from typing import ClassVar, List, Optional, Tuple, TypeVar import numpy as np import torch import torch.distributed as dist import torch_npu from torch import nn from vllm.attention.backends.utils import PAD_SLOT_ID 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, round_down from vllm.v1.attention.backends.utils import AttentionCGSupport from vllm.v1.kv_cache_interface import MLAAttentionSpec from vllm_ascend.ascend_forward_context import get_cos_and_sin from vllm_ascend.attention.mla_v1 import (AscendMLADecodeMetadata, AscendMLAImpl, AscendMLAMetadata, AscendMLAMetadataBuilder, AscendMLAPrefillMetadata, DecodeMLAPreprocessResult, PrefillMLAPreprocessResult) from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata, maybe_save_kv_layer_to_connector, split_decodes_and_prefills, wait_for_kv_layer_from_connector) from vllm_ascend.compilation.acl_graph import (get_graph_params, update_graph_params_workspaces) from vllm_ascend.ops.shared_weight_layer import ( is_hidden_layer, reach_layer_for_shared_weight_series) from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch from vllm_ascend.utils import weak_ref_tensors MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024 M = TypeVar("M", bound=AscendMLAMetadata) class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder): # Does this backend/builder support ACL Graphs for attention (default: no). aclgraph_support: ClassVar[AttentionCGSupport] = \ AttentionCGSupport.UNIFORM_BATCH """ 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: Optional[AscendMLAMetadata] = None): super().__init__(kv_cache_spec, layer_names, vllm_config, device, metadata_cls) 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) def build( self, common_prefix_len: int, common_attn_metadata: AscendCommonAttentionMetadata, model: nn.Module, ) -> AscendMLAMetadata: 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 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.") num_actual_tokens_pcp_padded = long_seq_metadata.num_actual_tokens_pcp_padded if num_actual_tokens_pcp_padded is None: num_actual_tokens_pcp_padded = num_actual_tokens 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 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 # If graph_pad_size > -1, mean is running in fullgraph mode. graph_pad_size = common_attn_metadata.graph_pad_size # NOTE: Maybe this block_table change can be removed when graph_pad_size > 1. if graph_pad_size > num_reqs and self.speculative_config.disable_padded_drafter_batch: block_table = ( common_attn_metadata.block_table_tensor[:graph_pad_size]) else: block_table = (common_attn_metadata.block_table_tensor[:num_reqs]) # NOTE: Currently, MTP-fullgraph is incompatibility pcp if self.pcp_size > 1: num_decodes_flatten = num_decodes * self.decode_threshold block_table = common_attn_metadata.block_table_tensor[: num_decodes_flatten + num_prefills] # NOTE: Currently, MTP-fullgraph is incompatibility pcp slot_mapping = common_attn_metadata.slot_mapping[: num_actual_tokens_pcp_padded] input_positions = common_attn_metadata.positions[: num_actual_tokens_pcp_padded].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: pcp_metadata = AscendMLAPrefillMetadata.AscendPCPMetadata( q_head_idx=long_seq_metadata.q_head_idx_tensor, q_tail_idx=long_seq_metadata.q_tail_idx_tensor, kv_with_q_head_nomask_idx=long_seq_metadata. kv_with_q_head_nomask_idx_tensor, kv_with_q_head_mask_idx=long_seq_metadata. kv_with_q_head_mask_idx_tensor, kv_with_q_tail_nomask_idx=long_seq_metadata. kv_with_q_tail_nomask_idx_tensor, kv_with_q_tail_mask_idx=long_seq_metadata. kv_with_q_tail_mask_idx_tensor, attn_mask_seqlens=long_seq_metadata.attn_mask_seqlens, head_attn_nomask_seqlens=long_seq_metadata. head_attn_nomask_seqlens, tail_attn_nomask_seqlens=long_seq_metadata. tail_attn_nomask_seqlens, q_full_idx=long_seq_metadata.q_full_idx, pcp_prefill_mask=long_seq_metadata.pcp_prefill_mask, pcp_allgather_restore_idx=long_seq_metadata. pcp_allgather_restore_idx) 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) local_context_lens_allranks = torch.tensor( num_computed_tokens_of_pcp_dcp[reqs_start:num_reqs] ).