mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
a7359d1c1d
* fix mtp entropy drop in RL * optimize usage and fix unit test * optimize padding_sampling_params speed(vectorized)
1162 lines
46 KiB
Python
1162 lines
46 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import multiprocessing
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import time
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from concurrent.futures import Future, ThreadPoolExecutor
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from typing import Any, List, Optional
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig
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from fastdeploy.envs import FD_FILL_BITMASK_BATCH
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from fastdeploy.model_executor.guided_decoding import LogitsProcessorBase
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from fastdeploy.model_executor.layers.sample.early_stopper import (
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get_early_stopper_cls_from_stragegy,
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)
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from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
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from fastdeploy.model_executor.layers.sample.ops import (
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apply_penalty_multi_scores,
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apply_speculative_penalty_multi_scores,
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min_p_sampling,
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speculate_get_target_logits,
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speculate_insert_first_token,
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top_k_top_p_sampling,
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)
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from fastdeploy.platforms import current_platform
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from fastdeploy.reasoning import ReasoningParser
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from fastdeploy.worker.output import LogprobsTensors, SamplerOutput
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def top_p_normalize_probs_paddle(
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probs: paddle.Tensor,
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top_ps: paddle.Tensor,
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):
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probs_idx = probs.argsort(axis=-1, descending=True)
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probs_sort = paddle.take_along_axis(probs, probs_idx, axis=-1)
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probs_sum = paddle.cumsum(probs_sort, axis=-1)
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probs_sort = paddle.where((probs_sum - probs_sort) > top_ps, paddle.zeros_like(probs_sort), probs_sort)
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probs_sort.divide_(probs_sort.sum(axis=-1, keepdim=True))
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return paddle.zeros_like(probs_sort).put_along_axis_(indices=probs_idx, values=probs_sort, axis=-1)
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def padding_sampling_params(top_p, top_k, infer_seed, seq_lens_this_time, seq_lens_encoder):
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real_bsz = seq_lens_this_time.shape[0]
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repeats = paddle.where(seq_lens_encoder[:real_bsz] == 0, seq_lens_this_time, paddle.ones_like(seq_lens_this_time))
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top_p_padding = paddle.repeat_interleave(top_p[:real_bsz], repeats).unsqueeze(1)
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top_k_padding = paddle.repeat_interleave(top_k[:real_bsz], repeats).unsqueeze(1)
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topp_seed = paddle.repeat_interleave(infer_seed[:real_bsz], repeats).unsqueeze(1)
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MAX_INFER_SEED = 9223372036854775806
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token_lens = paddle.where(
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seq_lens_encoder[:real_bsz] == 0,
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seq_lens_this_time,
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paddle.ones_like(seq_lens_this_time),
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)
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batch_start = (paddle.cumsum(token_lens, axis=0) - token_lens.astype("int64")).reshape(-1) # [B]
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token_batch_ids = paddle.repeat_interleave(
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paddle.arange(token_lens.shape[0], dtype="int64"),
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token_lens,
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)
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token_pos = paddle.arange(topp_seed.shape[0], dtype="int64")
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local_pos = token_pos - paddle.gather(batch_start, token_batch_ids)
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is_decoder = paddle.gather(seq_lens_encoder[:real_bsz] == 0, token_batch_ids).reshape(-1)
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offsets = paddle.where(
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is_decoder,
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local_pos * 4,
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paddle.zeros_like(local_pos),
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)
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topp_seed[:, 0] = (topp_seed[:, 0] + offsets) % MAX_INFER_SEED
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return top_p_padding, top_k_padding, topp_seed
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class GuidedDecoding:
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"""
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processor for guided decoding.
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"""
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def __init__(self, fd_config: FDConfig):
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self.token_bitmask = None
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self.max_num_seqs: int = int(
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fd_config.scheduler_config.max_num_seqs if fd_config.scheduler_config is not None else 1
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)
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self.logits_processors: List[Any] = [None] * self.max_num_seqs
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self.reasoning_parser = None
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self._prefill_done_idxs: List[bool] = [False] * self.max_num_seqs
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# for pd
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self._tokens_to_acc: List[None | List[int]] = [None] * self.max_num_seqs
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self.fill_bitmask_parallel_batch_size: int = FD_FILL_BITMASK_BATCH
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max_workers = max(
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1,
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min(multiprocessing.cpu_count() // 2, int(self.max_num_seqs) / int(self.fill_bitmask_parallel_batch_size)),
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)
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self.executor_for_fillmask = ThreadPoolExecutor(max_workers=int(max_workers))
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self._fillmask_futures: List[Future] = [None] * self.max_num_seqs
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self.is_cuda_platform = current_platform.is_cuda()
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logger.info(
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f"GuidedDecoding max_num_seqs={self.max_num_seqs} fill_bitmask_parallel_batch_size={self.fill_bitmask_parallel_batch_size} is_cuda_platform={self.is_cuda_platform} max_workers={max_workers}"
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)
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def apply_reasoning_parser(self, reasoning_parser: Optional[ReasoningParser] = None):
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self.reasoning_parser = reasoning_parser
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def add_logits_processor(
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self,
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idx: int,
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future: Optional[Any] = None,
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prefill_tokens: List[int] = [],
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):
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"""add logits processor to SamplerProcessor"""
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self._prefill_done_idxs[idx] = False
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if future is None:
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# normal request without guided_backend
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self.logits_processors[idx] = None
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return
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if len(prefill_tokens) != 0:
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# first_token from prefill node
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self._prefill_done_idxs[idx] = True
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if future.done():
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# cached xgrammar
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self.logits_processors[idx] = future.result()
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for token in prefill_tokens:
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self._accept_token(idx, token)
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else:
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# async
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self.logits_processors[idx] = future
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self._tokens_to_acc[idx] = prefill_tokens
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def should_fill_bitmask(self, idx: int) -> bool:
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"""
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Determines whether to fill a bitmask for the logits processor at the given index.
