Support minicpm for NPU C++ (#12434)
* support minicpm-1b * update * tune fused_layers * update readme.md
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Ruonan Wang
GitHub
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4 changed files with 208 additions and 24 deletions
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@ -9,6 +9,7 @@ In this directory, you will find a C++ example on how to run LLM models on Intel
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| Qwen2.5 | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| Llama2 | [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) |
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| Llama3 | [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
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| MiniCPM | [openbmb/MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16), [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) |
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## 0. Requirements
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To run this C++ example with IPEX-LLM on Intel NPUs, make sure to install the newest driver version of Intel NPU.
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@ -493,3 +493,36 @@ def convert_llm_for_deploy(model: torch.nn.Module,
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# save blob of lmhead and bin of embedding
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convert_lm_head_and_embedding(model, n_splits_linear,
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save_directory, weight_dir, True)
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elif model.config.model_type == "minicpm":
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layernorm_const = True
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fused_layers = 4
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update_dict = {"kv_len": kv_len,
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"num_head": model.model.layers[0].self_attn.num_heads,
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"head_dim": model.model.layers[0].self_attn.head_dim,
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"transpose_value_cache": transpose_value_cache,
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"max_prompt_len": max_prompt_len,
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"layernorm_const": layernorm_const,
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"group_size": group_size,
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"fused_layers": fused_layers,
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"qkv_bias": False,
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"use_prefill_sdp": False,
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"weight_num": 7,
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"weight_idx": 5,
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"model_type": "minicpm",
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"embedding_post": True}
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model.config.update(update_dict)
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model.config.save_pretrained(save_directory)
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from .minicpm import convert_minicpm_layer, convert_fused_minicpm_layer
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from .minicpm import convert_lm_head_and_embedding
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# save fused_layers blobs of fused decoder layers
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convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_down_proj,
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save_directory, weight_dir, transpose_value_cache, kv_len,
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group_size, layernorm_const, "decode")
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# save blob of single prefill layer
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convert_minicpm_layer(model, 0, n_splits_linear, n_splits_down_proj,
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save_directory, weight_dir, transpose_value_cache, max_prompt_len,
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group_size, layernorm_const, "prefill")
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# save blob of lmhead and bin of embedding
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convert_lm_head_and_embedding(model, n_splits_linear,
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save_directory, weight_dir, True, max_prompt_len)
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@ -147,10 +147,11 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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)
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else:
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# llama-3.2-3B & llama-3.2-1B
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embedding_layer = model.model.embed_tokens
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new_embedding = Llama32Embedding(
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vocab_size=model.config.vocab_size,
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embedding_dim=model.config.hidden_size,
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embedding_weight=model.model.embed_tokens.weight.to(torch.float16).detach().numpy(),
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embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
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padding_idx=model.config.pad_token_id,
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inv_freq=model.model.rotary_emb.inv_freq.to(torch.float16),
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attention_scaling=model.model.rotary_emb.attention_scaling,
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@ -67,6 +67,29 @@ class MiniCPMEmbedding(NNFactory):
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self.compile()
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class MiniCPMPostEmbedding(NNFactory):
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def __init__(
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self,
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input_size,
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embedding_dim,
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dtype, # fp16
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scale_emb,
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device: str = "NPU",
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):
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super().__init__(False, device)
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self.embedding_dim = embedding_dim
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self.dtype = dtype
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input = self.parameter((1, input_size, embedding_dim), dtype=dtype)
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res = input * scale_emb
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# define outputs
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res = self.convert_to_fp16(res)
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print("start compiling")
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self.compile()
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class MiniCPMLMHead(LLMBaseNNFactory):
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def __init__(
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self,
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@ -134,7 +157,8 @@ class MiniCPMLMHead(LLMBaseNNFactory):
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self.compile()
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def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir):
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def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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convert_model=False, max_prompt_len=1):
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
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head_dim = model.model.layers[0].self_attn.head_dim
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@ -180,7 +204,8 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir):
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vocab_size=vocab_size,
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n_splits=n_splits_linear
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)
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last_blob_path = update_names_of_IR_and_export_blob(new_lm_head, "lm_head", temp_dir)
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last_blob_path = update_names_of_IR_and_export_blob(new_lm_head, "lm_head", temp_dir,
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True, True)
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# save weights bins files
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if n_splits_linear == 1:
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@ -209,14 +234,31 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir):
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dtype=np.float16,
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scale_emb=model.config.scale_emb,
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)
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first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
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temp_dir)
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if convert_model:
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bin_file = os.path.join(weight_dir, f"model_embedding_input_0.bin")
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embedding_layer.weight.to(torch.float16).detach().numpy().tofile(bin_file)
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first_blob_path = None
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# save embedding post module
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embedding_post = MiniCPMPostEmbedding(1, model.config.hidden_size,
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dtype=np.float16,
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scale_emb=model.config.scale_emb)
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update_names_of_IR_and_export_blob(embedding_post, "embedding_post",
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temp_dir, True, False)
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embedding_post_prefill = MiniCPMPostEmbedding(max_prompt_len, model.config.hidden_size,
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dtype=np.float16,
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scale_emb=model.config.scale_emb)
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update_names_of_IR_and_export_blob(embedding_post_prefill,
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"embedding_post_prefill",
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temp_dir, True, False)
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else:
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first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
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temp_dir, True, False)
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return first_blob_path, last_blob_path
