mirror of
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452 lines
20 KiB
Python
452 lines
20 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
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from math import sqrt
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from typing import TYPE_CHECKING, Optional
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import paddle
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from paddle.nn.functional.flash_attention import flash_attn_unpadded
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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.ops.iluvatar import paged_attention
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if TYPE_CHECKING:
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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@dataclass
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class IluvatarAttentionMetadata(AttentionMetadata):
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"""
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IluvatarAttentionMetadata
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"""
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# flash_attn metadata
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cu_seqlens_q: Optional[paddle.Tensor] = None
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cu_seqlens_k: Optional[paddle.Tensor] = None
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fixed_seed_offset: Optional[paddle.Tensor] = None
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attn_mask: Optional[paddle.Tensor] = None
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attn_mask_start_row_indices: Optional[paddle.Tensor] = None
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dropout: float = 0.0
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causal: bool = True
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return_softmax: bool = False
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rng_name: str = ""
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# paged_attn metadata
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block_tables: Optional[paddle.Tensor] = None
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seq_lens: Optional[paddle.Tensor] = None
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num_kv_heads: int = 1
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scale: float = 1.0
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block_size: int = 1
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max_context_len: int = 1
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alibi_slopes: Optional[paddle.Tensor] = None
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# causal: bool = True
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window_left: int = -1
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window_right: int = -1
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softcap: float = 0.0
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use_cuda_graph: bool = False
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use_sqrt_alibi: bool = False
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# qk[seq, h, d], cos/sin [seq, 1, d]
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def apply_rope(qk, cos, sin):
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rotate_half = paddle.reshape(
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paddle.stack([-qk[..., 1::2], qk[..., 0::2]], axis=-1),
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paddle.shape(qk),
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)
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out = paddle.add(paddle.multiply(qk, cos), paddle.multiply(rotate_half, sin))
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return paddle.cast(out, qk.dtype)
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class IluvatarAttnBackend(AttentionBackend):
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"""
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The backend class that uses paddle native attention implementation.
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Which is used only for testing purpose.
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"""
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def __init__(self, fd_config: FDConfig, kv_num_heads: int, num_heads: int, head_dim: int):
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super().__init__()
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self.attention_metadata = IluvatarAttentionMetadata()
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self.attention_metadata.block_size = fd_config.parallel_config.block_size
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assert (
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fd_config.parallel_config.enc_dec_block_num == 0
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), f"Iluvatar does not support yet, {fd_config.parallel_config.enc_dec_block_num}"
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assert self.attention_metadata.block_size == 16, "Iluvatar paged attn requires block_size must be 16."
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self.attention_metadata.max_context_len = fd_config.parallel_config.max_model_len
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self.attention_metadata.causal = getattr(fd_config.model_config, "causal", True)
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self.speculate_method = getattr(fd_config.parallel_config, "speculate_method", None)
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self.use_speculate = self.speculate_method is not None
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self.attention_metadata.num_kv_heads = kv_num_heads
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self.attention_metadata.dropout = fd_config.model_config.hidden_dropout_prob
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self.num_heads = num_heads
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self.total_num_heads = num_heads + 2 * kv_num_heads
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self.head_dim = head_dim
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self.hidden_dim = num_heads * head_dim
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self.total_hidden_dim = self.total_num_heads * head_dim
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# note: scale need to change if using MLA
