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Commit f2218895 authored by zhuwenwen's avatar zhuwenwen
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[feat]支持mtp模型full_cuda_graph

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Showing with 16 additions and 601 deletions
vllm/v1/sample/rejection_sampler_mtp.py deleted 100644 → 0
View file @ 236f11df
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vllm/v1/spec_decode/eagle.py
View file @ f2218895
......@@ -98,7 +98,7 @@ class EagleProposer:
next_token_ids: torch.Tensor,
common_attn_metadata: CommonAttentionMetadata,
sampling_metadata: SamplingMetadata,
) -> tuple[torch.Tensor, torch.Tensor]:
) -> torch.Tensor:
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
......@@ -185,16 +185,13 @@ class EagleProposer:
last_hidden_states, hidden_states = ret_hidden_states
sample_hidden_states = last_hidden_states[last_token_indices]
logits = self.model.compute_logits(sample_hidden_states, None)
draft_token_ids = logits.argmax(dim=-1)
draft_prob = logits.softmax(dim=-1, dtype=torch.float32)
draft_probs_list = [draft_prob]
draft_token_ids = torch.argmax(logits, dim=-1)
# Early exit if there is only one draft token to be generated.
if self.num_speculative_tokens == 1:
# [batch_size, 1]
return draft_token_ids.view(-1, 1), draft_prob.view(-1, 1, draft_prob.shape[-1])
return draft_token_ids.view(-1, 1)
# TODO: Currently, MTP module released by deepseek only has
# one layer. Adapt this code to support multiple layers once
......@@ -345,15 +342,11 @@ class EagleProposer:
# TODO(wenlong): get more than one token for tree attention
draft_token_ids = logits.argmax(dim=-1)
draft_token_ids_list.append(draft_token_ids)
draft_prob = logits.softmax(dim=-1, dtype=torch.float32)
draft_probs_list.append(draft_prob)
# [batch_size, num_speculative_tokens]
draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
draft_probs = torch.stack(draft_probs_list, dim=1).contiguous()
return draft_token_ids, draft_probs
return draft_token_ids
def prepare_inputs(
self,
......
vllm/v1/spec_decode/utils.py
View file @ f2218895
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import msgspec
from abc import ABC
import torch
from vllm.sampling_params import SamplingParams
......@@ -16,41 +13,4 @@ def is_spec_decode_unsupported(sampling_params: SamplingParams) -> bool:
or sampling_params.repetition_penalty != 1.0
or sampling_params.min_p > _SAMPLING_EPS
or sampling_params.logprobs is not None)
class DraftProbs(ABC): # type: ignore[call-arg]
"""Draft probs corresponding to in-progress sequences."""
# spec tokens probs.
draft_probs: torch.Tensor
# The request id list.
_req_ids: list[str]
def __init__(self, draft_probs, req_ids):
assert len(req_ids) == len(draft_probs)
self.draft_probs = draft_probs
self._req_ids = req_ids
def update(self,
draft_probs: torch.Tensor,
tmp_req_ids: list[str]):
diff_req_ids = [item for item in self._req_ids if item not in tmp_req_ids]
index = [self._req_ids.index(req_id) for req_id in diff_req_ids]
self._req_ids = diff_req_ids
self.draft_probs = self.draft_probs[index]
self.draft_probs = torch.cat([self.draft_probs, draft_probs])
self._req_ids.extend(tmp_req_ids)
assert len(self._req_ids) == len(self.draft_probs)
def prune(self, req_ids: list[str]):
new_req_ids = [req_id for req_id in self._req_ids if req_id not in req_ids]
if new_req_ids != self._req_ids:
