# SPDX-License-Identifier: Apache-2.0 """ 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, N * 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(Skv, N, P) v = (kv_c @ W_UV).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] concatnated per head `q_b_proj` is [W_UQ; W_QR] concatnated per head `out_proj` is W_O ## Data-Movement Friendly Approach (i.e. "_forward_decode"): Ahead of time, compute: % this projects from q_c to [Sq, N * Lkv] W_UQ_UK = einsum("qnp,knp -> qnk" W_UQ.view(Lq, N, P), W_UK.view(Lkv, N, P) ).view(Lkv, N * Lkv) % this projects from attn output [Sq, N * Lkv] to [Sq, H] W_UV_O = einsum("knv,nvh -> nkh" W_UV.view(Lkv, N, V), W_O.view(N, V, H) ).view(N * Lkv, H) Runtime q_c = h_t @ W_DQ q_latent = q_c @ W_UQ_UK.view(Sq, N, Lkv) 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([q_latent, q_pe], dim=-1), torch.cat([kv_c, k_pe], dim=-1), kv_c ) return spda_o.reshape(-1, N * Lkv) @ W_UV_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(Sq, N, P) new_v = (new_kv_c @ W_UV).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 from typing import TYPE_CHECKING, Any, Generic, Optional, TypeVar import torch from compressed_tensors.quantization import QuantizationStrategy 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_flash_attn_version from vllm.attention.ops.triton_merge_attn_states import merge_attn_states from vllm.distributed import (get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from vllm.logger import init_logger from vllm.model_executor.layers.linear import (ColumnParallelLinear, LinearBase, RowParallelLinear, UnquantizedLinearMethod) from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import ( # noqa: E501 CompressedTensorsLinearMethod) from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsW8A8Fp8) from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod from vllm.model_executor.layers.quantization.utils.fp8_utils import ( Fp8LinearGenericOp, current_platform_fp8_dtype, is_fp8) from vllm.model_executor.layers.quantization.utils.quant_utils import ( scaled_quantize) from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding from vllm.platforms import current_platform from vllm.utils import cdiv, round_down try: from vllm.vllm_flash_attn import flash_attn_varlen_func except ImportError: # For rocm use upstream flash attention from flash_attn import flash_attn_varlen_func if TYPE_CHECKING: from vllm.v1.core.scheduler_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) @staticmethod def get_supported_head_sizes() -> list[int]: return [576] @staticmethod def use_cascade_attention(*args, **kwargs) -> bool: return False @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 # Input positions for rotrary embeddings since for MLA the rotary # position embeddings are applied inside the attention backend input_positions: torch.Tensor block_table: torch.Tensor query_start_loc: torch.Tensor max_query_len: int chunked_context: Optional[ChunkedContextMetadata] = None @dataclass class MLACommonDecodeMetadata: # Input positions for rotrary embeddings since for MLA the rotary # position embeddings are applied inside the attention backend input_positions: torch.Tensor 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 # For logging. num_input_tokens: int = 0 # Number of tokens including padding. # The dimension of the attention heads head_dim: Optional[int] = None decode: Optional[D] = None prefill: Optional[MLACommonPrefillMetadata] = None def __post_init__(self): supported_head_sizes = MLACommonBackend.get_supported_head_sizes() if self.head_dim is not None and self.head_dim \ not in supported_head_sizes: raise ValueError( f"Only {supported_head_sizes} are supported for head_dim,", f"received {self.head_dim}.") M = TypeVar("M", bound=MLACommonMetadata) class MLACommonMetadataBuilder(Generic[M]): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ def __init__(self, runner: "GPUModelRunner", 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 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.page_size = self.runner.block_size 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 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 == 1: decodes.append(i) num_decode_tokens += num_tokens else: prefills.append(i) num_prefill_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) first_prefill = 0 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 if decodes[num_decodes - i] >= num_decodes: input_batch.swap_states(prefills[first_prefill], decodes[num_decodes - i]) first_prefill += 1 modified_batch = True else: break # 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 return modified_batch def _build_decode(self, input_positions: torch.Tensor, block_table: torch.Tensor, seq_lens: torch.Tensor): return MLACommonDecodeMetadata( input_positions=input_positions, block_table=block_table, seq_lens=seq_lens, ) def build(self, num_reqs: int, num_actual_tokens: int, max_query_len: int, common_prefix_len: int) -> M: assert self._num_decodes + self._num_prefills == num_reqs device = self.runner.device query_start_loc = self.runner.query_start_loc_cpu[:num_reqs + 1].to( device, non_blocking=True) seq_lens = self.runner.seq_lens_cpu[:num_reqs].to(device, non_blocking=True) block_table = ( self.runner.input_batch.block_table.get_device_tensor()[:num_reqs]) slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to( device, non_blocking=True).long() input_positions = self.runner.positions_cpu[:num_actual_tokens].to( device, non_blocking=True).long() prefill_metadata = None if self._num_prefills > 0: reqs_start = self._num_decodes # prefill_start tokens_start = self._num_decode_tokens context_lens_cpu = self.runner.input_batch.\ num_computed_tokens_cpu_tensor[reqs_start:num_reqs] context_lens = context_lens_cpu.to(device, non_blocking=True) chunked_context_metadata = None if self.chunked_prefill_enabled and self._num_prefills > 0 \ and context_lens.max() > 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 num_prefills_with_context = (context_lens > 0).sum().item() # 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 # 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(context_lens.max(), 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]] chunk_starts = \ torch.arange(num_chunks, device=device, dtype=torch.int32) \ .unsqueeze(1).expand(-1, self._num_prefills) \ * max_context_chunk chunk_ends = torch.min(context_lens.unsqueeze(0), chunk_starts + max_context_chunk) chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0) _chunk_cu_seq_lens = chunk_seq_lens.cumsum(dim=1).to( torch.int32) zero = torch.zeros(num_chunks, dtype=torch.int32, device=device).unsqueeze(-1) chunked_context_metadata = \ MLACommonPrefillMetadata.ChunkedContextMetadata( cu_seq_lens=torch.cat( [zero, _chunk_cu_seq_lens], dim=1), starts=chunk_starts, 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( input_positions=input_positions[tokens_start:], block_table=block_table[reqs_start:, ...], query_start_loc=query_start_loc[reqs_start:] - query_start_loc[reqs_start], max_query_len=seq_lens[reqs_start:].max().item(), chunked_context=chunked_context_metadata, ) decode_metadata = None if self._num_decodes > 0: decode_metadata = self._build_decode( input_positions=input_positions[:self._num_decode_tokens], block_table=block_table[:self._num_decodes, ...], seq_lens=seq_lens[:self._num_decodes], ) 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, ) 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, # 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, rotary_emb: RotaryEmbedding, # q_proj should be q_b_proj if q_lora_rank is not None, but from an # attention backend perspective we rely on the layer to pass in the # correct matrix q_proj: ColumnParallelLinear, kv_b_proj: ColumnParallelLinear, o_proj: RowParallelLinear, ) -> None: 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.rotary_emb = rotary_emb if current_platform.is_cuda(): # Hack for V1 for now to avoid torch library overhead (since we are # already inside an attention custom op), pull out the forward # method from the rotary embedding and call it directly (and avoid # calling forward_native, when we can call forward_cuda) # TODO(lucas): we should probably find a cleaner way to do this self.rotary_emb = rotary_emb.forward_cuda self.q_proj = q_proj self.kv_b_proj = kv_b_proj self.o_proj = o_proj self.vllm_flash_attn_version = get_flash_attn_version() self.fp8_linear_generic = Fp8LinearGenericOp() # 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 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) def _v_up_proj_and_o_proj(self, x): if envs.VLLM_MLA_PERFORM_MATRIX_ABSORPTION: if is_fp8(self.W_UV_O): output_parallel = self.fp8_linear_generic.apply( x.flatten(start_dim=1), self.W_UV_O, self.W_UV_O_scales, self.reqaunt_input_group_shape, self.reqaunt_weight_group_shape) else: output_parallel = torch.matmul(x.flatten(start_dim=1), self.W_UV_O) if self.tp_size > 1: output = tensor_model_parallel_all_reduce(output_parallel) else: output = output_parallel return output else: x = torch.einsum("bnl,lnv->bnv", x, self.W_UV) return self.o_proj(x.reshape(-1, self.num_heads * self.v_head_dim))[0] def _q_proj_and_k_up_proj(self, x): if envs.VLLM_MLA_PERFORM_MATRIX_ABSORPTION: if is_fp8(self.W_Q_UK): return self.fp8_linear_generic.apply( x, self.W_Q_UK, self.W_Q_UK_scales, self.reqaunt_input_group_shape, self.reqaunt_weight_group_shape).view( -1, self.num_heads, self.kv_lora_rank) return torch.matmul(x, self.W_Q_UK)\ .view(-1, self.num_heads, self.kv_lora_rank) else: x = torch.matmul(x, self.W_Q)\ .view(-1, self.num_heads, self.qk_nope_head_dim) return torch.einsum("bnp,lnp->bnl", x, self.W_UK)\ .view(-1, self.num_heads, self.kv_lora_rank) def process_weights_after_loading(self, act_dtype: torch.dtype): # TODO(lucas) This is very gross, we need a more wide scale refactor of # all the FP8 code with a more standard way of # defining schemes/group-shapes, we should also potentially force # quant_methods to support a decompress function # # returns input_group_shape, weight_group_shape def get_scale_group_shapes_for_fp8(layer: LinearBase) -> \ tuple[tuple[int, int], tuple[int, int]]: if isinstance(layer.quant_method, Fp8LinearMethod): if layer.quant_method.block_quant: weight_block_size = \ layer.quant_method.quant_config.weight_block_size # per-token-group (1, X), block-quantized (X, Y) return (1, weight_block_size[-1]), weight_block_size else: return (-1, -1), (-1, -1) # per-tensor, per-tensor elif isinstance(layer.quant_method, CompressedTensorsLinearMethod)\ and isinstance(layer.scheme, CompressedTensorsW8A8Fp8): # this is hacky but we always assume the for # CompressedTensorsW8A8Fp8 the input is dynamic per-token # we ignore if it is static-per-tensor since we are going to # requantize after later anyways strategy = layer.scheme.strategy if strategy == QuantizationStrategy.TENSOR: return (1, -1), (-1, -1) # per-token, per-tensor elif strategy == QuantizationStrategy.CHANNEL: return (1, -1), (-1, 1) # per-token, per-channel else: raise NotImplementedError( f"QuantizationStrategy.{strategy} is not supported for " "fp8 MLA, please run with VLLM_MLA_DISABLE=1") else: raise NotImplementedError( "Can't determine scale group shapes for " f"{layer.quant_method}, please run with VLLM_MLA_DISABLE=1" ) def get_layer_weight(layer): if hasattr(layer, "weight"): return layer.weight elif hasattr(layer, "qweight"): return layer.qweight else: raise AttributeError( f"Layer '{layer}' has neither weight nor qweight") 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 weight_dtype = get_layer_weight(self.kv_b_proj).dtype assert get_layer_weight(self.o_proj).dtype == weight_dtype assert get_layer_weight(self.q_proj).dtype == weight_dtype 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) q_proj_weight = get_and_maybe_dequant_weights(self.q_proj).T\ .view(-1, self.num_heads, self.qk_head_dim) # can be W_Q or W_UQ depending q_lora_rank, the former if # q_lora_rank is None, the latter otherwise. From the Attention backend # perspective though we call these both W_Q and rely on the layer # to pass in the correct matrix W_Q = q_proj_weight[..., :self.qk_nope_head_dim] self.W_QR = q_proj_weight[..., self.qk_nope_head_dim:]\ .flatten(start_dim=1).contiguous() # W_QR is small so for simplicity we dont bother requantizing it self.W_QR = self.W_QR.to(act_dtype) if envs.VLLM_MLA_PERFORM_MATRIX_ABSORPTION: requantization_enabled = not envs.VLLM_MLA_DISABLE_REQUANTIZATION if is_fp8(weight_dtype) and requantization_enabled: # This assumes it wise to requantize using the same group shapes # (i.e. strategy, per-tensor, per-channel, block etc.) that the # weights were originally quantized requant_input_group_shape, requant_weight_group_shape = \ get_scale_group_shapes_for_fp8(self.q_proj) assert (requant_input_group_shape, requant_weight_group_shape)\ == get_scale_group_shapes_for_fp8(self.kv_b_proj) assert (requant_input_group_shape, requant_weight_group_shape)\ == get_scale_group_shapes_for_fp8(self.o_proj) self.reqaunt_input_group_shape = requant_input_group_shape self.reqaunt_weight_group_shape = requant_weight_group_shape # # Perform matrix-absorption following # https://github.com/flashinfer-ai/flashinfer/pull/551 # for decode, as a result