from typing import Optional, Tuple, TypeVar import numpy as np import torch import torch_npu from vllm.config import VllmConfig from vllm.distributed import (get_dcp_group, get_decode_context_model_parallel_rank, get_decode_context_model_parallel_world_size, get_pcp_group) from vllm.forward_context import ForwardContext, get_forward_context from vllm.utils.math_utils import cdiv from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec # isort: off from vllm_ascend.attention.mla_v1 import ( AscendMLADecodeMetadata, AscendMLAImpl, AscendMLAMetadata, AscendMLAMetadataBuilder, AscendMLAPrefillMetadata, DecodeMLAPreprocessResult, PrefillMLAPreprocessResult, BUILD_METADATA_STEP_PREFILL) #isort: on from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata) from vllm_ascend.attention.context_parallel.common_cp import ( AscendPCPMetadata, CPChunkedContextMetadata, _process_attn_out_lse, _npu_attention_update) from vllm_ascend.compilation.acl_graph import (get_draft_graph_params, get_graph_params, update_graph_params_workspaces) from vllm_ascend.utils import weak_ref_tensors, vllm_version_is if vllm_version_is('0.13.0'): from vllm.v1.attention.backends.utils import AttentionCGSupport else: from vllm.v1.attention.backend import AttentionCGSupport MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024 M = TypeVar("M", bound=AscendMLAMetadata) class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder): """ NOTE: Please read the comment at the top of the file before trying to understand this class """ def __init__( self, kv_cache_spec: MLAAttentionSpec, layer_names: list[str], vllm_config: VllmConfig, device: torch.device, metadata_cls: type[AscendMLAMetadata] | None = None, supports_dcp_with_varlen: bool = False, ): super().__init__(kv_cache_spec, layer_names, vllm_config, device, metadata_cls, supports_dcp_with_varlen) self.pcp_size = get_pcp_group().world_size self.pcp_rank = get_pcp_group( ).rank_in_group if self.pcp_size > 1 else 0 self.dcp_size = get_decode_context_model_parallel_world_size() self.dcp_rank = get_decode_context_model_parallel_rank( ) if self.dcp_size > 1 else 0 self.cp_local_block_size = vllm_config.parallel_config.cp_kv_cache_interleave_size self.cp_virtual_block_size = self.cp_local_block_size * self.dcp_size * self.pcp_size scheduler_config = vllm_config.scheduler_config decode_max_num_seqs = getattr(scheduler_config, 'decode_max_num_seqs', 0) max_num_seqs = max(scheduler_config.max_num_seqs, decode_max_num_seqs) self.batch_seq_mask_buf = torch.empty(max_num_seqs * self.decode_threshold, dtype=torch.uint8, device=device) self.block_size = (self.block_size * self.cp_virtual_block_size) // np.gcd( self.block_size, self.cp_virtual_block_size) def build( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, fast_build: bool = False, ) -> AscendMLAMetadata: metadata_cls = super().build(common_prefix_len, common_attn_metadata) if self.num_prefills == 0 and self.pcp_size > 1: self.slot_mapping[:self. num_decode_tokens] = self.slot_mapping[:self. num_decode_tokens * self. pcp_size: self. pcp_size] self.slot_mapping[self.num_decode_tokens:self.num_decode_tokens * self.pcp_size].fill_(-1) metadata_cls.slot_mapping = self.slot_mapping return metadata_cls @classmethod def get_cudagraph_support( cls: type["AscendMlaCPMetadataBuilder"], vllm_config: VllmConfig, kv_cache_spec: AttentionSpec, ) -> AttentionCGSupport: # Explicit override in case the underlying builder specialized this getter. # @override omitted only because of mypy limitation due to type variable. return AttentionCGSupport.UNIFORM_BATCH def set_num_actual_tokens( self, common_attn_metadata: AscendCommonAttentionMetadata, ): long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata if long_seq_metadata is None: raise AssertionError("long_seq_metadata should not be None.") # In dcp only spec decode graph padding case, # num_actual_tokens_pcp_padded may be less than num_actual_tokens self.num_actual_tokens = max( long_seq_metadata.num_actual_tokens_pcp_padded, common_attn_metadata.num_actual_tokens) def