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vllm_npu/sample/rejection_sampler.py Normal file
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# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import torch
import torch.nn as nn
import vllm.v1.sample.rejection_sampler as rs
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import (RejectionSampler, compute_probs,
generate_uniform_probs)
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
PLACEHOLDER_TOKEN_ID = -1
GREEDY_TEMPERATURE = -1
# 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 = 32
class AscendRejectionSampler(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 forward(
self,
metadata: SpecDecodeMetadata,
# [num_tokens, vocab_size]
draft_probs: Optional[torch.Tensor],
# [num_tokens, vocab_size]
target_logits: torch.Tensor,
# [batch_size, 1]
bonus_token_ids: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
'''
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.
target_logits (torch.Tensor):
Target model's logits probability distribution.
Shape is [num_tokens, vocab_size]. Here, probabilities from
different requests are flattened into a single tensor because
this is the shape of the output logits.
NOTE: `target_logits` can be updated in place to save memory.
bonus_token_ids_tensor (torch.Tensor):
A tensor containing bonus tokens. Shape is [batch_size, 1].
Bonus tokens are added to the end of the sequence if all
proposed tokens are accepted. We generate the bonus tokens
outside of the rejection sampler with the default sampling
strategy. It allows for more flexibility in the sampling
process such as top_p, top_k sampling.
sampling_metadata (SamplingMetadata):
Additional metadata needed for sampling, such as temperature,
top-k/top-p parameters, or other relevant information.
Returns:
output_token_ids (torch.Tensor):
A tensor containing the final output token IDs.
'''
assert metadata.max_spec_len <= MAX_SPEC_LEN
# [num_tokens, vocab_size]
# NOTE(woosuk): `target_logits` can be updated in place inside the
# `compute_probs` function.
target_probs = compute_probs(
target_logits,
metadata.cu_num_draft_tokens,
sampling_metadata,
)
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,
)
return output_token_ids
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: Optional[torch.Tensor],
# [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.empty(
(batch_size, max_spec_len + 1),
dtype=torch.int32, # Consistent with SamplerOutput.sampled_token_ids.
device=device,
)
output_token_ids.fill_(PLACEHOLDER_TOKEN_ID)
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)
if min(num_draft_tokens) == 1 and max(
num_draft_tokens) == 1 and sampling_metadata.all_greedy:
rejection_greedy_sample_spec_len_1_pytorch(
output_token_ids,
draft_token_ids,
target_argmax,
bonus_token_ids,
)
else:
rejection_greedy_sample_pytorch(
output_token_ids,
cu_num_draft_tokens,
draft_token_ids,
target_argmax,
bonus_token_ids,
num_draft_tokens,
max_spec_len,
is_greedy,
)
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_pytorch(
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,
IS_NGRAM=draft_probs is None,
# num_warps=1,
)
return output_token_ids
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_pytorch(
expanded_x,
x,
cu_num_tokens,
replace_from,
replace_to,
MAX_NUM_TOKENS=MAX_SPEC_LEN, # To avoid recompilation.
)
return expanded_x
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: Optional[torch.Tensor],
# [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_pytorch(
recovered_token_ids,
cu_num_draft_tokens,
draft_token_ids,
draft_probs,
target_probs,
q,
vocab_size,
IS_NGRAM=draft_probs is None,
)
return recovered_token_ids
def rejection_greedy_sample_spec_len_1_pytorch(
output_token_ids, # [batch_size, 2]
draft_token_ids, # [num_tokens]
target_argmax, # [num_tokens]
bonus_token_ids, # [batch_size]
):
batch_size = output_token_ids.size(0)
num_tokens = draft_token_ids.size(0)
assert batch_size == num_tokens
accept_req_mask = draft_token_ids == target_argmax
output_token_ids[:, 0] = target_argmax
bonus_token_ids = bonus_token_ids.squeeze(1)
output_token_ids[accept_req_mask, 1] = bonus_token_ids[accept_req_mask]
def rejection_greedy_sample_pytorch(
output_token_ids, # [batch_size, max_spec_len + 1]
cu_num_draft_tokens, # [batch_size]
draft_token_ids, # [num_tokens]
target_argmax, # [num_tokens]
bonus_token_ids, # [batch_size]
draft_tokens_per_req, # [batch_size], list
max_spec_len,
is_greedy=None, # [batch_size] or None
):
batch_size = output_token_ids.size(0)
num_tokens = draft_token_ids.size(0)
device = output_token_ids.device
draft_tokens_per_req = torch.tensor(draft_tokens_per_req).to(
device, non_blocking=True)
if is_greedy is None:
is_greedy = torch.ones(batch_size, dtype=torch.bool, device=device)
start_indices = cu_num_draft_tokens - draft_tokens_per_req
req_ids = torch.arange(batch_size, device=device)
token_req_ids = torch.repeat_interleave(req_ids, draft_tokens_per_req)
token_positions = torch.arange(
num_tokens, device=device) - start_indices[token_req_ids]
# Find the first mismatch position of each request.
mismatch_global = (draft_token_ids != target_argmax)
if max_spec_len == 0:
first_mismatch_pos_per_req = torch.zeros(batch_size,
dtype=torch.long,
device=device)
else:
# [bs, max_spec_len]
pos_matrix = torch.full((batch_size, max_spec_len),
-1,
dtype=torch.long,
device=device)
pos_matrix[token_req_ids, token_positions] = token_positions
mismatch_matrix = torch.full((batch_size, max_spec_len),
False,
dtype=torch.bool,
device=device)
mismatch_matrix[token_req_ids, token_positions] = mismatch_global
mismatch_positions = torch.where(mismatch_matrix, pos_matrix,
max_spec_len * 2)
first_mismatch_pos_per_req, _ = torch.min(mismatch_positions, dim=1)
no_mismatch_mask = (first_mismatch_pos_per_req == max_spec_len * 2)
first_mismatch_pos_per_req[no_mismatch_mask] = draft_tokens_per_req[
no_mismatch_mask]
