from dataclasses import dataclass from typing import (TYPE_CHECKING, ClassVar, NamedTuple, Optional, Tuple, Type, TypeVar) import torch import torch_npu from torch import nn from vllm.attention.backends.abstract import (AttentionBackend, AttentionMetadata, MLAAttentionImpl) from vllm.config import VllmConfig, get_current_vllm_config from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod) from vllm.utils import cdiv, round_down from vllm.v1.attention.backends.utils import AttentionCGSupport from vllm_npu.ascend_config import get_ascend_config from vllm_npu.attention.attention_v1 import AscendAttentionState from vllm_npu.attention.mla_v1 import AscendMLAMetadata from vllm_npu.attention.utils import (AscendCommonAttentionMetadata, split_decodes_and_prefills) from vllm_npu.multistream.base import MSAttentionMetadataSplitConfig from vllm_npu.multistream.ms_split import model_input_split_v1_mla_attn from vllm_npu.worker.npu_input_batch import InputBatch if TYPE_CHECKING: from vllm.v1.core.sched.output import SchedulerOutput class AscendSFABackend(AttentionBackend): accept_output_buffer: bool = True @staticmethod def get_name() -> str: return "ASCEND_SFA" @staticmethod def get_metadata_cls() -> type["AttentionMetadata"]: return AscendSFAMetadata @staticmethod def get_builder_cls(): return AscendSFAMetadataBuilder @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["AscendSFAImpl"]: return AscendSFAImpl @dataclass class AscendSFAPrefillMetadata: """ Prefill Specific Metadata for Ascend""" @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 chunk_seq_lens: torch.Tensor 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 sin: torch.Tensor cos: torch.Tensor chunked_context: Optional[ChunkedContextMetadata] = None @dataclass class AscendSFADecodeMetadata: # 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: torch.Tensor sin: torch.Tensor cos: torch.Tensor attn_mask: Optional[torch.Tensor] = None @dataclass class AscendSFAMetadata: """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[AscendSFADecodeMetadata] = None prefill: Optional[AscendSFAPrefillMetadata] = 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["AscendSFAMetadata"]: """Split metadata for multi-stream with AscendSFAMetadata""" return model_input_split_v1_mla_attn( ms_split_config=ms_split_config, attn_metadata=self, _metadata_cls=AscendMLAMetadata, ) M = TypeVar("M", bound=AscendSFAMetadata) class AscendSFAMetadataBuilder: # Does this backend/builder support ACL Graphs for attention (default: no). aclgraph_support: ClassVar[AttentionCGSupport] = \ AttentionCGSupport.NEVER """ 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[AscendSFAMetadata] = None): self.metadata_cls: Optional[AscendSFAMetadata] = metadata_cls \ if metadata_cls is not None else AscendSFAMetadata # 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 self.speculative_config = vllm_config.speculative_config self.decode_threshold = 1 if self.speculative_config: spec_token_num = self.speculative_config.num_speculative_tokens self.decode_threshold += spec_token_num assert self.decode_threshold <= 16, f"decode_threshold exceeded \ npu_fused_infer_attention_score TND layout's limit of 16, \ got {self.decode_threshold}" 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, ) 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] if num_tokens <= self.decode_threshold: 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 build( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, model: nn.Module, ) -> AscendSFAMetadata: 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 num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \ split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.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].to( device, non_blocking=True) input_positions = common_attn_metadata.positions[: num_actual_tokens].long( ) if self.cos_cache is None: self.cos_cache = model.model.layers[ model.model.start_layer].self_attn.rotary_emb.cos_cached self.sin_cache = model.model.layers[ model.model.start_layer].