from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar import numpy as np import torch 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 get_current_vllm_config from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod) from vllm_ascend.ascend_config import get_ascend_config from vllm_ascend.attention.attention_v1 import AscendAttentionState from vllm_ascend.multistream.base import MSAttentionMetadataSplitConfig from vllm_ascend.multistream.context import get_multistream_comm_context from vllm_ascend.multistream.ms_split import model_input_split_v1_mla_attn from vllm_ascend.ops.attention import vanilla_chunked_prefill_mla if TYPE_CHECKING: from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.worker.gpu_input_batch import InputBatch @dataclass class CommonAttentionMetadata: """ Attention metadata attributes that can be shared by layers in different KV cache groups and thus having different block table. """ query_start_loc: torch.Tensor """(batch_size + 1,), the start location of each request in query Tensor""" seq_lens: torch.Tensor """(batch_size,), the length of each request including both computed tokens and newly scheduled tokens""" class AscendMLABackend(AttentionBackend): accept_output_buffer: bool = True @staticmethod def get_name() -> str: return "VLLM_ASCEND_MLA" @staticmethod def get_metadata_cls() -> type["AttentionMetadata"]: return AscendMLAMetadata @staticmethod def get_builder_cls(): return AscendMLAMetadataBuilder @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 AscendMLAImpl @dataclass class AscendMLAPrefillMetadata: """ Prefill Specific Metadata for Ascend""" attn_mask: torch.Tensor query_lens: list[int] 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 @dataclass class AscendMLADecodeMetadata: # 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] attn_mask: Optional[torch.Tensor] = None @dataclass class AscendMLAMetadata: """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. with_prefill_across_dp: bool = False 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[AscendMLADecodeMetadata] = None prefill: Optional[AscendMLAPrefillMetadata] = None 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["AscendMLAMetadata"]: """Split metadata for multi-stream with AscendMLAMetadata""" return model_input_split_v1_mla_attn( ms_split_config=ms_split_config, attn_metadata=self, _metadata_cls=AscendMLAMetadata, ) M = TypeVar("M", bound=AscendMLAMetadata) class AscendMLAMetadataBuilder: """ NOTE: Please read the comment at the top of the file before trying to understand this class """ # _attn_mask_builder = None def __init__(self, runner, metadata_cls: Optional[AscendMLAMetadata] = None): self.metadata_cls: Optional[AscendMLAMetadata] = metadata_cls \ if metadata_cls is not None else AscendMLAMetadata # type: ignore self.runner = runner scheduler_config = runner.scheduler_config self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled 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 = [] 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] 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) num_decode_tokens += num_tokens else: prefills.append(i) num_prefill_tokens += num_tokens # For eager mode we treat spec decoding as chunked prefill. else: 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 _get_graph_runner_block_tables( self, num_seqs: int, block_tables: torch.Tensor) -> torch.Tensor: max_batch_size, max_blocks = self.runner.graph_block_tables.shape assert max_batch_size >= num_seqs if isinstance(self.runner.graph_block_tables, np.ndarray): graph_block_tables = torch.zeros((max_batch_size, max_blocks), dtype=block_tables.dtype, device=block_tables.device) else: graph_block_tables = self.runner.graph_block_tables.to( device=block_tables.device, dtype=block_tables.dtype) 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[:num_seqs, :max_blocks] def build_dummy(self, num_reqs: int, num_actual_tokens: int) -> AscendMLAMetadata: device = self.runner.device _, max_blocks = self.runner.graph_block_tables.shape block_table = torch.zeros((num_reqs, max_blocks), dtype=torch.int32, device=device) block_table = self._get_graph_runner_block_tables( num_reqs, block_table) seq_lens = torch.ones(num_reqs, dtype=torch.int32, device=device) input_positions = torch.zeros(num_reqs, dtype=torch.int32, device=device).long() slot_mapping = torch.full((num_reqs, ), PAD_SLOT_ID, dtype=torch.int32, device=device) query_start_loc = torch.full((num_reqs, ), -1, dtype=torch.int32, device=device) decode_metadata = AscendMLADecodeMetadata( input_positions=input_positions, block_table=block_table, seq_lens=seq_lens, seq_lens_list=seq_lens.tolist(), max_seq_lens=1, attn_mask=self.runner.spec_attn_mask) return self.metadata_cls( # type: ignore num_input_tokens=num_actual_tokens, num_actual_tokens=num_actual_tokens, slot_mapping=slot_mapping, head_dim=self.runner.model_config.get_head_size(), num_decodes=1, num_decode_tokens=1, num_prefills=0, attn_mask=self.runner.attn_mask, attn_state=AscendAttentionState.DecodeOnly, prefill=None, decode=decode_metadata, query_start_loc=query_start_loc, seq_lens=seq_lens, block_tables=block_table, ) def build( self, num_reqs: int, num_actual_tokens: int, max_query_len: int, common_attn_metadata: CommonAttentionMetadata, common_prefix_len: Optional[int] = None, graph_pad_size: int = -1, with_prefill_across_dp: bool = False, ) -> AscendMLAMetadata: 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.runner.input_batch.block_table[0]. get_device_tensor()[:num_reqs]) slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to( device, non_blocking=True) input_positions = self.runner.positions_cpu[:num_actual_tokens].to( device, non_blocking=True).long() seq_lens_cpu = self.runner.seq_lens_cpu[:num_reqs] query_lens = seq_lens_cpu - self.runner.input_batch.num_computed_tokens_cpu_tensor[: num_reqs] seq_lens = seq_lens_cpu max_query_len = query_lens.max().item() max_seq_lens = seq_lens.max().item() query_start_loc = common_attn_metadata.query_start_loc prefill_metadata = None if self._num_prefills > 0: reqs_start = self._num_decodes # prefill_start tokens_start = self._num_decode_tokens max_query_len = query_lens[tokens_start:].max().item() max_seq_lens = seq_lens[tokens_start:].max().item() query_start_loc = common_attn_metadata.query_start_loc prefill_query_start_loc = query_start_loc[ reqs_start:] - query_start_loc[reqs_start] prefill_metadata = AscendMLAPrefillMetadata( attn_mask=self.runner.attn_mask, query_lens=query_lens[tokens_start:], seq_lens=seq_lens, context_lens=seq_lens[tokens_start:], input_positions=input_positions[tokens_start:], 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, ) decode_metadata = None use_torchair_graph = graph_pad_size != -1 if self._num_decodes > 0: max_seq_lens = seq_lens[:self._num_decodes].max().item() seq_lens = seq_lens[:self._num_decode_tokens] input_positions = input_positions[:self._num_decode_tokens] block_table = block_table[:self._num_decode_tokens, ...] if use_torchair_graph and self.runner.attn_state == AscendAttentionState.DecodeOnly: num_seqs = len(seq_lens) if graph_pad_size != 0: pad_value = 1 padded_seq_lens = seq_lens.tolist() + [pad_value ] * graph_pad_size else: padded_seq_lens = seq_lens.tolist() seq_lens = torch.from_numpy( np.array(padded_seq_lens).astype(np.int32)) padding = torch.full((graph_pad_size, ), PAD_SLOT_ID, dtype=slot_mapping.dtype, device=slot_mapping.device) slot_mapping = torch.cat([slot_mapping, padding]) block_table_padding = torch.zeros( (graph_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_seqs + graph_pad_size, block_table) padding_0 = torch.zeros(graph_pad_size, dtype=input_positions.dtype, device=input_positions.device) input_positions = torch.cat([input_positions, padding_0]) decode_metadata = AscendMLADecodeMetadata( input_positions=input_positions, block_table=block_table, seq_lens=seq_lens, seq_lens_list=seq_lens.tolist(), max_seq_lens=max_seq_lens, attn_mask=self.runner.spec_attn_mask) return self.metadata_cls( # type: ignore