52863c4165
### What this PR does / why we need it?
1. Refactor eagle and mtp function: load_model and generate_token_ids
2. Remove redundant code in mtp and eagle file
3. Refactor the UT of file
2/N of Refactor and merge mtp and eagle
Relational RFC: https://github.com/vllm-project/vllm-ascend/issues/5467
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut and tests
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
988 lines
50 KiB
Python
988 lines
50 KiB
Python
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
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from vllm.distributed import get_pcp_group
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from vllm.forward_context import get_forward_context
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from vllm.logger import init_logger
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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.utils.platform_utils import is_pin_memory_available
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from vllm.v1.attention.backends.utils import 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.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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update_attn_dcp_pcp_params,
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update_attn_params,
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update_mla_attn_dcp_pcp_params,
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update_mla_attn_params)
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from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
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from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
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from vllm_ascend.utils import ProfileExecuteDuration, lmhead_tp_enable
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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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"PanguUltraMoEForCausalLM":
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("vllm.model_executor.models.openpangu_mtp", "OpenPanguMTP"),
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"DeepseekV32ForCausalLM":
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("vllm.model_executor.models.deepseek_mtp", "DeepSeekMTP"),
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"Qwen3NextForCausalLM":
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("vllm.model_executor.models.qwen3_next_mtp", "Qwen3NextMTP")
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}
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class MtpProposer(EagleProposer):
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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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# update full-graph params for one spec token
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def _update_full_graph_params(self, forward_context, num_tokens):
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if self.vllm_config.model_config.use_mla:
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if self.pcp_size * self.dcp_size > 1:
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update_mla_attn_dcp_pcp_params(self.update_stream,
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forward_context, num_tokens)
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else:
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update_mla_attn_params(self.update_stream, forward_context,
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num_tokens,
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self.vllm_config.speculative_config)
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else:
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if self.pcp_size * self.dcp_size > 1:
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update_attn_dcp_pcp_params(self.update_stream, forward_context,
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num_tokens)
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else:
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update_attn_params(self.update_stream, forward_context,
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num_tokens, self.vllm_config)
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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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in_graph_capturing: 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,
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dummy_compute_logits=lambda hidden_states: None,
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is_profile=False) -> 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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if not self.use_cuda_graph:
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# there is synchronization between mtp steps when enabling aclgraph,
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# disable aclgraph when use async scheduling to avoid the
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# synchronization overhead.
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# NOTE: we need to set aclgraph_runtime_mode to None in both dummy_run
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# and _propose.
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aclgraph_runtime_mode = CUDAGraphMode.NONE
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if 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.gpu[:num_reqs +
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1],
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query_start_loc_cpu=self.runner.query_start_loc.
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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.gpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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num_input_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(),
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slot_mapping=self.runner.input_batch.block_table[0].
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slot_mapping.gpu,
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positions=self.runner.positions.gpu,
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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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max_seq_len=0)
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if self.pcp_size * self.dcp_size > 1:
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# update long_seq related params and flatten block_table
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common_attn_metadata.prefill_context_parallel_metadata = \
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self.runner.pcp_manager.long_seq_metadata
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common_attn_metadata.block_table_tensor = \
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self.runner.input_batch.block_table[0].get_device_tensor()[
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:num_reqs * self.decode_threshold]
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builder = self.runner.attn_groups[0][0].get_metadata_builder()
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# `AscendAttentionState.SpecDecoding` is only designed for mla, `AscendAttentionState.ChunkedPrefill` is used in self-attention.
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attn_state = AscendAttentionState.SpecDecoding if self.vllm_config.model_config.use_mla else AscendAttentionState.ChunkedPrefill
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attn_metadata_mtp = builder.build_for_graph_capture(
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common_attn_metadata, attn_state)
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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 and not in_graph_capturing and aclgraph_runtime_mode == CUDAGraphMode.FULL:
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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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num_tokens_across_dp=num_tokens_across_dp,
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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_draft_model=True,
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in_profile_run=is_profile):
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if self.enable_shared_expert_dp:
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positions = positions.unsqueeze(-1)
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positions = torch.ops.vllm.maybe_pad_and_reduce(positions)
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positions = positions.squeeze(-1)
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previous_hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
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previous_hidden_states)
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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 and not self.use_sparse:
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self._update_full_graph_params(forward_context, num_tokens)
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if self.enable_shared_expert_dp:
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positions = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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positions, True)
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previous_hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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previous_hidden_states, True)
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dummy_compute_logits(previous_hidden_states)
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if with_prefill:
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break
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def _prepare_inputs(
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self,
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common_attn_metadata: CommonAttentionMetadata,
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sampled_token_ids: list[list[int]],
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num_draft_tokens: list[int],
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) -> tuple[CommonAttentionMetadata, torch.Tensor]:
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"""
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This function is used to prepare the inputs for speculative decoding.
