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Adapt for iluvatar gpu (#2684)
This commit is contained in:
liddk1121
@@ -0,0 +1,613 @@
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"""
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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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import paddle
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from dataclasses import dataclass
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from typing import Optional
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from math import sqrt
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from paddle.nn.functional.flash_attention import flash_attn_unpadded
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from fastdeploy.model_executor.ops.iluvatar import paged_attention
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.attention.base_attention_backend import (
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AttentionBackend, AttentionMetadata)
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from fastdeploy.worker.forward_meta import ForwardMeta
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@dataclass
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class 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),
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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, llm_config: FDConfig, kv_num_heads: int, num_heads: int,
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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 = llm_config.parallel_config.block_size
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assert llm_config.parallel_config.enc_dec_block_num == 0, "Iluvatar does not support yet"
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self.attention_metadata.max_context_len = llm_config.parallel_config.max_model_len
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self.attention_metadata.causal = getattr(llm_config.model_config,
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"causal", True)
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self.speculate_method = getattr(llm_config.parallel_config,
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"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 = llm_config.model_config.hidden_dropout_prob
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self.num_heads = num_heads
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self.head_dim = 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 = llm_config.model_config.num_layers
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self.record_block_table_metadata = {}
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self.only_use_flash_attn = int(
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os.getenv("FD_ILUVATAR_ONLY_USE_FLASH_ATTN", 0)) == 1
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self.do_check_kv_cache = int(
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os.getenv("FD_ILUVATAR_CHECK_KV_CACHE_CORRECTNESS", 0)) == 1
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if not self.only_use_flash_attn:
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assert self.attention_metadata.block_size == 16, "Iluvatar paged attn requires block_size must be 16."
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if self.do_check_kv_cache:
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self.record_batched_k = [{} for _ in range(self.num_layers)]
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self.record_batched_v = [{} for _ in range(self.num_layers)]
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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.attention_metadata.block_tables = forward_meta.block_tables
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self.attention_metadata.attn_mask = forward_meta.attn_mask
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self.attention_metadata.seq_lens = forward_meta.seq_lens_decoder
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self.attention_metadata.cu_seqlens_q = forward_meta.cu_seqlens_q
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self.attention_metadata.cu_seqlens_k = forward_meta.cu_seqlens_k
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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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):
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"""
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Caculate kv cache shape
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"""
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return (max_num_blocks, self.attention_metadata.num_kv_heads,
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self.attention_metadata.block_size, self.head_dim)
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def get_new_kv(self,
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k,
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v,
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k_cache_id: int,
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v_cache_id: int,
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forward_meta: ForwardMeta,
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debug_paged_attn=False):
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new_k = []
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new_v = []
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tensor_start = 0
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for batch_idx in range(forward_meta.block_tables.shape[0]):
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seq_len = forward_meta.seq_lens_this_time[batch_idx]
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if seq_len == 0:
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continue
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tensor_end = tensor_start + seq_len
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slice_k = k[tensor_start:tensor_end, :, :]
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slice_v = v[tensor_start:tensor_end, :, :]
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if seq_len > 1:
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# prefill
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new_k.append(slice_k)
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new_v.append(slice_v)
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else:
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# decode
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assert seq_len == 1
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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 !=
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-1]
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assert batch_idx in self.record_block_table_metadata, \
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f"Key error: {batch_idx} vs {self.record_block_table_metadata}."