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( context_lens_cpu, self.cp_virtual_block_size, ) * self.cp_local_block_size) padded_local_max_context_chunk_across_ranks = (cdiv( max_context_chunk, self.cp_virtual_block_size, ) * self.cp_local_block_size) local_chunk_starts = ( torch.arange(num_chunks, dtype=torch.int32).unsqueeze(1).expand( -1, 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( num_chunks, 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_context_metadata = AscendMLAPrefillMetadata.ChunkedContextMetadata( cu_seq_lens=cu_seq_lens_cpu.pin_memory().to( device, non_blocking=True), starts=local_chunk_starts.pin_memory().to( device, non_blocking=True), seq_tot=padded_local_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, 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(device, non_blocking=True), cu_seq_lens_lst=cu_seq_lens_cpu.tolist(), chunk_size=padded_local_max_context_chunk_across_ranks, ) prefill_input_positions = input_positions[tokens_start:] assert self.cos_cache is not None assert self.sin_cache is not None cos = self.cos_cache[prefill_input_positions].unsqueeze( 1).unsqueeze(2) sin = self.sin_cache[prefill_input_positions].unsqueeze( 1).unsqueeze(2) prefill_metadata = AscendMLAPrefillMetadata( attn_mask=common_attn_metadata.attn_mask, query_lens=query_lens[reqs_start:].to(torch.int32), seq_lens=seq_lens, 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, pcp_metadata=pcp_metadata, ) if self.pcp_size > 1: prefill_metadata.block_table = block_table[ num_decodes_flatten:, ...] decode_metadata = None if num_decodes > 0: cos, sin = get_cos_and_sin() # Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario actual_seq_lengths_q = query_start_loc_cpu[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] if self.pcp_size > 1: # For pcp + spec decode, we flatten seq_lens and block_table # to avoid irregular spec_attn_mask shape block_table = block_table[:num_decodes_flatten, ...] else: block_table = block_table[:num_decodes, ...] # NOTE: Currently, MTP-fullgraph is incompatibility pcp # NOTE: Maybe this block_table change can be removed when graph_pad_size > 1. if graph_pad_size > num_decodes and \ self.speculative_config.disable_padded_drafter_batch: block_table = block_table[:graph_pad_size, ...] seq_lens_list = seq_lens.tolist() # [bs, pcp_size, dcp_size] num_computed_tokens_of_cp_dcp_array = np.array( num_computed_tokens_of_pcp_dcp)[:num_decodes * self.decode_threshold] 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) if graph_pad_size > num_reqs: if self.speculative_config.disable_padded_drafter_batch: num_reqs_pad_size = graph_pad_size - num_reqs actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_disable_pad( num_reqs_pad_size, num_reqs, actual_seq_lengths_q) seq_lens_list = seq_lens_list + [0] * (graph_pad_size - num_decodes) num_block_pad_size = graph_pad_size - block_table.shape[0] if num_block_pad_size > 0: block_table_padding = torch.zeros( (num_block_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) else: 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) num_block_table_pad_size = ( graph_pad_size // common_attn_metadata.decode_token_per_req - num_decodes) seq_lens_list = seq_lens.tolist() + [0] * num_reqs_pad_size 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_block_table_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) 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_mtp_enable_pad( num_reqs_pad_size, num_reqs, actual_seq_lengths_q, common_attn_metadata) # TODO: After the fullgraph supports MTP, the if branch needs to deleted assert self.cos_cache is not None assert self.sin_cache is not None if cos is None and sin is None: 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 = AscendMLADecodeMetadata( 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, cp_seq_len=cp_seq_len, batch_seq_mask=batch_seq_mask) else: cos[:num_decode_tokens, ...] = self.cos_cache[input_positions].unsqueeze( 1).unsqueeze(2) sin[:num_decode_tokens, ...] = self.sin_cache[input_positions].unsqueeze( 1).unsqueeze(2) decode_metadata = AscendMLADecodeMetadata( 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[:num_decode_tokens, ...], cos=cos[:num_decode_tokens, ...], cp_seq_len=cp_seq_len, batch_seq_mask=batch_seq_mask) return self.metadata_cls( # type: ignore num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded, 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, ) 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 _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 _compute_prefill_context( self, q_nope: torch.Tensor, q_pe: torch.Tensor, kv_c_and_k_pe_cache: Tuple[torch.Tensor], rope_dim: int, attn_metadata: AscendMLAMetadata, 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) 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] # chunk_seq_lens will be padded when pcp&dcp context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[ i] context_seq_len_npu = prefill_metadata.chunked_context.padded_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=q_nope.dtype, device=q_nope.device) k_pe = torch.empty(toks, num_heads, rope_dim, dtype=q_nope.dtype, device=q_nope.