ae068a3342
The main purposes of this PR are as follows:
1. Remove the multicast-related code;
Reason:
1. In the scenario like a2 Dual-System Back-to-Back Networking，the
performance is worse than all_gather. Before the modification, in e2e
test, it was 3 tps; after the modification, it is 10 tps.
2. At the same time, we usually enable the SP feature，it is consistent
with the current logic.
3. The advantage of broadcast communication lies in the fact that it
does not suffer from uneven DP load and does not require the prefill ACL
graph to be enabled. But we support prefill Acl graph recently.
So we think there is no need to maintain the multicast as one choice in
moe communication.
Performance benefits are as follows:
When not enable_flashcomm1, TTFT remains relatively stable at around
43000ms, which is approximately 15000ms faster than before the
modification.
When enable_flashcomm1, there is no diffenence, TTFT remains relatively
stable at around 29000ms.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Signed-off-by: weijinqian0 <1184188277@qq.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
1126 lines
54 KiB
Python
1126 lines
54 KiB
Python
import importlib
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from typing import Optional, Union
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from vllm.config import (CUDAGraphMode, VllmConfig,
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get_layers_from_vllm_config, set_current_vllm_config)
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from vllm.forward_context import BatchDescriptor, get_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.model_loader import get_model_loader
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from vllm.model_executor.model_loader.utils import \
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process_weights_after_loading
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from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
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CommonAttentionMetadata)
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.utils import CpuGpuBuffer
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
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from vllm_ascend.compilation.acl_graph import (ACLGraphWrapper,
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set_mtp_graph_params,
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update_mla_attn_params)
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from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
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from vllm_ascend.utils import (ProfileExecuteDuration, lmhead_tp_enable,
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prefill_context_parallel_enable)
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if prefill_context_parallel_enable():
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from vllm.distributed import get_pcp_group
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.utils.torch_utils import set_default_torch_dtype
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logger = init_logger(__name__)
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PADDING_SLOT_ID = -1
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_MTP_MODELS = {
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"DeepseekV3ForCausalLM":
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("vllm.model_executor.models.deepseek_mtp", "DeepSeekMTP"),
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"Qwen3NextForCausalLM":
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("vllm_ascend.models.qwen3_next_mtp", "CustomQwen3NextMTP")
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}
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_DEFAULT_FIRST_LAYER = 'model.layers.0.self_attn.attn'
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_FIRST_LAYERS = {"Qwen3NextForCausalLM": 'model.layers.3.self_attn.attn'}
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def _load_model(architecture):
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if architecture not in _MTP_MODELS:
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raise ValueError("Invalid architecture for mtp.")
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module_name, model_name = _MTP_MODELS[architecture]
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module = importlib.import_module(module_name)
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model = getattr(module, model_name)
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return model
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class MtpProposer(Proposer):
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# TODO: Find out why ModelRunner does not this explicit typing?
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model: Union[nn.Module, ACLGraphWrapper]
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def __init__(
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self,
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vllm_config: VllmConfig,
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device,
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runner,
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):
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self.name = SpecDcodeType.MTP
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self.vllm_config = vllm_config
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self.speculative_config = vllm_config.speculative_config
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assert self.speculative_config is not None
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self.draft_model_config = self.speculative_config.draft_model_config
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self.method = self.speculative_config.method
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self.runner = runner
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self.device = device
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self.dtype = vllm_config.model_config.dtype
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self.max_model_len = vllm_config.model_config.max_model_len
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self.block_size = vllm_config.cache_config.block_size
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self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
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self.decode_threshold = 1 + self.num_speculative_tokens
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self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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self.token_arange_np = np.arange(self.max_num_tokens)
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# We need to get the hidden size from the draft model config because
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# the draft model's hidden size can be different from the target model's
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# hidden size (e.g., Llama 3.3 70B).
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self.hidden_size = self.draft_model_config.get_hidden_size()
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self.pcp_size = self.runner.pcp_size
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self.dcp_size = self.runner.dcp_size
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self.pcp_rank = self.runner.pcp_rank
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self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None
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self.draft_indexer_metadata_builder: Optional[
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AttentionMetadataBuilder] = None
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self.attn_layer_names: list[str] = []
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self.indexer_layer_names: list[str] = []
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self.use_aclgraph = self.runner._use_aclgraph()
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self.cudagraph_batch_sizes = (list(
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reversed(
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self.vllm_config.compilation_config.cudagraph_capture_sizes))
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if self.use_aclgraph else [])
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# persistent buffers for aclgraph graph
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self.input_ids = torch.zeros(self.max_num_tokens,
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dtype=torch.int32,
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device=device)
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self.uses_mrope = self.vllm_config.model_config.uses_mrope
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if self.uses_mrope:
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# M-RoPE need (3, max_num_tokens)
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self.mrope_positions = torch.zeros((3, self.max_num_tokens),
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dtype=torch.int64,
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device=device)
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else:
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# RoPE need (max_num_tokens,)
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self.positions = torch.zeros(self.max_num_tokens,
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dtype=torch.int64,
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device=device)
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self.hidden_states = torch.zeros(
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(self.max_num_tokens, self.hidden_size),
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dtype=self.dtype,
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device=device)
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self.full_indices = range(
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self.runner.max_num_tokens * self.pcp_size * self.dcp_size +
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self.pcp_size * self.dcp_size * self.runner.max_num_reqs)
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# We need +1 here because the arange is used to set query_start_loc,
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# which has one more element than batch_size.
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max_batch_size = vllm_config.scheduler_config.max_num_seqs
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max_num_slots_for_arange = max(max_batch_size + 1, self.max_num_tokens)
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self.arange = torch.arange(max_num_slots_for_arange,
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device=device,
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dtype=torch.int32)
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self.inputs_embeds = torch.zeros(
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(self.max_num_tokens, self.hidden_size),
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dtype=self.dtype,
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device=device)
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self.backup_next_token_ids = CpuGpuBuffer(
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max_batch_size,
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dtype=torch.int32,
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pin_memory=is_pin_memory_available(),
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device=device,
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with_numpy=True,
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)
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self.use_sparse = hasattr(vllm_config.model_config.hf_config,
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"index_topk")
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def load_model(self, model) -> None:
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loader = get_model_loader(self.vllm_config.load_config)
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target_attn_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config,
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AttentionLayerBase).keys())
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target_indexer_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config,
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DeepseekV32IndexerCache).keys())
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draft_model_config = \
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self.vllm_config.speculative_config.draft_model_config
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target_device = self.vllm_config.device_config.device
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with set_default_torch_dtype(
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draft_model_config.dtype), set_current_vllm_config(
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self.vllm_config):
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self._init_mtp_model()
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draft_attn_layer_names = (get_layers_from_vllm_config(
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self.vllm_config, AttentionLayerBase).keys() -
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target_attn_layer_names)
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indexer_layers = get_layers_from_vllm_config(self.vllm_config,
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DeepseekV32IndexerCache)
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draft_indexer_layer_names = indexer_layers.keys(
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) - target_indexer_layer_names
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# NOTE: Currently we don't have specific attention backend and attention metadata
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# for deepseek v3.2 indexer, so we just exclude the indexer layers here.