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cur_block_table_metadata = self.record_block_table_metadata[
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batch_idx]
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record_last_block_id = cur_block_table_metadata["block_id"]
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assert record_last_block_id != -1
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for block_id in cur_used_block_tables:
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if block_id == record_last_block_id:
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cache_end = cur_block_table_metadata["cache_end"]
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block_k_cache = forward_meta.caches[k_cache_id][
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block_id, :, 0:cache_end, :]
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block_v_cache = forward_meta.caches[v_cache_id][
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block_id, :, 0:cache_end, :]
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else:
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block_k_cache = forward_meta.caches[k_cache_id][
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block_id]
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block_v_cache = forward_meta.caches[v_cache_id][
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block_id]
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# [num_kv_heads, block_size, head_dim] -> [block_size, num_kv_heads, head_dim]
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new_k.append(
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block_k_cache.transpose([1, 0, 2]).contiguous())
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new_v.append(
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block_v_cache.transpose([1, 0, 2]).contiguous())
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if block_id == record_last_block_id:
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break
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# as line 301 show, record_block_table_metadata updates when executing the last layer,
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# so slice_k and slice_v has been updated in block_k_cache and block_v_cache
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if not (debug_paged_attn and
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(k_cache_id / 2 == self.num_layers - 1)):
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new_k.append(slice_k)
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new_v.append(slice_v)
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tensor_start = tensor_end
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if len(new_k) == 1:
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return new_k[0], new_v[0]
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else:
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new_k = paddle.concat(new_k, axis=0)
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new_v = paddle.concat(new_v, axis=0)
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return new_k, new_v
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def update_kv_cache(self,
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k,
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v,
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k_cache_id: int,
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v_cache_id: int,
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layer_id: int,
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forward_meta: ForwardMeta,
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specific_batch_ids=None,
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debug_paged_attn=False):
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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 range(forward_meta.block_tables.shape[0]):
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if specific_batch_ids is not None and batch_idx not in specific_batch_ids:
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continue
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seq_len = forward_meta.seq_lens_this_time[batch_idx]
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if seq_len == 0:
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continue
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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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# prefill
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if seq_len > 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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forward_meta.caches[k_cache_id][
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block_id, :,
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0:cache_end, :] = slice_trans_k[:, cache_start:
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seq_len, :]
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forward_meta.caches[v_cache_id][
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block_id, :,
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0:cache_end, :] = slice_trans_v[:, cache_start:
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seq_len, :]
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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
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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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forward_meta.caches[k_cache_id][
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block_id] = slice_trans_k[:,
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cache_start:cache_end, :]
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forward_meta.caches[v_cache_id][
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block_id] = slice_trans_v[:,
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cache_start:cache_end, :]
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cache_start += self.attention_metadata.block_size
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else:
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# decode
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assert seq_len == 1
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cur_last_block_id = cur_used_block_tables[-1].item()
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assert cur_last_block_id != -1
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assert batch_idx in self.record_block_table_metadata, \
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f"Key error: {batch_idx} vs {self.record_block_table_metadata}."
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cur_block_table_metadata = self.record_block_table_metadata[
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batch_idx]
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record_last_block_id = cur_block_table_metadata["block_id"]
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if cur_last_block_id == record_last_block_id:
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# not alloc new block in decode stage
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cache_start = cur_block_table_metadata["cache_end"]
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else:
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# alloc new block in decode stage
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cache_start = 0
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cache_end = cache_start + 1
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assert cache_end <= self.attention_metadata.block_size
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# paged attn API will update kv cache with inplace mode
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if not debug_paged_attn:
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forward_meta.caches[k_cache_id][
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cur_last_block_id, :,
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cache_start:cache_end, :] = slice_trans_k
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forward_meta.caches[v_cache_id][
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cur_last_block_id, :,
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cache_start:cache_end, :] = slice_trans_v
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# update record_block_table_metadata
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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"] = cur_last_block_id
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self.record_block_table_metadata[batch_idx][
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"cache_end"] = cache_end
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tensor_start = tensor_end
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def _check_new_kv_correctness(self, k, v, new_k, new_v, layer_id: int,
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forward_meta: ForwardMeta):
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tensor_start = 0
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for batch_idx, seq_lens_this_time in enumerate(
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forward_meta.seq_lens_this_time):
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if seq_lens_this_time == 0:
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continue
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# note: the second request will also use the batch_idx 0 instead of 1 in
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# the streaming inference mode, so use seq_lens_this_time > 1 with the same
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# batch_idx represents the second request comes.