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Args:
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idx (int): The index of the logits processor to check
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Returns:
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bool: True if the idx request bitmask should be filled
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"""
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if self.reasoning_parser is not None:
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if self.logits_processors[idx].enable_reasoning: # <think> guided
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return True
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if not self.logits_processors[idx].reasoning_ended:
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return False
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return True
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def reset_processor(self, idx: int):
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"""reset idx"""
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self._prefill_done_idxs[idx] = False
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self.logits_processors[idx] = None
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def update_vocab_mask(self, prefill_done_idxs: List[int] = []):
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"""update vocab mask. (cpu-heavy operation)"""
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for idx in prefill_done_idxs:
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if self.logits_processors[idx] is None:
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continue
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assert not self._prefill_done_idxs[idx]
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self._prefill_done_idxs[idx] = True
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if isinstance(self.logits_processors[idx], Future):
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continue
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idxs = []
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for idx, processor in enumerate(self.logits_processors):
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if processor is None or not self._prefill_done_idxs[idx]:
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continue
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# skip, join at apply_token_mask
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if isinstance(processor, Future):
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continue
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if processor.is_terminated:
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self.reset_processor(idx)
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continue
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self.accept_tokens_from_prefill_node(idx)
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if self.token_bitmask is None:
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self.token_bitmask = self.logits_processors[idx].allocate_token_bitmask()
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if self.should_fill_bitmask(idx):
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idxs.append(idx)
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self._async_batch_fill_token_bitmask(idxs)
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def batch_fill_token_bitmask(self, batch: List[int]):
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"""
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Fills the token bitmask for a batch of logits processor indices.
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This method is typically called asynchronously via a thread pool executor
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to parallelize the bitmask filling operation. It is important that any
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shared data structures accessed within this method (such as
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`self.token_bitmask` and `self.logits_processors`) are thread-safe or
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properly synchronized to avoid race conditions.
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Args:
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batch (List[int]): List of indices for which to fill the token bitmask.
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"""
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for idx in batch:
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self.logits_processors[idx].fill_token_bitmask(self.token_bitmask, idx)
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def _async_batch_fill_token_bitmask(self, idxs: List[int]):
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"""launch async fill"""
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batch: List[int] = []
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for idx in idxs:
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batch.append(idx)
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if len(batch) == self.fill_bitmask_parallel_batch_size:
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promise = self.executor_for_fillmask.submit(self.batch_fill_token_bitmask, batch[:])
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self._fillmask_futures[idx] = promise
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batch = []
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if batch:
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promise = self.executor_for_fillmask.submit(self.batch_fill_token_bitmask, batch[:])
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self._fillmask_futures[batch[-1]] = promise
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def join_async_fillmask(self):
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"""join all async fill futures"""
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for idx, furture in enumerate(self._fillmask_futures):
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if furture is not None:
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try:
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furture.result()
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except Exception as e:
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logger.error(f"Exception in async fillmask future at idx {idx}: {e}", exc_info=True)
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self._fillmask_futures[idx] = None
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def accept_tokens_from_prefill_node(self, idx: int):
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"""accept prefill token, not future"""
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if self._tokens_to_acc[idx] is not None:
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# accept token from prefill node first
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for token in self._tokens_to_acc[idx]:
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self._accept_token(idx, token)
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self._tokens_to_acc[idx] = None
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def apply_token_mask(self, logits: paddle.Tensor, prefill_done_idxs: List[int] = []):
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"""apply token mask to logits"""
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indices = []
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for idx, processor in enumerate(self.logits_processors):
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if processor is None or not self._prefill_done_idxs[idx]:
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continue
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# compiled done, check idx should fill, fill_token_bitmask done in preprocess
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if not isinstance(processor, Future):
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if self.should_fill_bitmask(idx):
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indices.append(idx)
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continue
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# is Future, processor async compiled not ready, need join and wait
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ts = time.time()
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wait = False
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if not processor.done():
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wait = True
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self.logits_processors[idx] = processor.result()
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if wait:
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logger.debug(f"[{idx} join async compile xgrammar, time_cost:{time.time() - ts}]")
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self.accept_tokens_from_prefill_node(idx)
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# Possible optimization: Extract 'think' content validation from logits_processors,
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# allowing join operations to complete immediately after 'think' terminates.
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# Furthermore, the current idx could be skipped, with compilation overhead
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# estimated at only a few milliseconds.
