from dataclasses import dataclass from typing import TYPE_CHECKING, Optional, Tuple, Type, TypeVar import numpy as np import torch import torch.nn as nn import torch_npu from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer, AttentionMetadata, MLAAttentionImpl) from vllm.attention.backends.utils import PAD_SLOT_ID from vllm.config import VllmConfig, get_current_vllm_config from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod) from vllm.utils import cdiv, round_down import vllm_npu.envs as envs_ascend from vllm_npu.ascend_config import get_ascend_config from vllm_npu.attention.attention_v1 import AscendAttentionState from vllm_npu.attention.utils import (AscendCommonAttentionMetadata, split_decodes_and_prefills) from vllm_npu.multistream.base import MSAttentionMetadataSplitConfig from vllm_npu.multistream.context import get_multistream_comm_context from vllm_npu.multistream.ms_split import model_input_split_v1_mla_attn from vllm_npu.ops.weight_prefetch import maybe_npu_prefetch from vllm_npu.torchair.utils import (TorchairCommonAttentionMetadata, npu_stream_switch, npu_wait_tensor) from vllm_npu.worker.npu_input_batch import InputBatch if TYPE_CHECKING: from vllm.v1.core.sched.output import SchedulerOutput class AscendMLATorchairBackend(AttentionBackend): accept_output_buffer: bool = True @staticmethod def get_name() -> str: return "ASCEND_MLA_TORCHAIR" @staticmethod def get_metadata_cls() -> type["AttentionMetadata"]: return AscendMLATorchairMetadata @staticmethod def get_builder_cls(): return AscendMLATorchairMetadataBuilder @staticmethod def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int, head_size: int) -> tuple[int, ...]: return (num_blocks, block_size, num_kv_heads, head_size) @staticmethod def get_impl_cls() -> Type["MLAAttentionImpl"]: return AscendMLATorchairImpl @dataclass class AscendMLATorchairPrefillMetadata: """ Prefill Specific Metadata for Ascend""" @dataclass class TorchairChunkedContextMetadata: # 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 chunk_seq_lens: torch.Tensor chunk_seq_lens_npu: torch.Tensor attn_mask: torch.Tensor query_lens: torch.Tensor seq_lens: list[int] context_lens: torch.Tensor input_positions: torch.Tensor query_start_loc: torch.Tensor block_table: torch.Tensor max_query_len: int max_seq_lens: int chunked_context: Optional[TorchairChunkedContextMetadata] = None sin: torch.Tensor = None cos: torch.Tensor = None @dataclass class AscendMLATorchairDecodeMetadata: # 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 max_seq_lens: int seq_lens_list: list[int] actual_seq_lengths_q: Optional[list[int]] = None attn_mask: Optional[torch.Tensor] = None sin: torch.Tensor = None cos: torch.Tensor = None @dataclass class AscendMLATorchairMetadata: """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. slot_mapping: torch.Tensor query_start_loc: torch.Tensor seq_lens: torch.Tensor block_tables: 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. query_lens: Optional[list[int]] = None # The dimension of the attention heads head_dim: Optional[int] = None attn_mask: torch.Tensor = None # chunked prefill by default if no attn_states passed attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill decode: Optional[AscendMLATorchairDecodeMetadata] = None prefill: Optional[AscendMLATorchairPrefillMetadata] = None enable_dbo_across_dp: bool = False def __post_init__(self): pass # supported_head_sizes = AscendMLABackend.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}.") def split_metadata_for_multistream( self, ms_split_config: MSAttentionMetadataSplitConfig, ) -> list["AscendMLATorchairMetadata"]: """Split metadata for multi-stream with AscendMLATorchairMetadata""" return model_input_split_v1_mla_attn( ms_split_config=ms_split_config, attn_metadata=self, _metadata_cls=AscendMLATorchairMetadata, ) M = TypeVar("M", bound=AscendMLATorchairMetadata) class AscendMLATorchairMetadataBuilder: """ NOTE: Please read the comment at the top of the file before trying to understand this class """ # _attn_mask_builder = None def __init__(self, kv_cache_spec, layer_names, vllm_config: VllmConfig, device: torch.device, metadata_cls: Optional[AscendMLATorchairMetadata] = None): self.metadata_cls: Optional[AscendMLATorchairMetadata] = metadata_cls \ if metadata_cls is not None else AscendMLATorchairMetadata # type: ignore self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.device = device scheduler_config = vllm_config.scheduler_config self.block_size = vllm_config.cache_config.block_size self.max_blocks = (vllm_config.model_config.max_model_len + self.block_size - 1) // self.block_size 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 * self.model_config.max_model_len, 4 * scheduler_config.max_num_seqs * self.