207 lines
9.2 KiB
Python
207 lines
9.2 KiB
Python
################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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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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################################################################################
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from typing import Any, Optional, Tuple
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import torch
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import torch_br
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from torch import nn
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from torch_br.supa.profiler_kineto import record_function
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from vllm.attention import Attention, AttentionType
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from vllm.config import CacheConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import (MRotaryEmbedding,
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get_rope)
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from vllm.model_executor.models.utils import extract_layer_index
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class AttentionSplit(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_heads: int,
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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rope_theta: int = 10000,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[Tuple] = None,
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attn_type: str = AttentionType.DECODER,
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prefix: str = "",
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dual_chunk_attention_config: Optional[dict[str, Any]] = None,
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bias: bool = False,
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) -> None:
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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_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 = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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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.rope_theta = rope_theta
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qconfig = None
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if quant_config is not None and quant_config.qkv_quantized:
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qconfig = quant_config
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self.q_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=self.q_size * tp_size,
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bias=bias,
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quant_config=qconfig,
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prefix=f"{prefix}.q_proj")
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self.k_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=self.kv_size * tp_size,
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bias=bias,
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quant_config=qconfig,
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prefix=f"{prefix}.k_proj")
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self.v_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=self.kv_size * tp_size,
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bias=bias,
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quant_config=qconfig,
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prefix=f"{prefix}.v_proj")
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj")
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=self.rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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attn_type=attn_type,
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prefix=f"{prefix}.attn",
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**{
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"layer_idx": extract_layer_index(prefix),
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"dual_chunk_attention_config": dual_chunk_attention_config,
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} if dual_chunk_attention_config else {})
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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forward_context: ForwardContext = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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if attn_metadata is None:
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## for dummy run
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return hidden_states
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seq_len = hidden_states.shape[-2]
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decode_seql = 512
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# numa weight and not use mrope (qwen-vl)
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if ((hasattr(self.q_proj, "qweight")
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and len(self.q_proj.qweight.shape) == 3) or
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(hasattr(self.q_proj, "weight")
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and len(self.q_proj.weight.shape) == 3)) and not isinstance(
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self.rotary_emb, MRotaryEmbedding) and seq_len <= decode_seql:
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if isinstance(attn_metadata, dict):
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attn_metadata = attn_metadata[self.attn.layer_name]
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kv_cache = self.attn.kv_cache[forward_context.virtual_engine]
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if kv_cache is not None:
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with record_function('attention qkv_rope'):
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# int8 weight version
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q_weight = self.q_proj.qweight if hasattr(
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self.q_proj, "qweight") else self.q_proj.weight
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k_weight = self.k_proj.qweight if hasattr(
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self.k_proj, "qweight") else self.k_proj.weight
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v_weight = self.v_proj.qweight if hasattr(
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self.v_proj, "qweight") else self.v_proj.weight
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q_scale = self.q_proj.scales if hasattr(
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self.q_proj, "scales") else None
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k_scale = self.k_proj.scales if hasattr(
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self.k_proj, "scales") else None
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v_scale = self.v_proj.scales if hasattr(
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self.v_proj, "scales") else None
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q_bias = self.q_proj.bias if hasattr(self.q_proj,
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"bias") else None
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k_bias = self.k_proj.bias if hasattr(self.k_proj,
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"bias") else None
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v_bias = self.v_proj.bias if hasattr(self.v_proj,
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"bias") else None
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q, k, v = torch_br.supa_qkv_rope_decode_infer(
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hidden_states,
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q_weight,
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k_weight,
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v_weight,
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self.rotary_emb.sin_cache,
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self.rotary_emb.cos_cache,
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kv_cache,
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positions,
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attn_metadata.slot_mapping,
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self.rotary_emb.head_size,
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self.q_size,
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self.kv_size,
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q_scale=q_scale,
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k_scale=k_scale,
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v_scale=v_scale,
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q_bias=q_bias,
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k_bias=k_bias,
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v_bias=v_bias)
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if hasattr(attn_metadata, 'do_cache'):
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attn_metadata.do_cache = False
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with record_function('attention'):
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attn_output = self.attn(q, k, v)
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with record_function('attention o_proj'):
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output, _ = self.o_proj(attn_output)
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return output
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else:
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return hidden_states
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else:
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# uma weight or use mrope (qwen-vl)
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q, _ = self.q_proj(hidden_states)
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k, _ = self.k_proj(hidden_states)
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v, _ = self.v_proj(hidden_states)
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q, k = self.rotary_emb(positions, q, k)
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if hasattr(attn_metadata, 'do_cache'):
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attn_metadata.do_cache = True
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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