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Merge 24bad77ebf into b92a14a841
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d61c8b06ce
@ -91,7 +91,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
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- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
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- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
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- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
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- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
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- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
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- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
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- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
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- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
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@ -111,6 +111,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
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- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
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- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
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- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
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- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
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- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
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</details>
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@ -642,7 +642,7 @@ class Model:
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if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
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# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
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res = "jina-v2-code"
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if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
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if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
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# ref: https://huggingface.co/THUDM/glm-4-9b-chat
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res = "chatglm-bpe"
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if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
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@ -4280,7 +4280,7 @@ class JaisModel(Model):
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self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
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@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
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@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
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class ChatGLMModel(Model):
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model_arch = gguf.MODEL_ARCH.CHATGLM
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@ -4386,14 +4386,23 @@ class ChatGLMModel(Model):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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vocab_size = hparams["padded_vocab_size"]
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vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
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assert max(tokenizer.get_vocab().values()) < vocab_size
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if(hparams["partial_rotary_factor"] == 1.0):
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# only for glm-edge series
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tokens, toktypes, tokpre = self.get_vocab_base()
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
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else:
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# for glm4 series
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tokpre = self.get_vocab_base_pre(tokenizer)
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.mergeable_ranks
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mergeable_ranks = tokenizer._mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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@ -4437,16 +4446,20 @@ class ChatGLMModel(Model):
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def set_gguf_parameters(self):
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n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
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n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
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n_head_kv = self.hparams.get("multi_query_group_num", n_head)
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n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
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self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
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self.gguf_writer.add_embedding_length(n_embed)
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self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed))
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self.gguf_writer.add_block_count(self.hparams["num_layers"])
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self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
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self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
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self.gguf_writer.add_head_count(n_head)
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self.gguf_writer.add_head_count_kv(n_head_kv)
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_rope_dimension_count(64)
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if "attention_dim" in self.hparams:
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rope_dim = self.hparams["attention_dim"]
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else:
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rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
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self.gguf_writer.add_add_bos_token(False)
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rope_freq = 10000
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if "rope_ratio" in self.hparams:
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@ -4456,7 +4469,7 @@ class ChatGLMModel(Model):
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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if name.endswith(".rotary_pos_emb.inv_freq"):
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if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
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return []
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name = name.removeprefix("transformer.")
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43
examples/llava/README-glmedge.md
Normal file
43
examples/llava/README-glmedge.md
Normal file
@ -0,0 +1,43 @@
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# GLMV-EDGE
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Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
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## Usage
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Build with cmake or run `make llama-llava-cli` to build it.
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After building, run: `./llama-llava-cli` to see the usage. For example:
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```sh
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./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
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```
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**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
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**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
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## GGUF conversion
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1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
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```sh
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git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
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```
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2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
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```sh
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python ./examples/llava/glmedge-surgery.py -m ../model_path
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```
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4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
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```sh
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python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
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```
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5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
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```sh
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python convert_hf_to_gguf.py ../model_path
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```
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Now both the LLM part and the image encoder are in the `model_path` directory.
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@ -101,6 +101,7 @@ static std::string format(const char * fmt, ...) {
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#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
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#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
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#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector"
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#define KEY_HAS_GLM_PROJ "clip.has_glm_projector"
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#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
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#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger"
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#define KEY_USE_GELU "clip.use_gelu"
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@ -159,6 +160,15 @@ static std::string format(const char * fmt, ...) {
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#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
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#define TN_MINICPMV_LN "resampler.ln_%s.%s"
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#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
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#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
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#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
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#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
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#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
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#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
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#define TN_GLM_BOI_W "adapter.boi"
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#define TN_GLM_EOI_W "adapter.eoi"
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enum projector_type {
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PROJECTOR_TYPE_MLP,
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@ -166,6 +176,7 @@ enum projector_type {
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PROJECTOR_TYPE_LDP,
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PROJECTOR_TYPE_LDPV2,
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PROJECTOR_TYPE_RESAMPLER,
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PROJECTOR_TYPE_ADAPTER,
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PROJECTOR_TYPE_MERGER,
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PROJECTOR_TYPE_UNKNOWN,
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};
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@ -175,6 +186,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
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{ PROJECTOR_TYPE_LDP, "ldp" },
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{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
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{ PROJECTOR_TYPE_RESAMPLER, "resampler"},
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{ PROJECTOR_TYPE_ADAPTER, "adapter"},
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{ PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
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};
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@ -499,6 +511,12 @@ struct clip_vision_model {
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struct ggml_tensor * mm_4_w = NULL;
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struct ggml_tensor * mm_4_b = NULL;
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//GLMV-Edge projection
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struct ggml_tensor * mm_model_adapter_conv_w;
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struct ggml_tensor * mm_model_adapter_conv_b;
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struct ggml_tensor * boi_w;
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struct ggml_tensor * eoi_w;
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// MobileVLM projection
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struct ggml_tensor * mm_model_mlp_1_w;
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struct ggml_tensor * mm_model_mlp_1_b;
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@ -559,6 +577,7 @@ struct clip_ctx {
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bool has_vision_encoder = false;
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bool has_llava_projector = false;
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bool has_minicpmv_projector = false;
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bool has_glm_projector = false;
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bool has_qwen2vl_merger = false;
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int minicpmv_version = 2;
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@ -637,7 +656,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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const int batch_size = imgs->size;
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if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
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if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
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GGML_ASSERT(batch_size == 1);
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}
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@ -730,8 +749,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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}
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// loop over layers
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if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
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// TODO: figure out why we doing thing in this way ???
