From 523e49b11174368cd73460fa5eae7b39d856f300 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 23:00:50 +0200 Subject: [PATCH 01/50] llm : fix falcon norm after refactoring (#3837) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index d0c4ef101..17cf364bb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3763,7 +3763,7 @@ struct llm_build_context { { if (model.layers[il].attn_norm_2) { // Falcon-40B - cur = llm_build_norm(ctx0, attn_norm, hparams, + cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il); From c43c2da8afacaddfe51c09b21dbd9922cd0ea46b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 1 Nov 2023 23:08:30 +0200 Subject: [PATCH 02/50] llm : fix llm_build_kqv taking unused tensor (benign, #3837) --- llama.cpp | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/llama.cpp b/llama.cpp index 17cf364bb..1c6d482f8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3345,7 +3345,6 @@ static struct ggml_tensor * llm_build_ffn( // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, - struct ggml_tensor * cur, const llama_hparams & hparams, const llama_kv_cache & kv, struct ggml_tensor * wo, @@ -3411,7 +3410,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); + struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); cb(cur, "kqv_merged_cont", il); cur = ggml_mul_mat(ctx, wo, cur); @@ -3565,7 +3564,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -3677,7 +3676,7 @@ struct llm_build_context { // apply ALiBi for 13B model const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il); cb(cur, "kqv_out", il); @@ -3795,7 +3794,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, attn_norm, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -3895,7 +3894,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, cur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -4100,7 +4099,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); // TODO: not tested, could be broken - cur = llm_build_kqv(ctx0, Q, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); cb(cur, "kqv_out", il); @@ -4191,7 +4190,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); cb(cur, "kqv_out", il); @@ -4288,7 +4287,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); cb(cur, "kqv_out", il); @@ -4382,7 +4381,7 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self, + cur = llm_build_kqv(ctx0, hparams, kv_self, model.layers[il].wo, NULL, Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il); cb(cur, "kqv_out", il); From 898aeca90a9bb992f506234cf3b8b7f7fa28a1df Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Wed, 1 Nov 2023 18:04:33 -0400 Subject: [PATCH 03/50] llama : implement YaRN RoPE scaling (#2268) Co-authored-by: cebtenzzre Co-authored-by: Jeffrey Quesnelle --- common/common.cpp | 79 +++++- common/common.h | 7 + convert-baichuan-hf-to-gguf.py | 3 +- convert.py | 97 ++++--- examples/finetune/finetune.cpp | 5 +- examples/server/server.cpp | 59 ++++- .../train-text-from-scratch.cpp | 6 +- ggml-cuda.cu | 153 ++++++++--- ggml-metal.m | 22 +- ggml-metal.metal | 61 ++++- ggml.c | 241 +++++++++++++----- ggml.h | 20 +- gguf-py/gguf/gguf.py | 29 ++- llama.cpp | 220 ++++++++++++---- llama.h | 18 +- 15 files changed, 763 insertions(+), 257 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 7a48e9d11..b182ffaae 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -219,12 +219,52 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.rope_freq_scale = std::stof(argv[i]); + } else if (arg == "--rope-scaling") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + else { invalid_param = true; break; } } else if (arg == "--rope-scale") { if (++i >= argc) { invalid_param = true; break; } params.rope_freq_scale = 1.0f/std::stof(argv[i]); + } else if (arg == "--yarn-orig-ctx") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_orig_ctx = std::stoi(argv[i]); + } else if (arg == "--yarn-ext-factor") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_ext_factor = std::stof(argv[i]); + } else if (arg == "--yarn-attn-factor") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_attn_factor = std::stof(argv[i]); + } else if (arg == "--yarn-beta-fast") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_fast = std::stof(argv[i]); + } else if (arg == "--yarn-beta-slow") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_slow = std::stof(argv[i]); } else if (arg == "--memory-f32") { params.memory_f16 = false; } else if (arg == "--top-p") { @@ -716,9 +756,16 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --cfg-negative-prompt-file FNAME\n"); printf(" negative prompt file to use for guidance. (default: empty)\n"); printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale); - printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale\n"); + printf(" --rope-scaling {none,linear,yarn}\n"); + printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); + printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n"); printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency linear scaling factor (default: loaded from model)\n"); + printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); + printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n"); + printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); + printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); + printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); + printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); @@ -826,17 +873,23 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { auto cparams = llama_context_default_params(); - cparams.n_ctx = params.n_ctx; - cparams.n_batch = params.n_batch; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; - cparams.seed = params.seed; - cparams.f16_kv = params.memory_f16; - cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; - cparams.rope_freq_base = params.rope_freq_base; - cparams.rope_freq_scale = params.rope_freq_scale; + cparams.n_ctx = params.n_ctx; + cparams.n_batch = params.n_batch; + cparams.n_threads = params.n_threads; + cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + cparams.mul_mat_q = params.mul_mat_q; + cparams.seed = params.seed; + cparams.f16_kv = params.memory_f16; + cparams.logits_all = params.logits_all; + cparams.embedding = params.embedding; + cparams.rope_scaling_type = params.rope_scaling_type; + cparams.rope_freq_base = params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.yarn_orig_ctx = params.yarn_orig_ctx; return cparams; } diff --git a/common/common.h b/common/common.h index 343b27217..7be69f925 100644 --- a/common/common.h +++ b/common/common.h @@ -9,6 +9,7 @@ #define LOG_NO_FILE_LINE_FUNCTION #include "log.h" +#include #include #include #include @@ -54,6 +55,12 @@ struct gpt_params { int32_t n_beams = 0; // if non-zero then use beam search of given width. float rope_freq_base = 0.0f; // RoPE base frequency float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = NAN; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f;// YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters struct llama_sampling_params sparams; diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 5ee99be73..67ccbe99f 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -163,7 +163,8 @@ gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: if "type" in hparams["rope_scaling"]: if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) + gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) # TOKENIZATION diff --git a/convert.py b/convert.py index bfbfab283..9110f1580 100755 --- a/convert.py +++ b/convert.py @@ -151,8 +151,11 @@ class Params: n_head_kv: int f_norm_eps: float + rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None f_rope_scale: float | None = None + n_orig_ctx: int | None = None + rope_finetuned: bool | None = None ftype: GGMLFileType | None = None @@ -198,20 +201,20 @@ class Params: def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) - n_vocab = config["vocab_size"] - n_embd = config["hidden_size"] - n_layer = config["num_hidden_layers"] - n_ff = config["intermediate_size"] - n_head = config["num_attention_heads"] - n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head - f_norm_eps = config["rms_norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - + rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None rope_scaling = config.get("rope_scaling") - if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear": - f_rope_scale = config["rope_scaling"].get("factor") - else: - f_rope_scale = None + + if rope_scaling is not None and (typ := rope_scaling.get("type")): + rope_factor = rope_scaling.get("factor") + f_rope_scale = rope_factor + if typ == "linear": + rope_scaling_type = gguf.RopeScalingType.LINEAR + elif typ == "yarn": + rope_scaling_type = gguf.RopeScalingType.YARN + n_orig_ctx = rope_scaling['original_max_position_embeddings'] + rope_finetuned = rope_scaling['finetuned'] + else: + raise NotImplementedError(f'Unknown rope scaling type: {typ}') if "max_sequence_length" in config: n_ctx = config["max_sequence_length"] @@ -222,16 +225,19 @@ class Params: "Suggestion: provide 'config.json' of the model in the same directory containing model files.") return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_layer = n_layer, - n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, - f_rope_scale = f_rope_scale, + n_vocab = config["vocab_size"], + n_embd = config["hidden_size"], + n_layer = config["num_hidden_layers"], + n_ctx = n_ctx, + n_ff = config["intermediate_size"], + n_head = (n_head := config["num_attention_heads"]), + n_head_kv = config.get("num_key_value_heads", n_head), + f_norm_eps = config["rms_norm_eps"], + f_rope_freq_base = config.get("rope_theta"), + rope_scaling_type = rope_scaling_type, + f_rope_scale = f_rope_scale, + n_orig_ctx = n_orig_ctx, + rope_finetuned = rope_finetuned, ) # LLaMA v2 70B params.json @@ -240,17 +246,8 @@ class Params: def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) - n_vocab = config["vocab_size"] if "vocab_size" in config else -1 - n_embd = config["dim"] - n_layer = config["n_layers"] - n_ff = -1 - n_head = config["n_heads"] - n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head - f_norm_eps = config["norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - # hack to determine LLaMA v1 vs v2 vs CodeLlama - if f_rope_freq_base == 1000000: + if config.get("rope_theta") == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: @@ -260,22 +257,16 @@ class Params: # LLaMA v1 n_ctx = 2048 - if n_vocab == -1: - n_vocab = model["tok_embeddings.weight"].shape[0] - - if n_ff == -1: - n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] - return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_layer = n_layer, + n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]), + n_embd = config["dim"], + n_layer = config["n_layers"], n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, + n_ff = model["layers.0.feed_forward.w1.weight"].shape[0], + n_head = (n_head := config["n_heads"]), + n_head_kv = config.get("n_kv_heads", n_head), + f_norm_eps = config["norm_eps"], + f_rope_freq_base = config.get("rope_theta"), ) @staticmethod @@ -831,8 +822,16 @@ class OutputFile: if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) - if params.f_rope_scale is not None: - self.gguf.add_rope_scale_linear(params.f_rope_scale) + if params.rope_scaling_type: + assert params.f_rope_scale is not None + self.gguf.add_rope_scaling_type(params.rope_scaling_type) + self.gguf.add_rope_scaling_factor(params.f_rope_scale) + + if params.n_orig_ctx is not None: + self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx) + + if params.rope_finetuned is not None: + self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) if params.ftype is not None: self.gguf.add_file_type(params.ftype) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 60c7faa79..649a3b7c1 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -642,8 +642,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( const int rope_mode = 0; return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, - rope_freq_base, rope_freq_scale); + t, KQ_pos, n_rot, rope_mode, n_ctx, 0, + rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f + ); }; set_name(tokens_input, "tokens_input"); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 47ae0d558..84b04d5a0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1755,12 +1755,18 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf("options:\n"); printf(" -h, --help show this help message and exit\n"); printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" --rope-scaling {none,linear,yarn}\n"); + printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n"); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); + printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); + printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); + printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); + printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) @@ -1881,6 +1887,19 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } + else if (arg == "--rope-scaling") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; } + else { invalid_param = true; break; } + } else if (arg == "--rope-freq-base") { if (++i >= argc) @@ -1899,6 +1918,38 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.rope_freq_scale = std::stof(argv[i]); } + else if (arg == "--yarn-ext-factor") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_ext_factor = std::stof(argv[i]); + } + else if (arg == "--yarn-attn-factor") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_attn_factor = std::stof(argv[i]); + } + else if (arg == "--yarn-beta-fast") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_fast = std::stof(argv[i]); + } + else if (arg == "--yarn-beta-slow") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--memory-f32" || arg == "--memory_f32") { params.memory_f16 = false; diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 1ce6cef29..2a257e632 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -349,9 +349,9 @@ static struct ggml_tensor * llama_build_train_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, - rope_freq_base, rope_freq_scale); + return ggml_rope_custom( + ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + ); }; set_name(tokens_input, "tokens_input"); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 4e6e7cd94..12ee10e3d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4493,11 +4493,41 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, cpy_1(cx + x_offset, cdst + dst_offset); } -// rope == RoPE == rotary positional embedding +static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} +struct rope_corr_dims { + float v[4]; +}; + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static __device__ void rope_yarn( + float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// rope == RoPE == rotary positional embedding template -static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale) { +static __global__ void rope( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); if (col >= ncols) { @@ -4509,10 +4539,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t const int i2 = row/p_delta_rows; const int p = has_pos ? pos[i2] : 0; - const float p0 = p*freq_scale; - const float theta = p0*powf(theta_scale, col/2); - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); + const float theta_base = p*powf(freq_base, -col/ncols); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + 1]; @@ -4522,8 +4552,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t } template -static __global__ void rope_neox(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale) { +static __global__ void rope_neox( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); if (col >= ncols) { @@ -4534,11 +4566,14 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in const int i = row*ncols + col/2; const int i2 = row/p_delta_rows; + // simplified from `(row * ncols + col) * (-1 / ncols)` + const float cur_rot = -col/ncols - row; + const int p = has_pos ? pos[i2] : 0; - const float p0 = p*freq_scale; - const float theta = p0*powf(theta_scale, col/2); - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); + const float theta_base = p*powf(freq_base, cur_rot); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + ncols/2]; @@ -4547,8 +4582,10 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in dst[i + ncols/2] = x0*sin_theta + x1*cos_theta; } -static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, const int n_ctx) { +static __global__ void rope_glm_f32( + const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + int n_ctx +) { const int col = blockDim.x*blockIdx.x + threadIdx.x; const int half_n_dims = ncols/4; @@ -4560,7 +4597,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol const int i = row*ncols + col; const int i2 = row/p_delta_rows; - const float col_theta_scale = powf(theta_scale, col); + const float col_theta_scale = powf(freq_base, -2.0f*col/ncols); // FIXME: this is likely wrong const int p = pos != nullptr ? pos[i2] : 0; @@ -5584,40 +5621,54 @@ static void clamp_f32_cuda(const float * x, float * dst, const float min, const } template -static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, cudaStream_t stream) { +static void rope_cuda( + const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); if (pos == nullptr) { - rope<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } else { - rope<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } } template -static void rope_neox_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, cudaStream_t stream) { +static void rope_neox_cuda( + const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream +) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); if (pos == nullptr) { - rope_neox<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope_neox<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } else { - rope_neox<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale); + rope_neox<<>>( + x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims + ); } } -static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale, - const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) { +static void rope_glm_f32_cuda( + const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, int n_ctx, cudaStream_t stream +) { GGML_ASSERT(ncols % 4 == 0); const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1); const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE; const dim3 block_nums(num_blocks_x, nrows, 1); - rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx); + rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx); } static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, @@ -6477,17 +6528,20 @@ inline void ggml_cuda_op_rope( const int64_t ne2 = dst->ne[2]; const int64_t nrows = ggml_nrows(src0); - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + // RoPE alteration for extended context - - float freq_base, freq_scale; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - - const float theta_scale = powf(freq_base, -2.0f/n_dims); + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); const int32_t * pos = nullptr; if ((mode & 1) == 0) { @@ -6499,24 +6553,39 @@ inline void ggml_cuda_op_rope( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + rope_corr_dims corr_dims; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + // compute if (is_glm) { GGML_ASSERT(false); - rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream); + rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); } else if (is_neox) { GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet"); if (src0->type == GGML_TYPE_F32) { - rope_neox_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_neox_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else if (src0->type == GGML_TYPE_F16) { - rope_neox_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_neox_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else { GGML_ASSERT(false); } } else { if (src0->type == GGML_TYPE_F32) { - rope_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_cuda( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else if (src0->type == GGML_TYPE_F16) { - rope_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream); + rope_cuda( + (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); } else { GGML_ASSERT(false); } diff --git a/ggml-metal.m b/ggml-metal.m index 1f0341507..611d5e173 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1400,14 +1400,18 @@ void ggml_metal_graph_compute( const int nth = MIN(1024, ne00); - const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; - float freq_base; - float freq_scale; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); switch (src0->type) { case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break; @@ -1439,6 +1443,10 @@ void ggml_metal_graph_compute( [encoder setBytes:&mode length:sizeof( int) atIndex:21]; [encoder setBytes:&freq_base length:sizeof(float) atIndex:22]; [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23]; + [encoder setBytes:&ext_factor length:sizeof(float) atIndex:24]; + [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25]; + [encoder setBytes:&beta_fast length:sizeof(float) atIndex:26]; + [encoder setBytes:&beta_slow length:sizeof(float) atIndex:27]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index f3152778a..471d7d390 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1061,6 +1061,45 @@ kernel void kernel_alibi_f32( } } +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) { + return n_dims * log(n_orig_ctx / (n_rot * 2 * M_PI_F)) / (2 * log(base)); +} + +static void rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base))); + dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base))); +} + typedef void (rope_t)( device const void * src0, device const int32_t * src1, @@ -1116,6 +1155,10 @@ kernel void kernel_rope( constant int & mode, constant float & freq_base, constant float & freq_scale, + constant float & ext_factor, + constant float & attn_factor, + constant float & beta_fast, + constant float & beta_slow, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], uint3 tgpig[[threadgroup_position_in_grid]]) { @@ -1125,19 +1168,22 @@ kernel void kernel_rope( const bool is_neox = mode & 2; + float corr_dims[2]; + rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + device const int32_t * pos = src1; const int64_t p = pos[i2]; - const float theta_0 = freq_scale * (float)p; + const float theta_0 = (float)p; const float inv_ndims = -1.f/n_dims; if (!is_neox) { for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { const float theta = theta_0 * pow(freq_base, inv_ndims*i0); - const float cos_theta = cos(theta); - const float sin_theta = sin(theta); + float cos_theta, sin_theta; + rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -1152,9 +1198,12 @@ kernel void kernel_rope( for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) { - const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib); - const float cos_theta = cos(theta); - const float sin_theta = sin(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + const float cur_rot = inv_ndims*ic - ib; + + const float theta = theta_0 * pow(freq_base, cur_rot); + float cos_theta, sin_theta; + rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); const int64_t i0 = ib*n_dims + ic/2; diff --git a/ggml.c b/ggml.c index 80d682255..2c7fe476b 100644 --- a/ggml.c +++ b/ggml.c @@ -1,4 +1,5 @@ #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows +#define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-impl.h" #include "ggml-quants.h" @@ -4845,8 +4846,13 @@ static struct ggml_tensor * ggml_rope_impl( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down, bool inplace) { @@ -4862,11 +4868,15 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; - memcpy(params + 4, &freq_base, sizeof(float)); - memcpy(params + 5, &freq_scale, sizeof(float)); - memcpy(params + 6, &xpos_base, sizeof(float)); - memcpy(params + 7, &xpos_down, sizeof(bool)); + int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + memcpy(params + 5, &freq_base, sizeof(float)); + memcpy(params + 6, &freq_scale, sizeof(float)); + memcpy(params + 7, &ext_factor, sizeof(float)); + memcpy(params + 8, &attn_factor, sizeof(float)); + memcpy(params + 9, &beta_fast, sizeof(float)); + memcpy(params + 10, &beta_slow, sizeof(float)); + memcpy(params + 11, &xpos_base, sizeof(float)); + memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE; @@ -4884,7 +4894,9 @@ struct ggml_tensor * ggml_rope( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false); + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ); } struct ggml_tensor * ggml_rope_inplace( @@ -4894,7 +4906,9 @@ struct ggml_tensor * ggml_rope_inplace( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true); + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ); } struct ggml_tensor * ggml_rope_custom( @@ -4904,9 +4918,17 @@ struct ggml_tensor * ggml_rope_custom( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ); } struct ggml_tensor * ggml_rope_custom_inplace( @@ -4916,9 +4938,17 @@ struct ggml_tensor * ggml_rope_custom_inplace( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale) { - return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true + ); } struct ggml_tensor * ggml_rope_xpos_inplace( @@ -4928,7 +4958,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace( int n_dims, float base, bool down) { - return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true); + return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); } // ggml_rope_back @@ -10901,6 +10931,45 @@ static void ggml_compute_forward_clamp( // ggml_compute_forward_rope +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) { + return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base))); + dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base))); +} + static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -10910,21 +10979,26 @@ static void ggml_compute_forward_rope_f32( return; } - float freq_base; - float freq_scale; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // these two only relevant for xPos RoPE: float xpos_base; bool xpos_down; - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool)); + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool)); GGML_TENSOR_UNARY_OP_LOCALS @@ -10952,6 +11026,9 @@ static void ggml_compute_forward_rope_f32( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -10965,18 +11042,18 @@ static void ggml_compute_forward_rope_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = (float)p; if (is_glm) { - theta = MIN(p, n_ctx - 2); + theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); - theta *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -10994,13 +11071,16 @@ static void ggml_compute_forward_rope_f32( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta + ); + // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta *= theta_scale; + theta_base *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11014,12 +11094,19 @@ static void ggml_compute_forward_rope_f32( } else { // TODO: this might be wrong for ne0 != n_dims - need double check // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + theta_base *= freq_scale; for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + float cur_rot = inv_ndims * ic - ib; - theta *= theta_scale; + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + &cos_theta, &sin_theta + ); + + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11048,15 +11135,19 @@ static void ggml_compute_forward_rope_f16( return; } - float freq_base; - float freq_scale; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS @@ -11084,6 +11175,9 @@ static void ggml_compute_forward_rope_f16( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -11097,18 +11191,18 @@ static void ggml_compute_forward_rope_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = (float)p; if (is_glm) { - theta = MIN(p, n_ctx - 2); + theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); - theta *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -11126,10 +11220,12 @@ static void ggml_compute_forward_rope_f16( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta + ); - theta *= theta_scale; + theta_base *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11143,12 +11239,19 @@ static void ggml_compute_forward_rope_f16( } else { // TODO: this might be wrong for ne0 != n_dims - need double check // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 + theta_base *= freq_scale; for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + // simplified from `(ib * n_dims + ic) * inv_ndims` + float cur_rot = inv_ndims * ic - ib; - theta *= theta_scale; + float cos_theta, sin_theta; + rope_yarn( + theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + &cos_theta, &sin_theta + ); + + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11256,17 +11359,18 @@ static void ggml_compute_forward_rope_back_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = freq_scale * (float)p; + float theta_base = freq_scale * (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); + // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta *= theta_scale; + theta_base *= theta_scale; const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11280,10 +11384,10 @@ static void ggml_compute_forward_rope_back_f32( } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -11356,14 +11460,14 @@ static void ggml_compute_forward_rope_back_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta_base = (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11377,10 +11481,10 @@ static void ggml_compute_forward_rope_back_f16( } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta); - const float sin_theta = sinf(theta); + const float cos_theta = cosf(theta_base); + const float sin_theta = sinf(theta_base); - theta *= theta_scale; + theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; @@ -15505,9 +15609,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src1, n_dims, mode, + 0, n_ctx, freq_base, freq_scale, + 0.0f, + 1.0f, + 0.0f, + 0.0f, xpos_base, xpos_down, false), diff --git a/ggml.h b/ggml.h index 9d16c5a72..70eb25a6b 100644 --- a/ggml.h +++ b/ggml.h @@ -219,7 +219,7 @@ #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 6 #define GGML_MAX_NAME 64 -#define GGML_MAX_OP_PARAMS 32 +#define GGML_MAX_OP_PARAMS 64 #define GGML_DEFAULT_N_THREADS 4 #if UINTPTR_MAX == 0xFFFFFFFF @@ -1326,8 +1326,13 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_custom_inplace( @@ -1337,8 +1342,17 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, - float freq_scale); + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); + + // compute correction dims for YaRN RoPE scaling + void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // xPos RoPE, in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 6b7d65429..727b4e554 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -7,7 +7,7 @@ import shutil import struct import sys import tempfile -from enum import IntEnum, auto +from enum import Enum, IntEnum, auto from io import BufferedWriter from pathlib import Path from typing import IO, Any, BinaryIO, Callable, Sequence @@ -53,9 +53,12 @@ KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" # RoPE -KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" -KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" -KEY_ROPE_SCALE_LINEAR = "{arch}.rope.scale_linear" +KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" +KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" +KEY_ROPE_SCALING_TYPE = "{arch}.rope.scaling.type" +KEY_ROPE_SCALING_FACTOR = "{arch}.rope.scaling.factor" +KEY_ROPE_SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" +KEY_ROPE_SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" # tokenization KEY_TOKENIZER_MODEL = "tokenizer.ggml.model" @@ -577,6 +580,11 @@ class TokenType(IntEnum): UNUSED = 5 BYTE = 6 +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + # # implementation # @@ -948,8 +956,17 @@ class GGUFWriter: def add_rope_freq_base(self, value: float): self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value) - def add_rope_scale_linear(self, value: float): - self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value) + def add_rope_scaling_type(self, value: RopeScalingType): + self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float): + self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int): + self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool): + self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value) def add_tokenizer_model(self, model: str): self.add_string(KEY_TOKENIZER_MODEL, model) diff --git a/llama.cpp b/llama.cpp index 1c6d482f8..685882c20 100644 --- a/llama.cpp +++ b/llama.cpp @@ -54,6 +54,7 @@ #include #include #include +#include #include #include #include @@ -235,6 +236,10 @@ enum llm_kv { LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, LLM_KV_ROPE_SCALE_LINEAR, + LLM_KV_ROPE_SCALING_TYPE, + LLM_KV_ROPE_SCALING_FACTOR, + LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, + LLM_KV_ROPE_SCALING_FINETUNED, LLM_KV_TOKENIZER_MODEL, LLM_KV_TOKENIZER_LIST, @@ -276,9 +281,13 @@ static std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, - { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, - { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, - { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, + { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, + { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, + { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, + { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, + { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, + { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, + { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, @@ -552,6 +561,22 @@ do { \ } \ } while (0) +static std::map LLAMA_ROPE_SCALING_TYPES = { + { LLAMA_ROPE_SCALING_NONE, "none" }, + { LLAMA_ROPE_SCALING_LINEAR, "linear" }, + { LLAMA_ROPE_SCALING_YARN, "yarn" }, +}; + +static int8_t llama_rope_scaling_type_from_string(const std::string & name) { + for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { + if (kv.second == name) { + return kv.first; + } + } + + return LLAMA_ROPE_SCALING_UNSPECIFIED; +} + // // ggml helpers // @@ -1035,8 +1060,11 @@ struct llama_hparams { float f_norm_eps; float f_norm_rms_eps; - float rope_freq_base_train; - float rope_freq_scale_train; + float rope_freq_base_train; + float rope_freq_scale_train; + uint32_t n_yarn_orig_ctx; + int8_t rope_scaling_type_train : 3; + bool rope_finetuned : 1; float f_clamp_kqv; float f_max_alibi_bias; @@ -1051,6 +1079,8 @@ struct llama_hparams { if (this->n_layer != other.n_layer) return true; if (this->n_rot != other.n_rot) return true; if (this->n_ff != other.n_ff) return true; + if (this->rope_finetuned != other.rope_finetuned) return true; + if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; const float EPSILON = 1e-9; @@ -1081,8 +1111,16 @@ struct llama_cparams { uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - float rope_freq_base; - float rope_freq_scale; + float rope_freq_base; + float rope_freq_scale; + + uint32_t n_yarn_orig_ctx; + // These hyperparameters are not exposed in GGUF, because all + // existing YaRN models use the same values for them. + float yarn_ext_factor; + float yarn_attn_factor; + float yarn_beta_fast; + float yarn_beta_slow; bool mul_mat_q; }; @@ -2014,14 +2052,30 @@ static void llm_load_hparams( hparams.n_head_kv = hparams.n_head; GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV)); + hparams.rope_finetuned = false; + GGUF_GET_KEY(ctx, hparams.rope_finetuned, gguf_get_val_bool, GGUF_TYPE_BOOL, false, + kv(LLM_KV_ROPE_SCALING_FINETUNED)); + + hparams.n_yarn_orig_ctx = hparams.n_ctx_train; + GGUF_GET_KEY(ctx, hparams.n_yarn_orig_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, + kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN)); + // rope_freq_base (optional) hparams.rope_freq_base_train = 10000.0f; GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); + std::string rope_scaling("linear"); + GGUF_GET_KEY(ctx, rope_scaling, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_ROPE_SCALING_TYPE)); + hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling); + GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED); + // rope_freq_scale (inverse of the kv) is optional - float ropescale = 1.0f; - GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); - hparams.rope_freq_scale_train = 1.0f/ropescale; + float ropescale = 0.0f; + GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALING_FACTOR)); + if (ropescale == 0.0f) { // try the old key name + GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); + } + hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale; // sanity check for n_rot (optional) { @@ -2371,6 +2425,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const auto & hparams = model.hparams; const auto & vocab = model.vocab; + const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); @@ -2389,8 +2445,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias); LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); + LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); + LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9); @@ -3047,21 +3106,11 @@ static void llm_load_tensors( model.t_load_us = ggml_time_us() - model.t_start_us; } -static bool llama_model_load( - const std::string & fname, - llama_model & model, - int n_gpu_layers, - int main_gpu, - const float * tensor_split, - bool use_mmap, - bool use_mlock, - bool vocab_only, - llama_progress_callback progress_callback, - void *progress_callback_user_data) { +static bool llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) { try { - llama_model_loader ml(fname, use_mmap); + llama_model_loader ml(fname, params.use_mmap); - model.hparams.vocab_only = vocab_only; + model.hparams.vocab_only = params.vocab_only; llm_load_arch (ml, model); llm_load_hparams(ml, model); @@ -3073,15 +3122,15 @@ static bool llama_model_load( throw std::runtime_error("vocab size mismatch"); } - if (vocab_only) { + if (params.vocab_only) { LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__); return true; } llm_load_tensors( - ml, model, n_gpu_layers, - main_gpu, tensor_split, - use_mlock, progress_callback, progress_callback_user_data); + ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock, + params.progress_callback, params.progress_callback_user_data + ); } catch (const std::exception & err) { LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); return false; @@ -3150,6 +3199,7 @@ static struct ggml_tensor * llm_build_inp_embd( static void llm_build_k_shift( struct ggml_context * ctx, const llama_hparams & hparams, + const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, llm_rope_type type, @@ -3162,6 +3212,11 @@ static void llm_build_k_shift( const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_gqa = hparams.n_embd_gqa(); const int64_t n_embd_head = hparams.n_embd_head(); + const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; + const float ext_factor = cparams.yarn_ext_factor; + const float attn_factor = cparams.yarn_attn_factor; + const float beta_fast = cparams.yarn_beta_fast; + const float beta_slow = cparams.yarn_beta_slow; GGML_ASSERT(n_embd_head % n_rot == 0); @@ -3185,7 +3240,8 @@ static void llm_build_k_shift( ggml_element_size(kv.k)*n_embd_head, ggml_element_size(kv.k)*n_embd_gqa, ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il), - K_shift, n_rot, rope_type, 0, freq_base, freq_scale); + K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); cb(tmp, "K_shifted", il); ggml_build_forward_expand(graph, tmp); } @@ -3442,12 +3498,17 @@ struct llm_build_context { const float freq_base; const float freq_scale; + const float ext_factor; + const float attn_factor; + const float beta_fast; + const float beta_slow; const float norm_eps; const float norm_rms_eps; const int32_t n_tokens; const int32_t n_kv; // size of KV cache to consider (n_kv <= n_ctx) const int32_t kv_head; // index of where we store new KV data in the cache + const int32_t n_orig_ctx; const bool do_rope_shift; @@ -3477,11 +3538,16 @@ struct llm_build_context { n_embd_gqa (hparams.n_embd_gqa()), freq_base (cparams.rope_freq_base), freq_scale (cparams.rope_freq_scale), + ext_factor (cparams.yarn_ext_factor), + attn_factor (cparams.yarn_attn_factor), + beta_fast (cparams.yarn_beta_fast), + beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), n_tokens (batch.n_tokens), n_kv (worst_case ? n_ctx : kv_self.n), kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), + n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), cb (cb), buf_compute (lctx.buf_compute) { @@ -3532,7 +3598,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3556,10 +3622,18 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Kcur, "Kcur", il); llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); @@ -3634,7 +3708,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3658,8 +3732,16 @@ struct llm_build_context { switch (model.type) { case MODEL_7B: - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); break; case MODEL_13B: Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens); @@ -3746,7 +3828,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -3786,10 +3868,16 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom( + ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom( + ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(Kcur, "Kcur", il); llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); @@ -3960,7 +4048,7 @@ struct llm_build_context { cb(KQ_mask, "KQ_mask", -1); if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4053,13 +4141,15 @@ struct llm_build_context { cb(kpass, "kpass", il); struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); + ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(qrotated, "qrotated", il); struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale - ); + ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); cb(krotated, "krotated", il); // ggml currently only supports concatenation on dim=2 @@ -7883,8 +7973,13 @@ struct llama_context_params llama_context_default_params() { /*.n_batch =*/ 512, /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, + /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, + /*.yarn_ext_factor =*/ NAN, + /*.yarn_attn_factor =*/ 1.0f, + /*.yarn_beta_fast =*/ 32.0f, + /*.yarn_beta_slow =*/ 1.0f, /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, @@ -7971,10 +8066,7 @@ struct llama_model * llama_load_model_from_file( }; } - if (!llama_model_load(path_model, *model, params.n_gpu_layers, - params.main_gpu, params.tensor_split, - params.use_mmap, params.use_mlock, params.vocab_only, - params.progress_callback, params.progress_callback_user_data)) { + if (!llama_model_load(path_model, *model, params)) { LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); delete model; return nullptr; @@ -8000,13 +8092,35 @@ struct llama_context * llama_new_context_with_model( const auto & hparams = model->hparams; auto & cparams = ctx->cparams; - cparams.n_batch = params.n_batch; - cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; - cparams.rope_freq_base = params.rope_freq_base == 0 ? hparams.rope_freq_base_train : params.rope_freq_base; - cparams.rope_freq_scale = params.rope_freq_scale == 0 ? hparams.rope_freq_scale_train : params.rope_freq_scale; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; + cparams.n_batch = params.n_batch; + cparams.n_threads = params.n_threads; + cparams.n_threads_batch = params.n_threads_batch; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.mul_mat_q = params.mul_mat_q; + + cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; + cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; + + cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx : + hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx : + hparams.n_ctx_train; + + auto rope_scaling_type = params.rope_scaling_type; + if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) { + rope_scaling_type = hparams.rope_scaling_type_train; + } + + if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) { + cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none + } + + if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set' + cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f; + } if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/llama.h b/llama.h index 75fe391ef..3f1becd76 100644 --- a/llama.h +++ b/llama.h @@ -106,6 +106,14 @@ extern "C" { LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; + enum llama_rope_scaling_type { + LLAMA_ROPE_SCALING_UNSPECIFIED = -1, + LLAMA_ROPE_SCALING_NONE = 0, + LLAMA_ROPE_SCALING_LINEAR = 1, + LLAMA_ROPE_SCALING_YARN = 2, + LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, + }; + typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token @@ -172,10 +180,16 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing + int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 - float rope_freq_base; // RoPE base frequency, 0 = from model - float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model + float rope_freq_base; // RoPE base frequency, 0 = from model + float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model + float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model + float yarn_attn_factor; // YaRN magnitude scaling factor + float yarn_beta_fast; // YaRN low correction dim + float yarn_beta_slow; // YaRN high correction dim + uint32_t yarn_orig_ctx; // YaRN original context size // Keep the booleans together to avoid misalignment during copy-by-value. bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) From d02e98cde035d91ed8032ab943d1d504fe9da394 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 1 Nov 2023 23:10:09 +0100 Subject: [PATCH 04/50] ggml-cuda : compute ptrs for cublasGemmBatchedEx in a kernel (#3891) * ggml-cuda : compute ptrs for cublasGemmBatchedEx in a kernel * fix warnings --- ggml-cuda.cu | 78 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 45 insertions(+), 33 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 12ee10e3d..61cd1747c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6696,8 +6696,10 @@ inline void ggml_cuda_op_clamp( GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const float min = ((float *) dst->op_params)[0]; - const float max = ((float *) dst->op_params)[1]; + float min; + float max; + memcpy(&min, dst->op_params, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); CUDA_CHECK(cudaGetLastError()); @@ -7221,6 +7223,30 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); } +__global__ void k_compute_batched_ptrs( + const half * src0_as_f16, const half * src1_as_f16, half * dst_f16, + void ** ptrs, + int ne12, int ne13, + int ne23, + int nb02, int nb03, + int nb12, int nb13, + int nb2, int nb3, + int r2, int r3) { + int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i12 = blockIdx.y * blockDim.y + threadIdx.y; + + if (i13 >= ne13 || i12 >= ne12) { + return; + } + + int i03 = i13 / r3; + int i02 = i12 / r2; + + ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; + ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* nb2/2 + i13* nb3/2; +} + static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); @@ -7322,49 +7348,35 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } else { // use cublasGemmBatchedEx - // TODO: https://github.com/ggerganov/llama.cpp/pull/3749#discussion_r1369997000 const int ne23 = ne12*ne13; - // TODO: avoid this alloc - void ** ptrs = (void **) malloc(3*ne23*sizeof(void *)); - - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; - - ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3]; - ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2; - ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2; - } - } - - // allocate device memory for pointers void ** ptrs_as = nullptr; - CUDA_CHECK(cudaMalloc(&ptrs_as, 3*ne23*sizeof(void *))); + size_t ptrs_s = 0; + ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); - // TODO: this does not work for some reason -- not sure why? - //size_t ptrs_s = 0; - //ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); - - // copy pointers to device - CUDA_CHECK(cudaMemcpy(ptrs_as, ptrs, 3*ne23*sizeof(void *), cudaMemcpyHostToDevice)); - - free(ptrs); + dim3 block_dims(ne13, ne12); + k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( + src0_as_f16, src1_as_f16, dst_f16, + ptrs_as, + ne12, ne13, + ne23, + nb02, nb03, + nb12, nb13, + dst->nb[2], dst->nb[3], + r2, r3); + CUDA_CHECK(cudaGetLastError()); CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const void **) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( void **) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, + &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + &beta_f16, ( void ** ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - // free device memory for pointers - CUDA_CHECK(cudaFree(ptrs_as)); - //ggml_cuda_pool_free(ptrs_as, ptrs_s); + ggml_cuda_pool_free(ptrs_as, ptrs_s); } #endif From 0eb332a10f3f14a3746c391bf80ff5e7bdf29d5d Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Wed, 1 Nov 2023 19:29:14 -0400 Subject: [PATCH 05/50] llama : fix llama_context_default_params after #2268 (#3893) --- llama.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/llama.cpp b/llama.cpp index 685882c20..32d7d23de 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7980,6 +7980,7 @@ struct llama_context_params llama_context_default_params() { /*.yarn_attn_factor =*/ 1.0f, /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, + /*.yarn_orig_ctx =*/ 0, /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, From 2fffa0d61fa10e4b466e78cabcc6a4e16717b580 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Thu, 2 Nov 2023 01:49:44 -0400 Subject: [PATCH 06/50] cuda : fix RoPE after #2268 (#3897) --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 61cd1747c..57a528ede 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4539,7 +4539,7 @@ static __global__ void rope( const int i2 = row/p_delta_rows; const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*powf(freq_base, -col/ncols); + const float theta_base = p*powf(freq_base, -float(col)/ncols); float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -4566,8 +4566,8 @@ static __global__ void rope_neox( const int i = row*ncols + col/2; const int i2 = row/p_delta_rows; - // simplified from `(row * ncols + col) * (-1 / ncols)` - const float cur_rot = -col/ncols - row; + // simplified from `(ib * ncols + col) * (-1 / ncols)`, where ib is assumed to be zero + const float cur_rot = -float(col)/ncols; const int p = has_pos ? pos[i2] : 0; const float theta_base = p*powf(freq_base, cur_rot); From 183b3fac6c28e65d23ac0230c1dd6fb84bf0154d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 08:33:37 +0200 Subject: [PATCH 07/50] metal : fix build errors and kernel sig after #2268 (#3898) --- ggml-metal.m | 57 ++++++++++++++++++++++++------------------------ ggml-metal.metal | 16 +++++++++----- 2 files changed, 40 insertions(+), 33 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 611d5e173..b33a3cb8f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1419,34 +1419,35 @@ void ggml_metal_graph_compute( default: GGML_ASSERT(false); }; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&freq_base length:sizeof(float) atIndex:22]; - [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23]; - [encoder setBytes:&ext_factor length:sizeof(float) atIndex:24]; - [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25]; - [encoder setBytes:&beta_fast length:sizeof(float) atIndex:26]; - [encoder setBytes:&beta_slow length:sizeof(float) atIndex:27]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; + [encoder setBytes:&mode length:sizeof( int) atIndex:21]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 471d7d390..7c35f23a7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1070,20 +1070,20 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta + thread float * cos_theta, thread float * sin_theta ) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; if (ext_factor != 0.0f) { - ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + mscale *= 1.0f + 0.1f * log(1.0f / freq_scale); } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; + *cos_theta = cos(theta) * mscale; + *sin_theta = sin(theta) * mscale; } // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get @@ -1123,8 +1123,13 @@ typedef void (rope_t)( constant int & n_past, constant int & n_dims, constant int & mode, + constant int & n_orig_ctx, constant float & freq_base, constant float & freq_scale, + constant float & ext_factor, + constant float & attn_factor, + constant float & beta_fast, + constant float & beta_slow, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], uint3 tgpig[[threadgroup_position_in_grid]]); @@ -1153,6 +1158,7 @@ kernel void kernel_rope( constant int & n_past, constant int & n_dims, constant int & mode, + constant int & n_orig_ctx, constant float & freq_base, constant float & freq_scale, constant float & ext_factor, From 4d719a6d4e74b9a98e75f826f865f3153717d54b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 08:35:10 +0200 Subject: [PATCH 08/50] cuda : check if this fixes Pascal card regression (#3882) --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 57a528ede..e46295126 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7420,7 +7420,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { From b12fa0d1c13596869c512f49a526b979c94787cc Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Thu, 2 Nov 2023 02:50:16 -0400 Subject: [PATCH 09/50] build : link against build info instead of compiling against it (#3879) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * cmake : fix build when .git does not exist * cmake : simplify BUILD_INFO target * cmake : add missing dependencies on BUILD_INFO * build : link against build info instead of compiling against it * zig : make build info a .cpp source instead of a header Co-authored-by: Matheus C. França * cmake : revert change to CMP0115 --------- Co-authored-by: Matheus C. França --- .gitignore | 2 +- CMakeLists.txt | 33 --------- Makefile | 71 ++++++++++---------- build.zig | 38 +++++------ common/CMakeLists.txt | 42 +++++++++++- common/build-info.cpp.in | 4 ++ common/common.cpp | 5 +- common/common.h | 12 +++- examples/benchmark/CMakeLists.txt | 5 +- examples/benchmark/benchmark-matmult.cpp | 1 - examples/embedding/CMakeLists.txt | 3 - examples/embedding/embedding.cpp | 1 - examples/infill/CMakeLists.txt | 3 - examples/infill/infill.cpp | 5 +- examples/llama-bench/CMakeLists.txt | 3 - examples/llama-bench/llama-bench.cpp | 5 +- examples/llava/CMakeLists.txt | 6 -- examples/main/CMakeLists.txt | 3 - examples/main/main.cpp | 5 +- examples/parallel/CMakeLists.txt | 3 - examples/parallel/parallel.cpp | 2 - examples/perplexity/CMakeLists.txt | 3 - examples/perplexity/perplexity.cpp | 1 - examples/quantize-stats/CMakeLists.txt | 2 +- examples/quantize-stats/quantize-stats.cpp | 1 - examples/quantize/CMakeLists.txt | 5 +- examples/quantize/quantize.cpp | 1 - examples/save-load-state/CMakeLists.txt | 3 - examples/save-load-state/save-load-state.cpp | 1 - examples/server/CMakeLists.txt | 3 - examples/server/server.cpp | 5 +- examples/speculative/CMakeLists.txt | 3 - examples/speculative/speculative.cpp | 2 - scripts/build-info.cmake | 30 +++++---- scripts/build-info.h.in | 9 --- scripts/build-info.sh | 13 ++-- 36 files changed, 143 insertions(+), 191 deletions(-) create mode 100644 common/build-info.cpp.in delete mode 100644 scripts/build-info.h.in diff --git a/.gitignore b/.gitignore index 5d7c5479e..50cbd0b47 100644 --- a/.gitignore +++ b/.gitignore @@ -65,7 +65,7 @@ models-mnt /parallel /train-text-from-scratch /vdot -build-info.h +/common/build-info.cpp arm_neon.h compile_commands.json CMakeSettings.json diff --git a/CMakeLists.txt b/CMakeLists.txt index 3659279e2..611ed3f4d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,39 +100,6 @@ option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALO option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ON) -# -# Build info header -# - -# Generate initial build-info.h -include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) - -if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git") - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/.git") - - # Is git submodule - if(NOT IS_DIRECTORY "${GIT_DIR}") - file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) - string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") - endif() - - # Add a custom target for build-info.h - add_custom_target(BUILD_INFO ALL DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h") - - # Add a custom command to rebuild build-info.h when .git/index changes - add_custom_command( - OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h" - COMMENT "Generating build details from Git" - COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake" - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS "${GIT_DIR}/index" - VERBATIM - ) -else() - message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") -endif() - # # Compile flags # diff --git a/Makefile b/Makefile index c53c1e726..300c1e6c7 100644 --- a/Makefile +++ b/Makefile @@ -542,9 +542,9 @@ llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h l $(CXX) $(CXXFLAGS) -c $< -o $@ COMMON_H_DEPS = common/common.h common/sampling.h common/log.h -COMMON_DEPS = common.o sampling.o grammar-parser.o +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o -common.o: common/common.cpp build-info.h $(COMMON_H_DEPS) +common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ sampling.o: common/sampling.cpp $(COMMON_H_DEPS) @@ -563,46 +563,46 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples # -main: examples/main/main.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo -infill: examples/infill/infill.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -simple: examples/simple/simple.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -batched: examples/batched/batched.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS) +batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS) +quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o $(OBJS) +quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) @@ -614,7 +614,7 @@ train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratc convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) @@ -623,19 +623,19 @@ llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -export-lora: examples/export-lora/export-lora.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ifdef LLAMA_METAL @@ -648,7 +648,7 @@ swift: examples/batched.swift (cd examples/batched.swift; make build) endif -build-info.h: $(wildcard .git/index) scripts/build-info.sh +common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh @sh scripts/build-info.sh $(CC) > $@.tmp @if ! cmp -s $@.tmp $@; then \ mv $@.tmp $@; \ @@ -656,13 +656,16 @@ build-info.h: $(wildcard .git/index) scripts/build-info.sh rm $@.tmp; \ fi +build-info.o: common/build-info.cpp + $(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@ + # # Tests # tests: $(TEST_TARGETS) -benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS) +benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) run-benchmark-matmult: benchmark-matmult @@ -676,40 +679,40 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) -tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h diff --git a/build.zig b/build.zig index 9b58b74ca..699738f3d 100644 --- a/build.zig +++ b/build.zig @@ -10,7 +10,6 @@ const Maker = struct { builder: *std.build.Builder, target: CrossTarget, optimize: Mode, - config_header: *ConfigHeader, enable_lto: bool, include_dirs: ArrayList([]const u8), @@ -41,26 +40,24 @@ const Maker = struct { const commit_hash = try std.ChildProcess.exec( .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } }, ); - const config_header = builder.addConfigHeader( - .{ .style = .blank, .include_path = "build-info.h" }, - .{ - .BUILD_NUMBER = 0, - .BUILD_COMMIT = commit_hash.stdout[0 .. commit_hash.stdout.len - 1], // omit newline - .BUILD_COMPILER = builder.fmt("Zig {s}", .{zig_version}), - .BUILD_TARGET = try target.allocDescription(builder.allocator), - }, - ); + try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt( + \\int LLAMA_BUILD_NUMBER = {}; + \\char const *LLAMA_COMMIT = "{s}"; + \\char const *LLAMA_COMPILER = "Zig {s}"; + \\char const *LLAMA_BUILD_TARGET = "{s}"; + \\ + , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) })); var m = Maker{ .builder = builder, .target = target, .optimize = builder.standardOptimizeOption(.{}), - .config_header = config_header, .enable_lto = false, .include_dirs = ArrayList([]const u8).init(builder.allocator), .cflags = ArrayList([]const u8).init(builder.allocator), .cxxflags = ArrayList([]const u8).init(builder.allocator), .objs = ArrayList(*Compile).init(builder.allocator), }; + try m.addCFlag("-std=c11"); try m.addCxxFlag("-std=c++11"); try m.addProjectInclude(&.{}); @@ -72,7 +69,7 @@ const Maker = struct { const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize }); if (o.target.getAbi() != .msvc) o.defineCMacro("_GNU_SOURCE", null); - o.addConfigHeader(m.config_header); + if (std.mem.endsWith(u8, src, ".c")) { o.addCSourceFiles(&.{src}, m.cflags.items); o.linkLibC(); @@ -85,7 +82,6 @@ const Maker = struct { o.linkLibCpp(); } } - o.addConfigHeader(m.config_header); for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i }); o.want_lto = m.enable_lto; return o; @@ -105,7 +101,6 @@ const Maker = struct { // linkLibCpp already add (libc++ + libunwind + libc) e.linkLibCpp(); } - e.addConfigHeader(m.config_header); m.builder.installArtifact(e); e.want_lto = m.enable_lto; return e; @@ -121,6 +116,7 @@ pub fn build(b: *std.build.Builder) !void { const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); const llama = make.obj("llama", "llama.cpp"); + const buildinfo = make.obj("common", "common/build-info.cpp"); const common = make.obj("common", "common/common.cpp"); const console = make.obj("console", "common/console.cpp"); const sampling = make.obj("sampling", "common/sampling.cpp"); @@ -128,14 +124,14 @@ pub fn build(b: *std.build.Builder) !void { const train = make.obj("train", "common/train.cpp"); const clip = make.obj("clip", "examples/llava/clip.cpp"); - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); + _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index fbb0ff095..0150114e3 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -1,8 +1,46 @@ # common + +# Build info header +# + +if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../.git") + + # Is git submodule + if(NOT IS_DIRECTORY "${GIT_DIR}") + file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) + string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") + endif() + + set(GIT_INDEX "${GIT_DIR}/index") +else() + message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") + set(GIT_INDEX "") +endif() + +# Add a custom command to rebuild build-info.cpp when .git/index changes +add_custom_command( + OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp" + COMMENT "Generating build details from Git" + COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} + -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake" + WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.." + DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX} + VERBATIM +) +set(TARGET build_info) +add_library(${TARGET} OBJECT build-info.cpp) +if (BUILD_SHARED_LIBS) + set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) +endif() + + set(TARGET common) -add_library(${TARGET} OBJECT +add_library(${TARGET} STATIC common.h common.cpp sampling.h @@ -21,4 +59,4 @@ endif() target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE llama) +target_link_libraries(${TARGET} PRIVATE llama build_info) diff --git a/common/build-info.cpp.in b/common/build-info.cpp.in new file mode 100644 index 000000000..0b945aa68 --- /dev/null +++ b/common/build-info.cpp.in @@ -0,0 +1,4 @@ +int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@; +char const *LLAMA_COMMIT = "@BUILD_COMMIT@"; +char const *LLAMA_COMPILER = "@BUILD_COMPILER@"; +char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@"; diff --git a/common/common.cpp b/common/common.cpp index b182ffaae..e938dee16 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,5 +1,4 @@ #include "common.h" -#include "build-info.h" #include "llama.h" #include @@ -1199,8 +1198,8 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { const llama_sampling_params & sparams = params.sparams; - fprintf(stream, "build_commit: %s\n", BUILD_COMMIT); - fprintf(stream, "build_number: %d\n", BUILD_NUMBER); + fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); + fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); diff --git a/common/common.h b/common/common.h index 7be69f925..72a49b890 100644 --- a/common/common.h +++ b/common/common.h @@ -26,11 +26,17 @@ #define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) -#define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); \ - fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); \ +#define print_build_info() do { \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) +// build info +extern int LLAMA_BUILD_NUMBER; +extern char const *LLAMA_COMMIT; +extern char const *LLAMA_COMPILER; +extern char const *LLAMA_BUILD_TARGET; + // // CLI argument parsing // diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt index 14916d831..2bb47bab5 100644 --- a/examples/benchmark/CMakeLists.txt +++ b/examples/benchmark/CMakeLists.txt @@ -1,9 +1,6 @@ set(TARGET benchmark) add_executable(${TARGET} benchmark-matmult.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index f1c382aa9..76e3f57cc 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "ggml.h" diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 0c752c7bb..8ffc33868 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 14075609e..3295cd240 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 57d01cb0b..e4e8028da 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 9c52b7bba..62f5ce3c1 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -2,7 +2,6 @@ #include "console.h" #include "llama.h" -#include "build-info.h" #include "grammar-parser.h" #include @@ -184,8 +183,8 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); + LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt index 7e395afd0..5bdbea4e2 100644 --- a/examples/llama-bench/CMakeLists.txt +++ b/examples/llama-bench/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} llama-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 780398184..9bd82d565 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -19,7 +19,6 @@ #include "ggml.h" #include "llama.h" #include "common.h" -#include "build-info.h" #include "ggml-cuda.h" // utils @@ -641,8 +640,8 @@ struct test { } }; -const std::string test::build_commit = BUILD_COMMIT; -const int test::build_number = BUILD_NUMBER; +const std::string test::build_commit = LLAMA_COMMIT; +const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::metal = !!ggml_cpu_has_metal(); diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 2d7979ecd..03d32c26e 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -5,9 +5,6 @@ target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) if (NOT MSVC) target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h - endif() -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) endif() set(TARGET llava) @@ -15,6 +12,3 @@ add_executable(${TARGET} llava.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index cc1888948..d532980b7 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 8a43b6ab8..8d985c82a 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -2,7 +2,6 @@ #include "console.h" #include "llama.h" -#include "build-info.h" #include #include @@ -153,8 +152,8 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); + LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index 0bbf89eae..319535a6e 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 9a0b9c183..a78df305f 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -1,8 +1,6 @@ // A basic application simulating a server with multiple clients. // The clients submite requests to the server and they are processed in parallel. -#include "build-info.h" - #include "common.h" #include "llama.h" diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index af00b4e16..3c76d3221 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bd2c73d87..de60c5227 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index db182e263..e31cf5e38 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -1,6 +1,6 @@ set(TARGET quantize-stats) add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index dd76b1cee..271282477 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -1,5 +1,4 @@ #define LLAMA_API_INTERNAL -#include "build-info.h" #include "common.h" #include "ggml.h" #include "llama.h" diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 4a8eed544..6f374a2bd 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,9 +1,6 @@ set(TARGET quantize) add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index be0b2fe1e..d27ea5e91 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index eadd13cdf..cc6ed8554 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 38d05f4d3..48d801110 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -1,4 +1,3 @@ -#include "build-info.h" #include "common.h" #include "llama.h" diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index a23ddcc55..1f0d26f77 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -11,6 +11,3 @@ if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 84b04d5a0..fd755327a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,5 @@ #include "common.h" #include "llama.h" -#include "build-info.h" #include "grammar-parser.h" #include "../llava/clip.h" @@ -2264,8 +2263,8 @@ int main(int argc, char **argv) llama_backend_init(params.numa); - LOG_INFO("build info", {{"build", BUILD_NUMBER}, - {"commit", BUILD_COMMIT}}); + LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, + {"commit", LLAMA_COMMIT}}); LOG_INFO("system info", { {"n_threads", params.n_threads}, diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt index 6c5c9456e..810f3c46a 100644 --- a/examples/speculative/CMakeLists.txt +++ b/examples/speculative/CMakeLists.txt @@ -3,6 +3,3 @@ add_executable(${TARGET} speculative.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -if(TARGET BUILD_INFO) - add_dependencies(${TARGET} BUILD_INFO) -endif() diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 323c74652..798684f66 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -1,5 +1,3 @@ -#include "build-info.h" - #include "common.h" #include "llama.h" diff --git a/scripts/build-info.cmake b/scripts/build-info.cmake index c86ab4379..73853dfa4 100644 --- a/scripts/build-info.cmake +++ b/scripts/build-info.cmake @@ -1,5 +1,5 @@ -set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.h.in") -set(HEADER_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h") +set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in") +set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp") set(BUILD_NUMBER 0) set(BUILD_COMMIT "unknown") set(BUILD_COMPILER "unknown") @@ -24,15 +24,21 @@ if(Git_FOUND) WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} OUTPUT_VARIABLE HEAD OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES ) + if (RES EQUAL 0) + set(BUILD_COMMIT ${HEAD}) + endif() execute_process( COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} OUTPUT_VARIABLE COUNT OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE RES ) - set(BUILD_COMMIT ${HEAD}) - set(BUILD_NUMBER ${COUNT}) + if (RES EQUAL 0) + set(BUILD_NUMBER ${COUNT}) + endif() endif() if(MSVC) @@ -53,22 +59,22 @@ else() set(BUILD_TARGET ${OUT}) endif() -# Only write the header if it's changed to prevent unnecessary recompilation -if(EXISTS ${HEADER_FILE}) - file(READ ${HEADER_FILE} CONTENTS) - string(REGEX MATCH "BUILD_COMMIT \"([^\"]*)\"" _ ${CONTENTS}) +# Only write the build info if it changed +if(EXISTS ${OUTPUT_FILE}) + file(READ ${OUTPUT_FILE} CONTENTS) + string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_COMMIT ${CMAKE_MATCH_1}) - string(REGEX MATCH "BUILD_COMPILER \"([^\"]*)\"" _ ${CONTENTS}) + string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_COMPILER ${CMAKE_MATCH_1}) - string(REGEX MATCH "BUILD_TARGET \"([^\"]*)\"" _ ${CONTENTS}) + string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS}) set(OLD_TARGET ${CMAKE_MATCH_1}) if ( NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR NOT OLD_TARGET STREQUAL BUILD_TARGET ) - configure_file(${TEMPLATE_FILE} ${HEADER_FILE}) + configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) endif() else() - configure_file(${TEMPLATE_FILE} ${HEADER_FILE}) + configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) endif() diff --git a/scripts/build-info.h.in b/scripts/build-info.h.in deleted file mode 100644 index e996faef0..000000000 --- a/scripts/build-info.h.in +++ /dev/null @@ -1,9 +0,0 @@ -#ifndef BUILD_INFO_H -#define BUILD_INFO_H - -#define BUILD_NUMBER @BUILD_NUMBER@ -#define BUILD_COMMIT "@BUILD_COMMIT@" -#define BUILD_COMPILER "@BUILD_COMPILER@" -#define BUILD_TARGET "@BUILD_TARGET@" - -#endif // BUILD_INFO_H diff --git a/scripts/build-info.sh b/scripts/build-info.sh index 3c8b1fb85..32682afbd 100755 --- a/scripts/build-info.sh +++ b/scripts/build-info.sh @@ -24,12 +24,7 @@ if out=$($CC -dumpmachine); then build_target=$out fi -echo "#ifndef BUILD_INFO_H" -echo "#define BUILD_INFO_H" -echo -echo "#define BUILD_NUMBER $build_number" -echo "#define BUILD_COMMIT \"$build_commit\"" -echo "#define BUILD_COMPILER \"$build_compiler\"" -echo "#define BUILD_TARGET \"$build_target\"" -echo -echo "#endif // BUILD_INFO_H" +echo "int LLAMA_BUILD_NUMBER = ${build_number};" +echo "char const *LLAMA_COMMIT = \"${build_commit}\";" +echo "char const *LLAMA_COMPILER = \"${build_compiler}\";" +echo "char const *LLAMA_BUILD_TARGET = \"${build_target}\";" From 1efae9b7dca2a5cc5aa21c1997b538022964ea19 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 09:54:18 +0200 Subject: [PATCH 10/50] llm : prevent from 1-D tensors being GPU split (#3697) --- llama.cpp | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 32d7d23de..bb60044b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1837,6 +1837,12 @@ struct llama_model_loader { throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } + if (backend == GGML_BACKEND_GPU_SPLIT) { + if (ne.size() == 1) { + throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str())); + } + } + { bool is_ok = true; for (size_t i = 0; i < ne.size(); ++i) { @@ -2817,8 +2823,8 @@ static void llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += @@ -2877,13 +2883,13 @@ static void llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend); @@ -2949,19 +2955,19 @@ static void llm_load_tensors( layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); if (backend == GGML_BACKEND_GPU) { vram_weights += From 2756c4fbffab097736d5116007872d86456a544a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 11:20:21 +0200 Subject: [PATCH 11/50] gguf : remove special-case code for GGUFv1 (#3901) ggml-ci --- ggml.c | 58 +++-------------------------------- models/ggml-vocab-llama.gguf | Bin 595423 -> 723676 bytes 2 files changed, 5 insertions(+), 53 deletions(-) diff --git a/ggml.c b/ggml.c index 2c7fe476b..d5a49d8e4 100644 --- a/ggml.c +++ b/ggml.c @@ -18811,8 +18811,7 @@ static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) return n == size; } -// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 -static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) { +static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { p->n = 0; p->data = NULL; @@ -18824,19 +18823,6 @@ static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset return ok; } -static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) { - p->n = 0; - p->data = NULL; - - bool ok = true; - - uint32_t n = 0; - ok = ok && gguf_fread_el(file, &n, sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n; - ok = ok && gguf_fread_el(file, p->data, p->n, offset); - - return ok; -} - struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); @@ -18895,21 +18881,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ctx->data = NULL; ok = ok && gguf_fread_el(file, &ctx->header.version, sizeof(ctx->header.version), &offset); - - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t n_tensors = 0; - uint32_t n_kv = 0; - - ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset); - ok = ok && gguf_fread_el(file, &n_kv, sizeof(n_kv), &offset); - - ctx->header.n_tensors = n_tensors; - ctx->header.n_kv = n_kv; - } else { - ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); - ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); - } + ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); + ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); @@ -18919,12 +18892,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p } } - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur; - if (ctx->header.version == 1) { - gguf_fread_str = gguf_fread_str_v1; - } - // read the kv pairs { ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv)); @@ -18955,15 +18922,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_ARRAY: { ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset); - - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t n = 0; - ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset); - kv->value.arr.n = n; - } else { - ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); - } + ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: @@ -19022,14 +18981,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_str(file, &info->name, &offset); ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims), &offset); for (uint32_t j = 0; j < info->n_dims; ++j) { - if (ctx->header.version == 1) { - // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 - uint32_t t = 0; - ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset); - info->ne[j] = t; - } else { - ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); - } + ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); } ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); diff --git a/models/ggml-vocab-llama.gguf b/models/ggml-vocab-llama.gguf index 63bfaf672f382c0f5bbcffe54736e2698ef3ac55..549eed8c53f438a61f1b00c9bd3b7d02325f2479 100644 GIT binary patch literal 723676 zcma&P`*UR1ap$Sn_e(o78#^2Oh~2PTy=H?Vo96o=l6K+M4GQRn05sb+Nl~||E>O2o zRkv6VpwVKREyR-=F{ggK}7oi~i+e-04-*vNN5H%m0Gk 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More options diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fd755327a..cbf36ad67 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -679,6 +679,7 @@ struct llama_server_context slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); @@ -1113,6 +1114,7 @@ struct llama_server_context {"temp", slot.sparams.temp}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, + {"min_p", slot.sparams.min_p}, {"tfs_z", slot.sparams.tfs_z}, {"typical_p", slot.sparams.typical_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, From a75fa576abba9d37f463580c379e4bbf1e1ad03c Mon Sep 17 00:00:00 2001 From: Galunid Date: Thu, 9 Nov 2023 11:09:29 +0100 Subject: [PATCH 42/50] scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py --- convert-bloom-hf-to-gguf.py | 247 --------- convert-falcon-hf-to-gguf.py | 253 --------- convert-gptneox-hf-to-gguf.py | 221 -------- convert-hf-to-gguf.py | 890 ++++++++++++++++++++++++++++++++ convert-mpt-hf-to-gguf.py | 227 -------- convert-refact-hf-to-gguf.py | 272 ---------- convert-starcoder-hf-to-gguf.py | 210 -------- convert.py | 4 +- mypy.ini | 1 + 9 files changed, 893 insertions(+), 1432 deletions(-) delete mode 100755 convert-bloom-hf-to-gguf.py delete mode 100755 convert-falcon-hf-to-gguf.py delete mode 100755 convert-gptneox-hf-to-gguf.py create mode 100755 convert-hf-to-gguf.py delete mode 100755 convert-mpt-hf-to-gguf.py delete mode 100755 convert-refact-hf-to-gguf.py delete mode 100755 convert-starcoder-hf-to-gguf.py diff --git a/convert-bloom-hf-to-gguf.py b/convert-bloom-hf-to-gguf.py deleted file mode 100755 index 6e866d943..000000000 --- a/convert-bloom-hf-to-gguf.