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503 lines
18 KiB
Python
503 lines
18 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 math
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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 paddle.nn.functional.flash_attention import flash_attn_unpadded
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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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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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from fastdeploy.platforms import current_platform
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if current_platform.is_cuda():
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from fastdeploy.model_executor.ops.gpu import (
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decode_mla_write_cache,
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multi_head_latent_attention,
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prefill_mla_write_cache,
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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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def yarn_get_mscale(scale=1, mscale=1):
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""" """
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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@dataclass
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class MLAAttentionMetadata(AttentionMetadata):
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"""
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MLAAttentionMetadata for Multi-Layer Attention
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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: 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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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 MLAAttentionBackend(AttentionBackend):
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"""
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MLA Attention Backend implementation.
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"""
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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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) -> None:
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"""
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MLAAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: MLAAttentionMetadata = None
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# 基础配置
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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 = (
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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)
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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.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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# For Multi Head Latent Attention
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self.kv_lora_rank: int = fd_config.model_config.kv_lora_rank
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self.qk_rope_head_dim: int = fd_config.model_config.qk_rope_head_dim
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self.qk_head_dim: int = fd_config.model_config.qk_nope_head_dim + fd_config.model_config.qk_rope_head_dim
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self.attn_softmax_scale: float = self.qk_head_dim**-0.5
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if fd_config.model_config.rope_scaling:
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mscale_all_dim = fd_config.model_config.rope_scaling.get("mscale_all_dim", False) # 1.0
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scaling_factor = fd_config.model_config.rope_scaling["factor"] # 40
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mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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self.attn_softmax_scale = self.attn_softmax_scale * mscale * mscale
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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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self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
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self.rank, self.device_id = init_rank_and_device_id(fd_config)
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def init_attention_metadata(self, forward_meta: ForwardMeta):
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"""Initialize attention metadata hence all layers in the forward pass can reuse it."""
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metadata = MLAAttentionMetadata()
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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 = 32768
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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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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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# MLA
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metadata.max_enc_len_this_time = metadata.set_max_lengths[1]
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metadata.max_dec_len_this_time = metadata.set_max_lengths[2]
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forward_meta.max_enc_len_this_time = metadata.set_max_lengths[1]
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forward_meta.max_dec_len_this_time = metadata.set_max_lengths[2]
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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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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(self, max_num_blocks: int) -> Tuple[int, int, int, int]:
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"""
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Calculate kv cache shape for MLA
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"""
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return (
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max_num_blocks,
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1,
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self.block_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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)
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def forward_extend(
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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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Prefill阶段的前向传播
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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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latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
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# 写入缓存
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prefill_mla_write_cache(
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compressed_kv,
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k_pe,
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latent_cache,
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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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forward_meta.cu_seqlens_q,
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metadata.block_tables,
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"none",
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getattr(forward_meta, "max_input_length", -1),
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)
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# Flash注意力计算
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fmha_out = flash_attn_unpadded(
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q,
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k,
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v,
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forward_meta.cu_seqlens_q,
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forward_meta.cu_seqlens_k,
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metadata.max_enc_len_this_time,
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metadata.max_enc_len_this_time,
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self.attn_softmax_scale,
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causal=True,
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training=False,
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)[0]
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return fmha_out
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def forward_decode(
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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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Decode阶段的前向传播
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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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latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
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# 获取推测解码参数
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speculate_decoder = self.speculative_method is not None
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speculate_max_tokens = self.speculate_max_draft_token_num
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# 写入缓存
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decode_mla_write_cache(
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compressed_kv,
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k_pe,
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latent_cache,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_encoder,
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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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"none",
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self.max_seq_len,
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speculate_decoder,
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)
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# 多头潜在注意力计算
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fmha_out = multi_head_latent_attention(
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q,
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latent_cache,
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latent_cache,
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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.cu_seqlens_q,
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forward_meta.batch_id_per_token,
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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,
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forward_meta.decoder_tile_ids_per_batch,
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metadata.decoder_num_blocks,
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metadata.decoder_num_blocks, # PaddleNLP 传入的是 decoder_num_blocks_cpu
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metadata.max_enc_len_this_time,
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metadata.max_dec_len_this_time,
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metadata.max_len_kv,
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None, # attn_mask
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None, # qkv_bias
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None, # qkv_out_scales
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None, # cache_k_quant_scales
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None, # cache_v_quant_scales
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None, # cache_k_dequant_scales
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None, # cache_v_dequant_scales
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None, # cache_k_zp
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None, # cache_v_zp
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None, # out_shifts
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None, # out_smooths
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metadata._fuse_kernel_compute_dtype,
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"none", # cache_quant_type
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self.kv_lora_rank,
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self.max_seq_len,
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self.attn_softmax_scale,
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0.0, # quant_max_bound
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0.0, # quant_min_bound
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0.0, # out_linear_in_scale
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speculate_max_tokens,
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True, # causal
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speculate_decoder,
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)
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return fmha_out
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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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Mixed模式的前向传播
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"""
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metadata = self.attention_metadata
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speculate_decoder = self.speculative_method is not None
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speculate_max_tokens = self.speculate_max_draft_token_num
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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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latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
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if k is not None:
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prefill_mla_write_cache(
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compressed_kv,
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k_pe,
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latent_cache,
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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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forward_meta.cu_seqlens_q,
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metadata.block_tables,
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"none",
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self.max_seq_len,
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)
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# FA
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fmha_out = flash_attn_unpadded(
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q,
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k,
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v,
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forward_meta.cu_seqlens_q,
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forward_meta.cu_seqlens_k,
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metadata.max_enc_len_this_time,
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metadata.max_enc_len_this_time,
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self.attn_softmax_scale,
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causal=True,
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training=False,
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)[0]
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return fmha_out
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# Decode
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if k is None:
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decode_mla_write_cache(
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compressed_kv,
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k_pe,
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latent_cache,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_encoder,
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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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"none",
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self.max_seq_len,
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speculate_decoder,
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)
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# 多头潜在注意力计算
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fmha_out = multi_head_latent_attention(
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q,
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latent_cache,
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latent_cache,
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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.cu_seqlens_q,
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forward_meta.batch_id_per_token,
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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,
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forward_meta.decoder_tile_ids_per_batch,
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metadata.decoder_num_blocks,
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metadata.decoder_num_blocks, # PaddleNLP 传入的是 decoder_num_blocks_cpu
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metadata.max_enc_len_this_time,
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metadata.max_dec_len_this_time,
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metadata.max_len_kv,
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None, # attn_mask
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None, # qkv_bias
|
|
None, # qkv_out_scales
|
|
None, # cache_k_quant_scales
|
|
None, # cache_v_quant_scales
|
|
None, # cache_k_dequant_scales
|
|
None, # cache_v_dequant_scales
|
|
None, # cache_k_zp
|
|
None, # cache_v_zp
|
|
None, # out_shifts
|
|
None, # out_smooths
|
|
metadata._fuse_kernel_compute_dtype,
|
|
"none", # cache_quant_type
|
|
self.kv_lora_rank,
|
|
self.max_seq_len,
|
|
self.attn_softmax_scale,
|
|
0.0, # quant_max_bound
|
|
0.0, # quant_min_bound
|
|
0.0, # out_linear_in_scale
|
|
speculate_max_tokens,
|
|
True, # causal
|
|
speculate_decoder,
|
|
)
|
|
|
|
return fmha_out
|