build_cp_metadata( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, ) -> AscendPCPMetadata | None: common_long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata assert common_long_seq_metadata is not None return AscendPCPMetadata( q_head_idx=common_long_seq_metadata.q_head_idx_tensor, q_tail_idx=common_long_seq_metadata.q_tail_idx_tensor, kv_with_q_head_nomask_idx=common_long_seq_metadata. kv_with_q_head_nomask_idx_tensor, kv_with_q_head_mask_idx=common_long_seq_metadata. kv_with_q_head_mask_idx_tensor, kv_with_q_tail_nomask_idx=common_long_seq_metadata. kv_with_q_tail_nomask_idx_tensor, kv_with_q_tail_mask_idx=common_long_seq_metadata. kv_with_q_tail_mask_idx_tensor, attn_mask_seqlens=common_long_seq_metadata.attn_mask_seqlens, head_attn_nomask_seqlens=common_long_seq_metadata. head_attn_nomask_seqlens, tail_attn_nomask_seqlens=common_long_seq_metadata. tail_attn_nomask_seqlens, q_full_idx=common_long_seq_metadata.q_full_idx, pcp_allgather_restore_idx=common_long_seq_metadata. pcp_allgather_restore_idx) def build_chunked_metadata( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, ): chunked_context_metadata = super().build_chunked_metadata( common_prefix_len, common_attn_metadata) if chunked_context_metadata is None: return None long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata assert long_seq_metadata is not None num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp assert num_computed_tokens_of_pcp_dcp is not None local_context_lens_allranks = torch.tensor( num_computed_tokens_of_pcp_dcp[self.num_decodes_flatten:]).reshape( -1, self.dcp_size * self.pcp_size) # Note(qcs): The max local context lengths # padded to `cp_local_block_size`. padded_local_context_lens_cpu = (cdiv( self.context_lens_cpu, self.cp_virtual_block_size, ) * self.cp_local_block_size) padded_local_max_context_chunk_across_ranks = (cdiv( self.max_context_chunk, self.cp_virtual_block_size, ) * self.cp_local_block_size) local_chunk_starts = (torch.arange( self.num_chunks, dtype=torch.int32).unsqueeze(1).expand( -1, self.num_prefills) * padded_local_max_context_chunk_across_ranks) local_chunk_ends = torch.min( padded_local_context_lens_cpu.unsqueeze(0), local_chunk_starts + padded_local_max_context_chunk_across_ranks, ) padded_local_chunk_seq_lens = (local_chunk_ends - local_chunk_starts).clamp(min=0) padded_local_cu_chunk_seq_lens_cpu = torch.zeros(self.num_chunks, self.num_prefills + 1, dtype=torch.int32, pin_memory=True) torch.cumsum( padded_local_chunk_seq_lens, dim=1, out=padded_local_cu_chunk_seq_lens_cpu[:, 1:], dtype=torch.int32, ) chunked_metadata = CPChunkedContextMetadata( cu_seq_lens=chunked_context_metadata.cu_seq_lens, starts=local_chunk_starts.pin_memory().to(self.device, non_blocking=True), seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(), max_seq_lens=chunked_context_metadata.max_seq_lens, chunk_seq_lens=self.chunk_seq_lens, chunk_seq_lens_npu=chunked_context_metadata.chunk_seq_lens_npu, workspace=chunked_context_metadata.workspace, padded_chunk_seq_lens_npu=padded_local_chunk_seq_lens.npu(), padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(), local_context_lens_allranks=local_context_lens_allranks.tolist(), padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu. pin_memory().to(self.device, non_blocking=True), cu_seq_lens_lst=self.cu_seq_lens_cpu.tolist(), chunk_size=padded_local_max_context_chunk_across_ranks, ) return chunked_metadata def get_block_table_size( self, common_attn_metadata: AscendCommonAttentionMetadata, build_metadata_step: int): self.num_decodes_flatten = self.query_lens[:self.num_decodes].sum( ).item() if build_metadata_step == BUILD_METADATA_STEP_PREFILL: # For pcp + spec decode, we flatten seq_lens and block_table # to avoid irregular attn_mask shape return self.num_decodes_flatten + self.num_prefills else: return self.num_decodes_flatten def build_prefill_metadata( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, ) -> AscendMLAPrefillMetadata: prefill_metadata = super().build_prefill_metadata( common_prefix_len, common_attn_metadata) prefill_metadata.pcp_metadata = self.build_cp_metadata( common_prefix_len, common_attn_metadata) prefill_metadata.block_table = self.block_table[ self.num_decodes_flatten:, ...] return prefill_metadata def build_decode_metadata( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, ) -> AscendMLADecodeMetadata: decode_metadata = super().build_decode_metadata( common_prefix_len, common_attn_metadata) long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata assert long_seq_metadata is not None num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp assert num_computed_tokens_of_pcp_dcp is not None # [bs, pcp_size, dcp_size] num_computed_tokens_of_cp_dcp_array = np.array( num_computed_tokens_of_pcp_dcp)[:self.num_decodes_flatten] cp_seq_len = num_computed_tokens_of_cp_dcp_array[:, self.pcp_rank, self.dcp_rank] cp_seq_len = torch.tensor(cp_seq_len, dtype=torch.int32) batch_seq_mask = (cp_seq_len == 0) self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_( batch_seq_mask, non_blocking=True) batch_seq_mask = self.batch_seq_mask_buf[:batch_seq_mask.shape[0]] cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len) decode_metadata.cp_seq_len = cp_seq_len decode_metadata.batch_seq_mask = batch_seq_mask return decode_metadata class AscendMlaCPImpl(AscendMLAImpl): """ 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, ): super().__init__(num_heads, head_size, scale, num_kv_heads, alibi_slopes, sliding_window, kv_cache_dtype, logits_soft_cap, attn_type, kv_sharing_target_layer_name, **kwargs) self.pcp_size = get_pcp_group().world_size self.pcp_rank = get_pcp_group( ).rank_in_group if self.pcp_size > 1 else 0 self.pcp_group = get_pcp_group( ).device_group if self.pcp_size > 1 else None self.dcp_size = get_decode_context_model_parallel_world_size() self.dcp_rank = get_decode_context_model_parallel_rank( ) if self.dcp_size > 1 else 0 self.dcp_group = get_dcp_group( ).device_group if self.dcp_size > 1 else None def get_num_actual_tokens(self, attn_metadata: M): if self.pcp_size > 1: return attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size else: return attn_metadata.num_actual_tokens def _v_up_proj(self, x): # Convert from (B, N, L) to (N, B, L) x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1) # # Multiply (N, B, L) x (N, L, V) -> (N, B, V) x = torch.bmm(x, self.W_UV) # # Convert from (N, B, V) to (B, N * V) x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim) return x def mla_preprocess_prefill(self, q_c, kv_no_split, kv_cache, attn_metadata): if not self.pcp_size > 1: return super().mla_preprocess_prefill(q_c, kv_no_split, kv_cache, attn_metadata) num_decode_tokens = attn_metadata.num_decode_tokens num_actual_tokens = (attn_metadata.num_actual_tokens_pcp_padded - self.pcp_size * num_decode_tokens ) // self.pcp_size + num_decode_tokens prefill_q_c = q_c[num_decode_tokens:num_actual_tokens] prefill_q = self.q_proj(prefill_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] cos = attn_metadata.prefill.cos[:num_actual_tokens - num_decode_tokens] sin = attn_metadata.prefill.sin[:num_actual_tokens - num_decode_tokens] prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin) prefill_kv_no_split = kv_no_split[:num_actual_tokens] kv_c, k_pe = prefill_kv_no_split.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) kv_c_normed = self.kv_a_layernorm(kv_c.contiguous()) assert len( kv_cache ) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)" kv_c_normed = kv_c_normed.view( [num_actual_tokens, self.num_kv_heads, -1]) k_pe = k_pe.unsqueeze(1) prefill_k_pe = k_pe prefill_k_pe[num_decode_tokens:num_actual_tokens] = self.rope_single( prefill_k_pe[num_decode_tokens:num_actual_tokens], cos, sin) prefill_k_c_normed = kv_c_normed[:num_actual_tokens] prefill_kv_c_k_pe = torch.cat([prefill_k_c_normed, prefill_k_pe], dim=-1) prefill_kv_c_k_pe = get_pcp_group().all_gather(prefill_kv_c_k_pe, 0) prefill_kv_c_k_pe = torch.index_select( prefill_kv_c_k_pe, 0, attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx) prefill_kv_c_k_pe = prefill_kv_c_k_pe[num_decode_tokens * self.pcp_size:] prefill_k_c_normed, prefill_k_pe = prefill_kv_c_k_pe.