EngineX-Mthreads/enginex-mthreads-vllm
4
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

Sync from v0.13

Browse Source
This commit is contained in:
xiezhongtao
2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
Download Patch File Download Diff File Expand all files Collapse all files

805
vllm/v1/sample/rejection_sampler.py Normal file
View File

@@ -0,0 +1,805 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from dataclasses import replace
import torch
import torch.nn as nn
from vllm.logger import init_logger
from vllm.triton_utils import tl, triton
from vllm.v1.outputs import LogprobsTensors, SamplerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.ops.bad_words import apply_bad_words_with_drafts
from vllm.v1.sample.ops.penalties import apply_all_penalties
from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
from vllm.v1.sample.sampler import Sampler
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
logger = init_logger(__name__)
PLACEHOLDER_TOKEN_ID: tl.constexpr = -1
GREEDY_TEMPERATURE: tl.constexpr = 0
# Maximum number of speculative draft tokens allowed per request in a single
# step. This value is chosen to be large enough to handle typical use cases.
MAX_SPEC_LEN = 128
class RejectionSampler(nn.Module):
"""
The implementation strictly follows the algorithm described in
https://arxiv.org/abs/2211.17192.
However, we want to clarify the terminology used in the implementation:
accepted tokens: tokens that are accepted based on the relationship
between the "raw" draft and target probabilities.
recovered tokens: tokens that are sampled based on the adjusted probability
distribution, which is derived from both the draft and target
probabilities.
bonus tokens:
If all proposed tokens are accepted, the bonus token is added to the
end of the sequence. The bonus token is only sampled from the target
probabilities. We pass in the bonus tokens instead of sampling them
in the rejection sampler to allow for more flexibility in the
sampling process. For example, we can use top_p, top_k sampling for
bonus tokens, while spec decode does not support these sampling
strategies.
output tokens:
Tokens are finally generated with the rejection sampler.
output tokens = accepted tokens + recovered tokens + bonus tokens
"""
def __init__(self, sampler: Sampler):
super().__init__()
self.sampler = sampler
logprobs_mode = self.sampler.logprobs_mode
self.is_processed_logprobs_mode = logprobs_mode.startswith("processed")
self.is_logits_logprobs_mode = logprobs_mode.endswith("logits")
def forward(
self,
metadata: SpecDecodeMetadata,
# [num_tokens, vocab_size]
draft_probs: torch.Tensor | None,
# [num_tokens + batch_size, vocab_size]
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> SamplerOutput:
"""
Args:
metadata:
Metadata for spec decoding.
draft_probs (Optional[torch.Tensor]):
Probability distribution for the draft tokens. Shape is
[num_tokens, vocab_size]. Can be None if probabilities are
not provided, which is the case for ngram spec decode.
logits (torch.Tensor):
Target model's logits probability distribution.
Shape is [num_tokens + batch_size, vocab_size]. Here,
probabilities from different requests are flattened into a
single tensor because this is the shape of the output logits.
NOTE: `logits` can be updated in place to save memory.
sampling_metadata (vllm.v1.sample.metadata.SamplingMetadata):
Additional metadata needed for sampling, such as temperature,
top-k/top-p parameters, or other relevant information.
Returns:
SamplerOutput:
Contains the final output token IDs and their logprobs if
requested.
"""
assert metadata.max_spec_len <= MAX_SPEC_LEN
bonus_logits_indices = metadata.bonus_logits_indices
target_logits_indices = metadata.target_logits_indices
# When indexing with a tensor (bonus_logits_indices), PyTorch
# creates a new tensor with separate storage from the original
# logits tensor. This means any in-place operations on bonus_logits
# won't affect the original logits tensor.
assert logits is not None
bonus_logits = logits[bonus_logits_indices]
bonus_sampler_output = self.sampler(
logits=bonus_logits,
sampling_metadata=replace(
sampling_metadata,
max_num_logprobs=-1,
),
predict_bonus_token=True,
# Override the logprobs mode to return logits because they are
# needed later to compute the accepted token logprobs.
logprobs_mode_override="processed_logits"
if self.is_processed_logprobs_mode
else "raw_logits",
)
bonus_token_ids = bonus_sampler_output.sampled_token_ids
# Just like `bonus_logits`, `target_logits` is a new tensor with
# separate storage from the original `logits` tensor. Therefore,
# it is safe to update `target_logits` in place.
raw_target_logits = logits[target_logits_indices]
