# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ # MLA Common Components This file implements common components for MLA implementations. First we define: Sq as Q sequence length Skv as KV sequence length MLA has two possible ways of computing, a data-movement friendly approach and a compute friendly approach, we generally want to use the compute friendly approach for "prefill" (i.e. the ratio Sq / Skv is "small", is near 1) and the data-movement friendly approach for "decode" (i.e. the ratio Sq / Skv is "large"). NOTE what we deem small and large is currently determined by if its labelled prefill or decode by the scheduler, but this is something we should probably tune. Main reference: DeepseekV2 paper, and FlashInfer Implementation (https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551). Deepseek's MLA attention works the following way: * Use a single latent vector to represent the per-token entry of the KV cache. * For decode (i.e. the memory friendly approach) the attention "simulates" a multi-head attention, while the compute is similar to multi-query attention. Below is example of both paths assuming batchsize = 1 ## More Extent Definitions: C Context length, `Skv - Sq` H hidden size N number of attention heads Lq latent dimension for Q 1536 in DSV3 Lkv latent dimension for K/V 512 in DSV3 P nope dimension, no rope. 128 in DSV3 R rope dimension, goes through rope. 64 in DSV3 V V head dim. 128 in DSV3 ## Vector/Matrix Definitions h_t hidden states (input to attention) shape [Sq, H] q_c latent/compressed Q shape [Sq, Lq] q_nope uncompressed Q (no-rope) shape [Sq, N, P] q_pe uncompressed Q (rope) shape [Sq, N, R] kv_c latent/compressed KV shape [Skv, Lkv] k_pe decoupled k position embeddings shape [Skv, R] new_kv_c new kv_c from current iter shape [Sq, Lkv] new_k_pe new k_pe from current iter shape [Sq, R] cache_kv_c cached k_c from previous iters shape [C, Lkv] cache_k_pe cached k_pe from previous iters shape [C, R] W_DQ project h_t to q_c shape [H, Lq] W_UQ project q_c to q_nope shape [Lq, N * P] W_QR project q_c to q_pe shape [Lq, N * R] W_DKV project h_t to kv_c shape [H, Lkv] W_UK project kv_c to k_nope shape [Lkv, N, P] W_KR project h_t to k_pe shape [H, R] W_UV project kv_c to v shape [Lkv, N, V] W_O project v to h_t shape [N * V, H] ## Compute Friendly Approach (i.e. "_forward_prefill"): q_c = h_t @ W_DQ q_nope = (q_c @ W_UQ).view(Sq, N, P) q_pe = RoPE(q_c @ W_QR).view(Sq, N, R) new_kv_c = h_t @ W_DKV new_k_pe = RoPE(h_t @ W_KR) kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0) k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0) k_nope = (kv_c @ W_UK.view(Lkv, N * P)).view(Skv, N, P) v = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V) // MHA with QK headdim = P + R // V headdim = V // spda_o shape [Sq, N, V] spda_o = scaled_dot_product_attention( torch.cat([q_nope, q_pe], dim=-1), torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1), v ) return spda_o @ W_O NOTE: in the actual code, `kv_b_proj` is [W_UK; W_UV] concatenated per head `q_b_proj` is [W_UQ; W_QR] concatenated per head `out_proj` is W_O ## Data-Movement Friendly Approach (i.e. "_forward_decode"): Runtime q_c = h_t @ W_DQ q_nope = (q_c @ W_UQ).view(-1, N, P) ql_nope = einsum("snh,lnh->snl", q, W_UK) q_pe = RoPE(q_c @ W_QR).view(Sq, N, R) new_kv_c = h_t @ W_DKV new_k_pe = RoPE(h_t @ W_KR) kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0) k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0) // MQA with QK headdim = Lkv + R // V headdim = Lkv // spda_o shape [Sq, N, Lkv] // NOTE: this is less compute-friendly since Lkv > P // but is more data-movement friendly since its MQA vs MHA spda_o = scaled_dot_product_attention( torch.cat([ql_nope, q_pe], dim=-1), torch.cat([kv_c, k_pe], dim=-1), kv_c ) o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV) return o.view(-1, N * V) @ self.num_heads @ W_O ## Chunked Prefill For chunked prefill we want to use the compute friendly algorithm. We are assuming sufficiently large Sq / Skv ratio, in the future may want to switch to the data-movement friendly approach if the chunk (i.e. `Sq`) is small. However, the compute-friendly approach can potentially run out of memory if Skv is large due to: `k_nope = (kv_c @ W_UK).view(Skv, N, P)` To mitigate this, we chunk the computation of attention with respect to the current context (i.e. `cache_kv_c` and `cache_k_pe`) so that we can used a fixed workspace size. The chunked prefill approach is as follows: MCC Max chunk of context to process per iter, computed dynamically, used to bound the memory usage q_c = h_t @ W_DQ q_nope = (q_c @ W_UQ).view(Sq, N, P) q_pe = RoPE(q_c @ W_QR).view(Sq, N, R) new_kv_c = h_t @ W_DKV new_k_pe = RoPE(h_t @ W_KR) new_k_nope = (new_kv_c @ W_UK.view(Lkv, N * P)).view(Sq, N, P) new_v = (new_kv_c @ W_UV.view(Lkv, N * V)).view(Sq, N, V) // MHA between queries and new KV // with QK headdim = P + R // V headdim = V // curr_o shape [Sq, N, V] // curr_lse shape [N, Sq], this is just order FA returns curr_o, curr_lse = scaled_dot_product_attention( torch.cat([q_nope, q_pe], dim=-1), torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1), new_v, casual=True, return_softmax_lse=True ) // Compute attention with the already existing context for chunk_idx in range(cdiv(C, MCC)): chunk_start = chunk_idx * MCC chunk_end = min(chunk_start + MCC, C) Sc = chunk_end - chunk_start cache_kv_c_chunk = cache_kv_c[chunk_start:chunk_end] cache_k_pe_chunk = cache_k_pe[chunk_start:chunk_end] cache_k_nope_chunk = (cache_kv_c_chunk @ W_UK).view(-1, N, P) cache_v_chunk = (cache_kv_c_chunk @ W_UV).view(-1, N, V) chunk_o, chunk_lse = scaled_dot_product_attention( torch.cat([q_nope, q_pe], dim=-1), torch.cat([cache_k_nope_chunk, cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)], dim=-1), cache_v_chunk, casual=False, return_softmax_lse=True ) curr_o, curr_lse = merge_attn_states( suffix_output=curr_o, suffix_lse=curr_lse, prefix_output=chunk_o, prefix_lse=chunk_lse, ) return curr_o @ W_O """ import functools from abc import abstractmethod from dataclasses import dataclass, field from enum import Enum from typing import ClassVar, Generic, TypeVar import torch from tqdm import tqdm from vllm import _custom_ops as ops from vllm import envs from vllm._aiter_ops import rocm_aiter_ops from vllm.config import ModelConfig, VllmConfig, get_current_vllm_config from vllm.distributed.parallel_state import get_dcp_group, is_global_first_rank from vllm.logger import init_logger from vllm.model_executor.custom_op import CustomOp from vllm.model_executor.layers.batch_invariant import ( vllm_is_batch_invariant, ) from vllm.model_executor.layers.linear import ( ColumnParallelLinear, ) from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8 from vllm.model_executor.layers.quantization.utils.quant_utils import ( GroupShape, get_and_maybe_dequant_weights, ) from vllm.platforms import current_platform from vllm.utils.flashinfer import has_nvidia_artifactory from vllm.utils.math_utils import cdiv, round_down from vllm.v1.attention.backend import ( AttentionBackend, AttentionLayer, AttentionMetadata, AttentionMetadataBuilder, CommonAttentionMetadata, MLAAttentionImpl, ) from vllm.v1.attention.backends.fa_utils import get_flash_attn_version from vllm.v1.attention.backends.utils import ( get_dcp_local_seq_lens, get_per_layer_parameters, infer_global_hyperparameters, split_decodes_and_prefills, ) from vllm.v1.attention.ops.common import cp_lse_ag_out_rs from vllm.v1.attention.ops.merge_attn_states import merge_attn_states from vllm.v1.kv_cache_interface import AttentionSpec class QueryLenSupport(Enum): """Defines the level of query length support for an attention backend's decode pipeline. - SINGLE_ONLY: Decode pipeline