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, ) 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_normed, k_pe = self._reorg_kvcache( allgatered_kv_c_normed, allgatered_k_pe, padded_local_chunk_seq_lens_lst=prefill_metadata. chunked_context.padded_local_chunk_seq_lens[i], local_context_lens_allranks=prefill_metadata.chunked_context. local_context_lens_allranks, sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i] [-1], max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i], chunk_size=prefill_metadata.chunked_context.chunk_size, chunk_idx=i, toks=toks, ) 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)) mask = attn_metadata.attn_mask 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=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( self, layer_name, hidden_states: torch.Tensor, # query in unified attn kv_cache: Tuple[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. if self.fc2_o_shared_enable and is_hidden_layer( self.vllm_config, self.o_proj): reach_layer_for_shared_weight_series(self.o_proj) return output.fill_(0) if self.pcp_size > 1: num_actual_tokens = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size else: 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_padded = output o_proj_input_shape = (get_forward_context().num_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) # MLA Preprocess forward_context = get_forward_context() if (self.enable_mlapo and (attn_metadata is None or not forward_context.with_prefill)): hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad( hidden_states.contiguous(), need_gather_q_kv) decode_preprocess_res, prefill_preprocess_res = self._mla_decode_preprocess( hidden_states, kv_cache, attn_metadata) else: decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess( layer_name, hidden_states, kv_cache, attn_metadata, need_gather_q_kv) if decode_preprocess_res is not None: # MLA Preprocess for decoding if self.pcp_size * self.dcp_size > 1: output_decode = self._forward_decode_pcp_dcp( decode_preprocess_res.ql_nope, decode_preprocess_res.q_pe, decode_preprocess_res.k_nope, decode_preprocess_res.k_pe, kv_cache[0].shape[1], attn_metadata, ) else: output_decode = self._forward_decode( decode_preprocess_res.ql_nope, decode_preprocess_res.q_pe, decode_preprocess_res.k_nope, decode_preprocess_res.k_pe, kv_cache[0].shape[1], attn_metadata) o_proj_input[:num_decode_tokens] = output_decode if prefill_preprocess_res is not None: # 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 if self.pcp_size > 1: output_prefill = self._forward_prefill_cp( prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe, prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe, prefill_preprocess_res.value, kv_cache, attn_metadata) else: output_prefill = self._forward_prefill( prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe, prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe, prefill_preprocess_res.value, kv_cache, attn_metadata) o_proj_input[num_decode_tokens:num_actual_tokens] = output_prefill # O proj MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024 maybe_npu_prefetch(inputs=self.o_proj.weight, dependency=o_proj_input, max_size=MAX_O_PROJ_PREFETCH_SIZE, enabled=self.enable_prefetch) output[...] = self.o_proj(o_proj_input, is_prefill=(prefill_preprocess_res is not None))[0] del o_proj_input has_prefill = attn_metadata.num_prefills > 0 if has_prefill: maybe_save_kv_layer_to_connector(layer_name, list(kv_cache)) return output_padded def _mla_preprocess(self, layer_name, hidden_states, kv_cache, attn_metadata, need_gather_q_kv): # MLA Preprocess: # 1. Perform fused_qkv_a_proj and q_a_layernorm to obtain q_c and kv_no_split # or # Perform kv_a_proj_with_mqa to obtain kv_no_split # 2. If need_gather_q_kv, perform all_gather. # 3. Preprocess decode tokens, write kv cache and get: # decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope # 4. 