# 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_table 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 from typing import TYPE_CHECKING, Any, Generic, Optional, TypeVar from itertools import chain import numpy as np import torch import os from vllm import _custom_ops as ops from vllm import envs from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer, AttentionMetadata, MLAAttentionImpl) from vllm.attention.backends.utils import get_mla_dims from vllm.attention.ops.merge_attn_states import merge_attn_states from vllm.attention.utils.fa_utils import get_flash_attn_version from vllm.logger import init_logger from vllm.model_executor.layers.linear import (ColumnParallelLinear, LinearBase, UnquantizedLinearMethod) from vllm.platforms import current_platform from vllm.utils import cdiv, round_down from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder, CommonAttentionMetadata) from vllm.v1.kv_cache_interface import AttentionSpec from vllm.v1.worker.block_table import BlockTable from vllm.v1.attention.backends.mla.concatv3Tritonfinalv2 import concat_helper try: from vllm.vllm_flash_attn import 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 is_vllm_fa = False if TYPE_CHECKING: from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.worker.gpu_input_batch import InputBatch from vllm.v1.worker.gpu_model_runner import GPUModelRunner logger = init_logger(__name__) class MLACommonBackend(AttentionBackend): accept_output_buffer: bool = True @staticmethod def get_name() -> str: return "TRITON_MLA_VLLM_V1" @staticmethod def get_metadata_cls() -> type["AttentionMetadata"]: return MLACommonMetadata @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, ) -> tuple[int, ...]: return (num_blocks, block_size, head_size) @classmethod def get_supported_head_sizes(cls) -> list[int]: return [576] @classmethod def validate_head_size(cls, head_size: int) -> None: supported_head_sizes = cls.get_supported_head_sizes() if head_size not in supported_head_sizes: attn_type = cls.__name__.removesuffix("Backend") raise ValueError( f"Head size {head_size} is not supported by {attn_type}. " f"Supported head sizes are: {supported_head_sizes}. " "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use " "FlexAttention backend which supports all head sizes.") @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] workspace: torch.Tensor block_table: torch.Tensor query_start_loc: torch.Tensor max_query_len: int chunked_context: Optional[ChunkedContextMetadata] = None @dataclass class MLACommonDecodeMetadata: block_table: torch.Tensor seq_lens: torch.Tensor D = TypeVar("D", bound=MLACommonDecodeMetadata) @dataclass class MLACommonMetadata(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_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: Optional[int] = None decode: Optional[D] = None prefill: Optional[MLACommonPrefillMetadata] = None def __post_init__(self): if self.head_dim is not None: MLACommonBackend.validate_head_size(self.head_dim) M = TypeVar("M", bound=MLACommonMetadata) class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ def __init__(self, runner: "GPUModelRunner", kv_cache_spec: AttentionSpec, block_table: BlockTable, metadata_cls: Optional[type[M]] = None): self.metadata_cls = metadata_cls \ if metadata_cls is not None else MLACommonMetadata self.runner = runner scheduler_config = runner.scheduler_config model_config = runner.model_config cache_config = runner.cache_config self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled self.num_heads = model_config.get_num_attention_heads( runner.parallel_config) self.mla_dims = get_mla_dims(model_config) self.aot_schedule = current_platform.is_cuda() self.kv_cache_spec = kv_cache_spec # Dont try to access the runner on AMD if self.aot_schedule: self.page_size = self.kv_cache_spec.block_size if self.chunked_prefill_enabled: self.chunked_prefill_workspace_size = min( # Max sure there is enough for 8 full length request or at least # 4 pages of cache 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) 128 * 1024) assert self.chunked_prefill_workspace_size >= \ scheduler_config.max_num_seqs * cache_config.block_size self.chunked_prefill_workspace = torch.empty( (self.chunked_prefill_workspace_size, model_config.get_head_size()), dtype=model_config.dtype, device=runner.device, ) self.block_table = block_table self.use_spec_decode = False self.num_scheduled_tokens_np = np.zeros(scheduler_config.max_num_seqs, dtype=np.int32) # support for cudagraph spec docoding self.spec_decode_block_table_tensor = None self.spec_decode_seq_lens = None def reorder_batch(self, input_batch: "InputBatch", scheduler_output: "SchedulerOutput") -> bool: # We now want to reorder the batch so that the "decode" requests are and # the front and the "prefill" requests are at the using the least amount # swaps possible. (NOTE for now we loosely use "decode" to mean requests # where attention is likely memory-bound and "prefill" to mean requests # where attention is likely