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if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
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n_layer += 1;
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}
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for (int il = 0; il < n_layer - 1; il++) {
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@ -1086,7 +1104,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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GGML_ASSERT(false);
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}
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}
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else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
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// glm projector
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else if(ctx->has_glm_projector){
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if (ctx->proj_type == PROJECTOR_TYPE_ADAPTER){
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size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
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embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
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embeddings = ggml_reshape_3d(ctx0,embeddings,gridsz,gridsz,embeddings->ne[1]);
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embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
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embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
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embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
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embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
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//GLU
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{
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embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
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embeddings = ggml_norm(ctx0, embeddings, eps);
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
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embeddings = ggml_gelu_inplace(ctx0, embeddings);
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struct ggml_tensor * x = embeddings;
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embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
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x = ggml_mul_mat(ctx0,model.mm_model_mlp_1_w,x);
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embeddings = ggml_silu_inplace(ctx0,embeddings);
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embeddings = ggml_mul(ctx0,embeddings,x);
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embeddings = ggml_mul_mat(ctx0,model.mm_model_mlp_3_w,embeddings);
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}
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}else{
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GGML_ABORT("fatel error");
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}
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}else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
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embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
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embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
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@ -1275,6 +1319,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
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}
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idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ);
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if (idx != -1) {
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new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx);
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}
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idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
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if (idx != -1) {
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new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
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@ -1299,6 +1348,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
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LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
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LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector);
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LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector);
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LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
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LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
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}
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@ -1566,6 +1616,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
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vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
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}
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else if(new_clip->proj_type == PROJECTOR_TYPE_ADAPTER){
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vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight"));
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vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias"));
|
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vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight"));
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vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight"));
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vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias"));
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vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
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vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight"));
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vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
|
||||
vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W);
|
||||
vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W);
|
||||
}
|
||||
else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
|
||||
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
|
||||
@ -2098,6 +2160,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
|
||||
return true;
|
||||
}
|
||||
|
||||
if(ctx->has_glm_projector){
|
||||
res_imgs->size = 1;
|
||||
res_imgs->data = new clip_image_f32[res_imgs->size];
|
||||
clip_image_u8 resized_image;
|
||||
int32_t sz=ctx->vision_model.hparams.image_size;
|
||||
bicubic_resize(*img, resized_image,sz,sz);
|
||||