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# check idx for fill_token_mask
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if not self.should_fill_bitmask(idx):
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continue
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indices.append(idx)
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if self.token_bitmask is None:
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self.token_bitmask = self.logits_processors[idx].allocate_token_bitmask()
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# launch async fill
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self._async_batch_fill_token_bitmask([idx])
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if len(indices) == 0:
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return logits
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self.join_async_fillmask()
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from fastdeploy.model_executor.guided_decoding.xgrammar_backend import (
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apply_token_mask,
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)
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return apply_token_mask(logits, self.token_bitmask, indices=indices, is_cuda_platform=self.is_cuda_platform)
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def _accept_token(self, idx: int, token: int):
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"""accept token"""
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if self.reasoning_parser is not None:
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if not self.logits_processors[idx].enable_reasoning:
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if not self.logits_processors[idx].reasoning_ended:
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reasoning_ended = self.reasoning_parser.is_reasoning_end([token])
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self.logits_processors[idx].reasoning_ended = reasoning_ended
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return
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if not self.logits_processors[idx].accept_token(token) or self.logits_processors[idx].is_terminated:
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self.reset_processor(idx)
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def update_output_tokens(self, next_tokens: paddle.Tensor):
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"""update output tokens"""
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if len(self.logits_processors) == 0:
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return
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token_ids = next_tokens.numpy().tolist()
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for idx, processor in enumerate(self.logits_processors):
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if not self._prefill_done_idxs[idx] or processor is None:
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continue
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if idx >= len(token_ids):
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continue
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token = token_ids[idx][0]
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if token < 0:
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self.reset_processor(idx)
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continue
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logger.debug(f"[{idx}]accept token{token}")
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self._accept_token(idx, token)
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def pre_process(self, prefill_done_idxs: List[int] = []):
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"""pre process before running"""
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self.update_vocab_mask(prefill_done_idxs)
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class Sampler(nn.Layer):
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"""
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Sampler for normal generation.
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"""
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def __init__(self, fd_config: FDConfig = None, logprobs_mode: str = "raw_logprobs"):
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""" """
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super().__init__()
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if (
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current_platform.is_cuda()
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or current_platform.is_xpu()
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or current_platform.is_iluvatar()
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or current_platform.is_gcu()
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or current_platform.is_dcu()
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or current_platform.is_maca()
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):
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self.forward = self.forward_cuda
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elif current_platform.is_intel_hpu():
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self.forward = self.forward_intel_hpu
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else:
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raise NotImplementedError
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self.guided_decoding = GuidedDecoding(fd_config)
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self.logprobs_mode = fd_config.model_config.logprobs_mode if fd_config is not None else logprobs_mode
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# Can only be created when fd_config.early_stopper_config.enable_early_stop = True
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if (
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fd_config is not None
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and fd_config.early_stop_config is not None
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and fd_config.early_stop_config.enable_early_stop
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):
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early_stopper_cls = get_early_stopper_cls_from_stragegy(fd_config.early_stop_config.strategy)
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self.early_stopper = early_stopper_cls()
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self.early_stopper.initialize(fd_config.scheduler_config.max_num_seqs, fd_config.early_stop_config)
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def set_reasoning_parser(self, reasoning_parser: Optional[ReasoningParser] = None):
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"""set reasoning parser"""
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self.guided_decoding.apply_reasoning_parser(reasoning_parser)
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def apply_logits_processor(
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self, ids: int, future: Future[LogitsProcessorBase] = None, prefill_tokens: List[int] = []
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):
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"""apply logits processor to sampler"""
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self.guided_decoding.add_logits_processor(ids, future, prefill_tokens)
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def pre_process(self, prefill_done_idxs: List[int] = []):
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"""pre process before running"""
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self.guided_decoding.pre_process(prefill_done_idxs)
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def post_process(self, next_tokens: paddle.Tensor):
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"""post process after running"""
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self.guided_decoding.update_output_tokens(next_tokens)
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def compute_logprobs(
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self,
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logits: paddle.Tensor,
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sampling_metadata: Optional[SamplingMetadata] = None,
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) -> paddle.Tensor:
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""" """
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if sampling_metadata is None:
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return F.log_softmax(logits, axis=-1)
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last_logits = logits
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real_bsz = last_logits.shape[0]
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temp_scaled_logprobs = sampling_metadata.temp_scaled_logprobs
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top_p_normalized_logprobs = sampling_metadata.top_p_normalized_logprobs
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share_inputs = sampling_metadata.share_inputs
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if temp_scaled_logprobs is not None and sampling_metadata.temp_scaled_logprobs_flag:
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real_bsz_temp_scaled = temp_scaled_logprobs[:real_bsz]
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temperature = sampling_metadata.temperature[:real_bsz]
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temp_temperature = paddle.where(real_bsz_temp_scaled, temperature, paddle.ones_like(temperature))
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last_logits = last_logits / temp_temperature
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last_logprobs = F.log_softmax(last_logits, axis=-1)
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top_p_logprob = None
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top_p_req_mask = None
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if (
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top_p_normalized_logprobs is not None
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and share_inputs is not None
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and sampling_metadata.top_p_normalized_logprobs_flag
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):
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seq_lens_this_time = share_inputs["seq_lens_this_time"].reshape([-1, 1])[:real_bsz]
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seq_lens_encoder = share_inputs["seq_lens_encoder"].reshape([-1, 1])[:real_bsz]
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seq_lens_decoder = share_inputs["seq_lens_decoder"].reshape([-1, 1])[:real_bsz]
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seq_lens_time_sum = seq_lens_this_time + seq_lens_encoder + seq_lens_decoder
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real_req_mask = seq_lens_time_sum > 0
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top_p_req_mask = paddle.logical_and(top_p_normalized_logprobs[:real_bsz], real_req_mask)
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real_req_top_p = sampling_metadata.top_p[:real_bsz]
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# Normalize logprobs if top_p normalization is enabled
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# NOTE: only normalize logprobs when top_p is set and not equal to 1.0
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top_p_req_mask = paddle.logical_and(top_p_req_mask, real_req_top_p != 1.0)
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if top_p_req_mask.any():
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probs = F.softmax(last_logits, axis=-1)
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probs = top_p_normalize_probs_paddle(probs, real_req_top_p)
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top_p_logprob = paddle.log(probs)
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if top_p_logprob is not None:
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last_logprobs = paddle.where(top_p_req_mask, top_p_logprob, last_logprobs)
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return last_logprobs
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def gather_logprobs(
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self,
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logprobs: paddle.Tensor,
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num_logprobs: int,
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token_ids: paddle.Tensor,
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) -> 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 == paddle.int64
|
|
logprobs.clip_(min=paddle.finfo(logprobs.dtype).min)