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[reqs_start:].max().item() max_seq_lens = seq_lens[reqs_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 = \ AscendSFAPrefillMetadata.ChunkedContextMetadata( cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True), starts=chunk_starts.to(device, non_blocking=True), seq_tot=chunk_seq_lens.sum(dim=1).tolist(), max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(), chunk_seq_lens=chunk_seq_lens, 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) actual_query_lens = torch.tensor(query_lens[reqs_start:], dtype=torch.int32).npu() query_lens_prefill_sfa = torch.cumsum(actual_query_lens, dim=0).to(torch.int32) seq_lens_prefill_sfa = seq_lens[reqs_start:].to(torch.int32).npu() prefill_metadata = AscendSFAPrefillMetadata( attn_mask=common_attn_metadata.attn_mask, query_lens=query_lens_prefill_sfa, seq_lens=seq_lens_prefill_sfa, context_lens=seq_lens[reqs_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 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].to( torch.int32).npu() max_seq_lens = seq_lens[:num_decodes].max().item() seq_lens = seq_lens[:num_decodes].to(torch.int32).npu() input_positions = input_positions[:num_decode_tokens] block_table = block_table[:num_decodes, ...] seq_lens_list = seq_lens.tolist() 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 = AscendSFADecodeMetadata( 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_input_tokens=common_attn_metadata.num_input_tokens, 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, ) class PrefillSFAPreprocessResult(NamedTuple): q_nope: Optional[torch.Tensor] = None q_pe: Optional[torch.Tensor] = None k_nope: Optional[torch.Tensor] = None k_pe: Optional[torch.Tensor] = None topk_indices: Optional[torch.Tensor] = None query_states: Optional[torch.Tensor] = None key_states: Optional[torch.Tensor] = None class DecodeSFAPreprocessResult(NamedTuple): q_nope: Optional[torch.Tensor] = None q_pe: Optional[torch.Tensor] = None # nope_cache: Optional[torch.Tensor] = None # rope_cache: Optional[torch.Tensor] = None topk_indices: Optional[torch.Tensor] = None query_states: Optional[torch.Tensor] = None key_states: Optional[torch.Tensor] = None bsz: Optional[int] = None class AscendSFAImpl(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'] self.kv_b_proj = kwargs['kv_b_proj'] self.o_proj = kwargs['o_proj'] self.indexer = kwargs['indexer'] 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.q_a_proj = kwargs.get('q_a_proj', None) self.q_a_layernorm = kwargs.get('q_a_layernorm', None) self.num_queries_per_kv = self.num_heads // self.num_kv_heads self.tp_size = get_tensor_model_parallel_world_size() self.num_heads_per_rank = self.num_heads // self.tp_size if self.q_a_proj is not None: self.q_b_proj = self.q_proj else: self.q_b_proj = None ascend_config = get_ascend_config() self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp self.enable_kv_nz = ascend_config.torchair_graph_config.enable_kv_nz vllm_config = get_current_vllm_config() self.ring_mla_mask_size = 512 self.prefill_mask = None # indexer param self.dim = self.indexer.dim self.n_heads: int = self.indexer.n_heads # 64 self.head_dim: int = self.indexer.head_dim # 128 self.index_topk: int = self.indexer.index_topk # 2048 self.wq_b = self.indexer.wq_b self.wk = self.indexer.wk self.weights_proj = self.indexer.weights_proj self.k_norm = self.indexer.k_norm self.softmax_scale = self.indexer.softmax_scale # Adapt torch air graph mode with spec decoding. speculative_config = 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 self.cp_size = 1 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, ) self.kv_b_proj_w_k, self.kv_b_proj_w_v = 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.kv_b_proj_w_v = self.kv_b_proj_w_v.transpose(0, 1).contiguous() # Convert from (L, N, P) to (N, P, L) self.kv_b_proj_w_k = self.kv_b_proj_w_k.