mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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9cab3f47ff
* [feature] Add temp_scaled_logprobs and top_p_normalized_logprobs parameters for logits and logprobs post processing * infer engine support temp_scaled_logprobs and top_p_normalized_logprobs * delete some code * code check * code check and add doc * fix tokenizer.decoder(-1), return 'Invalid Token' * add ci for temp_scaled and top_p logprobs * check test * check seq len time shape * logprob clip inf --------- Co-authored-by: sunlei1024 <sunlei5788@gmail.com>
513 lines
19 KiB
Python
513 lines
19 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 threading
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any, Dict, 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 fastdeploy.config import FDConfig
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from fastdeploy.model_executor.guided_decoding.base_guided_decoding import (
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LogitsProcessorBase,
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)
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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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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.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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class SamplerProcessor:
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"""
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SamplingProcessor for guided decoding.
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"""
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def __init__(self):
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self.async_step = None
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self.token_bitmask = None
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self.logits_processor: Dict[int, Optional[Any]] = dict()
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self.executor = ThreadPoolExecutor()
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self.logits_lock = threading.Lock()
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def add_logits_processor(
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self,
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ids: 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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with self.logits_lock:
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if future is None:
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if ids in self.logits_processor:
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del self.logits_processor[ids]
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return
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if isinstance(future, LogitsProcessorBase):
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self.logits_processor[ids] = future
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for token in prefill_tokens:
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self.logits_processor[ids].accept_token(token)
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elif future.done():
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self.logits_processor[ids] = future.result()
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for token in prefill_tokens:
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self.logits_processor[ids].accept_token(token)
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else:
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self.logits_processor[ids] = [future, prefill_tokens]
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def update_vocab_mask(self, skip_idx_list: List[int] = []):
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"""update vocab mask. (cpu-heavy operation)"""
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if len(self.logits_processor) == 0:
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return
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with self.logits_lock:
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for idx, processor in self.logits_processor.items():
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if processor is None:
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del self.logits_processor[idx]
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continue
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if not isinstance(processor, LogitsProcessorBase):
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future, prefill_tokens = self.logits_processor[idx]
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self.logits_processor[idx] = future.result()
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for token in prefill_tokens:
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self.logits_processor[idx].accept_token(token)
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available_processors = None
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for processor in self.logits_processor.values():
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if processor.is_terminated():
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continue
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available_processors = processor
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if available_processors is None:
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return
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# allocate token bitmask
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self.token_bitmask = available_processors.allocate_token_bitmask()
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with self.logits_lock:
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# fill token bitmask
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for idx, processor in self.logits_processor.items():
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if processor.is_terminated() or idx in skip_idx_list:
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continue
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processor.fill_token_bitmask(self.token_bitmask, idx)
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def apply_token_mask(self, logits: paddle.Tensor, skip_idx_list: List[int] = []):
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"""apply token mask to logits"""
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if len(self.logits_processor) == 0 or self.token_bitmask is None:
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return logits
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# self.async_step.result()
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available_processors = None
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with self.logits_lock:
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for processor in self.logits_processor.values():
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if processor.is_terminated():
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continue
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available_processors = processor
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if available_processors is None:
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return logits
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indices = list(self.logits_processor.keys())
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mask_idx = [i for i in indices if i not in skip_idx_list]
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return available_processors.apply_token_mask(logits, self.token_bitmask, indices=mask_idx)
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def _accept_token(self, idx: int, token: int):
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"""accept token"""
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if idx not in self.logits_processor:
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raise ValueError(f"Invalid index, idx: {idx}, logit_processors.keys: {self.logits_processor.keys()}")
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if self.logits_processor[idx].is_terminated():
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return
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self.logits_processor[idx].accept_token(token)
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def update_output_tokens(self, next_tokens: paddle.Tensor, skip_idx_list: List[int] = []):
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"""update output tokens"""
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if len(self.logits_processor) == 0:
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return
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token_ids = next_tokens.numpy().tolist()
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with self.logits_lock:
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for idx in self.logits_processor.keys():
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token = token_ids[idx][0]
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if token < 0 or self.logits_processor[idx] is None or idx in skip_idx_list:
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continue
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self._accept_token(idx, token)
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def pre_process(self, skip_idx_list: List[int] = []):
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"""pre process before running"""
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# create async operation for guided decoding
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# TODO: support async
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self.update_vocab_mask(skip_idx_list)
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# self.async_step = self.executor.submit(self.update_vocab_mask)
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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):
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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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else:
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raise NotImplementedError
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self.processor = SamplerProcessor()
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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.parallel_config.max_num_seqs, fd_config.early_stop_config)
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def apply_logits_processor(
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self,
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ids: 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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"""apply logits processor to sampler"""
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self.processor.add_logits_processor(ids, future, prefill_tokens)
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def pre_process(self, skip_idx_list: List[int] = []):
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"""pre process before running"""
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self.processor.pre_process(skip_idx_list)
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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: SamplingMetadata,
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) -> paddle.Tensor:
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""" """
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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:
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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 top_p_normalized_logprobs is not None and share_inputs is not None:
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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:
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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 == paddle.int64
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logprobs.clip_(min=paddle.finfo(logprobs.dtype).min)
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# Get with the logprob of the prompt or sampled token.
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token_logprobs = paddle.take_along_axis(logprobs, token_ids, axis=-1)
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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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if num_logprobs >= 1:
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# Find the topK values.