clip_image_f32 * res = clip_image_f32_init();
|
||||
//clip_image_save_to_bmp(resized_image, "resized.bmp");
|
||||
normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
|
||||
res_imgs->data[0] = *res;
|
||||
clip_image_f32_free(res);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool pad_to_square = true;
|
||||
if (!ctx->has_vision_encoder) {
|
||||
LOG_ERR("This gguf file seems to have no vision encoder\n");
|
||||
@ -2283,6 +2359,8 @@ void clip_free(clip_ctx * ctx) {
|
||||
}
|
||||
|
||||
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
|
||||
if(ctx->has_glm_projector)
|
||||
return (clip_n_patches(ctx)+2) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
||||
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
||||
}
|
||||
|
||||
@ -2325,7 +2403,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
|
||||
|
||||
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
|
||||
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_ADAPTER) {
|
||||
n_patches /= 4;
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
|
||||
if (ctx->minicpmv_version == 2) {
|
||||
@ -2455,6 +2533,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
||||
if (ctx->has_minicpmv_projector) {
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
}
|
||||
if(ctx->has_glm_projector){
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
ggml_tensor * boi = ctx->vision_model.boi_w;
|
||||
ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
|
||||
vec=(float*)(vec+ggml_nelements(boi)); //offset for boi
|
||||
}
|
||||
|
||||
// build the inference graph
|
||||
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
|
||||
@ -2604,7 +2688,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
||||
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
|
||||
free(positions_data);
|
||||
|
||||
{
|
||||
if (!ctx->has_glm_projector){
|
||||
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
|
||||
int* patches_data = (int*)malloc(ggml_nbytes(patches));
|
||||
for (int i = 0; i < num_patches; i++) {
|
||||
@ -2628,6 +2712,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
||||
// copy the embeddings to the location passed by the user
|
||||
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
|
||||
|
||||
if(ctx->has_glm_projector){
|
||||
//eoi
|
||||
ggml_tensor * eoi = ctx->vision_model.eoi_w;
|
||||
int offset=ggml_nelements(eoi)*clip_n_patches(ctx);
|
||||
ggml_backend_tensor_get(eoi,vec+offset,0,ggml_nbytes(eoi));
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
@ -2785,6 +2876,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
||||
return 3584;
|
||||
}
|
||||
}
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_ADAPTER){
|
||||
return ctx->vision_model.mm_model_mlp_3_w->ne[1];
|
||||
}
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
return ctx->vision_model.mm_1_b->ne[0];
|
||||
}
|
||||
@ -2800,6 +2894,9 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
bool clip_is_glm(const struct clip_ctx * ctx) {
|
||||
return ctx->has_glm_projector;
|
||||
}
|
||||
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
|
||||
return ctx->has_qwen2vl_merger;
|
||||
}
|
||||
|
||||
@ -93,6 +93,8 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
|
||||
|
||||
CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
|
||||
|
||||
CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
280
examples/llava/glmedge-convert-image-encoder-to-gguf.py
Normal file
280
examples/llava/glmedge-convert-image-encoder-to-gguf.py
Normal file
@ -0,0 +1,280 @@
|
||||
import argparse
|
||||
import os
|
||||
import json
|
||||
import re
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from gguf import *
|
||||
|
||||
TEXT = "clip.text"
|
||||
VISION = "clip.vision"
|
||||
from transformers import SiglipVisionModel, SiglipVisionConfig
|
||||
|
||||
def k(raw_key: str, arch: str) -> str:
|
||||
return raw_key.format(arch=arch)
|
||||
|
||||
|
||||
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
|
||||
if name in (
|
||||
"logit_scale",
|
||||
"text_model.embeddings.position_ids",
|
||||
"vision_model.embeddings.position_ids",
|
||||
):
|
||||
return True
|
||||
|
||||
if name in (
|
||||
"vision_model.head.probe",
|
||||
"vision_model.head.attention.in_proj_weight",
|
||||
"vision_model.head.attention.in_proj_bias",
|
||||
"vision_model.head.attention.out_proj.weight",
|
||||
"vision_model.head.attention.out_proj.bias",
|
||||
"vision_model.head.layernorm.weight",
|
||||
"vision_model.head.layernorm.bias",
|
||||
"vision_model.head.mlp.fc1.weight",
|
||||
"vision_model.head.mlp.fc1.bias",
|
||||
"vision_model.head.mlp.fc2.weight",
|
||||
"vision_model.head.mlp.fc2.bias"
|
||||
):
|
||||
return True
|
||||
|
||||
if name.startswith("v") and not has_vision:
|
||||
return True
|
||||
|
||||
if name.startswith("t") and not has_text:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def get_tensor_name(name: str) -> str:
|
||||
if "projection" in name:
|
||||
return name
|
||||
if "mm_projector" in name:
|
||||
name = name.replace("model.mm_projector", "mm")
|
||||
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
|
||||
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
|
||||
return name
|
||||
|
||||
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
|
||||
|
||||
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a significant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = (
|
||||
list(range(ord("!"), ord("~") + 1))
|
||||
+ list(range(ord("¡"), ord("¬") + 1))
|
||||
+ list(range(ord("®"), ord("ÿ") + 1))
|
||||
)
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8 + n)
|
||||
n += 1
|
||||
cs = [chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
|
||||
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
|
||||
ap.add_argument("--text-only", action="store_true", required=False,
|
||||
help="Save a text-only model. It can't be used to encode images")
|
||||
ap.add_argument("--vision-only", action="store_true", required=False,
|
||||
help="Save a vision-only model. It can't be used to encode texts")
|
||||
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
|
||||
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
|
||||
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
|
||||
help="The clip model is from openclip (for ViT-SO400M type))")
|
||||
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
|
||||
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
|
||||
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
|
||||
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
|
||||
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
|
||||
default_image_mean = [0.5, 0.5, 0.5]
|
||||
default_image_std = [0.5, 0.5, 0.5]
|
||||
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
|
||||
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
|
||||
|
||||
# with proper
|
||||
args = ap.parse_args()
|
||||
|
||||
|
||||
if args.text_only and args.vision_only:
|
||||
print("--text-only and --image-only arguments cannot be specified at the same time.")