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draft_attn_layer_names = draft_attn_layer_names - draft_indexer_layer_names
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assert len(draft_attn_layer_names) == 1
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self.attn_layer_name = list(draft_attn_layer_names)
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self.model.load_weights(
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loader.get_all_weights(
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self.vllm_config.speculative_config.draft_model_config,
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self.model))
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process_weights_after_loading(self.model, draft_model_config,
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target_device)
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if self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs(
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):
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self.update_stream: torch.npu.Stream = torch.npu.Stream()
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set_mtp_graph_params(
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self.vllm_config.compilation_config.cudagraph_capture_sizes)
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self.model = ACLGraphWrapper(self.model,
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self.vllm_config,
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runtime_mode=CUDAGraphMode.FULL)
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@torch.inference_mode()
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def dummy_run(self,
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num_tokens: int,
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with_prefill: bool = False,
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skip_attn: bool = False,
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num_reqs: int = 0,
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num_tokens_across_dp=None,
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aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
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batch_descriptor=None) -> None:
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(
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num_tokens,
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num_tokens_across_dp,
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with_prefill,
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) = self.runner._sync_metadata_across_dp(num_tokens, with_prefill)
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moe_comm_type = self.runner._select_moe_comm_method(num_tokens)
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if skip_attn:
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attn_metadata = None
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elif aclgraph_runtime_mode == CUDAGraphMode.FULL:
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if len(self.runner.attn_groups) > 0:
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num_computed_tokens_cpu = (
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self.runner.input_batch.
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num_computed_tokens_cpu_tensor[:num_reqs])
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common_attn_metadata = AscendCommonAttentionMetadata(
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query_start_loc=self.runner.query_start_loc[:num_reqs + 1],
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query_start_loc_cpu=self.runner.
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query_start_loc_cpu[:num_reqs + 1],
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seq_lens_cpu=self.runner.seq_lens_cpu,
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seq_lens=self.runner.seq_lens_cpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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max_query_len=self.num_speculative_tokens + 1,
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num_computed_tokens_cpu=num_computed_tokens_cpu,
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actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
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block_table_tensor=self.runner.input_batch.block_table[0].
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get_device_tensor()[:num_reqs],
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slot_mapping=self.runner.input_batch.block_table[0].
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slot_mapping,
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positions=self.runner.positions,
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attn_mask=self.runner.attn_mask,
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spec_attn_mask=self.runner.spec_attn_mask,
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attn_state=self.runner.attn_state,
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decode_token_per_req=self.runner.decode_token_per_req,
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cos=self.runner.cos,
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sin=self.runner.sin,
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)
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builder = self.runner.attn_groups[0][0].get_metadata_builder()
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attn_metadata_mtp = builder.build_for_graph_capture(
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common_attn_metadata, AscendAttentionState.SpecDecoding,
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self.runner.get_model())
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attn_metadata = {}
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for layer_name in self.attn_layer_name:
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attn_metadata[layer_name] = attn_metadata_mtp
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else:
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attn_metadata = None
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else:
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attn_metadata = None
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input_ids = self.input_ids[:num_tokens]
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positions = self.positions[:num_tokens]
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previous_hidden_states = self.hidden_states[:num_tokens]
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for i in range(self.num_speculative_tokens):
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if i > 0:
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aclgraph_runtime_mode = CUDAGraphMode.NONE
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with set_ascend_forward_context(
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attn_metadata,
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self.vllm_config,
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num_tokens=num_tokens,
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with_prefill=with_prefill,
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num_tokens_across_dp=num_tokens_across_dp,
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reserved_mc2_mask=self.runner.reserved_mc2_mask,
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moe_comm_type=moe_comm_type,
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in_profile_run=self.runner.in_profile_run,
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num_actual_tokens=0,
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aclgraph_runtime_mode=aclgraph_runtime_mode,
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batch_descriptor=batch_descriptor,
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is_mtp_model=True):
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self.model(input_ids=input_ids,
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positions=positions,
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hidden_states=previous_hidden_states)
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forward_context = get_forward_context()
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if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL and \
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not forward_context.capturing:
|
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if self.vllm_config.model_config.use_mla:
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update_mla_attn_params(
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self.update_stream, forward_context,
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positions.shape[0],
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self.vllm_config.speculative_config)
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if with_prefill:
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break
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def generate_token_ids(self,
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sampled_token_ids: list[list[int]],
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sampling_metadata: SamplingMetadata = None,
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scheduler_output: SchedulerOutput = None,
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spec_decode_metadata: SpecDecodeMetadata = None,
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positions: torch.Tensor = None,
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num_scheduled_tokens: int = 0,
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hidden_states: torch.Tensor = None,
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attn_metadata=None,
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aux_hidden_states: torch.Tensor = None):
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common_attn_metadata = self.runner.spec_decode_common_attn_metadata
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attn_metadata = self._get_attn_metadata(attn_metadata)
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if self.speculative_config.disable_padded_drafter_batch:
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# When padded-batch is disabled, the sampled_token_ids should be
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# the cpu-side list[list[int]] of valid sampled tokens for each
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# request, with invalid requests having empty lists.
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assert isinstance(sampled_token_ids, list), \
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"sampled_token_ids should be a python list when" \
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"padded-batch is disabled."
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next_token_ids = self.prepare_next_token_ids_cpu(
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sampled_token_ids, self.runner.requests,
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self.runner.input_batch, scheduler_output.num_scheduled_tokens)
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else:
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# When using padded-batch, the sampled_token_ids should be
|
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# the gpu tensor of sampled tokens for each request, of shape
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# (num_reqs, num_spec_tokens + 1) with rejected tokens having
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# value -1.
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assert isinstance(sampled_token_ids, torch.Tensor), \
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|
"sampled_token_ids should be a torch.Tensor when" \
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"padded-batch is enabled."
