forked from EngineX-Hygon/enginex-hygon-vllm
init src 0.9.2
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449
vllm/v1/attention/backends/triton_attn.py
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449
vllm/v1/attention/backends/triton_attn.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Attention layer with PagedAttention and Triton prefix prefill."""
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, ClassVar, Optional
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import torch
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from vllm import _custom_ops as ops
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from vllm import envs
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata, AttentionType)
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from vllm.attention.ops.chunked_prefill_paged_decode import (
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chunked_prefill_paged_decode)
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from vllm.attention.ops.paged_attn import PagedAttention
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from vllm.attention.ops.triton_unified_attention import unified_attention
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.attention.backends.utils import (
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AttentionMetadataBuilder, CommonAttentionMetadata,
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make_local_attention_virtual_batches)
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm.v1.worker.block_table import BlockTable
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if TYPE_CHECKING:
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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logger = init_logger(__name__)
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@dataclass
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class TritonAttentionMetadata:
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# NOTE(sang): Definition of context_len, query_len, and seq_len.
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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num_actual_tokens: int # Number of tokens excluding padding.
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max_query_len: int
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query_start_loc: torch.Tensor
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max_seq_len: int
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seq_lens: torch.Tensor
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block_table: torch.Tensor
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slot_mapping: torch.Tensor
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# For cascade attention.
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use_cascade: bool
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common_prefix_len: int
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cu_prefix_query_lens: Optional[torch.Tensor]
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prefix_kv_lens: Optional[torch.Tensor]
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suffix_kv_lens: Optional[torch.Tensor]
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# Optional aot scheduling
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scheduler_metadata: Optional[torch.Tensor] = None
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prefix_scheduler_metadata: Optional[torch.Tensor] = None
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# for local attention
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@dataclass
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class LocalAttentionMetadata:
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local_query_start_loc: torch.Tensor
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local_seqused_k: torch.Tensor
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local_block_table: torch.Tensor
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local_max_query_len: int
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local_max_seq_len: int
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local_scheduler_metadata: Optional[torch.Tensor]
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local_attn_metadata: Optional[LocalAttentionMetadata] = None
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class TritonAttentionMetadataBuilder(
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AttentionMetadataBuilder[TritonAttentionMetadata]):
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full_cudagraph_supported: ClassVar[bool] = True
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def __init__(self, runner: "GPUModelRunner", kv_cache_spec: AttentionSpec,
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block_table: BlockTable):
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self.runner = runner
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self.block_size = kv_cache_spec.block_size
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self.kv_cache_spec = kv_cache_spec
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self.block_table = block_table
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def build_for_cudagraph_capture(
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self, common_attn_metadata: CommonAttentionMetadata
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) -> TritonAttentionMetadata:
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attn_metadata = self.build(0, common_attn_metadata)
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# When doing full graph capture, setting seq_lens to
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# max_model_len will cause graph capture to be extremely
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# slow, so here we set it to 1.
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attn_metadata.seq_lens.fill_(1)
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return attn_metadata
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def build(
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self, common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata
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) -> TritonAttentionMetadata:
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num_reqs = common_attn_metadata.num_reqs
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num_actual_tokens = common_attn_metadata.num_actual_tokens
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max_query_len = common_attn_metadata.max_query_len
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max_seq_len = int(self.runner.seq_lens_np[:num_reqs].max())
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query_start_loc = common_attn_metadata.query_start_loc
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seq_lens = common_attn_metadata.seq_lens
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block_table = self.block_table
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block_table_tensor = block_table.get_device_tensor()[:num_reqs]
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block_table.slot_mapping[:num_actual_tokens].copy_(
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block_table.slot_mapping_cpu[:num_actual_tokens],
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non_blocking=True)
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# Fill unused with -1. Needed for reshape_and_cache in full cuda graph
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# mode.
