wehub-resource-sync
245 lines
9.3 KiB
Python
245 lines
9.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import numpy as np
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import torch
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import vllm.envs as envs
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from vllm.config.model import LogprobsMode
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from vllm.sampling_params import SamplingParams
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from vllm.v1.sample.ops.topk_topp_sampler import (
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apply_top_k_top_p,
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flashinfer_sample,
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flashinfer_sampler_supported,
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)
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from vllm.v1.worker.gpu.input_batch import InputBatch, get_num_sampled_and_rejected
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from vllm.v1.worker.gpu.metrics.logits import get_num_nans
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from vllm.v1.worker.gpu.sample.bad_words import BadWordsState
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from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
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from vllm.v1.worker.gpu.sample.logit_bias import LogitBiasState
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from vllm.v1.worker.gpu.sample.logprob import (
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LogprobTokenIdsState,
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compute_topk_logprobs,
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)
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from vllm.v1.worker.gpu.sample.output import SamplerOutput
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from vllm.v1.worker.gpu.sample.penalties import PenaltiesState
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from vllm.v1.worker.gpu.sample.states import NO_LOGPROBS, SamplingStates
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from vllm.v1.worker.gpu.states import RequestState
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class Sampler:
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def __init__(
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self,
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max_num_reqs: int,
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vocab_size: int,
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device: torch.device,
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req_states: RequestState,
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logprobs_mode: LogprobsMode = "raw_logprobs",
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num_speculative_tokens: int = 1,
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use_fp64_gumbel: bool = False,
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):
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if logprobs_mode not in ("processed_logprobs", "raw_logprobs"):
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raise NotImplementedError(f"Unsupported logprobs_mode: {logprobs_mode}")
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self.logprobs_mode = logprobs_mode
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self.compute_nans = envs.VLLM_COMPUTE_NANS_IN_LOGITS # False by default.
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self.use_fp64_gumbel = use_fp64_gumbel
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self.req_states = req_states
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self.sampling_states = SamplingStates(max_num_reqs, vocab_size)
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self.penalties_state = PenaltiesState(req_states)
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self.logit_bias_state = LogitBiasState(max_num_reqs, device)
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self.bad_words_state = BadWordsState(req_states)
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self.logprob_token_ids_state = LogprobTokenIdsState(max_num_reqs, device)
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self.num_speculative_tokens = num_speculative_tokens
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self.use_flashinfer = flashinfer_sampler_supported()
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def add_request(
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self, req_idx: int, prompt_len: int, sampling_params: SamplingParams
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) -> None:
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self.sampling_states.add_request(req_idx, sampling_params)
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self.penalties_state.add_request(req_idx, sampling_params)
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self.logit_bias_state.add_request(req_idx, prompt_len, sampling_params)
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self.bad_words_state.add_request(req_idx, sampling_params)
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self.logprob_token_ids_state.add_request(req_idx, sampling_params)
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def apply_staged_writes(self) -> None:
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self.sampling_states.apply_staged_writes()
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self.penalties_state.apply_staged_writes()
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self.logit_bias_state.apply_staged_writes()
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self.bad_words_state.apply_staged_writes()
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self.logprob_token_ids_state.apply_staged_writes()
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def __call__(
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self,
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logits: torch.Tensor,
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input_batch: InputBatch,
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) -> SamplerOutput:
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expanded_idx_mapping = input_batch.expanded_idx_mapping
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idx_mapping_np = input_batch.idx_mapping_np
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cu_num_logits_np = input_batch.cu_num_logits_np
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expanded_local_pos = input_batch.expanded_local_pos
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pos = input_batch.positions[input_batch.logits_indices]
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input_ids = input_batch.input_ids[input_batch.logits_indices]
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# NOTE(woosuk): We intentionally compute num_nans before sampling to make clear
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# that num_nans is computed before applying penalties and temperature.