only supports single-token queries (query_len=1) - UNIFORM: Decode pipeline supports uniform multi-token queries (all requests must have same query_len > 1) - VARLEN: Decode pipeline supports variable-length queries (mixed query lengths in same batch) """ SINGLE_ONLY = "single_only" UNIFORM = "uniform" VARLEN = "varlen" try: from vllm.vllm_flash_attn import ( # type: ignore[attr-defined] flash_attn_varlen_func, ) is_vllm_fa = True except ImportError: # For rocm use upstream flash attention if current_platform.is_rocm(): from flash_attn import flash_attn_varlen_func # type: ignore[no-redef] is_vllm_fa = False try: from flashinfer import BatchPrefillWithRaggedKVCacheWrapper from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache # noqa: F401 flashinfer_available = True except ImportError: BatchPrefillWithRaggedKVCacheWrapper = object flashinfer_available = False def dynamic_per_batched_tensor_quant( x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn ): DTYPE_MAX = torch.finfo(dtype).max min_val, max_val = x.aminmax() amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-10) scale = DTYPE_MAX / amax x_scl_sat = (x * scale).clamp(min=-DTYPE_MAX, max=DTYPE_MAX) return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal() logger = init_logger(__name__) @CustomOp.register("mla_decode_concat_quant_fp8") class _DecodeConcatQuantFP8(QuantFP8): """ QuantFP8 variant that concatenates decode_ql_nope and decode_q_pe before quantization. When disabled, forward_native is compiled via torch.compile, fusing cat/reshape/quant/view together. """ def _make_forward(quant_fn): # noqa: N805 """Factory to create forward methods that concat before quantization.""" def forward( self, decode_ql_nope: torch.Tensor, decode_q_pe: torch.Tensor, scale: torch.Tensor, scale_ub: torch.Tensor | None = None, ) -> torch.Tensor: decode_q0 = torch.cat((decode_ql_nope, decode_q_pe), dim=-1) decode_q_flat = decode_q0.reshape(decode_q0.shape[0], -1) decode_q, _ = quant_fn(self, decode_q_flat, scale, scale_ub) return decode_q.view(decode_q0.shape) return forward forward_native = _make_forward(QuantFP8.forward_native) # type: ignore[arg-type] forward_cuda = _make_forward(QuantFP8.forward_cuda) # type: ignore[arg-type] forward_hip = _make_forward(QuantFP8.forward_hip) # type: ignore[arg-type] CUDNN_WORKSPACE_SIZE = 12800 class MLACommonBackend(AttentionBackend): accept_output_buffer: bool = True @staticmethod def get_name() -> str: return "TRITON_MLA" @staticmethod def get_builder_cls() -> type["MLACommonMetadataBuilder"]: return MLACommonMetadataBuilder @staticmethod def get_kv_cache_shape( num_blocks: int, block_size: int, num_kv_heads: int, # assumed to be 1 for MLA head_size: int, cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return (num_blocks, block_size, head_size) @staticmethod def get_kv_cache_stride_order( include_num_layers_dimension: bool = False, ) -> tuple[int, ...]: # `stride_order` indicates the permutation that gets # us from `get_kv_cache_shape` to the actual memory layout we want. # (num_blocks, num_layers, block_size, head_size) return (1, 0, 2, 3) if include_num_layers_dimension else (0, 1, 2) @classmethod def get_supported_head_sizes(cls) -> list[int]: return [576] @classmethod def is_mla(cls) -> bool: return True @dataclass class MLACommonPrefillMetadata: """Prefill Specific Metadata""" @dataclass class ChunkedContextMetadata: # New for MLA (compared to FlashAttention) # For handling chunked prefill cu_seq_lens: torch.Tensor starts: torch.Tensor seq_tot: list[int] max_seq_lens: list[int] seq_lens: torch.Tensor workspace: torch.Tensor token_to_seq: torch.Tensor chunk_total_token: list[int] # for mla DCP padded_local_chunk_seq_lens: list[list[int]] | None = None local_context_lens_allranks: list[list[int]] | None = None padded_local_cu_seq_lens: torch.Tensor | None = None cu_seq_lens_lst: list[list[int]] | None = None chunk_size: int | None = None block_table: torch.Tensor query_start_loc: torch.Tensor max_query_len: int chunked_context: ChunkedContextMetadata | None = None query_seq_lens: torch.Tensor | None = None workspace_buffer: torch.Tensor | None = None q_data_type: torch.dtype | None = None @dataclass class FlashInferPrefillMetadata(MLACommonPrefillMetadata): prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = field( default_factory=list ) @dataclass class CudnnPrefillMetadata(MLACommonPrefillMetadata): class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata): seq_lens: torch.Tensor cudnn_workspace: torch.Tensor | None = None @dataclass class MLACommonDecodeMetadata: block_table: torch.Tensor seq_lens: torch.Tensor dcp_tot_seq_lens: torch.Tensor | None D = TypeVar("D", bound=MLACommonDecodeMetadata) @dataclass class MLACommonMetadata(AttentionMetadata, Generic[D]): """Metadata for MLACommon. NOTE: Please read the comment at the top of the file before trying to understand this class """ # NOTE(sang): Definition of context_len, query_len, and seq_len. # |---------- N-1 iteration --------| # |---------------- N iteration ---------------------| # |- tokenA -|......................|-- newTokens ---| # |---------- context_len ----------| # |-------------------- seq_len ---------------------| # |-- query_len ---| num_reqs: int max_query_len: int max_seq_len: int num_actual_tokens: int # Number of tokens excluding padding. query_start_loc: torch.Tensor slot_mapping: torch.Tensor # New for MLA (compared to FlashAttention) # For handling prefill decode split num_decodes: int num_decode_tokens: int num_prefills: int # The dimension of the attention heads head_dim: int | None = None decode: D | None = None prefill: ( MLACommonPrefillMetadata | FlashInferPrefillMetadata | CudnnPrefillMetadata | None ) = None def __post_init__(self): if self.head_dim is not None and not MLACommonBackend.supports_head_size( self.head_dim ): raise ValueError(f"Head dimension {self.head_dim} is not supported by MLA.") M = TypeVar("M", bound=MLACommonMetadata) A = TypeVar("A", bound=AttentionMetadata) def is_deepseek_r1_mla_compatible(vllm_config: VllmConfig) -> bool: # Check if model has DeepSeek R1 compatible MLA dimensions: # qk_nope_head_dim = 128, qk_rope_head_dim = 64, v_head_dim = 128 # which results in query/key head dim = 192. if vllm_config.model_config is None: return False hf_text_config = vllm_config.model_config.hf_text_config qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1) qk_rope_head_dim = getattr(hf_text_config, "qk_rope_head_dim", 1) v_head_dim = getattr(hf_text_config, "v_head_dim", 1) return qk_nope_head_dim == 128 and qk_rope_head_dim == 64 and v_head_dim == 128 def use_flashinfer_prefill() -> bool: # For blackwell default to flashinfer prefill if it's available since # it is faster than FA2. from vllm.config import get_current_vllm_config vllm_config = get_current_vllm_config() if not ( not vllm_config.attention_config.disable_flashinfer_prefill and flashinfer_available and not vllm_config.attention_config.use_cudnn_prefill and current_platform.is_device_capability_family(100) ): return False return is_deepseek_r1_mla_compatible(vllm_config) def use_cudnn_prefill() -> bool: from vllm.config import get_current_vllm_config vllm_config = get_current_vllm_config() return ( flashinfer_available and vllm_config.attention_config.use_cudnn_prefill and current_platform.is_device_capability_family(100) and has_nvidia_artifactory() ) def use_trtllm_ragged_deepseek_prefill() -> bool: """Check if TRT-LLM ragged DeepSeek prefill should be used.""" from vllm.config import get_current_vllm_config vllm_config = get_current_vllm_config() if not ( flashinfer_available and vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill and current_platform.is_device_capability_family(100) ): return False return