compute-bound, TODO(lucas): figure out a # better naming here) decodes = [] prefills = [] num_decode_tokens = 0 num_prefill_tokens = 0 use_spec_decode = len( scheduler_output.scheduled_spec_decode_tokens) > 0 for i, req_id in enumerate(input_batch.req_ids): num_tokens = scheduler_output.num_scheduled_tokens[req_id] # for now treat 1 scheduled token as "decode" even if its not, # we should update this to something like < 8 in the future but # currently the TritonMLA._forward_decode only supports # num_tokens = 1 # if num_tokens == 2 or num_tokens == 1: # decodes.append(i) # num_decode_tokens += num_tokens # else: # prefills.append(i) # num_prefill_tokens += num_tokens req_idx = input_batch.req_id_to_index[req_id] num_computed_tokens = input_batch.num_computed_tokens_cpu[req_idx] num_prompt_tokens = input_batch.num_prompt_tokens[req_idx] self.num_scheduled_tokens_np[i] = num_tokens if num_computed_tokens < num_prompt_tokens: prefills.append(i) num_prefill_tokens += num_tokens else: decodes.append(i) num_decode_tokens += num_tokens # We hope that this is fairly minimal since decodes # should be around for a number of iterations so hopefully they are # relatively stationary (and new request are generally appended to the # persistent batch so already should be at the back) # To achieve this we loop over the decodes in descending order and # the prefills in ascending order. We swap decodes from the "back" # i.e. past where the last decode should be in the reodorered with # prefills from the front of the batch. # `decodes` and `prefills` are already in ascending order just based on # the above loop num_decodes = len(decodes) num_prefills = len(prefills) modified_batch = False for i in range(1, min(num_decodes, num_prefills) + 1): # If the decode is at the "back" of the batch, i, we can swap it # with the prefill closest to the front of the batch decode_idx = decodes[num_decodes - i] if decode_idx < num_decodes: break input_batch.swap_states(prefills[i - 1], decode_idx) modified_batch = True # num_scheduled_tokens also need to be swapped tmp = self.num_scheduled_tokens_np[decode_idx] self.num_scheduled_tokens_np[decode_idx] = self.num_scheduled_tokens_np[prefills[i - 1]] self.num_scheduled_tokens_np[prefills[i - 1]] = tmp # Save for next `build` call # TODO(lucas): this is a bit of a hack, we should probably have a # better way of doing this self._num_decodes = num_decodes self._num_prefills = num_prefills self._num_decode_tokens = num_decode_tokens self._num_prefill_tokens = num_prefill_tokens self.use_spec_decode = use_spec_decode return modified_batch def _build_decode(self, block_table_tensor: torch.Tensor, seq_lens: torch.Tensor): return MLACommonDecodeMetadata( block_table=block_table_tensor, seq_lens=seq_lens, ) 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, \ # "MLA only supports decode-only full CUDAGraph capture. " \ # "Make sure all cudagraph capture sizes <= max_num_seq." #m.max_query_len = 1 # decode-only # Update state usually set in reorder_batch. self._num_decodes = m.num_reqs self._num_decode_tokens = m.num_actual_tokens self._num_prefills = 0 self._num_prefill_tokens = 0 self.use_spec_decode = m.num_speculative_tokens > 0 # support for cudagraph spec docoding if self.use_spec_decode: for i in range(m.num_reqs): self.num_scheduled_tokens_np[i] = m.num_actual_tokens // m.num_reqs if self.spec_decode_block_table_tensor is None: max_num_reqs = m.seq_lens.shape[0] block_table_tensor = self.block_table.get_device_tensor() tokens_per_seq = 1+m.num_speculative_tokens self.spec_decode_block_table_tensor = torch.zeros((block_table_tensor.shape[0]*tokens_per_seq, block_table_tensor.shape[1]), dtype=block_table_tensor.dtype, device=m.seq_lens.device) self.spec_decode_seq_lens = torch.zeros(max_num_reqs * tokens_per_seq, dtype=m.seq_lens.dtype, device=m.seq_lens.device) return self.build(0, m) def build(self, common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata) -> M: num_reqs = common_attn_metadata.num_reqs num_actual_tokens = common_attn_metadata.num_actual_tokens max_query_len = common_attn_metadata.max_query_len assert self._num_decodes + self._num_prefills == num_reqs # 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.runner.device block_table = self.block_table block_table_tensor = block_table.get_device_tensor()[:num_reqs] slot_mapping = common_attn_metadata.slot_mapping if slot_mapping is None: block_table.slot_mapping[:num_actual_tokens].copy_( block_table.slot_mapping_cpu[:num_actual_tokens], non_blocking=True) block_table.slot_mapping[num_actual_tokens:].fill_(-1) slot_mapping = block_table.slot_mapping[:num_actual_tokens] query_start_loc = common_attn_metadata.query_start_loc seq_lens = common_attn_metadata.seq_lens prefill_metadata = None if self._num_prefills > 0: reqs_start = self._num_decodes # prefill_start context_lens_cpu = self.runner.input_batch.