# Use float32 for the target_logits.
raw_target_logits = raw_target_logits.to(torch.float32)
target_logits = self.apply_logits_processors(
raw_target_logits, sampling_metadata, metadata
)
# [num_tokens, vocab_size]
# NOTE(woosuk): `target_logits` can be updated in place inside the
# `apply_sampling_constraints` function.
target_logits = apply_sampling_constraints(
target_logits,
metadata.cu_num_draft_tokens,
sampling_metadata,
)
# Compute probability distribution from target logits.
target_probs = target_logits.softmax(dim=-1, dtype=torch.float32)
output_token_ids = rejection_sample(
metadata.draft_token_ids,
metadata.num_draft_tokens,
metadata.max_spec_len,
metadata.cu_num_draft_tokens,
draft_probs,
target_probs,
bonus_token_ids,
sampling_metadata,
)
logprobs_tensors = None
if sampling_metadata.max_num_logprobs is not None:
logprobs_tensors = self._get_logprobs_tensors(
sampling_metadata.max_num_logprobs,
metadata,
logits,
target_logits if self.is_processed_logprobs_mode else raw_target_logits,
bonus_sampler_output.logprobs_tensors.logprobs,
output_token_ids,
)
return SamplerOutput(
sampled_token_ids=output_token_ids,
logprobs_tensors=logprobs_tensors,
)
def _get_logprobs_tensors(
self,
max_num_logprobs: int,
metadata: SpecDecodeMetadata,
logits: torch.Tensor,
target_logits: torch.Tensor,
bonus_logits: torch.Tensor,
sampled_token_ids: torch.Tensor,
) -> LogprobsTensors:
cu_num_sampled_tokens = torch.zeros_like(metadata.cu_num_sampled_tokens)
cu_num_sampled_tokens[1:] = metadata.cu_num_sampled_tokens[:-1]
# Collect target and bonus logits.
bonus_logits_indices = metadata.bonus_logits_indices
target_logits_indices = metadata.target_logits_indices
final_logits = torch.zeros_like(logits, dtype=torch.float32)
final_logits[target_logits_indices] = target_logits.to(torch.float32)
final_logits[bonus_logits_indices] = bonus_logits.to(torch.float32)
# Compute accepted token indices.
accepted_mask = sampled_token_ids != PLACEHOLDER_TOKEN_ID
num_accepted_tokens = accepted_mask.sum(dim=-1)
accepted_logit_indices = accepted_mask.nonzero(as_tuple=True)[1]
accepted_logit_indices += cu_num_sampled_tokens.repeat_interleave(
num_accepted_tokens
)
# Compute logprobs for accepted tokens.
accepted_logits = final_logits[accepted_logit_indices]
accepted_logprobs = (
accepted_logits
if self.is_logits_logprobs_mode
else self.sampler.compute_logprobs(accepted_logits)
)
accepted_tokens = sampled_token_ids[accepted_mask]
return self.sampler.gather_logprobs(
accepted_logprobs,
max_num_logprobs,
accepted_tokens.to(torch.int64),
)
@staticmethod
def parse_output(
output_token_ids: torch.Tensor,
vocab_size: int,
discard_req_indices: Sequence[int] = (),
return_cu_num_tokens: bool = False,
) -> tuple[list[list[int]], list[int] | None]:
"""Parse the output of the rejection sampler.
Args:
output_token_ids: The sampled token IDs in shape
[batch_size, max_spec_len + 1]. The rejected tokens are
replaced with `PLACEHOLDER_TOKEN_ID` by the rejection sampler
and will be filtered out in this function.
vocab_size: The size of the vocabulary.
discard_req_indices: Optional row indices to discard tokens in.
return_cu_num_tokens: Whether to also return cumulative token counts.
Returns:
A list of lists of token IDs.
"""
output_token_ids_np = output_token_ids.cpu().numpy()
# Create mask for valid tokens.
valid_mask = (output_token_ids_np != PLACEHOLDER_TOKEN_ID) & (
output_token_ids_np < vocab_size
)
cu_num_tokens = None
if return_cu_num_tokens:
cu_num_tokens = [0] + valid_mask.sum(axis=1).cumsum().tolist()
if len(discard_req_indices) > 0:
valid_mask[discard_req_indices] = False
outputs = [
row[valid_mask[i]].tolist() for i, row in enumerate(output_token_ids_np)
]
return outputs, cu_num_tokens
def apply_logits_processors(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
metadata: SpecDecodeMetadata,
) -> torch.Tensor:
has_penalties = not sampling_metadata.no_penalties
any_penalties_or_bad_words = (
sampling_metadata.bad_words_token_ids or has_penalties
)
output_token_ids = sampling_metadata.output_token_ids
if any_penalties_or_bad_words:
output_token_ids = self._combine_outputs_with_spec_tokens(
output_token_ids,
sampling_metadata.spec_token_ids,
)