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It updates to the common_attn_metadata to account for the rejected
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tokens (and newly sampled tokens). It also returns the token indices
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of the tokens that should be fed to the speculator.
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"""
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# E.g.
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# common_attn_metadata.query_start_loc{_cpu}:
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# [0, q1, q1 + q2, q1 + q2 + q3]
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# common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
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# num_rejected_tokens: [n1, n2, n3]
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# This function computes the intermediate values:
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# num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
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# And returns:
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# common_attn_metadata.query_start_loc{_cpu}:
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# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
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# common_attn_metadata.seq_lens{_cpu}:
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# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
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# token_indices: [0, 1, ..., q1 - n1 - 1,
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# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
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# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
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num_actual_reqs = len(num_draft_tokens)
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num_rejected_tokens = [
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n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
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for i, n in enumerate(num_draft_tokens)
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]
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num_rejected_tokens = torch.tensor(num_rejected_tokens,
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dtype=torch.int32)
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device = common_attn_metadata.query_start_loc.device
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query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
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num_actual_reqs
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+ 1]
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seq_lens_cpu = common_attn_metadata.seq_lens_cpu[:num_actual_reqs]
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new_seq_lens_cpu = seq_lens_cpu - num_rejected_tokens
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# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
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new_query_len_per_req = query_start_loc_cpu[
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1:] - query_start_loc_cpu[:-1]
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# [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
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new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
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new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
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# [q1 - n1, q2 - n2, q3 - n3] ->
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# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
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new_query_start_loc_cpu = torch.zeros(
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query_start_loc_cpu.shape,
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dtype=torch.int32,
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pin_memory=is_pin_memory_available(),
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)
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new_query_start_loc_np = new_query_start_loc_cpu.numpy()
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np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
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total_num_tokens = new_query_start_loc_np[-1]
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# Example assuming num_tokens_per_req_np = [2, 4, 3]
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# this implies that `new_query_start_locs` is:
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# [0, 2, 6, 9] ->
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# [0, 0, 2, 2, 2, 2, 6, 6, 6]
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# _r1_ ____r2____ ___r3__
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new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
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new_num_tokens_per_req_np)
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# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
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# [0, 1, 0, 1, 2, 3, 0, 1, 2]
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# _r1_ ____r2____ ___r3__
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token_offests = (self.token_arange_np[:total_num_tokens] -
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new_query_start_locs_expanded)
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# Expand starting positions to match token pattern
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# [0, q1, q1 + q2] ->
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# [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
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# _r1_ _____r2_______ ___________r3____________
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old_query_start_locs_expanded = np.repeat(
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query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
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# Final token indices are:
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# [0, 1, // req 1
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# q1 + 0, q1 + 1, q1 + 2, q1 + 3, // req 2
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# q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
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token_indices_np = token_offests + old_query_start_locs_expanded
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token_indices = torch.from_numpy(token_indices_np).to(
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device, non_blocking=True)
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common_attn_metadata.slot_mapping[:token_indices.shape[0]].copy_(
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common_attn_metadata.slot_mapping[token_indices])
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common_attn_metadata.slot_mapping[token_indices.shape[0]:].fill_(-1)
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# NOTE: Currently positions and seq_lens are not used in mla_v1 forward
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# so we do not need to fixed them. But if they are used in the future,
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# we should fixed them.
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spec_common_attn_metadata = AscendCommonAttentionMetadata(
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query_start_loc=new_query_start_loc_cpu.to(device,
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non_blocking=True),
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query_start_loc_cpu=new_query_start_loc_cpu,
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seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
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seq_lens_cpu=new_seq_lens_cpu,
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num_computed_tokens_cpu=common_attn_metadata.