|
||||
exit(1)
|
||||
|
||||
if args.use_f32:
|
||||
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
|
||||
|
||||
# output in the same directory as the model if output_dir is None
|
||||
dir_model = args.model_dir
|
||||
|
||||
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
|
||||
vocab = None
|
||||
tokens = None
|
||||
else:
|
||||
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
|
||||
vocab = json.load(f)
|
||||
tokens = [key for key in vocab]
|
||||
|
||||
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
|
||||
config = json.load(f)
|
||||
if args.clip_model_is_vision:
|
||||
v_hparams = config
|
||||
t_hparams = None
|
||||
else:
|
||||
v_hparams = config["vision_config"]
|
||||
t_hparams = None
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
#
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
ftype = 1
|
||||
if args.use_f32:
|
||||
ftype = 0
|
||||
|
||||
vision_config = SiglipVisionConfig(**v_hparams)
|
||||
model = SiglipVisionModel(vision_config)
|
||||
model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
|
||||
|
||||
fname_middle = None
|
||||
has_text_encoder = False
|
||||
has_vision_encoder = True
|
||||
has_glm_projector = True
|
||||
if args.text_only:
|
||||
fname_middle = "text-"
|
||||
has_vision_encoder = False
|
||||
elif args.llava_projector is not None:
|
||||
fname_middle = "mmproj-"
|
||||
has_text_encoder = False
|
||||
has_glm_projector = True
|
||||
elif args.vision_only:
|
||||
fname_middle = "vision-"
|
||||
has_text_encoder = False
|
||||
else:
|
||||
fname_middle = ""
|
||||
|
||||
output_dir = args.output_dir if args.output_dir is not None else dir_model
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
|
||||
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
|
||||
fout = GGUFWriter(path=fname_out, arch="clip")
|
||||
|
||||
fout.add_bool("clip.has_text_encoder", has_text_encoder)
|
||||
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
|
||||
fout.add_bool("clip.has_glm_projector", has_glm_projector)
|
||||
fout.add_file_type(ftype)
|
||||
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
|
||||
fout.add_name(model_name)
|
||||
if has_glm_projector:
|
||||
fout.add_description("image encoder for glm4v")
|
||||
fout.add_string("clip.projector_type", "adapter")
|
||||
else:
|
||||
fout.add_description("two-tower CLIP model")
|
||||
|
||||
if has_text_encoder:
|
||||
assert t_hparams is not None
|
||||
assert tokens is not None
|
||||
# text_model hparams
|
||||
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
|
||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
|
||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
|
||||
fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
|
||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
|
||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
|
||||
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
|
||||
fout.add_token_list(tokens)
|
||||
|
||||
if has_vision_encoder:
|
||||
# vision_model hparams
|
||||
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
|
||||
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
|
||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
|
||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
|
||||
fout.add_uint32("clip.vision.projection_dim", 0)
|
||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
|
||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
||||
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
|
||||
|
||||
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
|
||||
image_std = args.image_std if args.image_std is not None else default_image_std
|
||||
fout.add_array("clip.vision.image_mean", image_mean)
|
||||
fout.add_array("clip.vision.image_std", image_std)
|
||||
|
||||
fout.add_bool("clip.use_gelu", True)
|
||||
|
||||
|
||||
if has_glm_projector:
|
||||
# model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
|
||||
projector = torch.load(args.llava_projector)
|
||||
for name, data in projector.items():
|
||||
name = get_tensor_name(name)
|
||||
# pw and dw conv ndim==4
|
||||
if data.ndim == 2 or data.ndim == 4:
|
||||
data = data.squeeze().numpy().astype(np.float16)
|
||||
else:
|
||||
data = data.squeeze().numpy().astype(np.float32)
|
||||
if name.startswith("vision."):
|
||||
name=name.replace("vision.","")
|
||||
fout.add_tensor(name, data)
|
||||
print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
|
||||
# print(f"Projector {name} tensors added\n")
|