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self.attention_metadata.scale = 1.0 / sqrt(head_dim)
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self.num_layers = fd_config.model_config.num_hidden_layers
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self.dtype = paddle.get_default_dtype()
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self.record_block_table_metadata = {}
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self.enable_fused_attention = int(os.getenv("FD_ILUVATAR_ENABLE_FUSED_ATTN", 1))
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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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self.prefill_info_dict = {}
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self.decode_info_dict = {}
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prefill_non_zeros_ids = forward_meta.seq_lens_this_time > 1
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decode_non_zeros_ids = forward_meta.seq_lens_this_time == 1
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self.prefill_info_dict["batch_ids"] = paddle.where(prefill_non_zeros_ids)[0]
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self.decode_info_dict["batch_ids"] = paddle.where(decode_non_zeros_ids)[0]
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self.prefill_len = len(self.prefill_info_dict["batch_ids"])
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self.decode_len = len(self.decode_info_dict["batch_ids"])
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# only prefill
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if self.decode_len == 0:
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cu_seq_ids = list(range(self.prefill_len + 1))
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self.prefill_info_dict["cu_seqlens_q"] = forward_meta.cu_seqlens_q[cu_seq_ids]
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# only decode
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elif self.prefill_len == 0:
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pass
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# both prefill and decode
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else:
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prefill_num_tokens = paddle.sum(forward_meta.seq_lens_this_time[prefill_non_zeros_ids])
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decode_num_tokens = paddle.sum(forward_meta.seq_lens_this_time[decode_non_zeros_ids])
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self.prefill_info_dict["cu_seqlens_q"] = paddle.zeros(
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[self.prefill_len + 1], dtype=forward_meta.cu_seqlens_q.dtype
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)
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self.prefill_info_dict["cu_seqlens_q"][1:] = forward_meta.seq_lens_encoder[
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self.prefill_info_dict["batch_ids"], 0
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]
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self.prefill_info_dict["cu_seqlens_q"] = paddle.cumsum(self.prefill_info_dict["cu_seqlens_q"])
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self.prefill_qkv = paddle.zeros([prefill_num_tokens, self.total_hidden_dim], dtype=self.dtype)
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self.decode_qkv = paddle.zeros([decode_num_tokens, self.total_hidden_dim], dtype=self.dtype)
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self.merged_output = paddle.zeros(
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[prefill_num_tokens + decode_num_tokens, self.num_heads, self.head_dim], dtype=self.dtype
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)
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prefill_start, decode_start, start = 0, 0, 0
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non_zeros_ids = forward_meta.seq_lens_this_time != 0
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non_zeros_seq_lens = forward_meta.seq_lens_this_time[non_zeros_ids]
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end = non_zeros_seq_lens[0]
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if end > 1:
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last_stage = "prefill"
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prefill_end = end
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decode_end = 0
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else:
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last_stage = "decode"
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prefill_end = 0
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decode_end = end
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self.prefill_info_dict["id_group"] = []
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self.prefill_info_dict["reverse_id_group"] = []
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self.decode_info_dict["id_group"] = []
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self.decode_info_dict["reverse_id_group"] = []
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self.record_stages = []
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for seq_len in non_zeros_seq_lens[1:]:
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if seq_len > 1:
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if last_stage == "decode":
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self.record_stages.append((last_stage, len(self.decode_info_dict["id_group"])))
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self.decode_info_dict["id_group"].append((decode_start, decode_end))
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self.decode_info_dict["reverse_id_group"].append((start, end))
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decode_start = decode_end
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start = end
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last_stage = "prefill"
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prefill_end += seq_len
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end += seq_len
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else:
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if last_stage == "prefill":
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self.record_stages.append((last_stage, len(self.prefill_info_dict["id_group"])))
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self.prefill_info_dict["id_group"].append((prefill_start, prefill_end))