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num_nans = get_num_nans(logits) if self.compute_nans else None
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max_num_logprobs = self.sampling_states.max_num_logprobs(idx_mapping_np)
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max_per_req_token_ids = self.logprob_token_ids_state.max_num_token_ids(
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idx_mapping_np
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)
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return_logprobs = max_num_logprobs != NO_LOGPROBS or max_per_req_token_ids > 0
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sampled, processed_logits = self.sample(
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logits,
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expanded_idx_mapping,
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idx_mapping_np,
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pos,
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input_ids,
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expanded_local_pos,
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return_logprobs=return_logprobs,
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)
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if return_logprobs:
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if self.logprobs_mode == "processed_logprobs":
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logits = processed_logits
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expanded_logits = logits.shape[0] != idx_mapping_np.shape[0]
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cu_num_logits = cu_num_logits_np.tolist() if expanded_logits else None
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num_logprobs = max_num_logprobs if max_num_logprobs != NO_LOGPROBS else 0
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logprobs_tensors = compute_topk_logprobs(
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logits,
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num_logprobs,
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sampled,
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cu_num_logits,
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logprob_token_ids_state=self.logprob_token_ids_state,
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expanded_idx_mapping=input_batch.expanded_idx_mapping,
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max_per_req_token_ids=max_per_req_token_ids,
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)
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else:
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logprobs_tensors = None
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# 1 sampled token per request, except chunked-prefill requests
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# (seq_len < prefill_len) which aren't done prefilling and produce no
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# output token. num_rejected is always 0 here (one logit per request).
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num_sampled, num_rejected = get_num_sampled_and_rejected(
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input_batch.seq_lens.new_ones(input_batch.num_reqs),
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input_batch.seq_lens,
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input_batch.cu_num_logits,
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input_batch.idx_mapping,
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self.req_states.prefill_len.gpu,
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)
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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.view(-1, 1),
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logprobs_tensors=logprobs_tensors,
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num_nans=num_nans,
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num_sampled=num_sampled,
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num_rejected=num_rejected,
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)
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return sampler_output
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def apply_sampling_params(
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self,
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logits: torch.Tensor,
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expanded_idx_mapping: torch.Tensor,
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idx_mapping_np: np.ndarray,
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pos: torch.Tensor,
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input_ids: torch.Tensor,
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expanded_local_pos: torch.Tensor,
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skip_top_k_top_p: bool = False,
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) -> torch.Tensor:
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# Copy logits to a new FP32 tensor.
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logits = torch.empty_like(logits, dtype=torch.float32).copy_(logits)
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# Apply logit bias (e.g., allowed_token_ids, min_tokens) in place.
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self.logit_bias_state.apply_logit_bias(
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logits, expanded_idx_mapping, idx_mapping_np, pos
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)
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# Apply penalties in place.
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self.penalties_state.apply_penalties(
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logits,
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expanded_idx_mapping,
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idx_mapping_np,
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input_ids,
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expanded_local_pos,
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)
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# Apply bad words masking in place.
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self.bad_words_state.apply_bad_words(
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logits,
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expanded_idx_mapping,
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idx_mapping_np,
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input_ids,
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expanded_local_pos,
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)
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# Apply temperature in place.
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self.sampling_states.apply_temperature(
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logits, expanded_idx_mapping, idx_mapping_np
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)
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# Apply min_p in place.
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self.sampling_states.apply_min_p(logits, expanded_idx_mapping, idx_mapping_np)
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if skip_top_k_top_p:
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return logits
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# Apply top_k and/or top_p. This might or might not return a new tensor.
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return self.sampling_states.apply_top_k_top_p(
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logits, expanded_idx_mapping, idx_mapping_np
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)
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def sample(
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self,
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logits: torch.Tensor,
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expanded_idx_mapping: torch.Tensor,
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idx_mapping_np: np.ndarray,
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pos: torch.Tensor,
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input_ids: torch.Tensor,
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expanded_local_pos: torch.Tensor,
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return_logprobs: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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processed_logits = self.apply_sampling_params(
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logits,
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expanded_idx_mapping,
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idx_mapping_np,
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pos,
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input_ids,
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expanded_local_pos,
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skip_top_k_top_p=True,
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)
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top_k, top_p = self.sampling_states.get_top_k_top_p(
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expanded_idx_mapping, idx_mapping_np
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)
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use_flashinfer = self.use_flashinfer and not (
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# Don't use FI sampler if no requests use top_k/top_p, if there are
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# any greedy requests or per-request seeds, or if post-processed
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# logprobs need to be returned for any requests.
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(top_k is None and top_p is None)
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or (return_logprobs and self.logprobs_mode == "processed_logprobs")
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or self.sampling_states.any_greedy(idx_mapping_np)
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or self.sampling_states.any_explicit_seed(idx_mapping_np)
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)
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# Sample the next token.
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if use_flashinfer:
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sampled = flashinfer_sample(processed_logits, top_k, top_p).to(torch.int64)
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else:
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processed_logits = apply_top_k_top_p(processed_logits, top_k, top_p)
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sampled = gumbel_sample(
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processed_logits,
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expanded_idx_mapping,
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self.sampling_states.temperature.gpu,
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self.sampling_states.seeds.gpu,
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pos,
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apply_temperature=False,
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use_fp64=self.use_fp64_gumbel,
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)
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return sampled, processed_logits
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