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) kv_c_normed, k_pe = prefill_k_c_normed, prefill_k_pe prefill_k_c_normed = prefill_k_c_normed.squeeze() slot_mapping = attn_metadata.slot_mapping[self.pcp_size * num_decode_tokens:] 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=slot_mapping) prefill_k_nope, prefill_value = self.kv_b_proj( prefill_k_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) prefill_k_pe = prefill_k_pe.expand((*prefill_k_nope.shape[:-1], -1)) return PrefillMLAPreprocessResult(prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value) def mla_preprocess_decode(self, q_c, kv_no_split, kv_cache, attn_metadata): num_decode_tokens = attn_metadata.num_decode_tokens decode_q_c = q_c[:num_decode_tokens] cos = attn_metadata.decode.cos sin = attn_metadata.decode.sin decode_ql_nope, decode_q_pe = \ self._q_proj_and_k_up_proj(decode_q_c) decode_ql_nope, decode_q_pe = self.reorg_decode_q( decode_ql_nope, decode_q_pe) decode_q_pe = self.rope_single(decode_q_pe, cos, sin) decode_slots = attn_metadata.slot_mapping[:num_decode_tokens] decode_kv_no_split = kv_no_split[:num_decode_tokens] decode_k_pe, decode_k_nope = self.exec_kv_decode( decode_kv_no_split, cos, sin, kv_cache, decode_slots) return DecodeMLAPreprocessResult(decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe) def get_context_seq_len_npu(self, index: int, attn_metadata: AscendMLAMetadata): prefill_metadata = attn_metadata.prefill assert prefill_metadata is not None assert prefill_metadata.chunked_context is not None assert isinstance(prefill_metadata.chunked_context, CPChunkedContextMetadata) assert prefill_metadata.chunked_context.padded_chunk_seq_lens_npu is not None iters = len(prefill_metadata.chunked_context.seq_tot) assert 0 <= index < iters return prefill_metadata.chunked_context.padded_chunk_seq_lens_npu[ index] def reorg_decode_q(self, decode_q_nope, decode_q_pe): if self.dcp_size > 1: decode_q_no_split = torch.cat([decode_q_nope, decode_q_pe], dim=-1) decode_q_no_split = get_dcp_group().all_gather( decode_q_no_split, 1) decode_q_nope, decode_q_pe = decode_q_no_split.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) return decode_q_nope, decode_q_pe def _forward_prefill( self, q_nope: torch.Tensor, q_pe: torch.Tensor, k_nope: torch.Tensor, k_pe: torch.Tensor, value: torch.Tensor, kv_c_and_k_pe_cache: Tuple[torch.Tensor], attn_metadata: AscendMLAMetadata, ) -> torch.Tensor: if not self.pcp_size > 1: return super()._forward_prefill(q_nope, q_pe, k_nope, k_pe, value, kv_c_and_k_pe_cache, attn_metadata) assert attn_metadata.prefill is not None assert attn_metadata.prefill.pcp_metadata is not None num_tokens = q_nope.size(0) # Use precomputed indices from the metadata (already converted to tensors and on device) q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens output_head, lse_head = self._attention_with_mask_and_nomask( q_nope=torch.index_select(q_nope, 0, q_head_idx), q_pe=torch.index_select(q_pe, 0, q_head_idx), k_nope=k_nope, k_pe=k_pe, value=value, kv_mask_idx=kv_with_q_head_mask_idx, kv_nomask_idx=kv_with_q_head_nomask_idx, attn_mask_seqlens=attn_mask_seqlens, attn_nomask_seqlens=head_attn_nomask_seqlens, mask=attn_metadata.attn_mask) output_tail, lse_tail = self._attention_with_mask_and_nomask( q_nope=torch.index_select(q_nope, 0, q_tail_idx), q_pe=torch.index_select(q_pe, 0, q_tail_idx), k_nope=k_nope, k_pe=k_pe, value=value, kv_mask_idx=kv_with_q_tail_mask_idx, kv_nomask_idx=kv_with_q_tail_nomask_idx, attn_mask_seqlens=attn_mask_seqlens, attn_nomask_seqlens=tail_attn_nomask_seqlens, mask=attn_metadata.attn_mask) q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx attn_output = torch.index_select( torch.cat([output_head, output_tail], dim=0), 0, q_full_idx) attn_lse = torch.index_select(torch.cat([lse_head, lse_tail], dim=1), 1, q_full_idx) output, _ = self._compute_prefill_context(q_nope, q_pe, kv_c_and_k_pe_cache, self.qk_rope_head_dim, attn_metadata, attn_output, attn_lse) output = output.reshape([num_tokens, self.num_heads * self.v_head_dim]) return output def _attention_with_mask_and_nomask( self, q_nope: torch.Tensor, q_pe: torch.Tensor, k_nope: torch.Tensor, k_pe: torch.Tensor, value: torch.Tensor, kv_mask_idx: torch.Tensor, kv_nomask_idx: list[torch.Tensor], attn_mask_seqlens: torch.Tensor, attn_nomask_seqlens: list[torch.Tensor], mask: torch.Tensor, ): attn_output = torch.empty(q_nope.shape[0], self.num_heads, self.v_head_dim, dtype=k_pe.dtype, device=k_pe.device) attn_lse = torch.empty(self.num_heads, q_pe.shape[0], dtype=torch.float32, device=k_pe.device) # mask k_nope_mask = torch.index_select(k_nope, 0, kv_mask_idx) value_mask = torch.index_select(value, 0, kv_mask_idx) k_pe_mask = torch.index_select(k_pe, 0, kv_mask_idx) torch_npu.atb.npu_ring_mla(q_nope=q_nope, q_rope=q_pe, k_nope=k_nope_mask, k_rope=k_pe_mask, value=value_mask, mask=mask, seqlen=attn_mask_seqlens, 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) # nomask if not kv_nomask_idx or len(kv_nomask_idx[0]) == 0: return attn_output, attn_lse for kv_nomask_idx_split, attn_nomask_seqlens_split in zip( kv_nomask_idx, attn_nomask_seqlens): k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx_split) value_nomask = torch.index_select(value, 0, kv_nomask_idx_split) k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx_split) torch_npu.atb.npu_ring_mla( q_nope=q_nope, q_rope=q_pe, k_nope=k_nope_nomask, k_rope=k_pe_nomask, value=value_nomask, mask=mask, seqlen=attn_nomask_seqlens_split, head_num=self.num_heads, kv_head_num=self.num_heads, pre_out=attn_output, prev_lse=attn_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=attn_output, softmax_lse=attn_lse) return attn_output, attn_lse def _forward_decode( self, q_nope: torch.Tensor, q_pe: torch.Tensor, k_nope: torch.Tensor, k_pe: torch.Tensor, block_size: int, attn_metadata: AscendMLAMetadata, ) -> torch.Tensor: decode_meta = attn_metadata.decode assert decode_meta is not None num_tokens = q_nope.size(0) # 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] if self.dcp_size > 1: num_heads = self.num_heads * self.dcp_size else: num_heads = self.num_heads k_nope = k_nope.view(-1, block_size, self.num_kv_heads, self.kv_lora_rank) k_pe = k_pe.view(-1, block_size, self.num_kv_heads, self.qk_rope_head_dim) q_nope = q_nope.view(num_tokens, num_heads, -1) q_pe = q_pe.view(num_tokens, num_heads, -1) # use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask seq_len = decode_meta.cp_seq_len common_kwargs = { "return_lse": True, "calc_type": "calc_type_ring", } forward_context: ForwardContext = get_forward_context() if forward_context.is_draft_model: graph_params = get_draft_graph_params() else: graph_params = get_graph_params() if forward_context.capturing: stream = torch_npu.npu.current_stream() event = torch.npu.ExternalEvent() event.wait(stream) event.reset(stream) graph_params.events[num_tokens].append(event) workspace = graph_params.workspaces.get(num_tokens) if workspace is None: workspace = torch_npu.atb._npu_multi_head_latent_attention_get_workspace( q_nope, q_pe, k_nope, k_pe, decode_meta.block_table, seq_len, num_heads, self.scale, self.num_kv_heads, **common_kwargs) update_graph_params_workspaces(num_tokens, workspace) attn_output = torch.empty_like(q_nope) softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=q_nope.dtype, device=q_nope.device) graph_params.attn_params[num_tokens].append( (weak_ref_tensors(q_nope), weak_ref_tensors(q_pe), weak_ref_tensors(k_nope), weak_ref_tensors(k_pe), decode_meta.block_table, seq_len, num_heads, self.scale, self.num_kv_heads, weak_ref_tensors(attn_output), weak_ref_tensors(softmax_lse))) torch.npu.graph_task_group_begin(stream) torch_npu.atb.npu_multi_head_latent_attention( q_nope, q_pe, k_nope, k_pe, decode_meta.block_table, seq_len, num_heads, self.scale, self.num_kv_heads, **common_kwargs, workspace=workspace, output=attn_output, lse=softmax_lse) handle = torch.npu.graph_task_group_end(stream) graph_params.handles[num_tokens].append(handle) else: attn_output = torch.empty_like(q_nope) softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=q_nope.dtype, device=q_nope.device) torch_npu.atb.npu_multi_head_latent_attention( q_nope, q_pe, k_nope, k_pe, decode_meta.block_table, seq_len, num_heads, self.scale, self.num_kv_heads, return_lse=True, calc_type="calc_type_ring", output=attn_output, lse=softmax_lse) # Update