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self.prefill_info_dict["reverse_id_group"].append((start, end))
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prefill_start = prefill_end
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start = end
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last_stage = "decode"
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decode_end += seq_len
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end += seq_len
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if prefill_start < prefill_end:
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self.record_stages.append(("prefill", len(self.prefill_info_dict["id_group"])))
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self.prefill_info_dict["id_group"].append((prefill_start, prefill_end))
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self.prefill_info_dict["reverse_id_group"].append((start, end))
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if decode_start < decode_end:
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self.record_stages.append(("decode", len(self.decode_info_dict["id_group"])))
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self.decode_info_dict["id_group"].append((decode_start, decode_end))
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self.decode_info_dict["reverse_id_group"].append((start, end))
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def get_attntion_meta(self):
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"""get_attntion_meta"""
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return self.attention_metadata
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def get_kv_cache_shape(
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self,
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max_num_blocks: int,
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kv_cache_quant_type: str = None,
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):
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"""
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Caculate kv cache shape
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"""
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return (
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max_num_blocks,
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self.attention_metadata.num_kv_heads,
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self.attention_metadata.block_size,
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self.head_dim,
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)
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def prefill_update_kv_cache(
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self, k, v, k_cache_id: int, v_cache_id: int, layer_id: int, forward_meta: ForwardMeta, prefill_batch_ids: list
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):
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# [num_tokens, num_kv_heads, head_dim] -> [num_kv_heads, num_tokens, head_dim]
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trans_k = k.transpose([1, 0, 2]).contiguous()
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trans_v = v.transpose([1, 0, 2]).contiguous()
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tensor_start = 0
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for batch_idx in prefill_batch_ids:
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seq_len = forward_meta.seq_lens_this_time[batch_idx]
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tensor_end = tensor_start + seq_len
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slice_trans_k = trans_k[:, tensor_start:tensor_end, :]
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slice_trans_v = trans_v[:, tensor_start:tensor_end, :]
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cur_block_tables = forward_meta.block_tables[batch_idx]
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cur_used_block_tables = cur_block_tables[cur_block_tables != -1]
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cache_start = 0
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cur_used_num_blocks = cur_used_block_tables.shape[0]
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for i, block_id in enumerate(cur_used_block_tables):
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# last block: seq_len - cache_start <= block_size
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if i == cur_used_num_blocks - 1:
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cache_end = seq_len - cache_start
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assert cache_end <= self.attention_metadata.block_size
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paddle.assign(
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slice_trans_k[:, cache_start:seq_len, :],
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output=forward_meta.caches[k_cache_id][block_id, :, 0:cache_end, :],
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)
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paddle.assign(
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slice_trans_v[:, cache_start:seq_len, :],
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output=forward_meta.caches[v_cache_id][block_id, :, 0:cache_end, :],
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)
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if layer_id == self.num_layers - 1:
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self.record_block_table_metadata[batch_idx] = {
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"block_id": block_id.item(),
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"cache_end": cache_end.item(),
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}
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# non last block: seq_lens_this_time > block_size
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else:
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assert seq_len > self.attention_metadata.block_size
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cache_end = cache_start + self.attention_metadata.block_size
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paddle.assign(
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slice_trans_k[:, cache_start:cache_end, :], output=forward_meta.caches[k_cache_id][block_id]
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)
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paddle.assign(