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 _sfa_preprocess(self, hidden_states, kv_cache, attn_metadata, need_gather_q_kv): # SFA Preprocess: # 1. Perform q_a_proj and q_a_layernorm to obtain q_c # 2. Perform kv_a_proj_with_mqa to obtain kv_no_split # 3. If need_gather_q_kv, perform all_gather. # 4. Preprocess decode tokens, write kv cache and get: # decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope # 5. Preprocess prefill tokens, write kv cache and get: # prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value has_decode = attn_metadata.num_decodes > 0 has_prefill = attn_metadata.num_prefills > 0 num_decode_tokens = attn_metadata.num_decode_tokens num_actual_tokens = attn_metadata.num_actual_tokens if need_gather_q_kv: # q_c = get_tp_group().all_gather(q_c, 0) # kv_no_split = get_tp_group().all_gather(kv_no_split, 0) hidden_states = get_tp_group().all_gather(hidden_states, 0) # hidden_states_decode = hidden_states[:num_decode_tokens] # if self.q_a_proj is not None: # npu_prefetch(self.q_a_proj.weight, # hidden_states, # enabled=self.enable_prefetch) # ckq = self.q_a_proj(hidden_states) # q down # q_c = self.q_a_layernorm(ckq) # q down layernorm # else: # q_c = hidden_states # kv_no_split = self.kv_a_proj_with_mqa(hidden_states) # c_kv # Process for shared_expert_dp decode_preprocess_res = None prefill_preprocess_res = None # Preprocess for decode tokens if has_decode: q_len = 1 hidden_states_decode = hidden_states[:num_decode_tokens] decode_kq = self.q_a_proj(hidden_states_decode) # q down decode_q_c = self.q_a_layernorm(decode_kq) # q down layernorm decode_kv_no_split = self.kv_a_proj_with_mqa( hidden_states_decode) # c_kv # decode_q_c = q_c[:num_decode_tokens] decode_slot_mapping = attn_metadata.slot_mapping[: num_decode_tokens] # decode_kv_no_split = decode_kv_no_split[:num_decode_tokens] decode_q = self.q_b_proj(decode_q_c) bsz, _ = decode_q.shape decode_q = decode_q.view(bsz, self.num_heads, 1, self.qk_head_dim) decode_q_nope, decode_q_pe = torch.split( decode_q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) decode_q_nope = decode_q_nope.view( -1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1) decode_q_nope = (torch.matmul(decode_q_nope, self.kv_b_proj_w_k).transpose( 1, 0).view(bsz, q_len, self.num_heads, self.kv_lora_rank)) # stream2 kv key_cache = kv_cache[0] value_cache = kv_cache[1] cos = attn_metadata.decode.cos sin = attn_metadata.decode.sin cos_q, sin_q = cos, sin cos = cos.view(-1, 1, 1, self.qk_rope_head_dim) sin = sin.view(-1, 1, 1, self.qk_rope_head_dim) decode_kv_no_split = decode_kv_no_split.unsqueeze(1).unsqueeze(1) decode_k_rope, decode_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache( decode_kv_no_split, self.kv_a_layernorm.weight, cos, sin, decode_slot_mapping.to(torch.int64), value_cache, key_cache, c_kv_scale=None, epsilon=self.kv_a_layernorm.variance_epsilon, cache_mode='PA') # adapter NZ # nz_block_size = 16 # KVCACHE_NZ_DIM = 16 # decode_k_nope = decode_k_nope.view(block_num, 1, self.kv_lora_rank // nz_block_size, block_size, nz_block_size) # decode_k_rope = decode_k_rope.view(block_num, 1, self.qk_rope_head_dim // KVCACHE_NZ_DIM, block_size, KVCACHE_NZ_DIM) decode_q_pe = torch_npu.npu_interleave_rope(decode_q_pe, cos, sin) # BNSD decode_q_nope = decode_q_nope.view(bsz, self.num_heads, self.kv_lora_rank) decode_q_pe = decode_q_pe.view(bsz, self.num_heads, -1) topk_indices = self.indexer_select(hidden_states_decode, decode_q_c, attn_metadata=attn_metadata, cos=cos, sin=sin, kv_cache=kv_cache) query_states = (decode_q_nope, decode_q_pe) key_states = (decode_k_nope, decode_k_rope) decode_preprocess_res = DecodeSFAPreprocessResult( q_nope=decode_q_nope, q_pe=decode_q_pe, # nope_cache = nope_cache, # rope_cache = rope_cache, topk_indices=topk_indices, query_states=query_states, key_states=key_states, bsz=bsz, ) # Preprocess for prefill tokens if has_prefill: bsz = 1 hidden_states_prefill = hidden_states[ num_decode_tokens:num_actual_tokens] prefill_kq = self.q_a_proj(hidden_states_prefill) # q down prefill_q_c = self.q_a_layernorm(prefill_kq) # q down layernorm prefill_kv_no_split = self.kv_a_proj_with_mqa( hidden_states_prefill) # c_kv # prefill_q_c = q_c[ # num_decode_tokens:num_actual_tokens] prefill_slot_mapping = attn_metadata.slot_mapping[ num_decode_tokens:num_actual_tokens] # decode_kv_no_split = decode_kv_no_split[:num_decode_tokens] prefill_slot_mapping = attn_metadata.slot_mapping[ num_decode_tokens:num_actual_tokens] # prefill_kv_no_split = kv_no_split[ # num_decode_tokens:num_actual_tokens] # prefill_qr = prefill_q_c[num_decode_tokens:num_actual_tokens] prefill_qr = prefill_q_c prefill_q = self.q_b_proj(prefill_qr) prefill_q = prefill_q.view(-1, self.num_heads, self.qk_head_dim) prefill_q_nope, prefill_q_pe = torch.split( prefill_q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) prefill_q_nope = prefill_q_nope.view( -1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1) prefill_q_nope = (torch.matmul(prefill_q_nope, self.kv_b_proj_w_k).transpose( 1, 0).view(-1, self.num_heads, self.kv_lora_rank)) prefill_q_pe = prefill_q_pe.unsqueeze(2) # stream2 kv nope_cache = kv_cache[0] rope_cache = kv_cache[1] cos = attn_metadata.prefill.cos sin = attn_metadata.prefill.sin cos_q, sin_q = cos, sin # cos = cos.view(-1, 1, 1, self.qk_rope_head_dim) # sin = sin.view(-1, 1, 1, self.qk_rope_head_dim) prefill_q_pe = torch_npu.npu_interleave_rope( prefill_q_pe, cos_q, sin_q) # BNSD prefill_q_pe = prefill_q_pe.squeeze(2) #BSH # q[..., self.qk_nope_head_dim:] = prefill_q_pe # TODO:???? prefill_latent_cache = prefill_kv_no_split # (B,S,N,D) prefill_k_pe, prefill_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache( prefill_latent_cache.view( -1, 1, 1, self.kv_lora_rank + self.qk_rope_head_dim), self.kv_a_layernorm.weight, cos.view(-1, 1, 1, self.qk_rope_head_dim), sin.view(-1, 1, 1, self.qk_rope_head_dim), prefill_slot_mapping.to(torch.int64), rope_cache, nope_cache, k_rope_scale=None, c_kv_scale=None, k_rope_offset=None, c_kv_offset=None, epsilon=self.kv_a_layernorm.variance_epsilon, cache_mode="PA") topk_indices = self.indexer_select(x=hidden_states_prefill, qr=prefill_qr, kv_cache=kv_cache, cos=cos, sin=sin, attn_metadata=attn_metadata) query_states = (prefill_q_nope, prefill_q_pe) key_states = (prefill_k_nope, prefill_k_pe) prefill_preprocess_res = PrefillSFAPreprocessResult( q_nope=prefill_q_nope, q_pe=prefill_q_pe, topk_indices=topk_indices, k_nope=prefill_k_nope, k_pe=prefill_k_pe, query_states=query_states, key_states=key_states, ) return decode_preprocess_res, prefill_preprocess_res def forward( self, hidden_states: torch.Tensor, # query in unified attn kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor], attn_metadata: M, need_gather_q_kv: bool = False, 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.fill_(0) num_actual_tokens = attn_metadata.num_actual_tokens 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 num_decode_tokens = attn_metadata.num_decode_tokens # Inputs and outputs may be padded for CUDA graphs output = output[:num_actual_tokens, ...] o_proj_input_shape = (num_actual_tokens, self.num_heads * self.v_head_dim) o_proj_input = torch.empty(o_proj_input_shape, dtype=hidden_states.dtype, device=hidden_states.device) # SFA Preprocess decode_preprocess_res, prefill_preprocess_res = self._sfa_preprocess( hidden_states, kv_cache, attn_metadata, need_gather_q_kv) if decode_preprocess_res is not None: # bsz, q_len, _, _ = query_states[0].shape decode_attn_output = self.apply_attention_fusion( query_states=decode_preprocess_res.query_states, key_states=decode_preprocess_res.key_states, attn_metadata=attn_metadata, topk_indices=decode_preprocess_res.topk_indices) o_proj_input[:num_decode_tokens] = decode_attn_output if prefill_preprocess_res is not None: prefill_attn_output = self.apply_attention_fusion( query_states=prefill_preprocess_res.query_states, key_states=prefill_preprocess_res.key_states, attn_metadata=attn_metadata, topk_indices=prefill_preprocess_res.topk_indices) o_proj_input[num_decode_tokens:] = prefill_attn_output output[...] = self.mla_epilog(o_proj_input, absorb=True) return