||||
|
||||
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
|
||||
for name, data in state_dict.items():
|
||||
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
|
||||
# we don't need this
|
||||
print(f"skipping parameter: {name}")
|
||||
continue
|
||||
|
||||
name = get_tensor_name(name)
|
||||
data = data.squeeze().numpy()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
|
||||
# ftype == 0 -> float32, ftype == 1 -> float16
|
||||
ftype_cur = 0
|
||||
if n_dims == 4:
|
||||
print(f"tensor {name} is always saved in f16")
|
||||
data = data.astype(np.float16)
|
||||
ftype_cur = 1
|
||||
elif ftype == 1:
|
||||
if name[-7:] == ".weight" and n_dims == 2:
|
||||
# print(" Converting to float16")
|
||||
data = data.astype(np.float16)
|
||||
ftype_cur = 1
|
||||
else:
|
||||
# print(" Converting to float32")
|
||||
data = data.astype(np.float32)
|
||||
ftype_cur = 0
|
||||
else:
|
||||
if data.dtype != np.float32:
|
||||
# print(" Converting to float32")
|
||||
data = data.astype(np.float32)
|
||||
ftype_cur = 0
|
||||
print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
|
||||
# print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
|
||||
fout.add_tensor(name, data)
|
||||
|
||||
|
||||
fout.write_header_to_file()
|
||||
fout.write_kv_data_to_file()
|
||||
fout.write_tensors_to_file()
|
||||
fout.close()
|
||||
|
||||
print("Done. Output file: " + fname_out)
|
||||
33
examples/llava/glmedge-surgery.py
Normal file
33
examples/llava/glmedge-surgery.py
Normal file
@ -0,0 +1,33 @@
|
||||
import argparse
|
||||
import os
|
||||
import torch
|
||||
from transformers import AutoModel
|
||||
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("-m", "--model", help="Path to GLM model")
|
||||
args = ap.parse_args()
|
||||
|
||||
# find the model part that includes the the multimodal projector weights
|
||||
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
|
||||
checkpoint = model.state_dict()
|
||||
|
||||
# get a list of mm tensor names
|
||||
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
|
||||
|
||||
# store these tensors in a new dictionary and torch.save them
|
||||
projector = {name: checkpoint[name].float() for name in mm_tensors}
|
||||
torch.save(projector, f"{args.model}/glm.projector")
|
||||
|
||||
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
|
||||
if len(clip_tensors) > 0:
|
||||
clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
|
||||
torch.save(clip, f"{args.model}/glm.clip")
|
||||
|
||||
# added tokens should be removed to be able to convert Mistral models
|
||||
if os.path.exists(f"{args.model}/added_tokens.json"):
|
||||
with open(f"{args.model}/added_tokens.json", "w") as f:
|
||||
f.write("{}\n")
|
||||
|
||||
print("Done!")
|
||||
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
|
||||
print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")
|
||||
@ -314,6 +314,20 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
||||
clip_add_load_image_size(ctx_clip, load_image_size);
|
||||
LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
|
||||
}
|
||||
else if (clip_is_glm(ctx_clip)){
|
||||
struct clip_image_size * load_image_size = clip_image_size_init();
|
||||
load_image_size->width = img_res_v.data[0].nx;
|
||||
load_image_size->height = img_res_v.data[0].ny;
|
||||
clip_add_load_image_size(ctx_clip, load_image_size);
|
||||
|
||||
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
|
||||
int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
|
||||
*n_img_pos = (pos * pos + 2);
|
||||
if (!encoded){
|
||||
LOG_ERR("Unable to encode image \n");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
|
||||
// flat / default llava-1.5 type embedding
|
||||
*n_img_pos = clip_n_patches(ctx_clip);
|
||||
@ -398,6 +412,9 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
|
||||
if (clip_is_minicpmv(ctx_clip)) {
|
||||
num_max_patches = 10;
|
||||
}
|
||||
if (clip_is_glm(ctx_clip)) {
|
||||
num_max_patches = 1;
|
||||
}
|
||||
float * image_embd;
|
||||
if (clip_is_qwen2vl(ctx_clip)) {
|
||||
// qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
|
||||
|
||||
@ -1284,6 +1284,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
|
||||
@ -1440,6 +1440,9 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
||||
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
||||
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
@ -1715,6 +1718,7 @@ enum llm_chat_template {
|
||||
