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124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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"""Inference-only Qwen2-RM model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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import torch
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from torch import nn
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .interfaces_base import default_pooling_type
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from .qwen2 import Qwen2Model
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from .utils import AutoWeightsLoader, maybe_prefix
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class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
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is_pooling_model = True
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pooler: Pooler
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.model = Qwen2Model(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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self.head_dtype = vllm_config.model_config.head_dtype
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self.score = nn.Sequential(
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ColumnParallelLinear(
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config.hidden_size,
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config.hidden_size,
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quant_config=quant_config,
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params_dtype=self.head_dtype,
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return_bias=False,
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),
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nn.ReLU(),
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RowParallelLinear(
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config.hidden_size,
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config.num_labels,
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params_dtype=self.head_dtype,
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quant_config=quant_config,
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return_bias=False,
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),
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)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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hidden_states = hidden_states.to(self.head_dtype)
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logits = self.score(hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self, ignore_unexpected_prefixes=["lm_head."])
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return loader.load_weights(weights)
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@default_pooling_type("ALL")
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class Qwen2ForRewardModel(Qwen2RewardBaseModel):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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vllm_config.model_config.hf_config.num_labels = 1
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.pooler = DispatchPooler(
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{"token_classify": Pooler.for_token_classify(pooler_config)}
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)
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@default_pooling_type("STEP")
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class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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vllm_config.model_config.hf_config.num_labels = 2
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.pooler = DispatchPooler(
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{"token_classify": Pooler.for_token_classify(pooler_config)}
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)
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