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 * self.block_size self.chunked_prefill_workspace = torch.empty( (self.chunked_prefill_workspace_size, self.model_config.get_head_size()), dtype=self.model_config.dtype, device=device, ) ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim self.cos_cache = None self.sin_cache = 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 at # 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 = [] for i, req_id in enumerate(input_batch.req_ids): num_tokens = scheduler_output.num_scheduled_tokens[req_id] num_spec_tokens = len( scheduler_output.scheduled_spec_decode_tokens.get(req_id, [])) # For torch air graph mode we treat spec decoding as decode. if self.torchair_graph_enabled: if num_tokens - num_spec_tokens == 1: decodes.append(i) else: prefills.append(i) # For eager mode we treat spec decoding as chunked prefill. else: if num_tokens == 1: decodes.append(i) else: prefills.append(i) # 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 return modified_batch def _get_graph_runner_block_tables( self, num_seqs: int, block_tables: torch.Tensor) -> torch.Tensor: max_blocks = self.max_blocks graph_block_tables = torch.zeros((num_seqs, max_blocks), dtype=block_tables.dtype, device=block_tables.device) num_blocks = block_tables.size(1) if num_blocks <= max_blocks: graph_block_tables[:num_seqs, : num_blocks] = block_tables[:num_seqs, : num_blocks] else: graph_block_tables[:num_seqs, : max_blocks] = block_tables[:num_seqs, : max_blocks] return graph_block_tables[:, :max_blocks] def build_torchair_graph_dummy( self, common_attn_metadata: TorchairCommonAttentionMetadata, ) -> AscendMLATorchairMetadata: device = self.device num_reqs = common_attn_metadata.num_reqs block_table = torch.zeros((num_reqs, self.max_blocks), dtype=torch.int32, device=device) block_table = self._get_graph_runner_block_tables( num_reqs, block_table) num_tokens = num_reqs * common_attn_metadata.decode_token_per_req seq_lens = torch.zeros(num_reqs, dtype=torch.int32, device=device) seq_lens_list = [0] * num_reqs input_positions = torch.zeros(num_tokens, dtype=torch.int32, device=device).long() slot_mapping = torch.full((num_tokens, ), PAD_SLOT_ID, dtype=torch.int32, device=device) query_start_loc = torch.full((num_reqs, ), -1, dtype=torch.int32, device=device) sin = torch.ones(num_tokens, 1, 1, self.rope_dim, dtype=self.model_config.dtype, device=device) cos = torch.ones(num_tokens, 1, 1, self.rope_dim, dtype=self.model_config.dtype, device=device) if self.vllm_config.speculative_config is not None and\ self.vllm_config.speculative_config.method == 'deepseek_mtp': attn_state = AscendAttentionState.SpecDecoding num_decode_tokens = 2 else: attn_state = AscendAttentionState.DecodeOnly num_decode_tokens = 1 decode_metadata = AscendMLATorchairDecodeMetadata( input_positions=input_positions, block_table=block_table, seq_lens=seq_lens, seq_lens_list=seq_lens_list, max_seq_lens=1, attn_mask=common_attn_metadata.spec_attn_mask, actual_seq_lengths_q=common_attn_metadata. actual_seq_lengths_q[:num_reqs], sin=sin, cos=cos, ) return self.metadata_cls( # type: ignore num_input_tokens=common_attn_metadata.num_actual_tokens, num_actual_tokens=common_attn_metadata.num_actual_tokens, slot_mapping=slot_mapping, head_dim=self.model_config.get_head_size(), num_decodes=1, num_decode_tokens=num_decode_tokens, num_prefills=0, attn_mask=common_attn_metadata.attn_mask, attn_state=attn_state, prefill=None, decode=decode_metadata, query_start_loc=query_start_loc, seq_lens=seq_lens, block_tables=block_table, ) def build( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, model: nn.Module, ) -> AscendMLATorchairMetadata: num_reqs = common_attn_metadata.num_reqs num_actual_tokens = common_attn_metadata.num_actual_tokens query_start_loc = common_attn_metadata.query_start_loc query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu if self.torchair_graph_enabled and common_attn_metadata.attn_state in [ AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding ]: decode_threshold = common_attn_metadata.decode_token_per_req else: # TODO(xyx): remove the if condition after mla supports torch mode speculative decoding decode_threshold = 1 