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xiezhongtao
2026-01-19 10:38:50 +08:00
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vllm/v1/worker/gpu/input_batch.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Any
import numpy as np
import torch
from vllm.triton_utils import tl, triton
from vllm.utils import random_uuid
from vllm.utils.math_utils import cdiv
from vllm.v1.utils import CpuGpuBuffer
class InputBuffers:
def __init__(
self,
max_num_reqs: int,
max_num_tokens: int,
inputs_embeds_size: int,
vocab_size: int,
dtype: torch.dtype,
device: torch.device,
pin_memory: bool,
):
self.max_num_reqs = max_num_reqs
self.max_num_tokens = max_num_tokens
self.device = device
self.pin_memory = pin_memory
self.idx_mapping = self._make_buffer(max_num_reqs, 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)
# Structured outputs.
self.bitmask_indices = self._make_buffer(max_num_reqs, dtype=torch.int32)
self.grammar_bitmask = self._make_buffer(
max_num_reqs, cdiv(vocab_size, 32), dtype=torch.int32
)
def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
return CpuGpuBuffer(
*args, dtype=dtype, pin_memory=self.pin_memory, device=self.device
)
@dataclass
class InputBatch:
# batch_idx -> req_id
req_ids: list[str]
num_reqs: int
# batch_idx -> req_state_idx
idx_mapping: torch.Tensor
idx_mapping_np: np.ndarray
# [num_reqs]
# batch_idx -> num_scheduled_tokens
num_scheduled_tokens: np.ndarray
# sum(num_scheduled_tokens)
num_tokens: int
num_tokens_after_padding: int
num_draft_tokens: int
# [num_reqs + 1]
query_start_loc: torch.Tensor
query_start_loc_np: np.ndarray
# [num_reqs]
seq_lens: torch.Tensor
seq_lens_np: np.ndarray
# [num_tokens_after_padding]
input_ids: torch.Tensor
# [num_tokens_after_padding]
positions: torch.Tensor
# layer_name -> Metadata
attn_metadata: dict[str, Any]
# [total_num_logits]
logits_indices: torch.Tensor
# [num_reqs + 1]
cu_num_logits: torch.Tensor
@classmethod
def make_dummy(
cls,
num_reqs: int,
num_tokens: int,
input_buffers: InputBuffers,
device: torch.device,
) -> "InputBatch":
assert 0 < num_reqs <= num_tokens
req_ids = [f"req_{i}_{random_uuid()}" for i in range(num_reqs)]
idx_mapping_np = np.arange(num_reqs, dtype=np.int32)
idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=device)
num_scheduled_tokens = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
num_scheduled_tokens[-1] += num_tokens % num_reqs
assert int(num_scheduled_tokens.sum()) == num_tokens
input_buffers.query_start_loc.np[0] = 0
input_buffers.query_start_loc.np[1 : num_reqs + 1] = np.cumsum(
num_scheduled_tokens
)
input_buffers.query_start_loc.np[num_reqs + 1 :] = num_tokens
query_start_loc_np = input_buffers.query_start_loc.np[: num_reqs + 1]
query_start_loc = input_buffers.query_start_loc.copy_to_gpu()[: num_reqs + 1]
# seq_len equals to query_len
seq_lens_np = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
seq_lens_np[-1] += num_tokens % num_reqs
input_buffers.seq_lens[:num_reqs] = num_tokens // num_reqs
input_buffers.seq_lens[num_reqs - 1] += num_tokens % num_reqs
input_buffers.seq_lens[num_reqs:] = 0
seq_lens = input_buffers.seq_lens[:num_reqs]
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
cu_num_logits = torch.arange(num_reqs + 1, device=device, dtype=torch.int32)
return cls(
req_ids=req_ids,
num_reqs=num_reqs,
idx_mapping=idx_mapping,
idx_mapping_np=idx_mapping_np,
num_scheduled_tokens=num_scheduled_tokens,
num_tokens=num_tokens,
num_tokens_after_padding=num_tokens,
num_draft_tokens=0,
query_start_loc=query_start_loc,
query_start_loc_np=query_start_loc_np,
seq_lens=seq_lens,
seq_lens_np=seq_lens_np,
input_ids=input_ids,
positions=positions,
attn_metadata=None, # type: ignore
logits_indices=logits_indices,
cu_num_logits=cu_num_logits,
)
@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(
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:
num_reqs = idx_mapping.shape[0]
_prepare_prefill_inputs_kernel[(num_reqs,)](
input_ids,
next_prefill_tokens,
idx_mapping,
query_start_loc,
prefill_token_ids,
prefill_token_ids.stride(0),
prefill_len,
num_computed_tokens,
BLOCK_SIZE=1024,
)
@triton.jit
def _prepare_pos_seq_lens_kernel(
pos_ptr,
seq_lens_ptr,
idx_mapping_ptr,
query_start_loc_ptr,
num_computed_tokens_ptr,
max_num_reqs,
BLOCK_SIZE: tl.constexpr,
):
req_id = tl.program_id(0)
num_reqs = tl.num_programs(0) - 1
if req_id == num_reqs:
