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Merge 2116f48bec into 30caac3a68
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253578003e
@ -3379,6 +3379,24 @@ class CommandR2Model(Model):
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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@Model.register("Cohere2ForCausalLM")
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class Cohere2Model(Model):
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model_arch = gguf.MODEL_ARCH.COHERE2
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
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self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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rotary_pct = self.hparams["rotary_pct"]
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hidden_size = self.hparams["hidden_size"]
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num_attention_heads = self.hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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@Model.register("OlmoForCausalLM")
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@Model.register("OLMoForCausalLM")
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class OlmoModel(Model):
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@ -255,6 +255,7 @@ class MODEL_ARCH(IntEnum):
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MAMBA = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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COHERE2 = auto()
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DBRX = auto()
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OLMO = auto()
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OLMO2 = auto()
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@ -437,6 +438,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.COHERE2: "cohere2",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.OLMO2: "olmo2",
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@ -1136,6 +1138,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_Q_NORM,
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],
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MODEL_ARCH.COHERE2: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.DBRX: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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189
src/llama.cpp
189
src/llama.cpp
@ -179,6 +179,7 @@ enum llm_arch {
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LLM_ARCH_MAMBA,
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_COHERE2,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_OLMO2,
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@ -237,6 +238,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_COHERE2, "cohere2" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_OLMO2, "olmo2" },
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@ -1268,6 +1270,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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},
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},
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{
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LLM_ARCH_COHERE2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_DBRX,
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{
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@ -6151,6 +6168,16 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_COHERE2:
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{
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_8B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_DBRX:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -8970,6 +8997,32 @@ static bool llm_load_tensors(
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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} break;
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case LLM_ARCH_COHERE2:
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{
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model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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// init output from the input tok embed
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model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
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llama_model_loader::TENSOR_DUPLICATED);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
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}
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}
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break;
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case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
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{
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model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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@ -15051,6 +15104,137 @@ struct llm_build_context {
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}
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struct ggml_cgraph * build_cohere2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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const float f_logit_scale = hparams.f_logit_scale;
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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// cohere2 requires different mask for layers using sliding window (SWA)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
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// sliding window switch pattern
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const int32_t sliding_window_pattern = 4;
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for (int il = 0; il < n_layer; ++il) {
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// three layers sliding window attention (window size 4096) and ROPE
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// fourth layer uses global attention without positional embeddings
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const bool is_sliding = il % sliding_window_pattern < (sliding_window_pattern - 1);
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struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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struct ggml_tensor * ffn_inp = cur;
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// self-attention
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{
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// rope freq factors for 128k context
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struct ggml_tensor * rope_factors = build_rope_factors(il);
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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if (is_sliding) {
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Qcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor,
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beta_fast, beta_slow);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
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attn_factor, beta_fast, beta_slow);
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cb(Kcur, "Kcur", il);
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} else {
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// For non-sliding layers, just reshape without applying RoPE
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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cb(Kcur, "Kcur", il);
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}
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cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur,
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KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f / sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
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}
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struct ggml_tensor * attn_out = cur;
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// feed-forward network
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{
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cur = llm_build_ffn(ctx0, lctx, ffn_inp, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate,
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NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR,
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cb, il);
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cb(cur, "ffn_out", il);
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}
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// add together residual + FFN + self-attention
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cur = ggml_add(ctx0, cur, inpL);
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cur = ggml_add(ctx0, cur, attn_out);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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if (f_logit_scale) {
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cur = ggml_scale(ctx0, cur, f_logit_scale);
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}
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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// ref: https://allenai.org/olmo
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// based on the original build_llama() function, changes:
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// * non-parametric layer norm
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@ -17802,6 +17986,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_command_r();
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} break;
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case LLM_ARCH_COHERE2:
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{
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result = llm.build_cohere2();
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} break;
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case LLM_ARCH_DBRX:
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{
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result = llm.build_dbrx();
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@ -21075,6 +21263,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_MINICPM:
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case LLM_ARCH_XVERSE:
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case LLM_ARCH_COMMAND_R:
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case LLM_ARCH_COHERE2:
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case LLM_ARCH_OLMO:
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case LLM_ARCH_ARCTIC:
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case LLM_ARCH_DEEPSEEK:
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