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next_token_ids, valid_sampled_tokens_count = \
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self.prepare_next_token_ids_padded(
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common_attn_metadata,
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sampled_token_ids,
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self.runner.requests,
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self.runner.input_batch,
|
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self.runner.discard_request_indices.gpu,
|
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self.runner.num_discarded_requests
|
|
)
|
|
|
|
req_scheduled_tokens = scheduler_output.num_scheduled_tokens
|
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if self.pcp_size > 1:
|
|
long_seq_metadata = self.runner.long_seq_metadata
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input_ids_pcp_full = self.runner.input_ids_pcp_full
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|
query_start_loc_pcp_full = self.runner.query_start_loc_pcp_full
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|
query_start_loc_pcp_full_cpu = self.runner.query_start_loc_pcp_full_cpu
|
|
num_reqs = self.runner.input_batch.num_reqs
|
|
ori_query_lens = query_start_loc_pcp_full_cpu[1:num_reqs+1] - \
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query_start_loc_pcp_full_cpu[:num_reqs]
|
|
num_prefill_reqs = (ori_query_lens
|
|
> self.decode_threshold).sum().item()
|
|
num_decode_reqs = num_reqs - num_prefill_reqs
|
|
else:
|
|
long_seq_metadata = None
|
|
num_prefill_reqs = 0
|
|
num_decode_reqs = 0
|
|
if spec_decode_metadata is None:
|
|
# update pcp related params
|
|
if self.pcp_size > 1:
|
|
token_indices_to_sample = \
|
|
query_start_loc_pcp_full_cpu[1:num_reqs + 1] - 1
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target_token_ids = input_ids_pcp_full[:num_scheduled_tokens]
|
|
target_positions = positions[:num_scheduled_tokens]
|
|
target_hidden_states = hidden_states
|
|
else:
|
|
token_indices_to_sample = None
|
|
# input_ids can be None for multimodal models.
|
|
target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
|
|
target_positions = positions[:num_scheduled_tokens]
|
|
target_hidden_states = hidden_states[:num_scheduled_tokens]
|
|
else:
|
|
if self.pcp_size > 1:
|
|
common_attn_metadata.query_start_loc_cpu = \
|
|
query_start_loc_pcp_full_cpu[:num_reqs + 1]
|
|
common_attn_metadata.query_start_loc = \
|
|
query_start_loc_pcp_full[:num_reqs + 1]
|
|
if self.speculative_config.disable_padded_drafter_batch:
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility with pcp
|
|
token_indices_to_sample = None
|
|
common_attn_metadata, token_indices =\
|
|
self._prepare_inputs(
|
|
common_attn_metadata,
|
|
sampled_token_ids,
|
|
spec_decode_metadata.num_draft_tokens)
|
|
else:
|
|
common_attn_metadata, token_indices, \
|
|
token_indices_to_sample =\
|
|
self.prepare_inputs_padded(
|
|
common_attn_metadata,
|
|
spec_decode_metadata,
|
|
valid_sampled_tokens_count)
|
|
if self.pcp_size > 1:
|
|
target_token_ids = input_ids_pcp_full[token_indices]
|
|
target_positions = positions
|
|
target_hidden_states = hidden_states
|
|
else:
|
|
target_token_ids = self.runner.input_ids[token_indices]
|
|
target_positions = positions[token_indices]
|
|
target_hidden_states = hidden_states[token_indices]
|
|
|
|
draft_token_ids = self._propose(
|
|
target_token_ids=target_token_ids,
|
|
target_positions=target_positions,
|
|
target_hidden_states=target_hidden_states,
|
|
next_token_ids=next_token_ids,
|
|
last_token_indices=token_indices_to_sample,
|
|
common_attn_metadata=common_attn_metadata,
|
|
sampling_metadata=sampling_metadata,
|
|
req_scheduled_tokens=req_scheduled_tokens,
|
|
long_seq_metadata=long_seq_metadata,
|
|
num_prefill_reqs=num_prefill_reqs,
|
|
num_decode_reqs=num_decode_reqs,
|
|
scheduler_output=scheduler_output,
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
)
|
|
|
|
return draft_token_ids
|
|
|
|
def _init_mtp_model(self):
|
|
architecture = self.vllm_config.model_config.architecture
|
|
target_device = self.vllm_config.device_config.device
|
|
model = _load_model(architecture)
|
|
self.model = model(vllm_config=self.vllm_config).to(target_device)
|
|
|
|
def _get_attn_metadata(self, attn_metadata):
|
|
if attn_metadata is not None and isinstance(attn_metadata, dict):
|
|
architecture = self.vllm_config.model_config.architecture
|
|
layer_name = _FIRST_LAYERS.get(architecture, _DEFAULT_FIRST_LAYER)
|
|
attn_metadata = attn_metadata[layer_name]
|
|
|
|
return attn_metadata
|
|
|
|
def _prepare_inputs(
|
|
self,
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
sampled_token_ids: list[list[int]],
|
|
num_draft_tokens: list[int],
|
|
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
|
|
"""
|
|
This function is used to prepare the inputs for speculative decoding.
|
|
It updates to the common_attn_metadata to account for the rejected
|
|
tokens (and newly sampled tokens). It also returns the token indices
|
|
of the tokens that should be fed to the speculator.
|
|
"""
