# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import os from typing import ClassVar import torch import vllm._custom_ops as ops from vllm.config.cache import CacheDType from vllm.logger import init_logger from vllm.model_executor.layers.attention.mla_attention import ( MLACommonBackend, MLACommonImpl, MLACommonMetadata, MLACommonMetadataBuilder, ) from vllm.platforms.interface import DeviceCapability from vllm.v1.attention.backend import ( AttentionCGSupport, AttentionLayer, AttentionType, MultipleOf, is_quantized_kv_cache, ) logger = init_logger(__name__) class CutlassMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]): # enable full CUDA Graph support for decode-only capture _cudagraph_support: ClassVar[AttentionCGSupport] = ( AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE ) class CutlassMLABackend(MLACommonBackend): supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16] supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [ "auto", "bfloat16", "fp8", "fp8_e4m3", ] @staticmethod def get_supported_kernel_block_sizes() -> list[int | MultipleOf]: return [128] @staticmethod def get_name() -> str: return "CUTLASS_MLA" @staticmethod def get_impl_cls() -> type["CutlassMLAImpl"]: return CutlassMLAImpl @staticmethod def get_builder_cls() -> type["CutlassMLAMetadataBuilder"]: return CutlassMLAMetadataBuilder @classmethod def supports_compute_capability(cls, capability: DeviceCapability) -> bool: return capability.major == 10 class SM100Workspace: def __init__(self, initial_workspace_size): self._workspace_buf = torch.empty( initial_workspace_size, device="cuda", dtype=torch.uint8 ) self._block_size = 128 # Forced to 128 # Pre-compute sm_count to avoid recomputing it. Use device 0 as a proxy # (assumes all devices are similar) properties = torch.cuda.get_device_properties(torch.device("cuda:0")) self._sm_count = properties.multi_processor_count def get_buf(self): return self._workspace_buf def ensure_size(self, attn_metadata: MLACommonMetadata, num_kv_splits: int): batch_size = attn_metadata.num_reqs max_seq_len = attn_metadata.max_query_len workspace_size = ops.sm100_cutlass_mla_get_workspace_size( max_seq_len * self._block_size, batch_size, self._sm_count, num_kv_splits=num_kv_splits, ) if self._workspace_buf.shape[0] < workspace_size: self._workspace_buf.resize_(workspace_size) g_sm100_workspace = SM100Workspace(128 * 1024 * 1024) # 128MB MAX_HEADS = 128 class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]): can_return_lse_for_decode: bool = True def __init__( self, num_heads: int, head_size: int, scale: float, num_kv_heads: int, alibi_slopes: list[float] | None, sliding_window: int | None, kv_cache_dtype: str, logits_soft_cap: float | None, attn_type: str, kv_sharing_target_layer_name: str | None, # 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, q_pad_num_heads=MAX_HEADS, **mla_args, ) unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap] if any(unsupported_features): raise NotImplementedError( "CutlassMLAImpl does not support one of the following: " "alibi_slopes, sliding_window, logits_soft_cap" ) if attn_type != AttentionType.DECODER: raise NotImplementedError( "Encoder self-attention and " "encoder/decoder cross-attention " "are not implemented for " "CutlassMLAImpl" ) # TODO: Currently, num_kv_splits is limited to 16 to avoid hanging # issues. In case the code hangs, use: # FORCE_NUM_KV_SPLITS=1 force_num_kv_splits = os.environ.get("FORCE_NUM_KV_SPLITS", None) if force_num_kv_splits: logger.debug_once("Forcing num_kv_splits to %d", int(force_num_kv_splits)) self._num_kv_splits = int(force_num_kv_splits) else: self._num_kv_splits = -1 # => Auto-detect # Share workspace buffer across all executions self._workspace = g_sm100_workspace def _sm100_cutlass_mla_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, seq_lens: torch.Tensor, page_table: torch.Tensor, workspace: torch.Tensor, sm_scale: float, num_kv_splits: int, ) -> tuple[torch.Tensor, torch.Tensor]: assert q_nope.ndim == 3, f"q_nope must be a 3D tensor, but got {q_nope.ndim}" assert q_pe.ndim == 3, f"q_pe must be a 3D tensor, but got {q_pe.ndim}" assert kv_c_and_k_pe_cache.ndim == 3, ( "kv_c_and_k_pe_cache must be a 3D tensor, but got {}".format( kv_c_and_k_pe_cache.ndim ) ) B_q, H, D_q_nope = q_nope.shape B_q_2, H_2, D_q_pe = q_pe.shape assert (B_q == B_q_2) and (H == H_2) _, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape D_latent = 512 D_rope = 64 assert D_q_nope == D_latent assert D_q_pe == D_rope assert D_ckv == D_latent + D_rope MAX_HEADS = 128 assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}" assert len(page_table.shape) == 2 B_block_table, block_num = page_table.shape assert B_block_table == B_q assert block_num > 0, f"block num must be greater than 0, got {block_num}" assert block_num % (128 / PAGE_SIZE) == 0 assert q_nope.dtype in (torch.float16, torch.bfloat16, torch.float8_e4m3fn), ( f"q_nope.dtype needs to be fp16 or bf16 or e4m3 but got {q_nope.dtype}." ) assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype assert seq_lens.dtype == torch.int32, ( f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}." ) assert page_table.dtype == torch.int32, ( f"page_table.dtype needs to be int32 but got {page_table.dtype}." ) dtype = ( torch.bfloat16 if is_quantized_kv_cache(self.kv_cache_dtype) else q_nope.dtype ) out = q_nope.new_empty((B_q, MAX_HEADS, D_latent), dtype=dtype) lse = ( torch.empty((B_q, MAX_HEADS), dtype=torch.float32, device=q_nope.device) if self.need_to_return_lse_for_decode else torch.Tensor() ) ops.sm100_cutlass_mla_decode( out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, ) if H < MAX_HEADS: # Extract the subsets of the outputs lse = lse[:, :H] if self.need_to_return_lse_for_decode else lse out = out[:, :H] return out, lse def _forward_decode( self, q: torch.Tensor | tuple[torch.Tensor, torch.Tensor], kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, layer: AttentionLayer, ) -> tuple[torch.Tensor, torch.Tensor | None]: assert kv_c_and_k_pe_cache.numel() > 0 assert attn_metadata.decode is not None if type(q) is tuple: q_nope, q_pe = q else: q_nope, q_pe = torch.split( q, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 ) # Adjust workspace size (if necessary) self._workspace.ensure_size(attn_metadata, self._num_kv_splits) # Run MLA o, lse = self._sm100_cutlass_mla_decode( q_nope, q_pe, kv_c_and_k_pe_cache, attn_metadata.decode.seq_lens, attn_metadata.decode.block_table, self._workspace.get_buf(), self.scale, self._num_kv_splits, ) return o, (lse if self.need_to_return_lse_for_decode else None)