# Batch contents changed - prune removed sequences.
index = [self._req_ids.index(req_id) for req_id in new_req_ids]
self.draft_probs = self.draft_probs[index]
self._req_ids = new_req_ids
def get_probs(self, req_ids: list[str]):
index = [self._req_ids.index(req_id) for req_id in req_ids]
return self.draft_probs[index]
\ No newline at end of file
vllm/v1/worker/gpu_model_runner.py
View file @ f2218895
......@@ -60,13 +60,11 @@ from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
from vllm.v1.pool.metadata import PoolingMetadata
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import RejectionSampler
from vllm.v1.sample.rejection_sampler_mtp import MtpRejectionSampler
from vllm.v1.sample.sampler import Sampler
from vllm.v1.spec_decode.eagle import EagleProposer
from vllm.v1.spec_decode.medusa import MedusaProposer
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
from vllm.v1.spec_decode.utils import DraftProbs
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
from vllm.platforms import current_platform
......@@ -197,12 +195,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
raise ValueError("Unknown speculative decoding method: "
f"{self.speculative_config.method}")
self.use_mtp = self.speculative_config.method == "deepseek_mtp"
if not self.use_mtp:
self.rejection_sampler = RejectionSampler()
else:
self.rejection_sampler = MtpRejectionSampler()
self.rejection_sampler = RejectionSampler()
# Request states.
self.requests: dict[str, CachedRequestState] = {}
......@@ -328,8 +321,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# means this layer will perform attention using the keys and values
# from the KV cache of `shared_kv_cache_layers[layer_name]`.
self.shared_kv_cache_layers: dict[str, str] = {}
self.draft_probs : Optional[DraftProbs] = None
def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
"""
......@@ -398,10 +389,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# and handling the second as a new request.
for req_id in scheduler_output.finished_req_ids:
self.input_batch.remove_request(req_id)
# prune draft probs of finished requests
if self.use_mtp and self.draft_probs is not None and len(scheduler_output.finished_req_ids) > 0:
self.draft_probs.prune(list(scheduler_output.finished_req_ids))
# Free the cached encoder outputs.
for req_id, input_id in scheduler_output.free_encoder_input_ids:
......@@ -1556,8 +1543,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
target_logits = logits[spec_decode_metadata.target_logits_indices]
output_token_ids = self.rejection_sampler(
spec_decode_metadata,
self.draft_probs.get_probs(self.input_batch.req_ids) \
if self.draft_probs is not None else None, # draft_probs
None, # draft_probs
target_logits,
bonus_token_ids,
sampling_metadata,
......@@ -1643,7 +1629,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
spec_token_ids = None
else:
assert spec_decode_common_attn_metadata is not None
spec_token_ids, draft_probs = self.propose_draft_token_ids(
spec_token_ids = self.propose_draft_token_ids(
scheduler_output,
valid_sampled_token_ids,
sampling_metadata,
......@@ -1653,12 +1639,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
spec_decode_metadata,
spec_decode_common_attn_metadata,
)
if self.use_mtp:
if self.draft_probs is None:
self.draft_probs = DraftProbs(
draft_probs, self.input_batch.req_ids)
else:
self.draft_probs.update(draft_probs, self.input_batch.req_ids)
spec_token_ids = spec_token_ids.tolist()
......@@ -1687,8 +1667,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
aux_hidden_states: Optional[torch.Tensor],
spec_decode_metadata: Optional[SpecDecodeMetadata],
common_attn_metadata: CommonAttentionMetadata,
) -> tuple[list[list[int]], torch.Tensor]:
draft_probs = None
) -> list[list[int]]:
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
if self.speculative_config.method == "ngram":
assert isinstance(self.drafter, NgramProposer)
......@@ -1768,7 +1747,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
[h[token_indices] for h in aux_hidden_states], dim=-1)
else:
target_hidden_states = hidden_states[token_indices]
spec_token_ids, draft_probs = self.drafter.propose(
draft_token_ids = self.drafter.propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
......@@ -1777,8 +1756,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
common_attn_metadata=common_attn_metadata,
num_rejected_tokens=num_rejected_tokens
)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids, draft_probs
return spec_token_ids
@staticmethod
def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
......@@ -2292,10 +2272,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
draft_token_ids, self.device)
num_tokens = sum(len(ids) for ids in draft_token_ids)
draft_probs = torch.randn(
num_tokens, logits.shape[-1], device=self.device,
dtype=logits.dtype)
# draft_probs = None
# draft_probs = torch.randn(
# num_tokens, logits.shape[-1], device=self.device,
# dtype=logits.dtype)
draft_probs = None
target_logits = torch.randn(num_tokens,
logits.shape[-1],
......
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