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ac201a0eaf
Signed-off-by: hongchao <hongchao@msh.team> Signed-off-by: youkaichao <youkaichao@gmail.com> Co-authored-by: hongchao <hongchao@msh.team> Co-authored-by: youkaichao <youkaichao@gmail.com>
254 lines
10 KiB
Python
254 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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from typing import ClassVar, Optional, Union
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import torch
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import vllm.envs as envs
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from vllm.attention.backends.abstract import AttentionLayer
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from vllm.attention.ops.rocm_aiter_mla import aiter_mla_decode_fwd
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from vllm.config import VllmConfig
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from vllm.utils import cdiv
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# yapf conflicts with isort for this docstring
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# yapf: disable
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from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
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MLACommonDecodeMetadata,
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MLACommonImpl,
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MLACommonMetadata,
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MLACommonMetadataBuilder)
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm.v1.kv_cache_interface import AttentionSpec
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# yapf: enable
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def is_aiter_mla_enabled() -> bool:
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return envs.VLLM_ROCM_USE_AITER \
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and envs.VLLM_ROCM_USE_AITER_MLA
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class AiterMLABackend(MLACommonBackend):
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@staticmethod
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def get_name() -> str:
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return "ROCM_AITER_MLA_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["AiterMLAImpl"]:
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return AiterMLAImpl
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@staticmethod
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def get_metadata_cls() -> type["AiterMLAMetadata"]:
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return AiterMLAMetadata
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@staticmethod
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def get_builder_cls() -> type["AiterMLAMetadataBuilder"]:
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return AiterMLAMetadataBuilder
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@dataclass
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class AiterMLADecodeMetadata(MLACommonDecodeMetadata):
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# The indptr of the paged kv cache, shape: [batch_size + 1]
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paged_kv_indptr: Optional[torch.Tensor] = None
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# The page indices of the paged kv cache
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paged_kv_indices: Optional[torch.Tensor] = None
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# The number of entries in the last page of each request in
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# the paged kv cache, shape: [batch_size]
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paged_kv_last_page_len: Optional[torch.Tensor] = None
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# The query indptr, shape : [num_decode + 1]
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qo_indptr: Optional[torch.Tensor] = None
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class AiterMLAMetadata(MLACommonMetadata[AiterMLADecodeMetadata]):
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pass
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class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
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# TODO(luka, lucas): audit this as part of:
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# https://github.com/vllm-project/vllm/issues/22945
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cudagraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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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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AiterMLAMetadata)
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assert self.kv_cache_spec.block_size == 1, "AITER MLA" \
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"only supports block size 1."
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self.compilation_config = vllm_config.compilation_config
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max_num_pages_per_req = cdiv(vllm_config.model_config.max_model_len,
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self.kv_cache_spec.block_size)
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max_num_reqs = vllm_config.scheduler_config.max_num_seqs
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max_num_pages = max_num_reqs * max_num_pages_per_req
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# Preparing persistent buffers
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# TODO: we can disambiguate between decode and mixed-prefill decode here
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# so we can only use the persistent buffer if a cudagraph is actually
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# being used.