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slice_trans_v[:, cache_start:cache_end, :], output=forward_meta.caches[v_cache_id][block_id]
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)
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cache_start += self.attention_metadata.block_size
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tensor_start = tensor_end
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def get_splited_qkv(
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self, qkv: paddle.Tensor, forward_meta: ForwardMeta, cu_seqlens_q: paddle.Tensor, batch_ids=None
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):
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q_end = self.hidden_dim
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k_end = q_end + self.attention_metadata.num_kv_heads * self.head_dim
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v_end = k_end + self.attention_metadata.num_kv_heads * self.head_dim
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assert v_end == qkv.shape[-1], f"Shape mismatch: {v_end} vs {qkv.shape[-1]}"
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assert qkv.shape[0] == cu_seqlens_q[-1], f"Shape mismatch: {qkv.shape[0]} vs {cu_seqlens_q[-1]}"
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if batch_ids is None:
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batch_ids = list(range(forward_meta.seq_lens_this_time.shape[0]))
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q = qkv[..., 0:q_end]
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k = qkv[..., q_end:k_end]
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v = qkv[..., k_end:v_end]
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q = q.view([-1, self.num_heads, self.head_dim])
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k = k.view([-1, self.attention_metadata.num_kv_heads, self.head_dim])
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v = v.view([-1, self.attention_metadata.num_kv_heads, self.head_dim])
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for idx in range(len(cu_seqlens_q) - 1):
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batch_idx = batch_ids[idx]
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seq_len_i = forward_meta.seq_lens_this_time[batch_idx]
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if seq_len_i == 0:
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continue
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cached_kv_len = forward_meta.seq_lens_decoder[batch_idx][0]
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cu_seq_start_q = cu_seqlens_q[idx]
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cu_seq_end_q = cu_seqlens_q[idx + 1]
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# forward_meta.rotary_embs is [2, 1, S, 1, D]
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if forward_meta.rotary_embs is not None:
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cos = forward_meta.rotary_embs[0, 0, cached_kv_len : cached_kv_len + seq_len_i, :, :]
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sin = forward_meta.rotary_embs[1, 0, cached_kv_len : cached_kv_len + seq_len_i, :, :]
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q[cu_seq_start_q:cu_seq_end_q] = apply_rope(q[cu_seq_start_q:cu_seq_end_q], cos, sin)
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k[cu_seq_start_q:cu_seq_end_q] = apply_rope(k[cu_seq_start_q:cu_seq_end_q], cos, sin)
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return q, k, v
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def split_pd_qkv(self, qkv):
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for ids, reverse_ids in zip(self.prefill_info_dict["id_group"], self.prefill_info_dict["reverse_id_group"]):
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self.prefill_qkv[ids[0] : ids[1], :] = qkv[reverse_ids[0] : reverse_ids[1], :]
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for ids, reverse_ids in zip(self.decode_info_dict["id_group"], self.decode_info_dict["reverse_id_group"]):
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self.decode_qkv[ids[0] : ids[1], :] = qkv[reverse_ids[0] : reverse_ids[1], :]
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return self.prefill_qkv, self.decode_qkv
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def merge_pd_output(self, prefill_out, decode_out):
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for stage, idx in self.record_stages:
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if stage == "prefill":
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ids = self.prefill_info_dict["id_group"][idx]
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reverse_ids = self.prefill_info_dict["reverse_id_group"][idx]
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self.merged_output[reverse_ids[0] : reverse_ids[1], :, :] = prefill_out[ids[0] : ids[1], :, :]
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else:
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ids = self.decode_info_dict["id_group"][idx]
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reverse_ids = self.decode_info_dict["reverse_id_group"][idx]
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self.merged_output[reverse_ids[0] : reverse_ids[1], :, :] = decode_out[ids[0] : ids[1], :, :]
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return self.merged_output
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def forward_prefill(self, prefill_qkv, layer_id, k_cache_id, v_cache_id, forward_meta: ForwardMeta):
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prefill_q, prefill_k, prefill_v = self.get_splited_qkv(
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prefill_qkv,
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forward_meta,
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self.prefill_info_dict["cu_seqlens_q"],
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batch_ids=self.prefill_info_dict["batch_ids"],
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)
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prefill_out = flash_attn_unpadded(
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prefill_q,
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prefill_k,
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prefill_v,
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cu_seqlens_q=self.prefill_info_dict["cu_seqlens_q"],
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cu_seqlens_k=self.prefill_info_dict["cu_seqlens_q"],