|
|
# Get with the logprob of the prompt or sampled token.
|
|
if len(token_ids.shape) < len(logprobs.shape):
|
|
token_ids = token_ids.unsqueeze(-1)
|
|
token_logprobs = paddle.take_along_axis(logprobs, token_ids, axis=-1)
|
|
|
|
# Compute the ranks of the actual token.
|
|
token_ranks = (logprobs >= token_logprobs).sum(-1)
|
|
|
|
if num_logprobs >= 1:
|
|
# Find the topK values.
|
|
topk_logprobs, topk_indices = paddle.topk(logprobs, num_logprobs, axis=-1)
|
|
indices = paddle.concat([token_ids, topk_indices], axis=1)
|
|
top_logprobs = paddle.concat([token_logprobs, topk_logprobs], axis=1)
|
|
else:
|
|
indices = token_ids
|
|
top_logprobs = token_logprobs
|
|
indices = indices.cpu()
|
|
top_logprobs = top_logprobs.cpu()
|
|
token_ranks = token_ranks.cpu()
|
|
return LogprobsTensors(indices, top_logprobs, token_ranks)
|
|
|
|
def forward_cuda(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
p_done_idxs: List[int] = [],
|
|
) -> SamplerOutput:
|
|
""" """
|
|
logits = self.guided_decoding.apply_token_mask(logits, p_done_idxs)
|
|
|
|
num_logprobs = sampling_metadata.max_num_logprobs
|
|
if num_logprobs is not None:
|
|
if self.logprobs_mode == "raw_logprobs":
|
|
raw_logprobs = self.compute_logprobs(logits, sampling_metadata)
|
|
elif self.logprobs_mode == "raw_logits":
|
|
raw_logprobs = logits.clone()
|
|
|
|
for proc in sampling_metadata.logits_processors or []:
|
|
logits = proc.apply(logits)
|
|
|
|
logits = apply_penalty_multi_scores(
|
|
sampling_metadata.pre_token_ids,
|
|
sampling_metadata.prompt_ids,
|
|
sampling_metadata.prompt_lens,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
)
|
|
|
|
if num_logprobs is not None:
|
|
if self.logprobs_mode == "processed_logprobs":
|
|
raw_logprobs = self.compute_logprobs(logits, sampling_metadata)
|
|
elif self.logprobs_mode == "processed_logits":
|
|
raw_logprobs = logits.clone()
|
|
|
|
probs = F.softmax(logits)
|
|
|
|
probs = min_p_sampling(probs, sampling_metadata.min_p, sampling_metadata.min_p_list)
|
|
_, next_tokens = top_k_top_p_sampling(
|
|
probs,
|
|
sampling_metadata.top_p,
|
|
sampling_metadata.top_k,
|
|
sampling_metadata.top_k_list,
|
|
topp_seed=sampling_metadata.seed,
|
|
)
|
|
|
|
logprobs_tensors = (
|
|
None if num_logprobs is None else self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=next_tokens)
|
|
)
|
|
if sampling_metadata.enable_early_stop:
|
|
# will set the stop batch in stop_flags
|
|
assert sampling_metadata.stop_flags is not None, "need stop_flags for early stop"
|
|
self.early_stopper.process(probs, next_tokens, sampling_metadata.stop_flags)
|
|
|
|
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=next_tokens,
|
|
logprobs_tensors=logprobs_tensors,
|
|
)
|
|
|
|
return sampler_output
|
|
|
|
def forward_intel_hpu(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
batch_ids: paddle.Tensor,
|
|
max_batch: int,
|
|
rank: int,
|
|
local_rank: int,
|
|
) -> paddle.Tensor:
|
|
if logits.dtype != paddle.float32:
|
|
logits = paddle.cast(logits, paddle.float32)
|
|
|
|
from fastdeploy.model_executor.ops.intel_hpu import fused_sampler
|
|
|
|
_, next_tokens = fused_sampler(
|
|
sampling_metadata.pre_token_ids,
|
|
sampling_metadata.prompt_ids,
|
|
sampling_metadata.seq_lens_encoder,
|
|
sampling_metadata.seq_lens_decoder,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.stop_flags,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
sampling_metadata.top_p,
|
|
rank,
|
|
local_rank,
|
|
)
|
|
|
|
if next_tokens.shape[0] != max_batch:
|
|
dim = next_tokens.shape[-1]
|
|
tmp_tokens = paddle.full((max_batch, dim), -1 if local_rank == 0 else 0, dtype=next_tokens.dtype)
|
|
tmp_tokens = paddle.scatter(tmp_tokens, batch_ids, next_tokens[: batch_ids.shape[0], :])
|
|
return tmp_tokens
|
|
|
|
return next_tokens
|
|
|
|
|
|
class SpeculativeSampler(nn.Layer):
|
|
"""
|
|
Sampler for speculative generation.