\ num_computed_tokens_cpu_tensor[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 self.chunked_prefill_enabled and self._num_prefills > 0 \ and 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_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, self._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, self._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) chunked_context_metadata = \ MLACommonPrefillMetadata.ChunkedContextMetadata( 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(), workspace=self.chunked_prefill_workspace, ) assert max(chunked_context_metadata.max_seq_lens) <= \ self.chunked_prefill_workspace_size prefill_metadata = MLACommonPrefillMetadata( 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, ) decode_metadata = None if self._num_decodes > 0: if self.use_spec_decode and not common_attn_metadata.spec_layer_decoding: query_lens = self.num_scheduled_tokens_np[:self._num_decodes] cu_num_blocks = np.cumsum(query_lens) virtual_batches = cu_num_blocks[-1] block_offsets = np.repeat(cu_num_blocks - query_lens, query_lens) arange = np.arange(virtual_batches, dtype=np.int32) - block_offsets rarange = np.repeat(query_lens, query_lens) - arange - 1 repeats = torch.from_numpy(query_lens).pin_memory().to( block_table_tensor.device, non_blocking=True).contiguous() if envs.VLLM_ZERO_OVERHEAD: decode_block_table_tensor = torch.empty((self._num_decode_tokens, block_table_tensor.shape[1]), device=block_table_tensor.device) arange_np = np.arange(self._num_decodes) indices_np = np.repeat(arange_np, query_lens) indices = torch.from_numpy(indices_np).pin_memory().to( block_table_tensor.device, non_blocking=True) decode_block_table_tensor = block_table_tensor[indices].contiguous() decode_seq_lens = seq_lens[indices].contiguous() else: decode_block_table_tensor = torch.repeat_interleave( block_table_tensor[:self._num_decodes, ...], repeats, dim=0).contiguous() decode_seq_lens = torch.repeat_interleave(seq_lens[:self._num_decodes], repeats, dim=0).contiguous() seq_lens_minus = torch.from_numpy(rarange).to(torch.int32).pin_memory().to( seq_lens.device, non_blocking=True).contiguous() decode_seq_lens = decode_seq_lens - seq_lens_minus if self.spec_decode_block_table_tensor is not None: self.spec_decode_block_table_tensor[:self._num_decode_tokens].copy_(decode_block_table_tensor) self.spec_decode_seq_lens[:self._num_decode_tokens].copy_(decode_seq_lens) decode_metadata = self._build_decode( block_table_tensor=self.spec_decode_block_table_tensor[:self._num_decode_tokens, ...], seq_lens=self.spec_decode_seq_lens[:self._num_decode_tokens], ) else: decode_metadata = self._build_decode( block_table_tensor=decode_block_table_tensor, seq_lens=decode_seq_lens, ) else: self._num_decode_tokens = self._num_decodes if self.use_spec_decode and self.spec_decode_block_table_tensor is not None: self.spec_decode_block_table_tensor[:self._num_decode_tokens].copy_(block_table_tensor[:self._num_decode_tokens, ...]) self.spec_decode_seq_lens[:self._num_decode_tokens].copy_(seq_lens[:self._num_decode_tokens]) decode_metadata = self._build_decode( block_table_tensor=self.spec_decode_block_table_tensor[:self._num_decode_tokens, ...], seq_lens=self.spec_decode_seq_lens[:self._num_decode_tokens], ) else: decode_metadata = self._build_decode( block_table_tensor=block_table_tensor[:self._num_decode_tokens, ...], seq_lens=seq_lens[:self._num_decode_tokens], ) return self.metadata_cls( num_actual_tokens=num_actual_tokens, query_start_loc=query_start_loc, slot_mapping=slot_mapping, head_dim=self.runner.model_config.get_head_size(), # MLACommonMetadata Chunk prefill specific num_decodes=self._num_decodes, num_decode_tokens=self._num_decode_tokens, num_prefills=self._num_prefills, prefill=prefill_metadata, decode=decode_metadata, ) def can_run_in_cudagraph( self, common_attn_metadata: CommonAttentionMetadata) -> bool: if not self.use_spec_decode: return common_attn_metadata.max_query_len == 1 return self._num_prefills == 0 class MLACommonImpl(MLAAttentionImpl[M], Generic[M]): """ 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: Optional[list[float]], sliding_window: Optional[int], kv_cache_dtype: str, blocksparse_params: Optional[dict[str, Any]], logits_soft_cap: Optional[float], attn_type: str, kv_sharing_target_layer_name: Optional[str], # MLA Specific Arguments q_lora_rank: Optional[int], 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, ) -> 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 # 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) self.use_llama_nn = os.environ.get('LLAMA_NN') == '1' # 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 if not current_platform.is_rocm(): self._pad_v = self.vllm_flash_attn_version is None or not ( self.vllm_flash_attn_version == 3 and current_platform.get_device_capability()[0] == 9) else: self._pad_v = torch.cuda.get_device_properties(torch.cuda.current_device()).multi_processor_count == 120 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) maybe_padded_v = torch.nn.functional.pad( v, [0, q.shape[-1] - v.shape[-1]- 32], value=0) maybe_padded_v = maybe_padded_v[..., :-32].reshape(v.shape[0], v.shape[1],v.shape[2]) 