# Calculate indices of target logits.
if sampling_metadata.allowed_token_ids_mask is not None or has_penalties:
num_requests = len(sampling_metadata.output_token_ids)
num_draft_tokens = torch.tensor(metadata.num_draft_tokens, device="cpu")
original_indices = torch.arange(num_requests, device="cpu")
repeat_indices_cpu = original_indices.repeat_interleave(num_draft_tokens)
repeat_indices = repeat_indices_cpu.to(
device=logits.device, non_blocking=True
)
logits = self.apply_penalties(
logits, sampling_metadata, metadata, repeat_indices, output_token_ids
)
# Apply allowed token ids.
if sampling_metadata.allowed_token_ids_mask is not None:
token_mask = sampling_metadata.allowed_token_ids_mask[repeat_indices]
logits.masked_fill_(token_mask, float("-inf"))
# Apply bad words exclusion.
if bad_words_token_ids := sampling_metadata.bad_words_token_ids:
apply_bad_words_with_drafts(
logits, bad_words_token_ids, output_token_ids, metadata.num_draft_tokens
)
return logits
@staticmethod
def apply_penalties(
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
metadata: SpecDecodeMetadata,
repeat_indices: torch.Tensor,
output_token_ids: list[list[int]],
) -> torch.Tensor:
if sampling_metadata.no_penalties:
return logits
assert sampling_metadata.prompt_token_ids is not None
prompt_token_ids = sampling_metadata.prompt_token_ids[repeat_indices]
presence_penalties = sampling_metadata.presence_penalties[repeat_indices]
frequency_penalties = sampling_metadata.frequency_penalties[repeat_indices]
repetition_penalties = sampling_metadata.repetition_penalties[repeat_indices]
logits = apply_all_penalties(
logits,
prompt_token_ids,
presence_penalties,
frequency_penalties,
repetition_penalties,
output_token_ids,
)
return logits
@staticmethod
def _combine_outputs_with_spec_tokens(
output_token_ids: list[list[int]],
spec_token_ids: list[list[int]] | None = None,
) -> list[list[int]]:
if spec_token_ids is None:
return output_token_ids
result = []
for out, spec in zip(output_token_ids, spec_token_ids):
if len(spec) == 0:
continue
result.append(out)
for i in range(len(spec) - 1):
result.append([*result[-1], spec[i]])
return result
def rejection_sample(
# [num_tokens]
draft_token_ids: torch.Tensor,
# [batch_size]
num_draft_tokens: list[int],
max_spec_len: int,
# [batch_size]
cu_num_draft_tokens: torch.Tensor,
# [num_tokens, vocab_size]
draft_probs: torch.Tensor | None,
# [num_tokens, vocab_size]
target_probs: torch.Tensor,
# [batch_size, 1]
bonus_token_ids: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
assert draft_token_ids.ndim == 1
assert draft_probs is None or draft_probs.ndim == 2
assert cu_num_draft_tokens.ndim == 1
assert target_probs.ndim == 2
batch_size = len(num_draft_tokens)
num_tokens = draft_token_ids.shape[0]
vocab_size = target_probs.shape[-1]
device = target_probs.device
assert draft_token_ids.is_contiguous()
assert draft_probs is None or draft_probs.is_contiguous()
assert target_probs.is_contiguous()
assert bonus_token_ids.is_contiguous()
assert target_probs.shape == (num_tokens, vocab_size)
# Create output buffer.
output_token_ids = torch.full(
(batch_size, max_spec_len + 1),
PLACEHOLDER_TOKEN_ID,
dtype=torch.int32, # Consistent with SamplerOutput.sampled_token_ids.
