35ad11b637
RFC: https://github.com/vllm-project/vllm-ascend/issues/4629
Reason:
The metadata data class contains an excessive number of variables. We
will inherit the metadata of the community and simultaneously remove
some variables that are no longer needed at present.
Todo:
1. remove attn_state partly.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
783 lines
37 KiB
Python
783 lines
37 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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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from vllm.attention.layer import Attention
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from vllm.config import (CompilationMode, CUDAGraphMode, VllmConfig,
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get_layers_from_vllm_config)
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.logger import 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
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from vllm.model_executor.models import supports_multimodal
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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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.utils import CpuGpuBuffer
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
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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.ops.rotary_embedding import update_cos_sin
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from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
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PADDING_SLOT_ID = -1
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_DEFAULT_FIRST_LAYER = 'model.layers.0.self_attn.attn'
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_FIRST_LAYERS = {"Qwen3NextForCausalLM": 'model.layers.3.self_attn.attn'}
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class EagleProposer(Proposer):
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def __init__(self,
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vllm_config: VllmConfig,
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device: torch.device,
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runner=None):
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self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3
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self.vllm_config = vllm_config
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self.device = device
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self.runner = runner
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self.block_size = vllm_config.cache_config.block_size
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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 = vllm_config.speculative_config.draft_model_config.get_hidden_size(
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)
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self.use_cuda_graph = (self.vllm_config.compilation_config.mode
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== CompilationMode.VLLM_COMPILE and
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not self.vllm_config.model_config.enforce_eager)
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self.cudagraph_batch_sizes = list(
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sorted(
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self.vllm_config.compilation_config.cudagraph_capture_sizes))
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max_batch_size = vllm_config.scheduler_config.max_num_seqs
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# Currently we do not use pcp. This is used to adapt the pcp branch.
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self.pcp_size = 0
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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.decode_threshold = 1 + \
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self.vllm_config.speculative_config.num_speculative_tokens
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# persistent buffers for cuda graph
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self.input_ids = torch.zeros(
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self.vllm_config.scheduler_config.max_num_batched_tokens,
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dtype=torch.int32,
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device=device)
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self.positions = torch.zeros(
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self.vllm_config.scheduler_config.max_num_batched_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.vllm_config.scheduler_config.max_num_batched_tokens,
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self.hidden_size),
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dtype=self.vllm_config.model_config.dtype,
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device=device)
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self.max_num_tokens = (
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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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max_num_slots_for_arange = max(self.max_num_tokens, max_batch_size + 1)
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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.arange_cpu = torch.arange(max_num_slots_for_arange,
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device="cpu",
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dtype=torch.int32)
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self.attn_mask_builder = AttentionMaskBuilder(self.device)
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def load_model(self, model: nn.Module) -> None:
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target_attn_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config, Attention).keys())
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self.model = get_model(vllm_config=self.vllm_config,
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model_config=self.vllm_config.
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speculative_config.draft_model_config)
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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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self.attn_layer_name = next(iter(draft_attn_layer_names))
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# share embed_tokens with the target model if needed
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if get_pp_group().world_size == 1:
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logger.info(
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"The EAGLE head shares the same vocab embedding" \
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" with the target model."
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)
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self.model.model.embed_tokens = model.model.embed_tokens
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else:
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logger.info(
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"Since PP > 1, the EAGLE head loaded its own vocab embedding" \
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" weights instead of sharing them with the target model."
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)
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# share lm_head with the target model if needed
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# some model definition do not define lm_head explicitly
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# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
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if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
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logger.info("Loading EAGLE LM head weights from the target model.")
