296 lines
12 KiB
Python
296 lines
12 KiB
Python
# 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 typing import TYPE_CHECKING, Any, 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 (
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FlashAttentionMetadata, FlashAttentionMetadataBuilder)
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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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class TritonAttentionMetadataBuilder(FlashAttentionMetadataBuilder):
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def __init__(self, runner: "GPUModelRunner", kv_cache_spec: AttentionSpec,
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block_table: BlockTable):
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super().__init__(runner, kv_cache_spec, block_table)
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self.aot_schedule = False
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class TritonAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@staticmethod
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def get_supported_head_sizes() -> list[int]:
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return [32, 64, 96, 128, 160, 192, 224, 256]
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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 FlashAttentionMetadata
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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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sinks: Optional[torch.Tensor] = None,
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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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assert self.num_heads % self.num_kv_heads == 0
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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support_head_sizes = TritonAttentionBackend.get_supported_head_sizes()
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if head_size not in support_head_sizes:
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raise ValueError(
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f"Head size {head_size} is not supported by TritonAttention. "
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f"Supported head sizes are: {support_head_sizes}.")
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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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self.sinks = sinks
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if sinks is not None:
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assert sinks.shape[0] == num_heads, (
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"Sinks must have the same number of heads as the number of "
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f"heads in the layer. Sinks shape: {sinks.shape}, "
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f"num_heads: {num_heads}.")
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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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) -> 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 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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sinks=self.sinks)
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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,
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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,
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max_seqlen_k=max_seqlen_k,
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softmax_scale=self.scale,
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causal=True,
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alibi_slopes=self.alibi_slopes,
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window_size=self.sliding_window,
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block_table=block_table,
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softcap=self.logits_soft_cap,
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q_descale=None, # Not supported
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k_descale=layer._k_scale.expand(descale_shape),
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v_descale=layer._v_scale.expand(descale_shape),
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sinks=self.sinks,
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)
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return output
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