|
|
# E.g.
|
|
# common_attn_metadata.query_start_loc{_cpu}:
|
|
# [0, q1, q1 + q2, q1 + q2 + q3]
|
|
# common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
|
|
# num_rejected_tokens: [n1, n2, n3]
|
|
# This function computes the intermediate values:
|
|
# num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
|
|
# And returns:
|
|
# common_attn_metadata.query_start_loc{_cpu}:
|
|
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
|
# common_attn_metadata.seq_lens{_cpu}:
|
|
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
|
|
# token_indices: [0, 1, ..., q1 - n1 - 1,
|
|
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
|
|
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
|
|
|
|
num_rejected_tokens = [
|
|
n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
|
|
for i, n in enumerate(num_draft_tokens)
|
|
]
|
|
num_rejected_tokens = torch.tensor(num_rejected_tokens,
|
|
dtype=torch.int32)
|
|
|
|
device = common_attn_metadata.query_start_loc.device
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
|
new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
|
|
|
|
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
|
|
new_query_len_per_req = query_start_loc_cpu[
|
|
1:] - query_start_loc_cpu[:-1]
|
|
# [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
|
|
new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
|
|
new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
|
|
|
|
# [q1 - n1, q2 - n2, q3 - n3] ->
|
|
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
|
new_query_start_loc_cpu = torch.zeros(
|
|
query_start_loc_cpu.shape,
|
|
dtype=torch.int32,
|
|
pin_memory=is_pin_memory_available(),
|
|
)
|
|
new_query_start_loc_np = new_query_start_loc_cpu.numpy()
|
|
np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
|
|
|
|
total_num_tokens = new_query_start_loc_np[-1]
|
|
# Example assuming num_tokens_per_req_np = [2, 4, 3]
|
|
# this implies that `new_query_start_locs` is:
|
|
# [0, 2, 6, 9] ->
|
|
# [0, 0, 2, 2, 2, 2, 6, 6, 6]
|
|
# _r1_ ____r2____ ___r3__
|
|
new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
|
|
new_num_tokens_per_req_np)
|
|
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
|
|
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
|
|
# _r1_ ____r2____ ___r3__
|
|
token_offests = (self.token_arange_np[:total_num_tokens] -
|
|
new_query_start_locs_expanded)
|
|
|
|
# Expand starting positions to match token pattern
|
|
# [0, q1, q1 + q2] ->
|
|
# [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
|
|
# _r1_ _____r2_______ ___________r3____________
|
|
old_query_start_locs_expanded = np.repeat(
|
|
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
|
|
# Final token indices are:
|
|
# [0, 1, // req 1
|
|
# q1 + 0, q1 + 1, q1 + 2, q1 + 3, // req 2
|
|
# q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
|
|
token_indices_np = token_offests + old_query_start_locs_expanded
|
|
token_indices = torch.from_numpy(token_indices_np).to(
|
|
device, non_blocking=True)
|
|
|
|
common_attn_metadata.slot_mapping[:token_indices.shape[0]].copy_(
|
|
common_attn_metadata.slot_mapping[token_indices])
|
|
common_attn_metadata.slot_mapping[token_indices.shape[0]:].fill_(-1)
|
|
|
|
# NOTE: Currently positions and seq_lens are not used in mla_v1 forward
|
|
# so we do not need to fixed them. But if they are used in the future,
|
|
# we should fixed them.
|
|
spec_common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=new_query_start_loc_cpu.to(device,
|
|
non_blocking=True),
|
|
query_start_loc_cpu=new_query_start_loc_cpu,
|
|
seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
|
|
seq_lens_cpu=new_seq_lens_cpu,
|
|
num_computed_tokens_cpu=common_attn_metadata.
|
|
num_computed_tokens_cpu,
|
|
num_reqs=common_attn_metadata.num_reqs,
|
|
num_actual_tokens=total_num_tokens,
|
|
max_query_len=new_query_len_per_req.max().item(),
|
|
block_table_tensor=common_attn_metadata.block_table_tensor,
|
|
slot_mapping=common_attn_metadata.slot_mapping,
|
|
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
|
positions=common_attn_metadata.positions[token_indices],
|
|
attn_mask=self.runner.attn_mask,
|
|
spec_attn_mask=self.runner.spec_attn_mask,
|
|
attn_state=self.runner.attn_state,
|
|
graph_pad_size=self.runner.graph_pad_size,
|
|
decode_token_per_req=self.runner.decode_token_per_req,
|
|
)
|
|
return spec_common_attn_metadata, token_indices
|
|
|
|
def _propose(
|
|
self,
|
|
# [num_tokens]
|
|
target_token_ids: torch.Tensor,
|
|
# [num_tokens] or [3, num_tokens] when M-RoPE is enabled
|
|
target_positions: torch.Tensor,
|
|
# [num_tokens, hidden_size]
|
|
target_hidden_states: torch.Tensor,
|
|
# [batch_size]
|
|
next_token_ids: torch.Tensor,
|
|
last_token_indices: Optional[torch.Tensor],
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
sampling_metadata: SamplingMetadata,
|
|
mm_embed_inputs: Optional[tuple[list[torch.Tensor],
|
|
torch.Tensor]] = None,
|
|
req_scheduled_tokens=None,
|
|
long_seq_metadata=None,
|
|
num_prefill_reqs=0,
|
|
num_decode_reqs=0,
|
|
scheduler_output: SchedulerOutput = None,
|
|
num_scheduled_tokens: int = 0,
|
|
) -> torch.Tensor:
|
|
num_tokens = target_token_ids.shape[0]
|
|
batch_size = next_token_ids.shape[0]
|
|
|
|
if last_token_indices is None:
|
|
last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
|
|
|
|
if self.method == "eagle3":
|
|
assert isinstance(self.model, Eagle3LlamaForCausalLM)
|
|
target_hidden_states = self.model.combine_hidden_states(
|
|
target_hidden_states)
|
|
assert target_hidden_states.shape[-1] == self.hidden_size
|
|
|
|
# Shift the input ids by one token.
|
|
# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
|
|
self.input_ids[:num_tokens - 1] = target_token_ids[1:]