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python3 -# HF bloom --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import re -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -# Supported Models: -# https://huggingface.co/bigscience/bloom-1b7 -# https://huggingface.co/bigscience/bloom-3b -# https://huggingface.co/bigscience/bloom-7b1 -# https://huggingface.co/Langboat/bloom-1b4-zh -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "BloomForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.BLOOM -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layer"] - -gguf_writer.add_name("Bloom") -n_embed = hparams.get("hidden_size", hparams.get("n_embed")) -n_head = hparams.get("n_head", hparams.get("num_attention_heads")) -gguf_writer.add_context_length(hparams.get("seq_length", n_embed)) -gguf_writer.add_embedding_length(n_embed) -gguf_writer.add_feed_forward_length(4 * n_embed) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(n_head) -gguf_writer.add_head_count_kv(n_head) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH, block_count) - -# params for qkv transform -n_head_kv = hparams.get("n_head_kv", n_head) -head_dim = n_embed // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - has_lm_head = True - if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys(): - has_lm_head = False - - for original_name in model_part.keys(): - data = model_part[original_name] - name = re.sub(r'transformer\.', '', original_name) - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): - # Map bloom-style qkv_linear to gpt-style qkv_linear - # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa - # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa - qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) - data = np.concatenate( - (qkv_weights[:, 0, :, :].reshape((-1, n_embed)), - qkv_weights[:, 1, :, :].reshape((-1, n_embed)), - qkv_weights[:, 2, :, :].reshape((-1, n_embed))), - axis=0 - ) - print("re-format attention.linear_qkv.weight") - elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): - qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) - data = np.concatenate( - (qkv_bias[:, 0, :].reshape((n_embed,)), - qkv_bias[:, 1, :].reshape((n_embed,)), - qkv_bias[:, 2, :].reshape((n_embed,))), - axis=0 - ) - print("re-format attention.linear_qkv.bias") - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "word_embeddings.weight": - gguf_writer.add_tensor("output.weight", data) - print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) # noqa - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py deleted file mode 100755 index 8e8f3c3f8..000000000 --- a/convert-falcon-hf-to-gguf.py +++ /dev/null @@ -1,253 +0,0 @@ -#!/usr/bin/env python3 -# HF falcon--> gguf conversion - -from __future__ import annotations - -import argparse -import contextlib -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path, prefix: str) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith(prefix): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"): - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model, "model-00") -if num_parts: - is_safetensors = True - from safetensors import safe_open -else: - is_safetensors = False - num_parts = count_model_parts(dir_model, "pytorch_model-") - -ARCH=gguf.MODEL_ARCH.FALCON -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams.get("num_hidden_layers") -if block_count is None: - block_count = hparams["n_layer"] # old name - -n_head = hparams.get("num_attention_heads") -if n_head is None: - n_head = hparams["n_head"] # old name - -n_head_kv = hparams.get("num_kv_heads") -if n_head_kv is None: - n_head_kv = hparams.get("n_head_kv", 1) # old name - -gguf_writer.add_name("Falcon") -gguf_writer.add_context_length(2048) # not in config.json -gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(n_head) -gguf_writer.add_head_count_kv(n_head_kv) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - tokens.append(reverse_vocab[i]) - scores.append(0.0) # dummy - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -head_dim = hparams["hidden_size"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -elif is_safetensors: - part_names = ( - f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1) - ) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - if is_safetensors: - ctx = safe_open(dir_model / part_name, framework="pt", device="cpu") - else: - ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu")) - - with ctx as model_part: - for name in model_part.keys(): - data = model_part.get_tensor(name) if is_safetensors else model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - # QKV tensor transform - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - - if "query_key_value" in name: - qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - data = torch.cat((q,k,v)).reshape_as(data) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py deleted file mode 100755 index 02d1fdf16..000000000 --- a/convert-gptneox-hf-to-gguf.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 -# HF gptneox--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTNeoXForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.GPTNEOX -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_context_length(hparams["max_position_embeddings"]) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"]))) -gguf_writer.add_head_count(hparams["num_attention_heads"]) -gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"): - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py new file mode 100755 index 000000000..f7fe29fd4 --- /dev/null +++ b/convert-hf-to-gguf.py @@ -0,0 +1,890 @@ +#!/usr/bin/env python3 + +from __future__ import annotations + +import argparse +import contextlib +import json +import os +import re +import sys +from enum import IntEnum +from pathlib import Path +from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast + +import numpy as np +import torch + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + + +###### MODEL DEFINITIONS ###### + +class SentencePieceTokenTypes(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class Model: + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): + self.dir_model = dir_model + self.ftype = ftype + self.fname_out = fname_out + self.is_big_endian = is_big_endian + self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.is_safetensors = self._is_model_safetensors() + self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") + self.part_names = self._get_part_names() + self.hparams = Model.load_hparams(self.dir_model) + self.model_arch = self._get_model_architecture() + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess) + + def set_vocab(self): + self._set_vocab_gpt2() + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + for part_name in self.part_names: + print(f"gguf: loading model part '{part_name}'") + ctx: ContextManager[Any] + if self.is_safetensors: + from safetensors import safe_open + ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) + else: + ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu")) + + with ctx as model_part: + for name in model_part.keys(): + data = model_part.get_tensor(name) if self.is_safetensors else model_part[name] + yield name, data + + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_block_count(self.hparams.get( + "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")), + )) + if (n_ctx := self.hparams.get("max_position_embeddings")) is not None: + self.gguf_writer.add_context_length(n_ctx) + if (n_embd := self.hparams.get("hidden_size")) is not None: + self.gguf_writer.add_embedding_length(n_embd) + if (n_ff := self.hparams.get("intermediate_size")) is not None: + self.gguf_writer.add_feed_forward_length(n_ff) + if (n_head := self.hparams.get("num_attention_head")) is not None: + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + def write(self): + self.write_tensors() + self.gguf_writer.write_header_to_file() + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.write_tensors_to_file() + self.gguf_writer.close() + + def write_vocab(self): + self.gguf_writer.write_header_to_file() + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.close() + + @staticmethod + def count_model_parts(dir_model: Path, prefix: str) -> int: + num_parts = 0 + for filename in os.listdir(dir_model): + if filename.endswith(prefix): + num_parts += 1 + + return num_parts + + @staticmethod + def load_hparams(dir_model): + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def from_model_architecture(model_architecture): + if model_architecture == "StableLMEpochForCausalLM": + return StableLMModel + if model_architecture == "GPTNeoXForCausalLM": + return GPTNeoXModel + if model_architecture == "BloomForCausalLM": + return BloomModel + if model_architecture == "MPTForCausalLM": + return MPTModel + if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): + return BaichuanModel + if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): + return FalconModel + if model_architecture == "GPTBigCodeForCausalLM": + return StarCoderModel + if model_architecture == "GPTRefactForCausalLM": + return RefactModel + if model_architecture == "PersimmonForCausalLM": + return PersimmonModel + return Model + + def _is_model_safetensors(self) -> bool: + return Model.count_model_parts(self.dir_model, ".safetensors") > 0 + + def _get_part_names(self): + if self.is_safetensors: + if self.num_parts == 1: # there's only one .safetensors file + return ("model.safetensors",) + return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1)) + + if self.num_parts == 1: # there's only one .bin file + return ("pytorch_model.bin",) + return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) + + def _get_model_architecture(self) -> gguf.MODEL_ARCH: + arch = self.hparams["architectures"][0] + if arch == "GPTNeoXForCausalLM": + return gguf.MODEL_ARCH.GPTNEOX + if arch == "BloomForCausalLM": + return gguf.MODEL_ARCH.BLOOM + if arch == "MPTForCausalLM": + return gguf.MODEL_ARCH.MPT + if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): + return gguf.MODEL_ARCH.BAICHUAN + if arch == "FalconForCausalLM": + return gguf.MODEL_ARCH.FALCON + if arch == "GPTBigCodeForCausalLM": + return gguf.MODEL_ARCH.STARCODER + if arch == "GPTRefactForCausalLM": + return gguf.MODEL_ARCH.REFACT + if arch == "PersimmonForCausalLM": + return gguf.MODEL_ARCH.PERSIMMON + + raise NotImplementedError(f'Architecture "{arch}" not supported!') + + def _set_vocab_gpt2(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[bytearray] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer # type: ignore[attr-defined] + tokenizer = AutoTokenizer.from_pretrained(dir_model) + vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) + assert max(tokenizer.vocab.values()) < vocab_size + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + pad_token = f"[PAD{i}]".encode('utf-8') + tokens.append(bytearray(pad_token)) + toktypes.append(gguf.TokenType.USER_DEFINED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_sentencepiece(self): + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + +class StableLMModel(Model): + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_dimension_count( + int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_layer_norm_eps(1e-5) + + +class GPTNeoXModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count( + int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + + +class BloomModel(Model): + def set_gguf_parameters(self): + self.gguf_writer.add_name("Bloom") + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(4 * n_embed) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams["n_layer"] + tensors = dict(self.get_tensors()) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + has_lm_head = True + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + for name, data_torch in tensors.items(): + if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys(): + has_lm_head = False + + name = re.sub(r'transformer\.', '', name) + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) + data = np.concatenate( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + axis=0, + ) + print("re-format attention.linear_qkv.weight") + elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): + qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) + data = np.concatenate( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + axis=0, + ) + print("re-format attention.linear_qkv.bias") + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + if not has_lm_head and name == "word_embeddings.weight": + self.gguf_writer.add_tensor("output.weight", data) + print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + +class MPTModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["n_layers"] + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): + self.gguf_writer.add_head_count_kv(kv_n_heads) + self.gguf_writer.add_layer_norm_eps(1e-5) + if self.hparams["attn_config"]["clip_qkv"] is not None: + self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) + self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + # note: MPT output is tied to (same as) wte in original model; + # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ + if new_name == "token_embd.weight": + self.gguf_writer.add_tensor("output.weight", data) + + +class BaichuanModel(Model): + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + hf_repo = self.hparams.get("_name_or_path", "") + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + print("gguf: can not find ctx length parameter.") + sys.exit() + + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_source_hf_repo(hf_repo) + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def write_tensors(self): + # Collect tensors from generator object + model_kv = dict(self.get_tensors()) + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + for i in range(block_count): + if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: + print(f"Unpacking and permuting layer {i}") + model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ + self._reverse_hf_permute_part(w, 0, head_count, head_count) + model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ + self._reverse_hf_permute_part(w, 1, head_count, head_count_kv) + model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \ + self._reverse_hf_part(w, 2) + del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"] + + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + def _reverse_hf_permute_part( + self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, + ) -> Tensor: + r = weights.shape[0] // 3 + return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) + + def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: + r = weights.shape[0] // 3 + return weights[r * n_part:r * n_part + r, ...] + + +class FalconModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + self.gguf_writer.add_name("Falcon") + self.gguf_writer.add_context_length(2048) # not in config.json + self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + head_dim = self.hparams["hidden_size"] // n_head + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py + + if "query_key_value" in name: + qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data_torch = torch.cat((q, k, v)).reshape_as(data_torch) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + +class StarCoderModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_name("StarCoder") + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +class RefactModel(Model): + def set_gguf_parameters(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_name("Refact") + # refact uses Alibi. So this is from config.json which might be used by training. + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + + self.gguf_writer.add_feed_forward_length(ff_dim) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + n_head = self.hparams["n_head"] + n_head_kv = 1 + head_dim = self.hparams["n_embd"] // n_head + block_count = self.hparams["n_layer"] + + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + tensors = dict(self.get_tensors()) + for i in range(block_count): + if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None: + tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim] + tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:] + del tensors[f"transformer.h.{i}.attn.kv.weight"] + if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None: + tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w + del tensors[f"transformer.h.{i}.attn.q.weight"] + if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None: + tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim] + tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:] + del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"] + + for name, data_torch in tensors.items(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + +class PersimmonModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) + head_count = self.hparams["num_attention_heads"] + head_count_kv = head_count + hidden_size = self.hparams["hidden_size"] + + self.gguf_writer.add_name('persimmon-8b-chat') + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + + def set_vocab(self): + self._set_vocab_sentencepiece() + # self.gguf_writer.add_bos_token_id(71013) + # self.gguf_writer.add_eos_token_id(71013) + + def write_tensors(self): + block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + if name.endswith(".self_attention.rotary_emb.inv_freq"): + continue + old_dtype = data_torch.dtype + # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) + data = data_torch.to(torch.float32).squeeze().numpy() + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + n_dims = len(data.shape) + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + +###### CONVERSION LOGIC ###### + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file") + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16"], default="f16", + help="output format - use f32 for float32, f16 for float16", + ) + parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") + parser.add_argument( + "model", type=Path, + help="directory containing model file", + ) + + return parser.parse_args() + + +args = parse_args() + +dir_model = args.model +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file=sys.stderr) + sys.exit(1) + +ftype_map = { + "f32": gguf.GGMLQuantizationType.F32, + "f16": gguf.GGMLQuantizationType.F16, +} + +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + +print(f"Loading model: {dir_model.name}") + +hparams = Model.load_hparams(dir_model) + +model_class = Model.from_model_architecture(hparams["architectures"][0]) +model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + +print("Set model parameters") +model_instance.set_gguf_parameters() + +print("Set model tokenizer") +model_instance.set_vocab() + +if args.vocab_only: + print(f"Exporting model vocab to '{fname_out}'") + model_instance.write_vocab() +else: + print(f"Exporting model to '{fname_out}'") + model_instance.write() + +print(f"Model successfully exported to '{fname_out}'") diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py deleted file mode 100755 index 70d154b3f..000000000 --- a/convert-mpt-hf-to-gguf.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -# HF mpt--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert an MPT model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", type=int, choices=[0, 1], default=1, nargs='?', - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "MPTForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.MPT -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layers"] - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_context_length(hparams["max_seq_len"]) -gguf_writer.add_embedding_length(hparams["d_model"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(4 * hparams["d_model"]) -gguf_writer.add_head_count(hparams["n_heads"]) -if kv_n_heads := hparams["attn_config"].get("kv_n_heads"): - gguf_writer.add_head_count_kv(kv_n_heads) -gguf_writer.add_layer_norm_eps(1e-05) -if hparams["attn_config"]["clip_qkv"] is not None: - gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"]) -gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"]) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but -# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to -# accomodate some "reserved" tokens; this is causing problems down the line in -# llama.cpp, so we pad the vocab with dummy tokens: - -vocab_size = hparams["vocab_size"] - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Cannot map tensor '" + name + "'") - continue # for the sake of compatibility with some old published models, don't quit - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - # note: MPT output is tied to (same as) wte in original model; - # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ - if new_name == "token_embd.weight": - gguf_writer.add_tensor("output.weight", data) - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-refact-hf-to-gguf.py b/convert-refact-hf-to-gguf.py deleted file mode 100755 index f0cfe84d8..000000000 --- a/convert-refact-hf-to-gguf.py +++ /dev/null @@ -1,272 +0,0 @@ -#!/usr/bin/env python3 -# HF refact--> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import sys -from pathlib import Path - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if "NO_LOCAL_GGUF" not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf")) -import gguf - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Convert a Refact model to a GGML compatible file" - ) - parser.add_argument( - "--vocab-only", - action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--outfile", - type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "model", - type=Path, - help="directory containing model file, or model file itself (*.bin)", - ) - parser.add_argument( - "ftype", - type=int, - choices=[0, 1], - default=1, - nargs="?", - help="output format - use 0 for float32, 1 for float16", - ) - return parser.parse_args() - - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f"Error: {args.model} is not a directory", file=sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f"ggml-model-{ftype_str[ftype]}.gguf" - -print("gguf: loading model " + dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTRefactForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH = gguf.MODEL_ARCH.REFACT -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -# Get refact feed forward dimension -hidden_dim = hparams["n_embd"] -inner_dim = 4 * hidden_dim -hidden_dim = int(2 * inner_dim / 3) -multiple_of = 256 -ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - -block_count = hparams["n_layer"] - -gguf_writer.add_name("Refact") -# refact uses Alibi. So this is from config.json which might be used by training. -gguf_writer.add_context_length(hparams["n_positions"]) -gguf_writer.add_embedding_length(hparams["n_embd"]) - -gguf_writer.add_feed_forward_length(ff_dim) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(hparams["n_head"]) -gguf_writer.add_head_count_kv(1) -gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH, block_count) - -# params for qkv transform -n_head = hparams["n_head"] -n_head_kv = 1 - -head_dim = hparams["n_embd"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - for i in range(block_count): - if f"transformer.h.{i}.attn.kv.weight" in model_part: - data = model_part[f"transformer.h.{i}.attn.kv.weight"] - model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[ - : n_head_kv * head_dim - ] - model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[ - n_head_kv * head_dim : - ] - del model_part[f"transformer.h.{i}.attn.kv.weight"] - if f"transformer.h.{i}.attn.q.weight" in model_part: - model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[ - f"transformer.h.{i}.attn.q.weight" - ] - del model_part[f"transformer.h.{i}.attn.q.weight"] - if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part: - data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"] - model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim] - model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:] - del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"] - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ( - ftype == 1 - and data_dtype == np.float32 - and name.endswith(".weight") - and n_dims == 2 - ): - data = data.astype(np.float16) - - print( - new_name - + ", n_dims = " - + str(n_dims) - + ", " - + str(old_dtype) - + " --> " - + str(data.dtype) - ) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py deleted file mode 100755 index a9bfed85e..000000000 --- a/convert-starcoder-hf-to-gguf.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -# HF starcoder --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from transformers import AutoTokenizer # type: ignore[import] - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "GPTBigCodeForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit(1) - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.STARCODER -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["n_layer"] - -gguf_writer.add_name("StarCoder") -gguf_writer.add_context_length(hparams["n_positions"]) -gguf_writer.add_embedding_length(hparams["n_embd"]) -gguf_writer.add_feed_forward_length(4 * hparams["n_embd"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(hparams["n_head"]) -gguf_writer.add_head_count_kv(1) -gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) -gguf_writer.add_file_type(ftype) - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytearray] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -# gpt2 tokenizer -gguf_writer.add_tokenizer_model("gpt2") - -print("gguf: get gpt2 tokenizer vocab") - -# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py -tokenizer = AutoTokenizer.from_pretrained(dir_model) - -# The number of tokens in tokenizer.json can differ from the expected vocab size. -# This causes downstream issues with mismatched tensor sizes when running the inference -vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) -assert max(tokenizer.vocab.values()) < vocab_size - -added_vocab = tokenizer.get_added_vocab() -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - -for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_types(toktypes) -special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens)) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# params for qkv transform -n_head = hparams["n_head"] -n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 - -head_dim = hparams["n_embd"] // n_head - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert.py b/convert.py index 9110f1580..b0f44dbef 100755 --- a/convert.py +++ b/convert.py @@ -26,7 +26,7 @@ from pathlib import Path from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar import numpy as np -from sentencepiece import SentencePieceProcessor # type: ignore[import] +from sentencepiece import SentencePieceProcessor import os if 'NO_LOCAL_GGUF' not in os.environ: @@ -328,7 +328,7 @@ class BpeVocab: def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.bpe_tokenizer - from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] + from transformers.models.gpt2 import tokenization_gpt2 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()} for i, _ in enumerate(tokenizer): diff --git a/mypy.ini b/mypy.ini index 55c168f2d..7215a05dd 100644 --- a/mypy.ini +++ b/mypy.ini @@ -3,3 +3,4 @@ strict = true allow_untyped_calls = true allow_untyped_defs = true allow_incomplete_defs = true +disable_error_code = import-untyped From df9d1293defe783f42bc83af732d3c670552c541 Mon Sep 17 00:00:00 2001 From: Galunid Date: Fri, 10 Nov 2023 14:24:54 +0100 Subject: [PATCH 43/50] Unbreak persimmon after #3837 (#4010) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index d220ff3e9..d682d2864 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4209,7 +4209,7 @@ struct llm_build_context { struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); cb(Kcur, "Kcur", il); - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3)); + struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); cb(Q, "Q", il); Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); From 4a4fd3eefad5bd17ab6bcd8e2181b4f62eae76cf Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 11 Nov 2023 06:49:33 +0800 Subject: [PATCH 44/50] server : allow continue edit on completion mode (#3950) * server : allow continue edit on completion mode * server : handle abort case in runCompletion * server : style improvement --- examples/server/index.html.hpp | 4807 +++++++++++++++-------------- examples/server/public/index.html | 38 +- 2 files changed, 2468 insertions(+), 2377 deletions(-) diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 207412513..f22b77e7f 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -229,850 +229,860 @@ unsigned char index_html[] = { 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x5d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x30, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6b, 0x65, 0x79, 0x66, 0x72, - 0x61, 0x6d, 0x65, 0x73, 0x20, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, - 0x2d, 0x62, 0x67, 0x2d, 0x77, 0x69, 0x70, 0x65, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x30, 0x25, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, - 0x6f, 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transcriptUpdate([...session.value.transcript, ["", prompt]]); - await runLlama(prompt, { + runLlama(prompt, { ...params.value, slot_id: slot_id, stop: [], - }, ""); + }, "").finally(() => { + session.value.prompt = session.value.transcript.map(([_, data]) => + Array.isArray(data) ? data.map(msg => msg.content).join('') : data + ).join(''); + session.value.transcript = []; + }) } const stop = (e) => { @@ -573,6 +583,7 @@ } }, [messages]) + const isCompletionMode = session.value.type === 'completion' const chatLine = ([user, data], index) => { let message const isArrayMessage = Array.isArray(data) @@ -582,20 +593,31 @@ const text = isArrayMessage ? data.map(msg => msg.content).join('').replace(/^\s+/, '') : data; - message = html`<${Markdownish} text=${template(text)} />` + message = isCompletionMode ? + text : + html`<${Markdownish} text=${template(text)} />` } if (user) { return html`

${template(user)}: ${message}

` } else { - return html`

${message}

` + return isCompletionMode ? + html`${message}` : + html`

${message}

` } }; + const handleCompletionEdit = (e) => { + session.value.prompt = e.target.innerText; + session.value.transcript = []; + } + return html` -
+
- ${messages.flatMap(chatLine)} -
`; + + ${messages.flatMap(chatLine)} + + `; }; const ConfigForm = (props) => { From 34b0a082074b073eb14c2bd93c0c070e20ddcd16 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 10 Nov 2023 22:04:50 -0700 Subject: [PATCH 45/50] gguf-py: Refactor and allow reading/modifying existing GGUF files (#3981) * gguf-py: Refactor and add file reading support * Replay changes from #3871 Credit to @cebtenzzre for that pull * Various type annotation fixes. * sort imports with isort (again) * Fix missing return statement in add_tensor * style cleanup with flake8 * fix NamedTuple and Enum usage * Fix an issue with state init in GGUFReader Move examples to an examples/ directory Clean up examples Add an example of modifying keys in a GGUF file Update documentation with info on examples Try to support people importing gguf/gguf.py directly * Damagage is not a word. * Clean up gguf-py/examples/modify_gguf.py whitespace Co-authored-by: Jared Van Bortel * Update gguf-py/examples/modify_gguf.py formatting Co-authored-by: Jared Van Bortel * Update gguf-py/gguf/gguf_reader.py type hint Co-authored-by: Jared Van Bortel * Make examples executable, formatting changes * Add more information to GGUFReader and examples comments * Include a gguf Python package version bump * Add convert-gguf-endian.py script * cleanup * gguf-py : bump minor version * Reorganize scripts * Make GGUFReader endian detection less arbitrary * Add JSON dumping support to gguf-dump.py Which I kind of regret now * A few for gguf-dump.py cleanups * Murder accidental tuple in gguf-py/scripts/gguf-dump.py Co-authored-by: Jared Van Bortel * cleanup * constants : remove unneeded type annotations * fix python 3.8 compat * Set up gguf- scripts in pyproject.toml * And include scripts/__init__.py, derp * convert.py: We can't currently support Q8_0 on big endian. * gguf-py: SpecialVocab: Always try available sources for special token ids gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata u * cleanup * Promote add_X_token to GGUF metadata for BOS and EOS --------- Co-authored-by: Jared Van Bortel Co-authored-by: Jared Van Bortel --- convert-baichuan-hf-to-gguf.py | 2 +- convert-llama-ggml-to-gguf.py | 24 +- convert-persimmon-to-gguf.py | 2 +- convert.py | 16 +- .../convert-train-checkpoint-to-gguf.py | 2 +- gguf-py/README.md | 10 + gguf-py/examples/writer.py | 40 + gguf-py/gguf/__init__.py | 6 +- gguf-py/gguf/constants.py | 470 +++++++ gguf-py/gguf/gguf.py | 1149 +---------------- gguf-py/gguf/gguf_reader.py | 264 ++++ gguf-py/gguf/gguf_writer.py | 409 ++++++ gguf-py/gguf/tensor_mapping.py | 257 ++++ gguf-py/gguf/vocab.py | 164 +++ gguf-py/pyproject.toml | 8 +- gguf-py/scripts/__init__.py | 12 + gguf-py/scripts/gguf-convert-endian.py | 113 ++ gguf-py/scripts/gguf-dump.py | 116 ++ gguf-py/scripts/gguf-set-metadata.py | 90 ++ gguf-py/tests/test_gguf.py | 4 +- 20 files changed, 1982 insertions(+), 1176 deletions(-) create mode 100755 gguf-py/examples/writer.py create mode 100644 gguf-py/gguf/constants.py create mode 100644 gguf-py/gguf/gguf_reader.py create mode 100644 gguf-py/gguf/gguf_writer.py create mode 100644 gguf-py/gguf/tensor_mapping.py create mode 100644 gguf-py/gguf/vocab.py create mode 100644 gguf-py/scripts/__init__.py create mode 100755 gguf-py/scripts/gguf-convert-endian.py create mode 100755 gguf-py/scripts/gguf-dump.py create mode 100755 gguf-py/scripts/gguf-set-metadata.py diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 67ccbe99f..789602351 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -16,7 +16,7 @@ import torch from sentencepiece import SentencePieceProcessor # type: ignore[import] if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index 871add64d..d898d81c4 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -12,29 +12,9 @@ import numpy as np import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -# Note: Does not support GGML_QKK_64 -QK_K = 256 -# Items here are (block size, type size) -GGML_QUANT_SIZES = { - gguf.GGMLQuantizationType.F32 : (1, 4), - gguf.GGMLQuantizationType.F16 : (1, 2), - gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16), - gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16), - gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16), - gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16), - gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32), - gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32), - gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4), - gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12), - gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), -} - class GGMLFormat(IntEnum): GGML = 0 GGMF = 1 @@ -125,7 +105,7 @@ class Tensor: (n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12]) assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}' assert name_len < 4096, 'Absurd tensor name length' - quant = GGML_QUANT_SIZES.get(dtype) + quant = gguf.GGML_QUANT_SIZES.get(dtype) assert quant is not None, 'Unknown tensor type' (blksize, tysize) = quant offset += 12 diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index e022ffe46..240f87306 100644 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -6,7 +6,7 @@ import argparse from pathlib import Path from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf def _flatten_dict(dct, tensors, prefix=None): diff --git a/convert.py b/convert.py index b0f44dbef..a4b87e088 100755 --- a/convert.py +++ b/convert.py @@ -3,11 +3,9 @@ from __future__ import annotations import argparse import concurrent.futures -import copy import enum import faulthandler import functools -import io import itertools import json import math @@ -23,14 +21,14 @@ from abc import ABCMeta, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar +from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar import numpy as np from sentencepiece import SentencePieceProcessor import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf if TYPE_CHECKING: @@ -851,7 +849,7 @@ class OutputFile: elif isinstance(vocab, BpeVocab): self.gguf.add_tokenizer_model("gpt2") else: - raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab') + raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab') self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) @@ -905,7 +903,7 @@ class OutputFile: return dt.quantize(arr) @staticmethod - def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None: + def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out, endianess=endianess) @@ -1114,11 +1112,15 @@ def do_dump_model(model_plus: ModelPlus) -> None: def main(args_in: list[str] | None = None) -> None: + output_choices = ["f32", "f16"] + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # We currently only support Q8_0 output on little endian systems. + output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") + parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py index 887ed2e21..ed93673bc 100644 --- a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py +++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py @@ -9,7 +9,7 @@ import numpy as np from pathlib import Path if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py')) import gguf # gguf constants diff --git a/gguf-py/README.md b/gguf-py/README.md index a28d8c57a..502b6a510 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -11,6 +11,16 @@ as an example for its usage. pip install gguf ``` +## API Examples/Simple Tools + +[examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model. + +[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console. + +[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key. + +[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files. + ## Development Maintainers who participate in development of this package are advised to install it in editable mode: diff --git a/gguf-py/examples/writer.py b/gguf-py/examples/writer.py new file mode 100755 index 000000000..f39eed1af --- /dev/null +++ b/gguf-py/examples/writer.