device=device,
)
if sampling_metadata.all_greedy:
is_greedy = None
else:
is_greedy = sampling_metadata.temperature == GREEDY_TEMPERATURE
if not sampling_metadata.all_random:
# Rejection sampling for greedy sampling requests.
target_argmax = target_probs.argmax(dim=-1)
rejection_greedy_sample_kernel[(batch_size,)](
output_token_ids,
cu_num_draft_tokens,
draft_token_ids,
target_argmax,
bonus_token_ids,
is_greedy,
max_spec_len,
)
if sampling_metadata.all_greedy:
return output_token_ids
# Generate uniform probabilities for rejection sampling.
# [num_tokens]
uniform_probs = generate_uniform_probs(
num_tokens,
num_draft_tokens,
sampling_metadata.generators,
device,
)
# Sample recovered tokens for each position.
# [num_tokens]
recovered_token_ids = sample_recovered_tokens(
max_spec_len,
num_draft_tokens,
cu_num_draft_tokens,
draft_token_ids,
draft_probs,
target_probs,
sampling_metadata,
device,
)
# Rejection sampling for random sampling requests.
rejection_random_sample_kernel[(batch_size,)](
output_token_ids,
cu_num_draft_tokens,
draft_token_ids,
draft_probs,
target_probs,
bonus_token_ids,
recovered_token_ids,
uniform_probs,
is_greedy,
max_spec_len,
vocab_size,
NO_DRAFT_PROBS=draft_probs is None,
)
return output_token_ids
def apply_sampling_constraints(
logits: torch.Tensor, # [num_tokens, vocab_size]
cu_num_draft_tokens: torch.Tensor, # [batch_size]
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
"""Process logits based on sampling metadata.
This function applies temperature scaling to the logits,
as well as top-k and top-p. For greedy decoding, it returns
the original logits.
Args:
logits: Input logits tensor to be processed.
cu_num_draft_tokens: Cumulative number of draft tokens.
sampling_metadata: Metadata containing sampling parameters such as
temperature and whether greedy sampling is used.
Returns:
torch.Tensor: Processed logits if non-greedy sampling is used,
otherwise returns the original logits.
"""
assert logits.ndim == 2
assert cu_num_draft_tokens.ndim == 1
if sampling_metadata.all_greedy:
return logits
num_tokens = logits.shape[0]
temperature = expand_batch_to_tokens(
sampling_metadata.temperature,
cu_num_draft_tokens,
num_tokens,
replace_from=GREEDY_TEMPERATURE,
replace_to=1,
)
# NOTE(woosuk): Update `logits` in place to avoid allocating a new tensor.
logits.div_(temperature.unsqueeze(-1))
# Get expanded top_k and top_p tensors.
top_k = None
if sampling_metadata.top_k is not None:
top_k = expand_batch_to_tokens(
sampling_metadata.top_k,
cu_num_draft_tokens,
num_tokens,
)
top_p = None
if sampling_metadata.top_p is not None:
top_p = expand_batch_to_tokens(
sampling_metadata.top_p,
cu_num_draft_tokens,
num_tokens,
)
# NOTE(woosuk): `apply_top_k_top_p` uses sorting to calculate the mask,
# which is slow for large vocab sizes. This may cause performance issues.
return apply_top_k_top_p(logits, top_k, top_p)
def expand_batch_to_tokens(
x: torch.Tensor, # [batch_size]
cu_num_tokens: torch.Tensor, # [batch_size]
num_tokens: int,
replace_from: int = 0,
replace_to: int = 0,
) -> torch.Tensor:
"""Expand [batch_size] tensor to [num_tokens] tensor based on the number of
tokens per batch in cu_num_tokens.
For example, if x = [a, b, c] and cu_num_tokens = [2, 5, 6], then
num_tokens = 6, and expanded_x = [a, a, b, b, b, c].
Args:
x: [batch_size] tensor to expand.
cu_num_tokens: [batch_size] tensor containing the cumulative number of
tokens per batch. Each element represents the total number of
tokens up to and including that batch.
num_tokens: Total number of tokens.
replace_from: int = 0
Value to be replaced if it is found in x.
replace_to: int = 0
Value to replace with when replace_from is found.
Returns:
expanded_x: [num_tokens] tensor.
"""
batch_size = x.shape[0]
assert cu_num_tokens.shape[0] == batch_size
expanded_x = x.new_empty(num_tokens)
expand_kernel[(batch_size,)](
expanded_x,
x,
cu_num_tokens,
replace_from,
replace_to,
MAX_NUM_TOKENS=MAX_SPEC_LEN, # To avoid recompilation.
)
return expanded_x
def generate_uniform_probs(
num_tokens: int,
num_draft_tokens: list[int],
generators: dict[int, torch.Generator],
device: torch.device,
) -> torch.Tensor:
"""
Generates a batch of uniform random samples, with optional seeding
if available.
This method creates a tensor of shape `(num_tokens, )` filled
with uniform random values in the range [0, 1). If `generators` is provided,
the requests with their own seeds will use the provided `torch.Generator`
for reproducibility. The samples for the other requests will be generated
without a seed.