|
|
# Replace the last token with the next token.
|
|
# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
|
|
self.input_ids[last_token_indices] = next_token_ids
|
|
|
|
# update pcp related params
|
|
if self.pcp_size > 1:
|
|
assert long_seq_metadata is not None
|
|
common_attn_metadata.prefill_context_parallel_metadata = long_seq_metadata
|
|
# 1. preprocess decode/prefill input_ids & target_hidden_states
|
|
# decode input_ids: keep unchanged
|
|
# decode target_hidden_states: remove padding
|
|
# prefill input_ids: add padding and pcp split
|
|
# prefill target_hidden_states: pcp split
|
|
num_tokens_d = num_decode_reqs * self.decode_threshold
|
|
num_tokens_d_padded = num_tokens_d * self.pcp_size
|
|
input_ids_d = self.input_ids[:num_tokens_d]
|
|
input_ids_p = self.input_ids[num_tokens_d:num_tokens]
|
|
target_hidden_states_d_padded = \
|
|
target_hidden_states[:num_tokens_d_padded]
|
|
if num_tokens_d:
|
|
# remove padding (from pcp all-gather) in decode part
|
|
target_hidden_states_d = target_hidden_states_d_padded.reshape(
|
|
[
|
|
num_decode_reqs, self.decode_threshold * self.pcp_size,
|
|
-1
|
|
])[:, :self.decode_threshold, :].reshape(
|
|
[num_tokens_d, -1])
|
|
else:
|
|
target_hidden_states_d = target_hidden_states_d_padded
|
|
target_hidden_states_p = target_hidden_states[num_tokens_d_padded:]
|
|
req_scheduled_tokens_p = {}
|
|
for i, req_id in enumerate(self.runner.input_batch.req_ids):
|
|
if i >= num_decode_reqs:
|
|
req_scheduled_tokens_p[req_id] = \
|
|
req_scheduled_tokens[req_id]
|
|
(num_tokens_p, input_ids_p, target_hidden_states_p,
|
|
max_query_len_p, seq_lens_p, cu_num_tokens_p) = \
|
|
self._split_pcp_input(
|
|
req_scheduled_tokens_p, input_ids_p, target_hidden_states_p)
|
|
num_tokens = num_tokens_d + num_tokens_p
|
|
target_positions = target_positions[:num_tokens]
|
|
self.input_ids[:num_tokens].copy_(
|
|
torch.cat([input_ids_d, input_ids_p], dim=0))
|
|
target_hidden_states = torch.cat(
|
|
[target_hidden_states_d, target_hidden_states_p], dim=0)
|
|
# 2. update attn_metadata params that may be influenced by pcp
|
|
common_attn_metadata.num_actual_tokens = num_tokens
|
|
common_attn_metadata.max_query_len = max(self.decode_threshold,
|
|
max_query_len_p)
|
|
common_attn_metadata.seq_lens[num_decode_reqs:] = seq_lens_p
|
|
common_attn_metadata.seq_lens_cpu[num_decode_reqs:] = seq_lens_p
|
|
query_start_loc_p = cu_num_tokens_p[1:] + \
|
|
common_attn_metadata.query_start_loc[num_decode_reqs].item()
|
|
common_attn_metadata.query_start_loc[num_decode_reqs + 1:] = \
|
|
query_start_loc_p
|
|
common_attn_metadata.query_start_loc_cpu[num_decode_reqs + 1:] = \
|
|
query_start_loc_p
|
|
# 3. update sample_indices according to main model
|
|
if num_decode_reqs:
|
|
last_token_indices[:num_decode_reqs] = \
|
|
self.runner.logits_indices[last_token_indices[:num_decode_reqs]]
|
|
if num_prefill_reqs:
|
|
last_token_indices[-num_prefill_reqs:] = \
|
|
self.runner.logits_indices[-num_prefill_reqs:]
|
|
|
|
assert self.runner is not None
|
|
|
|
if self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs(
|
|
) and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]:
|
|
num_input_tokens = self.vllm_config.pad_for_cudagraph(
|
|
num_scheduled_tokens)
|
|
elif self.use_aclgraph and num_tokens <= self.cudagraph_batch_sizes[-1]:
|
|
# Acl graph mode, add padding to the batch size
|
|
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
|
|
else:
|
|
# Eager mode, no padding needed
|
|
num_input_tokens = num_tokens
|
|
|
|
# copy inputs to buffer for cudagraph
|
|
self.positions[:num_tokens] = target_positions
|
|
self.hidden_states[:num_tokens] = target_hidden_states
|
|
# eager/acl piecewise mode need to update num_tokens_across_dp
|
|
(num_input_tokens, num_tokens_across_dp,
|
|
with_prefill) = self.runner._sync_metadata_across_dp(
|
|
num_input_tokens, self.runner.with_prefill)
|
|
|
|
moe_comm_type = self.runner._select_moe_comm_method(num_input_tokens)
|
|
|
|
if scheduler_output:
|
|
max_query_len = common_attn_metadata.max_query_len
|
|
uniform_decode = (max_query_len in list(
|
|
range(1, self.num_speculative_tokens +
|
|
2))) and (scheduler_output.total_num_scheduled_tokens
|
|
== self.runner.input_batch.num_reqs *
|
|
(self.num_speculative_tokens + 1))
|
|
batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
|
|
uniform_decode=uniform_decode)
|
|
else:
|
|
batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
|
|
uniform_decode=False)
|
|
aclgraph_runtime_mode, batch_descriptor = \
|
|
self.runner.aclgraph_dispatcher.dispatch(batch_descriptor)
|
|
|
|
if self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs(
|
|
) and aclgraph_runtime_mode == CUDAGraphMode.FULL:
|
|
graph_pad_size = num_input_tokens
|
|
else:
|
|
# Currently, runner.graph_pad_size will always be -1.
|
|
graph_pad_size = self.runner.graph_pad_size
|
|
|
|
# If use fullgraph and disable_padded_drafter_batch=True, We need to
|
|
# update the graph_pad_size in common_attn_metadata, to tell the
|
|
# builder padding some elements.
|
|
common_attn_metadata.graph_pad_size = graph_pad_size
|
|
builder = self.runner.attn_groups[0][0].get_metadata_builder()
|
|
attn_metadata_mtp = builder.build(0, common_attn_metadata,
|
|
self.runner.get_model())
|
|
attn_metadata = {}
|
|
for layer_name in self.attn_layer_name:
|
|
attn_metadata[layer_name] = attn_metadata_mtp
|
|
|
|
for step in range(self.num_speculative_tokens):
|
|
with set_ascend_forward_context(
|
|
attn_metadata,
|
|
self.vllm_config,
|
|
num_tokens=num_input_tokens,
|
|
with_prefill=with_prefill,
|
|
num_tokens_across_dp=num_tokens_across_dp,
|
|
reserved_mc2_mask=self.runner.reserved_mc2_mask,
|
|
moe_comm_type=moe_comm_type,
|
|
aclgraph_runtime_mode=aclgraph_runtime_mode,
|
|
batch_descriptor=batch_descriptor,
|
|
in_profile_run=self.runner.in_profile_run,
|
|
num_actual_tokens=num_tokens,
|
|
is_mtp_model=True):
|
|
with ProfileExecuteDuration().capture_async('mtp_forward'):
|
|
model_kwargs = {}
|
|
model_kwargs["attn_metadata"] = attn_metadata
|
|
|
|
hidden_states = self.model(
|
|
input_ids=self.input_ids[:num_input_tokens],
|
|
positions=self.positions[:num_input_tokens],
|
|
hidden_states=self.hidden_states[:num_input_tokens])
|
|
forward_context = get_forward_context()
|
|
if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL:
|
|
if self.vllm_config.model_config.use_mla:
|
|
update_mla_attn_params(
|
|
self.update_stream, forward_context,
|
|
num_input_tokens,
|
|
self.vllm_config.speculative_config)
|
|
|
|
num_indices = last_token_indices.shape[0]
|
|
if lmhead_tp_enable():
|
|
if not self.runner.with_prefill:
|
|
max_num_reqs_across_dp = num_input_tokens
|
|
else:
|
|
max_num_reqs_across_dp = self.vllm_config.scheduler_config.max_num_seqs
|
|
last_token_indices = nn.functional.pad(
|
|
last_token_indices,
|
|
(0, max_num_reqs_across_dp - num_indices))
|
|
|
|
if self.pcp_size > 1:
|
|
hidden_states = get_pcp_group().all_gather(hidden_states, 0)
|
|
hidden_states = torch.index_select(
|
|
hidden_states, 0, self.runner.