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num_computed_tokens_cpu,
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num_reqs=common_attn_metadata.num_reqs,
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num_actual_tokens=total_num_tokens,
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num_input_tokens=common_attn_metadata.num_input_tokens,
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max_query_len=new_query_len_per_req.max().item(),
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block_table_tensor=common_attn_metadata.block_table_tensor,
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slot_mapping=common_attn_metadata.slot_mapping,
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actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
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positions=common_attn_metadata.positions[token_indices],
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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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max_seq_len=0)
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return spec_common_attn_metadata, token_indices
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def _propose(
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self,
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# [num_tokens]
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target_token_ids: torch.Tensor,
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# [num_tokens] or [3, num_tokens] when M-RoPE is enabled
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target_positions: torch.Tensor,
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# [num_tokens, hidden_size]
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target_hidden_states: torch.Tensor,
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# [batch_size]
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next_token_ids: torch.Tensor,
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last_token_indices: Optional[torch.Tensor],
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common_attn_metadata: CommonAttentionMetadata,
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sampling_metadata: SamplingMetadata,
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mm_embed_inputs: Optional[tuple[list[torch.Tensor],
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torch.Tensor]] = None,
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req_scheduled_tokens=None,
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long_seq_metadata=None,
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num_prefill_reqs=0,
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num_decode_reqs=0,
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scheduler_output: SchedulerOutput = None,
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num_scheduled_tokens: int = 0,
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) -> torch.Tensor:
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num_tokens = target_token_ids.shape[0]
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batch_size = next_token_ids.shape[0]
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if last_token_indices is None:
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last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
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if self.method == "eagle3":
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assert isinstance(self.model, Eagle3LlamaForCausalLM)
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target_hidden_states = self.model.combine_hidden_states(
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target_hidden_states)
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assert target_hidden_states.shape[-1] == self.hidden_size
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# Shift the input ids by one token.
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# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
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self.input_ids[:num_tokens - 1] = target_token_ids[1:]
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# Replace the last token with the next token.
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# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
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self.input_ids[last_token_indices] = next_token_ids
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# update pcp related params
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if self.pcp_size * self.dcp_size > 1:
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assert long_seq_metadata is not None
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common_attn_metadata.prefill_context_parallel_metadata = long_seq_metadata
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ori_last_token_indices = last_token_indices.clone()
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query_lens_d = self.runner.query_lens[:num_decode_reqs]
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if self.pcp_size > 1:
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# 1. preprocess decode/prefill input_ids & target_hidden_states
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# decode input_ids: keep unchanged
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# decode target_hidden_states: remove padding
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# prefill input_ids: add padding and pcp split
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# prefill target_hidden_states: pcp split
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num_tokens_d = query_lens_d.sum().item()
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num_tokens_d_padded = num_tokens_d * self.pcp_size
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input_ids_d = self.input_ids[:num_tokens_d]
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input_ids_p = self.input_ids[num_tokens_d:num_tokens]
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target_hidden_states_d_padded = \
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target_hidden_states[:num_tokens_d_padded]
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if num_tokens_d:
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# remove padding (from pcp all-gather) in decode part
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mask_start_loc = torch.cat([
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torch.tensor([0], dtype=torch.int32),
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torch.cumsum(query_lens_d * self.pcp_size, dim=0)[:-1]
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])
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mask_len = query_lens_d
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mask = []
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for req_id in range(num_decode_reqs):
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mask += list(
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range(mask_start_loc[req_id],
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mask_start_loc[req_id] + mask_len[req_id]))
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target_hidden_states_d = target_hidden_states_d_padded[mask]
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else:
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target_hidden_states_d = target_hidden_states_d_padded
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target_hidden_states_p = target_hidden_states[num_tokens_d_padded:]
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req_scheduled_tokens_p = {}
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for i, req_id in enumerate(self.runner.input_batch.req_ids):
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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 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:]
|
|
# 3. 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_prefill_reqs:] = seq_lens_p
|
|
common_attn_metadata.seq_lens_cpu[
|
|
-num_prefill_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_prefill_reqs:] = \
