499 lines
15 KiB
Python
499 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Dict, Iterable, Optional, Set, Tuple
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import torch
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from torch import nn
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.pooler import CrossEncodingPooler, Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
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from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix
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BertConfig = None
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class BertEmbedding(nn.Module):
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def __init__(self, config: BertConfig):
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super().__init__()
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self.size = config.hidden_size
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self.word_embeddings = VocabParallelEmbedding(
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config.vocab_size, config.hidden_size
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)
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self.position_embeddings = VocabParallelEmbedding(
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config.max_position_embeddings, config.hidden_size
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)
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self.token_type_embeddings = VocabParallelEmbedding(
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config.type_vocab_size, config.hidden_size
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)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.position_ids = nn.Parameter(
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torch.empty((1, config.max_position_embeddings)),
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)
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self.position_embedding_type = config.position_embedding_type
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if self.position_embedding_type != "absolute":
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raise ValueError(
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"Only 'absolute' position_embedding_type" + " is supported"
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)
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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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) -> torch.Tensor:
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input_shape = input_ids.size()
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# Input embeddings.
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inputs_embeds = self.word_embeddings(input_ids)
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# Position embeddings.
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position_embeddings = self.position_embeddings(positions)
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token_type_ids = forward_batch.token_type_ids
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if token_type_ids is None:
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token_type_ids = torch.zeros(
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input_shape, dtype=torch.long, device=inputs_embeds.device
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)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings + position_embeddings
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embeddings = self.LayerNorm(embeddings)
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return embeddings
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class BertPooler(nn.Module):
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def __init__(self, config: BertConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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# simply taking the hidden state corresponding
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first_token_tensor = hidden_states[0, :]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class BertEncoder(nn.Module):
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def __init__(
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self,
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config: BertConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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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.layer = nn.ModuleList(
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[
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BertLayer(
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config=config,
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layer_id=layer_idx,
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quant_config=quant_config,
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prefix=f"{prefix}.layer.{layer_idx}",
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)
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for layer_idx in range(config.num_hidden_layers)
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]
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)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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for layer in self.layer:
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hidden_states = layer(hidden_states, forward_batch)
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(
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self,
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config: BertConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.layer_id = layer_id
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self.attention = BertAttention(
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hidden_size=config.hidden_size,
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num_attention_heads=config.num_attention_heads,
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layer_id=layer_id,
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layer_norm_eps=config.layer_norm_eps,
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quant_config=quant_config,
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prefix=f"{prefix}.attention",
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)
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self.intermediate = BertIntermediate(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.intermediate",
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)
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self.output = BertOutput(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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layer_norm_eps=config.layer_norm_eps,
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quant_config=quant_config,
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prefix=f"{prefix}.output",
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)
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def forward(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
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attn_output = self.attention(hidden_states, forward_batch)
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intermediate_output = self.intermediate(attn_output)
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output = self.output(intermediate_output, attn_output)
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return output
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class BertAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_attention_heads: int,
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layer_norm_eps: float,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.self_attn = BertSelfAttention(
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hidden_size=hidden_size,
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num_attention_heads=num_attention_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=f"{prefix}.output",
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)
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self.output = BertSelfOutput(
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hidden_size=hidden_size,
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layer_norm_eps=layer_norm_eps,
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quant_config=quant_config,
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prefix=f"{prefix}.output",
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)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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self_output = self.self_attn(hidden_states, forward_batch)
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return self.output(self_output, hidden_states)
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class BertSelfAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_attention_heads: int,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = self.total_num_heads
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self.head_dim = self.hidden_size // self.total_num_heads
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assert self.head_dim * self.total_num_heads == self.hidden_size
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.attn = RadixAttention(
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num_heads=self.num_heads,
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head_dim=self.head_dim,
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scaling=self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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prefix=f"{prefix}.attn",
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attn_type=AttentionType.ENCODER_ONLY,
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)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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output = self.attn(q, k, v, forward_batch)
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return output
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class BertSelfOutput(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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layer_norm_eps: float,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = RowParallelLinear(
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input_size=hidden_size,
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output_size=hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(
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self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
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) -> torch.Tensor:
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hidden_states, _ = self.dense(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertIntermediate(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = ColumnParallelLinear(
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input_size=hidden_size,
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output_size=intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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self.intermediate_act_fn = get_act_fn(hidden_act)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BertOutput(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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layer_norm_eps: float,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(
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self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
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) -> torch.Tensor:
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hidden_states, _ = self.dense(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertModel(nn.Module):
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def __init__(
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self,
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*,
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config: BertConfig,
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quant_config: Optional[QuantizationConfig] = None,
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use_bert_pooler: bool = False,
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prefix: str = "",
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):
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super().__init__()
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self.use_bert_pooler = use_bert_pooler
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self.config = config
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self.embeddings = BertEmbedding(config)
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self.encoder = BertEncoder(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("encoder", prefix),
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)
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self.pooler = (
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BertPooler(config)
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if self.use_bert_pooler
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else Pooler(pooling_type=PoolingType.LAST, normalize=True)
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)
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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 = False,
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) -> torch.Tensor:
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assert get_embedding == True
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# Your tokenized IDs
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hidden_states = self.embeddings(
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input_ids=input_ids,
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positions=positions,
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forward_batch=forward_batch,
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)
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hidden_states = self.encoder(hidden_states, forward_batch=forward_batch)
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if not self.use_bert_pooler:
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hidden_states = self.pooler(hidden_states, forward_batch)
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return hidden_states
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "query", "q"),
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("qkv_proj", "key", "k"),
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("qkv_proj", "value", "v"),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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name = name.replace("self", "self_attn")
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if not self.use_bert_pooler and "pooler" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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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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class Contriever(BertModel):
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pass
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class BertForSequenceClassification(nn.Module):
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def __init__(
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self,
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*,
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config: BertConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.num_labels = config.num_labels
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self.bert = BertModel(
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config=config,
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quant_config=quant_config,
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use_bert_pooler=True,
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prefix=add_prefix("bert", prefix),
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)
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self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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self.pooler = CrossEncodingPooler(config, self.classifier, self.bert.pooler)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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self_weights = []
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def weight_filter():
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for name, weight in weights:
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if name.startswith("bert."):
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yield (name[len("bert.") :], weight)
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else:
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self_weights.append((name, weight))
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self.bert.load_weights(weight_filter())
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in self_weights:
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if name.startswith("classifier"):
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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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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 = False,
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) -> torch.Tensor:
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assert get_embedding == True
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hidden_states = self.bert(
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input_ids=input_ids,
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positions=positions,
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forward_batch=forward_batch,
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input_embeds=input_embeds,
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get_embedding=get_embedding,
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)
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return self.pooler(hidden_states, forward_batch)
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EntryClass = [BertModel, Contriever, BertForSequenceClassification]
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