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if seq_lens_this_time > 1 and batch_idx in self.record_batched_k[
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layer_id]:
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print(
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f"clear self.record_batched_batched_k: "
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f"layer_id={layer_id}, batch_id={batch_idx}, "
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f"record_lens={len(self.record_batched_k[layer_id][batch_idx])}"
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)
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self.record_batched_k[layer_id][batch_idx].clear()
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self.record_batched_v[layer_id][batch_idx].clear()
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tensor_end = tensor_start + seq_lens_this_time
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slice_k = k[tensor_start:tensor_end, :, :]
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slice_v = v[tensor_start:tensor_end, :, :]
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if batch_idx not in self.record_batched_k[layer_id]:
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self.record_batched_k[layer_id][batch_idx] = []
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self.record_batched_v[layer_id][batch_idx] = []
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self.record_batched_k[layer_id][batch_idx].append(slice_k)
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self.record_batched_v[layer_id][batch_idx].append(slice_v)
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tensor_start = tensor_end
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ref_k, ref_v = [], []
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for batch_idx, seq_lens_this_time in enumerate(
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forward_meta.seq_lens_this_time):
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if seq_lens_this_time == 0:
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continue
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bached_k_list = self.record_batched_k[layer_id][batch_idx]
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bached_v_list = self.record_batched_v[layer_id][batch_idx]
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ref_k.extend(bached_k_list)
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ref_v.extend(bached_v_list)
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ref_k = paddle.concat(ref_k, axis=0)
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ref_v = paddle.concat(ref_v, axis=0)
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print(
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f"_check_new_kv_correctness: layer_id={layer_id}, "
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f"k.shape={k.shape}, v.shape={v.shape}, "
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f"ref_k.shape={ref_k.shape}, ref_v.shape={ref_v.shape}, "
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f"new_k.shape={new_k.shape}, new_v.shape={new_v.shape}, "
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f"len(self.record_batched_k[layer_id])={len(self.record_batched_k[layer_id])}, "
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f"len(self.record_batched_k[layer_id][0])={len(self.record_batched_k[layer_id][0])}, "
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f"forward_meta.seq_lens_this_time={forward_meta.seq_lens_this_time}"
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f"ref_k[-2:, 0:2, 0:2]={ref_k[-2:, 0:2, 0:2]}, "
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f"ref_v[-2:, 0:2, 0:2]={ref_v[-2:, 0:2, 0:2]}, "
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f"new_k[-2:, 0:2, 0:2]={new_k[-2:, 0:2, 0:2]}, "
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f"new_v[-2:, 0:2, 0:2]={new_v[-2:, 0:2, 0:2]}")
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assert paddle.allclose(
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ref_k.to("cpu").to(paddle.float32),
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new_k.to("cpu").to(paddle.float32))
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assert paddle.allclose(
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ref_v.to("cpu").to(paddle.float32),