|
|
"""
|
|
|
|
def __init__(self, fd_config: FDConfig):
|
|
""" """
|
|
super().__init__()
|
|
if current_platform.is_cuda():
|
|
self.forward = self.forward_cuda
|
|
elif current_platform.is_xpu():
|
|
self.forward = self.forward_xpu
|
|
else:
|
|
raise NotImplementedError
|
|
self.logprobs_mode = fd_config.model_config.logprobs_mode
|
|
self.speculative_verify_window = fd_config.speculative_config.verify_window
|
|
self.speculative_max_candidate_len = fd_config.speculative_config.max_candidate_len
|
|
self.speculative_benchmark_mode = fd_config.speculative_config.benchmark_mode
|
|
|
|
def pre_process(self, skip_idx_list: List[int] = []):
|
|
"""pre process before running"""
|
|
pass
|
|
|
|
def set_reasoning_parser(self, reasoning_parser: Optional[ReasoningParser] = None):
|
|
"""set reasoning parser"""
|
|
pass
|
|
|
|
def post_process(self, next_tokens: paddle.Tensor, skip_idx_list: List[int] = []):
|
|
"""post process after running"""
|
|
pass
|
|
|
|
def apply_logits_processor(self, ids: int, future: Optional[Any] = None, prefill_tokens: List[int] = []):
|
|
"""apply logits processor to sampler"""
|
|
pass
|
|
|
|
def compute_logprobs(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> paddle.Tensor:
|
|
"""compute logprobs"""
|
|
share_inputs = sampling_metadata.share_inputs
|
|
last_logits = logits
|
|
real_bsz = share_inputs["seq_lens_this_time"].shape[0]
|
|
batch_token_num = share_inputs["accept_num"][:real_bsz]
|
|
|
|
temp_scaled_logprobs = sampling_metadata.temp_scaled_logprobs
|
|
top_p_normalized_logprobs = sampling_metadata.top_p_normalized_logprobs
|
|
if temp_scaled_logprobs is not None:
|
|
real_bsz_temp_scaled = temp_scaled_logprobs[:real_bsz]
|
|
temperature = sampling_metadata.temperature[:real_bsz]
|
|
real_bsz_temp_scaled = (
|
|
real_bsz_temp_scaled.astype("int32").squeeze(1).repeat_interleave(batch_token_num).astype("bool")
|
|
)
|
|
temperature = temperature.squeeze(1).repeat_interleave(batch_token_num)
|
|
temp_temperature = paddle.where(
|
|
real_bsz_temp_scaled, temperature, paddle.ones_like(temperature)
|
|
).unsqueeze(1)
|
|
last_logits = last_logits / temp_temperature
|
|
|
|
last_logprobs = F.log_softmax(last_logits, axis=-1)
|
|
top_p_logprob = None
|
|
top_p_token_mask = None
|
|
|
|
if top_p_normalized_logprobs is not None and share_inputs is not None:
|
|
real_token_top_p = (
|
|
sampling_metadata.top_p[:real_bsz].squeeze(1).repeat_interleave(batch_token_num).unsqueeze(1)
|
|
)
|
|
top_p_normalized_logprobs = (
|
|
top_p_normalized_logprobs[:real_bsz]
|
|
.astype("int32")
|
|
.squeeze(1)
|
|
.repeat_interleave(batch_token_num)
|
|
.astype("bool")
|
|
.unsqueeze(1)
|
|
)
|
|
top_p_token_mask = paddle.logical_and(top_p_normalized_logprobs, real_token_top_p != 1.0)
|
|
if top_p_token_mask.any():
|
|
probs = F.softmax(last_logits, axis=-1)
|
|
probs = top_p_normalize_probs_paddle(probs, real_token_top_p)
|
|
top_p_logprob = paddle.log(probs)
|
|
if top_p_logprob is not None:
|
|
last_logprobs = paddle.where(top_p_token_mask, top_p_logprob, last_logprobs)
|
|
return last_logprobs
|
|
|
|
def gather_logprobs(
|
|
self,
|
|
logprobs: paddle.Tensor,
|
|
num_logprobs: int,
|
|
token_ids: paddle.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 == paddle.int64
|
|
token_ids = token_ids.unsqueeze(1)
|
|
logprobs.clip_(min=paddle.finfo(logprobs.dtype).min)
|
|
# Get with the logprob of the prompt or sampled token.
|
|
token_logprobs = paddle.take_along_axis(logprobs, token_ids, axis=-1)
|
|
|
|
# Compute the ranks of the actual token.
|
|
token_ranks = (logprobs >= token_logprobs).sum(-1)
|
|
|
|
if num_logprobs >= 1:
|
|
# Find the topK values.
|
|
topk_logprobs, topk_indices = paddle.topk(logprobs, num_logprobs, axis=-1)
|
|
indices = paddle.concat([token_ids, topk_indices], axis=1)
|
|
top_logprobs = paddle.concat([token_logprobs, topk_logprobs], axis=1)
|
|
else:
|
|
indices = token_ids
|
|
top_logprobs = token_logprobs
|
|
|
|
return LogprobsTensors(indices, top_logprobs, token_ranks)
|
|
|
|
def forward_cuda(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
max_model_len: int,
|
|
share_inputs: List[paddle.Tensor],
|
|
accept_all_drafts: bool = False,
|
|
reject_all_drafts: bool = False,
|
|
) -> paddle.Tensor:
|
|
""" """
|
|
|
|
from fastdeploy.model_executor.ops.gpu import speculate_verify, top_p_candidates
|
|
|
|
logits = apply_speculative_penalty_multi_scores(
|
|
sampling_metadata.pre_token_ids,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["output_padding_offset"],
|
|
share_inputs["output_cum_offsets"],
|
|
max_model_len,
|
|
)
|
|
|
|
probs = F.softmax(logits)
|
|
|
|
top_p, top_k, topp_seed = padding_sampling_params(
|
|
sampling_metadata.top_p,
|
|
sampling_metadata.top_k,
|
|
sampling_metadata.seed,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["seq_lens_encoder"],
|
|
)
|
|
_, sampled_token_ids = top_k_top_p_sampling(probs, top_p=top_p, top_k=top_k, topp_seed=topp_seed)
|
|
|
|
verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
|
|
probs,
|
|
sampling_metadata.top_p,
|
|
share_inputs["output_padding_offset"],
|
|
self.speculative_max_candidate_len,
|
|
max_model_len,
|
|
)
|
|
|
|
speculate_verify(
|
|
sampled_token_ids,
|
|
share_inputs["accept_tokens"],
|
|
share_inputs["accept_num"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs[
|
|
"draft_tokens"
|
|
], # Both input and output, need to write the last 1 token accepted to position 0.
|
|
share_inputs["seq_lens_this_time"],
|
|
verify_tokens,
|
|
verify_scores,
|
|
share_inputs["max_dec_len"],
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["is_block_step"],
|
|
share_inputs["output_cum_offsets"],
|
|
actual_candidate_len,
|
|
share_inputs["actual_draft_token_num"],
|
|
sampling_metadata.top_p,
|
|
max_model_len,
|
|
self.speculative_verify_window,
|
|
True, # enable_topp
|
|
(self.speculative_benchmark_mode or reject_all_drafts),
|
|
accept_all_drafts,
|
|
)
|
|
|
|
num_logprobs = sampling_metadata.max_num_logprobs
|
|
batch_token_num = None
|
|
if num_logprobs is not None:
|
|
real_bsz = share_inputs["seq_lens_this_time"].shape[0]
|
|
batch_token_num = paddle.where(
|
|
share_inputs["seq_lens_encoder"][:real_bsz] != 0,
|
|
paddle.ones_like(share_inputs["seq_lens_encoder"][:real_bsz]),
|
|
share_inputs["seq_lens_this_time"],
|
|
).squeeze(1)
|
|
share_inputs["batch_token_num"] = batch_token_num
|
|
ori_cu_batch_token_offset = paddle.concat([paddle.to_tensor([0]), paddle.cumsum(batch_token_num)]).astype(
|
|
"int32"
|
|
)
|
|
cu_batch_token_offset = paddle.concat(
|
|
[paddle.to_tensor([0]), paddle.cumsum(share_inputs["accept_num"][:real_bsz])]
|
|
).astype("int32")
|
|
share_inputs["cu_batch_token_offset"] = cu_batch_token_offset
|
|
target_logits = paddle.empty(
|
|
[share_inputs["accept_num"][:real_bsz].sum(), logits.shape[1]], dtype=logits.dtype
|
|
)
|
|
speculate_get_target_logits(
|
|
target_logits,
|
|
logits,
|
|
cu_batch_token_offset,
|
|
ori_cu_batch_token_offset,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["accept_num"],
|
|
)
|
|
if self.logprobs_mode == "raw_logprobs":
|
|
raw_logprobs = self.compute_logprobs(target_logits, sampling_metadata)
|
|
elif self.logprobs_mode == "raw_logits":
|
|
raw_logprobs = target_logits.clone()
|
|
|
|
logprobs_tensors = None
|
|
token_ids = share_inputs["accept_tokens"]
|
|
if num_logprobs is not None:
|
|
token_ids = paddle.concat(
|
|
[share_inputs["accept_tokens"][i, : share_inputs["accept_num"][i]] for i in range(real_bsz)]
|
|
)
|
|
logprobs_tensors = self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=token_ids)
|
|
|
|
sampler_output = SamplerOutput(
|
|
sampled_token_ids=token_ids,
|
|
logprobs_tensors=logprobs_tensors,
|
|
token_num_per_batch=share_inputs["accept_num"],
|
|
cu_batch_token_offset=share_inputs["cu_batch_token_offset"],
|
|
)
|
|
|
|
return sampler_output
|
|
|
|
def forward_xpu(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
max_model_len: int,
|
|
share_inputs: List[paddle.Tensor],
|
|
accept_all_drafts: bool = False,
|
|
reject_all_drafts: bool = False,
|
|
) -> paddle.Tensor:
|
|
from fastdeploy.model_executor.ops.xpu import speculate_verify, top_p_candidates
|
|
|
|
logits = apply_speculative_penalty_multi_scores(
|
|
sampling_metadata.pre_token_ids,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["output_padding_offset"],
|
|
share_inputs["output_cum_offsets"],
|
|
max_model_len,
|
|
)
|
|
|
|
probs = F.softmax(logits)
|
|
|
|
verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
|
|
probs,
|
|
sampling_metadata.top_p,
|
|
share_inputs["output_padding_offset"],
|
|
self.speculative_max_candidate_len,
|
|
max_model_len,
|
|
)
|
|
|
|
speculate_verify(
|
|
share_inputs["accept_tokens"],
|
|
share_inputs["accept_num"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs[
|
|
"draft_tokens"
|
|
], # Both input and output, need to write the last 1 token accepted to position 0.