output def apply_attention_fusion(self, query_states, key_states, topk_indices, attn_metadata: M): # repeat k/v heads if n_kv_heads < n_heads q_nope, q_pe = query_states k_nope, k_rope = key_states if attn_metadata.prefill is not None: prefill_metadata = attn_metadata.prefill slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention( query=q_nope, key=k_nope, value=k_nope, sparse_indices=topk_indices, scale_value=self.scale, sparse_block_size=1, block_table=prefill_metadata.block_table, actual_seq_lengths_query=prefill_metadata.query_lens, actual_seq_lengths_kv=prefill_metadata.seq_lens, query_rope=q_pe, key_rope=k_rope, layout_query="TND", layout_kv="PA_BSND", sparse_mode=3, ) elif attn_metadata.decode is not None: decode_metadata = attn_metadata.decode slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention( query=q_nope, key=k_nope, value=k_nope, sparse_indices=topk_indices, scale_value=self.scale, sparse_block_size=1, block_table=attn_metadata.decode.block_table, actual_seq_lengths_query=decode_metadata.actual_seq_lengths_q, actual_seq_lengths_kv=decode_metadata.seq_lens, query_rope=q_pe, key_rope=k_rope, layout_query="TND", layout_kv="PA_BSND", sparse_mode=3, ) slc_fa_fusion = slc_fa_fusion.squeeze(1) slc_fa_fusion = slc_fa_fusion.transpose(0, 1) # input shape [N//attn_tp_size, T(bs*q_len), D] # output shape [T(bs*q_len), N//attn_tp_size, D] attn_output = torch.matmul(slc_fa_fusion, self.kv_b_proj_w_v).transpose(1, 0).reshape( -1, self.num_heads * self.v_head_dim) # Note: Considering the fusion rules of TBMM, attn_output shape requires a 3-dim shape, and # with appropriate tensor stride for the later 'view' operation if oproj_tp_size > 1. # after reshape: [T(bs*q_len), 1, N//attn_tp_size*D] # attn_output = attn_output.reshape(-1, self.num_heads * self.v_head_dim) return attn_output def mla_epilog(self, attn_output: torch.Tensor = None, absorb: bool = False): # TODO: need to check attn_output = self.o_proj(attn_output.reshape(attn_output.shape[0], -1), is_prefill=True, is_force_scatter=False) return attn_output def indexer_select( self, x: torch.Tensor, qr: torch.Tensor, kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor], cos, sin, attn_metadata: M, ): if attn_metadata.prefill is not None: actual_seq_lengths_query = attn_metadata.prefill.query_lens actual_seq_lengths_key = attn_metadata.prefill.seq_lens block_table = attn_metadata.prefill.block_table elif attn_metadata.decode is not None: actual_seq_lengths_query = attn_metadata.decode.actual_seq_lengths_q actual_seq_lengths_key = attn_metadata.decode.seq_lens block_table = attn_metadata.decode.block_table cos_q, sin_q = cos, sin cos = cos.view(-1, 1, 1, self.qk_rope_head_dim) sin = sin.view(-1, 1, 1, self.qk_rope_head_dim) # q process in new stream q = self.wq_b(qr) # [b,s,1536] @ [1536,64*128] = [b,s,64*128] q = q.view(-1, self.n_heads, self.head_dim) # [b,s,64,128] q_pe, q_nope = torch.split( q, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1) # [b,s,64,64+64] q_pe = q_pe.unsqueeze(2) q_pe = torch_npu.npu_interleave_rope(q_pe, cos_q, sin_q) q_pe = q_pe.squeeze(2) q = torch.cat([q_pe, q_nope], dim=-1) # [b*s,64,128] k_proj = self.wk(x) # [b,s,7168] @ [7168,128] = [b,s,128] k = self.k_norm(k_proj).unsqueeze(1) k_pe, k_nope = torch.split( k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1) # [b,s,64+64] k_pe = k_pe.unsqueeze(2) k_pe = torch_npu.npu_interleave_rope(k_pe, cos, sin) k_pe = k_pe.squeeze(2) k = torch.cat([k_pe, k_nope], dim=-1) # [b*s,128] if kv_cache is not None: torch_npu.npu_scatter_nd_update_(kv_cache[2].view(-1, k.shape[-1]), attn_metadata.slot_mapping.view( -1, 1), k.view(-1, k.shape[-1])) # b, s, n, d weights = self.weights_proj(x) topk_indices = torch.ops.custom.npu_lightning_indexer( query=q, key=kv_cache[2], weights=weights, actual_seq_lengths_query=actual_seq_lengths_query, actual_seq_lengths_key=actual_seq_lengths_key, block_table=block_table, layout_query="TND", layout_key="PA_BSND", sparse_count=2048, sparse_mode=3) return topk_indices