update

model_executor/models/qwen2_rm.py

@@ -1,123 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Adapted from
-# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py
-# Copyright 2024 The Qwen team.
-# Copyright 2023 The vLLM team.
-"""Inference-only Qwen2-RM model compatible with HuggingFace weights."""
-
-from collections.abc import Iterable
-
-import torch
-from torch import nn
-
-from vllm.config import VllmConfig
-from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
-from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
-from vllm.sequence import IntermediateTensors
-
-from .interfaces import SupportsLoRA, SupportsPP
-from .interfaces_base import default_pooling_type
-from .qwen2 import Qwen2Model
-from .utils import AutoWeightsLoader, maybe_prefix
-
-
-class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
-    is_pooling_model = True
-    pooler: Pooler
-
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-
-        self.config = config
-
-        self.quant_config = quant_config
-        self.model = Qwen2Model(
-            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
-        )
-        self.head_dtype = vllm_config.model_config.head_dtype
-
-        self.score = nn.Sequential(
-            ColumnParallelLinear(
-                config.hidden_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                params_dtype=self.head_dtype,
-                return_bias=False,
-            ),
-            nn.ReLU(),
-            RowParallelLinear(
-                config.hidden_size,
-                config.num_labels,
-                params_dtype=self.head_dtype,
-                quant_config=quant_config,
-                return_bias=False,
-            ),
-        )
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors
-        )
-
-    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.embed_input_ids(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor | IntermediateTensors:
-        hidden_states = self.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds
-        )
-        hidden_states = hidden_states.to(self.head_dtype)
-        logits = self.score(hidden_states)
-        return logits
-
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(self, ignore_unexpected_prefixes=["lm_head."])
-        return loader.load_weights(weights)
-
-
-@default_pooling_type("ALL")
-class Qwen2ForRewardModel(Qwen2RewardBaseModel):
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        vllm_config.model_config.hf_config.num_labels = 1
-        super().__init__(vllm_config=vllm_config, prefix=prefix)
-
-        pooler_config = vllm_config.model_config.pooler_config
-        assert pooler_config is not None
-
-        self.pooler = DispatchPooler(
-            {"token_classify": Pooler.for_token_classify(pooler_config)}
-        )
-
-
-@default_pooling_type("STEP")
-class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        vllm_config.model_config.hf_config.num_labels = 2
-        super().__init__(vllm_config=vllm_config, prefix=prefix)
-
-        pooler_config = vllm_config.model_config.pooler_config
-        assert pooler_config is not None
-
-        self.pooler = DispatchPooler(
-            {"token_classify": Pooler.for_token_classify(pooler_config)}
-        )