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max_seqlen_q=self.attention_metadata.max_context_len,
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max_seqlen_k=self.attention_metadata.max_context_len,
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scale=self.attention_metadata.scale,
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dropout=self.attention_metadata.dropout,
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causal=self.attention_metadata.causal,
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return_softmax=self.attention_metadata.return_softmax,
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)[0]
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self.prefill_update_kv_cache(
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prefill_k, prefill_v, k_cache_id, v_cache_id, layer_id, forward_meta, self.prefill_info_dict["batch_ids"]
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)
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return prefill_out
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def forward_decode(self, decode_qkv, k_cache_id, v_cache_id, forward_meta: ForwardMeta):
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k_cache = forward_meta.caches[k_cache_id]
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v_cache = forward_meta.caches[v_cache_id]
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if self.enable_fused_attention:
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rope_cos = forward_meta.rotary_embs[0, 0, :, :, :]
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rope_sin = forward_meta.rotary_embs[1, 0, :, :, :]
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decode_out = paged_attention(
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decode_qkv.view([-1, self.total_num_heads, self.head_dim]),
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k_cache,
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v_cache,
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block_tables=forward_meta.block_tables[self.decode_info_dict["batch_ids"], :],
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seq_lens=forward_meta.seq_lens_decoder[self.decode_info_dict["batch_ids"], 0] + 1,
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num_kv_heads=self.attention_metadata.num_kv_heads,
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scale=self.attention_metadata.scale,
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block_size=self.attention_metadata.block_size,
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max_context_len=self.attention_metadata.max_context_len,
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alibi_slopes=self.attention_metadata.alibi_slopes,
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causal=self.attention_metadata.causal,
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window_left=self.attention_metadata.window_left,
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window_right=self.attention_metadata.window_right,
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softcap=self.attention_metadata.softcap,
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use_cuda_graph=self.attention_metadata.use_cuda_graph,
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use_sqrt_alibi=self.attention_metadata.use_sqrt_alibi,
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merged_qkv=True,
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k=decode_qkv,
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v=decode_qkv,
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rope_sin=rope_sin,
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rope_cos=rope_cos,
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)
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else:
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decode_q, decode_k, decode_v = self.get_splited_qkv(
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decode_qkv,
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forward_meta,
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self.decode_info_dict["cu_seqlens_q"],
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batch_ids=self.decode_info_dict["batch_ids"],
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)
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decode_out = paged_attention(
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decode_q,
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k_cache,
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v_cache,
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|
block_tables=forward_meta.block_tables[self.decode_info_dict["batch_ids"], :],
|
|
seq_lens=forward_meta.seq_lens_decoder[self.decode_info_dict["batch_ids"], 0] + 1,
|
|
num_kv_heads=self.attention_metadata.num_kv_heads,
|
|
scale=self.attention_metadata.scale,
|
|
block_size=self.attention_metadata.block_size,
|
|
max_context_len=self.attention_metadata.max_context_len,
|
|
alibi_slopes=self.attention_metadata.alibi_slopes,
|
|
causal=self.attention_metadata.causal,
|
|
window_left=self.attention_metadata.window_left,
|
|
window_right=self.attention_metadata.window_right,
|
|
softcap=self.attention_metadata.softcap,
|
|
use_cuda_graph=self.attention_metadata.use_cuda_graph,
|
|
use_sqrt_alibi=self.attention_metadata.use_sqrt_alibi,
|
|
k=decode_k,
|
|
v=decode_v,
|
|
)
|
|
|
|
return decode_out
|
|
|
|
def forward_mixed(
|
|
self,
|
|
q: paddle.Tensor,
|
|
k: paddle.Tensor,
|
|
v: paddle.Tensor,
|
|
qkv: paddle.Tensor,
|
|
compressed_kv: paddle.Tensor,
|
|
k_pe: paddle.Tensor,
|
|
layer: Attention,
|
|
forward_meta: ForwardMeta,
|
|
):
|
|
"""
|
|
forward_mixed
|
|
"""
|
|
assert not self.use_speculate, "IluvatarAttnBackend cannot support speculate now"
|
|
layer_id = layer.layer_id
|
|
k_cache_id = layer_id * 2
|
|
v_cache_id = k_cache_id + 1
|
|
q_dim = qkv.dim()
|
|
assert q_dim == 2
|
|
|
|
if self.decode_len == 0:
|
|
output = self.forward_prefill(qkv, layer_id, k_cache_id, v_cache_id, forward_meta)
|
|
|
|
elif self.prefill_len == 0:
|
|
output = self.forward_decode(qkv, k_cache_id, v_cache_id, forward_meta)
|
|
else:
|
|
prefill_qkv, decode_qkv = self.split_pd_qkv(qkv)
|
|
prefill_output = self.forward_prefill(prefill_qkv, layer_id, k_cache_id, v_cache_id, forward_meta)
|
|
decode_output = self.forward_decode(decode_qkv, k_cache_id, v_cache_id, forward_meta)
|
|
output = self.merge_pd_output(prefill_output, decode_output)
|
|
|
|
output = output.view([-1, self.num_heads * self.head_dim])
|
|
return output
|