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFWriter # noqa: E402 + + +# Example usage: +def writer_example() -> None: + # Example usage with a file + gguf_writer = GGUFWriter("example.gguf", "llama") + + gguf_writer.add_architecture() + gguf_writer.add_block_count(12) + gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer + gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float + gguf_writer.add_custom_alignment(64) + + tensor1 = np.ones((32,), dtype=np.float32) * 100.0 + tensor2 = np.ones((64,), dtype=np.float32) * 101.0 + tensor3 = np.ones((96,), dtype=np.float32) * 102.0 + + gguf_writer.add_tensor("tensor1", tensor1) + gguf_writer.add_tensor("tensor2", tensor2) + gguf_writer.add_tensor("tensor3", tensor3) + + gguf_writer.write_header_to_file() + gguf_writer.write_kv_data_to_file() + gguf_writer.write_tensors_to_file() + + gguf_writer.close() + + +if __name__ == '__main__': + writer_example() diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index f9b70a85b..110ab342c 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -1 +1,5 @@ -from .gguf import * +from .constants import * +from .gguf_reader import * +from .gguf_writer import * +from .tensor_mapping import * +from .vocab import * diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py new file mode 100644 index 000000000..bf1ccf669 --- /dev/null +++ b/gguf-py/gguf/constants.py @@ -0,0 +1,470 @@ +from __future__ import annotations + +import sys +from enum import Enum, IntEnum, auto +from typing import Any + +# +# constants +# + +GGUF_MAGIC = 0x46554747 # "GGUF" +GGUF_VERSION = 3 +GGUF_DEFAULT_ALIGNMENT = 32 + +# +# metadata keys +# + + +class Keys: + class General: + ARCHITECTURE = "general.architecture" + QUANTIZATION_VERSION = "general.quantization_version" + ALIGNMENT = "general.alignment" + NAME = "general.name" + AUTHOR = "general.author" + URL = "general.url" + DESCRIPTION = "general.description" + LICENSE = "general.license" + SOURCE_URL = "general.source.url" + SOURCE_HF_REPO = "general.source.huggingface.repository" + FILE_TYPE = "general.file_type" + + class LLM: + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + + class Attention: + HEAD_COUNT = "{arch}.attention.head_count" + HEAD_COUNT_KV = "{arch}.attention.head_count_kv" + MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" + CLAMP_KQV = "{arch}.attention.clamp_kqv" + LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" + LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + + class Rope: + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + + class Tokenizer: + MODEL = "tokenizer.ggml.model" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + + +# +# recommended mapping of model tensor names for storage in gguf +# + + +class MODEL_ARCH(IntEnum): + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + PERSIMMON = auto() + REFACT = auto() + BERT = auto() + BLOOM = auto() + + +class MODEL_TENSOR(IntEnum): + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_NORM = auto() + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + + +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.PERSIMMON: "persimmon", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.BLOOM: "bloom", +} + +TENSOR_NAMES: dict[MODEL_TENSOR, str] = { + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", +} + +MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTNEOX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STARCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MPT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTJ: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.REFACT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BLOOM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPT2: [ + # TODO + ], + # TODO +} + +# tensors that will not be serialized +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.ROPE_FREQS, + ], +} + +# +# types +# + + +class TokenType(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + + +class GGMLQuantizationType(IntEnum): + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + + +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + + +class GGUFValueType(IntEnum): + UINT8 = 0 + INT8 = 1 + UINT16 = 2 + INT16 = 3 + UINT32 = 4 + INT32 = 5 + FLOAT32 = 6 + BOOL = 7 + STRING = 8 + ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 + + @staticmethod + def get_type(val: Any) -> GGUFValueType: + if isinstance(val, (str, bytes, bytearray)): + return GGUFValueType.STRING + elif isinstance(val, list): + return GGUFValueType.ARRAY + elif isinstance(val, float): + return GGUFValueType.FLOAT32 + elif isinstance(val, bool): + return GGUFValueType.BOOL + elif isinstance(val, int): + return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python + else: + print("Unknown type:", type(val)) + sys.exit() + + +# Note: Does not support GGML_QKK_64 +QK_K = 256 +# Items here are (block size, type size) +GGML_QUANT_SIZES = { + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), +} + + +# Aliases for backward compatibility. + +# general +KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE +KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION +KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT +KEY_GENERAL_NAME = Keys.General.NAME +KEY_GENERAL_AUTHOR = Keys.General.AUTHOR +KEY_GENERAL_URL = Keys.General.URL +KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION +KEY_GENERAL_LICENSE = Keys.General.LICENSE +KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL +KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO +KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE + +# LLM +KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH +KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH +KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT +KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH +KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL +KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT + +# attention +KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT +KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV +KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS +KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV +KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS +KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS + +# RoPE +KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT +KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE +KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR +KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN +KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED + +# tokenization +KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST +KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE +KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES +KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES +KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID +KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID +KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID +KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID +KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON +KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 7e495cb19..651a81eb8 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -1,1146 +1,15 @@ -#!/usr/bin/env python3 -from __future__ import annotations +# This file left for compatibility. If you want to use the GGUF API from Python +# then don't import gguf/gguf.py directly. If you're looking for examples, see the +# examples/ directory for gguf-py -import json -import os -import shutil -import struct +import importlib import sys -import tempfile -from enum import Enum, IntEnum, auto -from io import BufferedWriter from pathlib import Path -from typing import IO, Any, BinaryIO, Callable, Sequence -import numpy as np +sys.path.insert(0, str(Path(__file__).parent.parent)) -# -# constants -# +# Compatibility for people trying to import gguf/gguf.py directly instead of as a package. +importlib.invalidate_caches() +import gguf # noqa: E402 -GGUF_MAGIC = 0x46554747 -GGUF_VERSION = 3 -GGUF_DEFAULT_ALIGNMENT = 32 - - -# general -KEY_GENERAL_ARCHITECTURE = "general.architecture" -KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version" -KEY_GENERAL_ALIGNMENT = "general.alignment" -KEY_GENERAL_NAME = "general.name" -KEY_GENERAL_AUTHOR = "general.author" -KEY_GENERAL_URL = "general.url" -KEY_GENERAL_DESCRIPTION = "general.description" -KEY_GENERAL_LICENSE = "general.license" -KEY_GENERAL_SOURCE_URL = "general.source.url" -KEY_GENERAL_SOURCE_HF_REPO = "general.source.huggingface.repository" -KEY_GENERAL_FILE_TYPE = "general.file_type" - -# LLM -KEY_CONTEXT_LENGTH = "{arch}.context_length" -KEY_EMBEDDING_LENGTH = "{arch}.embedding_length" -KEY_BLOCK_COUNT = "{arch}.block_count" -KEY_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" -KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" -KEY_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - -# attention -KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count" -KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv" -KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" -KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv" -KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" -KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" - -# RoPE -KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" -KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" -KEY_ROPE_SCALING_TYPE = "{arch}.rope.scaling.type" -KEY_ROPE_SCALING_FACTOR = "{arch}.rope.scaling.factor" -KEY_ROPE_SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" -KEY_ROPE_SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" - -# tokenization -KEY_TOKENIZER_MODEL = "tokenizer.ggml.model" -KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens" -KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type" -KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores" -KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges" -KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id" -KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id" -KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id" -KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id" -KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id" -KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json" -KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world" - - -# -# recommended mapping of model tensor names for storage in gguf -# - - -class MODEL_ARCH(IntEnum): - LLAMA : int = auto() - FALCON : int = auto() - BAICHUAN : int = auto() - GPT2 : int = auto() - GPTJ : int = auto() - GPTNEOX : int = auto() - MPT : int = auto() - STARCODER : int = auto() - PERSIMMON : int = auto() - REFACT : int = auto() - BERT : int = auto() - BLOOM : int = auto() - - -class MODEL_TENSOR(IntEnum): - TOKEN_EMBD : int = auto() - TOKEN_EMBD_NORM : int = auto() - TOKEN_TYPES : int = auto() - POS_EMBD : int = auto() - OUTPUT : int = auto() - OUTPUT_NORM : int = auto() - ROPE_FREQS : int = auto() - ATTN_Q : int = auto() - ATTN_K : int = auto() - ATTN_V : int = auto() - ATTN_QKV : int = auto() - ATTN_OUT : int = auto() - ATTN_NORM : int = auto() - ATTN_NORM_2 : int = auto() - ATTN_ROT_EMBD : int = auto() - FFN_GATE : int = auto() - FFN_DOWN : int = auto() - FFN_UP : int = auto() - FFN_NORM : int = auto() - ATTN_Q_NORM : int = auto() - ATTN_K_NORM : int = auto() - - -MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { - MODEL_ARCH.LLAMA: "llama", - MODEL_ARCH.FALCON: "falcon", - MODEL_ARCH.BAICHUAN: "baichuan", - MODEL_ARCH.GPT2: "gpt2", - MODEL_ARCH.GPTJ: "gptj", - MODEL_ARCH.GPTNEOX: "gptneox", - MODEL_ARCH.MPT: "mpt", - MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", - MODEL_ARCH.REFACT: "refact", - MODEL_ARCH.BERT: "bert", - MODEL_ARCH.BLOOM: "bloom", -} - -TENSOR_NAMES: dict[MODEL_TENSOR, str] = { - MODEL_TENSOR.TOKEN_EMBD: "token_embd", - MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", - MODEL_TENSOR.TOKEN_TYPES: "token_types", - MODEL_TENSOR.POS_EMBD: "position_embd", - MODEL_TENSOR.OUTPUT_NORM: "output_norm", - MODEL_TENSOR.OUTPUT: "output", - MODEL_TENSOR.ROPE_FREQS: "rope_freqs", - MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", - MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", - MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", - MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", - MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", - MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", - MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", - MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", - MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", - MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", - MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", - MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", - MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", - MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", -} - -MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTNEOX: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.FALCON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_NORM_2, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.STARCODER: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BERT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_TYPES, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.MPT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTJ: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.REFACT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BLOOM: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_EMBD_NORM, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPT2: [ - # TODO - ], - # TODO -} - -# tensors that will not be serialized -MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ] -} - - -class TensorNameMap: - mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Token embeddings - MODEL_TENSOR.TOKEN_EMBD: ( - "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact - "transformer.word_embeddings", # falcon - "word_embeddings", # bloom - "model.embed_tokens", # llama-hf - "tok_embeddings", # llama-pth - "embeddings.word_embeddings", # bert - "language_model.embedding.word_embeddings", # persimmon - ), - - # Token type embeddings - MODEL_TENSOR.TOKEN_TYPES: ( - "embeddings.token_type_embeddings", # bert - ), - - # Normalization of token embeddings - MODEL_TENSOR.TOKEN_EMBD_NORM: ( - "word_embeddings_layernorm", # bloom - ), - - # Position embeddings - MODEL_TENSOR.POS_EMBD: ( - "transformer.wpe", # gpt2 - "embeddings.position_embeddings", # bert - ), - - # Output - MODEL_TENSOR.OUTPUT: ( - "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan - "output", # llama-pth bloom - "word_embeddings_for_head", # persimmon - ), - - # Output norm - MODEL_TENSOR.OUTPUT_NORM: ( - "gpt_neox.final_layer_norm", # gptneox - "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan - "norm", # llama-pth - "embeddings.LayerNorm", # bert - "transformer.norm_f", # mpt - "ln_f", # refact bloom - "language_model.encoder.final_layernorm", # persimmon - ), - - # Rope frequencies - MODEL_TENSOR.ROPE_FREQS: ( - "rope.freqs", # llama-pth - ), - } - - block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Attention norm - MODEL_TENSOR.ATTN_NORM: ( - "gpt_neox.layers.{bid}.input_layernorm", # gptneox - "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact - "transformer.blocks.{bid}.norm_1", # mpt - "transformer.h.{bid}.input_layernorm", # falcon7b - "h.{bid}.input_layernorm", # bloom - "transformer.h.{bid}.ln_mlp", # falcon40b - "model.layers.{bid}.input_layernorm", # llama-hf - "layers.{bid}.attention_norm", # llama-pth - "encoder.layer.{bid}.attention.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.input_layernorm", # persimmon - "model.layers.{bid}.ln1", # yi - ), - - # Attention norm 2 - MODEL_TENSOR.ATTN_NORM_2: ( - "transformer.h.{bid}.ln_attn", # falcon40b - ), - - # Attention query-key-value - MODEL_TENSOR.ATTN_QKV: ( - "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox - "transformer.h.{bid}.attn.c_attn", # gpt2 - "transformer.blocks.{bid}.attn.Wqkv", # mpt - "transformer.h.{bid}.self_attention.query_key_value", # falcon - "h.{bid}.self_attention.query_key_value", # bloom - "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon - ), - - # Attention query - MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf - "layers.{bid}.attention.wq", # llama-pth - "encoder.layer.{bid}.attention.self.query", # bert - "transformer.h.{bid}.attn.q_proj", # gpt-j - ), - - # Attention key - MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf - "layers.{bid}.attention.wk", # llama-pth - "encoder.layer.{bid}.attention.self.key", # bert - "transformer.h.{bid}.attn.k_proj", # gpt-j - ), - - # Attention value - MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf - "layers.{bid}.attention.wv", # llama-pth - "encoder.layer.{bid}.attention.self.value", # bert - "transformer.h.{bid}.attn.v_proj", # gpt-j - ), - - # Attention output - MODEL_TENSOR.ATTN_OUT: ( - "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact - "transformer.blocks.{bid}.attn.out_proj", # mpt - "transformer.h.{bid}.self_attention.dense", # falcon - "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf - "layers.{bid}.attention.wo", # llama-pth - "encoder.layer.{bid}.attention.output.dense", # bert - "transformer.h.{bid}.attn.out_proj", # gpt-j - "language_model.encoder.layers.{bid}.self_attention.dense" # persimmon - ), - - # Rotary embeddings - MODEL_TENSOR.ATTN_ROT_EMBD: ( - "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf - "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth - ), - - # Feed-forward norm - MODEL_TENSOR.FFN_NORM: ( - "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox - "transformer.h.{bid}.ln_2", # gpt2 refact - "h.{bid}.post_attention_layernorm", # bloom - "transformer.blocks.{bid}.norm_2", # mpt - "model.layers.{bid}.post_attention_layernorm", # llama-hf - "layers.{bid}.ffn_norm", # llama-pth - "encoder.layer.{bid}.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon - "model.layers.{bid}.ln2", # yi - ), - - # Feed-forward up - MODEL_TENSOR.FFN_UP: ( - "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox - "transformer.h.{bid}.mlp.c_fc", # gpt2 - "transformer.blocks.{bid}.ffn.up_proj", # mpt - "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon - "h.{bid}.mlp.dense_h_to_4h", # bloom - "model.layers.{bid}.mlp.up_proj", # llama-hf refact - "layers.{bid}.feed_forward.w3", # llama-pth - "encoder.layer.{bid}.intermediate.dense", # bert - "transformer.h.{bid}.mlp.fc_in", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon - ), - - # Feed-forward gate - MODEL_TENSOR.FFN_GATE: ( - "model.layers.{bid}.mlp.gate_proj", # llama-hf refact - "layers.{bid}.feed_forward.w1", # llama-pth - ), - - # Feed-forward down - MODEL_TENSOR.FFN_DOWN: ( - "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox - "transformer.h.{bid}.mlp.c_proj", # gpt2 refact - "transformer.blocks.{bid}.ffn.down_proj", # mpt - "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon - "h.{bid}.mlp.dense_4h_to_h", # bloom - "model.layers.{bid}.mlp.down_proj", # llama-hf - "layers.{bid}.feed_forward.w2", # llama-pth - "encoder.layer.{bid}.output.dense", # bert - "transformer.h.{bid}.mlp.fc_out", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon - ), - - MODEL_TENSOR.ATTN_Q_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.q_layernorm", - ), - - MODEL_TENSOR.ATTN_K_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.k_layernorm", - ), - - MODEL_TENSOR.ROPE_FREQS: ( - "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon - ) - } - - mapping: dict[str, tuple[MODEL_TENSOR, str]] - - def __init__(self, arch: MODEL_ARCH, n_blocks: int): - self.mapping = {} - for tensor, keys in self.mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor] - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - self.mapping[key] = (tensor, tensor_name) - for bid in range(n_blocks): - for tensor, keys in self.block_mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor].format(bid = bid) - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - key = key.format(bid = bid) - self.mapping[key] = (tensor, tensor_name) - - def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: - result = self.mapping.get(key) - if result is not None: - return result - for suffix in try_suffixes: - if key.endswith(suffix): - result = self.mapping.get(key[:-len(suffix)]) - if result is not None: - return (result[0], result[1] + suffix) - return None - - def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: - result = self.get_type_and_name(key, try_suffixes = try_suffixes) - if result is None: - return None - return result[1] - - def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: - result = self.get_type_and_name(key, try_suffixes = try_suffixes) - if result is None: - return None - return result[0] - - def __getitem__(self, key: str) -> str: - try: - return self.mapping[key][1] - except KeyError: - raise KeyError(key) - - def __contains__(self, key: str) -> bool: - return key in self.mapping - - def __repr__(self) -> str: - return repr(self.mapping) - -def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: - return TensorNameMap(arch, n_blocks) - -class TokenType(IntEnum): - NORMAL = 1 - UNKNOWN = 2 - CONTROL = 3 - USER_DEFINED = 4 - UNUSED = 5 - BYTE = 6 - -class RopeScalingType(Enum): - NONE = 'none' - LINEAR = 'linear' - YARN = 'yarn' - -# -# implementation -# - - -class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 - -class GGUFEndian(IntEnum): - LITTLE = 0 - BIG = 1 - - -class GGUFValueType(IntEnum): - UINT8 = 0 - INT8 = 1 - UINT16 = 2 - INT16 = 3 - UINT32 = 4 - INT32 = 5 - FLOAT32 = 6 - BOOL = 7 - STRING = 8 - ARRAY = 9 - UINT64 = 10 - INT64 = 11 - FLOAT64 = 12 - - @staticmethod - def get_type(val): - if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray): - return GGUFValueType.STRING - elif isinstance(val, list): - return GGUFValueType.ARRAY - elif isinstance(val, float): - return GGUFValueType.FLOAT32 - elif isinstance(val, bool): - return GGUFValueType.BOOL - elif isinstance(val, int): - return GGUFValueType.INT32 - # TODO: need help with 64-bit types in Python - else: - print("Unknown type: "+str(type(val))) - sys.exit() - - -class WriterState(Enum): - EMPTY = auto() - HEADER = auto() - KV_DATA = auto() - TI_DATA = auto() - - -class GGUFWriter: - fout: BufferedWriter - temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any]] - - @property - def pack_prefix(self): - if self.endianess==GGUFEndian.LITTLE: - return "<" - else: - return ">" - - def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE): - self.fout = open(path, "wb") - self.arch = arch - self.endianess = endianess - self._simple_value_packing = { - GGUFValueType.UINT8: f"{self.pack_prefix}B", - GGUFValueType.INT8: f"{self.pack_prefix}b", - GGUFValueType.UINT16: f"{self.pack_prefix}H", - GGUFValueType.INT16: f"{self.pack_prefix}h", - GGUFValueType.UINT32: f"{self.pack_prefix}I", - GGUFValueType.INT32: f"{self.pack_prefix}i", - GGUFValueType.FLOAT32: f"{self.pack_prefix}f", - GGUFValueType.UINT64: f"{self.pack_prefix}Q", - GGUFValueType.INT64: f"{self.pack_prefix}q", - GGUFValueType.FLOAT64: f"{self.pack_prefix}d", - GGUFValueType.BOOL: "?" , - } - self.offset_tensor = 0 - self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" - self.kv_data_count = 0 - self.ti_data = b"" - self.ti_data_count = 0 - self.use_temp_file = use_temp_file - self.temp_file = None - self.tensors = [] - endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" - print(f"This gguf file is for {endianess_str} only") - self.state = WriterState.EMPTY - - self.add_architecture() - - def write_header_to_file(self): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - self.fout.write(struct.pack(" 0: - ltype = GGUFValueType.get_type(val[0]) - if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): - raise ValueError("All items in a GGUF array should be of the same type") - self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype) - self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val)) - for item in val: - self.add_val(item, add_vtype=False) - else: - raise ValueError("Invalid GGUF metadata value type or value") - - @staticmethod - def ggml_pad(x: int, n: int) -> int: - return ((x + n - 1) // n) * n - - def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" - - encoded_name = name.encode("utf8") - self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name)) - self.ti_data += encoded_name - n_dims = len(tensor_shape) - self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims) - for i in range(n_dims): - self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i]) - if raw_dtype is None: - dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 - else: - dtype = raw_dtype - self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype) - self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor) - self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) - self.ti_data_count += 1 - - def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None): - if self.endianess == GGUFEndian.BIG: - tensor.byteswap(inplace=True) - if self.use_temp_file and self.temp_file is None: - fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) - fp.seek(0) - self.temp_file = fp - - shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape - self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) - - if self.temp_file is None: - self.tensors.append(tensor) - return - - tensor.tofile(self.temp_file) - self.write_padding(self.temp_file, tensor.nbytes) - - def write_padding(self, fp: IO[bytes], n: int, align: int | None = None): - pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n - if pad != 0: - fp.write(bytes([0] * pad)) - - def write_tensor_data(self, tensor: np.ndarray[Any, Any]): - if self.state is not WriterState.TI_DATA: - raise ValueError(f'Expected output file to contain tensor info, got {self.state}') - - if self.endianess==GGUFEndian.BIG: - tensor.byteswap(inplace=True) - self.write_padding(self.fout, self.fout.tell()) - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - - def write_tensors_to_file(self): - self.write_ti_data_to_file() - - self.write_padding(self.fout, self.fout.tell()) - - if self.temp_file is None: - while True: - try: - tensor = self.tensors.pop(0) - except IndexError: - break - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - return - - self.temp_file.seek(0) - - shutil.copyfileobj(self.temp_file, self.fout) - self.flush() - self.temp_file.close() - - def flush(self): - self.fout.flush() - - def close(self): - self.fout.close() - - def add_architecture(self): - self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch) - - def add_author(self, author: str): - self.add_string(KEY_GENERAL_AUTHOR, author) - - def add_tensor_data_layout(self, layout: str): - self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) - - def add_url(self, url: str): - self.add_string(KEY_GENERAL_URL, url) - - def add_description(self, description: str): - self.add_string(KEY_GENERAL_DESCRIPTION, description) - - def add_source_url(self, url: str): - self.add_string(KEY_GENERAL_SOURCE_URL, url) - - def add_source_hf_repo(self, repo: str): - self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo) - - def add_file_type(self, ftype: int): - self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype) - - def add_name(self, name: str): - self.add_string(KEY_GENERAL_NAME, name) - - def add_quantization_version(self, quantization_version: GGMLQuantizationType): - self.add_uint32( - KEY_GENERAL_QUANTIZATION_VERSION, quantization_version) - - def add_custom_alignment(self, alignment: int): - self.data_alignment = alignment - self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment) - - def add_context_length(self, length: int): - self.add_uint32( - KEY_CONTEXT_LENGTH.format(arch=self.arch), length) - - def add_embedding_length(self, length: int): - self.add_uint32( - KEY_EMBEDDING_LENGTH.format(arch=self.arch), length) - - def add_block_count(self, length: int): - self.add_uint32( - KEY_BLOCK_COUNT.format(arch=self.arch), length) - - def add_feed_forward_length(self, length: int): - self.add_uint32( - KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length) - - def add_parallel_residual(self, use: bool): - self.add_bool( - KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) - - def add_head_count(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count) - - def add_head_count_kv(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count) - - def add_max_alibi_bias(self, bias: float): - self.add_float32( - KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias) - - def add_clamp_kqv(self, value: float): - self.add_float32( - KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value) - - def add_layer_norm_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value) - - def add_layer_norm_rms_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value) - - def add_rope_dimension_count(self, count: int): - self.add_uint32( - KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count) - - def add_rope_freq_base(self, value: float): - self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value) - - def add_rope_scaling_type(self, value: RopeScalingType): - self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value) - - def add_rope_scaling_factor(self, value: float): - self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value) - - def add_rope_scaling_orig_ctx_len(self, value: int): - self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) - - def add_rope_scaling_finetuned(self, value: bool): - self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value) - - def add_tokenizer_model(self, model: str): - self.add_string(KEY_TOKENIZER_MODEL, model) - - def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): - self.add_array(KEY_TOKENIZER_LIST, tokens) - - def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): - self.add_array(KEY_TOKENIZER_MERGES, merges) - - def add_token_types(self, types: Sequence[TokenType] | Sequence[int]): - self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types) - - def add_token_scores(self, scores: Sequence[float]): - self.add_array(KEY_TOKENIZER_SCORES, scores) - - def add_bos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_BOS_ID, id) - - def add_eos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_EOS_ID, id) - - def add_unk_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_UNK_ID, id) - - def add_sep_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_SEP_ID, id) - - def add_pad_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_PAD_ID, id) - - -class SpecialVocab: - merges: list[str] - special_token_ids: dict[str, int] - - def __init__( - self, path: str | os.PathLike[str], load_merges: bool = False, - special_token_types: tuple[str, ...] | None = None, - n_vocab: int | None = None, - ): - self.special_token_ids = {} - self.n_vocab = n_vocab - self.load_merges = load_merges - self.merges = [] - if special_token_types is not None: - self.special_token_types = special_token_types - else: - self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') - self._load(Path(path)) - - def _load(self, path: Path) -> None: - if not self._try_load_from_tokenizer_json(path): - self._try_load_from_config_json(path) - - def _set_special_token(self, typ: str, tid: Any): - if not isinstance(tid, int) or tid < 0: - return - if self.n_vocab is None or tid < self.n_vocab: - self.special_token_ids[typ] = tid - return - print(f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', - file = sys.stderr) - - - def _try_load_from_tokenizer_json(self, path: Path) -> bool: - tokenizer_file = path / 'tokenizer.json' - if not tokenizer_file.is_file(): - return False - with open(tokenizer_file, encoding = 'utf-8') as f: - tokenizer = json.load(f) - if self.load_merges: - merges = tokenizer.get('model', {}).get('merges') - if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str): - self.merges = merges - tokenizer_config_file = path / 'tokenizer_config.json' - added_tokens = tokenizer.get('added_tokens') - if added_tokens is None or not tokenizer_config_file.is_file(): - return True - with open(tokenizer_config_file, encoding = 'utf-8') as f: - tokenizer_config = json.load(f) - for typ in self.special_token_types: - entry = tokenizer_config.get(f'{typ}_token') - if isinstance(entry, str): - tc_content = entry - elif isinstance(entry, dict): - entry_content = entry.get('content') - if not isinstance(entry_content, str): - continue - tc_content = entry_content - else: - continue - # We only need the first match here. - maybe_token_id = next(( - atok.get('id') for atok in added_tokens - if atok.get('content') == tc_content), None) - self._set_special_token(typ, maybe_token_id) - return True - - def _try_load_from_config_json(self, path: Path) -> bool: - config_file = path / 'config.json' - if not config_file.is_file(): - return False - with open(config_file, encoding = 'utf-8') as f: - config = json.load(f) - for typ in self.special_token_types: - self._set_special_token(typ, config.get(f'{typ}_token_id')) - return True - - def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: - if len(self.merges) > 0: - if not quiet: - print(f'gguf: Adding {len(self.merges)} merge(s).') - gw.add_token_merges(self.merges) - for typ, tokid in self.special_token_ids.items(): - handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) - if handler is None: - print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', file = sys.stderr) - continue - if not quiet: - print(f'gguf: Setting special token type {typ} to {tokid}') - handler(tokid) - - def __repr__(self) -> str: - return f'' - - -# Example usage: -if __name__ == "__main__": - # Example usage with a file - gguf_writer = GGUFWriter("example.gguf", "llama") - - gguf_writer.add_architecture() - gguf_writer.add_block_count(12) - gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer - gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float - gguf_writer.add_custom_alignment(64) - - tensor1 = np.ones((32,), dtype=np.float32) * 100.0 - tensor2 = np.ones((64,), dtype=np.float32) * 101.0 - tensor3 = np.ones((96,), dtype=np.float32) * 102.0 - - gguf_writer.add_tensor("tensor1", tensor1) - gguf_writer.add_tensor("tensor2", tensor2) - gguf_writer.add_tensor("tensor3", tensor3) - - gguf_writer.write_header_to_file() - gguf_writer.write_kv_data_to_file() - gguf_writer.write_tensors_to_file() - - gguf_writer.close() +importlib.reload(gguf) diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py new file mode 100644 index 000000000..8682765ed --- /dev/null +++ b/gguf-py/gguf/gguf_reader.py @@ -0,0 +1,264 @@ +# +# GGUF file reading/modification support. For API usage information, +# please see the files scripts/ for some fairly simple examples. +# +from __future__ import annotations + +import os +from collections import OrderedDict +from typing import Any, Literal, NamedTuple, TypeVar, Union + +import numpy as np +import numpy.typing as npt + +if __name__ == "__main__": + import sys + from pathlib import Path + + # Allow running file in package as a script. + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.constants import ( + GGML_QUANT_SIZES, + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFValueType, +) + + +READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] + + +class ReaderField(NamedTuple): + # Offset to start of this field. + offset: int + + # Name of the field (not necessarily from file data). + name: str + + # Data parts. Some types have multiple components, such as strings + # that consist of a length followed by the string data. + parts: list[npt.NDArray[Any]] = [] + + # Indexes into parts that we can call the actual data. For example + # an array of strings will be populated with indexes to the actual + # string data. + data: list[int] = [-1] + + types: list[GGUFValueType] = [] + + +class ReaderTensor(NamedTuple): + name: str + tensor_type: GGMLQuantizationType + shape: npt.NDArray[np.uint32] + n_elements: int + n_bytes: int + data_offset: int + data: npt.NDArray[Any] + field: ReaderField + + +class GGUFReader: + # I - same as host, S - swapped + byte_order: Literal['I' | 'S'] = 'I' + alignment: int = GGUF_DEFAULT_ALIGNMENT + + # Note: Internal helper, API may change. + gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = { + GGUFValueType.UINT8: np.uint8, + GGUFValueType.INT8: np.int8, + GGUFValueType.UINT16: np.uint16, + GGUFValueType.INT16: np.int16, + GGUFValueType.UINT32: np.uint32, + GGUFValueType.INT32: np.int32, + GGUFValueType.FLOAT32: np.float32, + GGUFValueType.UINT64: np.uint64, + GGUFValueType.INT64: np.int64, + GGUFValueType.FLOAT64: np.float64, + GGUFValueType.BOOL: np.bool_, + } + + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'): + self.data = np.memmap(path, mode = mode) + offs = 0 + if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: + raise ValueError('GGUF magic invalid') + offs += 4 + temp_version = self._get(offs, np.uint32) + if temp_version[0] & 65535 == 0: + # If we get 0 here that means it's (probably) a GGUF file created for + # the opposite byte order of the machine this script is running on. + self.byte_order = 'S' + temp_version = temp_version.newbyteorder(self.byte_order) + version = temp_version[0] + if version not in READER_SUPPORTED_VERSIONS: + raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle') + self.fields: OrderedDict[str, ReaderField] = OrderedDict() + self.tensors: list[ReaderTensor] = [] + offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32])) + temp_counts = self._get(offs, np.uint64, 2) + offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64])) + offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64])) + tensor_count, kv_count = temp_counts + offs = self._build_fields(offs, kv_count) + offs, tensors_fields = self._build_tensors_fields(offs, tensor_count) + new_align = self.fields.get('general.alignment') + if