mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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69be77c8c0
* [Feature] support prompt repetition_penalty (#2806) * [Bug Fix] fix bug of prompt penalty (#2888)
437 lines
16 KiB
Python
437 lines
16 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 as nn
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import paddle.nn.functional as F
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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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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, apply_speculative_penalty_multi_scores,
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top_k_top_p_sampling)
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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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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(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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""" 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,
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logits: paddle.Tensor,
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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,
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self.token_bitmask,
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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(
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f"Invalid index, idx: {idx}, logit_processors.keys: {self.logits_processor.keys()}"
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)
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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,
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next_tokens: paddle.Tensor,
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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[
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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):
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"""
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"""
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super().__init__()
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if current_platform.is_cuda() or current_platform.is_xpu(
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) or current_platform.is_iluvatar() or current_platform.is_gcu():
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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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def apply_logits_processor(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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""" 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(self, logits: paddle.Tensor) -> paddle.Tensor:
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"""
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"""
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return F.log_softmax(logits, axis=-1)
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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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# 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,
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num_logprobs,
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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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"""
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num_logprobs = sampling_metadata.max_num_logprobs
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if num_logprobs is not None:
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raw_logprobs = self.compute_logprobs(logits)
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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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_, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
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logprobs_tensors = None if num_logprobs is None else \
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self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=next_tokens)
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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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"""
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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(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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""" 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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"""
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from fastdeploy.model_executor.ops.gpu import (speculate_verify,
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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"], # 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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"""
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def __init__(self, fd_config: FDConfig):
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"""
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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 """
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pass
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def apply_logits_processor(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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""" 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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"""
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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["seq_lens_encoder"],
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share_inputs["seq_lens_decoder"],
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max_model_len,
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)
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probs = F.softmax(logits)
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_, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
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return next_tokens
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