|
|
pcp_allgather_restore_idx[:hidden_states.shape[0]])
|
|
|
|
sample_hidden_states = hidden_states[last_token_indices]
|
|
logits = self.model.compute_logits(sample_hidden_states)
|
|
if lmhead_tp_enable() and num_indices < logits.shape[0]:
|
|
logits = logits[:num_indices]
|
|
draft_token_ids = logits.argmax(dim=-1)
|
|
|
|
if self.num_speculative_tokens == 1:
|
|
# [batch_size, 1]
|
|
return draft_token_ids.view(-1, 1)
|
|
|
|
if step == 0:
|
|
draft_token_ids_list = [draft_token_ids]
|
|
else:
|
|
draft_token_ids_list.append(draft_token_ids)
|
|
|
|
# prepare next mtp inputs
|
|
# mtp>1: prefill skip or decode skip last loop
|
|
if with_prefill:
|
|
for _ in range(self.num_speculative_tokens - 1):
|
|
draft_token_ids_list.append(draft_token_ids)
|
|
if step == self.num_speculative_tokens - 1 or with_prefill:
|
|
break
|
|
|
|
attn_metadata_i = attn_metadata[self.attn_layer_name[0]]
|
|
|
|
if step == 0:
|
|
positions = target_positions[last_token_indices]
|
|
hidden_states = hidden_states[last_token_indices]
|
|
slot_mapping = attn_metadata_i.slot_mapping[last_token_indices]
|
|
attn_metadata_i.slot_mapping.fill_(-1)
|
|
attn_metadata_i.query_start_loc = self.arange[:batch_size + 1]
|
|
last_token_indices = self.arange[:batch_size]
|
|
if attn_metadata_i.num_decode_tokens != 0:
|
|
attn_metadata_i.num_decode_tokens = batch_size
|
|
|
|
input_ids = draft_token_ids_list[-1].int()
|
|
positions += 1
|
|
|
|
# When disable_padded_drafter_batch=False, it should not to be updating these params, maybe.
|
|
if self.speculative_config.disable_padded_drafter_batch or \
|
|
aclgraph_runtime_mode != CUDAGraphMode.FULL:
|
|
attn_metadata_i.decode.actual_seq_lengths_q = attn_metadata_i.query_start_loc[
|
|
1:batch_size + 1].tolist()
|
|
if aclgraph_runtime_mode == CUDAGraphMode.FULL:
|
|
attn_metadata_i.decode.actual_seq_lengths_q = \
|
|
builder.pad_actual_seq_len_q_mtp_disable_pad(
|
|
graph_pad_size - batch_size,
|
|
batch_size,
|
|
attn_metadata_i.decode.actual_seq_lengths_q)
|
|
attn_metadata_i.decode.cos = builder.cos_cache[
|
|
positions].unsqueeze(1).unsqueeze(2)
|
|
attn_metadata_i.decode.sin = builder.sin_cache[
|
|
positions].unsqueeze(1).unsqueeze(2)
|
|
# NOTE(woosuk): We should handle the case where the draft model
|
|
# generates tokens beyond the max model length. Since it is complex
|
|
# to remove such requests from the batch, we keep them in the batch
|
|
# but adjust the position ids and slot mappings to avoid the
|
|
# out-of-range access during the model execution. The draft tokens
|
|
# generated with this adjustment should be ignored.
|
|
exceeds_max_model_len = positions >= self.runner.model_config.max_model_len
|
|
# Mask out the position ids that exceed the max model length.
|
|
# Otherwise, we may get out-of-range error in RoPE.
|
|
clamped_positions = torch.where(exceeds_max_model_len, 0,
|
|
positions)
|
|
# Increment the sequence lengths.
|
|
attn_metadata_i.seq_lens[:batch_size] += 1
|
|
# For the requests that exceed the max model length, we set the
|
|
# sequence length to 1 to minimize their overheads in attention.
|
|
exceeds_max_model_len_cpu = exceeds_max_model_len.to(
|
|
attn_metadata_i.seq_lens.device, non_blocking=True)
|
|
attn_metadata_i.seq_lens[:batch_size].masked_fill_(
|
|
exceeds_max_model_len_cpu, 1)
|
|
# Mask out the slot mappings that exceed the max model length.
|
|
# Otherwise, the KV cache will be inadvertently updated with the
|
|
# padding tokens.
|
|
slot_mapping += 1
|
|
slot_mapping.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID)
|
|
|
|
# copy inputs to buffer for cudagraph
|
|
self.input_ids[:batch_size] = input_ids
|
|
self.positions[:batch_size] = clamped_positions
|
|
self.hidden_states[:hidden_states.shape[0]] = hidden_states
|
|
attn_metadata_i.slot_mapping[:batch_size] = slot_mapping
|
|
if self.speculative_config.disable_padded_drafter_batch:
|
|
self.positions[batch_size:num_input_tokens] = 0
|
|
self.input_ids[batch_size:num_input_tokens] = 0
|
|
self.hidden_states[batch_size:num_input_tokens].fill_(0)
|
|
|
|
if attn_metadata_i.prefill is not None:
|
|
attn_metadata_i.prefill.seq_lens = attn_metadata_i.seq_lens
|
|
attn_metadata_i.prefill.seq_lens_list = attn_metadata_i.prefill.seq_lens.tolist(
|
|
)
|
|
attn_metadata_i.prefill.context_lens = attn_metadata_i.seq_lens
|
|
attn_metadata_i.prefill.input_positions = self.positions[:
|
|
num_input_tokens]
|
|
attn_metadata_i.prefill.max_seq_lens += 1
|
|
attn_metadata_i.prefill.max_seq_lens = min(
|
|
attn_metadata_i.prefill.max_seq_lens,
|
|
self.runner.model_config.max_model_len)
|
|
if attn_metadata_i.decode is not None:
|
|
attn_metadata_i.decode.seq_lens = attn_metadata_i.seq_lens
|
|
attn_metadata_i.decode.seq_lens_list = attn_metadata_i.decode.seq_lens.tolist(
|
|
)
|
|
decode_seq_lens_list = attn_metadata_i.decode.seq_lens_list
|
|
if aclgraph_runtime_mode == CUDAGraphMode.FULL and \
|
|
self.speculative_config.disable_padded_drafter_batch:
|
|
attn_metadata_i.decode.seq_lens_list = decode_seq_lens_list + [
|
|
0
|
|
] * (graph_pad_size - len(decode_seq_lens_list))
|
|
attn_metadata_i.decode.input_positions = self.positions[:
|
|
num_input_tokens]
|
|
attn_metadata_i.decode.max_seq_lens += 1
|
|
attn_metadata_i.decode.max_seq_lens = min(
|
|
attn_metadata_i.decode.max_seq_lens,
|
|
self.runner.model_config.max_model_len)
|
|
|
|
# mtp>1: [batch_size, k]
|
|
draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
|
|
return draft_token_ids
|
|
|
|
# TODO Using torch instead of triton may result in poor performance
|
|
def _prepare_input_kernel(self, out_ptr: torch.Tensor,
|
|
cu_query_lens: torch.Tensor,
|
|
cu_num_tokens: torch.Tensor, block_size: int):
|
|
device = cu_query_lens.device
|
|
dtype = out_ptr.dtype
|
|
|
|
offsets = torch.arange(block_size, device=device, dtype=dtype)
|
|
start_pos = cu_num_tokens[:-1]
|
|
end_pos = cu_num_tokens[1:]
|
|
num_tokens = end_pos - start_pos
|
|
|
|
global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
|
|
values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
|
|
|
|
mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
|
|
|
|
global_indices_flat = global_indices[mask]
|
|
values_flat = values[mask]
|
|
out_ptr[global_indices_flat] = values_flat
|
|
|
|
def prepare_next_token_ids_cpu(
|
|
self,
|
|
sampled_token_ids: list[list[int]],
|
|
requests: dict[str, CachedRequestState],
|
|
gpu_input_batch: InputBatch,
|
|
num_scheduled_tokens: dict[str, int],
|
|
) -> torch.Tensor:
|
|
"""
|
|
This function is used to prepare the inputs for speculative decoding.