num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \ split_decodes_and_prefills(common_attn_metadata, decode_threshold=decode_threshold) assert num_decodes + num_prefills == num_reqs assert num_decode_tokens + num_prefill_tokens == num_actual_tokens # 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 = (common_attn_metadata.block_table_tensor[:num_reqs]) slot_mapping = common_attn_metadata.slot_mapping[:num_actual_tokens] input_positions = common_attn_metadata.positions[: num_actual_tokens].long( ) if self.cos_cache is None: self.cos_cache = model.model.layers[ 0].self_attn.rotary_emb.cos_cached self.sin_cache = model.model.layers[ 0].self_attn.rotary_emb.sin_cached if self.cos_cache.dtype != self.model_config.dtype: # type: ignore self.cos_cache = self.cos_cache.to( # type: ignore self.model_config.dtype) # type: ignore self.sin_cache = self.sin_cache.to( # type: ignore self.model_config.dtype) # type: ignore query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1] query_lens = query_seq_lens_cpu[:num_reqs] seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs] num_computed_tokens_cpu = (seq_lens - query_lens) prefill_metadata = None chunked_context_metadata = None if num_prefills > 0: reqs_start = num_decodes # prefill_start tokens_start = num_decode_tokens max_query_len = query_lens[tokens_start:].max().item() max_seq_lens = seq_lens[tokens_start:].max().item() prefill_query_start_loc = query_start_loc[ reqs_start:] - query_start_loc[reqs_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() if self.chunked_prefill_enabled and max_context_len_cpu > 0: max_context_chunk = (self.chunked_prefill_workspace_size // num_prefills_with_context_cpu) max_context_chunk = round_down(max_context_chunk, self.block_size) assert max_context_chunk > 0 num_chunks = cdiv(max_context_len_cpu, max_context_chunk) 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) chunked_context_metadata = \ AscendMLATorchairPrefillMetadata.TorchairChunkedContextMetadata( 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(), chunk_seq_lens=chunk_seq_lens, chunk_seq_lens_npu=chunk_seq_lens.npu(), workspace=self.chunked_prefill_workspace, ) prefill_input_positions = input_positions[tokens_start:] cos = self.cos_cache[ prefill_input_positions].unsqueeze( # type: ignore 1).unsqueeze(2) sin = self.sin_cache[ prefill_input_positions].unsqueeze( # type: ignore 1).unsqueeze(2) prefill_metadata = AscendMLATorchairPrefillMetadata( attn_mask=common_attn_metadata.attn_mask, query_lens=query_lens[tokens_start:].to(torch.int32), seq_lens=seq_lens, context_lens=seq_lens[tokens_start:], input_positions=prefill_input_positions, block_table=block_table[reqs_start:, ...], max_query_len=max_query_len, max_seq_lens=max_seq_lens, query_start_loc=prefill_query_start_loc, chunked_context=chunked_context_metadata, sin=sin, cos=cos, ) decode_metadata = None graph_pad_size = common_attn_metadata.graph_pad_size use_torchair_graph = graph_pad_size != -1 if num_decodes > 0: # Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario actual_seq_lengths_q = query_start_loc[1:num_decodes + 1].tolist() max_seq_lens = seq_lens[:num_decodes].max().item() seq_lens = seq_lens[:num_decodes] input_positions = input_positions[:num_decode_tokens] block_table = block_table[:num_decodes, ...] num_token_pad_size = 0 if use_torchair_graph and common_attn_metadata.attn_state in [ AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding ]: num_reqs_pad_size = 0 if graph_pad_size != 0: pad_value = 0 num_token_pad_size = graph_pad_size - num_decode_tokens num_reqs_pad_size = ( graph_pad_size // common_attn_metadata.decode_token_per_req - num_reqs) padded_seq_lens = seq_lens.tolist( ) + [pad_value] * num_reqs_pad_size else: padded_seq_lens = seq_lens.tolist() seq_lens = torch.from_numpy( np.array(padded_seq_lens).astype(np.int32)) seq_lens_list = padded_seq_lens slot_padding = torch.full((num_token_pad_size, ), PAD_SLOT_ID, dtype=slot_mapping.dtype, device=slot_mapping.device) slot_mapping = torch.cat([slot_mapping, slot_padding]) block_table_padding = torch.zeros( (num_reqs_pad_size, ) + block_table.shape[1:], dtype=block_table.dtype, device=block_table.device) block_table = torch.cat([block_table, block_table_padding], dim=0) block_table = self._get_graph_runner_block_tables( num_reqs + num_reqs_pad_size, block_table) position_padding = torch.zeros(num_token_pad_size, dtype=input_positions.dtype, device=input_positions.device) input_positions = torch.cat( [input_positions, position_padding]) actual_seq_lengths_q = self.pad_actual_seq_len_q( num_reqs_pad_size, num_reqs, actual_seq_lengths_q, common_attn_metadata) else: seq_lens_list = seq_lens.tolist() # mtp torchair + PD scenario, last element of actual_seq_lengths_q must equal to batch_size(num_tokens) batch_size = num_decode_tokens + num_token_pad_size if actual_seq_lengths_q[-1] != batch_size \ and common_attn_metadata.attn_state == AscendAttentionState.SpecDecoding: actual_seq_lengths_q[-1] = batch_size cos = self.cos_cache[input_positions].unsqueeze( # type: ignore 1).unsqueeze(2) sin = self.sin_cache[input_positions].unsqueeze( # type: ignore 1).unsqueeze(2) decode_metadata = AscendMLATorchairDecodeMetadata( input_positions=input_positions, block_table=block_table, seq_lens=seq_lens, seq_lens_list=seq_lens_list, max_seq_lens=max_seq_lens, attn_mask=common_attn_metadata.spec_attn_mask, actual_seq_lengths_q=actual_seq_lengths_q, sin=sin, cos=cos) return self.metadata_cls( # type: ignore num_actual_tokens=num_actual_tokens, query_lens=query_lens.tolist(), slot_mapping=slot_mapping, head_dim=self.model_config.get_head_size(), num_decodes=num_decodes, num_decode_tokens=num_decode_tokens, num_prefills=num_prefills, attn_mask=common_attn_metadata.attn_mask, attn_state=common_attn_metadata.attn_state, prefill=prefill_metadata, decode=decode_metadata, query_start_loc=query_start_loc, block_tables=block_table, seq_lens=seq_lens, enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp, ) def pad_actual_seq_len_q(self, num_reqs_pad_size, num_reqs, actual_seq_lengths_q, common_attn_metadata): """ Pads actual_seq_lengths_q evenly to not exceed 16 tokens per request in order to meet the requirement of npu_fused_infer_attention_score. In Torchair scenario, the lengths of the queries must be padded to the same length. And npu_fused_infer_attention_score constraint requires the last element must equal to batch_size(num_tokens). For example: batch_size=36, num_reqs_pad_size=2, num_reqs=16 By default, each request should have inference 2 token, which means actual_seq_lengths_q should be [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36]. However, mtp torchair + PD scenario, the actual_seq_lengths_q may be [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] before padding, since the first decode request only has 1 token. In order to meet the requirement of npu_fused_infer_attention_score, we need to pad actual_seq_lengths_q evenly to not exceed 16 tokens per request. after padding actual_seq_lengths_q should be similar to [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,36] """ FIA_SEQ_LEN_LIMIT = 16 need_padding = num_reqs_pad_size != 0 and \ len(common_attn_metadata.actual_seq_lengths_q) > num_reqs and \ common_attn_metadata.actual_seq_lengths_q[num_reqs] - actual_seq_lengths_q[-1] > FIA_SEQ_LEN_LIMIT if need_padding: padding_seq_len_q = common_attn_metadata.actual_seq_lengths_q[ num_reqs:num_reqs + num_reqs_pad_size] start_val = actual_seq_lengths_q[-1] end_val = padding_seq_len_q[-1] num_step = len(padding_seq_len_q) interpolated = np.round( np.linspace(start_val, end_val, num_step + 1)[1:]).astype(int).tolist() assert interpolated[-1] == end_val assert len(interpolated) == len(padding_seq_len_q) actual_seq_lengths_q = actual_seq_lengths_q + interpolated else: actual_seq_lengths_q = actual_seq_lengths_q + common_attn_metadata.actual_seq_lengths_q[ num_reqs:num_reqs + num_reqs_pad_size] return actual_seq_lengths_q class AscendMLATorchairImpl(MLAAttentionImpl): """ 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, logits_soft_cap: Optional[float], attn_type: str, kv_sharing_target_layer_name: Optional[str], **kwargs, ) -> 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 # MLA Args self.q_lora_rank = kwargs['q_lora_rank'] self.kv_lora_rank = kwargs['kv_lora_rank'] self.qk_nope_head_dim = kwargs['qk_nope_head_dim'] self.qk_rope_head_dim = kwargs['qk_rope_head_dim'] self.qk_head_dim = kwargs['qk_head_dim'] self.v_head_dim = kwargs['v_head_dim'] self.rotary_emb = kwargs['rotary_emb'] self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[ 'q_b_proj'] self.kv_b_proj = kwargs['kv_b_proj'] self.o_proj = kwargs['o_proj'] self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None) self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None) self.num_queries_per_kv = self.num_heads // self.num_kv_heads self.tp_size = get_tensor_model_parallel_world_size() ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled self.enable_kv_nz = ascend_config.torchair_graph_config.enable_kv_nz self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp self.running_in_graph = False self.prefill_mask = None self.ring_mla_mask_size = 512 self.speculative_config = get_current_vllm_config().speculative_config def _v_up_proj_and_o_proj(self, x, enable_multistream_mla: bool = False): # 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) x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim) if hasattr(self, "running_in_graph") and not self.running_in_graph: return x MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024 # 16MB maybe_npu_prefetch(self.o_proj.weight, x, max_size=MAX_O_PROJ_PREFETCH_SIZE, enabled=enable_multistream_mla) return self.o_proj(x, is_prefill=False)[0] # Return `ql_nope`, `q_pe` def _q_proj_and_k_up_proj(self, x): q_nope, q_pe = self.q_proj(x)[0]\ .view(-1, self.num_heads, self.qk_head_dim)\ .split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) # Convert from (B, N, P) to (N, B, P) q_nope = q_nope.transpose(0, 1) # Multiply (N, B, P) x (N, P, L) -> (N, B, L) ql_nope = torch.bmm(q_nope, self.W_UK_T) # Convert from (N, B, L) to (B, N, L) return ql_nope.transpose(0, 1), q_pe 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 # 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 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).contiguous() # Convert from (L, N, P) to (N, P, L) self.W_UK_T = W_UK.permute(1, 2, 0).contiguous() # Waiting for BMM NZ support # self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29) # self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29) def _compute_prefill_context( self, query: torch.Tensor, kv_c_and_k_pe_cache: Tuple[torch.Tensor], rope_dim: int, attn_metadata: AscendMLATorchairMetadata, prefix_output: torch.Tensor, prefix_lse: torch.Tensor, ): assert len(kv_c_and_k_pe_cache) > 1 prefill_metadata = attn_metadata.prefill if prefill_metadata is None or prefill_metadata.chunked_context is None: return prefix_output, prefix_lse iters = len(prefill_metadata.chunked_context.seq_tot) q_pe = query[..., self.qk_nope_head_dim:] q_nope = query[..., :self.qk_nope_head_dim] current_seq_len = torch.tensor(prefill_metadata.query_lens, dtype=torch.int32) cache_kv_c = kv_c_and_k_pe_cache[0] cache_k_pe = kv_c_and_k_pe_cache[1] num_heads = cache_k_pe.size(2) latent_kv_dim = kv_c_and_k_pe_cache[0].size(-1) for i in range(iters): toks = prefill_metadata.chunked_context.seq_tot[i] context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[ i] context_seq_len_npu = prefill_metadata.chunked_context.chunk_seq_lens_npu[ i] seq_len = torch.stack([current_seq_len, context_seq_len]) kv_c_normed = torch.empty(toks, num_heads, latent_kv_dim, dtype=query.dtype, device=query.device) k_pe = torch.empty(toks, num_heads, rope_dim, dtype=query.dtype, device=query.device) torch_npu.atb.npu_paged_cache_load( cache_kv_c, cache_k_pe, prefill_metadata.block_table, context_seq_len_npu, seq_starts=prefill_metadata.chunked_context.starts[i], key=kv_c_normed, value=k_pe, ) kv_c_normed = kv_c_normed.squeeze() 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_pe = k_pe.expand((*k_nope.shape[:-1], -1)) torch_npu.atb.npu_ring_mla( q_nope=q_nope, q_rope=q_pe, k_nope=k_nope, k_rope=k_pe, value=v, mask=self.prefill_mask, seqlen=seq_len, head_num=self.num_heads, kv_head_num=self.num_heads, pre_out=prefix_output, prev_lse=prefix_lse, qk_scale=self.scale, kernel_type="kernel_type_high_precision", mask_type="no_mask", input_layout="type_bsnd", calc_type="calc_type_default", output=prefix_output, softmax_lse=prefix_lse) return prefix_output, prefix_lse def _forward_prefill( self, query: torch.Tensor, kv_c_normed: torch.Tensor, k_pe: torch.Tensor, kv_c_and_k_pe_cache: Tuple[torch.Tensor], attn_metadata: AscendMLATorchairMetadata, ) -> torch.Tensor: assert attn_metadata.prefill is not None assert len(kv_c_and_k_pe_cache) > 1 num_tokens = query.size(0) attn_output = torch.empty(num_tokens, self.num_heads, self.v_head_dim, dtype=query.dtype, device=query.device) attn_lse = torch.empty(self.num_heads, num_tokens, dtype=torch.float32, device=query.device) k_nope, value = self.kv_b_proj(kv_c_normed)[0].view( -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim).split( [self.qk_nope_head_dim, self.v_head_dim], dim=-1) k_pe = k_pe.expand((*k_nope.shape[:-1], -1)) # Here is only 2 possibility of input, ChunkedPrefill or PrefillNoCache q_pe = query[..., self.qk_nope_head_dim:] q_nope = query[..., :self.qk_nope_head_dim] if self.prefill_mask is None: if q_nope.dtype == torch.float16: mask_value = torch.finfo(torch.float32).min else: mask_value = 1 prefill_mask = torch.triu( torch.ones(self.ring_mla_mask_size, self.ring_mla_mask_size, device=q_nope.device, dtype=q_nope.dtype), 1) self.prefill_mask = torch.where(prefill_mask == 1, mask_value, 0).to(q_nope.dtype) torch_npu.atb.npu_ring_mla(q_nope=q_nope, q_rope=q_pe, k_nope=k_nope, k_rope=k_pe, value=value, mask=self.prefill_mask, seqlen=attn_metadata.prefill.query_lens, head_num=self.num_heads, kv_head_num=self.num_heads, pre_out=None, prev_lse=None, qk_scale=self.scale, kernel_type="kernel_type_high_precision", mask_type="mask_type_triu", input_layout="type_bsnd", calc_type="calc_type_first_ring", output=attn_output, softmax_lse=attn_lse) attn_output, attn_lse = self._compute_prefill_context( \ query, kv_c_and_k_pe_cache, self.qk_rope_head_dim, attn_metadata, attn_output, attn_lse) attn_output = attn_output.reshape( [num_tokens, self.num_heads * self.v_head_dim]) return attn_output def exec_kv( self, hidden_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, kv_cache: Tuple, slots: torch.Tensor, ): B = hidden_states.shape[0] N = self.num_kv_heads S = 1 kv = self.kv_a_proj_with_mqa(hidden_states)[0] # npu_kv_rmsnorm_rope_cache needs [B, N, S, D] kv = kv.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim) cache_mode = "PA_NZ" if self.enable_kv_nz else "PA" k_pe, k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache( kv, self.kv_a_layernorm.weight, cos, sin, slots.to(torch.int64), kv_cache[1], kv_cache[0], epsilon=self.kv_a_layernorm.variance_epsilon, cache_mode=cache_mode, ) return k_pe, k_nope, kv def exec_kv_prefill( self, hidden_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, kv_cache: Tuple, slots: torch.Tensor, ): B = hidden_states.shape[0] N = self.num_kv_heads S = 1 kv = self.kv_a_proj_with_mqa(hidden_states)[0] # npu_kv_rmsnorm_rope_cache needs [B, N, S, D] kv = kv.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim) cache_mode = "PA_NZ" if self.enable_kv_nz else "PA" _, _, k_pe, k_nope = torch_npu.npu_kv_rmsnorm_rope_cache( kv, self.kv_a_layernorm.weight, cos, sin, slots.to(torch.int64), kv_cache[1], kv_cache[0], epsilon=self.kv_a_layernorm.variance_epsilon, cache_mode=cache_mode, is_output_kv=True, ) return k_pe, k_nope def rope_single( self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, ) -> torch.Tensor: B, N, D = x.shape S = 1 x = x.view(B, N, S, D) x = torch_npu.npu_interleave_rope(x, cos, sin) return x.view(B, N, D) def _forward_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, k_nope: torch.Tensor, k_pe: torch.Tensor, kv_c_and_k_pe_cache: Tuple[torch.Tensor], attn_metadata: AscendMLATorchairMetadata, enable_multistream_mla: bool = False, ) -> torch.Tensor: decode_meta = attn_metadata.decode assert decode_meta is not None num_tokens = q_nope.size(0) if self.running_in_graph or self.running_chunkprefilll_with_torchair: # shape of knope/k_pe for npu graph mode should be: # [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim] block_size = kv_c_and_k_pe_cache[0].shape[1] actual_seq_lengths = None if self.enable_kv_nz: k_nope = k_nope.view(-1, self.num_kv_heads, self.kv_lora_rank // 16, block_size, 16) k_pe = k_pe.view(-1, self.num_kv_heads, self.qk_rope_head_dim // 16, block_size, 16) input_layout = "BSND" else: k_nope = k_nope.view(-1, self.num_kv_heads, block_size, self.kv_lora_rank) k_pe = k_pe.view(-1, self.num_kv_heads, block_size, self.qk_rope_head_dim) input_layout = "BNSD" if attn_metadata.attn_state in [ AscendAttentionState.SpecDecoding, AscendAttentionState.ChunkedPrefill ] and self.speculative_config is not None: # Use TND layout for pure SpecDecoding and SpecDecoding in ChunkedPrefill input_layout = "TND" # [bs * q_seq_len, num_heads_per_rank, dim] q_nope = q_nope.view(num_tokens, self.num_heads, -1) q_pe = q_pe.view(num_tokens, self.num_heads, -1) sparse_mode = 3 spec_attn_mask = attn_metadata.decode.attn_mask # type:ignore actual_seq_lengths = decode_meta.actual_seq_lengths_q else: if self.enable_kv_nz: q_nope = q_nope.view(num_tokens, 1, self.num_heads, -1) q_pe = q_pe.view(num_tokens, 1, self.num_heads, -1) else: q_nope = q_nope.view(num_tokens, self.num_heads, 1, -1) q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1) sparse_mode = 0 spec_attn_mask = None attn_output, _ = torch_npu.npu_fused_infer_attention_score( q_nope, k_nope, k_nope, query_rope=q_pe, key_rope=k_pe, num_heads=self.num_heads, num_key_value_heads=self.num_kv_heads, input_layout=input_layout, atten_mask=spec_attn_mask, sparse_mode=sparse_mode, scale=self.scale, antiquant_mode=0, antiquant_scale=None, block_table=decode_meta.block_table, block_size=block_size, actual_seq_lengths_kv=decode_meta.seq_lens_list, actual_seq_lengths=actual_seq_lengths) else: # The MLA_PA path will be used as default path in the future, `_npu_paged_attention_mla` will # be removed after the torch_npu contains `torch_npu.atb.npu_multi_head_latent_attention` become # public available assert len(kv_c_and_k_pe_cache) > 1 if envs_ascend.vllm_npu_MLA_PA: attn_output = torch_npu.atb.npu_multi_head_latent_attention( q_nope, q_pe, kv_c_and_k_pe_cache[0], kv_c_and_k_pe_cache[1], attn_metadata.decode.block_table, attn_metadata.decode.seq_lens, self.num_heads, self.scale, self.num_kv_heads) else: q = torch.cat([q_nope, q_pe], dim=-1) attn_output = torch.empty( [num_tokens, self.num_heads, self.kv_lora_rank], dtype=q.dtype, device=q.device) k_cache = torch.cat( [kv_c_and_k_pe_cache[0], kv_c_and_k_pe_cache[1]], dim=-1) torch_npu._npu_paged_attention_mla( query=q, key_cache=k_cache, num_kv_heads=self.num_kv_heads, num_heads=self.num_heads, scale_value=self.scale, block_table=attn_metadata.decode. block_table, # type:ignore context_lens=attn_metadata.decode.seq_lens, # type:ignore mla_vheadsize=self.kv_lora_rank, out=attn_output) current_ms_metadata = get_multistream_comm_context() if current_ms_metadata is None: return self._v_up_proj_and_o_proj(attn_output, enable_multistream_mla) else: current_ms_metadata.before_comm_event.record() with torch.npu.stream(current_ms_metadata.comm_stream): current_ms_metadata.before_comm_event.wait() return self._v_up_proj_and_o_proj(attn_output) def forward( self, layer: AttentionLayer, hidden_states_or_q_c: torch.Tensor, # query in unified attn hidden_states_or_kv_c_normed: torch.Tensor, # key in unified attn k_pe: torch.Tensor, # value in unified attn kv_cache: Tuple[torch.Tensor], attn_metadata: M, output: Optional[torch.Tensor] = None, enable_multistream_mla: bool = False, ckq: 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.fill_(0) self.running_in_graph = self.torchair_graph_enabled and attn_metadata.attn_state in [ AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding ] self.running_chunkprefilll_with_torchair = self.torchair_graph_enabled and attn_metadata.attn_state == AscendAttentionState.ChunkedPrefill num_actual_toks = attn_metadata.num_actual_tokens if k_pe is None and not self.running_in_graph: kv_c, k_pe = self.kv_a_proj_with_mqa( hidden_states_or_kv_c_normed)[0].split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) kv_c_normed = self.kv_a_layernorm(kv_c.contiguous()) else: kv_c_normed = hidden_states_or_kv_c_normed 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 if not self.running_in_graph: # Inputs and outputs may be padded for CUDA graphs output_padded = output output = output[:num_actual_toks, ...] if not self.torchair_graph_enabled: kv_c_normed = kv_c_normed[:num_actual_toks, ...] prefill_k_c_normed = kv_c_normed[num_decode_tokens:] if not self.running_in_graph: hidden_states_or_q_c = hidden_states_or_q_c[:num_actual_toks, ...] prefill_hs_or_q_c = hidden_states_or_q_c[num_decode_tokens:] decode_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens] prefill_hs = hidden_states_or_kv_c_normed[num_decode_tokens:] # if not self.torchair_graph_enabled: k_pe = k_pe[:num_actual_toks, ...] k_pe = k_pe.unsqueeze(1) decode_k_pe = k_pe[:num_decode_tokens] prefill_k_pe = k_pe[num_decode_tokens:] else: decode_hs_or_q_c = hidden_states_or_q_c if has_decode: decode_k_nope = None assert attn_metadata.decode is not None if self.running_in_graph or self.running_chunkprefilll_with_torchair: cos = attn_metadata.decode.cos sin = attn_metadata.decode.sin if self.running_chunkprefilll_with_torchair: decode_hs = ( hidden_states_or_kv_c_normed[:num_decode_tokens]) slots = attn_metadata.slot_mapping[:num_decode_tokens] decode_k_pe, decode_k_nope, decode_kv = self.exec_kv( decode_hs, cos, sin, kv_cache, slots) else: with npu_stream_switch("mla_secondary", 0, enabled=enable_multistream_mla): npu_wait_tensor(hidden_states_or_kv_c_normed, ckq, enabled=enable_multistream_mla) decode_k_pe, decode_k_nope, decode_kv = self.exec_kv( hidden_states_or_kv_c_normed, cos, sin, kv_cache, attn_metadata.slot_mapping) # Without explicitly controlling the order, IndexByTensor