mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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301 lines
11 KiB
Python
301 lines
11 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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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, List, Optional
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import paddle
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from paddle.nn.functional.flash_attention import flash_attn_unpadded
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try:
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from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
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except:
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flash_attention_v3_varlen = None
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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,
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AttentionMetadata,
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)
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from fastdeploy.model_executor.layers.attention.ops import (
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get_block_shape_and_split_kv_block,
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gqa_rope_write_cache,
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init_kv_signal_per_query,
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init_signal_layerwise,
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open_shm_and_get_meta_signal,
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pre_cache_len_concat,
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)
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from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id
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if TYPE_CHECKING:
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from fastdeploy.model_executor.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: int = -1
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decoder_block_shape_q: int = -1
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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[Optional[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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__infer_dynamic_dims_fields__ = ["attention_metadata"]
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attention_metadata: FlashAttentionMetadata
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flash_attn_func: callable = None
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def __init__(
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self,
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fd_config: FDConfig,
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kv_num_heads: int,
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num_heads: int,
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head_dim: int,
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):
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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.attn_outputsize_tp = self.num_heads * self.head_dim
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self.block_size = fd_config.cache_config.block_size
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self.num_layers: int = fd_config.model_config.num_hidden_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.num_layers_draft_model: int = int(fd_config.speculative_config.method in ["mtp"])
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self.pd_disaggregation_mode: str = fd_config.parallel_config.pd_disaggregation_mode
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self.start_layer_index: int = fd_config.model_config.start_layer_index
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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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self.rank, self.device_id = init_rank_and_device_id(fd_config)
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if self.flash_attn_func is None:
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prop = paddle.device.cuda.get_device_properties()
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cc = prop.major * 10 + prop.minor
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is_current_sm_supported = cc >= 90
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is_paddle_supported = any(num >= 90 for num in paddle.version.cuda_archs())
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if is_current_sm_supported and is_paddle_supported:
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self.flash_attn_func = flash_attention_v3_varlen
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print("The current platform supports Flash Attention V3.")
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self.flash_attn_kwargs = {}
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else:
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self.flash_attn_func = flash_attn_unpadded
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self.flash_attn_kwargs = {"scale": self.head_dim**-0.5, "training": False}
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print(
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"The current platform does not support Flash Attention V3, so Flash Attention V2 will be used instead."
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)
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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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kv_cache_quant_type: str = None,
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):
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"""
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Caculate kv cache shape
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"""
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if kv_cache_quant_type is not None and kv_cache_quant_type == "int4_zp":
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return (
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max_num_blocks,
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self.kv_num_heads,
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self.block_size,
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self.head_dim // 2,
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)
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else:
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return (
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max_num_blocks,
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self.kv_num_heads,
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self.block_size,
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self.head_dim,
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)
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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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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.pd_disaggregation_mode == "per_chunk":
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if not self.keep_pd_step_flag:
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init_kv_signal_per_query(
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_this_time,
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forward_meta.seq_lens_decoder,
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self.rank,
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self.num_layers + self.num_layers_draft_model,
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)
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elif self.pd_disaggregation_mode == "per_query":
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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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)
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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_(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.pd_disaggregation_mode == "per_query":
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metadata.kv_signal_data_list[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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)
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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.batch_id_per_token,
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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].item(),
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self.max_seq_len,
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getattr(layer, "cache_quant_type_str", "none"),
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self.rope_3d,
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)
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res = self.flash_attn_func(
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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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**self.flash_attn_kwargs,
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)[0].reshape([-1, self.attn_outputsize_tp])
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return res
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