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if supports_multimodal(model):
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self.model.lm_head = model.get_language_model().lm_head
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else:
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self.model.lm_head = model.lm_head
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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: Optional[torch.Tensor] = 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):
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# update global cos, sin
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update_cos_sin(self.positions[:num_tokens])
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with set_ascend_forward_context(None,
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self.vllm_config,
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num_tokens=num_tokens):
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self.model(
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input_ids=self.input_ids[:num_tokens],
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positions=self.positions[:num_tokens],
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hidden_states=self.hidden_states[:num_tokens],
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)
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dummy_compute_logits(self.hidden_states)
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def generate_token_ids(self,
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sampled_token_ids: torch.Tensor | 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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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 self.vllm_config.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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self._copy_valid_sampled_token_count(next_token_ids,
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valid_sampled_tokens_count)
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req_scheduled_tokens = scheduler_output.num_scheduled_tokens
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if self.pcp_size > 1:
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long_seq_metadata = self.runner.long_seq_metadata
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input_ids_pcp_full = self.runner.input_ids_pcp_full
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query_start_loc_pcp_full = self.runner.query_start_loc_pcp_full
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query_start_loc_pcp_full_cpu = self.runner.query_start_loc_pcp_full_cpu
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num_reqs = self.runner.input_batch.num_reqs
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ori_query_lens = query_start_loc_pcp_full_cpu[1:num_reqs+1] - \
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query_start_loc_pcp_full_cpu[:num_reqs]
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num_prefill_reqs = (ori_query_lens
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> self.decode_threshold).sum().item()
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num_decode_reqs = num_reqs - num_prefill_reqs
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else:
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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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if spec_decode_metadata is None:
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# update pcp related params
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if self.pcp_size > 1:
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token_indices_to_sample = \
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query_start_loc_pcp_full_cpu[1:num_reqs + 1] - 1
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target_token_ids = input_ids_pcp_full[:num_scheduled_tokens]
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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.gpu[:
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num_scheduled_tokens]
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target_positions = positions[:num_scheduled_tokens]
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if self.name == SpecDcodeType.EAGLE3:
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target_hidden_states = torch.cat(
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[h[:num_scheduled_tokens] for h in aux_hidden_states],
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dim=-1)
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else:
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target_hidden_states = hidden_states[:num_scheduled_tokens]
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else:
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if self.pcp_size > 1:
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common_attn_metadata.query_start_loc_cpu = \
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query_start_loc_pcp_full_cpu[:num_reqs + 1]
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common_attn_metadata.query_start_loc = \
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query_start_loc_pcp_full[:num_reqs + 1]
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if self.vllm_config.speculative_config.disable_padded_drafter_batch:
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# NOTE: Currently, MTP-fullgraph is incompatibility with pcp
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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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if self.pcp_size > 1:
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target_token_ids = input_ids_pcp_full[token_indices]
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target_positions = positions
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target_hidden_states = hidden_states
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else:
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target_token_ids = self.runner.input_ids.gpu[token_indices]
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target_positions = positions[token_indices]
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if self.name == SpecDcodeType.EAGLE3:
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target_hidden_states = torch.cat(
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[h[token_indices] for h in aux_hidden_states], dim=-1)
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else:
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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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req_scheduled_tokens=req_scheduled_tokens,
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long_seq_metadata=long_seq_metadata,
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num_prefill_reqs=num_prefill_reqs,
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num_decode_reqs=num_decode_reqs,
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scheduler_output=scheduler_output,
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num_scheduled_tokens=num_scheduled_tokens,
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)
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return draft_token_ids
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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.name == SpecDcodeType.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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if self.use_cuda_graph and \
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num_tokens <= self.cudagraph_batch_sizes[-1]:
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num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
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else:
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num_input_tokens = num_tokens
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# copy inputs to buffer for cudagraph
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self.positions[:num_tokens] = target_positions
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self.hidden_states[:num_tokens] = target_hidden_states
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# FIXME(woosuk): The below two ops cause synchronization. Optimize.
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builder = self.runner.attn_groups[0][0].get_metadata_builder()
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attn_metadata = builder.build(0, common_attn_metadata,
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self.runner.get_model())
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# update global cos, sin
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update_cos_sin(self.positions[:num_input_tokens])
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with set_ascend_forward_context(attn_metadata,
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self.vllm_config,
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num_tokens=num_input_tokens):
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last_hidden_states, hidden_states = self.model(
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input_ids=self.input_ids[:num_input_tokens],
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positions=self.positions[:num_input_tokens],
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hidden_states=self.hidden_states[:num_input_tokens],
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)
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sample_hidden_states = last_hidden_states[last_token_indices]
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logits = self.model.compute_logits(sample_hidden_states)
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draft_token_ids = logits.argmax(dim=-1)
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# Early exit if there is only one draft token to be generated.
