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[Model Runner V2] Refactor prefill token preparation (#29712)

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon 2025-11-28 19:49:17 -08:00 committed by GitHub
parent 762a4a6ca9
commit ca1b1e7296
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GPG Key ID: B5690EEEBB952194
5 changed files with 83 additions and 78 deletions
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2
vllm/v1/worker/gpu/cudagraph_utils.py
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@ -78,7 +78,7 @@ class CudaGraphManager:
kv_cache_config: KVCacheConfig,
) -> None:
num_reqs = min(num_tokens, self.max_num_reqs)
input_ids = input_buffers.input_ids.gpu[:num_tokens]
input_ids = input_buffers.input_ids[:num_tokens]
positions = input_buffers.positions[:num_tokens]
attn_metadata = prepare_inputs_to_capture(
num_reqs,

86
vllm/v1/worker/gpu/input_batch.py
View File

@ -3,7 +3,6 @@
from dataclasses import dataclass
from typing import Any
import numba
import numpy as np
import torch
@ -30,15 +29,12 @@ class InputBuffers:
self.pin_memory = pin_memory
self.idx_mapping = self._make_buffer(max_num_reqs, dtype=torch.int32)
self.input_ids = self._make_buffer(max_num_tokens, dtype=torch.int32)
self.input_ids = torch.zeros(max_num_tokens, dtype=torch.int32, device=device)
self.positions = torch.zeros(max_num_tokens, dtype=torch.int64, device=device)
self.query_start_loc = self._make_buffer(max_num_reqs + 1, dtype=torch.int32)
self.seq_lens = torch.zeros(max_num_reqs, dtype=torch.int32, device=device)
self.cu_num_logits = self._make_buffer(max_num_reqs + 1, dtype=torch.int32)
# Spec decoding.
self.next_prefill_tokens = self._make_buffer(max_num_reqs, dtype=torch.int32)
# Structured outputs.
self.bitmask_indices = self._make_buffer(max_num_reqs, dtype=torch.int32)
self.grammar_bitmask = self._make_buffer(
@ -120,7 +116,7 @@ class InputBatch:
input_buffers.seq_lens[num_reqs:] = 0
seq_lens = input_buffers.seq_lens[:num_reqs]
input_ids = input_buffers.input_ids.copy_to_gpu(num_tokens)
input_ids = input_buffers.input_ids[:num_tokens]
positions = input_buffers.positions[:num_tokens]
# attn_metadata = defaultdict(lambda: None)
logits_indices = query_start_loc[1:] - 1
@ -146,41 +142,63 @@ class InputBatch:
)
@numba.njit(cache=True)
def _prepare_prefill_inputs(
idx_mapping: np.ndarray, # [B]
query_lens: np.ndarray, # [B]
query_start_loc: np.ndarray, # [B + 1]
prefill_token_ids: np.ndarray, # [N, max_model_len]
num_computed_prefill_tokens: np.ndarray, # [N]
input_ids: np.ndarray, # [num_input_tokens]
) -> None:
num_reqs = idx_mapping.shape[0]
query_starts = query_start_loc[:num_reqs]
query_ends = query_start_loc[1 : num_reqs + 1]
starts = num_computed_prefill_tokens[idx_mapping]
ends = starts + query_lens
for i in range(num_reqs):
input_ids[query_starts[i] : query_ends[i]] = prefill_token_ids[
idx_mapping[i], starts[i] : ends[i]
]
@triton.jit
def _prepare_prefill_inputs_kernel(
input_ids_ptr,
next_prefill_tokens_ptr,
idx_mapping_ptr,
query_start_loc_ptr,
prefill_token_ids_ptr,
prefill_token_ids_stride,
prefill_lens_ptr,
num_computed_tokens_ptr,
BLOCK_SIZE: tl.constexpr,
):
batch_idx = tl.program_id(0)
req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
prefill_len = tl.load(prefill_lens_ptr + req_state_idx)
num_computed = tl.load(num_computed_tokens_ptr + req_state_idx)
if num_computed >= prefill_len:
# Not prefill.
return
query_start = tl.load(query_start_loc_ptr + batch_idx)
query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
query_len = query_end - query_start
prefill_ptr = prefill_token_ids_ptr + req_state_idx * prefill_token_ids_stride
for i in range(0, query_len, BLOCK_SIZE):
block = i + tl.arange(0, BLOCK_SIZE)
mask = block < query_len
tokens = tl.load(prefill_ptr + num_computed + block, mask=mask)
tl.store(input_ids_ptr + query_start + block, tokens, mask=mask)
next_pos = num_computed + query_len
if next_pos < prefill_len:
next_token = tl.load(prefill_ptr + next_pos)
tl.store(next_prefill_tokens_ptr + req_state_idx, next_token)
def prepare_prefill_inputs(
idx_mapping: np.ndarray,
num_scheduled_tokens: np.ndarray,
query_start_loc: np.ndarray,
prefill_token_ids: np.ndarray,
num_computed_prefill_tokens: np.ndarray,
input_ids: np.ndarray,
input_ids: torch.Tensor,
next_prefill_tokens: torch.Tensor,
idx_mapping: torch.Tensor,
query_start_loc: torch.Tensor,
prefill_token_ids: torch.Tensor,
prefill_len: torch.Tensor,
num_computed_tokens: torch.Tensor,
) -> None:
_prepare_prefill_inputs(
num_reqs = idx_mapping.shape[0]
_prepare_prefill_inputs_kernel[(num_reqs,)](
input_ids,
next_prefill_tokens,
idx_mapping,
num_scheduled_tokens,
query_start_loc,
prefill_token_ids,
num_computed_prefill_tokens,
input_ids,
prefill_token_ids.stride(0),
prefill_len,
num_computed_tokens,
BLOCK_SIZE=1024,
)