new_align is not None: + if new_align.types != [GGUFValueType.UINT64]: + raise ValueError('Bad type for general.alignment field') + self.alignment = new_align.parts[-1][0] + padding = offs % self.alignment + if padding != 0: + offs += self.alignment - padding + self._build_tensors(offs, tensors_fields) + + _DT = TypeVar('_DT', bound = npt.DTypeLike) + + # Fetch a key/value metadata field by key. + def get_field(self, key: str) -> Union[ReaderField, None]: + return self.fields.get(key, None) + + # Fetch a tensor from the list by index. + def get_tensor(self, idx: int) -> ReaderTensor: + return self.tensors[idx] + + def _get( + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None, + ) -> npt.NDArray[Any]: + count = int(count) + itemsize = int(np.empty([], dtype = dtype).itemsize) + end_offs = offset + itemsize * count + return ( + self.data[offset:end_offs] + .view(dtype = dtype)[:count] + .newbyteorder(override_order or self.byte_order) + ) + + def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: + if field.name in self.fields: + raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + self.fields[field.name] = field + return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) + + def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: + slen = self._get(offset, np.uint64) + return slen, self._get(offset + 8, np.uint8, slen[0]) + + def _get_field_parts( + self, orig_offs: int, raw_type: int, + ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]: + offs = orig_offs + types: list[GGUFValueType] = [] + gtype = GGUFValueType(raw_type) + types.append(gtype) + # Handle strings. + if gtype == GGUFValueType.STRING: + sparts: list[npt.NDArray[Any]] = list(self._get_str(offs)) + size = sum(int(part.nbytes) for part in sparts) + return size, sparts, [1], types + # Check if it's a simple scalar type. + nptype = self.gguf_scalar_to_np.get(gtype) + if nptype is not None: + val = self._get(offs, nptype) + return int(val.nbytes), [val], [0], types + # Handle arrays. + if gtype == GGUFValueType.ARRAY: + raw_itype = self._get(offs, np.uint32) + offs += int(raw_itype.nbytes) + alen = self._get(offs, np.uint64) + offs += int(alen.nbytes) + aparts: list[npt.NDArray[Any]] = [raw_itype, alen] + data_idxs: list[int] = [] + for idx in range(alen[0]): + curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0]) + if idx == 0: + types += curr_types + idxs_offs = len(aparts) + aparts += curr_parts + data_idxs += (idx + idxs_offs for idx in curr_idxs) + offs += curr_size + return offs - orig_offs, aparts, data_idxs, types + # We can't deal with this one. + raise ValueError('Unknown/unhandled field type {gtype}') + + def _get_tensor(self, orig_offs: int) -> ReaderField: + offs = orig_offs + name_len, name_data = self._get_str(offs) + offs += int(name_len.nbytes + name_data.nbytes) + n_dims = self._get(offs, np.uint32) + offs += int(n_dims.nbytes) + dims = self._get(offs, np.uint64, n_dims[0]) + offs += int(dims.nbytes) + raw_dtype = self._get(offs, np.uint32) + offs += int(raw_dtype.nbytes) + offset_tensor = self._get(offs, np.uint64) + offs += int(offset_tensor.nbytes) + return ReaderField( + orig_offs, + str(bytes(name_data), encoding = 'utf-8'), + [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor], + [1, 3, 4, 5], + ) + + def _build_fields(self, offs: int, count: int) -> int: + for _ in range(count): + orig_offs = offs + kv_klen, kv_kdata = self._get_str(offs) + offs += int(kv_klen.nbytes + kv_kdata.nbytes) + raw_kv_type = self._get(offs, np.uint32) + offs += int(raw_kv_type.nbytes) + parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type] + idxs_offs = len(parts) + field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0]) + parts += field_parts + self._push_field(ReaderField( + orig_offs, + str(bytes(kv_kdata), encoding = 'utf-8'), + parts, + [idx + idxs_offs for idx in field_idxs], + field_types, + ), skip_sum = True) + offs += field_size + return offs + + def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]: + tensor_fields = [] + for _ in range(count): + field = self._get_tensor(offs) + offs += sum(int(part.nbytes) for part in field.parts) + tensor_fields.append(field) + return offs, tensor_fields + + def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: + tensors = [] + for field in fields: + _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + ggml_type = GGMLQuantizationType(raw_dtype[0]) + n_elems = np.prod(dims) + block_size, type_size = GGML_QUANT_SIZES[ggml_type] + n_bytes = n_elems * type_size // block_size + data_offs = int(start_offs + offset_tensor[0]) + item_type: npt.DTypeLike + if ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + else: + item_count = n_bytes + item_type = np.uint8 + tensors.append(ReaderTensor( + name = str(bytes(name_data), encoding = 'utf-8'), + tensor_type = ggml_type, + shape = dims, + n_elements = n_elems, + n_bytes = n_bytes, + data_offset = data_offs, + data = self._get(data_offs, item_type, item_count), + field = field, + )) + self.tensors = tensors diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py new file mode 100644 index 000000000..75fb6976f --- /dev/null +++ b/gguf-py/gguf/gguf_writer.py @@ -0,0 +1,409 @@ +from __future__ import annotations + +import os +import shutil +import struct +import tempfile +from enum import Enum, auto +from io import BufferedWriter +from typing import IO, Any, Sequence + +import numpy as np + +from .constants import ( + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFEndian, + GGUFValueType, + Keys, + RopeScalingType, + TokenType, +) + + +class WriterState(Enum): + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + + +class GGUFWriter: + fout: BufferedWriter + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[np.ndarray[Any, Any]] + _simple_value_packing = { + GGUFValueType.UINT8: "B", + GGUFValueType.INT8: "b", + GGUFValueType.UINT16: "H", + GGUFValueType.INT16: "h", + GGUFValueType.UINT32: "I", + GGUFValueType.INT32: "i", + GGUFValueType.FLOAT32: "f", + GGUFValueType.UINT64: "Q", + GGUFValueType.INT64: "q", + GGUFValueType.FLOAT64: "d", + GGUFValueType.BOOL: "?", + } + + def __init__( + self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True, + endianess: GGUFEndian = GGUFEndian.LITTLE, + ): + self.fout = open(path, "wb") + self.arch = arch + self.endianess = endianess + self.offset_tensor = 0 + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.kv_data = b"" + self.kv_data_count = 0 + self.ti_data = b"" + self.ti_data_count = 0 + self.use_temp_file = use_temp_file + self.temp_file = None + self.tensors = [] + print("gguf: This GGUF file is for {0} Endian only".format( + "Big" if self.endianess == GGUFEndian.BIG else "Little", + )) + self.state = WriterState.EMPTY + + self.add_architecture() + + def write_header_to_file(self) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + self._write_packed(" None: + if self.state is not WriterState.HEADER: + raise ValueError(f'Expected output file to contain the header, got {self.state}') + + self.fout.write(self.kv_data) + self.flush() + self.state = WriterState.KV_DATA + + def write_ti_data_to_file(self) -> None: + if self.state is not WriterState.KV_DATA: + raise ValueError(f'Expected output file to contain KV data, got {self.state}') + + self.fout.write(self.ti_data) + self.flush() + self.state = WriterState.TI_DATA + + def add_key(self, key: str) -> None: + self.add_val(key, GGUFValueType.STRING, add_vtype=False) + + def add_uint8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT8) + + def add_int8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT8) + + def add_uint16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT16) + + def add_int16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT16) + + def add_uint32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT32) + + def add_int32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT32) + + def add_float32(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT32) + + def add_uint64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT64) + + def add_int64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT64) + + def add_float64(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT64) + + def add_bool(self, key: str, val: bool) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.BOOL) + + def add_string(self, key: str, val: str) -> None: + if not val: + return + self.add_key(key) + self.add_val(val, GGUFValueType.STRING) + + def add_array(self, key: str, val: Sequence[Any]) -> None: + if not isinstance(val, Sequence): + raise ValueError("Value must be a sequence for array type") + + self.add_key(key) + self.add_val(val, GGUFValueType.ARRAY) + + def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None: + if vtype is None: + vtype = GGUFValueType.get_type(val) + + if add_vtype: + self.kv_data += self._pack("I", vtype) + self.kv_data_count += 1 + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf8") if isinstance(val, str) else val + self.kv_data += self._pack("Q", len(encoded_val)) + self.kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + self.kv_data += self._pack("I", ltype) + self.kv_data += self._pack("Q", len(val)) + for item in val: + self.add_val(item, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + @staticmethod + def ggml_pad(x: int, n: int) -> int: + return ((x + n - 1) // n) * n + + def add_tensor_info( + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], + tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + if raw_dtype is None and tensor_dtype not in (np.float32, np.float16): + raise ValueError("Only F32 and F16 tensors are supported for now") + + encoded_name = name.encode("utf8") + self.ti_data += self._pack("Q", len(encoded_name)) + self.ti_data += encoded_name + n_dims = len(tensor_shape) + self.ti_data += self._pack("I", n_dims) + for i in range(n_dims): + self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) + if raw_dtype is None: + dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 + else: + dtype = raw_dtype + self.ti_data += self._pack("I", dtype) + self.ti_data += self._pack("Q", self.offset_tensor) + self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) + self.ti_data_count += 1 + + def add_tensor( + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, + raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + if self.use_temp_file and self.temp_file is None: + fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) + fp.seek(0) + self.temp_file = fp + + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) + + if self.temp_file is None: + self.tensors.append(tensor) + return + + tensor.tofile(self.temp_file) + self.write_padding(self.temp_file, tensor.nbytes) + + def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None: + pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n + if pad != 0: + fp.write(bytes([0] * pad)) + + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: + if self.state is not WriterState.TI_DATA: + raise ValueError(f'Expected output file to contain tensor info, got {self.state}') + + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + self.write_padding(self.fout, self.fout.tell()) + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + + def write_tensors_to_file(self) -> None: + self.write_ti_data_to_file() + + self.write_padding(self.fout, self.fout.tell()) + + if self.temp_file is None: + while True: + try: + tensor = self.tensors.pop(0) + except IndexError: + break + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + return + + self.temp_file.seek(0) + + shutil.copyfileobj(self.temp_file, self.fout) + self.flush() + self.temp_file.close() + + def flush(self) -> None: + self.fout.flush() + + def close(self) -> None: + self.fout.close() + + def add_architecture(self) -> None: + self.add_string(Keys.General.ARCHITECTURE, self.arch) + + def add_author(self, author: str) -> None: + self.add_string(Keys.General.AUTHOR, author) + + def add_tensor_data_layout(self, layout: str) -> None: + self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) + + def add_url(self, url: str) -> None: + self.add_string(Keys.General.URL, url) + + def add_description(self, description: str) -> None: + self.add_string(Keys.General.DESCRIPTION, description) + + def add_source_url(self, url: str) -> None: + self.add_string(Keys.General.SOURCE_URL, url) + + def add_source_hf_repo(self, repo: str) -> None: + self.add_string(Keys.General.SOURCE_HF_REPO, repo) + + def add_file_type(self, ftype: int) -> None: + self.add_uint32(Keys.General.FILE_TYPE, ftype) + + def add_name(self, name: str) -> None: + self.add_string(Keys.General.NAME, name) + + def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: + self.add_uint32( + Keys.General.QUANTIZATION_VERSION, quantization_version) + + def add_custom_alignment(self, alignment: int) -> None: + self.data_alignment = alignment + self.add_uint32(Keys.General.ALIGNMENT, alignment) + + def add_context_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) + + def add_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + + def add_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_parallel_residual(self, use: bool) -> None: + self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) + + def add_head_count(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + + def add_head_count_kv(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + + def add_max_alibi_bias(self, bias: float) -> None: + self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) + + def add_clamp_kqv(self, value: float) -> None: + self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + + def add_layer_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) + + def add_layer_norm_rms_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + + def add_rope_dimension_count(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + + def add_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + + def add_rope_scaling_type(self, value: RopeScalingType) -> None: + self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: + self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool) -> None: + self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + + def add_tokenizer_model(self, model: str) -> None: + self.add_string(Keys.Tokenizer.MODEL, model) + + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.LIST, tokens) + + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.MERGES, merges) + + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + + def add_token_scores(self, scores: Sequence[float]) -> None: + self.add_array(Keys.Tokenizer.SCORES, scores) + + def add_bos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.BOS_ID, id) + + def add_eos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOS_ID, id) + + def add_unk_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.UNK_ID, id) + + def add_sep_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SEP_ID, id) + + def add_pad_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PAD_ID, id) + + def add_add_bos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_BOS, value) + + def add_add_eos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_EOS, value) + + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: + pack_prefix = '' + if not skip_pack_prefix: + pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' + return struct.pack(f'{pack_prefix}{fmt}', value) + + def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None: + self.fout.write(self._pack(fmt, value, skip_pack_prefix)) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py new file mode 100644 index 000000000..22ad8b8fc --- /dev/null +++ b/gguf-py/gguf/tensor_mapping.py @@ -0,0 +1,257 @@ +from __future__ import annotations + +from typing import Sequence + +from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES + + +class TensorNameMap: + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Token embeddings + MODEL_TENSOR.TOKEN_EMBD: ( + "gpt_neox.embed_in", # gptneox + "transformer.wte", # gpt2 gpt-j mpt refact + "transformer.word_embeddings", # falcon + "word_embeddings", # bloom + "model.embed_tokens", # llama-hf + "tok_embeddings", # llama-pth + "embeddings.word_embeddings", # bert + "language_model.embedding.word_embeddings", # persimmon + ), + + # Token type embeddings + MODEL_TENSOR.TOKEN_TYPES: ( + "embeddings.token_type_embeddings", # bert + ), + + # Normalization of token embeddings + MODEL_TENSOR.TOKEN_EMBD_NORM: ( + "word_embeddings_layernorm", # bloom + ), + + # Position embeddings + MODEL_TENSOR.POS_EMBD: ( + "transformer.wpe", # gpt2 + "embeddings.position_embeddings", # bert + ), + + # Output + MODEL_TENSOR.OUTPUT: ( + "embed_out", # gptneox + "lm_head", # gpt2 mpt falcon llama-hf baichuan + "output", # llama-pth bloom + "word_embeddings_for_head", # persimmon + ), + + # Output norm + MODEL_TENSOR.OUTPUT_NORM: ( + "gpt_neox.final_layer_norm", # gptneox + "transformer.ln_f", # gpt2 gpt-j falcon + "model.norm", # llama-hf baichuan + "norm", # llama-pth + "embeddings.LayerNorm", # bert + "transformer.norm_f", # mpt + "ln_f", # refact bloom + "language_model.encoder.final_layernorm", # persimmon + ), + + # Rope frequencies + MODEL_TENSOR.ROPE_FREQS: ( + "rope.freqs", # llama-pth + ), + } + + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact + "transformer.blocks.{bid}.norm_1", # mpt + "transformer.h.{bid}.input_layernorm", # falcon7b + "h.{bid}.input_layernorm", # bloom + "transformer.h.{bid}.ln_mlp", # falcon40b + "model.layers.{bid}.input_layernorm", # llama-hf + "layers.{bid}.attention_norm", # llama-pth + "encoder.layer.{bid}.attention.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.input_layernorm", # persimmon + "model.layers.{bid}.ln1", # yi + ), + + # Attention norm 2 + MODEL_TENSOR.ATTN_NORM_2: ( + "transformer.h.{bid}.ln_attn", # falcon40b + ), + + # Attention query-key-value + MODEL_TENSOR.ATTN_QKV: ( + "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox + "transformer.h.{bid}.attn.c_attn", # gpt2 + "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.h.{bid}.self_attention.query_key_value", # falcon + "h.{bid}.self_attention.query_key_value", # bloom + "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + ), + + # Attention query + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.h.{bid}.attn.q_proj", # gpt-j + ), + + # Attention key + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.h.{bid}.attn.k_proj", # gpt-j + ), + + # Attention value + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.h.{bid}.attn.v_proj", # gpt-j + ), + + # Attention output + MODEL_TENSOR.ATTN_OUT: ( + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + ), + + # Rotary embeddings + MODEL_TENSOR.ATTN_ROT_EMBD: ( + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + ), + + # Feed-forward norm + MODEL_TENSOR.FFN_NORM: ( + "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox + "transformer.h.{bid}.ln_2", # gpt2 refact + "h.{bid}.post_attention_layernorm", # bloom + "transformer.blocks.{bid}.norm_2", # mpt + "model.layers.{bid}.post_attention_layernorm", # llama-hf + "layers.{bid}.ffn_norm", # llama-pth + "encoder.layer.{bid}.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon + "model.layers.{bid}.ln2", # yi + ), + + # Feed-forward up + MODEL_TENSOR.FFN_UP: ( + "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox + "transformer.h.{bid}.mlp.c_fc", # gpt2 + "transformer.blocks.{bid}.ffn.up_proj", # mpt + "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon + "h.{bid}.mlp.dense_h_to_4h", # bloom + "model.layers.{bid}.mlp.up_proj", # llama-hf refact + "layers.{bid}.feed_forward.w3", # llama-pth + "encoder.layer.{bid}.intermediate.dense", # bert + "transformer.h.{bid}.mlp.fc_in", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon + ), + + # Feed-forward gate + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact + "layers.{bid}.feed_forward.w1", # llama-pth + ), + + # Feed-forward down + MODEL_TENSOR.FFN_DOWN: ( + "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox + "transformer.h.{bid}.mlp.c_proj", # gpt2 refact + "transformer.blocks.{bid}.ffn.down_proj", # mpt + "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon + "h.{bid}.mlp.dense_4h_to_h", # bloom + "model.layers.{bid}.mlp.down_proj", # llama-hf + "layers.{bid}.feed_forward.w2", # llama-pth + "encoder.layer.{bid}.output.dense", # bert + "transformer.h.{bid}.mlp.fc_out", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + ), + + MODEL_TENSOR.ATTN_Q_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.q_layernorm", + ), + + MODEL_TENSOR.ATTN_K_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.k_layernorm", + ), + + MODEL_TENSOR.ROPE_FREQS: ( + "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon + ), + } + + mapping: dict[str, tuple[MODEL_TENSOR, str]] + + def __init__(self, arch: MODEL_ARCH, n_blocks: int): + self.mapping = {} + for tensor, keys in self.mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor] + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + self.mapping[key] = (tensor, tensor_name) + for bid in range(n_blocks): + for tensor, keys in self.block_mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor].format(bid = bid) + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + key = key.format(bid = bid) + self.mapping[key] = (tensor, tensor_name) + + def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: + result = self.mapping.get(key) + if result is not None: + return result + for suffix in try_suffixes: + if key.endswith(suffix): + result = self.mapping.get(key[:-len(suffix)]) + if result is not None: + return result[0], result[1] + suffix + return None + + def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[1] + + def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[0] + + def __getitem__(self, key: str) -> str: + try: + return self.mapping[key][1] + except KeyError: + raise KeyError(key) + + def __contains__(self, key: str) -> bool: + return key in self.mapping + + def __repr__(self) -> str: + return repr(self.mapping) + + +def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: + return TensorNameMap(arch, n_blocks) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py new file mode 100644 index 000000000..71192a928 --- /dev/null +++ b/gguf-py/gguf/vocab.py @@ -0,0 +1,164 @@ +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path +from typing import Any, Callable + +from .gguf_writer import GGUFWriter + + +class SpecialVocab: + merges: list[str] + add_special_token: dict[str, bool] + special_token_ids: dict[str, int] + + def __init__( + self, path: str | os.PathLike[str], load_merges: bool = False, + special_token_types: tuple[str, ...] | None = None, + n_vocab: int | None = None, + ): + self.special_token_ids = {} + self.add_special_token = {} + self.n_vocab = n_vocab + self.load_merges = load_merges + self.merges = [] + if special_token_types is not None: + self.special_token_types = special_token_types + else: + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') + self._load(Path(path)) + + def __repr__(self) -> str: + return ''.format( + len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset", + ) + + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: + if self.merges: + if not quiet: + print(f'gguf: Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + elif self.load_merges: + print( + 'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.', + file = sys.stderr, + ) + for typ, tokid in self.special_token_ids.items(): + id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) + if id_handler is None: + print( + f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting special token type {typ} to {tokid}') + id_handler(tokid) + for typ, value in self.add_special_token.items(): + add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) + if add_handler is None: + print( + f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting add_{typ}_token to {value}') + add_handler(value) + + def _load(self, path: Path) -> None: + self._try_load_from_tokenizer_json(path) + self._try_load_from_config_json(path) + if self.load_merges and not self.merges: + self._try_load_merges_txt(path) + + def _try_load_merges_txt(self, path: Path) -> bool: + merges_file = path / 'merges.txt' + if not merges_file.is_file(): + return False + with open(merges_file, 'r') as fp: + first_line = next(fp, '').strip() + if not first_line.startswith('#'): + fp.seek(0) + line_num = 0 + else: + line_num = 1 + merges = [] + for line in fp: + line_num += 1 + line = line.strip() + if not line: + continue + parts = line.split(None, 3) + if len(parts) != 2: + print( + f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring', + file = sys.stderr, + ) + continue + merges.append(f'{parts[0]} {parts[1]}') + self.merges = merges + return True + + def _set_special_token(self, typ: str, tid: Any) -> None: + if not isinstance(tid, int) or tid < 0: + return + if self.n_vocab is None or tid < self.n_vocab: + if typ in self.special_token_ids: + return + self.special_token_ids[typ] = tid + return + print( + f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', + file = sys.stderr, + ) + + def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer_file = path / 'tokenizer.json' + if not tokenizer_file.is_file(): + return False + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges and isinstance(merges[0], str): + self.merges = merges + tokenizer_config_file = path / 'tokenizer_config.json' + added_tokens = tokenizer.get('added_tokens') + if added_tokens is None or not tokenizer_config_file.is_file(): + return True + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + for typ in self.special_token_types: + add_entry = tokenizer_config.get(f'add_{typ}_token') + if isinstance(add_entry, bool): + self.add_special_token[typ] = add_entry + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + # We only need the first match here. + maybe_token_id = next( + (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content), + None, + ) + self._set_special_token(typ, maybe_token_id) + return True + + def _try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + self._set_special_token(typ, config.get(f'{typ}_token_id')) + return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index c6cb2c37a..624e1cda6 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,11 +1,12 @@ [tool.poetry] name = "gguf" -version = "0.4.6" +version = "0.5.0" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, {include = "gguf/py.typed"}, + {include = "scripts"}, ] readme = "README.md" homepage = "https://ggml.ai" @@ -27,3 +28,8 @@ pytest = "^5.2" [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint" +gguf-dump = "scripts:gguf_dump_entrypoint" +gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint" diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py new file mode 100644 index 000000000..77132db7a --- /dev/null +++ b/gguf-py/scripts/__init__.py @@ -0,0 +1,12 @@ +import os + +from importlib import import_module + + +os.environ["NO_LOCAL_GGUF"] = "TRUE" + +gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main +gguf_dump_entrypoint = import_module("scripts.gguf-dump").main +gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main + +del import_module, os diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py new file mode 100755 index 000000000..b79d86e07 --- /dev/null +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf + + +def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # Host is little endian + host_endian = "little" + swapped_endian = "big" + else: + # Sorry PDP or other weird systems that don't use BE or LE. + host_endian = "big" + swapped_endian = "little" + if reader.byte_order == "S": + file_endian = swapped_endian + else: + file_endian = host_endian + if args.order == "native": + order = host_endian + print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") + if file_endian == order: + print(f"* File is already {order.upper()} endian. Nothing to do.") + sys.exit(0) + print("* Checking tensors for conversion compatibility") + for tensor in reader.tensors: + if tensor.tensor_type not in ( + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + gguf.GGMLQuantizationType.Q8_0, + ): + raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") + print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}") + if args.dry_run: + return + print("\n*** Warning *** Warning *** Warning **") + print("* This conversion process may damage the file. Ensure you have a backup.") + if order != host_endian: + print("* Requested endian differs from host, you will not be able to load the model on this machine.") + print("* The file will be modified immediately, so if conversion fails or is interrupted") + print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") + response = input("YES, I am sure> ") + if response != "YES": + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + print(f"\n* Converting fields ({len(reader.fields)})") + for idx, field in enumerate(reader.fields.values()): + print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") + for part in field.parts: + part.byteswap(inplace=True) + print(f"\n* Converting tensors ({len(reader.tensors)})") + for idx, tensor in enumerate(reader.tensors): + print( + f" - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, " + f"elements={tensor.n_elements}... ", + end="", + ) + tensor_type = tensor.tensor_type + for part in tensor.field.parts: + part.byteswap(inplace=True) + if tensor_type != gguf.GGMLQuantizationType.Q8_0: + tensor.data.byteswap(inplace=True) + print() + continue + # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes + block_size = 34 + n_blocks = len(tensor.data) // block_size + for block_num in range(n_blocks): + block_offs = block_num * block_size + # I know I said f16, but it doesn't matter here - any simple 16 bit type works. + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + if block_num % 100000 == 0: + print(f"[{(n_blocks - block_num) // 1000}K]", end="") + sys.stdout.flush() + print() + print("* Completion") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert GGUF file byte order") + parser.add_argument( + "model", type=str, + help="GGUF format model filename", + ) + parser.add_argument( + "order", type=str, choices=['big', 'little', 'native'], + help="Requested byte order", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="Don't actually change anything", + ) + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') + convert_byteorder(reader, args) + + +if __name__ == "__main__": + main() diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py new file mode 100755 index 000000000..5141873de --- /dev/null +++ b/gguf-py/scripts/gguf-dump.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Any + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader, GGUFValueType # noqa: E402 + + +def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: + host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG' + if reader.byte_order == 'S': + file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE' + else: + file_endian = host_endian + return (host_endian, file_endian) + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') + print(f'\n* Dumping {len(reader.fields)} key/value pair(s)') + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + print(f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '') + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '') + elif field.types[0] in reader.gguf_scalar_to_np: + print(' = {0}'.format(field.parts[-1][0]), end = '') + print() + if args.no_tensors: + return + print(f'\n* Dumping {len(reader.tensors)} tensor(s)') + for n, tensor in enumerate(reader.tensors, 1): + prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape))) + print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') + + +def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: + import json + host_endian, file_endian = get_file_host_endian(reader) + metadata: dict[str, Any] = {} + tensors: dict[str, Any] = {} + result = { + "filename": args.model, + "endian": file_endian, + "metadata": metadata, + "tensors": tensors, + } + for idx, field in enumerate(reader.fields.values()): + curr: dict[str, Any] = { + "index": idx, + "type": field.types[0].name if field.types else 'UNKNOWN', + "offset": field.offset, + } + metadata[field.name] = curr + if field.types[:1] == [GGUFValueType.ARRAY]: + curr["array_types"] = [t.name for t in field.types][1:] + if not args.json_array: + continue + itype = field.types[-1] + if itype == GGUFValueType.STRING: + curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data] + else: + curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()] + elif field.types[0] == GGUFValueType.STRING: + curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8") + else: + curr["value"] = field.parts[-1].tolist()[0] + for idx, tensor in enumerate(reader.tensors): + tensors[tensor.name] = { + "index": idx, + "shape": tensor.shape.tolist(), + "type": tensor.tensor_type.name, + "offset": tensor.field.offset, + } + json.dump(result, sys.stdout) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") + parser.add_argument("--json", action="store_true", help="Produce JSON output") + parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + if not args.json: + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r') + if args.json: + dump_metadata_json(reader, args) + else: + dump_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf-set-metadata.py new file mode 100755 index 000000000..3ebdfa898 --- /dev/null +++ b/gguf-py/scripts/gguf-set-metadata.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +import argparse +import os +import sys +from pathlib import Path + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader # noqa: E402 + + +def minimal_example(filename: str) -> None: + reader = GGUFReader(filename, 'r+') + field = reader.fields['tokenizer.ggml.bos_token_id'] + if field is None: + return + part_index = field.data[0] + field.parts[part_index][0] = 2 # Set tokenizer.ggml.bos_token_id to 2 + # + # So what's this field.data thing? It's helpful because field.parts contains + # _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists + # of: + # + # Part index 0: Key length (27) + # Part index 1: Key data ("tokenizer.ggml.bos_token_id") + # Part index 2: Field type (4, the id for GGUFValueType.UINT32) + # Part index 3: Field value + # + # Note also that each part is an NDArray slice, so even a part that + # is only a single value like the key length will be a NDArray of + # the key length type (numpy.uint32). + # + # The .data attribute in the Field is a list of relevant part indexes + # and doesn't contain internal GGUF details like the key length part. + # In this case, .data will be [3] - just the part index of the + # field value itself. + + +def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + field = reader.get_field(args.key) + if field is None: + print(f'! Field {repr(args.key)} not found', file = sys.stderr) + sys.exit(1) + # Note that field.types is a list of types. This is because the GGUF + # format supports arrays. For example, an array of UINT32 would + # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32] + handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None + if handler is None: + print( + f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}', + file = sys.stderr, + ) + sys.exit(1) + current_value = field.parts[field.data[0]][0] + new_value = handler(args.value) + print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') + if current_value == new_value: + print(f'- Key {repr(args.key)} already set to requested value {current_value}') + sys.exit(0) + if args.dry_run: + sys.exit(0) + if not args.force: + print('*** Warning *** Warning *** Warning **') + print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') + print('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + field.parts[field.data[0]][0] = new_value + print('* Field changed. Successful completion.') + + +def main() -> None: + parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("key", type=str, help="Metadata key to set") + parser.add_argument("value", type=str, help="Metadata value to set") + parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything") + parser.add_argument("--force", action="store_true", help="Change the field without confirmation") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+') + set_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py index 512531dd2..0adeb7d55 100644 --- a/gguf-py/tests/test_gguf.py +++ b/gguf-py/tests/test_gguf.py @@ -1,7 +1,7 @@ -import gguf +import gguf # noqa: F401 # TODO: add tests -def test_write_gguf(): +def test_write_gguf() -> None: pass From d96ca7ded77df764db797b68b4a29e34c5b56285 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 11 Nov 2023 05:48:21 +0000 Subject: [PATCH 46/50] server : fix crash when prompt exceeds context size (#3996) --- examples/server/server.cpp | 58 +++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index cbf36ad67..46862a84b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1557,6 +1557,35 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); + if (slot.params.n_keep < 0) + { + slot.params.n_keep = slot.num_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); + + // if input prompt is too big, truncate it + if (slot.num_prompt_tokens >= slot.n_ctx) + { + const int n_left = slot.n_ctx - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + slot.truncated = true; + prompt_tokens = new_tokens; + + slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + } + if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); @@ -1566,35 +1595,6 @@ struct llama_server_context } else { - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.num_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - slot.truncated = true; - prompt_tokens = new_tokens; - - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); - } - // push the prompt into the sampling context (do not apply grammar) for (auto &token : prompt_tokens) { From e86fc56f7521ca4b18d1d9939e82abd40c2f1c01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sat, 11 Nov 2023 18:35:31 +0300 Subject: [PATCH 47/50] Fix gguf-convert-endian script (#4037) * Fix gguf-convert-endian script * Bump version and update description --- gguf-py/pyproject.toml | 4 ++-- gguf-py/scripts/gguf-convert-endian.py | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 624e1cda6..e21c3cd94 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "gguf" -version = "0.5.0" -description = "Write ML models in GGUF for GGML" +version = "0.5.1" +description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py index b79d86e07..10a16ad06 100755 --- a/gguf-py/scripts/gguf-convert-endian.py +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -28,8 +28,7 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None file_endian = swapped_endian else: file_endian = host_endian - if args.order == "native": - order = host_endian + order = host_endian if args.order == "native" else args.order print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") if file_endian == order: print(f"* File is already {order.upper()} endian. Nothing to do.") From 532dd74e38c29e16ea1cfc4e7eedb4f2fab3f3cd Mon Sep 17 00:00:00 2001 From: Richard Kiss Date: Sat, 11 Nov 2023 22:04:58 -0800 Subject: [PATCH 48/50] Fix some documentation typos/grammar mistakes (#4032) * typos * Update examples/parallel/README.md Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --------- Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --- README.md | 2 +- docs/token_generation_performance_tips.md | 2 +- examples/main/README.md | 2 +- examples/parallel/README.md | 2 +- grammars/README.md | 4 ++-- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9c9e36ad0..af39e8c0e 100644 --- a/README.md +++ b/README.md @@ -424,7 +424,7 @@ Building the program with BLAS support may lead to some performance improvements ``` The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. - If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. + If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): | Option | Legal values | Default | Description | diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md index c9acff7d4..d7e863dff 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/token_generation_performance_tips.md @@ -17,7 +17,7 @@ llama_model_load_internal: [cublas] total VRAM used: 17223 MB If you see these lines, then the GPU is being used. ## Verifying that the CPU is not oversaturated -llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physicial CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. +llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. # Example of runtime flags effect on inference speed benchmark These runs were tested on the following machine: diff --git a/examples/main/README.md b/examples/main/README.md index a3428b487..c7997f665 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -142,7 +142,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by ### Extended Context Size -Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. +Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. - `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. diff --git a/examples/parallel/README.md b/examples/parallel/README.md index 4d0fe5cef..df0456733 100644 --- a/examples/parallel/README.md +++ b/examples/parallel/README.md @@ -1,3 +1,3 @@ # llama.cpp/example/parallel -Simplified simluation for serving incoming requests in parallel +Simplified simulation of serving incoming requests in parallel diff --git a/grammars/README.md b/grammars/README.md index 7f3b11ca5..e1383fa5c 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -55,7 +55,7 @@ The order of symbols in a sequence matter. For example, in `"1. " move " " move Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`. -Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optptional symbols (below) to a sequence. +Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optional symbols (below) to a sequence. ## Repetition and Optional Symbols @@ -67,7 +67,7 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte Comments can be specified with `#`: ``` -# defines optional whitspace +# defines optional whitespace ws ::= [ \t\n]+ ``` From 21fd874c8d2a14dea2d56724e4357c0824aee6a8 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 12 Nov 2023 16:39:37 -0700 Subject: [PATCH 49/50] gguf-py: gguf_writer: Use bytearray to build metadata (#4051) * gguf-py: gguf_writer: Use BytesIO to build metadata * Use bytearray instead Bump gguf-py package version --- gguf-py/gguf/gguf_writer.