mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
import torch
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from ..._utils import str_dtype_to_torch
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from .split_weights import shuffle_qkv_weights, split_weights_tp
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def load_weights_from_hf_model(hf_model, config):
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torch_dtype = str_dtype_to_torch(config.dtype)
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hf_state_dict = hf_model.state_dict()
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weights = {}
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config.quant_mode.is_weight_only()
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if config.quant_mode.is_int8_weight_only():
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torch.int8
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elif config.quant_mode.is_int4_weight_only():
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torch.quint4x2
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# replace key name
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for key, value in hf_state_dict.items():
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# Decoder Layers
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orig_key = key
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if "model.layers." in key:
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key = key.replace("model.layers.", "transformer.layers.")
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#Attention
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key = key.replace("self_attn.", "attention.")
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key = key.replace("query_key_value.", "qkv.") # small
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key = key.replace("Wqkv.weight", "qkv.weight")
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key = key.replace("qkv_proj.", "qkv.") #128k
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#MLP
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key = key.replace("mlp.fc1.", "mlp.fc.")
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key = key.replace("mlp.fc2.", "mlp.proj.")
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key = key.replace("mlp.gate_up_proj.", "mlp.fc.")
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key = key.replace("mlp.up_proj.", "mlp.fc." if config.architecture
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== 'Phi3SmallForCausalLM' else "mlp.gate.") #128k
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key = key.replace("mlp.down_proj.", "mlp.proj.") #128k
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key = key.replace("mlp.gate_proj.", "mlp.fc.") #128k
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key = key.replace("o_proj.", "dense.") #128k
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#Layer norm
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key = key.replace("post_attention_layernorm.",
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"post_layernorm.") #128k
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# Embedding
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key = key.replace("model.embed_tokens.weight",
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"transformer.vocab_embedding.weight")
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# Final Layer norm
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key = key.replace("model.final_layernorm.", "transformer.ln_f.")
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key = key.replace("model.norm.", "transformer.ln_f.") #128k
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if "mlp.gate_up_proj." in orig_key: #4k
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original_weights = value.contiguous().clone()
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half_split = original_weights.shape[0] // 2
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first_half, second_half = original_weights[:
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half_split, :], original_weights[
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half_split:, :]
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# Swap the halves
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value = torch.cat((second_half, first_half), dim=0)
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if "q_proj" in key: #128k
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q_param = value
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k_param = hf_state_dict[orig_key.replace("q_proj", "k_proj")]
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v_param = hf_state_dict[orig_key.replace("q_proj", "v_proj")]
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value = torch.cat([q_param, k_param, v_param], dim=0)
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key = key.replace("q_proj.weight", "qkv.weight")
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elif "k_proj" in key or "v_proj" in key:
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continue
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weights[key] = value.to(torch_dtype).cpu()
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if config.architecture == 'Phi3SmallForCausalLM':
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weights['lm_head.weight'] = weights[
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'transformer.vocab_embedding.weight'].clone()
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# Transform QKV weights from custom Phi3Small format to TRT-LLM format
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for key, value in weights.items():
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if "qkv." in key:
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weights[key] = shuffle_qkv_weights(weights[key], config)
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weights = split_weights_tp(config, weights, torch_dtype)
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return weights
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