[gpt-oss] Add gpt-oss bf16 support
This commit is contained in:
286
vllm/v1/sample/sampler.py
Normal file
286
vllm/v1/sample/sampler.py
Normal file
@@ -0,0 +1,286 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
"""A layer that samples the next tokens from the model's outputs."""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from vllm.utils import async_tensor_h2d, is_pin_memory_available
|
||||
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
|
||||
from vllm.v1.sample.ops.penalties import (apply_all_penalties,
|
||||
apply_min_token_penalties)
|
||||
from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler
|
||||
|
||||
_SAMPLING_EPS = 1e-5
|
||||
|
||||
|
||||
class Sampler(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.topk_topp_sampler = TopKTopPSampler()
|
||||
self.pin_memory = is_pin_memory_available()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> SamplerOutput:
|
||||
# NOTE(woosuk): Use the original logits (before any penalties or
|
||||
# temperature scaling) for the top-k logprobs.
|
||||
# This is different from the V0 sampler, which uses the logits that
|
||||
# is used for sampling (after penalties and temperature scaling).
|
||||
# TODO(rob): provide option for logprobs post sampling.
|
||||
# See https://vllm-dev.slack.com/archives/C07UUL8E61Z/p1735907856007919 # noqa: E501
|
||||
num_logprobs = sampling_metadata.max_num_logprobs
|
||||
if num_logprobs is not None:
|
||||
raw_logprobs = self.compute_logprobs(logits)
|
||||
|
||||
# Use float32 for the logits.
|
||||
logits = logits.to(torch.float32)
|
||||
# Apply allowed token ids.
|
||||
logits = self.apply_allowed_token_ids(logits, sampling_metadata)
|
||||
# Apply bad words exclusion.
|
||||
logits = self.apply_bad_words(logits, sampling_metadata)
|
||||
# Apply logits bias.
|
||||
logits = self.apply_logits_bias(logits, sampling_metadata)
|
||||
# Apply penalties (e.g., min_tokens, freq_penalties).
|
||||
logits = self.apply_penalties(logits, sampling_metadata)
|
||||
# Sample the next token.
|
||||
sampled = self.sample(logits, sampling_metadata)
|
||||
# Convert sampled token ids to int64 (long) type to ensure compatibility
|
||||
# with subsequent operations that may use these values as indices.
|
||||
# This conversion is necessary because FlashInfer sampling operations
|
||||
# return int32 (while PyTorch argmax and topk return int64).
|
||||
sampled = sampled.long()
|
||||
|
||||
# Gather the logprobs of the topk and sampled token (if requested).
|
||||
# Get logprobs and rank tensors (if requested)
|
||||
logprobs_tensors = None if num_logprobs is None else \
|
||||
self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=sampled)
|
||||
|
||||
# Use int32 to reduce the tensor size.
|
||||
sampled = sampled.to(torch.int32)
|
||||
|
||||
# These are GPU tensors.
|
||||
sampler_output = SamplerOutput(
|
||||
# The sampled tokens are expanded to 2D tensor with shape
|
||||
# [num_requests, 1], where each row represents one generated
|
||||
# token per request.
|
||||
sampled_token_ids=sampled.unsqueeze(-1),
|
||||
logprobs_tensors=logprobs_tensors,
|
||||
)
|
||||
return sampler_output
|
||||
|
||||
def apply_temperature(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
temp: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
# Use in-place division to avoid creating a new tensor.
|
||||
return logits.div_(temp.unsqueeze(dim=1))
|
||||
|
||||
def greedy_sample(self, logits: torch.Tensor) -> torch.Tensor:
|
||||
return logits.argmax(dim=-1).view(-1)
|
||||
|
||||
def sample(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> torch.Tensor:
|
||||
"""Sample logits based on sampling metadata.
|
||||
|
||||
The various logits processing functions called in this method
|
||||
may update the logits tensor in-place.
|
||||
"""
|
||||
|
||||
assert not (sampling_metadata.all_greedy
|
||||
and sampling_metadata.all_random)
|
||||
if sampling_metadata.all_random:
|
||||
greedy_sampled = None
|
||||
else:
|
||||
greedy_sampled = self.greedy_sample(logits)
|
||||
if sampling_metadata.all_greedy:
|
||||
return greedy_sampled
|
||||
|
||||
assert sampling_metadata.temperature is not None
|
||||
|
||||
# Apply temperature.
|
||||
logits = self.apply_temperature(logits, sampling_metadata.temperature)
|
||||
|
||||
# Apply min_p.
|
||||
if sampling_metadata.min_p is not None:
|
||||
logits = self.apply_min_p(logits, sampling_metadata.min_p)
|
||||
|
||||
# Apply top_k and/or top_p.
|
||||
random_sampled = self.topk_topp_sampler(
|
||||
logits,
|
||||
sampling_metadata.generators,
|
||||
sampling_metadata.top_k,
|
||||
sampling_metadata.top_p,
|
||||
)
|
||||
|
||||
if greedy_sampled is None:
|
||||
return random_sampled
|
||||
|
||||
sampled = torch.where(
|
||||
sampling_metadata.temperature < _SAMPLING_EPS,
|
||||
greedy_sampled,
|
||||
random_sampled,
|
||||
out=greedy_sampled, # Reuse tensor
|
||||
)
|
||||
return sampled
|
||||
|
||||
def compute_logprobs(self, logits: torch.Tensor) -> torch.Tensor:
|
||||
return logits.log_softmax(dim=-1, dtype=torch.float32)
|
||||
|
||||
def gather_logprobs(
|
||||
self,
|
||||
logprobs: torch.Tensor,
|
||||
num_logprobs: int,
|
||||
token_ids: torch.Tensor,
|
||||
) -> LogprobsTensors:
|
||||
"""
|
||||
Gather logprobs for topk and sampled/prompt token.