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 75fb6976f..c3b8c588f 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -57,9 +57,9 @@ class GGUFWriter: self.endianess = endianess self.offset_tensor = 0 self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" + self.kv_data = bytearray() self.kv_data_count = 0 - self.ti_data = b"" + self.ti_data = bytearray() self.ti_data_count = 0 self.use_temp_file = use_temp_file self.temp_file = None diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index e21c3cd94..af777c3e0 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.5.1" +version = "0.5.2" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From bb50a792ec2a49944470c82694fa364345e95170 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Mon, 13 Nov 2023 01:58:15 -0700 Subject: [PATCH 50/50] Add ReLU and SQR CUDA ops to (partially) fix Persimmon offloading (#4041) * Add ReLU and SQR CUDA ops to fix Persimmon offloading * Persimmon loader: More helpful error on CUDA/ROCM when offloading too many layers --- ggml-cuda.cu | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++ llama.cpp | 7 +++++ 2 files changed, 79 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f87f18802..8d03ba664 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -433,6 +433,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define CUDA_MUL_BLOCK_SIZE 256 #define CUDA_GELU_BLOCK_SIZE 256 #define CUDA_SILU_BLOCK_SIZE 256 +#define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 #define CUDA_CLAMP_BLOCK_SIZE 256 @@ -553,6 +555,24 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __global__ void relu_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fmaxf(x[i], 0); +} + +static __global__ void sqr_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -4759,6 +4779,16 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ silu_f32<<>>(x, dst, k); } +static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; + relu_f32<<>>(x, dst, k); +} + +static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE; + sqr_f32<<>>(x, dst, k); +} + static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); if (ncols < 1024) { @@ -6128,6 +6158,34 @@ inline void ggml_cuda_op_silu( (void) src1_dd; } +inline void ggml_cuda_op_relu( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_cuda_op_sqr( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_cuda_op_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { @@ -7160,6 +7218,14 @@ static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu); } +static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); +} + +static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr); +} + static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm); } @@ -7891,6 +7957,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_SILU: func = ggml_cuda_silu; break; + case GGML_UNARY_OP_RELU: + func = ggml_cuda_relu; + break; default: return false; } break; @@ -7909,6 +7978,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_SCALE: func = ggml_cuda_scale; break; + case GGML_OP_SQR: + func = ggml_cuda_sqr; + break; case GGML_OP_CLAMP: if (!any_on_device) { return false; diff --git a/llama.cpp b/llama.cpp index d682d2864..a5f3876cc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2877,6 +2877,13 @@ static void llm_load_tensors( ggml_backend_type backend_output; if (n_gpu_layers > int(n_layer)) { +#ifdef GGML_USE_CUBLAS + if (n_gpu_layers > int(n_layer + 1)) { + LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n", + __func__, n_layer + 1); + throw std::runtime_error("Persimmon CUDA offload failed"); + } +#endif // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32

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changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 611ed3f4d..3c49d645c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -44,7 +44,7 @@ endif() # general option(LLAMA_STATIC "llama: static link libraries" OFF) -option(LLAMA_NATIVE "llama: enable -march=native flag" ON) +option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) option(LLAMA_LTO "llama: enable link time optimization" OFF) # debug From 4ff1046d75e64f0e556d8dcd930ea25c23eb8b18 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 16:22:30 +0200 Subject: [PATCH 13/50] gguf : print error for GGUFv1 files (#3908) --- ggml.c | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ggml.c b/ggml.c index d5a49d8e4..605a27940 100644 --- a/ggml.c +++ b/ggml.c @@ -18884,6 +18884,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); + if (ctx->header.version == 1) { + fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } + if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); fclose(file); From d6069051de7165a4e06662c89257f5d2905bb156 Mon Sep 17 00:00:00 2001 From: Oleksii Maryshchenko Date: Thu, 2 Nov 2023 18:10:39 +0100 Subject: [PATCH 14/50] cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903) * Using cuda memory pools for async alloc/dealloc. * If cuda device doesnt support memory pool than use old implementation. * Removed redundant cublasSetStream --------- Co-authored-by: Oleksii Maryshchenko --- ggml-cuda.cu | 130 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 78 insertions(+), 52 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e46295126..58b58f331 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -181,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cudaError_t err_ = (err); \ if (err_ != cudaSuccess) { \ - int id; \ - cudaGetDevice(&id); \ + int dev_id; \ + cudaGetDevice(&dev_id); \ fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ cudaGetErrorString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ + fprintf(stderr, "current device: %d\n", dev_id); \ exit(1); \ } \ } while (0) @@ -195,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cublasStatus_t err_ = (err); \ if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int id; \ - cudaGetDevice(&id); \ + int dev_id; \ + cudaGetDevice(&dev_id); \ fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \ err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \ - fprintf(stderr, "current device: %d\n", id); \ + fprintf(stderr, "current device: %d\n", dev_id); \ exit(1); \ } \ } while (0) @@ -465,6 +465,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MAX_STREAMS 8 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr }; +static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr }; struct ggml_tensor_extra_gpu { void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -5772,6 +5773,16 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { return ptr; } +static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) { + if (g_cudaMemPools[id] == nullptr) { + return ggml_cuda_pool_malloc(size, actual_size); + } + void *ptr; + CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream)); + *actual_size = size; + return ptr; +} + static void ggml_cuda_pool_free(void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; @@ -5790,6 +5801,13 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { } +static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) { + if (g_cudaMemPools[id] == nullptr) { + return ggml_cuda_pool_free(ptr, actual_size); + } + CUDA_CHECK(cudaFreeAsync(ptr, stream)); +} + void ggml_init_cublas() { static bool initialized = false; @@ -5844,6 +5862,13 @@ void ggml_init_cublas() { // create cublas handle CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id])); CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH)); + + // configure memory pool + cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id); + if (err == cudaSuccess) { + size_t treshold = UINT64_MAX; + CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold)); + } } // configure logging to stdout @@ -6437,7 +6462,7 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = row_diff*ne00; - src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as); + src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream); to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream); } const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16; @@ -6448,13 +6473,12 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = src1_ncols*ne10; - src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); + src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - - size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); + size_t dst_f16_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -6472,14 +6496,15 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream); - ggml_cuda_pool_free(dst_f16, dst_as); - - if (src0_as != 0) { - ggml_cuda_pool_free(src0_as_f16, src0_as); + if (dst_f16_as != 0) { + ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream); } + if (src0_as != 0) { + ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream); + } if (src1_as != 0) { - ggml_cuda_pool_free(src1_as_f16, src1_as); + ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream); } } else { @@ -6489,7 +6514,7 @@ inline void ggml_cuda_op_mul_mat_cublas( if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT + src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream); } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32; @@ -6506,7 +6531,7 @@ inline void ggml_cuda_op_mul_mat_cublas( &beta, dst_dd_i, ldc)); if (src0_as != 0) { - ggml_cuda_pool_free(src0_ddq_as_f32, src0_as); + ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream); } } @@ -6929,21 +6954,22 @@ static void ggml_cuda_op_mul_mat( src0_dd[id] = (char *) src0_extra->data_device[id]; } else { const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream); } if (src1_on_device && src1_is_contiguous) { src1_ddf[id] = (float *) src1_extra->data_device[id]; } else { - src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; + src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream); if (src1_on_device && src1_is_contiguous) { quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); + // CUDA_CHECK(cudaGetLastError()); } } @@ -6951,7 +6977,7 @@ static void ggml_cuda_op_mul_mat( dst_dd[id] = (float *) dst_extra->data_device[id]; } else { const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]); + dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id, stream); } } @@ -7077,24 +7103,6 @@ static void ggml_cuda_op_mul_mat( } } - for (int64_t id = 0; id < g_device_count; ++id) { - CUDA_CHECK(ggml_cuda_set_device(id)); - - // free buffers again when done - if (src0_as[id] > 0) { - ggml_cuda_pool_free(src0_dd[id], src0_as[id]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src1_asq[id] > 0) { - ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); - } - if (dst_as[id] > 0) { - ggml_cuda_pool_free(dst_dd[id], dst_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; @@ -7112,6 +7120,21 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaDeviceSynchronize()); } + + for (int64_t id = 0; id < g_device_count; ++id) { + if (src0_as[id] > 0) { + ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]); + } + if (src1_asf[id] > 0) { + ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]); + } + if (src1_asq[id] > 0) { + ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]); + } + if (dst_as[id] > 0) { + ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]); + } + } } static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7298,11 +7321,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(to_fp16_cuda != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); + half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream); to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); + half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -7349,10 +7372,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const } else { // use cublasGemmBatchedEx const int ne23 = ne12*ne13; - - void ** ptrs_as = nullptr; + // allocate device memory for pointers size_t ptrs_s = 0; - ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s); + void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( @@ -7365,7 +7387,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dst->nb[2], dst->nb[3], r2, r3); CUDA_CHECK(cudaGetLastError()); - CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, @@ -7375,16 +7396,21 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - - ggml_cuda_pool_free(ptrs_as, ptrs_s); + // free device memory for pointers + if (ptrs_s != 0) { + ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream); + } } #endif const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); - - ggml_cuda_pool_free(src1_as_f16, src1_as); - ggml_cuda_pool_free(dst_f16, dst_as); + if (src1_as != 0) { + ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream); + } + if (dst_as != 0) { + ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream); + } } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { From c7743fe1c1cbda5a886362aa371480360580fdf0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 20:32:11 +0200 Subject: [PATCH 15/50] cuda : fix const ptrs warning causing ROCm build issues (#3913) --- ggml-cuda.cu | 37 +++++++++++++++++++++++-------------- 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 58b58f331..06c28f565 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7248,7 +7248,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor __global__ void k_compute_batched_ptrs( const half * src0_as_f16, const half * src1_as_f16, half * dst_f16, - void ** ptrs, + const void ** ptrs_src, void ** ptrs_dst, int ne12, int ne13, int ne23, int nb02, int nb03, @@ -7265,9 +7265,9 @@ __global__ void k_compute_batched_ptrs( int i03 = i13 / r3; int i02 = i12 / r2; - ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* nb2/2 + i13* nb3/2; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; } static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7372,14 +7372,20 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const } else { // use cublasGemmBatchedEx const int ne23 = ne12*ne13; - // allocate device memory for pointers - size_t ptrs_s = 0; - void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream); + + const void ** ptrs_src = nullptr; + void ** ptrs_dst = nullptr; + + size_t ptrs_src_s = 0; + size_t ptrs_dst_s = 0; + + ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream); + ptrs_dst = ( void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( src0_as_f16, src1_as_f16, dst_f16, - ptrs_as, + ptrs_src, ptrs_dst, ne12, ne13, ne23, nb02, nb03, @@ -7390,15 +7396,18 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float), - &beta_f16, ( void ** ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01, + &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half), + (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + &beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01, ne23, CUBLAS_COMPUTE_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - // free device memory for pointers - if (ptrs_s != 0) { - ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream); + + if (ptrs_src_s != 0) { + ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream); + } + if (ptrs_dst_s != 0) { + ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream); } } #endif From 224e7d5b14cbabab7ae45c64db2cfde979c8455d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 2 Nov 2023 20:44:12 +0200 Subject: [PATCH 16/50] readme : add notice about #3912 --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index b56ecaec7..9c9e36ad0 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,6 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) -[![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) @@ -11,8 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- LLaVA support: https://github.com/ggerganov/llama.cpp/pull/3436 -- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252) +- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912 ---- From 51b2fc11f7f605fff49725a4540e9a6ef7b51b70 Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Nov 2023 15:40:31 -0400 Subject: [PATCH 17/50] cmake : fix relative path to git submodule index (#3915) --- common/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 0150114e3..ac594b2ca 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -11,7 +11,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") if(NOT IS_DIRECTORY "${GIT_DIR}") file(READ ${GIT_DIR} REAL_GIT_DIR_LINK) string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK}) - set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}") + set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}") endif() set(GIT_INDEX "${GIT_DIR}/index") From 629f917cd6b96ba1274c49a8aab163b1b189229d Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Thu, 2 Nov 2023 13:58:22 -0600 Subject: [PATCH 18/50] cuda : add ROCM aliases for CUDA pool stuff (#3918) --- ggml-cuda.cu | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 06c28f565..baf02df2b 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -39,6 +39,10 @@ #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess +#define cudaDeviceGetMemPool hipDeviceGetMemPool +#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold +#define cudaMemPoolSetAttribute hipMemPoolSetAttribute +#define cudaMemPool_t hipMemPool_t #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t @@ -48,6 +52,7 @@ #define cudaEvent_t hipEvent_t #define cudaEventDestroy hipEventDestroy #define cudaFree hipFree +#define cudaFreeAsync hipFreeAsync #define cudaFreeHost hipHostFree #define cudaGetDevice hipGetDevice #define cudaGetDeviceCount hipGetDeviceCount @@ -55,6 +60,7 @@ #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError #define cudaMalloc hipMalloc +#define cudaMallocFromPoolAsync hipMallocFromPoolAsync #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #define cudaMemcpy hipMemcpy #define cudaMemcpy2DAsync hipMemcpy2DAsync From 3fdbe6b66b7b5c6ad3b2f245cbad1517c27ff776 Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Fri, 3 Nov 2023 02:31:58 -0400 Subject: [PATCH 19/50] llama : change yarn_ext_factor placeholder to -1 (#3922) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index bb60044b4..cc0211ceb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7982,7 +7982,7 @@ struct llama_context_params llama_context_default_params() { /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, - /*.yarn_ext_factor =*/ NAN, + /*.yarn_ext_factor =*/ -1.0f, /*.yarn_attn_factor =*/ 1.0f, /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, @@ -8125,7 +8125,7 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none } - if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set' + if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set' cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f; } From 05816027d649f977468fc804cdb54e99eac246d1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 3 Nov 2023 09:24:00 +0200 Subject: [PATCH 20/50] common : YAYF (yet another YARN fix) (#3925) ggml-ci --- common/common.h | 44 ++++++++++++++++++++++---------------------- llama.h | 10 +++++----- 2 files changed, 27 insertions(+), 27 deletions(-) diff --git a/common/common.h b/common/common.h index 72a49b890..9ad625633 100644 --- a/common/common.h +++ b/common/common.h @@ -43,29 +43,29 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 16; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_beams = 0; // if non-zero then use beam search of given width. - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - float yarn_ext_factor = NAN; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor - float yarn_beta_fast = 32.0f;// YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs + int32_t n_beams = 0; // if non-zero then use beam search of given width. + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters diff --git a/llama.h b/llama.h index 3f1becd76..e8dc04bb5 100644 --- a/llama.h +++ b/llama.h @@ -175,11 +175,11 @@ extern "C" { }; struct llama_context_params { - uint32_t seed; // RNG seed, -1 for random - uint32_t n_ctx; // text context, 0 = from model - uint32_t n_batch; // prompt processing maximum batch size - uint32_t n_threads; // number of threads to use for generation - uint32_t n_threads_batch; // number of threads to use for batch processing + uint32_t seed; // RNG seed, -1 for random + uint32_t n_ctx; // text context, 0 = from model + uint32_t n_batch; // prompt processing maximum batch size + uint32_t n_threads; // number of threads to use for generation + uint32_t n_threads_batch; // number of threads to use for batch processing int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 From 8f961abdc4e134c83bf8c2ad618ab256b4cae0f9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 3 Nov 2023 09:41:17 +0200 Subject: [PATCH 21/50] speculative : change default p_accept to 0.5 + CLI args (#3919) ggml-ci --- common/common.cpp | 14 ++++++++++++++ common/common.h | 8 ++++++-- examples/speculative/speculative.cpp | 8 +++++--- 3 files changed, 25 insertions(+), 5 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index e938dee16..20cc4a081 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -403,6 +403,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_sequences = std::stoi(argv[i]); + } else if (arg == "--p-accept" || arg == "-pa") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.p_accept = std::stof(argv[i]); + } else if (arg == "--p-split" || arg == "-ps") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.p_split = std::stof(argv[i]); } else if (arg == "-m" || arg == "--model") { if (++i >= argc) { invalid_param = true; @@ -778,6 +790,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); + printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); + printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); diff --git a/common/common.h b/common/common.h index 9ad625633..dd6b002eb 100644 --- a/common/common.h +++ b/common/common.h @@ -44,6 +44,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = -1; // RNG seed + int32_t n_threads = get_num_physical_cores(); int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_predict = -1; // new tokens to predict @@ -54,6 +55,8 @@ struct gpt_params { int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode + float p_accept = 0.5f; // speculative decoding accept probability + float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors @@ -66,7 +69,8 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment + // pinging @cebtenzzre // // sampling parameters struct llama_sampling_params sparams; @@ -90,7 +94,7 @@ struct gpt_params { int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line // (which is more convenient to use for plotting) // - bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 798684f66..3a8e27811 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -37,9 +37,11 @@ int main(int argc, char ** argv) { // max number of parallel drafting sequences (i.e. tree branches) const int n_seq_dft = params.n_parallel; - // TODO: make this configurable - const float p_accept = 0.80f; - const float p_split = 0.10f; + // probability threshold for accepting a token from the draft model + const float p_accept = params.p_accept; + + // probability threshold for splitting a draft branch (only for n_seq_dft > 1) + const float p_split = params.p_split; #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); From abb77e7319aabc0b5cfb7c22da690a692489b6b7 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 3 Nov 2023 12:13:09 +0100 Subject: [PATCH 22/50] ggml-cuda : move row numbers to x grid dim in mmv kernels (#3921) --- ggml-cuda.cu | 53 ++++++++++++++++++++++++++-------------------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index baf02df2b..bdbcca0ca 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -989,7 +989,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -1093,7 +1093,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -1197,7 +1197,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1451,7 +1451,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; @@ -4261,7 +4261,7 @@ template static __global__ void template static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { return; @@ -4301,7 +4301,7 @@ template static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { // qk = quantized weights per x block // qr = number of quantized weights per data value in x block - const int row = blockIdx.y*blockDim.y + threadIdx.y; + const int row = blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { return; @@ -4874,7 +4874,8 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4883,7 +4884,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4892,7 +4893,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4901,7 +4902,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4910,7 +4911,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); @@ -4920,7 +4921,7 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); } @@ -4929,7 +4930,7 @@ static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); } @@ -4938,7 +4939,7 @@ static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); } @@ -4953,7 +4954,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(32, ny, 1); dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); } @@ -4961,7 +4962,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK4_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4970,7 +4971,7 @@ static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK4_1 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4979,7 +4980,7 @@ static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK5_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4988,7 +4989,7 @@ static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK5_1 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -4997,7 +4998,7 @@ static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK8_0 == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5006,7 +5007,7 @@ static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5015,7 +5016,7 @@ static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5024,7 +5025,7 @@ static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5033,7 +5034,7 @@ static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5042,7 +5043,7 @@ static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); mul_mat_vec_q <<>>(vx, vy, dst, ncols, nrows); @@ -5061,7 +5062,7 @@ static void convert_fp32_to_fp16_cuda(const void * vx, half * y, const int k, cu static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(1, block_num_y, 1); + const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec<1, 1, convert_f16> <<>>(vx, y, dst, ncols, nrows); From 5ba37461711095c0284233dbd14f0d9010cdbf56 Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Fri, 3 Nov 2023 13:00:31 -0500 Subject: [PATCH 23/50] ggml-metal: fix yarn rope (#3937) --- ggml-metal.m | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index b33a3cb8f..acdb83843 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1403,7 +1403,8 @@ void ggml_metal_graph_compute( const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; + // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); From d9b33fe95bd257b36c84ee5769cc048230067d6f Mon Sep 17 00:00:00 2001 From: Peter Sugihara Date: Fri, 3 Nov 2023 12:18:18 -0700 Subject: [PATCH 24/50] metal : round up to 16 to fix MTLDebugComputeCommandEncoder assertion (#3938) --- ggml-metal.m | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index acdb83843..78ae4485d 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1017,7 +1017,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1348,7 +1348,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0]; const int64_t nrows = ggml_nrows(src0); From f28af0d81aa1010afa5de74cf627dcb04bea3157 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 4 Nov 2023 16:20:34 -0600 Subject: [PATCH 25/50] gguf-py: Support 01.AI Yi models (#3943) --- gguf-py/gguf/gguf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 727b4e554..a2271d225 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -393,6 +393,7 @@ class TensorNameMap: "layers.{bid}.attention_norm", # llama-pth "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon + "model.layers.{bid}.ln1", # yi ), # Attention norm 2 @@ -464,6 +465,7 @@ class TensorNameMap: "layers.{bid}.ffn_norm", # llama-pth "encoder.layer.{bid}.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon + "model.layers.{bid}.ln2", # yi ), # Feed-forward up From 48ade94538fa509465d71023e49d07aab0ec8cd5 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 5 Nov 2023 08:12:13 +0100 Subject: [PATCH 26/50] cuda : revert CUDA pool stuff (#3944) * Revert "cuda : add ROCM aliases for CUDA pool stuff (#3918)" This reverts commit 629f917cd6b96ba1274c49a8aab163b1b189229d. * Revert "cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903)" This reverts commit d6069051de7165a4e06662c89257f5d2905bb156. ggml-ci --- ggml-cuda.cu | 131 ++++++++++++++++++++------------------------------- 1 file changed, 50 insertions(+), 81 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bdbcca0ca..dc14f2f5d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -39,10 +39,6 @@ #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess -#define cudaDeviceGetMemPool hipDeviceGetMemPool -#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold -#define cudaMemPoolSetAttribute hipMemPoolSetAttribute -#define cudaMemPool_t hipMemPool_t #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t @@ -52,7 +48,6 @@ #define cudaEvent_t hipEvent_t #define cudaEventDestroy hipEventDestroy #define cudaFree hipFree -#define cudaFreeAsync hipFreeAsync #define cudaFreeHost hipHostFree #define cudaGetDevice hipGetDevice #define cudaGetDeviceCount hipGetDeviceCount @@ -60,7 +55,6 @@ #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError #define cudaMalloc hipMalloc -#define cudaMallocFromPoolAsync hipMallocFromPoolAsync #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #define cudaMemcpy hipMemcpy #define cudaMemcpy2DAsync hipMemcpy2DAsync @@ -187,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cudaError_t err_ = (err); \ if (err_ != cudaSuccess) { \ - int dev_id; \ - cudaGetDevice(&dev_id); \ + int id; \ + cudaGetDevice(&id); \ fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ cudaGetErrorString(err_)); \ - fprintf(stderr, "current device: %d\n", dev_id); \ + fprintf(stderr, "current device: %d\n", id); \ exit(1); \ } \ } while (0) @@ -201,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); do { \ cublasStatus_t err_ = (err); \ if (err_ != CUBLAS_STATUS_SUCCESS) { \ - int dev_id; \ - cudaGetDevice(&dev_id); \ + int id; \ + cudaGetDevice(&id); \ fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \ err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \ - fprintf(stderr, "current device: %d\n", dev_id); \ + fprintf(stderr, "current device: %d\n", id); \ exit(1); \ } \ } while (0) @@ -471,7 +465,6 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MAX_STREAMS 8 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr }; -static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr }; struct ggml_tensor_extra_gpu { void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -5780,16 +5773,6 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { return ptr; } -static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) { - if (g_cudaMemPools[id] == nullptr) { - return ggml_cuda_pool_malloc(size, actual_size); - } - void *ptr; - CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream)); - *actual_size = size; - return ptr; -} - static void ggml_cuda_pool_free(void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); int id; @@ -5808,13 +5791,6 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { } -static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) { - if (g_cudaMemPools[id] == nullptr) { - return ggml_cuda_pool_free(ptr, actual_size); - } - CUDA_CHECK(cudaFreeAsync(ptr, stream)); -} - void ggml_init_cublas() { static bool initialized = false; @@ -5869,13 +5845,6 @@ void ggml_init_cublas() { // create cublas handle CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id])); CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH)); - - // configure memory pool - cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id); - if (err == cudaSuccess) { - size_t treshold = UINT64_MAX; - CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold)); - } } // configure logging to stdout @@ -6469,7 +6438,7 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = row_diff*ne00; - src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream); + src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as); to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream); } const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16; @@ -6480,12 +6449,13 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); size_t ne = src1_ncols*ne10; - src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream); + src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - size_t dst_f16_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream); + + size_t dst_as = 0; + half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -6503,15 +6473,14 @@ inline void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream); - if (dst_f16_as != 0) { - ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream); - } + ggml_cuda_pool_free(dst_f16, dst_as); if (src0_as != 0) { - ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream); + ggml_cuda_pool_free(src0_as_f16, src0_as); } + if (src1_as != 0) { - ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream); + ggml_cuda_pool_free(src1_as_f16, src1_as); } } else { @@ -6521,7 +6490,7 @@ inline void ggml_cuda_op_mul_mat_cublas( if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT + src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream); } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32; @@ -6538,7 +6507,7 @@ inline void ggml_cuda_op_mul_mat_cublas( &beta, dst_dd_i, ldc)); if (src0_as != 0) { - ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream); + ggml_cuda_pool_free(src0_ddq_as_f32, src0_as); } } @@ -6961,22 +6930,21 @@ static void ggml_cuda_op_mul_mat( src0_dd[id] = (char *) src0_extra->data_device[id]; } else { const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream); + src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]); } if (src1_on_device && src1_is_contiguous) { src1_ddf[id] = (float *) src1_extra->data_device[id]; } else { - src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream); + src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); } if (convert_src1_to_q8_1) { - const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; - src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream); + src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); if (src1_on_device && src1_is_contiguous) { quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); - // CUDA_CHECK(cudaGetLastError()); + CUDA_CHECK(cudaGetLastError()); } } @@ -6984,7 +6952,7 @@ static void ggml_cuda_op_mul_mat( dst_dd[id] = (float *) dst_extra->data_device[id]; } else { const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id, stream); + dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]); } } @@ -7110,6 +7078,24 @@ static void ggml_cuda_op_mul_mat( } } + for (int64_t id = 0; id < g_device_count; ++id) { + CUDA_CHECK(ggml_cuda_set_device(id)); + + // free buffers again when done + if (src0_as[id] > 0) { + ggml_cuda_pool_free(src0_dd[id], src0_as[id]); + } + if (src1_asf[id] > 0) { + ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]); + } + if (src1_asq[id] > 0) { + ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]); + } + if (dst_as[id] > 0) { + ggml_cuda_pool_free(dst_dd[id], dst_as[id]); + } + } + // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; @@ -7127,21 +7113,6 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaDeviceSynchronize()); } - - for (int64_t id = 0; id < g_device_count; ++id) { - if (src0_as[id] > 0) { - ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]); - } - if (src1_asf[id] > 0) { - ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]); - } - if (src1_asq[id] > 0) { - ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]); - } - if (dst_as[id] > 0) { - ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]); - } - } } static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7328,11 +7299,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(to_fp16_cuda != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream); + half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream); + half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -7386,8 +7357,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const size_t ptrs_src_s = 0; size_t ptrs_dst_s = 0; - ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream); - ptrs_dst = ( void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream); + ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( @@ -7400,6 +7371,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dst->nb[2], dst->nb[3], r2, r3); CUDA_CHECK(cudaGetLastError()); + CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, @@ -7411,22 +7383,19 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (ptrs_src_s != 0) { - ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream); + ggml_cuda_pool_free(ptrs_src, ptrs_src_s); } if (ptrs_dst_s != 0) { - ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream); + ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s); } } #endif const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream); - if (src1_as != 0) { - ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream); - } - if (dst_as != 0) { - ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream); - } + + ggml_cuda_pool_free(src1_as_f16, src1_as); + ggml_cuda_pool_free(dst_f16, dst_as); } static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { From a7fac013cf1cc7bbc0160a226aa2412e9f22e78a Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sun, 5 Nov 2023 07:46:44 +0000 Subject: [PATCH 27/50] ci : use intel sde when ci cpu doesn't support avx512 (#3949) --- .github/workflows/build.yml | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 5af497a3c..bc295d52d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -288,6 +288,7 @@ jobs: OPENBLAS_VERSION: 0.3.23 OPENCL_VERSION: 2023.04.17 CLBLAST_VERSION: 1.6.0 + SDE_VERSION: 9.21.1-2023-04-24 strategy: matrix: @@ -383,11 +384,23 @@ jobs: - name: Test id: cmake_test - if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # Test AVX-512 only when possible + if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512 run: | cd build ctest -C Release --verbose --timeout 900 + - name: Test (Intel SDE) + id: cmake_test_sde + if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + run: | + curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz" + # for some weird reason windows tar doesn't like sde tar.xz + 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz + 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar + $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) + cd build + & $sde -future -- ctest -C Release --verbose --timeout 900 + - name: Determine tag name id: tag shell: bash From c41ea36eaa3548776de4cb3d5d49b925cd3fc0f2 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sun, 5 Nov 2023 08:03:09 +0000 Subject: [PATCH 28/50] cmake : MSVC instruction detection (fixed up #809) (#3923) * Add detection code for avx * Only check hardware when option is ON * Modify per code review sugguestions * Build locally will detect CPU * Fixes CMake style to use lowercase like everywhere else * cleanup * fix merge * linux/gcc version for testing * msvc combines avx2 and fma into /arch:AVX2 so check for both * cleanup * msvc only version * style * Update FindSIMD.cmake --------- Co-authored-by: Howard Su Co-authored-by: Jeremy Dunn --- CMakeLists.txt | 8 +++- cmake/FindSIMD.cmake | 100 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 106 insertions(+), 2 deletions(-) create mode 100644 cmake/FindSIMD.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 3c49d645c..7b4eb1840 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -10,7 +10,7 @@ endif() set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) -if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) +if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) set(LLAMA_STANDALONE ON) # configure project version @@ -44,7 +44,7 @@ endif() # general option(LLAMA_STATIC "llama: static link libraries" OFF) -option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) +option(LLAMA_NATIVE "llama: enable -march=native flag" ON) option(LLAMA_LTO "llama: enable link time optimization" OFF) # debug @@ -510,6 +510,10 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) message(STATUS "x86 detected") if (MSVC) + # instruction set detection for MSVC only + if (LLAMA_NATIVE) + include(cmake/FindSIMD.cmake) + endif () if (LLAMA_AVX512) add_compile_options($<$:/arch:AVX512>) add_compile_options($<$:/arch:AVX512>) diff --git a/cmake/FindSIMD.cmake b/cmake/FindSIMD.cmake new file mode 100644 index 000000000..33377ec44 --- /dev/null +++ b/cmake/FindSIMD.cmake @@ -0,0 +1,100 @@ +include(CheckCSourceRuns) + +set(AVX_CODE " + #include + int main() + { + __m256 a; + a = _mm256_set1_ps(0); + return 0; + } +") + +set(AVX512_CODE " + #include + int main() + { + __m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0); + __m512i b = a; + __mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ); + return 0; + } +") + +set(AVX2_CODE " + #include + int main() + { + __m256i a = {0}; + a = _mm256_abs_epi16(a); + __m256i x; + _mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code + return 0; + } +") + +set(FMA_CODE " + #include + int main() + { + __m256 acc = _mm256_setzero_ps(); + const __m256 d = _mm256_setzero_ps(); + const __m256 p = _mm256_setzero_ps(); + acc = _mm256_fmadd_ps( d, p, acc ); + return 0; + } +") + +macro(check_sse type flags) + set(__FLAG_I 1) + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) + foreach (__FLAG ${flags}) + if (NOT ${type}_FOUND) + set(CMAKE_REQUIRED_FLAGS ${__FLAG}) + check_c_source_runs("${${type}_CODE}" HAS_${type}_${__FLAG_I}) + if (HAS_${type}_${__FLAG_I}) + set(${type}_FOUND TRUE CACHE BOOL "${type} support") + set(${type}_FLAGS "${__FLAG}" CACHE STRING "${type} flags") + endif() + math(EXPR __FLAG_I "${__FLAG_I}+1") + endif() + endforeach() + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + + if (NOT ${type}_FOUND) + set(${type}_FOUND FALSE CACHE BOOL "${type} support") + set(${type}_FLAGS "" CACHE STRING "${type} flags") + endif() + + mark_as_advanced(${type}_FOUND ${type}_FLAGS) +endmacro() + +# flags are for MSVC only! +check_sse("AVX" " ;/arch:AVX") +if (NOT ${AVX_FOUND}) + set(LLAMA_AVX OFF) +else() + set(LLAMA_AVX ON) +endif() + +check_sse("AVX2" " ;/arch:AVX2") +check_sse("FMA" " ;/arch:AVX2") +if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND})) + set(LLAMA_AVX2 OFF) +else() + set(LLAMA_AVX2 ON) +endif() + +check_sse("AVX512" " ;/arch:AVX512") +if (NOT ${AVX512_FOUND}) + set(LLAMA_AVX512 OFF) +else() + set(LLAMA_AVX512 ON) +endif() From 3d48f42efcd05381221654376e9f6f69d76af739 Mon Sep 17 00:00:00 2001 From: Meng Zhang Date: Sun, 5 Nov 2023 04:40:08 -0800 Subject: [PATCH 29/50] llama : mark LLM_ARCH_STARCODER as full offload supported (#3945) as done in https://github.com/ggerganov/llama.cpp/pull/3827 --- llama.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index cc0211ceb..e16539000 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5164,11 +5164,12 @@ static int llama_decode_internal( // If all tensors can be run on the GPU then using more than 1 thread is detrimental. const bool full_offload_supported = - model.arch == LLM_ARCH_LLAMA || - model.arch == LLM_ARCH_BAICHUAN || - model.arch == LLM_ARCH_FALCON || - model.arch == LLM_ARCH_REFACT || - model.arch == LLM_ARCH_MPT; + model.arch == LLM_ARCH_LLAMA || + model.arch == LLM_ARCH_BAICHUAN || + model.arch == LLM_ARCH_FALCON || + model.arch == LLM_ARCH_REFACT || + model.arch == LLM_ARCH_MPT || + model.arch == LLM_ARCH_STARCODER; const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { From 132d25b8a62ea084447e0014a0112c1b371fb3f8 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sun, 5 Nov 2023 10:08:57 -0500 Subject: [PATCH 30/50] cuda : fix disabling device with --tensor-split 1,0 (#3951) Co-authored-by: slaren --- ggml-cuda.cu | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index dc14f2f5d..9f873035a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6893,6 +6893,8 @@ static void ggml_cuda_op_mul_mat( int64_t row_low[GGML_CUDA_MAX_DEVICES]; int64_t row_high[GGML_CUDA_MAX_DEVICES]; + int used_devices = 0; + for (int64_t id = 0; id < g_device_count; ++id) { // by default, use all rows row_low[id] = 0; @@ -6920,6 +6922,8 @@ static void ggml_cuda_op_mul_mat( continue; } + used_devices++; + const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device; const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device; @@ -6958,12 +6962,12 @@ static void ggml_cuda_op_mul_mat( // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data - if (split && g_device_count > 1) { + if (split && used_devices > 1) { CUDA_CHECK(ggml_cuda_set_device(g_main_device)); CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0])); } - const int64_t src1_col_stride = split && g_device_count > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; + const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; @@ -7079,6 +7083,9 @@ static void ggml_cuda_op_mul_mat( } for (int64_t id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device) || row_low[id] == row_high[id]) { + continue; + } CUDA_CHECK(ggml_cuda_set_device(id)); // free buffers again when done @@ -7103,6 +7110,9 @@ static void ggml_cuda_op_mul_mat( CUDA_CHECK(ggml_cuda_set_device(g_main_device)); for (int64_t id = 0; id < g_device_count; ++id) { + if (row_low[id] == row_high[id]) { + continue; + } for (int64_t is = 0; is < is_max; ++is) { CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0)); } @@ -7400,7 +7410,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = - (src0->backend == GGML_BACKEND_GPU) && + (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && (src1->backend == GGML_BACKEND_GPU) && ( dst->backend == GGML_BACKEND_GPU); From bb60fd0bf6bb270744d86dd45b3a95af01b7de45 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A1i=20Ho=C3=A0ng=20T=C3=A2m?= <75922889+RoyalHeart@users.noreply.github.com> Date: Sun, 5 Nov 2023 23:15:27 +0700 Subject: [PATCH 31/50] server : fix typo for --alias shortcut from -m to -a (#3958) --- examples/server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 715007735..089ebe2d1 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -7,7 +7,7 @@ Command line options: - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). -- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. +- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. From d9ccce2e339ca0396560d18b8637f2c848d72a08 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 5 Nov 2023 10:06:06 -0700 Subject: [PATCH 32/50] Allow common process_escapes to handle \x sequences (#3928) * Allow common process_escapes to handle \x sequences * Fix edge case when second hex digit is NUL --- common/common.cpp | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 20cc4a081..37e3ace8a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -90,6 +90,19 @@ void process_escapes(std::string& input) { case '\'': input[output_idx++] = '\''; break; case '\"': input[output_idx++] = '\"'; break; case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + // Intentionally fall through to default. + } default: input[output_idx++] = '\\'; input[output_idx++] = input[input_idx]; break; } From 2833a6f63c1b87c7f4ac574bcf7a15a2f3bf3ede Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 5 Nov 2023 18:45:16 +0100 Subject: [PATCH 33/50] ggml-cuda : fix f16 mul mat (#3961) * ggml-cuda : fix f16 mul mat ggml-ci * silence common.cpp warning (bonus) --- common/common.cpp | 2 +- ggml-cuda.cu | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 37e3ace8a..6a7114200 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -101,8 +101,8 @@ void process_escapes(std::string& input) { input[output_idx++] = char(val); break; } - // Intentionally fall through to default. } + // fall through default: input[output_idx++] = '\\'; input[output_idx++] = input[input_idx]; break; } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9f873035a..2d9ffffbf 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7414,6 +7414,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 (src1->backend == GGML_BACKEND_GPU) && ( dst->backend == GGML_BACKEND_GPU); + const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + int64_t min_compute_capability = INT_MAX; for (int64_t id = 0; id < g_device_count; ++id) { if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { @@ -7435,13 +7437,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { From 381efbf480959bb6d1e247a8b0c2328f22e350f8 Mon Sep 17 00:00:00 2001 From: Damian Stewart Date: Mon, 6 Nov 2023 22:36:23 +0100 Subject: [PATCH 34/50] llava : expose as a shared library for downstream projects (#3613) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * wip llava python bindings compatibility * add external llava API * add base64 in-prompt image support * wip refactor image loading * refactor image load out of llava init * cleanup * further cleanup; move llava-cli into its own file and rename * move base64.hpp into common/ * collapse clip and llava libraries * move llava into its own subdir * wip * fix bug where base64 string was not removed from the prompt * get libllava to output in the right place * expose llava methods in libllama.dylib * cleanup memory usage around clip_image_* * cleanup and refactor *again* * update headerdoc * build with cmake, not tested (WIP) * Editorconfig * Editorconfig * Build with make * Build with make * Fix cyclical depts on Windows * attempt to fix build on Windows * attempt to fix build on Windows * Upd TODOs * attempt to fix build on Windows+CUDA * Revert changes in cmake * Fix according to review comments * Support building as a shared library * address review comments --------- Co-authored-by: M. Yusuf Sarıgöz Co-authored-by: Jared Van Bortel --- .gitignore | 2 +- Makefile | 7 +- common/CMakeLists.txt | 1 + common/base64.hpp | 392 +++++++++++++++++++++++++++++++++ examples/llava/CMakeLists.txt | 46 +++- examples/llava/README.md | 7 +- examples/llava/clip.cpp | 86 +++++--- examples/llava/clip.h | 41 +++- examples/llava/llava-cli.cpp | 315 ++++++++++++++++++++++++++ examples/llava/llava-utils.h | 147 ------------- examples/llava/llava.cpp | 280 ++++++++++++----------- examples/llava/llava.h | 50 +++++ examples/server/CMakeLists.txt | 2 +- 13 files changed, 1022 insertions(+), 354 deletions(-) create mode 100644 common/base64.hpp create mode 100644 examples/llava/llava-cli.cpp delete mode 100644 examples/llava/llava-utils.h create mode 100644 examples/llava/llava.h diff --git a/.gitignore b/.gitignore index 50cbd0b47..708e8582e 100644 --- a/.gitignore +++ b/.gitignore @@ -46,7 +46,7 @@ models-mnt /infill /libllama.so /llama-bench -/llava +/llava-cli /main /metal /perplexity diff --git a/Makefile b/Makefile index 300c1e6c7..f2d4fd031 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf llama-bench llava baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o # Binaries only useful for tests @@ -617,7 +617,10 @@ convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggm llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS) +libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ $(LDFLAGS) -Wno-cast-qual + +llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index ac594b2ca..4f930bdc5 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -41,6 +41,7 @@ endif() set(TARGET common) add_library(${TARGET} STATIC + base64.hpp common.h common.cpp sampling.h diff --git a/common/base64.hpp b/common/base64.hpp new file mode 100644 index 000000000..563247a6e --- /dev/null +++ b/common/base64.hpp @@ -0,0 +1,392 @@ +/* +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to +*/ + +#ifndef PUBLIC_DOMAIN_BASE64_HPP_ +#define PUBLIC_DOMAIN_BASE64_HPP_ + +#include +#include +#include +#include + +class base64_error : public std::runtime_error +{ +public: + using std::runtime_error::runtime_error; +}; + +class base64 +{ +public: + enum class alphabet + { + /** the alphabet is detected automatically */ + auto_, + /** the standard base64 alphabet is used */ + standard, + /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/ + url_filename_safe + }; + + enum class decoding_behavior + { + /** if the input is not padded, the remaining bits are ignored */ + moderate, + /** if a padding character is encounter decoding is finished */ + loose + }; + + /** + Encodes all the elements from `in_begin` to `in_end` to `out`. + + @warning The source and destination cannot overlap. The destination must be able to hold at least + `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than + 8 bits + @tparam Output_iterator the destination; the elements written to it are from the type `char` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @returns the iterator to the next element past the last element copied + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::standard) + { + constexpr auto pad = '='; + const char* alpha = alphabet == alphabet::url_filename_safe + ? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_" + : "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; + + while (in_begin != in_end) { + std::uint8_t i0 = 0, i1 = 0, i2 = 0; + + // first character + i0 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[i0 >> 2 & 0x3f]; + ++out; + + // part of first character and second + if (in_begin != in_end) { + i1 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)]; + ++out; + } else { + *out = alpha[(i0 & 0x3) << 4]; + ++out; + + // last padding + *out = pad; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // part of second character and third + if (in_begin != in_end) { + i2 = static_cast(*in_begin); + ++in_begin; + + *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)]; + ++out; + } else { + *out = alpha[(i1 & 0xf) << 2]; + ++out; + + // last padding + *out = pad; + ++out; + + break; + } + + // rest of third + *out = alpha[i2 & 0x3f]; + ++out; + } + + return out; + } + /** + Encodes a string. + + @param str the string that should be encoded + @param alphabet which alphabet should be used + @returns the encoded base64 string + @throws see base64::encode() + */ + static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(str.length()) + 1); + + encode(str.begin(), str.end(), std::back_inserter(result), alphabet); + + return result; + } + /** + Encodes a char array. + + @param buffer the char array + @param size the size of the array + @param alphabet which alphabet should be used + @returns the encoded string + */ + static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard) + { + std::string result; + + result.reserve(required_encode_size(size) + 1); + + encode(buffer, buffer + size, std::back_inserter(result), alphabet); + + return result; + } + /** + Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`, + in other words: inplace decoding is possible. + + @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`, + otherwise the behavior depends on the output iterator. + + @tparam Input_iterator the source; the returned elements are cast to `char` + @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t` + @param in_begin the beginning of the source + @param in_end the ending of the source + @param out the destination iterator + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the iterator to the next element past the last element copied + @throws base64_error depending on the set behavior + @throws see `Input_iterator` and `Output_iterator` + */ + template + static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out, + alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + //constexpr auto pad = '='; + std::uint8_t last = 0; + auto bits = 0; + + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c == '=') { + break; + } + + auto part = _base64_value(alphabet, c); + + // enough bits for one byte + if (bits + 6 >= 8) { + *out = (last << (8 - bits)) | (part >> (bits - 2)); + ++out; + + bits -= 2; + } else { + bits += 6; + } + + last = part; + } + + // check padding + if (behavior != decoding_behavior::loose) { + while (in_begin != in_end) { + auto c = *in_begin; + ++in_begin; + + if (c != '=') { + throw base64_error("invalid base64 character."); + } + } + } + + return out; + } + /** + Decodes a string. + + @param str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(str.length())); + + decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string. + + @param buffer the base64 encoded buffer + @param size the size of the buffer + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the decoded string + @throws see base64::decode() + */ + static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + std::string result; + + result.reserve(max_decode_size(size)); + + decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior); + + return result; + } + /** + Decodes a string inplace. + + @param[in,out] str the base64 encoded string + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @throws base64::decode_inplace() + */ + static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin()); + } + /** + Decodes a char array inplace. + + @param[in,out] str the string array + @param size the length of the array + @param alphabet which alphabet should be used + @param behavior the behavior when an error was detected + @returns the pointer to the next element past the last element decoded + @throws base64::decode_inplace() + */ + static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_, + decoding_behavior behavior = decoding_behavior::moderate) + { + return decode(str, str + size, str, alphabet, behavior); + } + /** + Returns the required decoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{4} \rceil \cdot 3 + $$ + + @param size the size of the encoded input + @returns the size of the resulting decoded buffer; this the absolute maximum + */ + static std::size_t max_decode_size(std::size_t size) noexcept + { + return (size / 4 + (size % 4 ? 1 : 0)) * 3; + } + /** + Returns the required encoding size for a given size. The value is calculated with the following formula: + + $$ + \lceil \frac{size}{3} \rceil \cdot 4 + $$ + + @param size the size of the decoded input + @returns the size of the resulting encoded buffer + */ + static std::size_t required_encode_size(std::size_t size) noexcept + { + return (size / 3 + (size % 3 ? 1 : 0)) * 4; + } + +private: + static std::uint8_t _base64_value(alphabet& alphabet, char c) + { + if (c >= 'A' && c <= 'Z') { + return c - 'A'; + } else if (c >= 'a' && c <= 'z') { + return c - 'a' + 26; + } else if (c >= '0' && c <= '9') { + return c - '0' + 52; + } + + // comes down to alphabet + if (alphabet == alphabet::standard) { + if (c == '+') { + return 62; + } else if (c == '/') { + return 63; + } + } else if (alphabet == alphabet::url_filename_safe) { + if (c == '-') { + return 62; + } else if (c == '_') { + return 63; + } + } // auto detect + else { + if (c == '+') { + alphabet = alphabet::standard; + + return 62; + } else if (c == '/') { + alphabet = alphabet::standard; + + return 63; + } else if (c == '-') { + alphabet = alphabet::url_filename_safe; + + return 62; + } else if (c == '_') { + alphabet = alphabet::url_filename_safe; + + return 63; + } + } + + throw base64_error("invalid base64 character."); + } +}; + +#endif // !PUBLIC_DOMAIN_BASE64_HPP_ diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 03d32c26e..8ea3e5c83 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -1,14 +1,36 @@ -set(TARGET clip) -add_library(${TARGET} clip.cpp clip.h) -install(TARGETS ${TARGET} LIBRARY) -target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) -if (NOT MSVC) - target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h +add_library(llava OBJECT + llava.cpp + llava.h + clip.cpp + clip.h + ) + +target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) + +target_include_directories(llava PUBLIC .) +target_include_directories(llava PUBLIC ../..) +target_include_directories(llava PUBLIC ../../common) + +target_compile_features(llava PRIVATE cxx_std_11) + +add_library(llava_static STATIC $) +if (BUILD_SHARED_LIBS) + set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD) + add_library(llava_shared SHARED $) + target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT}) + install(TARGETS llava_shared LIBRARY) endif() -set(TARGET llava) -add_executable(${TARGET} llava.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +if (NOT MSVC) + target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h + endif() +if(TARGET BUILD_INFO) + add_dependencies(llava BUILD_INFO) +endif() + +set(TARGET llava-cli) +add_executable(llava-cli llava-cli.cpp) +install(TARGETS llava-cli RUNTIME) +target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(llava PRIVATE cxx_std_11) diff --git a/examples/llava/README.md b/examples/llava/README.md index fc3446b60..323c5fdd0 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -9,12 +9,12 @@ models are available. After API is confirmed, more models will be supported / uploaded. ## Usage -Build with cmake or run `make llava` to build it. +Build with cmake or run `make llava-cli` to build it. -After building, run: `./llava` to see the usage. For example: +After building, run: `./llava-cli` to see the usage. For example: ```sh -./llava -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -51,7 +51,6 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director ## TODO -- [ ] Support server mode. - [ ] Support non-CPU backend for the image encoding part. - [ ] Support different sampling methods. - [ ] Support more model variants. diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 61932e659..3c909c7d3 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -680,26 +680,44 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return new_clip; } -clip_image_u8 * make_clip_image_u8() { return new clip_image_u8(); } - +clip_image_u8 * make_clip_image_u8() { + auto img = new clip_image_u8(); + return img; +} clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); } -bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { - int nx, ny, nc; - auto data = stbi_load(fname, &nx, &ny, &nc, 3); - if (!data) { - fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname); - return false; - } +void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; } +void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; } +static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { img->nx = nx; img->ny = ny; img->size = nx * ny * 3; img->data = new uint8_t[img->size](); memcpy(img->data, data, img->size); +} +bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { + int nx, ny, nc; + auto data = stbi_load(fname, &nx, &ny, &nc, 3); + if (!data) { + fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); + return false; + } + build_clip_img_from_data(data, nx, ny, img); stbi_image_free(data); + return true; +} +bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) { + int nx, ny, nc; + auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); + if (!data) { + fprintf(stderr, "%s: failed to decode image bytes\n", __func__); + return false; + } + build_clip_img_from_data(data, nx, ny, img); + stbi_image_free(data); return true; } @@ -714,39 +732,40 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 - clip_image_u8 temp; // we will keep the input image data here temporarily + clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily if (pad2square && img->nx != img->ny) { int longer_side = std::max(img->nx, img->ny); - temp.nx = longer_side; - temp.ny = longer_side; - temp.size = 3 * longer_side * longer_side; - temp.data = new uint8_t[temp.size](); + temp->nx = longer_side; + temp->ny = longer_side; + temp->size = 3 * longer_side * longer_side; + temp->data = new uint8_t[temp->size](); uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA // fill with background color - for (size_t i = 0; i < temp.size; i++) { - temp.data[i] = bc[i % 3]; + for (size_t i = 0; i < temp->size; i++) { + temp->data[i] = bc[i % 3]; } // copy from the input image for (int y = 0; y < img->ny; y++) { for (int x = 0; x < img->nx; x++) { const int i = 3 * (y * img->nx + x); - const int j = 3 * (y * temp.nx + x); - temp.data[j] = img->data[i]; - temp.data[j+1] = img->data[i+1]; - temp.data[j+2] = img->data[i+2]; + const int j = 3 * (y * temp->nx + x); + temp->data[j] = img->data[i]; + temp->data[j+1] = img->data[i+1]; + temp->data[j+2] = img->data[i+2]; } } } else { - temp.nx = img->nx; - temp.ny = img->ny; - temp.size = img->size; - temp.data = img->data; + temp->nx = img->nx; + temp->ny = img->ny; + temp->size = img->size; + temp->data = new uint8_t[temp->size](); + *temp->data = *img->data; // copy } - const int nx = temp.nx; - const int ny = temp.ny; + const int nx = temp->nx; + const int ny = temp->ny; const int nx2 = ctx->vision_model.hparams.image_size; const int ny2 = ctx->vision_model.hparams.image_size; @@ -785,10 +804,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int j10 = 3 * (y1 * nx + x0) + c; const int j11 = 3 * (y1 * nx + x1) + c; - const float v00 = temp.data[j00]; - const float v01 = temp.data[j01]; - const float v10 = temp.data[j10]; - const float v11 = temp.data[j11]; + const float v00 = temp->data[j00]; + const float v01 = temp->data[j01]; + const float v10 = temp->data[j10]; + const float v11 = temp->data[j11]; const float v0 = v00 * (1.0f - dx) + v01 * dx; const float v1 = v10 * (1.0f - dx) + v11 * dx; @@ -803,6 +822,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip } } } + clip_image_u8_free(temp); return true; } @@ -1049,16 +1069,16 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i return true; } -int clip_n_mmproj_embd(struct clip_ctx * ctx) { +int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_2_b->ne[0]; } -int clip_n_patches(struct clip_ctx * ctx) { +int clip_n_patches(const struct clip_ctx * ctx) { auto & params = ctx->vision_model.hparams; return (params.image_size / params.patch_size) * (params.image_size / params.patch_size); } -size_t clip_embd_nbytes(struct clip_ctx * ctx) { +size_t clip_embd_nbytes(const struct clip_ctx * ctx) { return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); } diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 3d7261e29..f11df85de 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -1,7 +1,22 @@ #ifndef CLIP_H #define CLIP_H -#include "ggml.h" +#include +#include + +#ifdef LLAMA_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef LLAMA_BUILD +# define CLIP_API __declspec(dllexport) +# else +# define CLIP_API __declspec(dllimport) +# endif +# else +# define CLIP_API __attribute__ ((visibility ("default"))) +# endif +#else +# define CLIP_API +#endif struct clip_ctx; @@ -20,19 +35,20 @@ struct clip_vision_hparams { float eps; }; -struct clip_ctx * clip_model_load(const char * fname, const int verbosity); +/** load mmproj model */ +CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity); +/** free mmproj model */ +CLIP_API void clip_free(struct clip_ctx * ctx); -void clip_free(struct clip_ctx * ctx); - -size_t clip_embd_nbytes(struct clip_ctx * ctx); -int clip_n_patches(struct clip_ctx * ctx); -int clip_n_mmproj_embd(struct clip_ctx * ctx); +size_t clip_embd_nbytes(const struct clip_ctx * ctx); +int clip_n_patches(const struct clip_ctx * ctx); +int clip_n_mmproj_embd(const struct clip_ctx * ctx); // RGB uint8 image struct clip_image_u8 { int nx; int ny; - uint8_t * data; + uint8_t * data = NULL; size_t size; }; @@ -41,7 +57,7 @@ struct clip_image_u8 { struct clip_image_f32 { int nx; int ny; - float * data; + float * data = NULL; size_t size; }; @@ -57,7 +73,12 @@ struct clip_image_f32_batch { struct clip_image_u8 * make_clip_image_u8(); struct clip_image_f32 * make_clip_image_f32(); -bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); +CLIP_API void clip_image_u8_free(clip_image_u8 * img); +CLIP_API void clip_image_f32_free(clip_image_f32 * img); +CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); +/** interpret bytes as an image file with length bytes_length, and use the result to populate img */ +CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); + bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square); bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp new file mode 100644 index 000000000..19374c67f --- /dev/null +++ b/examples/llava/llava-cli.cpp @@ -0,0 +1,315 @@ +#include "ggml.h" +#include "common.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" + +#include "base64.hpp" + +#include +#include +#include + +static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { + int N = (int) tokens.size(); + for (int i = 0; i < N; i += n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + return false; + } + *n_past += n_eval; + } + return true; +} + +static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(ctx_llama, tokens, 1, n_past); +} + +static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ + std::string str2 = str; + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); + eval_tokens(ctx_llama, embd_inp, n_batch, n_past); + return true; +} + +// TODO: use common/sampling.h +static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { + auto & sparams = params.sparams; + + // out of user input, sample next token + const float temp = sparams.temp; + const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; + const float top_p = sparams.top_p; + const float tfs_z = sparams.tfs_z; + const float typical_p = sparams.typical_p; + // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; + // const float repeat_penalty = sparams.repeat_penalty; + // const float alpha_presence = sparams.presence_penalty; + // const float alpha_frequency = sparams.frequency_penalty; + const int mirostat = sparams.mirostat; + const float mirostat_tau = sparams.mirostat_tau; + const float mirostat_eta = sparams.mirostat_eta; + // const bool penalize_nl = sparams.penalize_nl; + + llama_token id = 0; + { + auto logits = llama_get_logits(ctx_llama); + auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); + + // Apply params.logit_bias map + for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx_llama, &candidates_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); + llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); + llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); + llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); + llama_sample_temp(ctx_llama, &candidates_p, temp); + id = llama_sample_token(ctx_llama, &candidates_p); + } + } + } + + return id; +} + +static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { + int id = sample_id(ctx_llama, params); + static std::string ret; + if (id == llama_token_eos(llama_get_model(ctx_llama))) { + ret = ""; + } else { + ret = llama_token_to_piece(ctx_llama, id); + } + eval_id(ctx_llama, id, n_past); + return ret.c_str(); +} + +static const char* IMG_BASE64_TAG_BEGIN = ""; + +static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) { + begin_out = prompt.find(IMG_BASE64_TAG_BEGIN); + end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out); +} + +static bool prompt_contains_image(const std::string& prompt) { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + return (begin != std::string::npos); +} + +// replaces the base64 image tag in the prompt with `replacement` +static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) { + size_t img_base64_str_start, img_base64_str_end; + find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); + if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { + fprintf(stderr, "%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + return NULL; + } + + auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN); + auto base64_bytes_count = img_base64_str_end - base64_bytes_start; + auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count ); + + auto required_bytes = base64::required_encode_size(base64_str.size()); + auto img_bytes = std::vector(required_bytes); + base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin()); + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); + if (!embed) { + fprintf(stderr, "%s: could not load image from base64 string.