Args:
num_tokens: int
Total number of tokens.
num_draft_tokens: List[List[int]]
Number of draft tokens per request.
generators: Optional[Dict[int, torch.Generator]]
A dictionary mapping indices in the batch to
`torch.Generator` objects.
device: torch.device
The device on which to allocate the tensor.
Returns:
uniform_rand: torch.Tensor
A tensor of shape `(num_tokens, )` containing uniform
random values in the range [0, 1).
"""
# NOTE(woosuk): We deliberately use float64 instead of float32 here
# because when using float32, there's a non-negligible chance that
# uniform_prob is sampled to be exact 0.0 as reported in
# https://github.com/pytorch/pytorch/issues/16706. Using float64
# mitigates the issue.
uniform_probs = torch.rand(
(num_tokens,),
dtype=torch.float64,
device=device,
)
start_idx = 0
for req_idx, n in enumerate(num_draft_tokens):
# Do not generate random numbers for requests with no draft tokens.
# This can be important for reproducibility.
if n == 0:
continue
end_idx = start_idx + n
generator = generators.get(req_idx)
if generator is not None:
uniform_probs[start_idx:end_idx].uniform_(generator=generator)
start_idx = end_idx
return uniform_probs
def sample_recovered_tokens(
max_spec_len: int,
num_draft_tokens: list[int],
# [batch_size]
cu_num_draft_tokens: torch.Tensor,
# [num_tokens]
draft_token_ids: torch.Tensor,
# [num_tokens, vocab_size]
draft_probs: torch.Tensor | None,
# [num_tokens, vocab_size]
target_probs: torch.Tensor,
sampling_metadata: SamplingMetadata,
device: torch.device,
) -> torch.Tensor:
# NOTE(woosuk): Create only one distribution for each request.
batch_size = len(num_draft_tokens)
vocab_size = target_probs.shape[-1]
q = torch.empty(
(batch_size, vocab_size),
dtype=torch.float32,
device=device,
)
q.exponential_()
for i, generator in sampling_metadata.generators.items():
# Do not generate random numbers for requests with no draft tokens.
# This can be important for reproducibility.
if num_draft_tokens[i] > 0:
q[i].exponential_(generator=generator)
recovered_token_ids = torch.empty_like(draft_token_ids)
sample_recovered_tokens_kernel[(batch_size, max_spec_len)](
recovered_token_ids,
cu_num_draft_tokens,
draft_token_ids,
draft_probs,
target_probs,
q,
vocab_size,
triton.next_power_of_2(vocab_size),
NO_DRAFT_PROBS=draft_probs is None,
)
return recovered_token_ids
# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
@triton.jit(do_not_specialize=["max_spec_len"])
def rejection_greedy_sample_kernel(
output_token_ids_ptr, # [batch_size, max_spec_len + 1]
cu_num_draft_tokens_ptr, # [batch_size]
draft_token_ids_ptr, # [num_tokens]
target_argmax_ptr, # [num_tokens]
bonus_token_ids_ptr, # [batch_size]
is_greedy_ptr, # [batch_size] or None
max_spec_len,
):
req_idx = tl.program_id(0)
# FIXME(woosuk): Because is_greedy_ptr is not None at profiling run,
# re-compilation may happen during runtime when is_greedy_ptr is None.
is_greedy = True if is_greedy_ptr is None else tl.load(is_greedy_ptr + req_idx)
if not is_greedy:
# Early exit for non-greedy sampling requests.
return
start_idx = 0 if req_idx == 0 else tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
num_draft_tokens = end_idx - start_idx
rejected = False
for pos in range(num_draft_tokens):
if not rejected:
draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos)
tl.store(
output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
target_argmax_id,
)
if draft_token_id != target_argmax_id:
# Reject.
rejected = True
if not rejected:
# If all tokens are accepted, append the bonus token.
bonus_token_id = tl.load(bonus_token_ids_ptr + req_idx)
tl.store(
output_token_ids_ptr + req_idx * (max_spec_len + 1) + num_draft_tokens,
bonus_token_id,
)
# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
@triton.jit(do_not_specialize=["max_spec_len"])
def rejection_random_sample_kernel(
output_token_ids_ptr, # [batch_size, max_spec_len + 1]
cu_num_draft_tokens_ptr, # [batch_size]
draft_token_ids_ptr, # [num_tokens]
draft_probs_ptr, # [num_tokens, vocab_size] or None
target_probs_ptr, # [num_tokens, vocab_size]
bonus_token_ids_ptr, # [batch_size]
recovered_token_ids_ptr, # [num_tokens]
uniform_probs_ptr, # [num_tokens]
is_greedy_ptr, # [batch_size]
max_spec_len,
vocab_size,
NO_DRAFT_PROBS: tl.constexpr,
):
req_idx = tl.program_id(0)
is_greedy = tl.load(is_greedy_ptr + req_idx)
if is_greedy:
# Early exit for greedy sampling requests.
return
start_idx = 0 if req_idx == 0 else tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
num_draft_tokens = end_idx - start_idx
rejected = False
for pos in range(num_draft_tokens):
if not rejected:
draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
if NO_DRAFT_PROBS:
draft_prob = 1
else:
draft_prob = tl.load(
draft_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id
)
target_prob = tl.load(
target_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id
)
uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
# NOTE(woosuk): While the draft probability should never be 0,
# we check it to avoid NaNs. If it happens to be 0, we reject.
if draft_prob > 0 and target_prob / draft_prob >= uniform_prob:
# Accept.
token_id = draft_token_id
else:
# Reject. Use recovered token.
rejected = True
token_id = tl.load(recovered_token_ids_ptr + start_idx + pos)
tl.store(
output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos, token_id
)
if not rejected:
# If all tokens are accepted, append the bonus token.
bonus_token_id = tl.load(bonus_token_ids_ptr + req_idx)
tl.store(
output_token_ids_ptr + req_idx * (max_spec_len + 1) + num_draft_tokens,
bonus_token_id,
)
# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
@triton.jit(do_not_specialize=["replace_from", "replace_to"])
def expand_kernel(
output_ptr, # [num_tokens]
input_ptr, # [batch_size]
cu_num_tokens_ptr, # [batch_size]
replace_from,
replace_to,
MAX_NUM_TOKENS: tl.constexpr,
):
req_idx = tl.program_id(0)
if req_idx == 0: # noqa: SIM108
start_idx = 0
else:
start_idx = tl.load(cu_num_tokens_ptr + req_idx - 1)
end_idx = tl.load(cu_num_tokens_ptr + req_idx)
num_tokens = end_idx - start_idx
src_val = tl.load(input_ptr + req_idx)
src_val = tl.where(src_val == replace_from, replace_to, src_val)
offset = tl.arange(0, MAX_NUM_TOKENS)
tl.store(output_ptr + start_idx + offset, src_val, mask=offset < num_tokens)
@triton.jit
def sample_recovered_tokens_kernel(
output_token_ids_ptr, # [num_tokens]
cu_num_draft_tokens_ptr, # [batch_size]
draft_token_ids_ptr, # [num_tokens]
draft_probs_ptr, # [num_tokens, vocab_size] or None
target_probs_ptr, # [num_tokens, vocab_size]
q_ptr, # [batch_size, vocab_size]
vocab_size,
PADDED_VOCAB_SIZE: tl.constexpr,
NO_DRAFT_PROBS: tl.constexpr,
):
req_idx = tl.program_id(0)
start_idx = 0 if req_idx == 0 else tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
num_draft_tokens = end_idx - start_idx
# Early exit for out-of-range positions.
pos = tl.program_id(1)
if pos >= num_draft_tokens:
return
vocab_offset = tl.arange(0, PADDED_VOCAB_SIZE)
if NO_DRAFT_PROBS:
draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
prob = tl.load(
target_probs_ptr + (start_idx + pos) * vocab_size + vocab_offset,
mask=((vocab_offset < vocab_size) & (vocab_offset != draft_token_id)),
other=0,
)
else:
draft_prob = tl.load(
draft_probs_ptr + (start_idx + pos) * vocab_size + vocab_offset,
mask=vocab_offset < vocab_size,
other=0,
)
target_prob = tl.load(
target_probs_ptr + (start_idx + pos) * vocab_size + vocab_offset,
mask=vocab_offset < vocab_size,
other=0,
)
prob = tl.maximum(target_prob - draft_prob, 0)
# NOTE(woosuk): We don't need `prob = prob / tl.sum(prob)` here because
# `tl.argmax` will select the maximum value.
q = tl.load(
q_ptr + req_idx * vocab_size + vocab_offset,
mask=vocab_offset < vocab_size,
other=float("-inf"),
)
recovered_id = tl.argmax(prob / q, axis=-1)
tl.store(output_token_ids_ptr + start_idx + pos, recovered_id)