658 lines
27 KiB
Python
658 lines
27 KiB
Python
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################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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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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################################################################################
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"""Attention layer with FlashAttention."""
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, ClassVar, Optional, Tuple
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import torch
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import torch_br
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionType,
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is_quantized_kv_cache)
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from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
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get_flash_attn_version)
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from vllm.config import VllmConfig
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from vllm.logger import logger
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from vllm.v1.attention.backends.flash_attn import _get_sliding_window_configs
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from vllm.v1.attention.backends.utils import (AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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get_kv_cache_layout,
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split_decodes_and_prefills)
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm_br.config.compilation import SUPAGraphMode
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if TYPE_CHECKING:
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pass
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# from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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class SUPAFlashAttentionBackend(AttentionBackend):
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# NOTE: When piecewise cudagraph is enabled, this
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# makes sure the output tensor is allocated inside the cudagraph.
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# NOTE: currently, we do not support accept_output_buffer=True
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accept_output_buffer: bool = False
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supports_quant_query_input: bool = True
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@classmethod
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def get_supported_dtypes(cls) -> list[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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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 "SUPAFLASH_ATTN_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["SUPAFlashAttentionImpl"]:
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return SUPAFlashAttentionImpl
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@staticmethod
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def get_metadata_cls() -> type["SUPAFlashAttentionMetadata"]:
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return SUPAFlashAttentionMetadata
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@staticmethod
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def get_builder_cls() -> type["SUPAFlashAttentionMetadataBuilder"]:
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return SUPAFlashAttentionMetadataBuilder
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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 get_kv_cache_usharp_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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th_gran = SUPAFlashAttentionBackend.get_kv_cache_usharp_alignment(
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block_size)
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n_block = max(1, (num_blocks + th_gran - 1) // th_gran)
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logger.debug(
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f'Origin kv cache shape is [2, {num_blocks}, {block_size}, {num_kv_heads}, {head_size}, For SUPA Speed up, use [2, {n_block}, {th_gran * block_size}, {num_kv_heads * head_size}]' # noqa: G004
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)
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return (2, n_block, th_gran * block_size, num_kv_heads * head_size)
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@staticmethod
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def get_kv_cache_usharp_alignment(block_size: int) -> int:
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max_h_limit = 2048
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return max_h_limit // block_size
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@staticmethod
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def get_kv_cache_stride_order() -> tuple[int, ...]:
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# `stride_order` indicates the permutation that gets
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# us from `get_kv_cache_shape` to the actual memory layout we want.
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cache_layout = get_kv_cache_layout()
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if cache_layout == "NHD":
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stride_order = (0, 1, 2, 3, 4)
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elif cache_layout == "HND":
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stride_order = (0, 1, 3, 2, 4)
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else:
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raise ValueError(f"Unknown cache layout format {cache_layout}.")
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return stride_order
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@staticmethod
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def get_fp8_dtype_for_flashattn(kv_cache_dtype: str) -> torch.dtype:
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if kv_cache_dtype in ("fp8", "fp8_e4m3"):
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return torch.float8_e4m3fn
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else:
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raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
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@dataclass
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class SUPAFlashAttentionMetadata:
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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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# BIREN Attention Params
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seq_start_loc: torch.Tensor
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context_lens: torch.Tensor
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max_decode_seq_len: int
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do_cache: bool # when use attentionsplit, do cache = False
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num_actual_reqs: torch.Tensor
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# Graph mode
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supagraph_runtime_mode: SUPAGraphMode
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# For handling prefill decode split
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num_decodes: int
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num_decode_tokens: int
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num_prefills: int
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num_prefill_tokens: int
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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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max_num_splits: int = 0
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causal: bool = True
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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 SUPAFlashAttentionMetadataBuilder(
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AttentionMetadataBuilder[SUPAFlashAttentionMetadata]):
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cudagraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.ALWAYS
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reorder_batch_threshold: int = 1
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def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
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vllm_config: VllmConfig, device: torch.device):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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self.model_config = vllm_config.model_config
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self.parallel_config = vllm_config.parallel_config
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self.cache_config = vllm_config.cache_config
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self.compilation_config = vllm_config.compilation_config
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self.num_heads_q = self.model_config.get_num_attention_heads(
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self.parallel_config)
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self.num_heads_kv = self.model_config.get_num_kv_heads(
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self.parallel_config)
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self.kv_cache_dtype = kv_cache_spec.dtype
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self.headdim = self.model_config.get_head_size()
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self.block_size = kv_cache_spec.block_size
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supports_spec_as_decode = True
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self._init_reorder_batch_threshold(1, supports_spec_as_decode)
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self.max_num_splits = 0 # No upper bound on the number of splits.