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 self.fused_qkv_a_proj is not None: maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states, enabled=self.enable_prefetch) qkv_lora = self.fused_qkv_a_proj(hidden_states)[0] q_c, kv_no_split = qkv_lora.split( [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1, ) q_c = self.q_a_layernorm(q_c) # allgather need contiguous data kv_no_split = kv_no_split.contiguous() else: q_c = hidden_states kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0] # Process for Flash Comm V1 q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad( q_c.contiguous(), need_gather_q_kv) kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad( kv_no_split.contiguous(), need_gather_q_kv) if self.fc2_o_shared_enable and is_hidden_layer( self.vllm_config, self.o_proj): reach_layer_for_shared_weight_series(self.o_proj) decode_preprocess_res = None prefill_preprocess_res = None if has_prefill: wait_for_kv_layer_from_connector(layer_name) # Preprocess for decode tokens if has_decode: 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) if self.dcp_size > 1: decode_q_no_split = torch.cat([decode_ql_nope, decode_q_pe], dim=-1) decode_q_no_split = get_dcp_group().all_gather( decode_q_no_split, 1) decode_ql_nope, decode_q_pe = decode_q_no_split.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) decode_q_pe = self.rope_single(decode_q_pe, cos, sin) decode_slots = attn_metadata.slot_mapping[:num_decode_tokens * self.pcp_size:self. pcp_size] 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) decode_preprocess_res = DecodeMLAPreprocessResult( decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe) # Preprocess for prefill tokens if has_prefill: if self.pcp_size > 1: num_actual_tokens = (attn_metadata.num_actual_tokens_pcp_padded - self.pcp_size * num_decode_tokens ) // self.pcp_size + num_decode_tokens prefill_kv_no_split = kv_no_split[ num_decode_tokens:num_actual_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] if self.pcp_size > 1: cos = attn_metadata.prefill.cos[:num_actual_tokens - num_decode_tokens] sin = attn_metadata.prefill.sin[:num_actual_tokens - num_decode_tokens] else: cos = attn_metadata.prefill.cos sin = attn_metadata.prefill.sin prefill_slots = attn_metadata.slot_mapping[ num_decode_tokens:num_actual_tokens] prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin) if self.pcp_size > 1: 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) else: prefill_k_pe, prefill_k_c_normed = self.exec_kv_prefill( prefill_kv_no_split, cos, sin, kv_cache, prefill_slots) 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) if not self.pcp_size > 1: prefill_k_pe = prefill_k_pe.view(prefill_q_c.shape[0], self.num_kv_heads, -1) prefill_k_pe = prefill_k_pe.expand( (*prefill_k_nope.shape[:-1], -1)) prefill_preprocess_res = PrefillMLAPreprocessResult( prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value) return decode_preprocess_res, prefill_preprocess_res def _mla_decode_preprocess(self, hidden_states, kv_cache, attn_metadata): bsz = attn_metadata.num_decode_tokens hidden_states = hidden_states[:bsz] cos_shape = attn_metadata.decode.cos.shape cos = attn_metadata.decode.cos.view(cos_shape[0], cos_shape[-1]) sin = attn_metadata.decode.sin.view(cos_shape[0], cos_shape[-1]) decode_k_nope, decode_k_pe = kv_cache[0], kv_cache[1] decode_q_nope = torch.empty( (hidden_states.shape[0], self.W_UK_T.shape[0], decode_k_nope.shape[-1]), dtype=hidden_states.dtype, device=hidden_states.device, ) decode_q_pe = torch.empty( (hidden_states.shape[0], self.W_UK_T.shape[0], decode_k_pe.shape[-1]), dtype=hidden_states.dtype, device=hidden_states.device, ) torch.ops._C_ascend.mla_preprocess( hidden_states, self.wd_qkv, self.deq_scale_qkv, self.gamma1, self.beta1, self.wu_q, self.qb_deq_scl, self.gamma2, cos, sin, self.W_UK_T, decode_k_nope, decode_k_pe, attn_metadata.slot_mapping[:bsz].flatten(), quant_scale0=self.quant_scale0, quant_offset0=self.quant_offset0, bias0=self.quant_bias_qkv, quant_scale1=self.quant_scale1, quant_offset1=self.quant_offset1, bias1=self.qb_qt_bias, ctkv_scale=self.ctkv_scale, q_nope_scale=self.q_nope_scale, cache_mode="krope_ctkv", quant_mode="per_tensor_quant_asymm", q_out0=decode_q_nope, kv_cache_out0=decode_k_nope, q_out1=decode_q_pe, kv_cache_out1=decode_k_pe, enable_inner_out=False, inner_out=torch.tensor([], device=hidden_states.device)) 