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 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 _v_up_proj(self, x): # Convert from (B, N, L) to (N, B, L) x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1) # Multiply (N, B, L) x (N, L, V) -> (N, B, V) x = torch.bmm(x, self.W_UV) # Convert from (N, B, V) to (B, N * V) return x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim) def process_weights_after_loading(self, act_dtype: torch.dtype): def get_layer_weight(layer): WEIGHT_NAMES = ("weight", "qweight", "weight_packed") for attr in WEIGHT_NAMES: if hasattr(layer, attr): return getattr(layer, attr) raise AttributeError( f"Layer '{layer}' has no recognized weight attribute:" f" {WEIGHT_NAMES}.") def get_and_maybe_dequant_weights(layer: LinearBase): if not isinstance(layer.quant_method, UnquantizedLinearMethod): # NOTE: This should only be used offline, since it's O(N^3) eye = torch.eye(layer.input_size_per_partition, dtype=act_dtype, device=get_layer_weight(layer).device) dequant_weights = layer.quant_method.apply(layer, eye, bias=None) del eye # standardize to (output, input) return dequant_weights.T return layer.weight if not envs.VLLM_USE_NN else layer.weight.T # we currently do not have quantized bmm's which are needed for # `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform # the bmm's in 16-bit, the extra memory overhead of this is fairly low if self.use_llama_nn and isinstance(self.kv_b_proj.quant_method, UnquantizedLinearMethod): kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj) else: kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).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) # 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 _compute_prefill_context( self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: MLACommonMetadata, ): 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_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], batch_size=attn_metadata.num_prefills, 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) if envs.VLLM_USE_TRITON_CAT: k = concat_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)), dim=-1) else: k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1) attn_output, attn_softmax_lse = \ self._flash_attn_varlen_diff_headdims( q=q, k=k, v=v, cu_seqlens_q=prefill_metadata.query_start_loc, cu_seqlens_k=prefill_metadata.chunked_context.cu_seq_lens[i], max_seqlen_q=prefill_metadata.max_query_len, max_seqlen_k=prefill_metadata.chunked_context.max_seq_lens[i], softmax_scale=self.scale, causal=False, # Context is unmasked return_softmax_lse=True, ) 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, ) -> torch.Tensor: assert attn_metadata.prefill is not None if envs.VLLM_HAS_CONTEXT_DEFAULT: has_context = attn_metadata.prefill.chunked_context is not None else: has_context = False 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) if envs.VLLM_USE_TRITON_CAT: k = concat_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)), dim=-1) else: k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1) output = self._flash_attn_varlen_diff_headdims( q=q, k=k, v=v, cu_seqlens_q=attn_metadata.prefill.query_start_loc, cu_seqlens_k=attn_metadata.prefill.query_start_loc, max_seqlen_q=attn_metadata.prefill.max_query_len, max_seqlen_k=attn_metadata.prefill.max_query_len, softmax_scale=self.scale, causal=True, return_softmax_lse=has_context, ) if has_context: suffix_output, suffix_lse = output context_output, context_lse = self._compute_prefill_context( \ q, kv_c_and_k_pe_cache, attn_metadata) output = torch.empty_like(suffix_output) merge_attn_states( output=output, prefix_output=context_output, prefix_lse=context_lse, suffix_output=suffix_output, suffix_lse=suffix_lse, ) # unpad if necessary if self._pad_v: output = output[..., :v.shape[-1]] return output.flatten(start_dim=-2) @abstractmethod def _forward_decode( self, ql_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: M, ) -> torch.Tensor: 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: Optional[torch.Tensor] = None, output_scale: Optional[torch.Tensor] = None, ) -> torch.Tensor: assert output is not None, "Output tensor must be provided." if output_scale is not None: raise NotImplementedError( "fused output quantization is not yet supported" " for MLACommonImpl") if attn_metadata is None: # 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) 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 has_prefill: output[num_decode_tokens:] = self._forward_prefill( prefill_q, prefill_k_c_normed, prefill_k_pe, kv_cache, attn_metadata) 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) # Multiply (N, B, P) x (N, P, L) -> (N, B, L) decode_ql_nope = torch.bmm(decode_q_nope, self.W_UK_T) # Convert from (N, B, L) to (B, N, L) decode_ql_nope = decode_ql_nope.transpose(0, 1) output[:num_decode_tokens] = self._forward_decode( decode_ql_nope, decode_q_pe, kv_cache, attn_metadata, layer._k_scale, self.kv_cache_dtype) return output_padded