[gpt-oss] Add gpt-oss bf16 support

vllm/model_executor/models/qwen2_rm.py

@@ -0,0 +1,132 @@
+# 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
+from typing import Optional, Union
+
+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 Pooler, PoolingType, SimplePooler
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import IntermediateTensors, PoolerOutput
+
+from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
+from .qwen2 import Qwen2Model
+from .utils import AutoWeightsLoader, maybe_prefix
+
+
+class ReLU(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.activation = nn.ReLU()
+
+    def forward(self, input):
+        input, _ = input
+        return self.activation(input)
+
+
+class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
+                           SupportsV0Only):
+    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
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = Qwen2Model(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+
+        self.score = nn.Sequential(
+            ColumnParallelLinear(config.hidden_size,
+                                 config.hidden_size,
+                                 quant_config=quant_config),
+            ReLU(),
+            RowParallelLinear(config.hidden_size,
+                              config.num_labels,
+                              quant_config=quant_config),
+        )
+        self._pooler: SimplePooler
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        logits, _ = self.score(hidden_states)
+        return logits
+
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return self._pooler(hidden_states, pooling_metadata)
+
+    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)
+
+
+class Qwen2ForRewardModel(Qwen2RewardBaseModel):
+
+    def __init__(self, *, vllm_config, prefix=""):
+        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
+        self._pooler = Pooler.from_config_with_defaults(
+            pooler_config,
+            pooling_type=PoolingType.ALL,
+            normalize=False,
+            softmax=False)
+
+
+class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
+
+    def __init__(self, *, vllm_config, prefix=""):
+        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
+        self._pooler = Pooler.from_config_with_defaults(
+            pooler_config,
+            pooling_type=PoolingType.STEP,
+            normalize=False,
+            softmax=True,
+            step_tag_id=151651,
+        )