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block_table.slot_mapping[num_actual_tokens:].fill_(-1)
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slot_mapping = block_table.slot_mapping[:num_actual_tokens]
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# for local attention
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local_attn_metadata = None
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if self.runner.attention_chunk_size is not None:
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seqlens_q_local_np, virt_q_cu_seqlens_np, virt_k_seqlens_np, \
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virt_block_table_tensor = make_local_attention_virtual_batches(
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self.runner.attention_chunk_size,
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self.runner.query_start_loc_np[:num_reqs + 1],
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self.runner.seq_lens_np[:num_reqs],
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block_table_tensor,
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self.block_size,
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)
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local_query_start_loc = torch.from_numpy(virt_q_cu_seqlens_np).to(
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self.runner.device, non_blocking=True)
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local_seqused_k = torch.from_numpy(virt_k_seqlens_np).to(
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self.runner.device, non_blocking=True)
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local_max_query_len = seqlens_q_local_np.max()
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local_max_seq_len = virt_k_seqlens_np.max()
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local_attn_metadata = TritonAttentionMetadata \
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.LocalAttentionMetadata(
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local_query_start_loc=local_query_start_loc,
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local_seqused_k=local_seqused_k,
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local_block_table=virt_block_table_tensor,
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local_max_query_len=local_max_query_len,
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local_max_seq_len=local_max_seq_len,
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local_scheduler_metadata=None,
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)
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use_cascade = common_prefix_len > 0
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if use_cascade:
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cu_prefix_query_lens = torch.tensor([0, num_actual_tokens],
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dtype=torch.int32,
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device=self.runner.device)
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prefix_kv_lens = torch.tensor([common_prefix_len],
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dtype=torch.int32,
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device=self.runner.device)
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suffix_kv_lens = (self.runner.seq_lens_np[:num_reqs] -
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common_prefix_len)
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suffix_kv_lens = torch.from_numpy(suffix_kv_lens).to(
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self.runner.device)
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else:
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cu_prefix_query_lens = None
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prefix_kv_lens = None
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suffix_kv_lens = None
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prefix_scheduler_metadata = None
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attn_metadata = TritonAttentionMetadata(
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num_actual_tokens=num_actual_tokens,
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max_query_len=max_query_len,
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query_start_loc=query_start_loc,
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max_seq_len=max_seq_len,
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seq_lens=seq_lens,
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block_table=block_table_tensor,
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slot_mapping=slot_mapping,
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use_cascade=use_cascade,
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common_prefix_len=common_prefix_len,
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cu_prefix_query_lens=cu_prefix_query_lens,
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prefix_kv_lens=prefix_kv_lens,
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suffix_kv_lens=suffix_kv_lens,
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local_attn_metadata=local_attn_metadata,
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prefix_scheduler_metadata=prefix_scheduler_metadata,
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)
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return attn_metadata
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def can_run_in_cudagraph(
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self, common_attn_metadata: CommonAttentionMetadata) -> bool:
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# Full CUDA Graph always supported
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return True
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class TritonAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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return [32, 64, 96, 128, 160, 192, 224, 256]
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@classmethod
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def validate_head_size(cls, head_size: int) -> None:
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supported_head_sizes = cls.get_supported_head_sizes()
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if head_size not in supported_head_sizes:
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attn_type = cls.__name__.removesuffix("Backend")
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raise ValueError(
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f"Head size {head_size} is not supported by {attn_type}. "
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f"Supported head sizes are: {supported_head_sizes}. "
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"Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
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"FlexAttention backend which supports all head sizes.")
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@staticmethod
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def get_name() -> str:
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return "TRITON_ATTN_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["TritonAttentionImpl"]:
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return TritonAttentionImpl
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return TritonAttentionMetadata
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> tuple[int, ...]:
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if block_size % 16 != 0:
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raise ValueError("Block size must be a multiple of 16.")
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return (2, num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def use_cascade_attention(*args, **kwargs) -> bool:
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return False
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@staticmethod
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def get_builder_cls() -> type["TritonAttentionMetadataBuilder"]:
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return TritonAttentionMetadataBuilder
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class TritonAttentionImpl(AttentionImpl):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[dict[str, Any]] = None,
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logits_soft_cap: Optional[float] = None,
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attn_type: AttentionType = AttentionType.DECODER,
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kv_sharing_target_layer_name: Optional[int] = None,
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use_irope: bool = False,
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) -> None:
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if blocksparse_params is not None:
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raise ValueError(
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"TritonAttention does not support block-sparse attention.")
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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if alibi_slopes is not None:
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alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
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self.alibi_slopes = alibi_slopes
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if sliding_window is None:
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self.sliding_window = (-1, -1)
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else:
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self.sliding_window = (sliding_window - 1, 0)
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self.kv_cache_dtype = kv_cache_dtype
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if logits_soft_cap is None:
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# In flash-attn, setting logits_soft_cap as 0 means no soft cap.
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logits_soft_cap = 0
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self.logits_soft_cap = logits_soft_cap
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self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
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self.use_irope = use_irope
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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TritonAttentionBackend.validate_head_size(head_size)
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if attn_type != AttentionType.DECODER:
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raise NotImplementedError("Encoder self-attention and "
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"encoder/decoder cross-attention "
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"are not implemented for "
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"TritonAttentionImpl")
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self.fp8_dtype = current_platform.fp8_dtype()
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self.force_prefill_decode_attn = \
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envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
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def forward(
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self,
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layer: torch.nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: FlashAttentionMetadata,
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output: Optional[torch.Tensor] = None,
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output_scale: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Forward pass with FlashAttention.
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Args:
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query: shape = [num_tokens, num_heads, head_size]
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key: shape = [num_tokens, num_kv_heads, head_size]
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value: shape = [num_tokens, num_kv_heads, head_size]
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kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size]
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attn_metadata: Metadata for attention.
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Returns:
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shape = [num_tokens, num_heads * head_size]
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"""
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assert output is not None, "Output tensor must be provided."