|
|
query_start_loc_p
|
|
common_attn_metadata.query_start_loc_cpu[-num_prefill_reqs:] = \
|
|
query_start_loc_p
|
|
|
|
assert self.runner is not None
|
|
|
|
# Note(qcs): We may need to refactor these check logics.
|
|
if self.runner.use_aclgraph and num_scheduled_tokens <= self.runner.cudagraph_batch_sizes[
|
|
-1]:
|
|
num_input_tokens = self.vllm_config.pad_for_cudagraph(
|
|
num_scheduled_tokens)
|
|
elif self.use_aclgraph and num_tokens <= self.runner.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)
|
|
|
|
# Enable shared_expert_dp and MTP FULL graph may cause accuracy issues.
|
|
if scheduler_output and not self.enable_shared_expert_dp:
|
|
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))
|
|
else:
|
|
uniform_decode = False
|
|
has_lora = len(self.runner.input_batch.lora_id_to_lora_request) > 0
|
|
aclgraph_runtime_mode, batch_descriptor = \
|
|
self.runner.cudagraph_dispatcher.dispatch(num_tokens=num_input_tokens, uniform_decode=uniform_decode, has_lora=has_lora)
|
|
if not self.use_cuda_graph:
|
|
# there is synchronization between mtp steps when enabling aclgraph,
|
|
# disable aclgraph when use async scheduling to avoid the
|
|
# synchronization overhead.
|
|
# NOTE: we need to set aclgraph_runtime_mode to None in both dummy_run
|
|
# and _propose.
|
|
aclgraph_runtime_mode = CUDAGraphMode.NONE
|
|
|
|
if self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs(
|
|
) and aclgraph_runtime_mode == CUDAGraphMode.FULL:
|
|
graph_pad_size = num_input_tokens
|
|
else:
|
|
graph_pad_size = -1
|
|
|
|
# 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,
|
|
num_tokens_across_dp=num_tokens_across_dp,
|
|
aclgraph_runtime_mode=aclgraph_runtime_mode,
|
|
batch_descriptor=batch_descriptor,
|
|
num_actual_tokens=num_tokens,
|
|
is_draft_model=True):
|
|
with ProfileExecuteDuration().capture_async('mtp_forward'):
|
|
model_kwargs = {}
|
|
model_kwargs["attn_metadata"] = attn_metadata
|
|
input_ids = self.input_ids[:num_input_tokens]
|
|
positions = self.positions[:num_input_tokens]
|
|
hidden_states = self.hidden_states[:num_input_tokens]
|
|
|
|
if self.enable_shared_expert_dp:
|
|
# positions [N] -> [N, 1] for padding
|
|
positions = positions.unsqueeze(-1)
|
|
positions = torch.ops.vllm.maybe_pad_and_reduce(
|
|
positions)
|
|
positions = positions.squeeze(-1)
|
|
hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
|
|
hidden_states)
|
|
|
|
for layer_name in self.attn_layer_name:
|
|
decode_metadata = getattr(attn_metadata[layer_name],
|
|
"decode", None)
|
|
if self.use_async_scheduling and decode_metadata is not None:
|
|
actual_size = len(
|
|
decode_metadata.actual_seq_lengths_q)
|
|
|
|
decode_metadata.seq_lens_list = \
|
|
decode_metadata.seq_lens_list[:actual_size]
|
|
decode_metadata.block_table = \
|
|
decode_metadata.block_table[:actual_size]
|
|
|
|
hidden_states = self.model(input_ids=input_ids,
|
|
positions=positions,
|
|
hidden_states=hidden_states)
|
|
forward_context = get_forward_context()
|
|
if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL and not self.use_sparse:
|
|
self._update_full_graph_params(forward_context,
|
|
num_input_tokens)
|
|
|
|
if self.enable_shared_expert_dp:
|
|
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
hidden_states.contiguous(), True)
|
|
positions = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
positions.contiguous(), True)
|
|
|
|
num_indices = last_token_indices.shape[0]
|
|
if lmhead_tp_enable():
|
|
max_num_reqs_across_dp = self.vllm_config.scheduler_config.max_num_seqs * self.runner.uniform_decode_query_len
|
|
last_token_indices = nn.functional.pad(
|
|
last_token_indices,
|
|
(0, max_num_reqs_across_dp - num_indices))
|
|
|
|
if self.pcp_size > 1 and step == 0:
|
|
# remove graph padding before all_gather
|
|
hidden_states = hidden_states[:num_tokens]
|
|
hidden_states = get_pcp_group().all_gather(hidden_states, 0)
|
|
hidden_states = torch.index_select(
|
|
hidden_states, 0, self.runner.pcp_manager.