\n", __func__); + return NULL; + } + + return embed; +} + +static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + if (begin == std::string::npos || end == std::string::npos) { + return prompt; + } + auto pre = prompt.substr(0, begin); + auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END)); + return pre + replacement + post; +} + +struct llava_context { + struct clip_ctx * ctx_clip = NULL; + struct llama_context * ctx_llama = NULL; + struct llama_model * model = NULL; +}; + +static void show_additional_info(int /*argc*/, char ** argv) { + printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); +} + +static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { + + // load and preprocess the image + llava_image_embed * embed = NULL; + auto prompt = params->prompt; + if (prompt_contains_image(prompt)) { + if (!params->image.empty()) { + printf("using base64 encoded image instead of command line image path\n"); + } + embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); + if (!embed) { + fprintf(stderr, "%s: can't load image from prompt\n", __func__); + return NULL; + } + params->prompt = remove_image_from_prompt(prompt); + } else { + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); + if (!embed) { + fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str()); + return NULL; + } + } + + return embed; +} + +static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) { + int n_past = 0; + + const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; + + // llava chat format is "\nUSER:\n\nASSISTANT:" + eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, true); + llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); + eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false); + + // generate the response + + printf("\n"); + + for (int i = 0; i < max_tgt_len; i++) { + const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past); + if (strcmp(tmp, "") == 0) break; + + printf("%s", tmp); + fflush(stdout); + } + + printf("\n"); +} + + +static struct llava_context * llava_init(gpt_params * params) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + + llama_backend_init(params->numa); + + llama_model_params model_params = llama_model_default_params(); + llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); + if (model == NULL) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return NULL; + } + + llama_context_params ctx_params = llama_context_default_params(); + + ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings + ctx_params.n_threads = params->n_threads; + ctx_params.n_threads_batch = params->n_threads_batch == -1 ? params->n_threads : params->n_threads_batch; + + llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); + + if (ctx_llama == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return NULL; + } + + auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + + ctx_llava->ctx_llama = ctx_llama; + ctx_llava->ctx_clip = ctx_clip; + ctx_llava->model = model; + return ctx_llava; +} + +static void llava_free(struct llava_context * ctx_llava) { + if (ctx_llava->ctx_clip) { + clip_free(ctx_llava->ctx_clip); + ctx_llava->ctx_clip = NULL; + } + + llama_free(ctx_llava->ctx_llama); + llama_free_model(ctx_llava->model); + llama_backend_free(); +} + +int main(int argc, char ** argv) { + ggml_time_init(); + + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + show_additional_info(argc, argv); + return 1; + } + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { + gpt_print_usage(argc, argv, params); + show_additional_info(argc, argv); + return 1; + } + + auto ctx_llava = llava_init(¶ms); + if (ctx_llava == NULL) { + fprintf(stderr, "%s: error: failed to init llava\n", __func__); + return 1; + } + + auto image_embed = load_image(ctx_llava, ¶ms); + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_print_timings(ctx_llava->ctx_llama); + + llava_image_embed_free(image_embed); + llava_free(ctx_llava); + return 0; +} diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h deleted file mode 100644 index 320c71967..000000000 --- a/examples/llava/llava-utils.h +++ /dev/null @@ -1,147 +0,0 @@ -#pragma once - -// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now - -#include "common.h" -#include "llama.h" - -#include -#include -#include - -inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) { - int n_embd = llama_n_embd(llama_get_model(ctx_llama)); - - for (int i = 0; i < N; i += n_batch) { - int n_eval = N - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - } - return true; -} - -inline bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { - int N = (int) tokens.size(); - for (int i = 0; i < N; i += n_batch) { - int n_eval = (int) tokens.size() - i; - if (n_eval > n_batch) { - n_eval = n_batch; - } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return false; - } - *n_past += n_eval; - } - return true; -} - -inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { - std::vector tokens; - tokens.push_back(id); - return eval_tokens(ctx_llama, tokens, 1, n_past); -} - -inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ - std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); - eval_tokens(ctx_llama, embd_inp, n_batch, n_past); - return true; -} - -// TODO: use common/sampling.h -inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { - auto & sparams = params.sparams; - - // out of user input, sample next token - const float temp = sparams.temp; - const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; - const float top_p = sparams.top_p; - const float tfs_z = sparams.tfs_z; - const float typical_p = sparams.typical_p; - // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; - // const float repeat_penalty = sparams.repeat_penalty; - // const float alpha_presence = sparams.presence_penalty; - // const float alpha_frequency = sparams.frequency_penalty; - const int mirostat = sparams.mirostat; - const float mirostat_tau = sparams.mirostat_tau; - const float mirostat_eta = sparams.mirostat_eta; - // const bool penalize_nl = sparams.penalize_nl; - - llama_token id = 0; - { - auto logits = llama_get_logits(ctx_llama); - auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); - - // Apply params.logit_bias map - for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - // TODO: Apply penalties - // float nl_logit = logits[llama_token_nl(ctx)]; - // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - // llama_sample_repetition_penalty(ctx, &candidates_p, - // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - // last_n_repeat, repeat_penalty); - // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, - // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - // last_n_repeat, alpha_frequency, alpha_presence); - // if (!penalize_nl) { - // logits[llama_token_nl(ctx)] = nl_logit; - // } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx_llama, &candidates_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token(ctx_llama, &candidates_p); - } - } - } - - return id; -} - -inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { - int id = sample_id(ctx_llama, params); - static std::string ret; - if (id == llama_token_eos(llama_get_model(ctx_llama))) { - ret = ""; - } else { - ret = llama_token_to_piece(ctx_llama, id); - } - eval_id(ctx_llama, id, n_past); - return ret.c_str(); -} diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index f0974d5bc..d10bcf2d2 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -1,164 +1,156 @@ #include "clip.h" -#include "llava-utils.h" #include "common.h" #include "llama.h" +#include "llava.h" #include #include #include -static void show_additional_info(int /*argc*/, char ** argv) { - printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); -} +#include "base64.hpp" -int main(int argc, char ** argv) { - ggml_time_init(); - - gpt_params params; - - if (!gpt_params_parse(argc, argv, params)) { - show_additional_info(argc, argv); - return 1; +static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { + clip_image_f32 * img_res = make_clip_image_f32(); + if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) { + fprintf(stderr, "%s: unable to preprocess image\n", __func__); + clip_image_f32_free(img_res); + return false; } - if (params.mmproj.empty() || params.image.empty()) { - gpt_print_usage(argc, argv, params); - show_additional_info(argc, argv); - return 1; - } - - const char * clip_path = params.mmproj.c_str(); - const char * img_path = params.image.c_str(); - - if (params.prompt.empty()) { - params.prompt = "describe the image in detail."; - } - - auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - - // load and preprocess the image - clip_image_u8 img; - clip_image_f32 img_res; - - if (!clip_image_load_from_file(img_path, &img)) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, img_path); - - clip_free(ctx_clip); - return 1; - } - - if (!clip_image_preprocess(ctx_clip, &img, &img_res, /*pad2square =*/ true)) { - fprintf(stderr, "%s: unable to preprocess %s\n", __func__, img_path); - - clip_free(ctx_clip); - return 1; - } - - int n_img_pos = clip_n_patches(ctx_clip); - int n_img_embd = clip_n_mmproj_embd(ctx_clip); - - float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)); - - if (!image_embd) { - fprintf(stderr, "Unable to allocate memory for image embeddings\n"); - - return 1; - } + *n_img_pos = clip_n_patches(ctx_clip); const int64_t t_img_enc_start_us = ggml_time_us(); - if (!clip_image_encode(ctx_clip, params.n_threads, &img_res, image_embd)) { + bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); + clip_image_f32_free(img_res); + if (!encoded) { fprintf(stderr, "Unable to encode image\n"); - return 1; + return false; } + const int64_t t_img_enc_end_us = ggml_time_us(); + float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - // we get the embeddings, free up the memory required for CLIP - clip_free(ctx_clip); + printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); - llama_backend_init(params.numa); - - llama_model_params model_params = llama_model_default_params(); - model_params.n_gpu_layers = params.n_gpu_layers; - model_params.main_gpu = params.main_gpu; - model_params.tensor_split = params.tensor_split; - model_params.use_mmap = params.use_mmap; - model_params.use_mlock = params.use_mlock; - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); - if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); - return 1; - } - - llama_context_params ctx_params = llama_context_default_params(); - - ctx_params.n_ctx = params.n_ctx < 2048 ? 2048 : params.n_ctx; // we need a longer context size to process image embeddings - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - ctx_params.seed = params.seed; - - llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); - - if (ctx_llama == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); - return 1; - } - - // make sure that the correct mmproj was used, i.e., compare apples to apples - const int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); - - if (n_img_embd != n_llama_embd) { - printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_llama_embd); - - llama_free(ctx_llama); - llama_free_model(model); - llama_backend_free(); - free(image_embd); - - return 1; - } - - // process the prompt - // llava chat format is "USER: \n\nASSISTANT:" - - int n_past = 0; - - const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict; - - eval_string(ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params.n_batch, &n_past, true); - eval_image_embd(ctx_llama, image_embd, n_img_pos, params.n_batch, &n_past); - eval_string(ctx_llama, (params.prompt + "\nASSISTANT:").c_str(), params.n_batch, &n_past, false); - - // generate the response - - printf("\n"); - printf("prompt: '%s'\n", params.prompt.c_str()); - printf("\n"); - - for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(ctx_llama, params, &n_past); - if (strcmp(tmp, "") == 0) break; - - printf("%s", tmp); - fflush(stdout); - } - - printf("\n"); - - { - const float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - - printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / n_img_pos); - } - - llama_print_timings(ctx_llama); - - llama_free(ctx_llama); - llama_free_model(model); - llama_backend_free(); - free(image_embd); - - return 0; + return true; +} + +bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) { + // make sure that the correct mmproj was used, i.e., compare apples to apples + int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); + auto n_image_embd = clip_n_mmproj_embd(ctx_clip); + if (n_image_embd != n_llama_embd) { + printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); + return false; + } + return true; +} + +static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { + float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)); + if (!image_embd) { + fprintf(stderr, "Unable to allocate memory for image embeddings\n"); + free(image_embd); + return false; + } + + int n_img_pos; + if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { + fprintf(stderr, "%s: cannot encode image, aborting\n", __func__); + free(image_embd); + return false; + } + *image_embd_out = image_embd; + *n_img_pos_out = n_img_pos; + + return true; +} + +bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { + int n_embd = llama_n_embd(llama_get_model(ctx_llama)); + + for (int i = 0; i < image_embed->n_image_pos; i += n_batch) { + int n_eval = image_embed->n_image_pos - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; + if (llama_decode(ctx_llama, batch)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + *n_past += n_eval; + } + return true; +} + +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { + clip_image_u8 * img = make_clip_image_u8(); + if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { + clip_image_u8_free(img); + fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); + return NULL; + } + + float* image_embed = NULL; + int n_image_pos = 0; + bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); + if (!image_embed_result) { + clip_image_u8_free(img); + fprintf(stderr, "%s: coulnd't embed the image\n", __func__); + return NULL; + } + + clip_image_u8_free(img); + auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed)); + result->embed = image_embed; + result->n_image_pos = n_image_pos; + return result; +} + +static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { + auto file = fopen(path, "rb"); + if (file == NULL) { + fprintf(stderr, "%s: can't read file %s\n", __func__, path); + return false; + } + + fseek(file, 0, SEEK_END); + auto fileSize = ftell(file); + fseek(file, 0, SEEK_SET); + + auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data + if (buffer == NULL) { + fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); + perror("Memory allocation error"); + fclose(file); + return false; + } + fread(buffer, 1, fileSize, file); // Read the file into the buffer + fclose(file); // Close the file + + *bytesOut = buffer; + *sizeOut = fileSize; + return true; +} + +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) { + unsigned char* image_bytes; + long image_bytes_length; + auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); + if (!loaded) { + fprintf(stderr, "%s: failed to load %s\n", __func__, image_path); + return NULL; + } + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length); + free(image_bytes); + + return embed; +} + +LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) { + free(embed->embed); + free(embed); } diff --git a/examples/llava/llava.h b/examples/llava/llava.h new file mode 100644 index 000000000..e08ce7883 --- /dev/null +++ b/examples/llava/llava.h @@ -0,0 +1,50 @@ +#ifndef LLAVA_H +#define LLAVA_H + +#include "ggml.h" + + +#ifdef LLAMA_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef LLAMA_BUILD +# define LLAVA_API __declspec(dllexport) +# else +# define LLAVA_API __declspec(dllimport) +# endif +# else +# define LLAVA_API __attribute__ ((visibility ("default"))) +# endif +#else +# define LLAVA_API +#endif + +struct clip_ctx; + +#ifdef __cplusplus +extern "C" { +#endif + +struct llava_image_embed { + float * embed; + int n_image_pos; +}; + +/** sanity check for clip <-> llava embed size match */ +LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip); + +/** build an image embed from image file bytes */ +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); +/** build an image embed from a path to an image filename */ +LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path); +LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); +/** free an embedding made with llava_image_embed_make_* */ + +/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ +LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past); + + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 1f0d26f77..859cd12c6 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() From 46876d2a2c92e60579dc732cdb8cbd243b06f317 Mon Sep 17 00:00:00 2001 From: Meng Zhang Date: Mon, 6 Nov 2023 22:49:08 -0800 Subject: [PATCH 35/50] cuda : supports running on CPU for GGML_USE_CUBLAS=ON build (#3946) * protyping the idea that supports running on CPU for a GGML_USE_CUBLAS=on build * doc: add comments to ggml_cublas_loaded() * fix defined(...) --- ggml-cuda.cu | 17 ++++- ggml-cuda.h | 5 ++ llama.cpp | 181 ++++++++++++++++++++++++++++++--------------------- 3 files changed, 127 insertions(+), 76 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2d9ffffbf..f87f18802 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5790,6 +5790,11 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) { CUDA_CHECK(cudaFree(ptr)); } +static bool g_cublas_loaded = false; + +bool ggml_cublas_loaded(void) { + return g_cublas_loaded; +} void ggml_init_cublas() { static bool initialized = false; @@ -5803,7 +5808,12 @@ void ggml_init_cublas() { CUDA_CHECK(cudaDeviceSynchronize()); #endif - CUDA_CHECK(cudaGetDeviceCount(&g_device_count)); + if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) { + initialized = true; + g_cublas_loaded = false; + return; + } + GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; #if defined(GGML_CUDA_FORCE_MMQ) @@ -5851,6 +5861,7 @@ void ggml_init_cublas() { // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); initialized = true; + g_cublas_loaded = true; } } @@ -7158,6 +7169,8 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src } bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { + if (!g_cublas_loaded) return false; + const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; @@ -7843,6 +7856,8 @@ void ggml_cuda_free_scratch() { } bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + if (!g_cublas_loaded) return false; + ggml_cuda_func_t func; const bool any_on_device = tensor->backend == GGML_BACKEND_GPU || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) diff --git a/ggml-cuda.h b/ggml-cuda.h index 57adc9cf3..528e66c33 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -17,7 +17,12 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 +// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`. GGML_API void ggml_init_cublas(void); + +// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`. +GGML_API bool ggml_cublas_loaded(void); + GGML_API void * ggml_cuda_host_malloc(size_t size); GGML_API void ggml_cuda_host_free(void * ptr); diff --git a/llama.cpp b/llama.cpp index e16539000..d220ff3e9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -596,19 +596,37 @@ static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * // llama helpers // +inline void * llama_host_malloc(size_t n) { #ifdef GGML_USE_CUBLAS -# define llama_host_malloc(n) ggml_cuda_host_malloc(n) -# define llama_host_free(data) ggml_cuda_host_free(data) + if (ggml_cublas_loaded()) { + return ggml_cuda_host_malloc(n); + } else { + return malloc(n); + } #elif GGML_USE_METAL -# define llama_host_malloc(n) ggml_metal_host_malloc(n) -# define llama_host_free(data) ggml_metal_host_free(data) + return ggml_metal_host_malloc(n); #elif GGML_USE_CPU_HBM -# define llama_host_malloc(n) hbw_malloc(n) -# define llama_host_free(data) if (data != NULL) hbw_free(data) + return hbw_malloc(n); #else -# define llama_host_malloc(n) malloc(n) -# define llama_host_free(data) free(data) + return malloc(n); #endif +} + +inline void llama_host_free(void * ptr) { +#ifdef GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + return ggml_cuda_host_free(ptr); + } else { + return free(ptr); + } +#elif GGML_USE_METAL + return ggml_metal_host_free(ptr); +#elif GGML_USE_CPU_HBM + return hbw_free(ptr); +#else + return free(ptr); +#endif +} #if defined(_WIN32) static std::string llama_format_win_err(DWORD err) { @@ -1200,9 +1218,11 @@ struct llama_kv_cache { } #ifdef GGML_USE_CUBLAS - ggml_cuda_free_data(k); - ggml_cuda_free_data(v); -#endif // GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + ggml_cuda_free_data(k); + ggml_cuda_free_data(v); + } +#endif } }; @@ -1302,11 +1322,15 @@ struct llama_model { } #ifdef GGML_USE_CUBLAS - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cuda_free_data(tensors_by_name[i].second); + if (ggml_cublas_loaded()) { + for (size_t i = 0; i < tensors_by_name.size(); ++i) { + ggml_cuda_free_data(tensors_by_name[i].second); + } + ggml_cuda_free_scratch(); } - ggml_cuda_free_scratch(); -#elif defined(GGML_USE_CLBLAST) +#endif + +#if defined(GGML_USE_CLBLAST) for (size_t i = 0; i < tensors_by_name.size(); ++i) { ggml_cl_free_data(tensors_by_name[i].second); } @@ -1418,23 +1442,26 @@ static bool llama_kv_cache_init( ggml_set_name(cache.v, "cache_v"); (void) n_gpu_layers; -#ifdef GGML_USE_CUBLAS - size_t vram_kv_cache = 0; - if (n_gpu_layers > (int)n_layer + 1) { - ggml_cuda_assign_buffers_no_scratch(cache.v); - LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__); - vram_kv_cache += ggml_nbytes(cache.v); +#ifdef GGML_USE_CUBLAS + if (ggml_cublas_loaded()) { + size_t vram_kv_cache = 0; + + if (n_gpu_layers > (int)n_layer + 1) { + ggml_cuda_assign_buffers_no_scratch(cache.v); + LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__); + vram_kv_cache += ggml_nbytes(cache.v); + } + if (n_gpu_layers > (int)n_layer + 2) { + ggml_cuda_assign_buffers_no_scratch(cache.k); + LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__); + vram_kv_cache += ggml_nbytes(cache.k); + } + if (vram_kv_cache > 0) { + LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); + } } - if (n_gpu_layers > (int)n_layer + 2) { - ggml_cuda_assign_buffers_no_scratch(cache.k); - LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__); - vram_kv_cache += ggml_nbytes(cache.k); - } - if (vram_kv_cache > 0) { - LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); - } -#endif // GGML_USE_CUBLAS +#endif return true; } @@ -2521,18 +2548,22 @@ static void llm_load_tensors( } (void) main_gpu; + + enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; + enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; + #ifdef GGML_USE_CUBLAS - LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); - ggml_cuda_set_main_device(main_gpu); -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT + if (ggml_cublas_loaded()) { + LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); + ggml_cuda_set_main_device(main_gpu); + + llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT; + } #elif defined(GGML_USE_CLBLAST) - LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU -#else -#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU -#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU + LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); + llama_backend_offload = GGML_BACKEND_GPU; + llama_backend_offload_split = GGML_BACKEND_GPU; #endif // prepare memory for the weights @@ -2559,12 +2590,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2588,8 +2619,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2625,12 +2656,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2654,8 +2685,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2695,12 +2726,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2726,8 +2757,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2772,12 +2803,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2803,8 +2834,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2849,12 +2880,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2877,8 +2908,8 @@ static void llm_load_tensors( const int i_gpu_start = n_layer - n_gpu_layers; model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); @@ -2915,12 +2946,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -2946,8 +2977,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; @@ -2993,12 +3024,12 @@ static void llm_load_tensors( // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 - backend_norm = LLAMA_BACKEND_OFFLOAD; + backend_norm = llama_backend_offload; #else - backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; #endif // _WIN32 - backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT; + backend_output = llama_backend_offload_split; } else { backend_norm = GGML_BACKEND_CPU; backend_output = GGML_BACKEND_CPU; @@ -3022,8 +3053,8 @@ static void llm_load_tensors( model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT auto & layer = model.layers[i]; From 54b4df8886103b436a4bb3b60f4d84824f9e8868 Mon Sep 17 00:00:00 2001 From: Matthew Tejo Date: Mon, 6 Nov 2023 23:43:59 -0800 Subject: [PATCH 36/50] Use params when loading models in llava-cli (#3976) llava-cli was loading models with default params and ignoring settings from the cli. This switches to a generic function to load the params from the cli options. --- examples/llava/llava-cli.cpp | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 19374c67f..633afd1da 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -242,18 +242,16 @@ static struct llava_context * llava_init(gpt_params * params) { llama_backend_init(params->numa); - llama_model_params model_params = llama_model_default_params(); + llama_model_params model_params = llama_model_params_from_gpt_params(*params); + llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); if (model == NULL) { fprintf(stderr , "%s: error: unable to load model\n" , __func__); return NULL; } - llama_context_params ctx_params = llama_context_default_params(); - + llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings - ctx_params.n_threads = params->n_threads; - ctx_params.n_threads_batch = params->n_threads_batch == -1 ? params->n_threads : params->n_threads_batch; llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); From e9c1cecb9d7d743d30b4a29ecd56a411437def0a Mon Sep 17 00:00:00 2001 From: xaedes Date: Tue, 7 Nov 2023 09:04:51 +0100 Subject: [PATCH 37/50] ggml : fix backward rope after YaRN (#3974) * fix backward process of rope rope backward process was broken after YaRN RoPE (#2268) implementation, due to missing changes in backward functions. the code for the backward process is nearly identically to the forward process: the only difference is the sign of the sin-values. to avoid future regressions remove the near-duplicate backward functions and reuse the forward code: for this a new function argument `bool forward` was added to `ggml_compute_forward_rope_f32` and `ggml_compute_forward_rope_f16`. the sin-values will be negated when forward is false. * fix finetune rope call to use correct default attn_factor of 1.0f * remove unused `ggml_rope_xpos_back` it is better to have only one `ggml_rope_back` function that accepts all rope parameters, so that `ggml_compute_backward` can propagate all parameters without having to switch between different rope_back variants. * fix comments explaining the sinus sign in ggml_forward_rope * add missing function arguments in declaration * fix function argument type in declaration --- examples/finetune/finetune.cpp | 2 +- ggml.c | 330 ++++++++------------------------- ggml.h | 5 + 3 files changed, 84 insertions(+), 253 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 649a3b7c1..fa7dbe496 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -643,7 +643,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( return ggml_rope_custom(ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, - rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f + rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/ggml.c b/ggml.c index 605a27940..009d5b398 100644 --- a/ggml.c +++ b/ggml.c @@ -4970,8 +4970,13 @@ struct ggml_tensor * ggml_rope_back( int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down) { GGML_ASSERT(ggml_is_vector(b)); @@ -4988,11 +4993,15 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; - memcpy(params + 4, &freq_base, sizeof(float)); - memcpy(params + 5, &freq_scale, sizeof(float)); - memcpy(params + 6, &xpos_base, sizeof(float)); - memcpy(params + 7, &xpos_down, sizeof(bool)); + int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + memcpy(params + 5, &freq_base, sizeof(float)); + memcpy(params + 6, &freq_scale, sizeof(float)); + memcpy(params + 7, &ext_factor, sizeof(float)); + memcpy(params + 8, &attn_factor, sizeof(float)); + memcpy(params + 9, &beta_fast, sizeof(float)); + memcpy(params + 10, &beta_slow, sizeof(float)); + memcpy(params + 11, &xpos_base, sizeof(float)); + memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE_BACK; @@ -10974,7 +10983,8 @@ static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + const bool forward) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -11033,6 +11043,11 @@ static void ggml_compute_forward_rope_f32( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { @@ -11049,9 +11064,9 @@ static void ggml_compute_forward_rope_f32( float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); + const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta); + const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; @@ -11075,6 +11090,7 @@ static void ggml_compute_forward_rope_f32( rope_yarn( theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; @@ -11105,6 +11121,7 @@ static void ggml_compute_forward_rope_f32( theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11130,7 +11147,8 @@ static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + const bool forward) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -11182,6 +11200,11 @@ static void ggml_compute_forward_rope_f16( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { @@ -11198,9 +11221,9 @@ static void ggml_compute_forward_rope_f16( float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); + const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta); + const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; @@ -11224,6 +11247,7 @@ static void ggml_compute_forward_rope_f16( rope_yarn( theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11250,6 +11274,7 @@ static void ggml_compute_forward_rope_f16( theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); + sin_theta *= sin_sign; theta_base *= theta_scale; @@ -11279,11 +11304,11 @@ static void ggml_compute_forward_rope( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_f16(params, src0, src1, dst); + ggml_compute_forward_rope_f16(params, src0, src1, dst, true); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, src1, dst); + ggml_compute_forward_rope_f32(params, src0, src1, dst, true); } break; default: { @@ -11294,216 +11319,6 @@ static void ggml_compute_forward_rope( // ggml_compute_forward_rope_back -static void ggml_compute_forward_rope_back_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { - return; - } - - // y = rope(x, src1) - // dx = rope_back(dy, src1) - // src0 is dy, src1 contains options - - float freq_base; - float freq_scale; - - // these two only relevant for xPos RoPE: - float xpos_base; - bool xpos_down; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx); - memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - assert(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - const bool is_neox = mode & 2; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - float theta_base = freq_scale * (float)p; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - // zeta scaling for xPos only: - float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; - if (xpos_down) zeta = 1.0f / zeta; - - theta_base *= theta_scale; - - const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = dy[0]; - const float dy1 = dy[1]; - - dx[0] = dy0*cos_theta*zeta + dy1*sin_theta*zeta; - dx[1] = - dy0*sin_theta*zeta + dy1*cos_theta*zeta; - } - } else { - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; - - const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = dy[0]; - const float dy1 = dy[n_dims/2]; - - dx[0] = dy0*cos_theta + dy1*sin_theta; - dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta; - } - } - } - } - } - } -} - -static void ggml_compute_forward_rope_back_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { - return; - } - - // y = rope(x, src1) - // dx = rope_back(dy, src1) - // src0 is dy, src1 contains options - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - assert(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(10000.0, -2.0f/n_dims); - - const bool is_neox = mode & 2; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - float theta_base = (float)p; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = GGML_FP16_TO_FP32(dy[0]); - const float dy1 = GGML_FP16_TO_FP32(dy[1]); - - dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); - dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); - } - } else { - for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { - for (int64_t ic = 0; ic < n_dims; ic += 2) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base); - - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; - - const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float dy0 = GGML_FP16_TO_FP32(dy[0]); - const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]); - - dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); - dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); - } - } - } - } - } - } -} - static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -11512,11 +11327,11 @@ static void ggml_compute_forward_rope_back( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_back_f16(params, src0, src1, dst); + ggml_compute_forward_rope_f16(params, src0, src1, dst, false); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_back_f32(params, src0, src1, dst); + ggml_compute_forward_rope_f32(params, src0, src1, dst, false); } break; default: { @@ -15559,17 +15374,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - float freq_base; - float freq_scale; - float xpos_base; - bool xpos_down; - memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool)); + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + + memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -15579,8 +15397,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor n_dims, mode, n_ctx, + n_orig_ctx, freq_base, freq_scale, + ext_factor, + attn_factor, + beta_fast, + beta_slow, xpos_base, xpos_down), zero_table); @@ -15590,17 +15413,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - float freq_base; - float freq_scale; - float xpos_base; - bool xpos_down; - memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool)); + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + + memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); + memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -15609,14 +15435,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src1, n_dims, mode, - 0, n_ctx, + n_orig_ctx, freq_base, freq_scale, - 0.0f, - 1.0f, - 0.0f, - 0.0f, + ext_factor, + attn_factor, + beta_fast, + beta_slow, xpos_base, xpos_down, false), diff --git a/ggml.h b/ggml.h index 70eb25a6b..26654fc8e 100644 --- a/ggml.h +++ b/ggml.h @@ -1372,8 +1372,13 @@ extern "C" { int n_dims, int mode, int n_ctx, + int n_orig_ctx, float freq_base, float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, float xpos_base, bool xpos_down); From 413503d4b92500d82b002d03c580a71a54747138 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 7 Nov 2023 19:25:32 +0200 Subject: [PATCH 38/50] make : do not add linker flags when compiling static llava lib (#3977) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index f2d4fd031..d6be254a0 100644 --- a/Makefile +++ b/Makefile @@ -618,7 +618,7 @@ llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ $(LDFLAGS) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual From 0a7c980b6f94a049cb804573df2d8092a34df8e4 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 7 Nov 2023 12:43:04 -0500 Subject: [PATCH 39/50] gguf : track writer state, free unneeded tensors, cleanup (#3871) --- gguf-py/gguf/gguf.py | 82 +++++++++++++++++++++++++++--------------- gguf-py/pyproject.toml | 2 +- 2 files changed, 54 insertions(+), 30 deletions(-) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index a2271d225..7e495cb19 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -646,18 +646,17 @@ class GGUFValueType(IntEnum): sys.exit() +class WriterState(Enum): + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + + class GGUFWriter: fout: BufferedWriter - arch: str - offset_tensor = 0 - data_alignment = GGUF_DEFAULT_ALIGNMENT - kv_data = b"" - kv_data_count = 0 - ti_data = b"" - ti_data_count = 0 - use_temp_file: bool - temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None - tensors: list[tuple[np.ndarray[Any, Any], int]] + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[np.ndarray[Any, Any]] @property def pack_prefix(self): @@ -683,27 +682,47 @@ class GGUFWriter: GGUFValueType.FLOAT64: f"{self.pack_prefix}d", GGUFValueType.BOOL: "?" , } - self.add_architecture() + self.offset_tensor = 0 + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.kv_data = b"" + self.kv_data_count = 0 + self.ti_data = b"" + self.ti_data_count = 0 self.use_temp_file = use_temp_file + self.temp_file = None self.tensors = [] endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" print(f"This gguf file is for {endianess_str} only") + self.state = WriterState.EMPTY + + self.add_architecture() def write_header_to_file(self): + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + self.fout.write(struct.pack(" None: diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index f0741a7c2..c6cb2c37a 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.4.5" +version = "0.4.6" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 875fb42871a0f5a88fbe31a0b5edd697b84038e4 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 8 Nov 2023 13:15:14 +0100 Subject: [PATCH 40/50] ggml-alloc : fix backend assignments of views (#3982) --- ggml-alloc.c | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 34eba3f83..b553eb7c1 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -378,9 +378,13 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) { +static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) { assert(view->view_src != NULL && view->view_src->data != NULL); - view->backend = view->view_src->backend; + + if (update_backend) { + view->backend = view->view_src->backend; + } + view->buffer = view->view_src->buffer; view->data = (char *)view->view_src->data + view->view_offs; @@ -394,7 +398,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) struct hash_node * ht = alloc->hash_table; if (node->data == NULL) { if (ggml_is_view(node)) { - init_view(alloc, node); + init_view(alloc, node, true); } else { // see if we can reuse a parent's buffer (inplace) if (ggml_op_can_inplace(node->op)) { @@ -424,15 +428,14 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); node->view_src = view_src; view_src_hn->n_views += 1; - init_view(alloc, node); + init_view(alloc, node, false); return; } - } - else { + } else { AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); node->view_src = parent; p_hn->n_views += 1; - init_view(alloc, node); + init_view(alloc, node, false); return; } } @@ -463,7 +466,7 @@ size_t ggml_allocr_alloc_graph_n( hash_get(ht, view_src)->n_views += 1; if (node->buffer == NULL && node->data != NULL) { // view of a pre-allocated tensor, didn't call init_view() yet - init_view(alloc, node); + init_view(alloc, node, true); } } @@ -474,7 +477,7 @@ size_t ggml_allocr_alloc_graph_n( } hash_get(ht, parent)->n_children += 1; if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { - init_view(alloc, parent); + init_view(alloc, parent, true); } } } From 57ad015dc3011b046ed5a23186c86ea55f987c54 Mon Sep 17 00:00:00 2001 From: Mihai Date: Thu, 9 Nov 2023 04:00:34 +0200 Subject: [PATCH 41/50] server : add min_p param (#3877) * Update server.cpp with min_p after it was introduced in https://github.com/ggerganov/llama.cpp/pull/3841 * Use spaces instead of tabs * Update index.html.hpp after running deps.sh * Fix test - fix line ending --- examples/server/README.md | 2 + examples/server/index.html.hpp | 4396 +++++++++++++++-------------- examples/server/public/index.html | 2 + examples/server/server.cpp | 2 + 4 files changed, 2211 insertions(+), 2191 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 089ebe2d1..a6eda3b32 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -122,6 +122,8 @@ node index.js `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). + `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05). + `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 5d3bdfbdd..207412513 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -374,1189 +374,1161 @@ unsigned char index_html[] = { 0x7a, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, - 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x66, 0x73, - 0x5f, 0x7a, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, - 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, - 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, - 0x69, 0x63, 0x61, 0x6c, 0x5f, 0x70, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, - 0x20, 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min_p: 0.05, // 0 = disabled tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled @@ -744,6 +745,7 @@ ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} + ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}