|
|
share_inputs["seq_lens_this_time"],
|
|
verify_tokens,
|
|
verify_scores,
|
|
share_inputs["max_dec_len"],
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["is_block_step"],
|
|
share_inputs["output_cum_offsets"],
|
|
actual_candidate_len,
|
|
share_inputs["actual_draft_token_num"],
|
|
sampling_metadata.top_p,
|
|
max_model_len,
|
|
self.speculative_verify_window,
|
|
True, # enable_topp
|
|
(self.speculative_benchmark_mode or reject_all_drafts),
|
|
accept_all_drafts,
|
|
)
|
|
# TODO(chenhuan09): support return logprobs
|
|
token_ids = share_inputs["accept_tokens"]
|
|
sampler_output = SamplerOutput(
|
|
sampled_token_ids=token_ids,
|
|
logprobs_tensors=None,
|
|
token_num_per_batch=share_inputs["accept_num"],
|
|
cu_batch_token_offset=None,
|
|
)
|
|
return sampler_output
|
|
|
|
|
|
class MTPSampler(nn.Layer):
|
|
""" """
|
|
|
|
def __init__(self, fd_config: FDConfig):
|
|
""" """
|
|
super().__init__()
|
|
if current_platform.is_cuda():
|
|
self.forward = self.forward_cuda
|
|
elif current_platform.is_xpu():
|
|
self.forward = self.forward_xpu
|
|
else:
|
|
raise NotImplementedError
|
|
self.logprobs_mode = fd_config.model_config.logprobs_mode
|
|
|
|
def pre_process(self, skip_idx_list: List[int] = []):
|
|
"""pre process before running"""
|
|
pass
|
|
|
|
def apply_logits_processor(
|
|
self,
|
|
ids: int,
|
|
future: Optional[Any] = None,
|
|
prefill_tokens: List[int] = [],
|
|
):
|
|
"""apply logits processor to sampler"""
|
|
pass
|
|
|
|
def set_reasoning_parser(self, reasoning_parser: Optional[ReasoningParser] = None):
|
|
"""set reasoning parser"""
|
|
pass
|
|
|
|
def post_process(self, next_tokens: paddle.Tensor, skip_idx_list: List[int] = []):
|
|
"""post process after running"""
|
|
pass
|
|
|
|
def compute_logprobs(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> paddle.Tensor:
|
|
"""compute logprobs"""
|
|
share_inputs = sampling_metadata.share_inputs
|
|
real_bsz = share_inputs["seq_lens_this_time"].shape[0]
|
|
last_logits = logits
|
|
temp_scaled_logprobs = sampling_metadata.temp_scaled_logprobs
|
|
top_p_normalized_logprobs = sampling_metadata.top_p_normalized_logprobs
|
|
if temp_scaled_logprobs is not None:
|
|
real_bsz_temp_scaled = temp_scaled_logprobs[:real_bsz]
|
|
temperature = sampling_metadata.temperature[:real_bsz]
|
|
real_bsz_temp_scaled = (
|
|
real_bsz_temp_scaled.astype("int32")
|
|
.squeeze(1)
|
|
.repeat_interleave(share_inputs["batch_token_num"][:real_bsz])
|
|
.astype("bool")
|
|
)
|
|
temperature = temperature.squeeze(1).repeat_interleave(share_inputs["batch_token_num"][:real_bsz])
|
|
temp_temperature = paddle.where(
|
|
real_bsz_temp_scaled, temperature, paddle.ones_like(temperature)
|
|
).unsqueeze(1)
|
|
last_logits = last_logits / temp_temperature
|
|
|
|
last_logprobs = F.log_softmax(last_logits, axis=-1)
|
|
top_p_logprob = None
|
|
top_p_token_mask = None
|
|
|
|
if top_p_normalized_logprobs is not None and share_inputs is not None:
|
|
real_token_top_p = (
|
|
sampling_metadata.top_p[:real_bsz]
|
|
.squeeze(1)
|
|
.repeat_interleave(share_inputs["batch_token_num"][:real_bsz])
|
|
.unsqueeze(1)
|
|
)
|
|
top_p_normalized_logprobs = (
|
|
top_p_normalized_logprobs[:real_bsz]
|
|
.astype("int32")
|
|
.squeeze(1)
|
|
.repeat_interleave(share_inputs["batch_token_num"][:real_bsz])
|
|
.astype("bool")
|
|
.unsqueeze(1)
|
|
)
|
|
top_p_token_mask = paddle.logical_and(top_p_normalized_logprobs, real_token_top_p != 1.0)
|
|
|
|
if top_p_token_mask.any():
|
|
probs = F.softmax(last_logits, axis=-1)
|
|
probs = top_p_normalize_probs_paddle(probs, real_token_top_p)
|
|
top_p_logprob = paddle.log(probs)
|
|
if top_p_logprob is not None:
|
|
last_logprobs = paddle.where(top_p_token_mask, top_p_logprob, last_logprobs)
|
|
return last_logprobs
|
|
|
|
def gather_logprobs(
|
|
self,
|
|
logprobs: paddle.Tensor,
|
|
num_logprobs: int,
|
|
token_ids: paddle.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 == paddle.int64
|
|
token_ids = token_ids.unsqueeze(1)
|
|
logprobs.clip_(min=paddle.finfo(logprobs.dtype).min)