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# self.aot_schedule = (get_flash_attn_version() == 3)
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self.aot_schedule = False
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self.use_full_cuda_graph = \
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self.compilation_config.cudagraph_mode.has_full_cudagraphs()
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self.max_cudagraph_size = self.compilation_config.max_capture_size
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# if self.use_full_cuda_graph and self.aot_schedule:
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# if self.max_cudagraph_size > 992:
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# # This condition derives from FA3's internal heuristic.
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# # TODO(woosuk): Support larger cudagraph sizes.
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# raise ValueError(
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# "Capture size larger than 992 is not supported for "
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# "full cuda graph.")
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# self.scheduler_metadata = torch.zeros(
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# vllm_config.scheduler_config.max_num_seqs + 1,
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# dtype=torch.int32,
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# device=self.device,
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# )
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# # When using cuda graph, we need to set the upper bound of the
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# # number of splits so that large enough intermediate buffers are
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# # pre-allocated during capture.
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# self.max_num_splits = (
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# envs.VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH)
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# Sliding window size to be used with the AOT scheduler will be
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# populated on first build() call.
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self.aot_sliding_window: Optional[tuple[int, int]] = None
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# model_config = runner.model_config
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# self.runner = runner
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# self.num_heads_q = model_config.get_num_attention_heads(
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# runner.parallel_config)
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# self.num_heads_kv = model_config.get_num_kv_heads(
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# runner.parallel_config)
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# self.headdim = model_config.get_head_size()
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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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# self.aot_schedule = False
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# logger.warning(
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# "AOT Schedule is disabled when using SUPAFlashAttention.")
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# # Sliding window size to be used with the AOT scheduler will be
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# # populated on first build() call.
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# self.aot_sliding_window: Optional[tuple[int, int]] = None
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def build(self,
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common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata,
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fast_build: bool = False) -> SUPAFlashAttentionMetadata:
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"""
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fast_build disables AOT scheduling, used when there will be few
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iterations i.e. spec-decode
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"""
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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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num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens =\
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split_decodes_and_prefills(common_attn_metadata,
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decode_threshold=self.reorder_batch_threshold,
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require_uniform=True)
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max_query_len = common_attn_metadata.max_query_len
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max_seq_len = common_attn_metadata.max_seq_len
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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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seq_lens_cpu = common_attn_metadata.seq_lens_cpu
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block_table_tensor = common_attn_metadata.block_table_tensor
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slot_mapping = common_attn_metadata.slot_mapping
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causal = common_attn_metadata.causal
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num_actual_reqs = common_attn_metadata.num_actual_reqs
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seq_start_loc = common_attn_metadata.seq_start_loc
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context_lens = common_attn_metadata.context_lens
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# the overhead of the aot schedule is not worth it for spec-decode
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aot_schedule = self.aot_schedule and not fast_build
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if self.aot_sliding_window is None:
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self.aot_sliding_window = (-1, -1)
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# For the AOT scheduler we need the sliding window value to be
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# constant for all layers to. We have to populate this on the first
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# build() call so the layers are constructed (cannot populate)
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# in __init__.
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if aot_schedule:
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sliding_window_configs = _get_sliding_window_configs(
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self.vllm_config)
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if len(sliding_window_configs) == 1:
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sliding_window_config = sliding_window_configs.pop()
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if sliding_window_config is not None:
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self.aot_sliding_window = sliding_window_config
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elif len(sliding_window_configs) > 1:
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self.aot_schedule = False
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aot_schedule = False
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max_num_splits = 0 # 0 means use FA3's heuristics, not CG compatible
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if self.use_full_cuda_graph and \
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num_actual_tokens <= self.max_cudagraph_size:
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# NOTE(woosuk): Setting num_splits > 1 may increase the memory
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# usage, because the intermediate buffers of size [num_splits,
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# num_heads, num_tokens, head_size] are allocated. Therefore,
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# we only set num_splits when using cuda graphs.