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new_v.to("cpu").to(paddle.float32))
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def get_splited_qkv(self, qkv: paddle.Tensor, forward_meta: ForwardMeta):
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q_end = self.num_heads * self.head_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[
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-1], f"Shape mistach: {v_end} vs {qkv.shape[-1]}"
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assert qkv.shape[0] == forward_meta.cu_seqlens_q[-1]
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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]).contiguous()
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k = k.view([-1, self.attention_metadata.num_kv_heads,
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self.head_dim]).contiguous()
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v = v.view([-1, self.attention_metadata.num_kv_heads,
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self.head_dim]).contiguous()
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# forward_meta.seq_lens_this_time [max_batch,]
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for batch_idx in range(forward_meta.seq_lens_this_time.shape[0]):
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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 = forward_meta.cu_seqlens_q[batch_idx]
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cu_seq_end_q = forward_meta.cu_seqlens_q[batch_idx + 1]
|
||||
# forward_meta.rotary_embs is [2, 1, S, 1, D]
|
||||
if forward_meta.rotary_embs is not None:
|
||||
cos = forward_meta.rotary_embs[0, 0,
|
||||
cached_kv_len:cached_kv_len +
|
||||
seq_len_i, :, :]
|
||||
sin = forward_meta.rotary_embs[1, 0,
|
||||
cached_kv_len:cached_kv_len +
|
||||
seq_len_i, :, :]
|
||||
q[cu_seq_start_q:cu_seq_end_q] = apply_rope(
|
||||
q[cu_seq_start_q:cu_seq_end_q], cos, sin)
|
||||
k[cu_seq_start_q:cu_seq_end_q] = apply_rope(
|
||||
k[cu_seq_start_q:cu_seq_end_q], cos, sin)
|
||||
|
||||
return q, k, v
|
||||
|
||||
def get_splited_info_by_stage(self, q, k, v, forward_meta: ForwardMeta):
|
||||
prefill_info_dict = {"q": [], "k": [], "v": [], "batch_ids": []}
|
||||
decode_info_dict = {"q": [], "k": [], "v": [], "batch_ids": []}
|
||||
tensor_start = 0
|
||||
for batch_idx, seq_lens_this_time in enumerate(
|
||||
forward_meta.seq_lens_this_time):
|
||||
if seq_lens_this_time == 0:
|
||||
continue
|
||||
tensor_end = tensor_start + seq_lens_this_time
|
||||
slice_q = q[tensor_start:tensor_end, :, :]
|
||||
slice_k = k[tensor_start:tensor_end, :, :]
|
||||
slice_v = v[tensor_start:tensor_end, :, :]
|
||||
if seq_lens_this_time > 1:
|
||||
prefill_info_dict["q"].append(slice_q)
|
||||
prefill_info_dict["k"].append(slice_k)
|
||||
prefill_info_dict["v"].append(slice_v)
|
||||
prefill_info_dict["batch_ids"].append(batch_idx)
|
||||
else:
|
||||
assert seq_lens_this_time == 1
|
||||
decode_info_dict["q"].append(slice_q)
|
||||
decode_info_dict["k"].append(slice_k)
|
||||
decode_info_dict["v"].append(slice_v)
|
||||
decode_info_dict["batch_ids"].append(batch_idx)
|
||||
tensor_start = tensor_end
|
||||
|
||||
if len(prefill_info_dict["batch_ids"]) > 0:
|
||||
prefill_info_dict["q"] = paddle.concat(prefill_info_dict["q"],
|
||||
axis=0)
|
||||
prefill_info_dict["k"] = paddle.concat(prefill_info_dict["k"],
|
||||
axis=0)
|
||||
prefill_info_dict["v"] = paddle.concat(prefill_info_dict["v"],
|
||||
axis=0)
|
||||
cu_seq_ids = list(
|
||||
map(lambda x: x + 1, prefill_info_dict["batch_ids"]))
|
||||
prefill_info_dict["cu_seq_ids"] = [0, *cu_seq_ids]
|
||||
|
||||
if len(decode_info_dict["batch_ids"]) > 0:
|
||||
decode_info_dict["q"] = paddle.concat(decode_info_dict["q"],
|
||||
axis=0)
|
||||
decode_info_dict["k"] = paddle.concat(decode_info_dict["k"],
|
||||
axis=0)
|
||||
decode_info_dict["v"] = paddle.concat(decode_info_dict["v"],
|
||||
axis=0)
|
||||
|
||||
return prefill_info_dict, decode_info_dict
|
||||
|
||||
def merge_output(self, prefill_out, decode_out, forward_meta: ForwardMeta):
|
||||
assert not (prefill_out is None and decode_out
|
||||
is None), "prefill and decode output cannot both be None"
|
||||
if prefill_out is None:
|
||||
return decode_out
|
||||
elif decode_out is None:
|
||||
return prefill_out
|
||||
else:
|
||||
merged_output = []
|
||||
prefill_tensor_start = 0
|
||||
decode_tensor_start = 0
