65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.internlm2 import InternLM2ForCausalLM, InternLM2Model
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from sglang.srt.utils import add_prefix
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class InternLM2ForRewardModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.vocab_size = config.vocab_size
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self.model = InternLM2Model(
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config, quant_config, prefix=add_prefix("model", prefix)
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)
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self.v_head = nn.Linear(config.hidden_size, 1, bias=False)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
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@torch.no_grad()
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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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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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get_embedding: bool = True,
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) -> EmbeddingPoolerOutput:
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assert get_embedding, "InternLM2ForRewardModel is only used for embedding"
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
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scores = self.v_head(last_token_hidden)
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return EmbeddingPoolerOutput(scores)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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return InternLM2ForCausalLM.load_weights(self, weights)
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EntryClass = InternLM2ForRewardModel
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