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max_num_splits = self.max_num_splits
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def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
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max_seq_len, causal):
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if self.aot_schedule:
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raise NotImplementedError(
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'aot schedule not support in SUPA attention')
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return None
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# for local attention
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|
# local_attn_metadata = None
|
||
|
|
# if self.runner.attention_chunk_size is not None:
|
||
|
|
# seqlens_q_local_np, virt_q_cu_seqlens_np, virt_k_seqlens_np, \
|
||
|
|
# virt_block_table_tensor = make_local_attention_virtual_batches(
|
||
|
|
# self.runner.attention_chunk_size,
|
||
|
|
# self.runner.query_start_loc_np[:num_reqs + 1],
|
||
|
|
# self.runner.seq_lens_np[:num_reqs],
|
||
|
|
# block_table_tensor,
|
||
|
|
# self.block_size,
|
||
|
|
# )
|
||
|
|
# local_query_start_loc = torch.from_numpy(virt_q_cu_seqlens_np).to(
|
||
|
|
# self.runner.device, non_blocking=False)
|
||
|
|
# local_seqused_k = torch.from_numpy(virt_k_seqlens_np).to(
|
||
|
|
# self.runner.device, non_blocking=False)
|
||
|
|
# local_max_query_len = seqlens_q_local_np.max()
|
||
|
|
# local_max_seq_len = virt_k_seqlens_np.max()
|
||
|
|
# local_scheduler_metadata = schedule(
|
||
|
|
# batch_size=local_query_start_loc.shape[0] - 1,
|
||
|
|
# cu_query_lens=local_query_start_loc,
|
||
|
|
# max_query_len=local_max_query_len,
|
||
|
|
# seqlens=local_seqused_k,
|
||
|
|
# max_seq_len=local_max_seq_len,
|
||
|
|
# causal=True)
|
||
|
|
|
||
|
|
# local_attn_metadata = SUPAFlashAttentionMetadata.LocalAttentionMetadata(
|
||
|
|
# local_query_start_loc=local_query_start_loc,
|
||
|
|
# local_seqused_k=local_seqused_k,
|
||
|
|
# local_block_table=virt_block_table_tensor,
|
||
|
|
# local_max_query_len=local_max_query_len,
|
||
|
|
# local_max_seq_len=local_max_seq_len,
|
||
|
|
# local_scheduler_metadata=local_scheduler_metadata,
|
||
|
|
# )
|
||
|
|
|
||
|
|
use_cascade = common_prefix_len > 0
|
||
|
|
|
||
|
|
if use_cascade:
|
||
|
|
cu_prefix_query_lens = torch.tensor([0, num_actual_tokens],
|
||
|
|
dtype=torch.int32,
|
||
|
|
device=self.runner.device)
|
||
|
|
prefix_kv_lens = torch.tensor([common_prefix_len],
|
||
|
|
dtype=torch.int32,
|
||
|
|
device=self.runner.device)
|
||
|
|
suffix_kv_lens = (seq_lens_cpu[:num_reqs] - common_prefix_len).to(
|
||
|
|
self.device, non_blocking=True)
|
||
|
|
prefix_scheduler_metadata = schedule(
|
||
|
|
batch_size=1,
|
||
|
|
cu_query_lens=cu_prefix_query_lens,
|
||
|
|
max_query_len=num_actual_tokens,
|
||
|
|
seqlens=prefix_kv_lens,
|
||
|
|
max_seq_len=common_prefix_len,
|
||
|
|
causal=False)
|
||
|
|
scheduler_metadata = schedule(batch_size=num_reqs,
|
||
|
|
cu_query_lens=query_start_loc,
|
||
|
|
max_query_len=max_query_len,
|
||
|
|
