mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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221 lines
8.0 KiB
Python
221 lines
8.0 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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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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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 XPUAttentionMetadata(AttentionMetadata):
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"""
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XPUAttentionMetadata
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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 XPUAttentionBackend(AttentionBackend):
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"""
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XPUAttentionBackend backend implementation.
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"""
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__infer_dynamic_dims_fields__ = ["attention_metadata"]
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attention_metadata: XPUAttentionMetadata
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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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):
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"""
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XPUAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: XPUAttentionMetadata = 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.model_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.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 = head_dim
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self.num_layers: int = fd_config.model_config.num_hidden_layers
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# pd_disaggregation
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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.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 attntion metadata hence all layers in the forward pass can reuse it."""
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metadata = XPUAttentionMetadata()
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metadata.max_partition_size = 32768
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metadata.encoder_max_partition_size = 32768
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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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# 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" and not forward_meta.is_profiling:
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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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) -> Tuple[list, list]:
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"""
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Calculate kv cache shape
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"""
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key_cache_shape = value_cache_shape = [max_num_blocks, self.kv_num_heads, self.block_size, self.head_dim]
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return key_cache_shape, value_cache_shape
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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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k_quant_scale = getattr(layer, "cache_k_scale", None)
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v_quant_scale = getattr(layer, "cache_v_scale", None)
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cache_k_out_scale = getattr(layer, "cache_k_out_scale", None)
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cache_v_out_scale = getattr(layer, "cache_v_out_scale", None)
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k_zp = getattr(self, "cache_k_zp", None)
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v_zp = getattr(self, "cache_v_zp", None)
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from fastdeploy.model_executor.ops.xpu import block_attn
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res = block_attn(
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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.cum_offsets,
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metadata.rotary_embs,
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metadata.block_tables,
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forward_meta.prefix_block_tables,
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forward_meta.len_info_cpu,
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forward_meta.encoder_seq_lod_cpu,
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forward_meta.decoder_seq_lod_cpu,
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forward_meta.encoder_kv_lod_cpu,
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forward_meta.encoder_batch_map_cpu,
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forward_meta.decoder_context_len_cpu,
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forward_meta.decoder_context_len_cache_cpu,
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forward_meta.decoder_batch_map_cpu,
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forward_meta.prefix_len_cpu,
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k_quant_scale,
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v_quant_scale,
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cache_k_out_scale,
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cache_v_out_scale,
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k_zp, # zero_point_quant_scale
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v_zp, # zero_point_quant_scale
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None, # shift
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None, # smooth
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metadata.kv_signal_data_list[layer.layer_id], # kv_signal_data
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forward_meta.kv_signal_sender, # kv_signal_sender
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layer.use_neox_rotary_style,
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self.rope_3d,
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)
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return res
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