406 lines
15 KiB
Python
406 lines
15 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 dataclasses import dataclass
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from typing import ClassVar
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import torch
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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QuantKey,
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kFp8StaticTensorSym,
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)
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from vllm.platforms import current_platform
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from vllm.v1.attention.backend import (
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AttentionBackend,
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AttentionCGSupport,
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AttentionImpl,
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AttentionMetadataBuilder,
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AttentionType,
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CommonAttentionMetadata,
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MultipleOf,
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)
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.attention.ops.chunked_prefill_paged_decode import (
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chunked_prefill_paged_decode,
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)
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from vllm.v1.attention.ops.paged_attn import PagedAttention
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from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
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triton_reshape_and_cache_flash,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec
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logger = init_logger(__name__)
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@dataclass
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class RocmAttentionMetadata:
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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: torch.Tensor | None
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prefix_kv_lens: torch.Tensor | None
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suffix_kv_lens: torch.Tensor | None
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# Optional aot scheduling
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scheduler_metadata: torch.Tensor | None = None
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prefix_scheduler_metadata: torch.Tensor | None = None
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class RocmAttentionMetadataBuilder(AttentionMetadataBuilder[RocmAttentionMetadata]):
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_cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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self.block_size = kv_cache_spec.block_size
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model_config = vllm_config.model_config
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self.num_heads_q = model_config.get_num_attention_heads(
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vllm_config.parallel_config
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)
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self.num_heads_kv = model_config.get_num_kv_heads(vllm_config.parallel_config)
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self.headdim = model_config.get_head_size()
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def build_for_cudagraph_capture(
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self, common_attn_metadata: CommonAttentionMetadata
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) -> RocmAttentionMetadata:
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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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# Here we set the query start locs to 0. This is to
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# cover up an invalid memory access in the prefix_prefil kernel
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# that we run into during graph capture (#25985)
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common_attn_metadata.query_start_loc.zero_()
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common_attn_metadata.query_start_loc_cpu.zero_()
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return attn_metadata
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def build(
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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,
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) -> RocmAttentionMetadata:
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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 = 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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block_table_tensor = common_attn_metadata.block_table_tensor
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slot_mapping = common_attn_metadata.slot_mapping
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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(
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[0, num_actual_tokens], dtype=torch.int32, device=self.device
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)
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prefix_kv_lens = torch.tensor(
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[common_prefix_len], dtype=torch.int32, device=self.device
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)
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suffix_kv_lens = common_attn_metadata.seq_lens.cpu() - common_prefix_len
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suffix_kv_lens = suffix_kv_lens.to(self.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 = RocmAttentionMetadata(
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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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prefix_scheduler_metadata=prefix_scheduler_metadata,
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)
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return attn_metadata
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class RocmAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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supported_dtypes: ClassVar[list[torch.dtype]] = [
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torch.float16,
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torch.bfloat16,
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torch.float32,
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]
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@staticmethod
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def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
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# ROCM paged attention kernel only supports block sizes 16 and 32
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# due to shared memory (LDS) constraints on AMD GPUs.
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# See csrc/rocm/attention.cu CALL_CUSTOM_LAUNCHER_BLK macro.
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# However, The limitations in [16, 32] are reasonable for a native C++ kernel,
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# but vLLM should allow support for non-standard sizes via the Triton path,
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# as addressed in this PR: https://github.com/vllm-project/vllm/pull/31380,
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# where the Triton kernel under rocm_atten does not support inference
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# for a non-standard qwen3-next model with a block_size of 544.
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# We have fixed the Triton kernel so that the standard model uses the original
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# bit-addressing logic, while the non-standard model
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# uses our optimized kernel logic.
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return [16, 32, 544]
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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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if not cls.supports_head_size(head_size):
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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: {cls.get_supported_head_sizes()}. "
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"Set --attention-backend=FLEX_ATTENTION to use "
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"FlexAttention backend which supports all head sizes."
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)
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@staticmethod
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def get_name() -> str:
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return "ROCM_ATTN"
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@staticmethod
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def get_impl_cls() -> type["RocmAttentionImpl"]:
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return RocmAttentionImpl
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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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cache_dtype_str: str = "auto",
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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["RocmAttentionMetadataBuilder"]:
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return RocmAttentionMetadataBuilder
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class RocmAttentionImpl(AttentionImpl):
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def fused_output_quant_supported(self, quant_key: QuantKey):
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return quant_key == kFp8StaticTensorSym
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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: list[float] | None,
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sliding_window: int | None,
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kv_cache_dtype: str,
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logits_soft_cap: float | None = None,
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attn_type: AttentionType = AttentionType.DECODER,
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kv_sharing_target_layer_name: int | None = None,
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sinks: torch.Tensor | None = None,
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) -> None:
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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.num_queries_per_kv = self.num_heads // self.num_kv_heads
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RocmAttentionBackend.validate_head_size(head_size)
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if attn_type not in [AttentionType.DECODER, AttentionType.ENCODER_DECODER]:
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raise NotImplementedError(
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"Encoder self-attention is not implemented for RocmAttentionImpl"
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)
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self.fp8_dtype = current_platform.fp8_dtype()
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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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)
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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: torch.Tensor | None = None,
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output_scale: torch.Tensor | None = None,
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output_block_scale: torch.Tensor | None = 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: shape =
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[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_block_scale is not None:
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raise NotImplementedError(
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"fused block_scale output quantization is not yet supported"
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" for RocmAttentionImpl"
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)
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if attn_metadata is None:
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# Profiling run.
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return output.fill_(0)
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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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num_actual_tokens = attn_metadata.num_actual_tokens
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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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)
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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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# Get the actual block_size from value_cache
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# value_cache shape: [num_blocks, num_heads, head_size, block_size]
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block_size = value_cache.shape[3]
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# Determine if it is a power of 2
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is_pow2 = block_size > 0 and (block_size & (block_size - 1) == 0)
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if is_pow2:
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# Normal 16, 32, 64, etc., use vLLM native HIP C++ logic
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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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# Case B: Non-standard blocks (e.g., 544 in Qwen3),
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# force using our modified Triton logic
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triton_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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assert layer._q_scale_float == 1.0, (
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"A non 1.0 q_scale is not currently supported."
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)
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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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# Compute attention and update output up to `num_actual_tokens`.
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chunked_prefill_paged_decode(
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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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output_scale=output_scale,
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sinks=self.sinks,
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)
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return output
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