seqlens=suffix_kv_lens,
|
||
|
|
max_seq_len=max_seq_len -
|
||
|
|
common_prefix_len,
|
||
|
|
causal=True)
|
||
|
|
else:
|
||
|
|
cu_prefix_query_lens = None
|
||
|
|
prefix_kv_lens = None
|
||
|
|
suffix_kv_lens = None
|
||
|
|
prefix_scheduler_metadata = None
|
||
|
|
scheduler_metadata = schedule(batch_size=num_reqs,
|
||
|
|
cu_query_lens=query_start_loc,
|
||
|
|
max_query_len=max_query_len,
|
||
|
|
seqlens=seq_lens,
|
||
|
|
max_seq_len=max_seq_len,
|
||
|
|
causal=causal)
|
||
|
|
|
||
|
|
if common_attn_metadata.max_decode_seq_len is None:
|
||
|
|
max_decode_seq_len = max_decode_seq_len = int(
|
||
|
|
seq_lens.max().item())
|
||
|
|
else:
|
||
|
|
max_decode_seq_len = common_attn_metadata.max_decode_seq_len
|
||
|
|
|
||
|
|
attn_metadata = SUPAFlashAttentionMetadata(
|
||
|
|
num_actual_tokens=num_actual_tokens,
|
||
|
|
max_query_len=max_query_len,
|
||
|
|
query_start_loc=query_start_loc,
|
||
|
|
max_seq_len=max_seq_len,
|
||
|
|
seq_lens=seq_lens,
|
||
|
|
block_table=block_table_tensor,
|
||
|
|
slot_mapping=slot_mapping,
|
||
|
|
use_cascade=use_cascade,
|
||
|
|
common_prefix_len=common_prefix_len,
|
||
|
|
scheduler_metadata=scheduler_metadata,
|
||
|
|
cu_prefix_query_lens=cu_prefix_query_lens,
|
||
|
|
prefix_kv_lens=prefix_kv_lens,
|
||
|
|
suffix_kv_lens=suffix_kv_lens,
|
||
|
|
# local_attn_metadata=local_attn_metadata,
|
||
|
|
prefix_scheduler_metadata=prefix_scheduler_metadata,
|
||
|
|
max_num_splits=max_num_splits,
|
||
|
|
causal=causal,
|
||
|
|
# Biren Attention Params
|
||
|
|
seq_start_loc=seq_start_loc,
|
||
|
|
context_lens=context_lens,
|
||
|
|
max_decode_seq_len=max_decode_seq_len,
|
||
|
|
num_prefills=num_prefills,
|
||
|
|
num_decodes=num_decodes,
|
||
|
|
num_prefill_tokens=num_prefill_tokens,
|
||
|
|
num_decode_tokens=num_decode_tokens,
|
||
|
|
do_cache=True,
|
||
|
|
num_actual_reqs=num_actual_reqs,
|
||
|
|
supagraph_runtime_mode=common_attn_metadata.supagraph_runtime_mode)
|
||
|
|
|
||
|
|
return attn_metadata
|
||
|
|
|
||
|
|
def use_cascade_attention(self, *args, **kwargs) -> bool:
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
class SUPAFlashAttentionImpl(AttentionImpl):
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_heads: int,
|
||
|
|
head_size: int,
|
||
|
|
scale: float,
|
||
|
|
num_kv_heads: int,
|
||
|
|
alibi_slopes: Optional[list[float]],
|
||
|
|
sliding_window: Optional[int],
|
||
|
|
kv_cache_dtype: str,
|
||
|
|
logits_soft_cap: Optional[float] = None,
|
||
|
|
attn_type: AttentionType = AttentionType.DECODER,
|
||
|
|
kv_sharing_target_layer_name: Optional[str] = None,
|
||
|
|
sinks: Optional[torch.Tensor] = None,
|
||
|
|
) -> None:
|
||
|
|
self.num_heads = num_heads
|
||
|
|
self.head_size = head_size
|
||
|
|
self.scale = float(scale)
|
||
|
|
self.num_kv_heads = num_kv_heads
|
||
|
|
if alibi_slopes is not None:
|
||
|
|
alibi_slopes = torch.tensor(alibi_slopes,
|
||
|
|
dtype=torch.float32,
|
||
|
|
device="cpu")
|
||
|
|
self.alibi_slopes = alibi_slopes
|
||
|
|
self.sliding_window = sliding_window or None
|
||
|
|
self.kv_cache_dtype = kv_cache_dtype
|
||
|
|
if logits_soft_cap is None:
|
||
|
|
# In flash-attn, setting logits_soft_cap as 0 means no soft cap.
|
||
|
|
logits_soft_cap = 0
|
||
|
|
self.logits_soft_cap = logits_soft_cap
|
||
|
|
self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
|
||
|
|
|
||
|
|
assert self.num_heads % self.num_kv_heads == 0
|
||
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
||
|
|
|
||
|
|
SUPAFlashAttentionBackend.validate_head_size(head_size)
|
||
|
|
|
||
|
|
self.attn_type = attn_type
|
||
|
|
|
||
|
|
if attn_type not in (AttentionType.DECODER,
|
||
|
|
AttentionType.ENCODER_ONLY):
|
||
|
|
raise NotImplementedError("Encoder self-attention and "
|
||
|
|
"encoder/decoder cross-attention "
|
||
|
|
"are not implemented for "
|
||
|
|
"FlashAttentionImpl")
|
||
|
|
self.vllm_flash_attn_version = get_flash_attn_version()
|
||
|
|
if is_quantized_kv_cache(self.kv_cache_dtype) \
|
||
|
|
and not flash_attn_supports_fp8():
|
||
|
|
raise NotImplementedError(
|
||
|
|
"FlashAttention does not support fp8 kv-cache on this device.")
|
||
|
|
|
||
|
|
self.sinks: Optional[torch.Tensor] = None
|
||
|
|
if sinks is not None:
|
||
|
|
if sinks.shape[0] != num_heads:
|
||
|
|
raise ValueError(
|
||
|
|
"Sinks must have the same number of heads as the number of "
|
||
|
|
f"heads in the layer. Expected {num_heads}, but got "
|
||
|
|
f"{sinks.shape[0]}.")
|
||
|
|
if sinks.dtype != torch.float32:
|
||
|
|
raise ValueError("Sinks must be of type float32, but got "
|
||
|
|
f"{sinks.dtype}.")
|
||
|
|
self.sinks = sinks
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
layer: torch.nn.Module,
|
||
|
|
query: torch.Tensor,
|
||
|
|
key: torch.Tensor,
|
||
|
|
value: torch.Tensor,
|
||
|
|
kv_cache: torch.Tensor,
|
||
|
|
attn_metadata: SUPAFlashAttentionMetadata,
|
||
|
|
output: Optional[torch.Tensor] = None,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
"""Forward pass with FlashAttention.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query: shape = [num_tokens, num_heads, head_size]
|
||
|
|
key: shape = [num_tokens, num_kv_heads, head_size]
|
||
|
|
value: shape = [num_tokens, num_kv_heads, head_size]
|
||
|
|
kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size]
|
||
|
|
attn_metadata: Metadata for attention.
|
||
|
|
Returns:
|
||
|
|
shape = [num_tokens, num_heads * head_size]
|
||
|
|
NOTE: FP8 quantization, flash-attn expect the size of
|
||
|
|
{q,k,v}_descale to be (num_sequences, num_kv_heads).
|
||
|
|
We use torch's .expand() to avoid duplicating values
|
||
|
|
"""
|
||
|
|
assert output is None, "Output tensor should not provided."
|
||
|
|
if attn_metadata is None:
|
||
|
|
# FIXME: this may lead to wrong block estimatation
|
||
|
|
# Profiling run.
|
||
|
|
return query
|
||
|
|
|
||
|
|
is_encoder = self.attn_type in (AttentionType.ENCODER_ONLY,
|
||
|
|
AttentionType.ENCODER)
|
||
|
|
# NOTE: supa attn use [batch_size, num_tokens, num_heads * head_size] as shape
|
||
|
|
if kv_cache is not None and attn_metadata.do_cache and not is_encoder:
|
||
|
|
torch_br.supa_kvcache_store_infer_v2(
|
||
|
|
kv_cache,
|
||
|
|
key,
|
||
|
|
value, # type: ignore
|
||
|
|
attn_metadata.slot_mapping,
|
||
|
|
self.head_size)
|
||
|
|
|
||
|
|
if self.sinks is not None:
|
||
|
|
return self.forward_sw_sinks(query, kv_cache, attn_metadata)
|
||
|
|
|
||
|
|
if self.attn_type in (AttentionType.ENCODER_ONLY,
|
||
|
|
AttentionType.ENCODER):
|
||
|
|
assert len(query.shape) == 3