# Pad unused seq_lens as 0 for full CUDA graphs.
for i in tl.range(num_reqs, max_num_reqs, BLOCK_SIZE):
block = i + tl.arange(0, BLOCK_SIZE)
mask = block < max_num_reqs
tl.store(seq_lens_ptr + block, 0, mask=mask)
return
req_state_idx = tl.load(idx_mapping_ptr + req_id)
num_computed_tokens = tl.load(num_computed_tokens_ptr + req_state_idx)
start = tl.load(query_start_loc_ptr + req_id)
end = tl.load(query_start_loc_ptr + req_id + 1)
query_len = end - start
seq_len = num_computed_tokens + query_len
tl.store(seq_lens_ptr + req_id, seq_len)
for i in tl.range(0, query_len, BLOCK_SIZE):
block = i + tl.arange(0, BLOCK_SIZE)
mask = block < query_len
pos = num_computed_tokens + block
tl.store(pos_ptr + start + block, pos, mask=mask)
def prepare_pos_seq_lens(
idx_mapping: torch.Tensor,
query_start_loc: torch.Tensor,
num_computed_tokens: torch.Tensor,
pos: torch.Tensor,
seq_lens: torch.Tensor,
) -> None:
num_reqs = idx_mapping.shape[0]
# NOTE(woosuk): We do +1 because the last thread block is used
# to pad unused seq_lens as 0 for full CUDA graphs.
_prepare_pos_seq_lens_kernel[(num_reqs + 1,)](
pos,
seq_lens,
idx_mapping,
query_start_loc,
num_computed_tokens,
seq_lens.shape[0],
BLOCK_SIZE=1024,
)
@triton.jit
def _combine_sampled_and_draft_tokens_kernel(
input_ids_ptr,
idx_mapping_ptr,
last_sampled_tokens_ptr,
query_start_loc_ptr,
seq_lens_ptr,
prefill_len_ptr,
draft_tokens_ptr,
draft_tokens_stride,
cu_num_logits_ptr,
logits_indices_ptr,
BLOCK_SIZE: tl.constexpr,
):
batch_idx = tl.program_id(0)
req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
# Get the number of logits and draft tokens.
cu_num_logits_start = tl.load(cu_num_logits_ptr + batch_idx)
cu_num_logits_end = tl.load(cu_num_logits_ptr + batch_idx + 1)
num_logits = cu_num_logits_end - cu_num_logits_start
num_draft_tokens = num_logits - 1
# Compute the logits indices.
block = tl.arange(0, BLOCK_SIZE)
query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
logits_start = query_end - num_logits
tl.store(
logits_indices_ptr + cu_num_logits_start + block,
logits_start + block,
mask=block < num_logits,
)
seq_len = tl.load(seq_lens_ptr + batch_idx)
prefill_len = tl.load(prefill_len_ptr + req_state_idx)
if seq_len <= prefill_len:
# Handling prefill tokens. No sampled or draft tokens.
return
# Write the last sampled token ID to input_ids.
last_token_id = tl.load(last_sampled_tokens_ptr + req_state_idx)
tl.store(input_ids_ptr + query_end - num_logits, last_token_id)
# Write the draft tokens (if any) to input_ids.
if num_draft_tokens > 0:
mask = block < num_draft_tokens
draft_tokens = tl.load(
draft_tokens_ptr + req_state_idx * draft_tokens_stride + block,
mask=mask,
)
tl.store(
input_ids_ptr + query_end - num_draft_tokens + block,
draft_tokens,
mask=mask,
)
def combine_sampled_and_draft_tokens(
input_ids: torch.Tensor,
idx_mapping: torch.Tensor,
last_sampled_tokens: torch.Tensor,
query_start_loc: torch.Tensor,
seq_lens: torch.Tensor,
prefill_len: torch.Tensor,
draft_tokens: torch.Tensor,
cu_num_logits: torch.Tensor,
num_logits: int,
) -> torch.Tensor:
num_reqs = seq_lens.shape[0]
num_speculative_steps = draft_tokens.shape[-1]
logits_indices = torch.empty(
num_logits,
dtype=torch.int64,
device=input_ids.device,
)
