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48fb076cbc
Signed-off-by: Nick Hill <nhill@redhat.com> Signed-off-by: Andrew Feldman <afeldman@neuralmagic.com> Signed-off-by: Andrew Feldman <afeldman@redhat.com> Co-authored-by: Nick Hill <nhill@redhat.com>
227 lines
8.2 KiB
Python
227 lines
8.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""A layer that samples the next tokens from the model's outputs."""
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import torch
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import torch.nn as nn
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from vllm.utils import is_pin_memory_available
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from vllm.v1.outputs import LogprobsTensors, SamplerOutput
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.ops.bad_words import apply_bad_words
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from vllm.v1.sample.ops.penalties import apply_all_penalties
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from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler
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_SAMPLING_EPS = 1e-5
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class Sampler(nn.Module):
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def __init__(self):
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super().__init__()
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self.topk_topp_sampler = TopKTopPSampler()
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self.pin_memory = is_pin_memory_available()
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def forward(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> SamplerOutput:
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# NOTE(woosuk): Use the original logits (before any penalties or
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# temperature scaling) for the top-k logprobs.
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# This is different from the V0 sampler, which uses the logits that
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# is used for sampling (after penalties and temperature scaling).
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# TODO(rob): provide option for logprobs post sampling.
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# See https://vllm-dev.slack.com/archives/C07UUL8E61Z/p1735907856007919 # noqa: E501
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num_logprobs = sampling_metadata.max_num_logprobs
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if num_logprobs is not None:
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raw_logprobs = self.compute_logprobs(logits)
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# Use float32 for the logits.
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logits = logits.to(torch.float32)
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# Apply allowed token ids.
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logits = self.apply_allowed_token_ids(logits, sampling_metadata)
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# Apply bad words exclusion.
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logits = self.apply_bad_words(logits, sampling_metadata)
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# Apply logits processors which can impact greedy sampling
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for processor in (sampling_metadata.logitsprocs.non_argmax_invariant):
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logits = processor.apply(logits)
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# Apply penalties (e.g., min_tokens, freq_penalties).
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logits = self.apply_penalties(logits, sampling_metadata)
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# Sample the next token.
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sampled = self.sample(logits, sampling_metadata)
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# Convert sampled token ids to int64 (long) type to ensure compatibility
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# with subsequent operations that may use these values as indices.
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# This conversion is necessary because FlashInfer sampling operations
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# return int32 (while PyTorch argmax and topk return int64).
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sampled = sampled.long()
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# Gather the logprobs of the topk and sampled token (if requested).
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# Get logprobs and rank tensors (if requested)
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logprobs_tensors = None if num_logprobs is None else \
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self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=sampled)
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# Use int32 to reduce the tensor size.
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sampled = sampled.to(torch.int32)
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# These are GPU tensors.
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sampler_output = SamplerOutput(
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# The sampled tokens are expanded to 2D tensor with shape
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# [num_requests, 1], where each row represents one generated
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# token per request.
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sampled_token_ids=sampled.unsqueeze(-1),
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logprobs_tensors=logprobs_tensors,
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)
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return sampler_output
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def apply_temperature(
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self,
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logits: torch.Tensor,
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temp: torch.Tensor,
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) -> torch.Tensor:
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# Use in-place division to avoid creating a new tensor.
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return logits.div_(temp.unsqueeze(dim=1))
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def greedy_sample(self, logits: torch.Tensor) -> torch.Tensor:
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return logits.argmax(dim=-1).view(-1)
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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"""Sample logits based on sampling metadata.
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The various logits processing functions called in this method
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may update the logits tensor in-place.
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"""
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assert not (sampling_metadata.all_greedy
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and sampling_metadata.all_random)
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if sampling_metadata.all_random:
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greedy_sampled = None
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else:
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greedy_sampled = self.greedy_sample(logits)
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if sampling_metadata.all_greedy:
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return greedy_sampled
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assert sampling_metadata.temperature is not None
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# Apply temperature.
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logits = self.apply_temperature(logits, sampling_metadata.temperature)
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# Apply logits processors that only apply to random sampling
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# (argmax invariant)
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for processor in sampling_metadata.logitsprocs.argmax_invariant:
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logits = processor.apply(logits)
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# Apply top_k and/or top_p.
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random_sampled = self.topk_topp_sampler(
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logits,
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sampling_metadata.generators,
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sampling_metadata.top_k,
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sampling_metadata.top_p,
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)
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if greedy_sampled is None:
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return random_sampled
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sampled = torch.where(
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sampling_metadata.temperature < _SAMPLING_EPS,
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greedy_sampled,
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random_sampled,
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out=greedy_sampled, # Reuse tensor
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)
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return sampled
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def compute_logprobs(self, logits: torch.Tensor) -> torch.Tensor:
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return logits.log_softmax(dim=-1, dtype=torch.float32)
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def gather_logprobs(
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self,
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logprobs: torch.Tensor,
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num_logprobs: int,
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token_ids: torch.Tensor,
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) -> LogprobsTensors:
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"""
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Gather logprobs for topk and sampled/prompt token.
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Args:
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logprobs: (num tokens) x (vocab) tensor
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num_logprobs: minimum number of logprobs to
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retain per token
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token_ids: prompt tokens (if prompt logprobs)
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or sampled tokens (if sampled
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logprobs); 1D token ID tensor
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with (num tokens) elements
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Must be int64.
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Returns:
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Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
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Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
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Sampled token rank tensor, (num tokens)
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"""
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assert token_ids.dtype == torch.int64
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# Find the topK values.
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topk_logprobs, topk_indices = torch.topk(logprobs,
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num_logprobs,
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dim=-1)
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# Get with the logprob of the prompt or sampled token.
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token_ids = token_ids.unsqueeze(-1)
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token_logprobs = logprobs.gather(-1, token_ids)
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# Compute the ranks of the actual token.
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token_ranks = (logprobs >= token_logprobs).sum(-1)
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# Concatenate together with the topk.
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indices = torch.cat((token_ids, topk_indices), dim=1)
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logprobs = torch.cat((token_logprobs, topk_logprobs), dim=1)
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# Use int32 to reduce the tensor size.
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indices = indices.to(torch.int32)
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return LogprobsTensors(indices, logprobs, token_ranks)
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def apply_penalties(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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if not sampling_metadata.no_penalties:
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assert sampling_metadata.prompt_token_ids is not None
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logits = apply_all_penalties(
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logits,
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sampling_metadata.prompt_token_ids,
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sampling_metadata.presence_penalties,
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sampling_metadata.frequency_penalties,
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sampling_metadata.repetition_penalties,
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sampling_metadata.output_token_ids,
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)
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return logits
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def apply_allowed_token_ids(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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if sampling_metadata.allowed_token_ids_mask is not None:
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logits.masked_fill_(sampling_metadata.allowed_token_ids_mask,
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float("-inf"))
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return logits
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def apply_bad_words(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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if sampling_metadata.bad_words_token_ids:
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apply_bad_words(
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logits,
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sampling_metadata.bad_words_token_ids,
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sampling_metadata.output_token_ids,
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)
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return logits
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