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if output_scale is not None:
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raise NotImplementedError(
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"fused output quantization is not yet supported"
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" for TritonAttentionImpl")
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if attn_metadata is None:
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# Profiling run.
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return output
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assert attn_metadata.use_cascade is False
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# IMPORTANT!
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# NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
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# eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
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# in this method. For example, `view` and `slice` (or `[:n]`) operations
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# are surprisingly slow even in the case they do not invoke any GPU ops.
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# Minimize the PyTorch ops in this method as much as possible.
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# Whenever making a change in this method, please benchmark the
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# performance to make sure it does not introduce any overhead.
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use_prefill_decode_attn = self.force_prefill_decode_attn
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num_actual_tokens = attn_metadata.num_actual_tokens
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if use_prefill_decode_attn:
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key_cache, value_cache = PagedAttention.split_kv_cache(
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kv_cache, self.num_kv_heads, self.head_size)
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else:
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key_cache, value_cache = kv_cache.unbind(0)
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if self.kv_sharing_target_layer_name is None:
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# Reshape the input keys and values and store them in the cache.
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# Skip this if sharing KV cache with an earlier attention layer.
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if use_prefill_decode_attn:
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PagedAttention.write_to_paged_cache(
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key,
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value,
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key_cache,
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value_cache,
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attn_metadata.slot_mapping,
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self.kv_cache_dtype,
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layer._k_scale,
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layer._v_scale,
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)
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else:
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torch.ops._C_cache_ops.reshape_and_cache_flash(
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key,
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value,
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key_cache,
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value_cache,
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attn_metadata.slot_mapping,
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self.kv_cache_dtype,
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layer._k_scale,
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layer._v_scale,
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)
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if self.kv_cache_dtype.startswith("fp8"):
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key_cache = key_cache.view(self.fp8_dtype)
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value_cache = value_cache.view(self.fp8_dtype)
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num_tokens, num_heads, head_size = query.shape
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assert layer._q_scale == 1.0, \
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"A non 1.0 q_scale is not currently supported."
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if not current_platform.is_rocm():
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# Skip Q quantization on ROCm, since dequantizing back to
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# f32 in the attention kernel is not supported.
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query, _ = ops.scaled_fp8_quant(
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query.reshape(
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(num_tokens, num_heads * head_size)).contiguous(),
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layer._q_scale)
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query = query.reshape((num_tokens, num_heads, head_size))
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use_local_attn = \
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(self.use_irope and attn_metadata.local_attn_metadata is not None)
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if use_local_attn:
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assert attn_metadata.local_attn_metadata is not None
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local_metadata = attn_metadata.local_attn_metadata
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cu_seqlens_q = local_metadata.local_query_start_loc
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seqused_k = local_metadata.local_seqused_k
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max_seqlen_q = local_metadata.local_max_query_len
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max_seqlen_k = local_metadata.local_max_seq_len
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block_table = local_metadata.local_block_table
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else:
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cu_seqlens_q = attn_metadata.query_start_loc
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seqused_k = attn_metadata.seq_lens
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max_seqlen_q = attn_metadata.max_query_len
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max_seqlen_k = attn_metadata.max_seq_len
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block_table = attn_metadata.block_table
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if use_prefill_decode_attn:
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# Compute attention and update output up to `num_actual_tokens`.
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chunked_prefill_paged_decode(query=query[:num_actual_tokens],
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key=key[:num_actual_tokens],
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value=value[:num_actual_tokens],
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output=output[:num_actual_tokens],
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kv_cache_dtype=self.kv_cache_dtype,
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key_cache=key_cache,
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value_cache=value_cache,
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block_table=block_table,
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query_start_loc=cu_seqlens_q,
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seq_lens=seqused_k,
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max_seq_len=max_seqlen_k,
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max_query_len=max_seqlen_q,
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k_scale=layer._k_scale,
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v_scale=layer._v_scale,
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alibi_slopes=self.alibi_slopes,
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sliding_window=self.sliding_window[0],
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sm_scale=self.scale)
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else:
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descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
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unified_attention(
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q=query[:num_actual_tokens],
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||||
k=key_cache,
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v=value_cache,
|
||||
out=output[:num_actual_tokens],
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||||
cu_seqlens_q=cu_seqlens_q,
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max_seqlen_q=max_seqlen_q,
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||||
seqused_k=seqused_k,
|
||||
max_seqlen_k=max_seqlen_k,
|
||||
softmax_scale=self.scale,
|
||||
causal=True,
|
||||
alibi_slopes=self.alibi_slopes,
|
||||
window_size=self.sliding_window,
|
||||
block_table=block_table,
|
||||
softcap=self.logits_soft_cap,
|
||||
q_descale=None, # Not supported
|
||||
k_descale=layer._k_scale.expand(descale_shape),
|
||||
v_descale=layer._v_scale.expand(descale_shape),
|
||||
)
|
||||
|
||||
return output
|
||||
Reference in New Issue
Block a user