num_actual_tokens=num_actual_tokens, query_lens=query_lens.tolist(), slot_mapping=slot_mapping, head_dim=self.runner.model_config.get_head_size(), num_decodes=self._num_decodes, num_decode_tokens=self._num_decode_tokens, num_prefills=self._num_prefills, attn_mask=self.runner.attn_mask, attn_state=self.runner.attn_state, prefill=prefill_metadata, decode=decode_metadata, query_start_loc=query_start_loc, block_tables=block_table, seq_lens=seq_lens, with_prefill_across_dp=with_prefill_across_dp, ) class AscendMLAImpl(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, blocksparse_params: Optional[dict[str, Any]], logits_soft_cap: Optional[float], attn_type: str, kv_sharing_target_layer_name: Optional[str] = None, **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'] 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) ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled # Adapt torch air graph mode with spec decoding. speculative_config = get_current_vllm_config().speculative_config if speculative_config is not None: self.spec_token_num = speculative_config.num_speculative_tokens assert self.spec_token_num > 0 def _v_up_proj_and_o_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) x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim) return self.o_proj(x)[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() 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 _forward_prefill( self, query: torch.Tensor, kv_c_normed: torch.Tensor, k_pe: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, attn_metadata: AscendMLAMetadata, ) -> torch.Tensor: assert attn_metadata.prefill is not None num_tokens = query.size(0) attn_output = None # Here is only 2 possibility of input, ChunkedPrefill or PrefillNoCache if attn_metadata.attn_state in [ AscendAttentionState.ChunkedPrefill, AscendAttentionState.SpecDecoding ]: attn_output = torch.empty(num_tokens, self.num_heads * self.v_head_dim, dtype=query.dtype, device=query.device) # current requests is chunked in prefill, disable flash attention with chunked prefill vanilla_chunked_prefill_mla( output=attn_output, query=query, kv_cache=kv_c_and_k_pe_cache, block_tables=attn_metadata.prefill.block_table, query_lens=attn_metadata.prefill.query_lens, context_lens=attn_metadata.prefill.context_lens, kv_b_proj=self.kv_b_proj, max_query_len=attn_metadata.prefill.max_query_len, max_context_len=attn_metadata.prefill.max_seq_lens, nope_dim=self.qk_nope_head_dim, rope_dim=self.qk_rope_head_dim, v_head_dim=self.v_head_dim, scale=self.scale, alibi_slopes=None, causal=True) elif attn_metadata.attn_state == AscendAttentionState.PrefillNoCache: attn_output = torch.empty(num_tokens, self.num_heads, self.v_head_dim, dtype=query.dtype, 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) key = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1) torch_npu._npu_flash_attention( query=query, key=key, value=value, mask=attn_metadata.attn_mask, seq_len=attn_metadata.prefill.context_lens, scale_value=self.scale, num_heads=self.num_heads, num_kv_heads=self.num_heads, out=attn_output) attn_output = attn_output.view(-1, self.num_heads, self.v_head_dim) else: raise RuntimeError( "Unexpected path reached, AscendMLAImpl should only have PrefillNoCache, ChunkedPrefill and SpecDecoding scenario in forward prefill, please file a bug to vllm-ascend !" ) attn_output = attn_output.reshape( [num_tokens, self.num_heads * self.v_head_dim]) current_ms_metadata = get_multistream_comm_context() if current_ms_metadata is None: return self.o_proj(attn_output)[0] 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.o_proj(attn_output)[0] 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) 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="PA", ) 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: torch.Tensor, attn_metadata: AscendMLAMetadata, ) -> torch.Tensor: decode_meta = attn_metadata.decode assert decode_meta is not None q = torch.cat([q_nope, q_pe], dim=-1) num_tokens = q.size(0) attn_output = torch.empty( [num_tokens, self.num_heads, self.kv_lora_rank], dtype=q.dtype, device=q.device) if self.running_in_graph: # TorchAir's shape is [bs, num_heads_per_rank, q_seq_len, dim] if attn_metadata.attn_state == AscendAttentionState.SpecDecoding: assert num_tokens % self.spec_token_num == 0 q_nope = (q_nope.view( num_tokens // (self.spec_token_num + 1), self.spec_token_num + 1, self.num_heads, -1, ).transpose(1, 2).contiguous()) q_pe = (q_pe.view( num_tokens // (self.spec_token_num + 1), self.spec_token_num + 1, self.num_heads, -1, ).transpose(1, 2).contiguous()) sparse_mode = 3 spec_attn_mask = attn_metadata.decode.attn_mask # type:ignore 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 # 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] 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) attn_output, _ = torch.ops.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="BNSD", 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, ) else: torch_npu._npu_paged_attention_mla( query=q, key_cache=kv_c_and_k_pe_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) 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: 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 self.running_in_graph = self.torchair_graph_enabled and attn_metadata.attn_state in [ AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding ] 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, ...] 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, ...] decode_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens] prefill_hs_or_q_c = hidden_states_or_q_c[num_decode_tokens:] 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 decode_ql_nope, decode_q_pe = \ self._q_proj_and_k_up_proj(decode_hs_or_q_c) if self.running_in_graph: seq_len = self.rotary_emb.max_position_embeddings cos = self.rotary_emb.cos_cached[:seq_len].to( dtype=decode_q_pe.dtype) sin = self.rotary_emb.sin_cached[:seq_len].to( dtype=decode_q_pe.dtype) cos = cos[attn_metadata.decode.input_positions] sin = sin[attn_metadata.decode.input_positions] cos = cos[:, None, None, :] sin = sin[:, None, None, :] decode_q_pe = self.rope_single(decode_q_pe, cos, sin) decode_k_pe, decode_k_nope = self.exec_kv( hidden_states_or_kv_c_normed, cos, sin, kv_cache, attn_metadata.slot_mapping) else: decode_q_pe[...], decode_k_pe[...] = self.rotary_emb( attn_metadata.decode.input_positions, decode_q_pe.contiguous(), decode_k_pe, max_seq_len=attn_metadata.decode.max_seq_lens) 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] prefill_k_pe = prefill_k_pe.view(num_tokens, self.num_kv_heads, -1) if self.rotary_emb.__class__.__name__ == 'RotaryEmbedding': # NOTE: When scaling not specified ori_q_pe_shape, ori_k_pe_shape = prefill_q_pe.shape, prefill_k_pe.shape prefill_q_pe = prefill_q_pe.reshape(num_tokens, -1) prefill_k_pe = prefill_k_pe.reshape(num_tokens, -1) prefill_q_pe, prefill_k_pe = self.rotary_emb( attn_metadata.prefill.input_positions, prefill_q_pe, prefill_k_pe) prefill_q_pe = prefill_q_pe.view(ori_q_pe_shape) prefill_k_pe = prefill_k_pe.view(ori_k_pe_shape) else: prefill_q_pe, prefill_k_pe = self.rotary_emb( attn_metadata.prefill.input_positions, prefill_q_pe, prefill_k_pe) 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, max_seq_len=attn_metadata.prefill.max_seq_lens) if self.torchair_graph_enabled: if len(kv_cache) > 0 and kv_cache[0].numel( ) > 0 and attn_metadata.attn_state == AscendAttentionState.PrefillNoCache: 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) elif kv_cache.numel() > 0: key = torch.cat([ kv_c_normed.view([num_actual_toks, self.num_kv_heads, -1]), k_pe ], dim=2) torch_npu._npu_reshape_and_cache_siso( key=key, key_cache=kv_cache, slot_indices=attn_metadata.slot_mapping.flatten()) 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: with torch.npu.stream(current_ms_metadata.comm_stream): output[num_decode_tokens:] = output_prefill current_ms_metadata.after_comm_event.record() else: output[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) 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): output[:num_decode_tokens] = output_decode current_ms_metadata.after_comm_event.record() else: output[:num_decode_tokens] = output_decode return output_padded