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