|
|
pcp_allgather_restore_idx.gpu[: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]
|
|
last_token_indices = last_token_indices[: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 getattr(attn_metadata_i, "num_decode_tokens", 0):
|
|
attn_metadata_i.num_decode_tokens = batch_size
|
|
if self.pcp_size * self.dcp_size > 1:
|
|
positions = target_positions[ori_last_token_indices]
|
|
# For pcp/dcp, tokens are split across different cp ranks,
|
|
# so we can not simply update slot_mapping by += 1.
|
|
# Instead, we pre-allocate mtp slot_mapping in model_runner
|
|
# (_generate_pcp_mtp_input), and use updated slot_indices
|
|
# to get corresponding slot_mapping in each step.
|
|
num_reject_tokens = torch.tensor(
|
|
self.runner.pcp_manager.cu_num_tokens_pcp_full,
|
|
dtype=torch.int32).to(
|
|
self.device) - ori_last_token_indices - 1
|
|
num_accept_tokens = \
|
|
query_lens_d.to(self.device) - num_reject_tokens
|
|
ori_seq_len = attn_metadata_i.seq_lens
|
|
mtp_slot_mapping = self.runner.pcp_manager.mtp_slot_pad
|
|
|
|
# slot_mapping index base offset:
|
|
# scheduled tokens + pre-allocated mtp tokens + accepted tokens
|
|
slot_idx_base = (
|
|
torch.cat([
|
|
torch.tensor(
|
|
[0], dtype=torch.int32, device=self.device),
|
|
(torch.cumsum(query_lens_d, dim=0)[:-1] *
|
|
self.pcp_size).to(self.device)
|
|
]) +
|
|
torch.arange(num_decode_reqs, device=self.device) *
|
|
(self.num_speculative_tokens - 1) * self.pcp_size +
|
|
(num_accept_tokens - 1) * self.pcp_size)
|
|
slot_indices_list = []
|
|
for req_id in range(num_decode_reqs):
|
|
slot_indices_list.append(
|
|
torch.arange(slot_idx_base[req_id],
|
|
slot_idx_base[req_id] + self.pcp_size,
|
|
device=self.device))
|
|
slot_indices = torch.cat(slot_indices_list, dim=0)
|
|
|
|
# fold block_table (restore it to original size before flattened)
|
|
block_indices = torch.cat([
|
|
torch.tensor([0], dtype=torch.int32),
|
|
torch.cumsum(query_lens_d, dim=0)[:-1]
|
|
])
|
|
attn_metadata_i.decode.block_table[:batch_size] = \
|
|
attn_metadata_i.decode.block_table[block_indices]
|
|
attn_metadata_i.decode.block_table = \
|
|
attn_metadata_i.decode.block_table[:batch_size]
|
|
|
|
input_ids = draft_token_ids_list[-1].int()
|
|
positions += 1
|
|
|
|
decode_metadata = getattr(attn_metadata_i, "decode", None)
|
|
prefill_metadata = getattr(attn_metadata_i, "prefill", None)
|
|
# When disable_padded_drafter_batch=False, it should not to be updating these params, maybe.
|
|
if decode_metadata is not None and (self.speculative_config.disable_padded_drafter_batch or \
|
|
aclgraph_runtime_mode != CUDAGraphMode.FULL):
|
|
decode_metadata.actual_seq_lengths_q = self.arange_cpu[
|
|
1:batch_size + 1].tolist()
|
|
if aclgraph_runtime_mode == CUDAGraphMode.FULL:
|
|
decode_metadata.actual_seq_lengths_q = \
|
|
builder.pad_actual_seq_len_q_mtp_disable_pad(
|
|
graph_pad_size - batch_size,
|
|
batch_size,
|
|
decode_metadata.actual_seq_lengths_q)
|
|
decode_metadata.cos, decode_metadata.sin = get_cos_and_sin_mla(
|
|
positions[:batch_size])
|
|
# 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[:
|
|
batch_size] >= 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[:batch_size])