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topk_logprobs, topk_indices = paddle.topk(logprobs, num_logprobs, axis=-1)
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indices = paddle.concat([token_ids, topk_indices], axis=1)
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top_logprobs = paddle.concat([token_logprobs, topk_logprobs], axis=1)
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else:
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indices = token_ids
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top_logprobs = token_logprobs
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return LogprobsTensors(indices, top_logprobs, token_ranks)
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def forward_cuda(
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self,
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logits: paddle.Tensor,
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sampling_metadata: SamplingMetadata,
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skip_idx_list: List[int] = [],
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) -> SamplerOutput:
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""" """
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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, sampling_metadata)
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logits = self.processor.apply_token_mask(logits, skip_idx_list)
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logits = apply_penalty_multi_scores(
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sampling_metadata.pre_token_ids,
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sampling_metadata.prompt_ids,
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sampling_metadata.prompt_lens,
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logits,
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sampling_metadata.repetition_penalties,
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sampling_metadata.frequency_penalties,
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sampling_metadata.presence_penalties,
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sampling_metadata.temperature,
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sampling_metadata.bad_words_token_ids,
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sampling_metadata.step_idx,
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sampling_metadata.min_dec_lens,
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sampling_metadata.eos_token_ids,
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)
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probs = F.softmax(logits)
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probs = min_p_sampling(probs, sampling_metadata.min_p, sampling_metadata.min_p_list)
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_, next_tokens = top_k_top_p_sampling(
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probs,
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sampling_metadata.top_p,
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sampling_metadata.top_k,
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sampling_metadata.top_k_list,
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seed=sampling_metadata.seed[0, 0],
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)
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logprobs_tensors = (
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None if num_logprobs is None else self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=next_tokens)
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)
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if sampling_metadata.enable_early_stop:
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# will set the stop batch in stop_flags
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assert sampling_metadata.stop_flags is not None, "need stop_flags for eary stop"
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self.early_stopper.process(probs, next_tokens, sampling_metadata.stop_flags)
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self.processor.update_output_tokens(next_tokens, skip_idx_list)
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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=next_tokens,
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logprobs_tensors=logprobs_tensors,
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)
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return sampler_output
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class SpeculativeSampler(nn.Layer):
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"""
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Sampler for speculative generation.
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"""
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def __init__(self, fd_config: FDConfig):
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""" """
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super().__init__()
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if current_platform.is_cuda():
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self.forward = self.forward_cuda
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else:
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raise NotImplementedError
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self.speculative_verify_window = fd_config.speculative_config.verify_window
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self.speculative_max_candidate_len = fd_config.speculative_config.max_candidate_len
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self.speculative_benchmark_mode = fd_config.speculative_config.benchmark_mode
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def pre_process(self, skip_idx_list: List[int] = []):
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"""pre process before running"""
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pass
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def apply_logits_processor(
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self,
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ids: 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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"""apply logits processor to sampler"""
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pass
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def forward_cuda(
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self,
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logits: paddle.Tensor,
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sampling_metadata: SamplingMetadata,
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max_model_len: int,
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share_inputs: List[paddle.Tensor],
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) -> paddle.Tensor:
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""" """
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from fastdeploy.model_executor.ops.gpu import speculate_verify, top_p_candidates
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logits = apply_speculative_penalty_multi_scores(
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sampling_metadata.pre_token_ids,
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logits,
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sampling_metadata.repetition_penalties,
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sampling_metadata.frequency_penalties,
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sampling_metadata.presence_penalties,
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sampling_metadata.temperature,
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sampling_metadata.bad_words_token_ids,
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sampling_metadata.step_idx,
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sampling_metadata.min_dec_lens,
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sampling_metadata.eos_token_ids,
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share_inputs["seq_lens_this_time"],
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share_inputs["output_padding_offset"],
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share_inputs["output_cum_offsets"],
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max_model_len,
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)
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probs = F.softmax(logits)
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verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
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probs,
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sampling_metadata.top_p,
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share_inputs["output_padding_offset"],
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self.speculative_max_candidate_len,
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max_model_len,
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)
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speculate_verify(
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share_inputs["accept_tokens"],
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share_inputs["accept_num"],
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share_inputs["step_idx"],
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share_inputs["stop_flags"],
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share_inputs["seq_lens_encoder"],
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share_inputs["seq_lens_decoder"],
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share_inputs[
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"draft_tokens"
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], # Both input and output, need to write the last 1 token accepted to position 0.
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share_inputs["seq_lens_this_time"],
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verify_tokens,
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verify_scores,
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share_inputs["max_dec_len"],
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sampling_metadata.eos_token_ids,
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share_inputs["is_block_step"],
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share_inputs["output_cum_offsets"],
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actual_candidate_len,
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share_inputs["actual_draft_token_num"],
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sampling_metadata.top_p,
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max_model_len,
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self.speculative_verify_window,
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True, # enable_topp
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self.speculative_benchmark_mode,
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)
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return None
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class MTPSampler(nn.Layer):
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""" """
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def __init__(self, fd_config: FDConfig):
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""" """
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super().__init__()
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if current_platform.is_cuda():
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self.forward = self.forward_cuda
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else:
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raise NotImplementedError
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def pre_process(self, skip_idx_list: List[int] = []):
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"""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 forward_cuda(
|
|
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
|
|
)
|
|
return next_tokens
|