|
|
It calculates the next token ids for each request based on the sampled
|
|
token ids from the CPU. If a request has no sampled token ids (e.g.,
|
|
during the initial decoding steps), it falls back to using the request
|
|
state to get the next token id.
|
|
"""
|
|
req_ids = gpu_input_batch.req_ids
|
|
next_token_ids: list[int] = []
|
|
for i, token_ids in enumerate(sampled_token_ids):
|
|
if token_ids:
|
|
# Common case.
|
|
next_token_id = token_ids[-1]
|
|
else:
|
|
# Partial prefill (rare case).
|
|
# Get the next token id from the request state.
|
|
req_id = req_ids[i]
|
|
req_state = requests[req_id]
|
|
seq_len = req_state.num_computed_tokens + num_scheduled_tokens[
|
|
req_id]
|
|
next_token_id = req_state.get_token_id(seq_len)
|
|
next_token_ids.append(next_token_id)
|
|
next_token_ids = torch.tensor(next_token_ids,
|
|
dtype=torch.int32,
|
|
device=self.input_ids.device)
|
|
return next_token_ids
|
|
|
|
def prepare_next_token_ids_padded(
|
|
self,
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
sampled_token_ids: torch.Tensor,
|
|
requests: dict[str, CachedRequestState],
|
|
gpu_input_batch: InputBatch,
|
|
discard_request_indices: torch.Tensor,
|
|
num_discarded_requests: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
This function is used to prepare the inputs for speculative decoding.
|
|
It calculates the next token ids and the number of valid sampled tokens
|
|
for each request, considering the "discarded" requests whose next token
|
|
is not sampled and comes from `request.get_token_id()` instead.
|
|
It also accounts for the rejected tokens in `sampled_token_ids`.
|
|
This function must use device functions to operate on the inputs, and
|
|
should not introduce any blocking CPU-GPU synchronization.
|
|
"""
|
|
# TODO(Ben): Combine this into a custom fused kernel
|
|
|
|
# Precompute get_token_id for when there is no valid next token
|
|
num_reqs = gpu_input_batch.num_reqs
|
|
self.backup_next_token_ids.np[:num_reqs] = np.array([
|
|
requests[gpu_input_batch.req_ids[i]].get_token_id(
|
|
common_attn_metadata.seq_lens_cpu[i].item())
|
|
for i in range(num_reqs)
|
|
])
|
|
self.backup_next_token_ids.copy_to_gpu(num_reqs)
|
|
|
|
# Mask out the sampled tokens indices that should not be sampled.
|
|
discard_sampled_tokens_req_indices = discard_request_indices[:
|
|
num_discarded_requests]
|
|
|
|
valid_sampled_token_ids_gpu = sampled_token_ids.clone()
|
|
valid_sampled_token_ids_gpu.index_fill_(
|
|
0, discard_sampled_tokens_req_indices, -1)
|
|
|
|
# Generate a mask for all valid tokens within those requests
|
|
valid_mask = (valid_sampled_token_ids_gpu != -1) & (
|
|
valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size)
|
|
|
|
# Count the number of valid tokens in each request
|
|
valid_sampled_tokens_count = valid_mask.sum(dim=1)
|
|
|
|
# Get the rightmost valid index per row
|
|
last_valid_indices = valid_sampled_tokens_count - 1
|
|
last_valid_indices_safe = torch.clamp(last_valid_indices, min=0)
|
|
|
|
# Get last valid token from each row
|
|
# (assume undefined state where there is no valid token)
|
|
selected_tokens = torch.gather(
|
|
valid_sampled_token_ids_gpu, 1,
|
|
last_valid_indices_safe.unsqueeze(1)).squeeze(1)
|
|
|
|
# Use last token if valid, pre-computed backup if not
|
|
batch_size = valid_sampled_token_ids_gpu.shape[0]
|
|
next_token_ids = torch.where(
|
|
last_valid_indices != -1,
|
|
selected_tokens,
|
|
self.backup_next_token_ids.gpu[:batch_size],
|
|
)
|
|
|
|
return next_token_ids, valid_sampled_tokens_count
|
|
|
|
def prepare_inputs_padded(
|
|
self,
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
spec_decode_metadata: SpecDecodeMetadata,
|
|
valid_sampled_tokens_count: torch.Tensor,
|
|
) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
|
|
"""
|
|
This function is used to prepare the inputs for speculative decoding
|
|
It updates the common_attn_metadata for speculative decoding,
|
|
but does not consider the rejected tokens. Instead, all tokens
|
|
are included as inputs to the speculator, with the rejected tokens
|
|
used as padding and filtered out later by `token_indices_to_sample`.
|
|
No blocking CPU operations should be introduced in this function.