46
vllm/v1/worker/gpu/model_runner.py
View File

@ -104,11 +104,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
if self.use_async_scheduling:
self.input_prep_event = torch.cuda.Event()
self.structured_outputs_event = torch.cuda.Event()
self.spec_decode_event = torch.cuda.Event()
else:
self.input_prep_event = None
self.structured_outputs_event = None
self.spec_decode_event = None
if self.speculative_config is not None:
self.do_spec_decode = True
@ -412,9 +410,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
cu_num_new_blocks[i].append(x + len(block_ids))
new_block_ids[i].extend(block_ids)
overwrite.append(True)
# Update the GPU tensors for request states.
if scheduler_output.scheduled_new_reqs:
self.req_states.prefill_len.copy_to_gpu()
# Add new blocks for the existing requests.
cached_reqs = scheduler_output.scheduled_cached_reqs
@ -507,16 +502,16 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
query_start_loc_cpu = self.input_buffers.query_start_loc.cpu[: num_reqs + 1]
query_start_loc_np = self.input_buffers.query_start_loc.np[: num_reqs + 1]
# Copy prefill tokens from CPU to GPU.
# Get prefill tokens.
prepare_prefill_inputs(
idx_mapping_np,
num_scheduled_tokens,
query_start_loc_np,
self.req_states.prefill_token_ids.np,
self.req_states.num_computed_prefill_tokens,
self.input_buffers.input_ids.np,
self.input_buffers.input_ids,
self.req_states.next_prefill_tokens,
idx_mapping,
query_start_loc_gpu,
self.req_states.prefill_token_ids.gpu,
self.req_states.prefill_len.gpu,
self.req_states.num_computed_tokens,
)
self.input_buffers.input_ids.copy_to_gpu(num_tokens)
# Prepare positions and seq_lens.
prepare_pos_seq_lens(
@ -531,7 +526,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
# Some input token ids are directly read from the last sampled tokens
# and draft tokens. Also, get the logits indices to sample tokens from.
logits_indices = combine_sampled_and_draft_tokens(
self.input_buffers.input_ids.gpu,
self.input_buffers.input_ids,
idx_mapping,
self.req_states.last_sampled_tokens,
query_start_loc_gpu,
@ -572,7 +567,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
kv_cache_config=self.kv_cache_config,
)
input_ids = self.input_buffers.input_ids.gpu[:num_tokens_after_padding]
input_ids = self.input_buffers.input_ids[:num_tokens_after_padding]
positions = self.input_buffers.positions[:num_tokens_after_padding]
return InputBatch(
req_ids=req_ids,
@ -782,20 +777,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
num_sampled: torch.Tensor,
num_rejected: torch.Tensor,
) -> torch.Tensor:
num_reqs = input_batch.num_reqs
idx_mapping_np = input_batch.idx_mapping_np
with async_barrier(self.spec_decode_event):
self.input_buffers.next_prefill_tokens.np[:num_reqs] = (
self.req_states.prefill_token_ids.np[
idx_mapping_np,
self.req_states.num_computed_prefill_tokens[idx_mapping_np],
]
)
next_prefill_tokens = self.input_buffers.next_prefill_tokens.copy_to_gpu(
num_reqs
)
assert self.speculator is not None
last_sampled_tokens = self.req_states.last_sampled_tokens[
input_batch.idx_mapping
]
next_prefill_tokens = self.req_states.next_prefill_tokens[
input_batch.idx_mapping
]
draft_tokens = self.speculator.propose(
input_batch,
sampling_metadata,
@ -803,7 +791,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
aux_hidden_states,
num_sampled,
num_rejected,
self.req_states.last_sampled_tokens,
last_sampled_tokens,
next_prefill_tokens,
)
return draft_tokens

