wehub-resource-sync
93 lines
4.4 KiB
Python
93 lines
4.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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import regex
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import torch
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from vllm.model_executor.models.deepseek_v2 import DeepseekV3ForCausalLM
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from vllm.model_executor.models.utils import AutoWeightsLoader, WeightsMapper
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class MistralLarge3ForCausalLM(DeepseekV3ForCausalLM):
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# WeightsMapper applies all matching patterns sequentially (no break on first
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# match). This is safe here because every pattern is anchored at both ends
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# (\A...\Z) and after substitution the resulting key always starts with
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# "model." or "lm_head.", so no later pattern can accidentally match again.
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_regex={ # noqa: B950
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regex.compile(
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r"\Alayers\.(\d+)\.attention_norm\.weight\Z"
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): r"model.layers.\1.input_layernorm.weight",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.wq_a\.(\w+)\Z"
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): r"model.layers.\1.self_attn.q_a_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.q_a_norm\.weight\Z"
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): r"model.layers.\1.self_attn.q_a_layernorm.weight",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.wq_b\.(\w+)\Z"
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): r"model.layers.\1.self_attn.q_b_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.wkv_a_with_mqa\.(\w+)\Z"
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): r"model.layers.\1.self_attn.kv_a_proj_with_mqa.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.kv_a_norm\.weight\Z"
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): r"model.layers.\1.self_attn.kv_a_layernorm.weight",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.wkv_b\.(\w+)\Z"
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): r"model.layers.\1.self_attn.kv_b_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.attention\.wo\.(\w+)\Z"
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): r"model.layers.\1.self_attn.o_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.ffn_norm\.weight\Z"
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): r"model.layers.\1.post_attention_layernorm.weight",
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regex.compile(
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r"\Alayers\.(\d+)\.feed_forward\.w1\.(\w+)\Z"
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): r"model.layers.\1.mlp.gate_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.feed_forward\.w2\.(\w+)\Z"
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): r"model.layers.\1.mlp.down_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.feed_forward\.w3\.(\w+)\Z"
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): r"model.layers.\1.mlp.up_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.gate\.weight\Z"
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): r"model.layers.\1.mlp.gate.weight",
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regex.compile(
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r"\Alayers\.(\d+)\.shared_experts\.w1\.(\w+)\Z"
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): r"model.layers.\1.mlp.shared_experts.gate_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.shared_experts\.w2\.(\w+)\Z"
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): r"model.layers.\1.mlp.shared_experts.down_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.shared_experts\.w3\.(\w+)\Z"
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): r"model.layers.\1.mlp.shared_experts.up_proj.\2",
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regex.compile(
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r"\Alayers\.(\d+)\.experts\.(\d+)\.w1\.(\w+)\Z"
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): r"model.layers.\1.mlp.experts.\2.gate_proj.\3",
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regex.compile(
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r"\Alayers\.(\d+)\.experts\.(\d+)\.w2\.(\w+)\Z"
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): r"model.layers.\1.mlp.experts.\2.down_proj.\3",
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regex.compile(
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r"\Alayers\.(\d+)\.experts\.(\d+)\.w3\.(\w+)\Z"
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): r"model.layers.\1.mlp.experts.\2.up_proj.\3",
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regex.compile(r"\Anorm\.weight\Z"): "model.norm.weight",
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regex.compile(r"\Atok_embeddings\.weight\Z"): "model.embed_tokens.weight",
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regex.compile(r"\Aoutput\.weight\Z"): "lm_head.weight",
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},
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orig_to_new_suffix={
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".qscale_act": ".input_scale",
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".qscale_weight": ".weight_scale",
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},
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)
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# Bypass super().load_weights() and construct AutoWeightsLoader(self)
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# directly (same pattern as Qwen2ForCausalLM). Any logic in the parent
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# class's load_weights is a thin wrapper around AutoWeightsLoader, and
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# we must apply hf_to_vllm_mapper before the loader walks the tree.
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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