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if self.vllm_config.speculative_config.num_speculative_tokens == 1:
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# [batch_size, 1]
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return draft_token_ids.view(-1, 1)
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# Generate the remaining draft tokens.
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draft_token_ids_tensor = torch.zeros(
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(self.vllm_config.speculative_config.num_speculative_tokens,
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*draft_token_ids.shape),
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dtype=draft_token_ids.dtype,
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device=self.device)
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draft_token_ids_tensor[0] = draft_token_ids
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positions = target_positions[last_token_indices]
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hidden_states = hidden_states[last_token_indices]
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last_token_indices = self.arange[:batch_size]
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if self.use_cuda_graph and \
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batch_size <= self.cudagraph_batch_sizes[-1]:
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input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
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else:
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input_batch_size = batch_size
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attn_metadata.num_actual_tokens = batch_size
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attn_metadata.max_query_len = 1
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attn_metadata.query_start_loc = self.arange_cpu[:batch_size + 1]
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attn_metadata.num_decodes, attn_metadata.num_prefills, attn_metadata.num_decode_tokens, attn_metadata.num_prefill_tokens = 0, batch_size, 0, batch_size
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attn_metadata.num_actual_tokens_pcp_padded = attn_metadata.num_decode_tokens + attn_metadata.num_prefill_tokens
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attn_metadata.actual_seq_lengths_q = attn_metadata.query_start_loc[
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1:].tolist()
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attn_metadata.seq_lens_list = attn_metadata.seq_lens.tolist()
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attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
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for now_speculative in range(
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self.vllm_config.speculative_config.num_speculative_tokens -
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1):
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# Update the inputs.
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# cast to int32 is crucial when eagle model is compiled.
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# tensor.argmax() returns int64 by default.
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input_ids = draft_token_ids_tensor[now_speculative]
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positions += 1
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# NOTE(woosuk): We should handle the case where the draft model
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# generates tokens beyond the max model length. Since it is complex
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# to remove such requests from the batch, we keep them in the batch
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# but adjust the position ids and slot mappings to avoid the
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# out-of-range access during the model execution. The draft tokens
|
|
# generated with this adjustment should be ignored.
|
|
exceeds_max_model_len = positions >= self.vllm_config.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)
|
|
|
|
# TODO: Increment the sequence lengths.
|
|
|
|
attn_metadata.seq_lens = attn_metadata.seq_lens + 1
|
|
attn_metadata.seq_lens_list = [
|
|
_ + 1 for _ in attn_metadata.seq_lens_list
|
|
]
|
|
# TODO: Consider max model length.
|
|
# attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
|
|
# self.max_model_len)
|
|
# For the requests that exceed the max model length, we set the
|
|
# TODO: sequence length to 1 to minimize their overheads in attention.
|
|
|
|
# Compute the slot mapping.
|
|
block_numbers = (clamped_positions // self.block_size)
|
|
block_ids = attn_metadata.block_tables.gather(
|
|
dim=1, index=block_numbers.view(-1, 1))
|
|
block_ids = block_ids.view(-1)
|
|
slot_mapping_tmp = (
|
|
block_ids * self.vllm_config.cache_config.block_size +
|
|
clamped_positions % self.block_size)
|
|
|
|
# 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_tmp.masked_fill_(exceeds_max_model_len,
|
|
PADDING_SLOT_ID)
|
|
# NOTE: ASCEND slot_mapping must on cpu
|
|
attn_metadata.slot_mapping[:slot_mapping_tmp.shape[0]].copy_(
|
|
slot_mapping_tmp.to(torch.int32))
|
|
# copy inputs to buffer for cudagraph
|
|
self.input_ids[:batch_size] = input_ids
|
|
self.positions[:batch_size] = clamped_positions
|
|
self.hidden_states[:batch_size] = hidden_states
|
|
attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask()