LLM_CHAT_TEMPLATE_LLAMA_3,
|
||||
LLM_CHAT_TEMPLATE_CHATGML_3,
|
||||
LLM_CHAT_TEMPLATE_CHATGML_4,
|
||||
LLM_CHAT_TEMPLATE_GLMEDGE,
|
||||
LLM_CHAT_TEMPLATE_MINICPM,
|
||||
LLM_CHAT_TEMPLATE_EXAONE_3,
|
||||
LLM_CHAT_TEMPLATE_RWKV_WORLD,
|
||||
@ -1749,6 +1753,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
|
||||
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
|
||||
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 },
|
||||
{ "chatglm4", LLM_CHAT_TEMPLATE_CHATGML_4 },
|
||||
{ "glmedge", LLM_CHAT_TEMPLATE_GLMEDGE },
|
||||
{ "minicpm", LLM_CHAT_TEMPLATE_MINICPM },
|
||||
{ "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 },
|
||||
{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
|
||||
@ -6300,8 +6305,20 @@ static void llm_load_hparams(
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
switch (hparams.n_layer) {
|
||||
case 28: model.type = e_model::MODEL_6B; break;
|
||||
case 40: model.type = e_model::MODEL_9B; break;
|
||||
case 28: {
|
||||
if(hparams.n_head(0)==16){
|
||||
model.type = e_model::MODEL_1_6B;
|
||||
}else{
|
||||
model.type = e_model::MODEL_6B;
|
||||
}
|
||||
}break;
|
||||
case 40:{
|
||||
if(hparams.n_head(0)==24){
|
||||
model.type = e_model::MODEL_4B;
|
||||
}else{
|
||||
model.type = e_model::MODEL_9B;
|
||||
}
|
||||
} break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
@ -9434,9 +9451,14 @@ static bool llm_load_tensors(
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
if(model.type == e_model::MODEL_1_6B || model.type == e_model::MODEL_4B){
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
||||
}else{
|
||||
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
||||
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
||||
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
||||
|
||||
@ -16827,20 +16849,28 @@ struct llm_build_context {
|
||||
struct ggml_tensor * Qcur = nullptr;
|
||||
struct ggml_tensor * Kcur = nullptr;
|
||||
struct ggml_tensor * Vcur = nullptr;
|
||||
|
||||
if(model.type == e_model::MODEL_1_6B || model.type == e_model::MODEL_4B){
|
||||
Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}else{
|
||||
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
if(model.layers[il].bqkv){
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
}
|
||||
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}
|
||||
|
||||
//printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
|
||||
@ -22921,6 +22951,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
|
||||
return LLM_CHAT_TEMPLATE_CHATGML_3;
|
||||
} else if (tmpl_contains("[gMASK]<sop>")) {
|
||||
return LLM_CHAT_TEMPLATE_CHATGML_4;
|
||||
} else if (tmpl_contains("<|user|>") && tmpl_contains("<|assistant|>") && !tmpl_contains("<|end|>") && !tmpl_contains("</s>")) {
|
||||
return LLM_CHAT_TEMPLATE_GLMEDGE;
|
||||
} else if (tmpl_contains(LU8("<用户>"))) {
|
||||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||||
return LLM_CHAT_TEMPLATE_MINICPM;
|
||||
@ -23204,6 +23236,14 @@ static int32_t llama_chat_apply_template_internal(
|
||||
if (add_ass) {
|
||||
ss << "<|assistant|>";
|
||||
}
|
||||
} else if(tmpl == LLM_CHAT_TEMPLATE_GLMEDGE){
|
||||
for (auto message : chat) {
|
||||
std::string role(message->role);
|
||||
ss << "<|" << role << "|>" << "\n" << message->content;
|
||||
}
|
||||
if (add_ass) {
|
||||
ss << "<|assistant|>";
|
||||
}
|
||||
} else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
|
||||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||||
for (auto message : chat) {
|
||||
|
||||
@ -61,6 +61,8 @@ int main(void) {
|
||||
"{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
|
||||
// ChatGLM4
|
||||
u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
|
||||
// GLM-edge
|
||||
"{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>\n",
|
||||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||||
u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
|
||||
// DeepSeek-V2
|
||||
@ -119,6 +121,8 @@ int main(void) {
|
||||
"[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n I am an assistant <|user|>\n Another question<|assistant|>",
|
||||
// ChatGLM4
|
||||
"[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>",
|
||||
// GLM-Edge
|
||||
"<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>",
|
||||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||||
u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
|
||||
// DeepSeek-V2
|
||||
|
||||
Loading…
Reference in New Issue
Block a user