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* support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * delete use_fast_ffn
244 lines
9.0 KiB
Python
244 lines
9.0 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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 __future__ import annotations
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import os
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from dataclasses import dataclass, field
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from typing import List, Optional
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import paddle
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from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.attention.base_attention_backend import (
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AttentionBackend, AttentionMetadata)
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from fastdeploy.model_executor.layers.attention.ops import (
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get_block_shape_and_split_kv_block, gqa_rope_write_cache,
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init_signal_layerwise, open_shm_and_get_meta_signal, pre_cache_len_concat)
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from fastdeploy.worker.forward_meta import ForwardMeta
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@dataclass
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class FlashAttentionMetadata(AttentionMetadata):
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"""
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FlashAttentionMetadata
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"""
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max_len_kv: paddle.Tensor = None
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set_max_lengths: int = -1
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rotary_embs: Optional[paddle.Tensor] = None
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block_tables: Optional[paddle.Tensor] = None
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encoder_batch_ids: paddle.Tensor = None
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encoder_tile_ids_per_batch: paddle.Tensor = None
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encoder_num_blocks: paddle.Tensor = None
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kv_batch_ids: paddle.Tensor = None
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kv_tile_ids_per_batch: paddle.Tensor = None
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kv_num_blocks: paddle.Tensor = None
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decoder_batch_ids: paddle.Tensor = None
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decoder_tile_ids_per_batch: paddle.Tensor = None
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decoder_num_blocks: paddle.Tensor = None
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encoder_block_shape_q: Optional[paddle.Tensor] = None
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decoder_block_shape_q: Optional[paddle.Tensor] = None
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cu_seqlens_q: paddle.Tensor = None
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cu_seqlens_k: paddle.Tensor = None
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max_seqlen_q: int = 0
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max_seqlen_k: int = 0
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pre_cache_batch_ids = None
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pre_cache_tile_ids_per_batch = None
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pre_cache_num_blocks_cpu = None
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kv_token_num_cpu = None
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# pd_disaggregation
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kv_signal_metadata: Optional[paddle.Tensor] = None
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kv_signal_data_list: List[paddle.Tensor] = field(default_factory=list)
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class FlashAttentionBackend(AttentionBackend):
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"""
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FlashAttentionBackend backend implementation
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"""
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def __init__(self, fd_config: FDConfig, kv_num_heads: int, num_heads: int,
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head_dim: int):
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"""
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FlashAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: FlashAttentionMetadata = None
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self.max_seq_len = fd_config.parallel_config.max_model_len
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self.causal = getattr(fd_config.model_config, "causal", True)
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self.kv_num_heads = kv_num_heads
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self.num_heads = num_heads
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self.head_dim = fd_config.model_config.head_dim
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self.hidden_size = fd_config.model_config.hidden_size
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self.block_size = fd_config.parallel_config.block_size
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self.num_layers: int = fd_config.model_config.num_layers
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self.speculative_method = fd_config.speculative_config.method
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self.use_speculate = self.speculative_method is not None
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self.speculate_max_draft_token_num = fd_config.speculative_config.num_speculative_tokens
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self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
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self.rank: int = fd_config.parallel_config.tensor_parallel_rank
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# pd_disaggregation
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self.use_pd_disaggregation: int = int(
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os.getenv("FLAGS_use_pd_disaggregation", 0))
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self.start_layer_index: int = fd_config.model_config.start_layer_index
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self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
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if fd_config.parallel_config.expert_parallel_rank is None:
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fd_config.parallel_config.expert_parallel_rank = 0
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device_id = self.rank + fd_config.parallel_config.tensor_parallel_degree * \
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fd_config.parallel_config.expert_parallel_rank
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if self.device_id is None:
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self.device_id = device_id
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else:
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self.device_id = self.device_id.split(",")[device_id]
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def get_attntion_meta(self):
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"""get_attntion_meta"""
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return self.attention_metadata
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def get_kv_cache_shape(
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self,
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max_num_blocks: int,
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):
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"""
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Caculate kv cache shape
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"""