|
|
# Increment the sequence lengths.
|
|
# This is an out-of-place operation to avoid modifying the original tensor
|
|
# when enable async_scheduling.
|
|
attn_metadata_i.seq_lens = attn_metadata_i.seq_lens + 1
|
|
# For the requests that exceed the max model length, we set the
|
|
# sequence length to 1 to minimize their overheads in attention.
|
|
exceeds_mask = attn_metadata_i.seq_lens[:batch_size] > \
|
|
self.runner.model_config.max_model_len
|
|
attn_metadata_i.seq_lens[:batch_size].masked_fill_(exceeds_mask, 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
|
|
if self.pcp_size > 1:
|
|
exceeds_max_model_len = exceeds_max_model_len.repeat_interleave(
|
|
slot_mapping.size(0) // exceeds_max_model_len.size(0))
|
|
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
|
|
if self.pcp_size * self.dcp_size > 1:
|
|
# update local seq_len and batch_seq_mask
|
|
num_computed_tokens_of_pcp_dcp = self.runner.pcp_manager._get_cp_local_seq_lens(
|
|
ori_seq_len + step + 1,
|
|
self.pcp_size,
|
|
self.dcp_size,
|
|
self.runner.parallel_config.cp_kv_cache_interleave_size,
|
|
)
|
|
cp_seq_len = \
|
|
num_computed_tokens_of_pcp_dcp[:, self.pcp_rank, self.dcp_rank]
|
|
batch_seq_mask = (cp_seq_len == 0)
|
|
builder.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
|
|
batch_seq_mask, non_blocking=True)
|
|
batch_seq_mask = builder.batch_seq_mask_buf[:batch_seq_mask.
|
|
shape[0]]
|
|
cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len)
|
|
attn_metadata_i.decode.cp_seq_len = cp_seq_len
|
|
attn_metadata_i.decode.batch_seq_mask = batch_seq_mask
|
|
# update slot_mapping
|
|
slot_indices += self.pcp_size
|
|
slot_mapping = mtp_slot_mapping[slot_indices]
|
|
attn_metadata_i.slot_mapping[:batch_size *
|
|
self.pcp_size] = slot_mapping
|
|
else:
|
|
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 prefill_metadata is not None:
|
|
prefill_metadata.seq_lens = attn_metadata_i.seq_lens
|
|
prefill_metadata.seq_lens_list = prefill_metadata.seq_lens.tolist(
|
|
)
|
|
prefill_metadata.context_lens = attn_metadata_i.seq_lens
|
|
prefill_metadata.input_positions = self.positions[:
|
|
num_input_tokens]
|
|
prefill_metadata.max_seq_lens += 1
|
|
prefill_metadata.max_seq_lens = min(
|
|
prefill_metadata.max_seq_lens,
|
|
self.runner.model_config.max_model_len)
|
|
if decode_metadata is not None:
|
|
decode_metadata.seq_lens = attn_metadata_i.seq_lens
|
|
decode_metadata.seq_lens_list = decode_metadata.seq_lens.tolist(
|
|
)
|
|
decode_seq_lens_list = decode_metadata.seq_lens_list
|
|
if aclgraph_runtime_mode == CUDAGraphMode.FULL and \
|
|
self.speculative_config.disable_padded_drafter_batch:
|
|
decode_metadata.seq_lens_list = decode_seq_lens_list + [
|
|
0
|
|
] * (graph_pad_size - len(decode_seq_lens_list))
|
|
decode_metadata.input_positions = self.positions[:
|
|
num_input_tokens]
|
|
decode_metadata.max_seq_lens += 1
|
|
decode_metadata.max_seq_lens = min(
|
|
decode_metadata.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_cpu,
|
|
num_reqs=common_attn_metadata.num_reqs,
|
|
num_actual_tokens=total_num_tokens,
|
|
num_input_tokens=common_attn_metadata.num_input_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,
|
|
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,
|
|
max_seq_len=0)
|
|
|
|
query_start_loc = common_attn_metadata.query_start_loc[
|
|
1:1 + num_rejected_tokens_gpu.shape[0]]
|
|
token_indices_to_sample = query_start_loc - 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
|