e9bb4491ec
### What this PR does / why we need it?
This PR fixes deepseek v3.2 mtp bug.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
All existed ci tests should pass.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
887 lines
42 KiB
Python
887 lines
42 KiB
Python
from typing import Optional
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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
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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_mtp import DeepSeekMTP
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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 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.utils import (AscendCommonAttentionMetadata,
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AscendPrefillContextParallelMetadata)
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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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vllm_version_is)
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if vllm_version_is("0.11.0"):
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from vllm.model_executor.model_loader.utils import set_default_torch_dtype
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from vllm.utils import is_pin_memory_available
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else:
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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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class MtpProposer(Proposer):
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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.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.model = DeepSeekMTP(
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vllm_config=self.vllm_config).to(target_device)
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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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@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(
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num_tokens, with_prefill)
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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 _ in range(self.num_speculative_tokens):
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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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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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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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if attn_metadata is not None and isinstance(attn_metadata, dict):
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attn_metadata = attn_metadata['model.layers.0.self_attn.attn']
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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
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)
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is_prefill = len(scheduler_output.scheduled_new_reqs) > 0
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req_scheduled_tokens = scheduler_output.num_scheduled_tokens
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long_seq_metadata: AscendPrefillContextParallelMetadata = \
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self.runner.long_seq_metadata if self.pcp_size > 1 else None
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if spec_decode_metadata is None:
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# update pcp related params
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if self.pcp_size > 1 and is_prefill:
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token_indices_to_sample = None
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target_token_ids = self.runner.input_ids_pcp_full[:
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num_scheduled_tokens]
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target_positions = positions[:num_scheduled_tokens]
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target_hidden_states = hidden_states
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else:
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token_indices_to_sample = None
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# input_ids can be None for multimodal models.
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target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
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target_positions = positions[:num_scheduled_tokens]
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target_hidden_states = hidden_states[:num_scheduled_tokens]
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else:
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if self.speculative_config.disable_padded_drafter_batch:
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token_indices_to_sample = None
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common_attn_metadata, token_indices =\
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self._prepare_inputs(
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common_attn_metadata,
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sampled_token_ids,
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spec_decode_metadata.num_draft_tokens)
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else:
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common_attn_metadata, token_indices, \
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token_indices_to_sample =\
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self.prepare_inputs_padded(
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common_attn_metadata,
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spec_decode_metadata,
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valid_sampled_tokens_count)
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target_token_ids = self.runner.input_ids[token_indices]
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target_positions = positions[token_indices]
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target_hidden_states = hidden_states[token_indices]
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draft_token_ids = self._propose(
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target_token_ids=target_token_ids,
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target_positions=target_positions,
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target_hidden_states=target_hidden_states,
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next_token_ids=next_token_ids,
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last_token_indices=token_indices_to_sample,
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common_attn_metadata=common_attn_metadata,
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sampling_metadata=sampling_metadata,
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is_prefill=is_prefill,
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req_scheduled_tokens=req_scheduled_tokens,
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long_seq_metadata=long_seq_metadata,
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)
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return draft_token_ids
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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_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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new_seq_lens_cpu = common_attn_metadata.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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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.
|
|
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[token_indices],
|
|
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,
|
|
is_prefill=False,
|
|
req_scheduled_tokens=None,
|
|
long_seq_metadata=None,
|
|
) -> 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 and is_prefill:
|
|
num_tokens, input_ids, target_hidden_states, max_query_len, seq_lens, cu_num_tokens = \
|
|
self._split_pcp_input(req_scheduled_tokens, num_tokens, target_hidden_states)
|
|
# graph mode padding not considered now
|
|
num_input_tokens = num_tokens
|
|
self.input_ids[:num_input_tokens].copy_(input_ids)
|
|
common_attn_metadata.prefill_context_parallel_metadata = long_seq_metadata
|
|
common_attn_metadata.num_actual_tokens = num_tokens
|
|
common_attn_metadata.max_query_len = max_query_len
|
|
common_attn_metadata.seq_lens_cpu = seq_lens.cpu()
|
|
common_attn_metadata.query_start_loc = \
|
|
cu_num_tokens[:batch_size + 1]
|
|
common_attn_metadata.query_start_loc_cpu = \
|
|
cu_num_tokens[:batch_size + 1].cpu()
|
|
|
|
assert self.runner is not None
|
|
|
|
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
|
|
|
|
if 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, with_prefill)
|
|
batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
|
|
uniform_decode=False)
|
|
aclgraph_runtime_mode, batch_descriptor = \
|
|
self.runner.aclgraph_dispatcher.dispatch(batch_descriptor)
|
|
if aclgraph_runtime_mode not in [
|
|
CUDAGraphMode.PIECEWISE, CUDAGraphMode.NONE
|
|
]:
|
|
# Fallback to piecewise graph, when acl full graph is enabled
|
|
logger.debug(
|
|
"Currently the eagle proposer only supports cudagraph_mode "
|
|
f"PIECEWISE, and is forced to set graph mode from {aclgraph_runtime_mode} "
|
|
"to CUDAGraphMode.PIECEWISE")
|
|
aclgraph_runtime_mode = CUDAGraphMode.PIECEWISE
|
|
|
|
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):
|
|
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])
|
|
|
|
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))
|
|
|
|
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
|
|
|
|
attn_metadata_i.decode.actual_seq_lengths_q = attn_metadata_i.query_start_loc[
|
|
1:batch_size + 1].tolist()
|
|
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 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(
|
|
)
|
|
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]
|
|
|
|
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,
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|
num_computed_tokens_cpu=common_attn_metadata.
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|
num_computed_tokens_cpu,
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|
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, num_tokens,
|
|
target_hidden_states):
|
|
"""
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|
Split input_ids and target_hidden_states in pcp group.
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|
1. input_ids padding: [t0, t1, t2, t3, t4, t5] -> [t0, t1, t2, t3, t4, t5, pad, pad]
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|
2. split input_ids: pcp0 [t0, t1, pad, pad], pcp1 [t2, t3, t4, t5]
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|
3. split target_hidden_states (already include cp padding):
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|
[h0, h1, h2, h3, h4, h5, pad, pad] -> pcp0 [h0, h1, pad, pad], pcp1 [h2, h3, h4, h5]
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|
4. also update max_query_len, seq_lens, cu_num_tokens according to pcp split.
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|
"""
|
|
|
|
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 = []
|
|
input_ids_list = self.input_ids[:num_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_list[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
|