|
|
"""
|
|
num_draft_tokens_gpu = torch.cat([
|
|
spec_decode_metadata.cu_num_draft_tokens[0:1],
|
|
spec_decode_metadata.cu_num_draft_tokens[1:] -
|
|
spec_decode_metadata.cu_num_draft_tokens[:-1],
|
|
])
|
|
|
|
num_rejected_tokens_gpu = torch.where(
|
|
num_draft_tokens_gpu > 0,
|
|
num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
|
|
torch.zeros_like(num_draft_tokens_gpu),
|
|
)
|
|
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
|
|
|
new_query_len_per_req = query_start_loc_cpu[
|
|
1:] - query_start_loc_cpu[:-1]
|
|
|
|
total_num_tokens = query_start_loc_cpu[-1].item()
|
|
token_indices = self.arange[:total_num_tokens]
|
|
|
|
# NOTE: Currently positions and seq_lens are not used in mla_v1 forward
|
|
# so we do not need to fixed them. But if they are used in the future,
|
|
# we should fixed them.
|
|
spec_common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=common_attn_metadata.query_start_loc,
|
|
query_start_loc_cpu=query_start_loc_cpu,
|
|
seq_lens_cpu=common_attn_metadata.seq_lens,
|
|
num_reqs=common_attn_metadata.num_reqs,
|
|
num_actual_tokens=total_num_tokens,
|
|
max_query_len=new_query_len_per_req.max().item(),
|
|
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
|
block_table_tensor=common_attn_metadata.block_table_tensor,
|
|
slot_mapping=common_attn_metadata.slot_mapping,
|
|
positions=common_attn_metadata.positions,
|
|
attn_mask=self.runner.attn_mask,
|
|
spec_attn_mask=self.runner.spec_attn_mask,
|
|
attn_state=self.runner.attn_state,
|
|
graph_pad_size=self.runner.graph_pad_size,
|
|
decode_token_per_req=self.runner.decode_token_per_req,
|
|
num_computed_tokens_cpu=common_attn_metadata.
|
|
num_computed_tokens_cpu,
|
|
seq_lens=common_attn_metadata.seq_lens)
|
|
|
|
token_indices_to_sample = (common_attn_metadata.query_start_loc[1:] -
|
|
1 - num_rejected_tokens_gpu)
|
|
|
|
return spec_common_attn_metadata, token_indices, token_indices_to_sample
|
|
|
|
def _split_pcp_input(self, req_scheduled_tokens, input_ids,
|
|
target_hidden_states):
|
|
"""
|
|
Split prefill input_ids and target_hidden_states in pcp group.
|
|
1. input_ids padding: [t0, t1, t2, t3, t4, t5] -> [t0, t1, t2, t3, t4, t5, pad, pad]
|
|
2. split input_ids: pcp0 [t0, t1, pad, pad], pcp1 [t2, t3, t4, t5]
|
|
3. split target_hidden_states (already include pcp padding):
|
|
[h0, h1, h2, h3, h4, h5, pad, pad] -> pcp0 [h0, h1, pad, pad], pcp1 [h2, h3, h4, h5]
|
|
4. also update max_query_len, seq_lens, cu_num_tokens according to pcp split.
|
|
"""
|
|
if len(req_scheduled_tokens) == 0:
|
|
# no prefill inputs to split, return empty result
|
|
return (
|
|
0,
|
|
torch.zeros([0], device='npu'),
|
|
torch.zeros([0, target_hidden_states.size(1)], device='npu'),
|
|
0,
|
|
torch.zeros([0]),
|
|
torch.tensor([0], dtype=torch.int32),
|
|
)
|
|
|
|
def _pcp_pad_and_split(num_tokens):
|
|
num_pcp_padded_scheduled_tokens = cdiv(
|
|
num_tokens, 2 * self.pcp_size) * 2 * self.pcp_size
|
|
pcp_pad = num_pcp_padded_scheduled_tokens - num_tokens
|
|
chunk_size = num_pcp_padded_scheduled_tokens // (2 * self.pcp_size)
|
|
|
|
# split position_ids (and use split position_ids to split input_ids afterwards)
|
|
req_position_cp: list[int] = []
|
|
req_position_cp.extend(
|
|
self.full_indices[self.pcp_rank *
|
|
chunk_size:(self.pcp_rank + 1) * chunk_size])
|
|
req_position_cp.extend(
|
|
self.full_indices[num_pcp_padded_scheduled_tokens -
|
|
(self.pcp_rank + 1) *
|
|
chunk_size:num_pcp_padded_scheduled_tokens -
|
|
self.pcp_rank * chunk_size])
|
|
|
|
return req_position_cp, num_pcp_padded_scheduled_tokens, pcp_pad
|
|
|
|
num_pcp_scheduled_tokens = []
|
|
ori_start_index = 0
|
|
pad_start_index = 0
|
|
pcp_split_input_ids_list = []
|
|
pcp_split_hidden_states_list = []
|
|
for ori_num_tokens in req_scheduled_tokens.values():
|
|
req_position_pcp, num_pcp_padded_scheduled_tokens, num_pcp_pad = \
|
|
_pcp_pad_and_split(ori_num_tokens)
|
|
actual_num_tokens = len(req_position_pcp)
|
|
num_pcp_scheduled_tokens.append(actual_num_tokens)
|
|
pad_input_ids = F.pad(
|
|
input_ids[ori_start_index:ori_start_index + ori_num_tokens],
|
|
(0, num_pcp_pad))
|
|
ori_start_index += ori_num_tokens
|
|
pcp_chunk_indices = [
|
|
pad_start_index + pos for pos in req_position_pcp
|
|
]
|
|
pcp_split_input_ids = pad_input_ids[req_position_pcp]
|
|
pcp_split_hidden_states = target_hidden_states[pcp_chunk_indices]
|
|
pcp_split_input_ids_list.append(pcp_split_input_ids)
|
|
pcp_split_hidden_states_list.append(pcp_split_hidden_states)
|
|
pad_start_index += num_pcp_padded_scheduled_tokens
|
|
num_tokens = sum(num_pcp_scheduled_tokens)
|
|
input_ids = torch.cat(pcp_split_input_ids_list)
|
|
target_hidden_states = torch.cat(pcp_split_hidden_states_list, dim=0)
|
|
max_query_len = max(num_pcp_scheduled_tokens)
|
|
seq_lens = torch.tensor(num_pcp_scheduled_tokens, dtype=torch.int32)
|
|
cu_num_tokens = torch.tensor(
|
|
np.insert(np.cumsum(np.array(num_pcp_scheduled_tokens)), 0, 0))
|
|
return num_tokens, input_ids, target_hidden_states, max_query_len, seq_lens, cu_num_tokens
|