# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import os from typing import ClassVar, Optional import torch import vllm._custom_ops as ops from vllm.attention.backends.abstract import (AttentionType, is_quantized_kv_cache) from vllm.logger import init_logger from vllm.v1.attention.backends.mla.common import (MLACommonBackend, MLACommonImpl, MLACommonMetadata, MLACommonMetadataBuilder) from vllm.v1.attention.backends.utils import AttentionCGSupport 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): @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 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 class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]): 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) 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") if is_quantized_kv_cache(self.kv_cache_dtype): raise NotImplementedError( "CutlassMLA V1 with FP8 KV cache not yet supported") self._use_old_cutlass_mla = False force_old_cutlass = os.environ.get("FORCE_OLD_CUTLASS_MLA", None) if force_old_cutlass: logger.warning("Forcing old cutlass mla kernel") self._use_old_cutlass_mla = True # 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.warning("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, ) -> 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}" if H < MAX_HEADS: q_nope_padded = q_nope.new_empty((B_q, MAX_HEADS, D_q_nope)) q_nope_padded[:, :H] = q_nope q_nope = q_nope_padded q_pe_padded = q_pe.new_empty((B_q, MAX_HEADS, D_q_pe)) q_pe_padded[:, :H] = q_pe q_pe = q_pe_padded 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 # TODO(kaixih@nvidia): support fp8 assert q_nope.dtype in ( torch.float16, torch.bfloat16, ), f"q_nope.dtype needs to be fp16 or bf16 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}." out = q_nope.new_empty((B_q, MAX_HEADS, D_latent)) ops.sm100_cutlass_mla_decode( out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, ) return out[:, :H].contiguous() def _sm100_forward_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, ) -> torch.Tensor: assert kv_c_and_k_pe_cache.numel() > 0 assert attn_metadata.decode is not None if self.kv_cache_dtype.startswith("fp8"): raise NotImplementedError("FP8 Cutlass MLA not yet supported") # Adjust workspace size (if necessary) self._workspace.ensure_size(attn_metadata, self._num_kv_splits) # Run MLA # Clone q_nope and q_pe to make sure strides computation is correct. # TODO: Check if we really need it q_nope = q_nope.clone() q_pe = q_pe.clone() o = 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 self._v_up_proj(o) # TODO: Currently we leave it here only for backup in case something is # wrong with the new SM100 CUTLASS MLA kernel def _old_forward_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, ) -> torch.Tensor: assert kv_c_and_k_pe_cache.numel() > 0 assert attn_metadata.decode is not None if self.kv_cache_dtype.startswith("fp8"): raise NotImplementedError("FP8 Cutlass MLA not yet supported") B = q_nope.shape[0] o = torch.empty((B, self.num_heads, self.kv_lora_rank), dtype=q_nope.dtype, device=q_nope.device) # Run MLA # Clone q_nope and q_pe to make sure strides computation is correct. q_nope = q_nope.clone() q_pe = q_pe.clone() ops.cutlass_mla_decode(o, q_nope, q_pe, kv_c_and_k_pe_cache, attn_metadata.decode.seq_lens, attn_metadata.decode.block_table, self.scale) return self._v_up_proj(o) def _forward_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, ) -> torch.Tensor: if self._use_old_cutlass_mla: # TODO: Remove the old cutlass MLA kernel after more extensive # testing return self._old_forward_decode(q_nope, q_pe, kv_c_and_k_pe_cache, attn_metadata) return self._sm100_forward_decode(q_nope, q_pe, kv_c_and_k_pe_cache, attn_metadata)