out&lse attn_out_lse = _process_attn_out_lse(attn_output, softmax_lse, decode_meta.batch_seq_mask) attn_output = _npu_attention_update(self.kv_lora_rank, attn_out_lse) return self._v_up_proj(attn_output) def _out_lse_reshape(self, attn_out: torch.Tensor, attn_lse: torch.Tensor) -> torch.Tensor: attn_out = attn_out.contiguous().view( attn_out.shape[0] * attn_out.shape[1], attn_out.shape[2]) attn_lse = attn_lse.contiguous().view( attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2]) return attn_out, attn_lse def _reorg_kvcache( self, kv_c_normed: torch.Tensor, k_pe: torch.Tensor, chunked_context: CPChunkedContextMetadata, chunk_idx: int, toks: int, ) -> tuple[torch.Tensor, torch.Tensor]: """ reorg and unpad kvcache after cp local gather to tp layout for attn kernel. e.g. kv_c_normed in rank0 = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...] kv_c_normed in rank1 = [T0_4, T0_5, pad, pad, T1_2, pad, ...] allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ..., T0_4, T0_5, pad, pad, T1_2, pad, ...] -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5, T1_0, T1_1, T1_2, ...] Args: padded_local_chunk_seq_lens_lst: local chunk context lengths under current CP rank. local_context_lens_allranks: local context lengths on each CP rank. sum_seq_len: the sum of cp_chunk_seq_lens_lst. max_seq_len: the max value of cp_chunk_seq_lens_lst. chunk_size: the local padded max context chunk from chunked_context_metadata building. chunk_idx: chunk idx of chunked_prefill. toks: the number of tokens for local gather cache. """ assert chunked_context is not None assert chunked_context.padded_local_chunk_seq_lens is not None assert chunked_context.local_context_lens_allranks is not None assert chunked_context.cu_seq_lens_lst is not None assert chunked_context.max_seq_lens is not None assert chunked_context.chunk_size is not None padded_local_chunk_seq_lens_lst = chunked_context.padded_local_chunk_seq_lens[ chunk_idx] local_context_lens_allranks = chunked_context.local_context_lens_allranks sum_seq_len = chunked_context.cu_seq_lens_lst[chunk_idx][-1] max_seq_len = chunked_context.max_seq_lens[chunk_idx] chunk_size: int = chunked_context.chunk_size cache_kv_c_k_pe = torch.cat([kv_c_normed, k_pe], dim=-1) if self.dcp_size > 1: cache_kv_c_k_pe = get_dcp_group().all_gather(cache_kv_c_k_pe, 0) if self.pcp_size > 1: cache_kv_c_k_pe = get_pcp_group().all_gather(cache_kv_c_k_pe, 0) allgatered_kv_c_normed, allgatered_k_pe = cache_kv_c_k_pe.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) kv_c_segments = [] k_pe_segments = [] src_token_idx = 0 max_seq_len_check = 0 for padded_local_chunk_seq_len, local_context_lens in zip( padded_local_chunk_seq_lens_lst, local_context_lens_allranks): cur_seq_len = 0 for rank, local_context_len in enumerate(local_context_lens): # Note(qcs): We split the context into multiple chunks, # depending on the size of the workspace. # local_context in dcp0: |-----------------| # local_context in dcp1: |--------------| # n*padded_local_chunk: |-----|-----|-----| # local_chunk_len in dcp1: |-----|-----|--| # so we need update the last chunk length in dcp1. local_chunk_len = min( max(0, local_context_len - chunk_idx * chunk_size), padded_local_chunk_seq_len, ) if local_chunk_len != 0: kv_c_segment = allgatered_kv_c_normed[rank * toks + src_token_idx:rank * toks + src_token_idx + local_chunk_len] k_pe_segment = allgatered_k_pe[rank * toks + src_token_idx:rank * toks + src_token_idx + local_chunk_len] kv_c_segments.append(kv_c_segment) k_pe_segments.append(k_pe_segment) cur_seq_len += local_chunk_len max_seq_len_check = max(max_seq_len_check, cur_seq_len) src_token_idx += padded_local_chunk_seq_len reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0) reorganized_k_pe = torch.cat(k_pe_segments, dim=0) assert reorganized_kv_c_normed.shape[0] == sum_seq_len assert reorganized_k_pe.shape[0] == sum_seq_len assert max_seq_len_check == max_seq_len return reorganized_kv_c_normed, reorganized_k_pe