|
||||
for seq_lens_this_time in forward_meta.seq_lens_this_time:
|
||||
if seq_lens_this_time == 0:
|
||||
continue
|
||||
if seq_lens_this_time > 1:
|
||||
tensor_end = prefill_tensor_start + seq_lens_this_time
|
||||
merged_output.append(
|
||||
prefill_out[prefill_tensor_start:tensor_end, :, :])
|
||||
prefill_tensor_start = tensor_end
|
||||
else:
|
||||
assert seq_lens_this_time == 1
|
||||
tensor_end = decode_tensor_start + seq_lens_this_time
|
||||
merged_output.append(
|
||||
decode_out[decode_tensor_start:tensor_end, :, :])
|
||||
decode_tensor_start = tensor_end
|
||||
|
||||
assert prefill_tensor_start == prefill_out.shape[0], \
|
||||
f"prefill merged unfinished: {prefill_tensor_start} vs {prefill_out.shape[0]}"
|
||||
assert decode_tensor_start == decode_out.shape[0], \
|
||||
f"decode merged unfinished: {decode_tensor_start} vs {decode_out.shape[0]}"
|
||||
merged_output = paddle.concat(merged_output, axis=0)
|
||||
return merged_output
|
||||
|
||||
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
|
||||
|
||||
assert qkv is not None
|
||||
q_dim = qkv.dim()
|
||||
q, k, v = self.get_splited_qkv(qkv, forward_meta)
|
||||
|
||||
if self.only_use_flash_attn:
|
||||
new_k, new_v = self.get_new_kv(k, v, k_cache_id, v_cache_id,
|
||||
forward_meta)
|
||||
if self.do_check_kv_cache:
|
||||
self._check_new_kv_correctness(k, v, new_k, new_v, layer_id,
|
||||
forward_meta)
|
||||
|
||||
out = flash_attn_unpadded(
|
||||
q,
|
||||
new_k,
|
||||
new_v,
|
||||
cu_seqlens_q=self.attention_metadata.cu_seqlens_q,
|
||||
cu_seqlens_k=self.attention_metadata.cu_seqlens_k,
|
||||
max_seqlen_q=self.attention_metadata.max_context_len,
|
||||
max_seqlen_k=self.attention_metadata.max_context_len,
|
||||
scale=self.attention_metadata.scale,
|
||||
dropout=self.attention_metadata.dropout,
|
||||
causal=self.attention_metadata.causal,
|
||||
return_softmax=self.attention_metadata.return_softmax)[0]
|
||||
|
||||
self.update_kv_cache(k, v, k_cache_id, v_cache_id, layer_id,
|
||||
forward_meta)
|
||||
else:
|
||||
prefill_info_dict, decode_info_dict = self.get_splited_info_by_stage(
|
||||
q, k, v, forward_meta)
|
||||
prefill_out, decode_out = None, None
|
||||
|
||||
if len(prefill_info_dict["batch_ids"]) > 0:
|
||||
prefill_out = flash_attn_unpadded(
|
||||
prefill_info_dict["q"],
|
||||
prefill_info_dict["k"],
|
||||
prefill_info_dict["v"],
|
||||
cu_seqlens_q=forward_meta.cu_seqlens_q[
|
||||
prefill_info_dict["cu_seq_ids"]],
|
||||
cu_seqlens_k=forward_meta.cu_seqlens_k[
|
||||
prefill_info_dict["cu_seq_ids"]],
|
||||
max_seqlen_q=self.attention_metadata.max_context_len,
|
||||
max_seqlen_k=self.attention_metadata.max_context_len,
|
||||
scale=self.attention_metadata.scale,
|
||||
dropout=self.attention_metadata.dropout,
|
||||
causal=self.attention_metadata.causal,
|
||||
return_softmax=self.attention_metadata.return_softmax)[0]
|
||||
self.update_kv_cache(
|
||||
prefill_info_dict["k"],
|
||||
prefill_info_dict["v"],
|
||||
k_cache_id,
|
||||
v_cache_id,
|
||||
layer_id,
|
||||
forward_meta,
|
||||
specific_batch_ids=prefill_info_dict['batch_ids'])
|
||||
|
||||
if len(decode_info_dict["batch_ids"]) > 0:
|
||||
k_cache = forward_meta.caches[k_cache_id]
|
||||
v_cache = forward_meta.caches[v_cache_id]
|
||||
|
||||
decode_out = paged_attention(
|
||||
decode_info_dict["q"],
|
||||
k_cache,
|
||||
v_cache,
|
||||
block_tables=forward_meta.block_tables[
|
||||
decode_info_dict["batch_ids"], :],
|
||||
seq_lens=forward_meta.seq_lens_decoder[
|
||||
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_info_dict["k"],
|
||||
v=decode_info_dict["v"])
|
||||
|
||||
if self.do_check_kv_cache:
|
||||
self.update_kv_cache(
|
||||
decode_info_dict['k'],
|
||||
decode_info_dict['v'],
|
||||
k_cache_id,
|
||||
v_cache_id,
|
||||
layer_id,
|
||||
forward_meta,
|
||||
specific_batch_ids=decode_info_dict['batch_ids'],
|
||||
debug_paged_attn=True)
|
||||
|
||||
if self.do_check_kv_cache:
|
||||
new_k, new_v = self.get_new_kv(k,
|
||||
v,
|
||||
k_cache_id,
|
||||
v_cache_id,
|
||||
forward_meta,
|
||||
debug_paged_attn=True)
|
||||
self._check_new_kv_correctness(k, v, new_k, new_v, layer_id,
|
||||
forward_meta)
|
||||
|
||||
out = self.merge_output(prefill_out, decode_out, forward_meta)
|
||||
|
||||
if q_dim == 2:
|
||||
out = out.view([-1, self.num_heads * self.head_dim])
|
||||
|
||||
return out
|
||||
Reference in New Issue
Block a user