147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
"""
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 LlamaConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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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.llama import LlamaForCausalLM, LlamaModel
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class LlamaForSequenceClassification(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config=None,
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) -> None:
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super().__init__()
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self.config = config
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self.torchao_config = None
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self.quant_config = quant_config
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self.num_labels = config.num_labels
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self.model = LlamaModel(config, quant_config=quant_config)
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self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
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self.eos_token_id = config.eos_token_id
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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 (
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get_embedding
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), "LlamaForSequenceClassification is only used for embedding"
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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scores = self.score(hidden_states)
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return self.pooler(scores, forward_batch)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "classification_head" in name:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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elif "lm_head" in name:
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continue
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else:
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LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
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class LlamaForSequenceClassificationWithNormal_Weights(LlamaForSequenceClassification):
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class Weights(torch.nn.Module):
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def __init__(self, hidden_size, num_label):
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super().__init__()
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self.fc = torch.nn.Sequential(
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torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
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torch.nn.SELU(),
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torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
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torch.nn.SELU(),
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torch.nn.Linear(hidden_size, num_label // 2, dtype=torch.float16),
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)
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def forward(self, x):
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return self.fc(x.to(torch.float16))
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def __init__(
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self,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config=None,
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) -> None:
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super().__init__(config, quant_config, cache_config)
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self.weights = self.Weights(config.hidden_size, self.num_labels)
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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 (
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get_embedding
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), "LlamaForSequenceClassification is only used for embedding"
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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logits = self.score(hidden_states)
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weights = self.weights(hidden_states)
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pooled_logits = self.pooler(logits, forward_batch).embeddings
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pooled_weights = self.pooler(weights, forward_batch).embeddings
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rews = pooled_logits.view(-1, self.num_labels // 2, 2)[:, :, 0].view(
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-1, self.num_labels // 2
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)
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scores = (rews * pooled_weights).sum(dim=-1).view(-1, 1)
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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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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "classification_head" in name:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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elif "lm_head" in name:
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continue
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else:
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LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
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EntryClass = [
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LlamaForSequenceClassification,
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LlamaForSequenceClassificationWithNormal_Weights,
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]
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