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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
257 lines
10 KiB
Python
257 lines
10 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 TYPE_CHECKING, List, Optional, Tuple
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import paddle
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from fastdeploy.model_executor.layers.attention.ops import (
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append_attention, get_block_shape_and_split_kv_block,
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init_signal_layerwise, open_shm_and_get_meta_signal)
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if TYPE_CHECKING:
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from paddle._typing.dtype_like import _DTypeLiteral
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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.worker.forward_meta import ForwardMeta
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@dataclass
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class AppendAttentionMetadata(AttentionMetadata):
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"""
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AppendAttentionMetadata
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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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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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_dtype: _DTypeLiteral = paddle.bfloat16
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encoder_max_partition_size: int = 32768
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max_partition_size: int = 32768
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block_tables: Optional[paddle.Tensor] = None
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rotary_embs: Optional[paddle.Tensor] = None
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attn_mask: Optional[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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_fuse_kernel_compute_dtype: str = "bf16"
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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 AppendAttentionBackend(AttentionBackend):
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"""
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AppendAttentionBackend 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) -> None:
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"""
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AppendAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: AppendAttentionMetadata = None
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self.block_size: int = fd_config.parallel_config.block_size
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self.max_seq_len: int = fd_config.parallel_config.max_model_len
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self.rope_theta: float = (10000.0
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if fd_config.model_config.rope_theta is None
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else fd_config.model_config.rope_theta)
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self.rope_3d: bool = getattr(fd_config.model_config, "rope_3d", False)
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self.causal: bool = getattr(fd_config.model_config, "causal", True)
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self.speculative_method: str = fd_config.speculative_config.method
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self.use_speculate: bool = self.speculative_method is not None
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self.speculate_max_draft_token_num: int = 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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self.kv_num_heads: int = kv_num_heads
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self.num_heads: int = num_heads
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self.head_dim: int = fd_config.model_config.head_dim
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self.num_layers: int = fd_config.model_config.num_layers
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self.max_partition_size: int = int(
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os.getenv("FLAGS_max_partition_size", 32768))
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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 init_attention_metadata(self, forward_meta: ForwardMeta):
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"""Initialize attntion metadata hence all layers in the forward pass can reuse it."""
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metadata = AppendAttentionMetadata()
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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.max_partition_size = self.max_partition_size
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metadata.encoder_max_partition_size = self.max_seq_len
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metadata._dtype = paddle.get_default_dtype()
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if metadata._dtype == "bfloat16":
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metadata._fuse_kernel_compute_dtype = "bf16"
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elif metadata._dtype == "float16":
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metadata._fuse_kernel_compute_dtype = "fp16"
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elif metadata._dtype == "float32":
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metadata._fuse_kernel_compute_dtype = "fp32"
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metadata.block_tables = forward_meta.block_tables
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metadata.rotary_embs = forward_meta.rotary_embs
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metadata.attn_mask = forward_meta.attn_mask
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metadata.pre_caches_length = forward_meta.pre_caches_length
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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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# 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: AttentionMetadata = 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 get_attntion_meta(self) -> AttentionMetadata:
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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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) -> Tuple[int, int, int, int]:
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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 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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) -> paddle.Tensor:
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"""
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forward_mixed
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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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res = append_attention(
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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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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.padding_offset,
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forward_meta.cum_offsets,
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metadata.block_tables,
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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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forward_meta.decoder_batch_ids, # from buffer
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forward_meta.decoder_tile_ids_per_batch, # from buffer
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metadata.decoder_num_blocks,
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metadata.set_max_lengths,
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metadata.max_len_kv,
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metadata.rotary_embs,
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metadata.attn_mask,
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layer.qkv_bias,
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layer.qkv_scale,
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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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layer.linear_shift,
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layer.linear_smooth,
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metadata.kv_signal_data_list[layer.layer_id],
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metadata._fuse_kernel_compute_dtype,
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getattr(layer, "cache_quant_type_str", "none"),
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layer.use_neox_rotary_style,
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self.rope_3d,
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self.max_seq_len,
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getattr(layer, "quant_max_bound", 0.0),
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getattr(layer, "quant_min_bound", 0.0),
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getattr(layer, "out_scale", -1.0),
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metadata.encoder_block_shape_q,
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metadata.decoder_block_shape_q,
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metadata.max_partition_size,
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metadata.encoder_max_partition_size,
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self.speculate_max_draft_token_num + 1,
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self.causal,
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self.speculative_method is not None,
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)[0]
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return res
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