operations # would be placed after `matmul W_KV_T` hindering the overlapping of # KvRmsNormRopeCache and SingleRope. npu_wait_tensor(decode_hs_or_q_c, cos, enabled=enable_multistream_mla) npu_wait_tensor(decode_hs_or_q_c, sin, enabled=enable_multistream_mla) npu_wait_tensor(decode_hs_or_q_c, decode_kv, enabled=enable_multistream_mla) decode_ql_nope, decode_q_pe = \ self._q_proj_and_k_up_proj(decode_hs_or_q_c) if self.running_in_graph: with npu_stream_switch("mla_secondary", 0, enabled=enable_multistream_mla): npu_wait_tensor(decode_q_pe, decode_k_pe, enabled=enable_multistream_mla) decode_q_pe = self.rope_single(decode_q_pe, cos, sin) elif self.running_chunkprefilll_with_torchair: decode_q_pe = self.rope_single(decode_q_pe, cos, sin) else: decode_q_pe[...], decode_k_pe[...] = self.rotary_emb( attn_metadata.decode.input_positions, decode_q_pe.contiguous(), 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_nope = prefill_q[..., :self.qk_nope_head_dim] if self.torchair_graph_enabled: num_tokens = prefill_hs_or_q_c.shape[0] cos = attn_metadata.prefill.cos sin = attn_metadata.prefill.sin prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin) prefill_k_pe, prefill_k_nope = self.exec_kv_prefill( prefill_hs, cos, sin, kv_cache, attn_metadata.slot_mapping[num_decode_tokens:]) kv_c_normed = prefill_k_nope[:num_actual_toks, ...] prefill_k_c_normed = prefill_k_nope prefill_k_pe = prefill_k_pe.view(num_tokens, self.num_kv_heads, -1) prefill_q = torch.cat([prefill_q_nope, prefill_q_pe], dim=-1) else: prefill_q_pe[...], prefill_k_pe[...] = self.rotary_emb( attn_metadata.prefill.input_positions, prefill_q_pe.contiguous(), prefill_k_pe) assert len( kv_cache ) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)" if self.torchair_graph_enabled: if kv_cache[0].numel() > 0 and has_prefill: slots = attn_metadata.slot_mapping # NOTE: Separate the kv cache in advance to avoid OOM or other issues torch_npu._npu_reshape_and_cache( key=kv_c_normed.view(num_tokens, self.num_kv_heads, -1), value=prefill_k_pe, key_cache=kv_cache[0], value_cache=kv_cache[1], slot_indices=slots[num_decode_tokens:]) else: kv_c_normed = kv_c_normed.view( [num_actual_toks, self.num_kv_heads, -1]) torch_npu._npu_reshape_and_cache( key=kv_c_normed, value=k_pe, key_cache=kv_cache[0], value_cache=kv_cache[1], slot_indices=attn_metadata.slot_mapping) if not self.running_in_graph: o_proj_input_shape = (num_actual_toks, self.num_heads * self.v_head_dim) o_proj_input = torch.empty(o_proj_input_shape, dtype=hidden_states_or_q_c.dtype, device=hidden_states_or_q_c.device) if has_prefill: # FIX: aicore move should be also placed on the comm stream in dbo, # otherwise it may affect the accuracy # TODO: use an elegant way to overlap output_prefill = self._forward_prefill(prefill_q, prefill_k_c_normed, prefill_k_pe, kv_cache, attn_metadata) current_ms_metadata = get_multistream_comm_context() if current_ms_metadata is not None: current_ms_metadata.before_comm_event.record() with torch.npu.stream(current_ms_metadata.comm_stream): current_ms_metadata.before_comm_event.wait() o_proj_input[num_decode_tokens:] = output_prefill else: o_proj_input[num_decode_tokens:] = output_prefill if has_decode: if self.running_in_graph: return self._forward_decode(decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe, kv_cache, attn_metadata, enable_multistream_mla) else: output_decode = self._forward_decode(decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe, kv_cache, attn_metadata) current_ms_metadata = get_multistream_comm_context() if current_ms_metadata is not None: with torch.npu.stream(current_ms_metadata.comm_stream): o_proj_input[:num_decode_tokens] = output_decode else: o_proj_input[:num_decode_tokens] = output_decode current_ms_metadata = get_multistream_comm_context() MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024 # 16MB if current_ms_metadata is None: maybe_npu_prefetch(self.o_proj.weight, o_proj_input, max_size=MAX_O_PROJ_PREFETCH_SIZE, enabled=enable_multistream_mla) output[...] = self.o_proj( o_proj_input, is_prefill=True, is_force_scatter=self.enable_shared_expert_dp)[0] else: with torch.npu.stream(current_ms_metadata.comm_stream): maybe_npu_prefetch(self.o_proj.weight, o_proj_input, max_size=MAX_O_PROJ_PREFETCH_SIZE, enabled=enable_multistream_mla) output[...] = self.o_proj( o_proj_input, is_prefill=True, is_force_scatter=self.enable_shared_expert_dp)[0] current_ms_metadata.after_comm_event.record() del o_proj_input return output_padded