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def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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temp_dir, weight_dir, transpose_value_cache, kv_len, group_size,
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layernorm_const):
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layernorm_const, mode="decode"):
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
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head_dim = model.model.layers[0].self_attn.head_dim
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@ -252,8 +294,16 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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else: # FP16 Linear
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np_dtype = np.float16
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if mode == "decode":
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input_len = 1
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decoder_name = f"decoder_layer_{layer_idx}"
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else:
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input_len = kv_len
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decoder_name = "decoder_layer_prefill"
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layernorm_const = False
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single_decoder = LowBitMinicpmMultiDecoderlayer(
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[1, 1, num_heads * head_dim],
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[1, input_len, num_heads * head_dim],
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input_layernorm_weights=[layer_norm_0] if layernorm_const else None,
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post_attn_layernorm_weights=[layer_norm_1] if layernorm_const else None,
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cached_cos=cached_cos,
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@ -266,7 +316,7 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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intermediate_size=intermediate_size,
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scale_depth=scale_depth,
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num_hidden_layers=num_hidden_layers,
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mode="decode",
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mode=mode,
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transpose_value=transpose_value_cache,
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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@ -274,20 +324,119 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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group_size=group_size
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)
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rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
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f"decoder_layer_{layer_idx}",
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temp_dir)
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decoder_name,
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temp_dir,
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True, True)
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if layernorm_const:
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st_idx = 5
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else:
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input_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_3.bin")
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post_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_4.bin")
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layer_norm_0.data.numpy().tofile(input_lm_bin_file)
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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st_idx = 7
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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del single_decoder
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if mode == "decode":
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if layernorm_const:
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st_idx = 5
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else:
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input_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_3.bin")
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post_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_4.bin")
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layer_norm_0.data.numpy().tofile(input_lm_bin_file)
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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st_idx = 7
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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del single_decoder
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def convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_down_proj,
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save_dir, weight_dir, transpose_value_cache, kv_len, group_size,
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layernorm_const, mode="decode"):
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
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head_dim = model.model.layers[0].self_attn.head_dim
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intermediate_size = model.config.intermediate_size
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rms_norm_eps = model.config.rms_norm_eps
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num_hidden_layers = model.config.num_hidden_layers
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scale_depth = model.model.config.scale_depth
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layer_num = len(model.model.layers)
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fused_layer_num = layer_num // fused_layers
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from ipex_llm.transformers.npu_models.minicpm_mp import LowBitMinicpmMultiDecoderlayer
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for i in range(fused_layers):
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layer_start = i * fused_layer_num
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layer_end = min((i + 1) * fused_layer_num, layer_num)
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layer_weights = []
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input_layer_norm_weights = []
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post_attn_layernorm_weights = []
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layer_indexs = range(layer_start, layer_end)
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n_splits_linear = len(model.model.layers[0].mlp.gate_proj_dq_list)
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n_splits_down_proj = len(model.model.layers[0].mlp.down_proj_dq_list)
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for layer_idx in layer_indexs:
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curr_layer = model.model.layers[layer_idx]
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attn_layer = curr_layer.self_attn
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mlp_layer = curr_layer.mlp
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weights = []
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for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
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attn_layer.v_proj_dq_list, attn_layer.o_proj_dq_list,
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mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list,
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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for l in layer_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
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layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
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layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)
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layer_weights.extend(weights)
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input_layer_norm_weights.append(layer_norm_0)
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post_attn_layernorm_weights.append(layer_norm_1)
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# save weight
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input_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_3.bin")
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post_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_4.bin")
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layer_norm_0.data.numpy().tofile(input_lm_bin_file)
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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st_idx = 5
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# 6, 7 are past k/v
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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if isinstance(weights[0], tuple):
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np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
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else: # FP16 Linear
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np_dtype = np.float16
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fused_decoder = LowBitMinicpmMultiDecoderlayer(
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[1, 1, num_heads * head_dim],
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input_layernorm_weights=input_layer_norm_weights,
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post_attn_layernorm_weights=post_attn_layernorm_weights,
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cached_cos=cached_cos,
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cached_sin=cached_sin,
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num_heads=num_heads,
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num_key_value_heads=num_key_value_heads,
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num_layers=fused_layer_num,
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max_seq_len=kv_len,
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rms_norm_eps=rms_norm_eps,
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intermediate_size=intermediate_size,
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scale_depth=scale_depth,
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num_hidden_layers=num_hidden_layers,
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mode=mode,
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transpose_value=transpose_value_cache,
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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)
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update_names_of_IR_and_export_blob(fused_decoder,
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f"decoder_layer_{i}",
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save_dir,
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compile_blob=True,
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keep_ir=False)
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return 0
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