127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
# Copyright 2023-2024 SGLang Team
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
from typing import Iterable, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import LlamaConfig
|
|
|
|
from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
|
|
from sglang.srt.models.llama import LlamaForCausalLM, LlamaModel
|
|
from sglang.srt.utils import add_prefix
|
|
|
|
|
|
class LlamaForSequenceClassification(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: LlamaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.num_labels = config.num_labels
|
|
self.model = LlamaModel(
|
|
config, quant_config=quant_config, prefix=add_prefix("model", prefix)
|
|
)
|
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
|
|
|
|
self.eos_token_id = config.eos_token_id
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
get_embedding: bool = True,
|
|
) -> EmbeddingPoolerOutput:
|
|
assert (
|
|
get_embedding
|
|
), "LlamaForSequenceClassification is only used for embedding"
|
|
|
|
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
|
last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
|
|
scores = self.score(last_token_hidden)
|
|
|
|
return EmbeddingPoolerOutput(scores)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
return LlamaForCausalLM.load_weights(self, weights)
|
|
|
|
|
|
class LlamaForSequenceClassificationWithNormal_Weights(LlamaForSequenceClassification):
|
|
class Weights(torch.nn.Module):
|
|
def __init__(self, hidden_size, num_label):
|
|
super().__init__()
|
|
self.fc = torch.nn.Sequential(
|
|
torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
|
|
torch.nn.SELU(),
|
|
torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
|
|
torch.nn.SELU(),
|
|
torch.nn.Linear(hidden_size, num_label // 2, dtype=torch.float16),
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.fc(x.to(torch.float16))
|
|
|
|
def __init__(
|
|
self,
|
|
config: LlamaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(config, quant_config, prefix=prefix)
|
|
self.weights = self.Weights(config.hidden_size, self.num_labels)
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
get_embedding: bool = True,
|
|
) -> EmbeddingPoolerOutput:
|
|
assert (
|
|
get_embedding
|
|
), "LlamaForSequenceClassification is only used for embedding"
|
|
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
|
logits = self.score(hidden_states)
|
|
weights = self.weights(hidden_states)
|
|
|
|
pooled_logits = self.pooler(logits, forward_batch).embeddings
|
|
pooled_weights = self.pooler(weights, forward_batch).embeddings
|
|
|
|
rews = pooled_logits.view(-1, self.num_labels // 2, 2)[:, :, 0].view(
|
|
-1, self.num_labels // 2
|
|
)
|
|
scores = (rews * pooled_weights).sum(dim=-1).view(-1, 1)
|
|
return EmbeddingPoolerOutput(scores)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
return super().load_weights(weights)
|
|
|
|
|
|
EntryClass = [
|
|
LlamaForSequenceClassification,
|
|
LlamaForSequenceClassificationWithNormal_Weights,
|
|
]
|