is_deepseek_r1_mla_compatible(vllm_config) @dataclass class MLADims: q_lora_rank: int | None kv_lora_rank: int qk_nope_head_dim: int qk_rope_head_dim: int v_head_dim: int def get_mla_dims(model_config: ModelConfig) -> MLADims: hf_text_config = model_config.hf_text_config return MLADims( q_lora_rank=getattr(hf_text_config, "q_lora_rank", None), kv_lora_rank=hf_text_config.kv_lora_rank, qk_nope_head_dim=hf_text_config.qk_nope_head_dim, qk_rope_head_dim=hf_text_config.qk_rope_head_dim, v_head_dim=hf_text_config.v_head_dim, ) class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ # Defines the level of query length support for this backend. # - SINGLE_ONLY: Only single-token queries (no spec decode support) # - UNIFORM: Supports uniform multi-token queries (spec decode with uniform lengths) # - VARLEN: Supports variable-length queries (spec decode with mixed lengths) # If set to UNIFORM or VARLEN, this will increase `reorder_batch_threshold` when # speculative decoding is enabled. query_len_support: ClassVar[QueryLenSupport] = QueryLenSupport.SINGLE_ONLY # The threshold for reordering the batch into decode and prefill requests. # If > 1, the batch will be reordered such that requests with # query length <= threshold are classified as decode requests. # Use `query_len_support` (above) to set this automatically # when speculative decoding is enabled. reorder_batch_threshold: int = 1 @staticmethod def determine_chunked_prefill_workspace_size(vllm_config: VllmConfig) -> int: scheduler_config = vllm_config.scheduler_config cache_config = vllm_config.cache_config model_config = vllm_config.model_config chunked_prefill_workspace_size = min( # Try for 8 full length request or at least 4 pages per-request max( 8 * model_config.max_model_len, 4 * scheduler_config.max_num_seqs * cache_config.block_size, ), # For long-context models try not to over-allocate limiting # kv-cache space, limiting it to 64k tokens, # which would result in the workspace being: # 2*(576)*(64*1024) = 144mb # (assuming 576 MLA head dim, and fp16) # which would result in up-projected context being # 2*(192*128)*(64*1024) = 3gb # (assuming 192 QK head dim, 128 heads, and fp16) 64 * 1024, ) # Enforce that we enough for at least 1 page per request chunked_prefill_workspace_size = max( chunked_prefill_workspace_size, scheduler_config.max_num_seqs * cache_config.block_size, ) return chunked_prefill_workspace_size def __init__( self, kv_cache_spec: AttentionSpec, layer_names: list[str], vllm_config: VllmConfig, device: torch.device, metadata_cls: type[M] | None = None, supports_dcp_with_varlen: bool = False, ): self.metadata_cls = ( metadata_cls if metadata_cls is not None else MLACommonMetadata ) self.kv_cache_spec = kv_cache_spec scheduler_config = vllm_config.scheduler_config self.model_config = vllm_config.model_config parallel_config = vllm_config.parallel_config self.compilation_config = vllm_config.compilation_config self.vllm_config = vllm_config self.device = device self.num_heads = self.model_config.get_num_attention_heads(parallel_config) self.mla_dims = get_mla_dims(self.model_config) self.aot_schedule = current_platform.is_cuda() try: self.dcp_world_size = get_dcp_group().world_size self.dcp_rank = get_dcp_group().rank_in_group except AssertionError: # DCP might not be initialized in testing self.dcp_world_size = 1 self.dcp_rank = 0 self.dcp_local_block_size = parallel_config.cp_kv_cache_interleave_size self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size self.cp_kv_cache_interleave_size = parallel_config.cp_kv_cache_interleave_size # Don't try to access the runner on AMD if self.aot_schedule: self.page_size = self.kv_cache_spec.block_size self.chunked_prefill_workspace_size = ( self.determine_chunked_prefill_workspace_size(vllm_config) ) if self.dcp_world_size > 1: # Note(hc): The local kvcache is incomplete when DCP is triggered, # an additional kvcache allgather across the DCP group is therefore # required, so the workspace has to be enlarged by 1/DCP relative # to the original TP allocation. assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0 self.chunked_prefill_workspace = torch.empty( ( self.chunked_prefill_workspace_size + self.chunked_prefill_workspace_size // self.dcp_world_size, self.model_config.get_head_size(), ), dtype=self.model_config.dtype, device=device, ) else: self.chunked_prefill_workspace = torch.empty( ( self.chunked_prefill_workspace_size, self.model_config.get_head_size(), ), dtype=self.model_config.dtype, device=device, ) self._use_cudnn_prefill = use_cudnn_prefill() self._use_fi_prefill = use_flashinfer_prefill() self._use_trtllm_ragged_prefill = use_trtllm_ragged_deepseek_prefill() self.prefill_metadata_cls = ( FlashInferPrefillMetadata if self._use_fi_prefill else CudnnPrefillMetadata if self._use_cudnn_prefill else MLACommonPrefillMetadata ) if self._use_fi_prefill: self._workspace_buffer = torch.empty( envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=device, ) self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = [] self._global_hyperparameters = infer_global_hyperparameters( get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl) # type: ignore[type-abstract] ) if self._use_trtllm_ragged_prefill: self._workspace_buffer = torch.empty( envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=device, ) if self._use_cudnn_prefill: self.cudnn_workspace = torch.empty( CUDNN_WORKSPACE_SIZE * scheduler_config.max_num_seqs, dtype=torch.int8, device=device, ) supports_spec_decode = self.query_len_support != QueryLenSupport.SINGLE_ONLY self._init_reorder_batch_threshold( self.reorder_batch_threshold, supports_spec_decode, supports_dcp_with_varlen ) # Validate consistency between query_len_support and reorder_batch_threshold if self.query_len_support == QueryLenSupport.SINGLE_ONLY: assert self.reorder_batch_threshold == 1, ( f"reorder_batch_threshold must be 1 when query_len_support is " f"SINGLE_ONLY, got {self.reorder_batch_threshold}" ) def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata): qo_indptr = prefill.query_start_loc has_context = False if prefill.chunked_context is not None: chunked_context = prefill.chunked_context has_context = True if self._fi_prefill_main is None: self._fi_prefill_main = BatchPrefillWithRaggedKVCacheWrapper( self._workspace_buffer, "NHD", backend="cutlass" ) if has_context: num_chunks = chunked_context.cu_seq_lens.shape[0] # Allocate more prefill chunk wrappers if needed if len(self._fi_prefill_chunks) < num_chunks: for _ in range(len(self._fi_prefill_chunks), num_chunks): self._fi_prefill_chunks.append( BatchPrefillWithRaggedKVCacheWrapper( self._workspace_buffer, "NHD", backend="cutlass" ) ) assert num_chunks <= len(self._fi_prefill_chunks) # In MLA, the non-latent num_qo_heads == num_kv_heads num_qo_heads = self.num_heads num_kv_heads = num_qo_heads # Sanity: Verify that num_kv_heads == 1 since it is latent space assert self.kv_cache_spec.num_kv_heads == 1 # Get non-latent head_dim_qk and head_dim_vo head_dim_qk = self.mla_dims.qk_nope_head_dim + self.mla_dims.qk_rope_head_dim head_dim_vo = self.mla_dims.v_head_dim # For main run, qo_indptr == kv_indptr kv_indptr = qo_indptr.clone() # Prepare main prefill self._fi_prefill_main.plan( qo_indptr=qo_indptr, kv_indptr=kv_indptr, num_qo_heads=num_qo_heads, num_kv_heads=num_kv_heads, head_dim_qk=head_dim_qk, head_dim_vo=head_dim_vo, causal=True, # This is main run sm_scale=self._global_hyperparameters.sm_scale, window_left=self._global_hyperparameters.window_left, logits_soft_cap=self._global_hyperparameters.logits_soft_cap, q_data_type=self.model_config.dtype, ) # Prepare context prefills if has_context: for i in range(num_chunks): kv_indptr_chunk = chunked_context.cu_seq_lens[i] self._fi_prefill_chunks[i].plan( qo_indptr=qo_indptr, kv_indptr=kv_indptr_chunk, num_qo_heads=num_qo_heads, num_kv_heads=num_kv_heads, head_dim_qk=head_dim_qk, head_dim_vo=head_dim_vo, causal=False, # This is context run sm_scale=self._global_hyperparameters.sm_scale, window_left=self._global_hyperparameters.window_left, logits_soft_cap=self._global_hyperparameters.logits_soft_cap, q_data_type=self.model_config.dtype, ) prefill.prefill_main = self._fi_prefill_main prefill.prefill_chunks = self._fi_prefill_chunks def _build_decode( self, block_table_tensor: torch.Tensor, seq_lens_device: torch.Tensor, max_seq_len: int, query_start_loc_cpu: torch.Tensor, query_start_loc_device: torch.Tensor, num_decode_tokens: int, dcp_tot_seq_lens_device: torch.Tensor | None, ) -> MLACommonDecodeMetadata: return MLACommonDecodeMetadata( block_table=block_table_tensor, seq_lens=seq_lens_device, dcp_tot_seq_lens=dcp_tot_seq_lens_device, ) def build_for_cudagraph_capture( self, common_attn_metadata: CommonAttentionMetadata ) -> M: """ This method builds the metadata for full cudagraph capture. Currently, only decode is supported for full cudagraphs with MLA. """ m = common_attn_metadata assert m.num_reqs <= (m.num_actual_tokens * self.reorder_batch_threshold), ( "MLA only supports decode-only full CUDAGraph capture. " "Make sure all cudagraph capture sizes <= max_num_seq." ) assert m.max_query_len <= self.reorder_batch_threshold # decode only return self.build(0, m) def build( self, common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata, fast_build: bool = False, ) -> M: num_reqs = common_attn_metadata.num_reqs num_tokens = common_attn_metadata.num_actual_tokens max_query_len = common_attn_metadata.max_query_len max_seq_len = common_attn_metadata.max_seq_len # Note(simon): be careful about the CPU <> GPU memory movement in this # function. We should avoid GPU -> CPU sync as much as possible because # it blocks on all previous kernels. device = self.device block_table_tensor = common_attn_metadata.block_table_tensor slot_mapping = common_attn_metadata.slot_mapping query_start_loc = common_attn_metadata.query_start_loc query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu seq_lens = common_attn_metadata.seq_lens dcp_local_seq_lens = common_attn_metadata.dcp_local_seq_lens num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = ( split_decodes_and_prefills( common_attn_metadata, decode_threshold=self.reorder_batch_threshold, require_uniform=(self.query_len_support != QueryLenSupport.VARLEN), ) ) assert num_decodes + num_prefills == num_reqs assert num_decode_tokens + num_prefill_tokens == num_tokens prefill_metadata = None if num_prefills > 0: num_computed_tokens_cpu = ( common_attn_metadata.compute_num_computed_tokens().cpu() ) reqs_start = num_decodes # prefill_start context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs] max_context_len_cpu = context_lens_cpu.max().item() num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item() prefill_query_start_loc = ( query_start_loc[reqs_start:] - query_start_loc[reqs_start] ) chunked_context_metadata = None if max_context_len_cpu > 0: # NOTE: it is recommend you read the `Chunked Prefill` section # in the comment at the top of the file before trying to # understand the following code # currently we allocate an equal amount of workspace for each # prefill in the batch, we could probably use a more advanced # algorithm here and allocate more workspace to prefills with # longer context lengths max_context_chunk = ( self.chunked_prefill_workspace_size // num_prefills_with_context_cpu ) if self.aot_schedule: # align max_context_chunk to page_size by rounding down, # currently the `gather_and_maybe_dequant_cache` kernel # cannot handle `context_chunk_starts` that are not aligned # to page_size max_context_chunk = round_down(max_context_chunk, self.page_size) assert max_context_chunk > 0 num_chunks = cdiv(max_context_len_cpu, max_context_chunk) # if `max_context_chunk = 256`, `num_chunks = 3`, and # `num_prefills_with_context = 4`, create a tensor that looks # like # [[0, 0, 0, 0], [256, 256, 256, 256], [512, 512, 512, 512]] # Note(simon): this is done in CPU because of downstream's # of `to_list`. chunk_starts = ( torch.arange(num_chunks, dtype=torch.int32) .unsqueeze(1) .expand(-1, num_prefills) * max_context_chunk ) chunk_ends = torch.min( context_lens_cpu.unsqueeze(0), chunk_starts + max_context_chunk ) chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0) cu_seq_lens_cpu = torch.zeros( num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True ) torch.cumsum( chunk_seq_lens, dim=1, out=cu_seq_lens_cpu[:, 1:], dtype=torch.int32 ) chunk_total_token = cu_seq_lens_cpu[:, -1] max_token_num_over_chunk = chunk_total_token.max().item() token_to_seq_tensor_cpu = torch.zeros( [num_chunks, max_token_num_over_chunk], dtype=torch.int32 ) range_idx = torch.arange(num_prefills, dtype=torch.int32) for i in range(num_chunks): chunk_token_to_seq_tensor = torch.repeat_interleave( range_idx, chunk_seq_lens[i] ) chunk_len = chunk_token_to_seq_tensor.shape[0] token_to_seq_tensor_cpu[i, :chunk_len] = chunk_token_to_seq_tensor if self.dcp_world_size > 1: local_context_lens_allranks = get_dcp_local_seq_lens( context_lens_cpu, self.dcp_world_size, None, self.dcp_local_block_size, ) # Note(qcs): The max local context lengths # padded to `dcp_local_block_size`. padded_local_context_lens_cpu: torch.Tensor = ( cdiv( context_lens_cpu, self.dcp_virtual_block_size, ) * self.dcp_local_block_size ) # Note(hc): The above max_context_chunk already enforces # block_size alignment, DCP just need the block_size can # be divisible by dcp_world_size, because DCP use # cp_gather_cache which not require `cp_chunk_starts` # aligned to page_size. assert max_context_chunk % self.dcp_world_size == 0 padded_local_max_context_chunk_across_ranks = ( cdiv( max_context_chunk, self.dcp_virtual_block_size, ) * self.dcp_local_block_size ) local_chunk_starts = ( torch.arange(num_chunks, dtype=torch.int32) .unsqueeze(1) .expand(-1, num_prefills) * padded_local_max_context_chunk_across_ranks ) local_chunk_ends = torch.min( padded_local_context_lens_cpu.unsqueeze(0), local_chunk_starts + padded_local_max_context_chunk_across_ranks, ) padded_local_chunk_seq_lens = ( local_chunk_ends - local_chunk_starts ).clamp(min=0) padded_local_cu_chunk_seq_lens_cpu = torch.zeros( num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True ) torch.cumsum( padded_local_chunk_seq_lens, dim=1, out=padded_local_cu_chunk_seq_lens_cpu[:, 1:], dtype=torch.int32, ) chunked_context_metadata_cls = ( CudnnPrefillMetadata.ChunkedContextMetadata if self._use_cudnn_prefill else MLACommonPrefillMetadata.ChunkedContextMetadata ) if self.dcp_world_size > 1: chunked_context_metadata = chunked_context_metadata_cls( cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True), starts=local_chunk_starts.to(device, non_blocking=True), seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(), max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(), seq_lens=chunk_seq_lens, token_to_seq=token_to_seq_tensor_cpu.to( device, non_blocking=True ), chunk_total_token=chunk_total_token.tolist(), workspace=self.chunked_prefill_workspace, padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(), local_context_lens_allranks=local_context_lens_allranks.tolist(), padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu.to( device, non_blocking=True ), cu_seq_lens_lst=cu_seq_lens_cpu.tolist(), chunk_size=padded_local_max_context_chunk_across_ranks, ) else: chunked_context_metadata = chunked_context_metadata_cls( cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True), starts=chunk_starts.to(device, non_blocking=True), seq_tot=chunk_seq_lens.sum(dim=1).tolist(), max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(), seq_lens=chunk_seq_lens, token_to_seq=token_to_seq_tensor_cpu.to( device, non_blocking=True ), chunk_total_token=chunk_total_token, workspace=self.chunked_prefill_workspace, ) if self._use_cudnn_prefill: chunked_context_metadata.seq_lens = chunk_seq_lens assert ( max(chunked_context_metadata.max_seq_lens) <= self.chunked_prefill_workspace_size ) prefill_metadata = self.prefill_metadata_cls( block_table=block_table_tensor[reqs_start:, ...], query_start_loc=prefill_query_start_loc, max_query_len=max_query_len, chunked_context=chunked_context_metadata, ) if self._use_cudnn_prefill: assert isinstance(prefill_metadata, CudnnPrefillMetadata) prefill_metadata.query_seq_lens = ( prefill_query_start_loc[1:] - prefill_query_start_loc[:-1] ) prefill_metadata.cudnn_workspace = self.cudnn_workspace if self._use_trtllm_ragged_prefill: prefill_metadata.query_seq_lens = ( prefill_query_start_loc[1:] - prefill_query_start_loc[:-1] ) prefill_metadata.workspace_buffer = self._workspace_buffer decode_metadata = None if num_decodes > 0: dcp_tot_seq_lens_device = None if self.dcp_world_size > 1: dcp_tot_seq_lens_device = seq_lens[:num_decodes] seq_lens = dcp_local_seq_lens # After DCP distribution, the maximum number of tokens for any rank is # ceil(L / (N * I)) * I, where L is max_seq_len, N is dcp_world_size, # and I is cp_kv_cache_interleave_size. # This eliminates GPU->CPU sync while minimizing workspace # over-allocation. num_partitions = self.dcp_world_size * self.cp_kv_cache_interleave_size max_seq_len = ( (max_seq_len + num_partitions - 1) // num_partitions ) * self.cp_kv_cache_interleave_size decode_metadata = self._build_decode( block_table_tensor=block_table_tensor[:num_decodes, ...], seq_lens_device=seq_lens[:num_decodes], max_seq_len=max_seq_len, query_start_loc_cpu=query_start_loc_cpu[: num_decodes + 1], query_start_loc_device=query_start_loc[: num_decodes + 1], num_decode_tokens=num_decode_tokens, dcp_tot_seq_lens_device=dcp_tot_seq_lens_device, ) attn_metadata = self.metadata_cls( num_reqs=common_attn_metadata.num_reqs, max_query_len=common_attn_metadata.max_query_len, max_seq_len=max_seq_len, num_actual_tokens=num_tokens, query_start_loc=query_start_loc, slot_mapping=slot_mapping, head_dim=self.model_config.get_head_size(), # MLACommonMetadata Chunk prefill specific num_decodes=num_decodes, num_decode_tokens=num_decode_tokens, num_prefills=num_prefills, prefill=prefill_metadata, decode=decode_metadata, ) if self._use_fi_prefill and num_prefills > 0: assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata) self._build_fi_prefill_wrappers(attn_metadata.prefill) return attn_metadata def reorg_kvcache( allgatered_kv_c_normed: torch.Tensor, allgatered_k_pe: torch.Tensor, padded_local_chunk_seq_lens_lst: list[int], local_context_lens_allranks: list[list[int]], sum_seq_len: int, max_seq_len: int, chunk_size: int, chunk_idx: int, toks: int, ) -> tuple[torch.Tensor, torch.Tensor]: """ reorg and unpad kvcache after cp local gather to tp layout for attn kernel. e.g. allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ..., T0_4, T0_5, pad, pad, T1_2, pad, ...] -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5, T1_0, T1_1, T1_2, ...] Args: padded_local_chunk_seq_lens_lst: local chunk context lengths under current CP rank. local_context_lens_allranks: local context lengths on each CP rank. sum_seq_len: the sum of cp_chunk_seq_lens_lst. max_seq_len: the max value of cp_chunk_seq_lens_lst. chunk_size: the local padded max context chunk from chunked_context_metadata building. chunk_idx: chunk idx of chunked_prefill. toks: the number of tokens for local gather cache. """ kv_c_segments = [] k_pe_segments = [] src_token_idx = 0 max_seq_len_check = 0 for padded_local_chunk_seq_len, local_context_lens in zip( padded_local_chunk_seq_lens_lst, local_context_lens_allranks ): cur_seq_len = 0 for rank, local_context_len in enumerate(local_context_lens): # Note(qcs): We split the context into multiple chunks, # depending on the size of the workspace. # local_context in dcp0: |-----------------| # local_context in dcp1: |--------------| # n*padded_local_chunk: |-----|-----|-----| # local_chunk_len in dcp1: |-----|-----|--| # so we need update the last chunk length in dcp1. local_chunk_len = min( max(0, local_context_len - chunk_idx * chunk_size), padded_local_chunk_seq_len, ) if local_chunk_len != 0: kv_c_segment = allgatered_kv_c_normed[ rank * toks + src_token_idx : rank * toks + src_token_idx + local_chunk_len ] k_pe_segment = allgatered_k_pe[ rank * toks + src_token_idx : rank * toks + src_token_idx + local_chunk_len ] kv_c_segments.append(kv_c_segment) k_pe_segments.append(k_pe_segment) cur_seq_len += local_chunk_len max_seq_len_check = max(max_seq_len_check, cur_seq_len) src_token_idx += padded_local_chunk_seq_len reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0) reorganized_k_pe = torch.cat(k_pe_segments, dim=0) assert reorganized_kv_c_normed.shape[0] == sum_seq_len assert reorganized_k_pe.shape[0] == sum_seq_len assert max_seq_len_check == max_seq_len return reorganized_kv_c_normed, reorganized_k_pe # TODO(Lucas): rename MLACommonBaseImpl -> MLACommonImpl, # and MLACommonImpl -> MLACommonDenseImpl or somthing like that class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ 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 q_lora_rank: int | None, kv_lora_rank: int, qk_nope_head_dim: int, qk_rope_head_dim: int, qk_head_dim: int, v_head_dim: int, kv_b_proj: ColumnParallelLinear, indexer=None, q_pad_num_heads: int | None = None, ) -> None: if kv_sharing_target_layer_name is not None: raise NotImplementedError("KV sharing is not supported for MLA") self.num_heads = num_heads self.head_size = head_size self.scale = float(scale) self.num_kv_heads = num_kv_heads self.kv_cache_dtype = kv_cache_dtype self.q_lora_rank = q_lora_rank self.kv_lora_rank = kv_lora_rank self.qk_nope_head_dim = qk_nope_head_dim self.qk_rope_head_dim = qk_rope_head_dim self.qk_head_dim = qk_head_dim self.v_head_dim = v_head_dim self.kv_b_proj = kv_b_proj self.indexer = indexer self.q_pad_num_heads = q_pad_num_heads self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled() # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported self.is_aiter_triton_fp4_bmm_enabled = ( rocm_aiter_ops.is_fp4bmm_enabled() and self.kv_b_proj.weight.dtype == torch.bfloat16 ) def process_weights_after_loading(self, act_dtype: torch.dtype): # we currently do not have quantized bmm's which are needed for # `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform # the bmm's in 16-bit, the extra memory overhead of this is fairly low kv_b_proj_weight = get_and_maybe_dequant_weights( self.kv_b_proj, out_dtype=act_dtype ).T assert kv_b_proj_weight.shape == ( self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), ), ( f"{kv_b_proj_weight.shape=}, " f"{self.kv_lora_rank=}, " f"{self.num_heads=}, " f"{self.qk_nope_head_dim=}, " f"{self.v_head_dim=}" ) kv_b_proj_weight = kv_b_proj_weight.view( self.kv_lora_rank, self.num_heads, self.qk_nope_head_dim + self.v_head_dim, ) W_UK, W_UV = kv_b_proj_weight.split( [self.qk_nope_head_dim, self.v_head_dim], dim=-1 ) # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported if self.is_aiter_triton_fp4_bmm_enabled: from vllm.model_executor.layers.quantization.quark.utils import ( quark_quantize_weight_to_mxfp4, ) self.W_K, self.W_K_scale = quark_quantize_weight_to_mxfp4(W_UK) # Convert from (L, N, P) to (N, L, P) self.W_K = self.W_K.transpose(0, 1) self.W_K_scale = self.W_K_scale.transpose(0, 1) self.W_V, self.W_V_scale = quark_quantize_weight_to_mxfp4( W_UV.permute(1, 2, 0) ) elif self.is_aiter_triton_fp8_bmm_enabled: W_K = W_UK.transpose(0, 1) # 