|
||
|
|
return torch_br.supa_flash_attention_infer( # type: ignore
|
||
|
|
query,
|
||
|
|
key,
|
||
|
|
value,
|
||
|
|
attn_metadata.query_start_loc,
|
||
|
|
self.head_size,
|
||
|
|
len(attn_metadata.query_start_loc), # type: ignore
|
||
|
|
self.alibi_slopes,
|
||
|
|
softmax_scale=self.scale,
|
||
|
|
is_causal=_get_causal_option(self.attn_type))
|
||
|
|
|
||
|
|
num_prefill_tokens = attn_metadata.num_prefill_tokens
|
||
|
|
if attn_metadata.supagraph_runtime_mode is None or (
|
||
|
|
attn_metadata.supagraph_runtime_mode
|
||
|
|
in (SUPAGraphMode.NONE, SUPAGraphMode.FULL_DECODE_ONLY)):
|
||
|
|
# prefill + decode(non-mtp)
|
||
|
|
if num_prefill_tokens > 0:
|
||
|
|
output_prefill = torch_br.br_flash_attn_with_kvcache_infer( # type: ignore
|
||
|
|
query,
|
||
|
|
kv_cache,
|
||
|
|
attn_metadata.query_start_loc,
|
||
|
|
attn_metadata.seq_start_loc,
|
||
|
|
attn_metadata.block_table,
|
||
|
|
self.head_size,
|
||
|
|
alibi_slopes=self.alibi_slopes,
|
||
|
|
softmax_scale=self.scale,
|
||
|
|
num_reqs=attn_metadata.num_actual_reqs)
|
||
|
|
return output_prefill
|
||
|
|
## decode only
|
||
|
|
output_decode = torch_br.supa_attention_decoder_infer_v2( # type: ignore
|
||
|
|
query, # type: ignore
|
||
|
|
kv_cache,
|
||
|
|
attn_metadata.block_table,
|
||
|
|
attn_metadata.seq_lens,
|
||
|
|
attn_metadata.max_decode_seq_len,
|
||
|
|
self.head_size,
|
||
|
|
attn_metadata.num_prefills,
|
||
|
|
self.alibi_slopes,
|
||
|
|
softmax_scale=self.scale)
|
||
|
|
return output_decode
|
||
|
|
else:
|
||
|
|
output_prefill = torch_br.br_flash_attn_with_kvcache_infer( # type: ignore
|
||
|
|
query,
|
||
|
|
kv_cache,
|
||
|
|
attn_metadata.query_start_loc,
|
||
|
|
attn_metadata.seq_start_loc,
|
||
|
|
attn_metadata.block_table,
|
||
|
|
self.head_size,
|
||
|
|
alibi_slopes=self.alibi_slopes,
|
||
|
|
softmax_scale=self.scale,
|
||
|
|
num_reqs=attn_metadata.num_actual_reqs)
|
||
|
|
return output_prefill
|
||
|
|
|
||
|
|
# sliding window with sinks impl
|
||
|
|
def forward_sw_sinks(
|
||
|
|
self,
|
||
|
|
query: torch.Tensor,
|
||
|
|
kv_cache: torch.Tensor,
|
||
|
|
attn_metadata: SUPAFlashAttentionMetadata,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
# prefix-enabled attention
|
||
|
|
output = torch_br.supa_flash_attn_cache_infer( # type: ignore
|
||
|
|
query,
|
||
|
|
kv_cache,
|
||
|
|
attn_metadata.query_start_loc,
|
||
|
|
attn_metadata.seq_start_loc,
|
||
|
|
attn_metadata.block_table,
|
||
|
|
attn_metadata.context_lens,
|
||
|
|
attn_metadata.slot_mapping,
|
||
|
|
attn_metadata.max_seq_len,
|
||
|
|
self.head_size,
|
||
|
|
window_size=self.sliding_window,
|
||
|
|
sinks=self.sinks)
|
||
|
|
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
def _get_causal_option(attn_type: str) -> bool:
|
||
|
|
"""
|
||
|
|
Determine whether the given attention type is suitable for causal
|
||
|
|
attention mechanisms.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
attn_type (AttentionType): The type of attention being evaluated
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
bool: Returns `True` if the attention type is suitable for causal
|
||
|
|
attention (i.e., not encoder, encoder-only, or encoder-decoder),
|
||
|
|
otherwise returns `False`.
|
||
|
|
"""
|
||
|
|
return not (attn_type == AttentionType.ENCODER
|
||
|
|
or attn_type == AttentionType.ENCODER_ONLY
|
||
|
|
or attn_type == AttentionType.ENCODER_DECODER)
|