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return (max_num_blocks, self.kv_num_heads, self.block_size,
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self.head_dim)
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def init_attention_metadata(self, forward_meta: ForwardMeta):
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metadata = FlashAttentionMetadata()
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metadata.encoder_block_shape_q = 64
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metadata.decoder_block_shape_q = 16
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metadata.cu_seqlens_q = forward_meta.cu_seqlens_q
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metadata.rotary_embs = forward_meta.rotary_embs
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metadata.block_tables = forward_meta.block_tables
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(
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metadata.encoder_batch_ids,
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metadata.encoder_tile_ids_per_batch,
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metadata.encoder_num_blocks,
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metadata.kv_batch_ids,
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metadata.kv_tile_ids_per_batch,
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metadata.kv_num_blocks,
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metadata.decoder_batch_ids,
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metadata.decoder_tile_ids_per_batch,
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metadata.decoder_num_blocks,
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metadata.max_len_kv,
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metadata.set_max_lengths,
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) = get_block_shape_and_split_kv_block(
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_this_time,
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forward_meta.cum_offsets,
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metadata.encoder_block_shape_q,
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metadata.decoder_block_shape_q,
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self.num_heads // self.kv_num_heads,
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self.block_size,
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self.speculate_max_draft_token_num + 1,
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)
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(
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metadata.cu_seqlens_k,
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metadata.pre_cache_batch_ids,
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metadata.pre_cache_tile_ids_per_batch,
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metadata.pre_cache_num_blocks_cpu,
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metadata.kv_token_num_cpu,
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) = pre_cache_len_concat(
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_this_time,
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metadata.set_max_lengths[2],
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self.block_size,
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)
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# pd_disaggregation
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metadata.kv_signal_data_list = [None] * self.num_layers
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if self.use_pd_disaggregation:
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metadata.kv_signal_metadata = open_shm_and_get_meta_signal(
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self.rank, int(self.device_id), self.keep_pd_step_flag)
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self.attention_metadata = metadata
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forward_meta.decoder_batch_ids.copy_(metadata.decoder_batch_ids, False)
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forward_meta.decoder_tile_ids_per_batch.copy_(
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metadata.decoder_tile_ids_per_batch, False)
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def forward_mixed(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: Attention,
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forward_meta: ForwardMeta,
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):
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metadata = self.attention_metadata
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if self.use_pd_disaggregation:
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metadata.kv_signal_data_list[
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layer.layer_id] = init_signal_layerwise(
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metadata.kv_signal_metadata,
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layer.layer_id + self.start_layer_index)
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q, k, v, _ = gqa_rope_write_cache(
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qkv,
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forward_meta.caches[2 * layer.layer_id],
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forward_meta.caches[2 * layer.layer_id + 1],
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metadata.cu_seqlens_q,
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metadata.cu_seqlens_k,
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metadata.rotary_embs,
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forward_meta.seq_lens_this_time,
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.padding_offset,
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forward_meta.cum_offsets,
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metadata.block_tables,
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metadata.kv_batch_ids,
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metadata.kv_tile_ids_per_batch,
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metadata.kv_num_blocks,
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metadata.pre_cache_batch_ids,
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metadata.pre_cache_tile_ids_per_batch,
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metadata.pre_cache_num_blocks_cpu,
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getattr(layer, "cache_k_scale", None),
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getattr(layer, "cache_v_scale", None),
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getattr(layer, "cache_k_out_scale", None),
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getattr(layer, "cache_v_out_scale", None),
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getattr(layer, "cache_k_zp", None),
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getattr(layer, "cache_v_zp", None),
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metadata.kv_signal_data_list[layer.layer_id],
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metadata.kv_token_num_cpu[0],
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self.max_seq_len,
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getattr(layer, "cache_quant_type_str", "none"),
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)
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res = flash_attention_v3_varlen(
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q,
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k,
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v,
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metadata.cu_seqlens_q,
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metadata.cu_seqlens_k,
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max_seqlen_q=metadata.set_max_lengths[0],
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max_seqlen_k=metadata.set_max_lengths[3],
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causal=self.causal,
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)[0].reshape([-1, self.hidden_size])
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return res
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