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vllm/v1/attention/backends/rocm_aiter_unified_attn.py
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249
vllm/v1/attention/backends/rocm_aiter_unified_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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import torch
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from vllm import _custom_ops as ops
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from vllm._aiter_ops import rocm_aiter_ops
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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.v1.attention.backend import AttentionLayer, AttentionType
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.attention.backends.rocm_attn import (
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RocmAttentionBackend,
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RocmAttentionImpl,
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RocmAttentionMetadataBuilder,
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)
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logger = init_logger(__name__)
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class RocmAiterUnifiedAttentionBackend(RocmAttentionBackend):
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accept_output_buffer: bool = True
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forward_includes_kv_cache_update: bool = False
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@staticmethod
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def get_name() -> str:
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return "ROCM_AITER_UNIFIED_ATTN"
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@staticmethod
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def get_impl_cls() -> type["RocmAiterUnifiedAttentionImpl"]:
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return RocmAiterUnifiedAttentionImpl
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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 RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
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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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super().__init__(
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num_heads,
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head_size,
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scale,
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num_kv_heads,
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alibi_slopes,
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sliding_window,
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kv_cache_dtype,
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logits_soft_cap,
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attn_type,
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kv_sharing_target_layer_name,
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sinks,
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)
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logger.info_once(
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"Using aiter unified attention for RocmAiterUnifiedAttentionImpl"
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)
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from aiter.ops.triton.unified_attention import unified_attention
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self.unified_attention = unified_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: 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 = kv_cache.unbind(0)
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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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descale_shape = (
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cu_seqlens_q.shape[0] - 1,
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key.shape[1] if key is not None else self.num_kv_heads,
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)
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self.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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output_scale=output_scale,
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)
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return output
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def do_kv_cache_update(
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self,
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layer: AttentionLayer,
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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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slot_mapping: torch.Tensor,
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):
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key_cache, value_cache = kv_cache.unbind(0)
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# Reshape the input keys and values and store them in the cache.
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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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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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def fused_rope_kvcache_supported(self):
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return rocm_aiter_ops.is_enabled()
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def do_rope_and_kv_cache_update(
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self,
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layer: AttentionLayer,
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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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positions: torch.Tensor,
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cos_sin_cache: torch.Tensor,
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is_neox: bool,
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kv_cache: torch.Tensor,
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layer_slot_mapping: torch.Tensor,
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):
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key_cache, value_cache = kv_cache.unbind(0)
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flash_layout = True
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is_fp8_kv_cache = self.kv_cache_dtype.startswith("fp8")
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if is_fp8_kv_cache:
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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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rocm_aiter_ops.triton_rope_and_cache(
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query,
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key,
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value,
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positions,
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cos_sin_cache,
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is_neox,
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key_cache,
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value_cache,
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layer_slot_mapping,
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layer._k_scale,
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layer._v_scale,
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flash_layout,
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is_fp8_kv_cache,
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)
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