16 512 128 W_V = W_UV.permute(1, 2, 0) # 16 128 512 self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant( W_K, dtype=current_platform.fp8_dtype() ) self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant( W_V, dtype=current_platform.fp8_dtype() ) # The kernel operates on non-padded inputs. Hence, pre-compiling # triton kernel to avoid runtime compilation for unseen batch sizes # Pre-compile for batch sizes 1 to 1024 to cover most use-cases. # On DS-R1, this step adds roughly 50s to the model loading time. max_batch_size = 1024 # [ToDo] Find the optimal upper limit pre_compilation_list = list(range(1, max_batch_size + 1)) if is_global_first_rank(): pre_compilation_list = tqdm( pre_compilation_list, desc="[Aiter Triton] Pre-compiling fp8 BMM kernel", total=max_batch_size, ) for m in pre_compilation_list: x = torch.empty( (self.W_K.shape[0], m, self.W_K.shape[2]), dtype=torch.bfloat16, device=self.W_K.device, ) rocm_aiter_ops.triton_fp8_bmm( x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True ) x = torch.empty( (self.W_V.shape[0], m, self.W_V.shape[2]), dtype=torch.bfloat16, device=self.W_V.device, ) rocm_aiter_ops.triton_fp8_bmm( x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True ) else: # Convert from (L, N, V) to (N, L, V) self.W_UV = W_UV.transpose(0, 1) # Convert from (L, N, P) to (N, P, L) self.W_UK_T = W_UK.permute(1, 2, 0) def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor): # Convert from (B, N, L) to (N, B, L) x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1) out = out.view(-1, self.num_heads, self.v_head_dim) if self.is_aiter_triton_fp4_bmm_enabled: out = rocm_aiter_ops.batched_gemm_a16wfp4( x, self.W_V, self.W_V_scale, out, transpose_bm=True, prequant=True, y_scale=None, ) x = out.view(-1, self.num_heads * self.v_head_dim) elif self.is_aiter_triton_fp8_bmm_enabled: # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V) x = rocm_aiter_ops.triton_fp8_bmm( x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out ) else: # Convert from (B, N * V) to (N, B, V) out = out.transpose(0, 1) # Multiply (N, B, L) x (N, L, V) -> (N, B, V) torch.bmm(x, self.W_UV, out=out) # Reuse "out" to make it "hot" # Convert from (N, B, V) to (B, N * V) out_new = out.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim) # Adjust output buffer shape back to the original (B, N * V) N, B, V = out.shape out.resize_((B, N * V)) out.copy_(out_new) # Copy result class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) if use_trtllm_ragged_deepseek_prefill(): logger.info_once( "Using TRT-LLM ragged DeepSeek prefill for MLA", scope="local" ) self._run_prefill_context_chunk = ( self._run_prefill_context_chunk_trtllm_ragged ) self._run_prefill_new_tokens = self._run_prefill_new_tokens_trtllm_ragged self._pad_v = False elif use_flashinfer_prefill(): logger.info_once("Using FlashInfer prefill for MLA", scope="local") self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi self._run_prefill_new_tokens = self._run_prefill_new_tokens_fi self._pad_v = False elif use_cudnn_prefill(): logger.info_once("Using CUDNN prefill for MLA", scope="local") self._run_prefill_context_chunk = self._run_prefill_context_chunk_cudnn self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn self._pad_v = False else: # Use FlashAttention logger.info_once("Using FlashAttention prefill for MLA", scope="local") self._run_prefill_context_chunk = self._run_prefill_context_chunk_fa self._run_prefill_new_tokens = self._run_prefill_new_tokens_fa # Handle the differences between the flash_attn_varlen from # flash_attn and the one from vllm_flash_attn. The former is used on # RoCM and the latter has an additional parameter to control # FA2 vs FA3 self.flash_attn_varlen_func = flash_attn_varlen_func self.vllm_flash_attn_version = get_flash_attn_version() if self.vllm_flash_attn_version is not None: self.flash_attn_varlen_func = functools.partial( flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version ) # For MLA the v head dim is smaller than qk head dim so we pad out # v with 0s to match the qk head dim for attention backends that do # not support different headdims # We don't need to pad V if we are on a hopper system with FA3 device_capability = current_platform.get_device_capability() self._pad_v = self.vllm_flash_attn_version is None or not ( self.vllm_flash_attn_version == 3 and device_capability is not None and device_capability[0] == 9 ) self.dcp_world_size: int = -1 self.chunked_prefill_workspace_size = ( MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size( get_current_vllm_config() ) ) self.cp_kv_cache_interleave_size: int = ( get_current_vllm_config().parallel_config.cp_kv_cache_interleave_size ) self._decode_concat_quant_fp8_op = _DecodeConcatQuantFP8( static=True, group_shape=GroupShape.PER_TENSOR, compile_native=True, ) def _flash_attn_varlen_diff_headdims( self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs ): maybe_padded_v = v if self._pad_v: maybe_padded_v = torch.nn.functional.pad( v, [0, q.shape[-1] - v.shape[-1]], value=0 ) if is_vllm_fa: kwargs["return_softmax_lse"] = return_softmax_lse else: # ROCm leverages the upstream flash_attn, which takes a parameter # called "return_attn_probs" instead of return_softmax_lse kwargs["return_attn_probs"] = return_softmax_lse if vllm_is_batch_invariant(): kwargs["num_splits"] = 1 attn_out = self.flash_attn_varlen_func( q=q, k=k, v=maybe_padded_v, softmax_scale=softmax_scale, **kwargs, ) # Unpack the output if there is multiple results lse = None if isinstance(attn_out, tuple): attn_out, lse = attn_out[0], attn_out[1] # Remain consistent with old `flash_attn_varlen_func` where there # is only one output tensor if `return_softmax_lse` is False. if return_softmax_lse: return attn_out, lse return attn_out def _run_prefill_new_tokens_fa( self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse ): return self._flash_attn_varlen_diff_headdims( q=q, k=k, v=v, cu_seqlens_q=prefill.query_start_loc, cu_seqlens_k=prefill.query_start_loc, max_seqlen_q=prefill.max_query_len, max_seqlen_k=prefill.max_query_len, softmax_scale=self.scale, causal=True, return_softmax_lse=return_softmax_lse, ) def _run_prefill_new_tokens_fi( self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse ): assert isinstance(prefill, FlashInferPrefillMetadata) assert prefill.prefill_main is not None ret = prefill.prefill_main.run( q=q, k=k, v=v, return_lse=return_softmax_lse, ) if isinstance(ret, tuple): return ret[0], ret[1].transpose(0, 1).contiguous() return ret def _run_prefill_new_tokens_cudnn( self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse ): assert isinstance(prefill, CudnnPrefillMetadata) assert prefill.query_seq_lens is not None output, lse = cudnn_batch_prefill_with_kv_cache( q=q, k_cache=k, v_cache=v, scale=self.scale, workspace_buffer=prefill.cudnn_workspace, max_token_per_sequence=prefill.max_query_len, max_sequence_kv=prefill.max_query_len, actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1), actual_seq_lens_kv=prefill.query_seq_lens.view(-1, 1, 1, 1), causal=True, # Do not support False for now return_lse=True, # Indicates actual_seq_lens are on GPU or CPU. is_cuda_graph_compatible=True, ) if return_softmax_lse: return output, lse return output def _run_prefill_context_chunk_fa( self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v ): assert prefill.chunked_context is not None return self._flash_attn_varlen_diff_headdims( q=q, k=k, v=v, cu_seqlens_q=prefill.query_start_loc, cu_seqlens_k=prefill.chunked_context.cu_seq_lens[chunk_idx], max_seqlen_q=prefill.max_query_len, max_seqlen_k=prefill.chunked_context.max_seq_lens[chunk_idx], softmax_scale=self.scale, causal=False, # Context is unmasked return_softmax_lse=True, ) def _run_prefill_context_chunk_fi( self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v ): assert isinstance(prefill, FlashInferPrefillMetadata) attn_out, lse = prefill.prefill_chunks[chunk_idx].run( q=q, k=k, v=v, return_lse=True, ) # Convert from (q_len, num_heads) to (num_heads, q_len) return attn_out, lse.transpose(0, 1).contiguous() def _run_prefill_context_chunk_cudnn( self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v ): assert isinstance(prefill, CudnnPrefillMetadata) assert prefill.chunked_context is not None assert prefill.chunked_context.seq_lens[chunk_idx] is not None assert prefill.query_seq_lens is not None return cudnn_batch_prefill_with_kv_cache( q=q, k_cache=k, v_cache=v, scale=self.scale, workspace_buffer=prefill.cudnn_workspace, max_token_per_sequence=prefill.max_query_len, max_sequence_kv=prefill.chunked_context.max_seq_lens[chunk_idx], actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1), actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].view( -1, 1, 1, 1 ), causal=False, return_lse=True, # Indicates actual_seq_lens are on GPU or CPU. is_cuda_graph_compatible=True, ) def _run_prefill_new_tokens_trtllm_ragged( self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse ): """TRT-LLM ragged attention for new tokens (causal).""" from flashinfer.prefill import trtllm_ragged_attention_deepseek assert prefill.query_seq_lens is not None assert prefill.workspace_buffer is not None ret = trtllm_ragged_attention_deepseek( query=q, key=k, value=v, workspace_buffer=prefill.workspace_buffer, seq_lens=prefill.query_seq_lens, max_q_len=prefill.max_query_len, max_kv_len=prefill.max_query_len, bmm1_scale=self.scale, bmm2_scale=1.0, o_sf_scale=1.0, batch_size=prefill.query_seq_lens.shape[0], window_left=-1, cum_seq_lens_q=prefill.query_start_loc, cum_seq_lens_kv=prefill.query_start_loc, enable_pdl=False, is_causal=True, return_lse=return_softmax_lse, ) if isinstance(ret, tuple): # Convert from (q_len, num_heads) to (num_heads, q_len) return ret[0], ret[1].transpose(0, 1).contiguous() return ret def _run_prefill_context_chunk_trtllm_ragged( self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v ): """TRT-LLM ragged attention for context chunks (non-causal).""" from flashinfer.prefill import trtllm_ragged_attention_deepseek assert prefill.chunked_context is not None assert prefill.chunked_context.seq_lens[chunk_idx] is not None assert prefill.workspace_buffer is not None out = torch.zeros( q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype, ) prefill.workspace_buffer.fill_(0) attn_out, lse = trtllm_ragged_attention_deepseek( query=q, key=k, value=v, workspace_buffer=prefill.workspace_buffer, seq_lens=prefill.chunked_context.seq_lens[chunk_idx], max_q_len=prefill.max_query_len, max_kv_len=prefill.chunked_context.max_seq_lens[chunk_idx], bmm1_scale=self.scale, bmm2_scale=1.0, o_sf_scale=1.0, batch_size=prefill.chunked_context.seq_lens[chunk_idx].shape[0], window_left=-1, cum_seq_lens_q=prefill.query_start_loc, cum_seq_lens_kv=prefill.chunked_context.cu_seq_lens[chunk_idx], enable_pdl=False, is_causal=False, return_lse=True, out=out, ) # Convert from (q_len, num_heads) to (num_heads, q_len) return attn_out, lse.transpose(0, 1).contiguous() def _concat_k_nope_k_pe( self, k_nope: torch.Tensor, k_pe: torch.Tensor ) -> torch.Tensor: """ Efficiently concatenate k_nope and k_pe tensors along the last dimension. This function avoids the performance penalty of torch.cat with expanded non-contiguous tensors by pre-allocating the output and using direct copies. Args: k_nope: Tensor of shape [..., nope_dim] k_pe: Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim] or [..., pe_dim] Returns: Tensor of shape [..., nope_dim + pe_dim] """ k = torch.empty( (*k_nope.shape[:-1], k_nope.shape[-1] + k_pe.shape[-1]), dtype=k_nope.dtype, device=k_nope.device, ) # Direct copies with efficient broadcasting k[..., : k_nope.shape[-1]] = k_nope k[..., k_nope.shape[-1] :] = k_pe return k def _compute_prefill_context( self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, k_scale: torch.Tensor, ): assert attn_metadata.prefill is not None prefill_metadata = attn_metadata.prefill assert prefill_metadata.chunked_context is not None output = None iters = len(prefill_metadata.chunked_context.seq_tot) workspace = prefill_metadata.chunked_context.workspace for i in range(iters): toks = prefill_metadata.chunked_context.seq_tot[i] ops.gather_and_maybe_dequant_cache( src_cache=kv_c_and_k_pe_cache, dst=workspace, block_table=prefill_metadata.block_table, cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i], token_to_seq=prefill_metadata.chunked_context.token_to_seq[i], num_tokens=prefill_metadata.chunked_context.chunk_total_token[i], kv_cache_dtype=self.kv_cache_dtype, scale=k_scale, seq_starts=prefill_metadata.chunked_context.starts[i], ) kv_c_normed = workspace[:toks][..., : self.kv_lora_rank] k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1) kv_nope = self.kv_b_proj(kv_c_normed)[0].view( -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim ) k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1) k = self._concat_k_nope_k_pe(k_nope, k_pe) attn_output, attn_softmax_lse = self._run_prefill_context_chunk( prefill=prefill_metadata, chunk_idx=i, q=q, k=k, v=v, ) if output is None: output = attn_output output_lse = attn_softmax_lse else: output_tmp = torch.empty_like(output) output_lse_tmp = torch.empty_like(output_lse) merge_attn_states( output=output_tmp, output_lse=output_lse_tmp, prefix_output=output, prefix_lse=output_lse, suffix_output=attn_output, suffix_lse=attn_softmax_lse, ) output = output_tmp output_lse = output_lse_tmp return output, output_lse def _context_parallel_compute_prefill_context( self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, k_scale: torch.Tensor, dcp_world_size: int, ): assert k_scale is None, "DCP not support scaled kvcache now." assert attn_metadata.prefill is not None prefill_metadata = attn_metadata.prefill assert prefill_metadata.chunked_context is not None assert prefill_metadata.chunked_context.padded_local_chunk_seq_lens is not None assert prefill_metadata.chunked_context.local_context_lens_allranks is not None assert prefill_metadata.chunked_context.padded_local_cu_seq_lens is not None assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None assert prefill_metadata.chunked_context.chunk_size is not None output = None iters = len(prefill_metadata.chunked_context.seq_tot) workspace = prefill_metadata.chunked_context.workspace for i in range(iters): toks = prefill_metadata.chunked_context.seq_tot[i] ops.cp_gather_cache( src_cache=kv_c_and_k_pe_cache, dst=workspace, block_table=prefill_metadata.block_table, cu_seq_lens=prefill_metadata.chunked_context.padded_local_cu_seq_lens[ i ], batch_size=attn_metadata.num_prefills, seq_starts=prefill_metadata.chunked_context.starts[i], ) # workspace # |------- N tokens --------|--------- N*dcp_size tokens ----------| # |<- use for loca_gather ->|<--------- use for allgather -------->| allgather_offset = workspace.shape[0] // (dcp_world_size + 1) assert allgather_offset * (dcp_world_size + 1) == workspace.shape[0] assert toks <= allgather_offset local_gathered_kvcache = workspace[:toks] cur_allgather_workspace = workspace[ allgather_offset : allgather_offset * (1 + dcp_world_size) ] assert toks * dcp_world_size <= cur_allgather_workspace.shape[0] cur_allgather_kvcache = cur_allgather_workspace[: toks * dcp_world_size] cur_allgather_kvcache.copy_( get_dcp_group().all_gather(local_gathered_kvcache, dim=0) ) assert ( cur_allgather_kvcache.shape[-1] == self.kv_lora_rank + self.qk_rope_head_dim ) allgatered_kv_c_normed, allgatered_k_pe = cur_allgather_kvcache.unsqueeze( 1 ).split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) kv_c_normed, k_pe = reorg_kvcache( allgatered_kv_c_normed, allgatered_k_pe, padded_local_chunk_seq_lens_lst=prefill_metadata.chunked_context.padded_local_chunk_seq_lens[ i ], local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks, sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1], max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i], chunk_size=prefill_metadata.chunked_context.chunk_size, chunk_idx=i, toks=toks, ) kv_nope = self.kv_b_proj(kv_c_normed)[0].view( -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim ) k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1) k = self._concat_k_nope_k_pe(k_nope, k_pe) attn_output, attn_softmax_lse = self._run_prefill_context_chunk( prefill=prefill_metadata, chunk_idx=i, q=q, k=k, v=v, ) if output is None: output = attn_output output_lse = attn_softmax_lse else: output_tmp = torch.empty_like(output) output_lse_tmp = torch.empty_like(output_lse) merge_attn_states( output=output_tmp, output_lse=output_lse_tmp, prefix_output=output, prefix_lse=output_lse, suffix_output=attn_output, suffix_lse=attn_softmax_lse, ) output = output_tmp output_lse = output_lse_tmp return output, output_lse def _forward_prefill( self, q: torch.Tensor, kv_c_normed: torch.Tensor, k_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, k_scale: torch.Tensor, output: torch.Tensor, ) -> None: # TODO (zyongye): Prefill function here assert attn_metadata.prefill is not None assert self.dcp_world_size != -1 has_context = attn_metadata.prefill.chunked_context is not None kv_nope = self.kv_b_proj(kv_c_normed)[0].view( -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim ) k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1) k = self._concat_k_nope_k_pe(k_nope, k_pe) output_prefill = self._run_prefill_new_tokens( prefill=attn_metadata.prefill, q=q, k=k, v=v, return_softmax_lse=has_context, ) if has_context: suffix_output, suffix_lse = output_prefill if self.dcp_world_size > 1: context_output, context_lse = ( self._context_parallel_compute_prefill_context( q, kv_c_and_k_pe_cache, attn_metadata, k_scale=None, dcp_world_size=self.dcp_world_size, ) ) else: context_output, context_lse = self._compute_prefill_context( q, kv_c_and_k_pe_cache, attn_metadata, k_scale ) # unpad if necessary if self._pad_v: context_output = context_output[..., : v.shape[-1]] suffix_output = suffix_output[..., : v.shape[-1]] output = output.view(-1, self.num_heads, self.v_head_dim) merge_attn_states( output=output, prefix_output=context_output, prefix_lse=context_lse, suffix_output=suffix_output, suffix_lse=suffix_lse, ) else: output_prefill = output_prefill[..., : v.shape[-1]].flatten(start_dim=-2) output.copy_(output_prefill) @abstractmethod def _forward_decode( self, q: torch.Tensor | tuple[torch.Tensor, torch.Tensor], kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: M, layer: AttentionLayer, ) -> tuple[torch.Tensor, torch.Tensor | None]: raise NotImplementedError def forward( self, layer: AttentionLayer, q: torch.Tensor, k_c_normed: torch.Tensor, # key in unified attn k_pe: torch.Tensor, # value in unified attn kv_cache: torch.Tensor, attn_metadata: M, output: torch.Tensor | None = None, output_scale: torch.Tensor | None = None, output_block_scale: torch.Tensor | None = None, ) -> torch.Tensor: assert output is not None, "Output tensor must be provided." if output_scale is not None or output_block_scale is not None: raise NotImplementedError( "fused output quantization is not yet supported for MLACommonImpl" ) if attn_metadata is None: # During the profile run try to simulate to worse case output size # for `self.kv_b_proj(kv_c_normed)` in `_compute_prefill_context` # since this can be large _ = torch.empty( ( self.chunked_prefill_workspace_size, self.num_heads, self.qk_nope_head_dim + self.v_head_dim, ), device=k_c_normed.device, dtype=k_c_normed.dtype, ) # The zero fill is required when used with DP + EP # to ensure all ranks within a DP group compute the # same expert outputs. return output.fill_(0) if self.dcp_world_size == -1: self.dcp_world_size = get_dcp_group().world_size fp8_attention = self.kv_cache_dtype.startswith("fp8") num_actual_toks = attn_metadata.num_actual_tokens # Inputs and outputs may be padded for CUDA graphs output_padded = output output = output[:num_actual_toks, ...] q = q[:num_actual_toks, ...] k_c_normed = k_c_normed[:num_actual_toks, ...] k_pe = k_pe[:num_actual_toks, ...] assert ( attn_metadata.num_decodes is not None and attn_metadata.num_prefills is not None and attn_metadata.num_decode_tokens is not None ) has_decode = attn_metadata.num_decodes > 0 has_prefill = attn_metadata.num_prefills > 0 num_decode_tokens = attn_metadata.num_decode_tokens decode_q = q[:num_decode_tokens] prefill_q = q[num_decode_tokens:] prefill_k_pe = k_pe[num_decode_tokens:] prefill_k_c_normed = k_c_normed[num_decode_tokens:] # write the latent and rope to kv cache if kv_cache.numel() > 0: ops.concat_and_cache_mla( k_c_normed, k_pe.squeeze(1), kv_cache, attn_metadata.slot_mapping.flatten(), kv_cache_dtype=self.kv_cache_dtype, scale=layer._k_scale, ) if fp8_attention: kv_cache = kv_cache.view(current_platform.fp8_dtype()) if has_prefill: self._forward_prefill( prefill_q, prefill_k_c_normed, prefill_k_pe, kv_cache, attn_metadata, layer._k_scale, output=output[num_decode_tokens:], ) if has_decode: assert attn_metadata.decode is not None decode_q_nope, decode_q_pe = decode_q.split( [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1 ) # Convert from (B, N, P) to (N, B, P) decode_q_nope = decode_q_nope.transpose(0, 1) if self.q_pad_num_heads is not None: B, N, L = decode_q_pe.shape decode_pe_padded = decode_q_pe.new_empty((B, self.q_pad_num_heads, L)) decode_pe_padded.resize_((B, N, L)) decode_pe_padded.copy_(decode_q_pe) decode_q_pe = decode_pe_padded if self.is_aiter_triton_fp4_bmm_enabled: from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4 decode_ql_nope = batched_gemm_a16wfp4( decode_q_nope, self.W_K, self.W_K_scale, transpose_bm=True, prequant=True, y_scale=layer._q_scale if fp8_attention else None, ) elif self.is_aiter_triton_fp8_bmm_enabled: # Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L) decode_ql_nope = rocm_aiter_ops.triton_fp8_bmm( decode_q_nope, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True, ) else: # Pads the head_dim if necessary (for the underlying kernel) N, B, P = decode_q_nope.shape _, _, L = self.W_UK_T.shape if self.q_pad_num_heads is not None: decode_ql_nope = decode_q_nope.new_empty( (self.q_pad_num_heads, B, L) ) decode_ql_nope.resize_((N, B, L)) else: decode_ql_nope = decode_q_nope.new_empty((N, B, L)) # Multiply (N, B, P) x (N, P, L) -> (N, B, L) torch.bmm(decode_q_nope, self.W_UK_T, out=decode_ql_nope) # Convert from (N, B, L) to (B, N, L) decode_ql_nope = decode_ql_nope.transpose(0, 1) if fp8_attention: assert decode_ql_nope.shape[0] == decode_q_pe.shape[0] assert decode_ql_nope.shape[1] == decode_q_pe.shape[1] decode_q = self._decode_concat_quant_fp8_op( decode_ql_nope, decode_q_pe, layer._q_scale ) else: decode_q = (decode_ql_nope, decode_q_pe) if self.dcp_world_size > 1: assert not fp8_attention, "DCP not support fp8 kvcache now." # concatenate decode_ql_nope and decode_q_pe -> (B, N, L + P) decode_q = torch.cat(decode_q, dim=-1) # decode_q do allgather in head dim. decode_q = get_dcp_group().all_gather(decode_q, dim=1) # call decode attn attn_out, lse = self._forward_decode( decode_q, kv_cache, attn_metadata, layer ) # correct dcp attn_out with lse. if self.dcp_world_size > 1: attn_out = cp_lse_ag_out_rs( attn_out, lse, get_dcp_group(), is_lse_base_on_e=not getattr(self, "_use_fi_prefill", False), ) # v_up projection self._v_up_proj(attn_out, out=output[:num_decode_tokens]) return output_padded