# Copy matched target tokens into output.
copy_len = torch.minimum(first_mismatch_pos_per_req + 1,
draft_tokens_per_req)
copy_indices = torch.arange(max_spec_len + 1,
device=device).expand(batch_size, -1)
copy_mask = copy_indices < copy_len.unsqueeze(1)
greedy_mask = is_greedy.unsqueeze(1)
final_copy_mask = copy_mask & greedy_mask
global_idx = start_indices.unsqueeze(1) + copy_indices
output_token_ids[final_copy_mask] = target_argmax[
global_idx[final_copy_mask]].to(output_token_ids.dtype)
# Fill bonus token.
needs_bonus = is_greedy & (first_mismatch_pos_per_req
>= draft_tokens_per_req)
if torch.any(needs_bonus):
bonus_rows = torch.where(needs_bonus)[0]
bonus_cols = draft_tokens_per_req[bonus_rows]
bonus_token_ids = bonus_token_ids.squeeze(1)
output_token_ids[bonus_rows, bonus_cols] = bonus_token_ids[bonus_rows]
def rejection_random_sample_pytorch(
output_token_ids, # [batch_size, max_spec_len + 1]
cu_num_draft_tokens, # [batch_size]
draft_token_ids, # [num_tokens]
draft_probs, # [num_tokens, vocab_size] or None
target_probs, # [num_tokens, vocab_size]
bonus_token_ids, # [batch_size]
recovered_token_ids, # [num_tokens]
uniform_probs, # [num_tokens]
is_greedy, # [batch_size]
max_spec_len,
vocab_size,
IS_NGRAM=False,
):
batch_size = output_token_ids.shape[0]
for req_idx in range(batch_size):
if is_greedy[req_idx]:
continue
if req_idx == 0:
start_idx = 0
else:
start_idx = cu_num_draft_tokens[req_idx - 1].item()
end_idx = cu_num_draft_tokens[req_idx].item()
num_draft_tokens = end_idx - start_idx
rejected = False
for pos in range(num_draft_tokens):
if not rejected:
draft_token_id = draft_token_ids[start_idx + pos].item()
if IS_NGRAM:
draft_prob = 1.0
else:
draft_prob = draft_probs[start_idx + pos,
draft_token_id].item()
target_prob = target_probs[start_idx + pos,
draft_token_id].item()
uniform_prob = uniform_probs[start_idx + pos].item()
if draft_prob > 0 and target_prob / draft_prob >= uniform_prob:
token_id = draft_token_id
else:
rejected = True
token_id = recovered_token_ids[start_idx + pos].item()
output_token_ids[req_idx, pos] = token_id
if not rejected:
bonus_token_id = bonus_token_ids[req_idx].item()
output_token_ids[req_idx, num_draft_tokens] = bonus_token_id
def expand_pytorch(
output_ptr, # [num_tokens]
input_ptr, # [batch_size]
cu_num_tokens_ptr, # [batch_size]
replace_from,
replace_to,
MAX_NUM_TOKENS,
):
batch_size = len(input_ptr)
for req_idx in range(batch_size):
start_idx = 0 if req_idx == 0 else cu_num_tokens_ptr[req_idx - 1]
end_idx = cu_num_tokens_ptr[req_idx]
num_tokens = end_idx - start_idx
src_val = input_ptr[req_idx]
src_val = replace_to if src_val == replace_from else src_val
offset = torch.arange(MAX_NUM_TOKENS, device=num_tokens.device)
mask = offset < num_tokens
output_slice = start_idx + offset[mask]
output_ptr[output_slice] = src_val
def sample_recovered_tokens_pytorch(
output_token_ids, # [num_tokens]
cu_num_draft_tokens, # [batch_size]
draft_token_ids, # [num_tokens]
draft_probs, # [num_tokens, vocab_size] or None
target_probs, # [num_tokens, vocab_size]
q, # [batch_size, vocab_size]
vocab_size,
IS_NGRAM=False,
):
batch_size = len(cu_num_draft_tokens)
for req_idx in range(batch_size):
start_idx = 0 if req_idx == 0 else cu_num_draft_tokens[req_idx - 1]
end_idx = cu_num_draft_tokens[req_idx]
num_draft_tokens = end_idx - start_idx
for pos in range(num_draft_tokens):
token_idx = start_idx + pos
if IS_NGRAM:
draft_token_id = draft_token_ids[token_idx]
orig_prob = target_probs[token_idx, draft_token_id].item()
target_probs[token_idx, draft_token_id] = 0
prob = target_probs[token_idx].clone()
else:
draft_p = draft_probs[token_idx].clone()
target_p = target_probs[token_idx].clone()
prob = torch.maximum(target_p - draft_p,
torch.tensor(0.0, device=target_p.device))
q_values = torch.full((vocab_size, ),
float('-inf'),
device=q.device)
q_values[:vocab_size] = q[req_idx, :vocab_size]
recovered_id = torch.argmax(prob / q_values).item()
output_token_ids[token_idx] = recovered_id
if IS_NGRAM:
target_probs[token_idx, draft_token_id] = orig_prob
rs.expand_batch_to_tokens = expand_batch_to_tokens