_combine_sampled_and_draft_tokens_kernel[(num_reqs,)](
input_ids,
idx_mapping,
last_sampled_tokens,
query_start_loc,
seq_lens,
prefill_len,
draft_tokens,
draft_tokens.stride(0),
cu_num_logits,
logits_indices,
# NOTE(woosuk): Add 1 to ensure the block can cover the last sampled token
# in addition to all draft tokens.
BLOCK_SIZE=triton.next_power_of_2(num_speculative_steps + 1),
)
return logits_indices
@triton.jit
def _get_num_sampled_and_rejected_kernel(
num_sampled_ptr,
num_rejected_ptr,
seq_lens_ptr,
cu_num_logits_ptr,
idx_mapping_ptr,
prefill_len_ptr,
):
batch_idx = tl.program_id(0)
req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
seq_len = tl.load(seq_lens_ptr + batch_idx)
prefill_len = tl.load(prefill_len_ptr + req_state_idx)
is_chunked_prefilling = seq_len < prefill_len
num_sampled = tl.load(num_sampled_ptr + batch_idx)
num_sampled = tl.where(is_chunked_prefilling, 0, num_sampled)
tl.store(num_sampled_ptr + batch_idx, num_sampled)
logits_start = tl.load(cu_num_logits_ptr + batch_idx)
logits_end = tl.load(cu_num_logits_ptr + batch_idx + 1)
num_logits = logits_end - logits_start
num_rejected = num_logits - num_sampled
num_rejected = tl.where(is_chunked_prefilling, 0, num_rejected)
tl.store(num_rejected_ptr + batch_idx, num_rejected)
def get_num_sampled_and_rejected(
num_sampled: torch.Tensor,
seq_lens: torch.Tensor,
cu_num_logits: torch.Tensor,
idx_mapping: torch.Tensor,
prefill_len: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
num_reqs = idx_mapping.shape[0]
num_rejected = torch.empty_like(num_sampled)
_get_num_sampled_and_rejected_kernel[(num_reqs,)](
num_sampled,
num_rejected,
seq_lens,
cu_num_logits,
idx_mapping,
prefill_len,
)
return num_sampled, num_rejected
@triton.jit
def _post_update_kernel(
idx_mapping_ptr,
num_computed_tokens_ptr,
last_sampled_tokens_ptr,
output_bin_counts_ptr,
output_bin_counts_stride,
sampled_tokens_ptr,
sampled_tokens_stride,
num_sampled_ptr,
num_rejected_ptr,
query_start_loc_ptr,
):
req_id = tl.program_id(0)
req_state_idx = tl.load(idx_mapping_ptr + req_id)
num_sampled = tl.load(num_sampled_ptr + req_id)
if num_sampled > 0:
token_id = tl.load(
sampled_tokens_ptr + req_id * sampled_tokens_stride + num_sampled - 1
)
tl.store(last_sampled_tokens_ptr + req_state_idx, token_id)
for i in range(num_sampled):
token_id = tl.load(sampled_tokens_ptr + req_id * sampled_tokens_stride + i)
token_ptr = (
output_bin_counts_ptr + req_state_idx * output_bin_counts_stride + token_id
)
count = tl.load(token_ptr)
count += 1
tl.store(token_ptr, count)
query_start = tl.load(query_start_loc_ptr + req_id)
query_end = tl.load(query_start_loc_ptr + req_id + 1)
query_len = query_end - query_start
num_rejected = tl.load(num_rejected_ptr + req_id)
num_computed = tl.load(num_computed_tokens_ptr + req_state_idx)
num_computed += query_len - num_rejected
tl.store(num_computed_tokens_ptr + req_state_idx, num_computed)
def post_update(
# [num_reqs]
idx_mapping: torch.Tensor,
# [max_num_reqs]
num_computed_tokens: torch.Tensor,
# [max_num_reqs]
last_sampled_tokens: torch.Tensor,
# [max_num_reqs, vocab_size]
output_bin_counts: torch.Tensor,
# [num_reqs, num_speculative_steps + 1]
sampled_tokens: torch.Tensor,
# [num_reqs]
num_sampled: torch.Tensor,
# [num_reqs]
num_rejected: torch.Tensor,
# [num_reqs + 1]
query_start_loc: torch.Tensor,
) -> None:
num_reqs = idx_mapping.shape[0]
_post_update_kernel[(num_reqs,)](
idx_mapping,
num_computed_tokens,
last_sampled_tokens,
output_bin_counts,
output_bin_counts.stride(0),
sampled_tokens,
sampled_tokens.stride(0),
num_sampled,
num_rejected,
query_start_loc,
num_warps=1,
)