we end up with absorbed weights for decode # and another copy of raw weights for prefill. # self.W_UK, self.W_UV = kv_b_proj_weight.split( [self.qk_nope_head_dim, self.v_head_dim], dim=-1) # We absorb `W_UK` into `W_Q` resulting in either W_Q_UK or W_UQ_UK # depending q_lora_rank, the former if q_lora_rank is None, the # latter otherwise # basically if q_lora_rank is none we are absorbing into q_proj # instead of UQ W_Q_UK = torch.einsum("qnd,lnd -> qnl", W_Q, W_UK)\ .flatten(start_dim=1).contiguous() if is_fp8(weight_dtype) and requantization_enabled: W_Q_UK, W_Q_UK_scales = scaled_quantize( W_Q_UK, self.reqaunt_weight_group_shape, quant_dtype=current_platform_fp8_dtype) # For FP8 save the transpose so we can use # `apply_w8a8_block_fp8_linear` directly self.W_Q_UK = W_Q_UK.T.contiguous() self.W_Q_UK_scales = W_Q_UK_scales.T.contiguous() else: self.W_Q_UK = W_Q_UK.to(act_dtype) W_O = get_and_maybe_dequant_weights(self.o_proj)\ .view(-1, self.num_heads, self.v_head_dim) W_UV_O = torch.einsum("lnd,hnd -> nlh", W_UV, W_O)\ .flatten(start_dim=0, end_dim=1).contiguous() if is_fp8(weight_dtype) and requantization_enabled: W_UV_O, W_UV_O_scales = scaled_quantize( W_UV_O, self.reqaunt_weight_group_shape, quant_dtype=current_platform_fp8_dtype) # For FP8 save the transpose so we can use # `apply_w8a8_block_fp8_linear` directly self.W_UV_O = W_UV_O.T.contiguous() self.W_UV_O_scales = W_UV_O_scales.T.contiguous() else: self.W_UV_O = W_UV_O.to(act_dtype) self.tp_size = get_tensor_model_parallel_world_size() else: if is_fp8(weight_dtype): raise NotImplementedError( "Currently fp8 requires matrix absorption") self.W_UV = W_UV self.W_UK = W_UK self.W_Q = W_Q.flatten(start_dim=1) 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) k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-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 v_padded = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]], value=0) attn_output, attn_softmax_lse = self.flash_attn_varlen_func( q=q, k=k, v=v_padded, 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 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 = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-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 v_padded = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]], value=0) output = self.flash_attn_varlen_func( q=q, k=k, v=v_padded, 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, ) # slice by `:v.shape[-1]` in order to remove v headdim padding output = output\ .view(-1, self.num_heads, q.shape[-1])[..., :v.shape[-1]]\ .reshape(-1, self.num_heads * v.shape[-1]) return self.o_proj(output)[0] @abstractmethod def _forward_decode( self, q_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, hidden_states_or_q_c: torch.Tensor, # query in unified attn 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, ) -> torch.Tensor: assert output is not None, "Output tensor must be provided." if attn_metadata is None: # Profiling run. return output 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, ...] hidden_states_or_q_c = hidden_states_or_q_c[:num_actual_toks, ...] k_c_normed = k_c_normed[:num_actual_toks, ...] k_pe = k_pe[:num_actual_toks, ...] # Restore head dim (for rotary embedding) k_pe = k_pe.unsqueeze(1) 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_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens] decode_k_pe = k_pe[:num_decode_tokens] prefill_hs_or_q_c = hidden_states_or_q_c[num_decode_tokens:] prefill_k_pe = k_pe[num_decode_tokens:] prefill_k_c_normed = k_c_normed[num_decode_tokens:] if has_decode: assert attn_metadata.decode is not None decode_q_nope = self._q_proj_and_k_up_proj(decode_hs_or_q_c) decode_q_pe = torch.matmul(decode_hs_or_q_c, self.W_QR)\ .view(-1, self.num_heads, self.qk_rope_head_dim) decode_q_pe[...], decode_k_pe[...] = self.rotary_emb( attn_metadata.decode.input_positions, decode_q_pe, decode_k_pe) if has_prefill: assert attn_metadata.prefill is not None prefill_q = self.q_proj(prefill_hs_or_q_c)[0]\ .view(-1, self.num_heads, self.qk_head_dim) prefill_q_pe = prefill_q[..., self.qk_nope_head_dim:] prefill_q_pe[...], prefill_k_pe[...] = self.rotary_emb( attn_metadata.prefill.input_positions, prefill_q_pe, prefill_k_pe) # 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: output[:num_decode_tokens] = self._forward_decode( decode_q_nope, decode_q_pe, kv_cache, attn_metadata) return output_padded