|
|
|
|
attn_metadata.attn_mask = attn_mask
|
|
# Run the model.
|
|
|
|
# update global cos, sin
|
|
update_cos_sin(self.positions[:input_batch_size])
|
|
|
|
with set_ascend_forward_context(attn_metadata,
|
|
self.vllm_config,
|
|
num_tokens=input_batch_size):
|
|
|
|
last_hidden_states, hidden_states = self.model(
|
|
input_ids=self.input_ids[:input_batch_size],
|
|
positions=self.positions[:input_batch_size],
|
|
hidden_states=self.hidden_states[:input_batch_size],
|
|
)
|
|
hidden_states = hidden_states[:batch_size]
|
|
logits = self.model.compute_logits(last_hidden_states[:batch_size])
|
|
|
|
# TODO(wenlong): get more than one token for tree attention
|
|
draft_token_ids = logits.argmax(dim=-1)
|
|
draft_token_ids_tensor[now_speculative + 1] = draft_token_ids
|
|
|
|
# [batch_size, num_speculative_tokens]
|
|
draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
|
|
return draft_token_ids
|
|
|
|
def _get_attn_metadata(self, attn_metadata):
|
|
if attn_metadata is not None and isinstance(attn_metadata, dict):
|
|
architecture = self.vllm_config.model_config.architecture
|
|
layer_name = _FIRST_LAYERS.get(architecture, _DEFAULT_FIRST_LAYER)
|
|
attn_metadata = attn_metadata[layer_name]
|
|
|
|
return attn_metadata
|
|
|
|
def prepare_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 _copy_valid_sampled_token_count(
|
|
self, next_token_ids: torch.Tensor,
|
|
valid_sampled_tokens_count: torch.Tensor) -> None:
|
|
if self.runner.valid_sampled_token_count_event is not None:
|
|
default_stream = torch.npu.current_stream()
|
|
# initialize a new stream to overlap the copy operation with
|
|
# prepare_input of draft model.
|
|
with torch.npu.stream(
|
|
self.runner.valid_sampled_token_count_copy_stream):
|
|
self.runner.valid_sampled_token_count_copy_stream.wait_stream(
|
|
default_stream) # type: ignore
|
|
self.runner.valid_sampled_token_count_cpu[:
|
|
valid_sampled_tokens_count
|
|
.shape[0]].copy_(
|
|
valid_sampled_tokens_count,
|
|
non_blocking=True
|
|
)
|
|
self.runner.valid_sampled_token_count_event.record()
|
|
|
|
self.runner.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(
|
|
1)
|
|
|
|
def prepare_inputs(
|
|
self,
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
sampled_token_ids: list[list[int]],
|
|
num_draft_tokens: list[int],
|
|
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
|
|
"""
|
|
This function is used to prepare the inputs for speculative decoding.
|
|
It updates to the common_attn_metadata to account for the rejected
|
|
tokens (and newly sampled tokens). It also returns the token indices
|
|
of the tokens that should be fed to the speculator.
|
|
"""