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if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
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self.paged_kv_indptr = torch.zeros(max_num_reqs + 1,
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dtype=torch.int32,
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device=device)
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self.paged_kv_indices = torch.zeros(max_num_pages,
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dtype=torch.int32,
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device=device)
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self.paged_kv_last_page_len = torch.zeros(max_num_reqs,
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dtype=torch.int32,
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device=device)
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self.qo_indptr = torch.arange(0,
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max_num_reqs + 1,
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dtype=torch.int32,
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device=device)
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def _build_decode(
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self, block_table_tensor: torch.Tensor, seq_lens_cpu: torch.Tensor,
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seq_lens_device: torch.Tensor, query_start_loc_cpu: torch.Tensor,
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query_start_loc_device: torch.Tensor) -> AiterMLADecodeMetadata:
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page_size = self.kv_cache_spec.block_size
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block_table_bounds = (seq_lens_device + page_size - 1) // page_size
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device = self.device
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num_reqs = seq_lens_device.size(0)
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mask = (torch.arange(block_table_tensor.size(1),
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dtype=block_table_tensor.dtype,
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device=device).unsqueeze(0)
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< block_table_bounds.unsqueeze(1))
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paged_kv_indices = block_table_tensor[mask]
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paged_kv_last_page_len = seq_lens_device % page_size
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paged_kv_last_page_len = torch.where(paged_kv_last_page_len == 0,
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page_size, paged_kv_last_page_len)
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paged_kv_indptr = torch.cat([
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torch.zeros(1, dtype=block_table_bounds.dtype, device=device),
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block_table_bounds.cumsum(dim=0, dtype=torch.int32)
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])
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if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
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num_actual_pages = paged_kv_indices.size(0)
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self.paged_kv_indices[:num_actual_pages].copy_(paged_kv_indices,
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non_blocking=True)
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self.paged_kv_indices[num_actual_pages:].fill_(-1)
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paged_kv_indices = self.paged_kv_indices[:num_actual_pages]
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self.paged_kv_indptr[:1 + num_reqs].copy_(paged_kv_indptr,
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non_blocking=True)
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self.paged_kv_indptr[1 + num_reqs:].fill_(paged_kv_indptr[-1])
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paged_kv_indptr = self.paged_kv_indptr[:1 + num_reqs]
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self.paged_kv_last_page_len[:num_reqs].copy_(
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paged_kv_last_page_len, non_blocking=True)
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self.paged_kv_last_page_len[num_reqs:].fill_(1)
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paged_kv_last_page_len = self.paged_kv_last_page_len[:num_reqs]
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qo_indptr = self.qo_indptr[:1 + num_reqs]
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else:
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qo_indptr = torch.arange(0,
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num_reqs + 1,
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step=1,
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dtype=torch.int32,
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device=device)
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attn_metadata = AiterMLADecodeMetadata(
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block_table=block_table_tensor,
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seq_lens=seq_lens_device,
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paged_kv_indptr=paged_kv_indptr,
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paged_kv_indices=paged_kv_indices,
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paged_kv_last_page_len=paged_kv_last_page_len,
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qo_indptr=qo_indptr)
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return attn_metadata
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class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
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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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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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# MLA Specific Arguments
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**mla_args) -> None:
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super().__init__(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window, kv_cache_dtype,
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logits_soft_cap, attn_type,
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kv_sharing_target_layer_name, **mla_args)
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assert (num_heads == 16 or num_heads == 128), (
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f"Aiter MLA only supports 16 or 128 number of heads.\n"
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f"Provided {num_heads} number of heads.\n"
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"Try adjusting tensor_parallel_size value.")
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unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
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if any(unsupported_features):
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raise NotImplementedError(
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"Aiter MLA does not support one of the following: "
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"alibi_slopes, sliding_window, logits_soft_cap")
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from aiter import flash_attn_varlen_func
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self.flash_attn_varlen_func = flash_attn_varlen_func
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def _flash_attn_varlen_diff_headdims(self,
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q,
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k,
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v,
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return_softmax_lse=False,
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softmax_scale=None,
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**kwargs):
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output = self.flash_attn_varlen_func(
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q=q,
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k=k,
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v=v,
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softmax_scale=softmax_scale,
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return_lse=return_softmax_lse,
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**kwargs,
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)
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return output
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def _forward_decode(
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self,
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q: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
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kv_c_and_k_pe_cache: torch.Tensor,
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attn_metadata: AiterMLAMetadata,
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layer: AttentionLayer,
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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assert kv_c_and_k_pe_cache.numel() > 0
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assert attn_metadata.decode is not None
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if type(q) is tuple:
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q = torch.cat(q, dim=-1)
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assert isinstance(q, torch.Tensor)
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B = q.shape[0]
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o = torch.zeros(B,
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self.num_heads,
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self.kv_lora_rank,
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dtype=q.dtype,
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device=q.device)
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kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
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# max_seqlen_qo must be 1 except for MTP
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# TODO: Find the best value for MTP
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max_seqlen_qo = 1
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aiter_mla_decode_fwd(q, kv_buffer, o, self.scale,
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attn_metadata.decode.qo_indptr, max_seqlen_qo,
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attn_metadata.decode.paged_kv_indptr,
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attn_metadata.decode.paged_kv_indices,
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attn_metadata.decode.paged_kv_last_page_len)
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return o, None
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