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) 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) decode_preprocess_res = DecodeMLAPreprocessResult( decode_q_nope, decode_q_pe, decode_k_nope, decode_k_pe) return decode_preprocess_res, None def _forward_prefill_cp( 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: 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 mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask 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=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=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: torch.Tensor, attn_mask_seqlens: torch.Tensor, attn_nomask_seqlens: 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 kv_nomask_idx.shape[0] == 0: return attn_output, attn_lse k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx) value_nomask = torch.index_select(value, 0, kv_nomask_idx) k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx) 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, 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_pcp_dcp( 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", } graph_params = get_graph_params() forward_context: ForwardContext = get_forward_context() 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_list = self._process_attn_out_lse(attn_output, softmax_lse, decode_meta) attn_output = self._npu_attention_update(attn_out_lse_list) return self._v_up_proj(attn_output) def _npu_attention_update( self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor: attn_out_split_cp = [] attn_lse_split_cp = [] for attn_out_lse in attn_out_lse_list: attn_out_allgather, attn_lse_allgather = self._out_lse_reshape( *torch.split(attn_out_lse, [self.kv_lora_rank, 1], dim=-1)) attn_out_split_cp.append(attn_out_allgather) attn_lse_split_cp.append(attn_lse_allgather) attn_out, _ = torch_npu.npu_attention_update(attn_lse_split_cp, attn_out_split_cp, 0) attn_out = attn_out.view(-1, attn_out_lse_list[0].shape[1], self.kv_lora_rank) return attn_out 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 _process_attn_out_lse( self, attn_output: torch.Tensor, softmax_lse: torch.Tensor, decode_meta: AscendMLADecodeMetadata, ) -> List[torch.Tensor]: attn_out_lse_list = [] out_mask = decode_meta.batch_seq_mask[:, None, None].expand_as(attn_output) attn_output = torch.where(out_mask, 0, attn_output) lse_mask = decode_meta.batch_seq_mask[:, None, None].expand_as(softmax_lse) softmax_lse = torch.where(lse_mask, -torch.inf, softmax_lse) softmax_lse = softmax_lse.to(torch.float32) attn_output = attn_output.to(torch.float32) # Concat out&lse: [bs,num_heads,v_head_dim] + [bs,num_heads,1] -> [bs,num_heads,v_head_dim+1] attn_out_lse = torch.cat([attn_output, softmax_lse], dim=-1) if self.dcp_size > 1: # permute: [bs, num_heads, v_head_dim+1] -> [num_heads, v_head_dim+1, bs] attn_out_lse = attn_out_lse.permute([1, 2, 0]).contiguous() attn_out_lse_all2all = torch.empty_like(attn_out_lse) dist.all_to_all_single(attn_out_lse_all2all, attn_out_lse, group=self.dcp_group) # permute: [num_heads, v_head_dim+1, bs] -> [bs, num_heads, v_head_dim+1] attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1]) if self.pcp_size > 1: attn_out_lse = attn_out_lse_all2all.contiguous() attn_out_lse_list = list( torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1)) if self.pcp_size > 1: # AllGather out&lse within PCP group attn_out_lse_list = [ torch.empty_like(attn_out_lse) for _ in range(self.pcp_size) ] dist.all_gather(attn_out_lse_list, attn_out_lse, group=self.pcp_group) if self.dcp_size > 1 and self.pcp_size > 1: attn_out_lse_list_pcp_dcp = [] for s in attn_out_lse_list: attn_out_lse_list_split = list( torch.chunk(s, self.dcp_size, dim=1)) attn_out_lse_list_pcp_dcp += attn_out_lse_list_split attn_out_lse_list = attn_out_lse_list_pcp_dcp return attn_out_lse_list def _reorg_kvcache( self, allgatered_kv_c_normed: torch.Tensor, allgatered_k_pe: torch.Tensor, padded_local_chunk_seq_lens_lst: list[int], local_context_lens_allranks: list[list[int]], sum_seq_len: int, max_seq_len: int, chunk_size: int, 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. """ 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