|
|
# E.g.
|
|
# common_attn_metadata.query_start_loc{_cpu}:
|
|
# [0, q1, q1 + q2, q1 + q2 + q3]
|
|
# common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
|
|
# num_rejected_tokens: [n1, n2, n3]
|
|
# This function computes the intermediate values:
|
|
# num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
|
|
# And returns:
|
|
# common_attn_metadata.query_start_loc{_cpu}:
|
|
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
|
# common_attn_metadata.seq_lens{_cpu}:
|
|
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
|
|
# token_indices: [0, 1, ..., q1 - n1 - 1,
|
|
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
|
|
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
|
|
|
|
num_actual_reqs = len(num_draft_tokens)
|
|
num_rejected_tokens = [
|
|
n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
|
|
for i, n in enumerate(num_draft_tokens)
|
|
]
|
|
num_rejected_tokens = torch.tensor(num_rejected_tokens,
|
|
dtype=torch.int32)
|
|
|
|
device = common_attn_metadata.query_start_loc.device
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
|
|
num_actual_reqs
|
|
+ 1]
|
|
seq_lens_cpu = common_attn_metadata.seq_lens_cpu[:num_actual_reqs]
|
|
new_seq_lens_cpu = seq_lens_cpu - num_rejected_tokens
|
|
|
|
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
|
|
new_query_len_per_req = query_start_loc_cpu[
|
|
1:] - query_start_loc_cpu[:-1]
|
|
# [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
|
|
new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
|
|
new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
|
|
|
|
# [q1 - n1, q2 - n2, q3 - n3] ->
|
|
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
|
new_query_start_loc_cpu = torch.zeros(
|
|
query_start_loc_cpu.shape,
|
|
dtype=torch.int32,
|
|
pin_memory=is_pin_memory_available(),
|
|
)
|
|
new_query_start_loc_np = new_query_start_loc_cpu.numpy()
|
|
np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
|
|
|
|
total_num_tokens = new_query_start_loc_np[-1]
|
|
# Example assuming num_tokens_per_req_np = [2, 4, 3]
|
|
# this implies that `new_query_start_locs` is:
|
|
# [0, 2, 6, 9] ->
|
|
# [0, 0, 2, 2, 2, 2, 6, 6, 6]
|
|
# _r1_ ____r2____ ___r3__
|
|
new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
|
|
new_num_tokens_per_req_np)
|
|
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
|
|
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
|
|
# _r1_ ____r2____ ___r3__
|
|
token_offests = (self.token_arange_np[:total_num_tokens] -
|
|
new_query_start_locs_expanded)
|
|
|
|
# Expand starting positions to match token pattern
|
|
# [0, q1, q1 + q2] ->
|
|
# [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
|
|
# _r1_ _____r2_______ ___________r3____________
|
|
old_query_start_locs_expanded = np.repeat(
|
|
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
|
|
# Final token indices are:
|
|
# [0, 1, // req 1
|
|
# q1 + 0, q1 + 1, q1 + 2, q1 + 3, // req 2
|
|
# q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
|
|
token_indices_np = token_offests + old_query_start_locs_expanded
|
|
token_indices = torch.from_numpy(token_indices_np).to(
|
|
device, non_blocking=True)
|
|
|
|
common_attn_metadata.slot_mapping[:token_indices.shape[0]].copy_(
|
|
common_attn_metadata.slot_mapping[token_indices])
|
|
common_attn_metadata.slot_mapping[token_indices.shape[0]:].fill_(-1)
|
|
|
|
# NOTE: Currently positions and seq_lens are not used in mla_v1 forward
|
|
# so we do not need to fixed them. But if they are used in the future,
|
|
# we should fixed them.
|
|
spec_common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=new_query_start_loc_cpu.to(device,
|
|
non_blocking=True),
|
|
query_start_loc_cpu=new_query_start_loc_cpu,
|
|
seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
|
|
seq_lens_cpu=new_seq_lens_cpu,
|
|
num_computed_tokens_cpu=common_attn_metadata.
|
|
num_computed_tokens_cpu,
|
|
num_reqs=common_attn_metadata.num_reqs,
|
|
num_actual_tokens=total_num_tokens,
|
|
num_input_tokens=common_attn_metadata.num_input_tokens,
|
|
max_query_len=new_query_len_per_req.max().item(),
|
|
block_table_tensor=common_attn_metadata.block_table_tensor,
|
|
slot_mapping=common_attn_metadata.slot_mapping,
|
|
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
|
positions=common_attn_metadata.positions[token_indices],
|
|
attn_mask=self.runner.attn_mask,
|
|
spec_attn_mask=self.runner.spec_attn_mask,
|
|
attn_state=self.runner.attn_state,
|
|
decode_token_per_req=self.runner.decode_token_per_req,
|
|
)
|
|
return spec_common_attn_metadata, token_indices
|
|
|
|
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)
|
|
|
|
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
|