19
vllm/v1/worker/gpu/spec_decode/eagle.py
View File

@ -121,7 +121,7 @@ class EagleSpeculator:
num_tokens_across_dp=num_tokens_across_dp,
):
ret_hidden_states = self.model(
input_ids=self.input_buffers.input_ids.gpu[:num_tokens],
input_ids=self.input_buffers.input_ids[:num_tokens],
positions=self.input_buffers.positions[:num_tokens],
hidden_states=self.hidden_states[:num_tokens],
)
@ -194,7 +194,7 @@ class EagleSpeculator:
num_sampled: torch.Tensor,
# [num_reqs]
num_rejected: torch.Tensor,
# [max_num_reqs, 1]
# [num_reqs]
last_sampled: torch.Tensor,
# [num_reqs]
next_prefill_tokens: torch.Tensor,
@ -316,7 +316,6 @@ def _prepare_eagle_inputs_kernel(
eagle_positions_ptr,
target_input_ids_ptr,
target_positions_ptr,
idx_mapping_ptr,
last_sampled_ptr,
next_prefill_tokens_ptr,
num_sampled_ptr,
@ -335,8 +334,7 @@ def _prepare_eagle_inputs_kernel(
num_sampled = tl.load(num_sampled_ptr + batch_idx)
if num_sampled > 0:
req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
next_token = tl.load(last_sampled_ptr + req_state_idx).to(tl.int32)
next_token = tl.load(last_sampled_ptr + batch_idx).to(tl.int32)
else:
# Chunked prefilling.
# Get the next prefill token.
@ -368,9 +366,9 @@ def prepare_eagle_inputs(
num_sampled: torch.Tensor,
# [num_reqs]
num_rejected: torch.Tensor,
# [max_num_reqs, 1]
# [num_reqs]
last_sampled: torch.Tensor,
# [max_num_reqs]
# [num_reqs]
next_prefill_tokens: torch.Tensor,
) -> torch.Tensor:
num_reqs = input_batch.num_reqs
@ -381,11 +379,10 @@ def prepare_eagle_inputs(
)
_prepare_eagle_inputs_kernel[(num_reqs,)](
last_token_indices,
input_buffers.input_ids.gpu,
input_buffers.input_ids,
input_buffers.positions,
input_batch.input_ids,
input_batch.positions,
input_batch.idx_mapping,
last_sampled,
next_prefill_tokens,
num_sampled,
@ -485,7 +482,7 @@ def prepare_eagle_decode(
last_token_indices,
target_seq_lens,
num_rejected,
input_buffers.input_ids.gpu,
input_buffers.input_ids,
input_buffers.positions,
input_hidden_states,
input_hidden_states.stride(0),
@ -553,7 +550,7 @@ def update_eagle_inputs(
):
num_reqs, hidden_size = output_hidden_states.shape
_update_eagle_inputs_kernel[(num_reqs,)](
input_buffers.input_ids.gpu,
input_buffers.input_ids,
input_buffers.positions,
hidden_states,
hidden_states.stride(0),

8
vllm/v1/worker/gpu/states.py
View File

@ -117,8 +117,7 @@ class RequestState:
self.prefill_token_ids = UvaBuffer(
self.max_num_reqs, self.max_model_len, dtype=torch.int32
)
self.prefill_len = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
self.prefill_len = UvaBuffer(self.max_num_reqs, dtype=torch.int32)
# Number of computed tokens.
self.num_computed_prefill_tokens = np.zeros(self.max_num_reqs, dtype=np.int32)
self.num_computed_tokens = torch.zeros(
@ -140,6 +139,9 @@ class RequestState:
dtype=torch.int64,
device=device,
)
self.next_prefill_tokens = torch.zeros(
self.max_num_reqs, dtype=torch.int32, device=device
)
# LoRA.
self.lora_ids = np.zeros(self.max_num_reqs, dtype=np.int32)
@ -380,13 +382,13 @@ def _expand_sampling_metadata_kernel(
expanded_top_p_ptr,
top_k_ptr,
expanded_top_k_ptr,
seeds_ptr,
rep_penalty_ptr,
expanded_rep_penalty_ptr,
freq_penalty_ptr,
expanded_freq_penalty_ptr,
pres_penalty_ptr,
expanded_pres_penalty_ptr,
seeds_ptr,
expanded_seeds_ptr,
cu_num_logits_ptr,
BLOCK_SIZE: tl.constexpr,