|
|
# Get with the logprob of the prompt or sampled token.
|
|
token_logprobs = paddle.take_along_axis(logprobs, token_ids, axis=-1)
|
|
|
|
# Compute the ranks of the actual token.
|
|
token_ranks = (logprobs >= token_logprobs).sum(-1)
|
|
|
|
if num_logprobs >= 1:
|
|
# Find the topK values.
|
|
topk_logprobs, topk_indices = paddle.topk(logprobs, num_logprobs, axis=-1)
|
|
indices = paddle.concat([token_ids, topk_indices], axis=1)
|
|
top_logprobs = paddle.concat([token_logprobs, topk_logprobs], axis=1)
|
|
else:
|
|
indices = token_ids
|
|
top_logprobs = token_logprobs
|
|
|
|
return LogprobsTensors(indices, top_logprobs, token_ranks)
|
|
|
|
def forward_cuda(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
max_model_len: int,
|
|
share_inputs: List[paddle.Tensor],
|
|
) -> paddle.Tensor:
|
|
""" """
|
|
num_logprobs = sampling_metadata.max_num_logprobs
|
|
real_bsz = share_inputs["seq_lens_this_time"].shape[0]
|
|
if num_logprobs is not None and share_inputs["substep"] == 0:
|
|
real_token_num = share_inputs["batch_token_num"][:real_bsz].sum()
|
|
if self.logprobs_mode == "raw_logprobs":
|
|
raw_logprobs = self.compute_logprobs(
|
|
share_inputs["draft_logits"][:real_token_num, :], sampling_metadata
|
|
)
|
|
elif self.logprobs_mode == "raw_logits":
|
|
raw_logprobs = share_inputs["draft_logits"][:real_token_num, :].clone()
|
|
|
|
logits = apply_speculative_penalty_multi_scores(
|
|
sampling_metadata.pre_token_ids,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["output_padding_offset"],
|
|
share_inputs["output_cum_offsets"],
|
|
max_model_len,
|
|
)
|
|
probs = F.softmax(logits)
|
|
|
|
next_tokens = paddle.argmax(probs, axis=-1)
|
|
|
|
token_ids = None
|
|
logprobs_tensors = None
|
|
if num_logprobs is not None and share_inputs["substep"] == 0:
|
|
token_ids = paddle.empty(real_token_num, dtype="int64")
|
|
speculate_insert_first_token(
|
|
token_ids,
|
|
share_inputs["accept_tokens"],
|
|
next_tokens,
|
|
share_inputs["cu_next_token_offset"],
|
|
share_inputs["cu_batch_token_offset"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["seq_lens_encoder"],
|
|
)
|
|
|
|
logprobs_tensors = self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=token_ids)
|
|
|
|
sampler_output = SamplerOutput(
|
|
sampled_token_ids=token_ids,
|
|
logprobs_tensors=logprobs_tensors,
|
|
token_num_per_batch=share_inputs["batch_token_num"][:real_bsz],
|
|
cu_batch_token_offset=share_inputs["cu_batch_token_offset"],
|
|
)
|
|
return next_tokens, sampler_output
|
|
|
|
def forward_xpu(
|
|
self,
|
|
logits: paddle.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
max_model_len: int,
|
|
share_inputs: List[paddle.Tensor],
|
|
) -> paddle.Tensor:
|
|
|
|
logits = apply_speculative_penalty_multi_scores(
|
|
sampling_metadata.pre_token_ids,
|
|
logits,
|
|
sampling_metadata.repetition_penalties,
|
|
sampling_metadata.frequency_penalties,
|
|
sampling_metadata.presence_penalties,
|
|
sampling_metadata.temperature,
|
|
sampling_metadata.bad_words_token_ids,
|
|
sampling_metadata.step_idx,
|
|
sampling_metadata.min_dec_lens,
|
|
sampling_metadata.eos_token_ids,
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["output_padding_offset"],
|
|
share_inputs["output_cum_offsets"],
|
|
max_model_len,
|
|
)
|
|
probs = F.softmax(logits)
|
|
|
|
_, next_tokens = top_k_top_p_sampling(
|
|
probs, sampling_metadata.top_p, sampling_metadata.top_k, sampling_metadata.top_k_list
|
|
)
|
|
# TODO(chenhuan09): add support for logprobs
|
|
token_ids = None
|
|
logprobs_tensors = None
|
|
|
|
sampler_output = SamplerOutput(
|
|
sampled_token_ids=token_ids,
|
|
logprobs_tensors=logprobs_tensors,
|
|
token_num_per_batch=None,
|
|
cu_batch_token_offset=None,
|
|
)
|
|
return next_tokens, sampler_output
|