mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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385 lines
16 KiB
Python
385 lines
16 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
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import paddle
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from fastdeploy.model_executor.layers.attention.ops import (
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append_attention,
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append_attention_with_output,
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get_block_shape_and_split_kv_block,
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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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)
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if TYPE_CHECKING:
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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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.utils import init_rank_and_device_id
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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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_dtype: paddle.dtype = 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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_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[Optional[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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__infer_dynamic_dims_fields__ = ["attention_metadata"]
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attention_metadata: AppendAttentionMetadata
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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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encoder_block_shape_q: int = -1,
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decoder_block_shape_q: int = -1,
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) -> 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.cache_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 = (
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10000.0 if fd_config.model_config.rope_theta is None else fd_config.model_config.rope_theta
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)
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self.rope_3d: bool = getattr(fd_config.model_config, "rope_3d", False) or getattr(
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fd_config.model_config, "use_3d_rope", False
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)
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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.num_layers_draft_model: int = int(fd_config.speculative_config.method in ["mtp"])
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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.group_size: int = self.num_heads // self.kv_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_hidden_layers
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self.max_partition_size: int = int(os.getenv("FLAGS_max_partition_size", 1024))
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self.encoder_block_shape_q: int = encoder_block_shape_q
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self.decoder_block_shape_q: int = decoder_block_shape_q
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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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self.use_output = not fd_config.graph_opt_config.full_cuda_graph
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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.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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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.decoder_batch_ids,
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forward_meta.decoder_tile_ids_per_batch,
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forward_meta.decoder_num_blocks_cpu,
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forward_meta.decoder_num_blocks_device,
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forward_meta.decoder_chunk_size_device,
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forward_meta.max_len_tensor_cpu,
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forward_meta.encoder_batch_ids,
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forward_meta.encoder_tile_ids_per_batch,
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forward_meta.encoder_num_blocks_x_cpu,
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forward_meta.kv_batch_ids,
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forward_meta.kv_tile_ids_per_batch,
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forward_meta.kv_num_blocks_x_cpu,
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forward_meta.max_len_kv_cpu,
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self.encoder_block_shape_q,
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self.decoder_block_shape_q,
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self.group_size,
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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.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: AttentionMetadata = metadata
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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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kv_cache_quant_type: str = None,
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):
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"""
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Calculate 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 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.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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cache_quant_type_str = getattr(layer, "cache_quant_type_str", "none")
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if cache_quant_type_str == "block_wise_fp8":
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cache_k = forward_meta.caches[4 * layer.layer_id]
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cache_v = forward_meta.caches[4 * layer.layer_id + 1]
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cache_k_scales = forward_meta.caches[4 * layer.layer_id + 2]
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cache_v_scales = forward_meta.caches[4 * layer.layer_id + 3]
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else:
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cache_k = forward_meta.caches[2 * layer.layer_id]
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cache_v = forward_meta.caches[2 * layer.layer_id + 1]
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cache_k_scales = getattr(layer, "cache_k_scale", None)
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cache_v_scales = getattr(layer, "cache_v_scale", None)
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if self.use_output:
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quant_max_bound = getattr(layer, "quant_max_bound", 0.0)
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cache_quant_type = getattr(layer, "cache_quant_type_str", "none")
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compute_type = metadata._fuse_kernel_compute_dtype
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out_scale = getattr(layer, "out_scale", -1.0)
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# 1. get output datatype
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qkv_dtype = qkv.dtype
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if qkv_dtype == paddle.float16:
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D_type = paddle.float16
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elif qkv_dtype == paddle.bfloat16:
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D_type = paddle.bfloat16
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elif qkv_dtype == paddle.int32:
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if compute_type == "bf16":
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D_type = paddle.bfloat16
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elif compute_type == "fp16":
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D_type = paddle.float16
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else:
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raise NotImplementedError("Only supported attr of qkv_type in ['float16', 'bfloat16'].")
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else:
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raise NotImplementedError("Only supported attr of qkv_type in ['float16', 'bfloat16', 'int32'].")
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# 2.Extract related parameters
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token_nums = qkv.shape[0]
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head_dims = self.head_dim if cache_quant_type != "cache_int4_zp" else self.head_dim * 2
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q_num_heads = self.num_heads
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# 3. generate output tensor of different dtypes
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if out_scale > 0.0:
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if abs(quant_max_bound - 127) < 0.000001:
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res = paddle.empty([token_nums, q_num_heads * head_dims], dtype="int8").to(qkv.place)
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elif abs(quant_max_bound - 448) < 0.000001:
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res = paddle.empty([token_nums, q_num_heads * head_dims], dtype="float8_e4m3fn").to(qkv.place)
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else:
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raise NotImplementedError("Only supported attr of quant_max_bound in ['127', '448'].")
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else:
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res = paddle.empty([token_nums, q_num_heads * head_dims], dtype=D_type).to(qkv.place)
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append_attention_with_output(
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qkv,
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cache_k,
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cache_v,
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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.batch_id_per_token,
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forward_meta.cu_seqlens_q,
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metadata.block_tables,
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forward_meta.encoder_batch_ids,
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forward_meta.encoder_tile_ids_per_batch,
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forward_meta.encoder_num_blocks_x_cpu,
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forward_meta.kv_batch_ids,
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forward_meta.kv_tile_ids_per_batch,
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forward_meta.kv_num_blocks_x_cpu,
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forward_meta.decoder_batch_ids,
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forward_meta.decoder_tile_ids_per_batch,
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forward_meta.decoder_num_blocks_cpu,
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forward_meta.max_len_tensor_cpu,
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forward_meta.max_len_kv_cpu,
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res,
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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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cache_k_scales,
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cache_v_scales,
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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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forward_meta.attn_mask_offsets,
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metadata.kv_signal_data_list[layer.layer_id],
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getattr(layer, "q_norm_weight", None),
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getattr(layer, "k_norm_weight", None),
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getattr(layer, "rms_norm_eps", 1e-6),
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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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self.encoder_block_shape_q,
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self.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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)
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else:
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res = append_attention(
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qkv,
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cache_k,
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cache_v,
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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.batch_id_per_token,
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forward_meta.cu_seqlens_q,
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metadata.block_tables,
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forward_meta.encoder_batch_ids,
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forward_meta.encoder_tile_ids_per_batch,
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forward_meta.encoder_num_blocks_x_cpu,
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forward_meta.kv_batch_ids,
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forward_meta.kv_tile_ids_per_batch,
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forward_meta.kv_num_blocks_x_cpu,
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forward_meta.decoder_batch_ids,
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forward_meta.decoder_tile_ids_per_batch,
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forward_meta.decoder_num_blocks_cpu,
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forward_meta.max_len_tensor_cpu,
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forward_meta.max_len_kv_cpu,
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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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cache_k_scales,
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cache_v_scales,
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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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forward_meta.attn_mask_offsets,
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metadata.kv_signal_data_list[layer.layer_id],
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getattr(layer, "q_norm_weight", None),
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getattr(layer, "k_norm_weight", None),
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getattr(layer, "rms_norm_eps", 1e-6),
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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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self.encoder_block_shape_q,
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self.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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)
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return res
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