|
||||
|
||||
Args:
|
||||
logprobs: (num tokens) x (vocab) tensor
|
||||
num_logprobs: minimum number of logprobs to
|
||||
retain per token
|
||||
token_ids: prompt tokens (if prompt logprobs)
|
||||
or sampled tokens (if sampled
|
||||
logprobs); 1D token ID tensor
|
||||
with (num tokens) elements
|
||||
Must be int64.
|
||||
|
||||
Returns:
|
||||
Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
|
||||
Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
|
||||
Sampled token rank tensor, (num tokens)
|
||||
"""
|
||||
assert token_ids.dtype == torch.int64
|
||||
# Find the topK values.
|
||||
topk_logprobs, topk_indices = torch.topk(logprobs,
|
||||
num_logprobs,
|
||||
dim=-1)
|
||||
|
||||
# Get with the logprob of the prompt or sampled token.
|
||||
token_ids = token_ids.unsqueeze(-1)
|
||||
token_logprobs = logprobs.gather(-1, token_ids)
|
||||
|
||||
# Compute the ranks of the actual token.
|
||||
token_ranks = (logprobs >= token_logprobs).sum(-1)
|
||||
|
||||
# Concatenate together with the topk.
|
||||
indices = torch.cat((token_ids, topk_indices), dim=1)
|
||||
logprobs = torch.cat((token_logprobs, topk_logprobs), dim=1)
|
||||
|
||||
# Use int32 to reduce the tensor size.
|
||||
indices = indices.to(torch.int32)
|
||||
|
||||
return LogprobsTensors(indices, logprobs, token_ranks)
|
||||
|
||||
def apply_penalties(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> torch.Tensor:
|
||||
if sampling_metadata.min_tokens:
|
||||
apply_min_token_penalties(logits,
|
||||
sampling_metadata.output_token_ids,
|
||||
sampling_metadata.min_tokens)
|
||||
if not sampling_metadata.no_penalties:
|
||||
assert sampling_metadata.prompt_token_ids is not None
|
||||
logits = apply_all_penalties(
|
||||
logits,
|
||||
sampling_metadata.prompt_token_ids,
|
||||
sampling_metadata.presence_penalties,
|
||||
sampling_metadata.frequency_penalties,
|
||||
sampling_metadata.repetition_penalties,
|
||||
sampling_metadata.output_token_ids,
|
||||
)
|
||||
return logits
|
||||
|
||||
def apply_min_p(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
min_p: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Filters logits using adaptive probability thresholding.
|
||||
"""
|
||||
# Convert logits to probability distribution
|
||||
probability_values = torch.nn.functional.softmax(logits, dim=-1)
|
||||
# Calculate maximum probabilities per sequence
|
||||
max_probabilities = torch.amax(probability_values,
|
||||
dim=-1,
|
||||
keepdim=True)
|
||||
# Reshape min_p for broadcasting
|
||||
adjusted_min_p = min_p.unsqueeze(1) * max_probabilities
|
||||
# Identify valid tokens using threshold comparison
|
||||
valid_token_mask = probability_values >= adjusted_min_p
|
||||
# Apply mask using boolean indexing
|
||||
logits[~valid_token_mask] = -float('inf')
|
||||
return logits
|
||||
|
||||
def apply_logits_bias(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> torch.Tensor:
|
||||
# TODO(houseroad): this implementation is extremely inefficient.
|
||||
# One idea is implement this as a PyTorch C++ op, and we may
|
||||
# even optimize the logit_bias layout.
|
||||
|
||||
rows: list[int] = []
|
||||
cols: list[int] = []
|
||||
vals: list[float] = []
|
||||
|
||||
# Get vocabulary size from logits
|
||||
vocab_size = logits.shape[-1]
|
||||
|
||||
for i, logit_bias in enumerate(sampling_metadata.logit_bias):
|
||||
if logit_bias:
|
||||
for token_id, bias in logit_bias.items():
|
||||
# Check token_id bounds to ensure within vocabulary
|
||||
if token_id < 0 or token_id >= vocab_size:
|
||||
raise ValueError(
|
||||
f"token_id {token_id} in logit_bias contains "
|
||||
f"out-of-vocab token id. Vocabulary size: "
|
||||
f"{vocab_size}")
|
||||
rows.append(i)
|
||||
cols.append(token_id)
|
||||
vals.append(bias)
|
||||
|
||||
if rows:
|
||||
indices = async_tensor_h2d([rows, cols], torch.int64,
|
||||
logits.device, self.pin_memory)
|
||||
values = async_tensor_h2d(vals, torch.float, logits.device,
|
||||
self.pin_memory)
|
||||
logits.index_put_(tuple(indices), values=values, accumulate=True)
|
||||
return logits
|
||||
|
||||
def apply_allowed_token_ids(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> torch.Tensor:
|
||||
if sampling_metadata.allowed_token_ids_mask is not None:
|
||||
logits.masked_fill_(sampling_metadata.allowed_token_ids_mask,
|
||||
float("-inf"))
|
||||
return logits
|
||||
|
||||
def apply_bad_words(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> torch.Tensor:
|
||||
if sampling_metadata.bad_words_token_ids:
|
||||
apply_bad_words(
|
||||
logits,
|
||||
sampling_metadata.bad_words_token_ids,
|
||||
sampling_metadata.output_token_ids,
|
||||
)
|
||||
return logits
|
||||
Reference in New Issue
Block a user