275 lines
12 KiB
Python
275 lines
12 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2023 PKU-Alignment Team. 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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"""Utilities for score models."""
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from __future__ import annotations
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import inspect
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from dataclasses import dataclass
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from typing import Optional, Tuple
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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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# use LlamaPretrainingCriterion as common PretrainingCriterion
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from paddlenlp.transformers import LlamaPretrainingCriterion as PretrainingCriterion
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from paddlenlp.transformers.model_outputs import ModelOutput
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@dataclass
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class PolicyOutput(ModelOutput):
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loss: Optional[paddle.Tensor] = None
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logits: paddle.Tensor = None
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# logits_entropy: Optional[paddle.Tensor] = None
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past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None
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hidden_states: Optional[Tuple[paddle.Tensor]] = None
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attentions: Optional[Tuple[paddle.Tensor]] = None
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cross_attentions: Optional[Tuple[paddle.Tensor]] = None
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@dataclass
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class ValueOutput(ModelOutput):
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loss: Optional[paddle.Tensor] = None
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value: paddle.Tensor = None
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reward: paddle.Tensor = None
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past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None
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hidden_states: Optional[Tuple[paddle.Tensor]] = None
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attentions: Optional[Tuple[paddle.Tensor]] = None
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cross_attentions: Optional[Tuple[paddle.Tensor]] = None
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def merge_fwd_labels(loss_cls):
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"""
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PipelineParallel and trainer.criterion both use labels as tuple, thus wrap.
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"""
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ori_fwd = loss_cls.forward
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def loss_fwd(self, predict, labels):
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return ori_fwd(self, predict, *labels)
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fwd_params = inspect.signature(ori_fwd).parameters
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# forward(self, predict, label1, label2, ...)
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loss_cls.label_names = list(fwd_params.keys())[2:]
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loss_cls.label_default_values = {}
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for label_name in loss_cls.label_names:
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if fwd_params[label_name].default is not inspect.Parameter.empty:
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loss_cls.label_default_values[label_name] = fwd_params[label_name].default
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loss_cls.forward = loss_fwd
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return loss_cls
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def create_loss(loss_cls, config, extra_args, merge_labels=None):
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"""
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loss_cls(paddle.nn.Layer): loss class
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config(PratrainedConfig): model config, to be consistent with loss defined
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in transformers
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extra_args(dict): create loss with more args not in config
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merge_labels: use a wrapped loss_cls whose label args are merged into one arg,
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this is useful to PipelineParallel and trainer.criterion since they only
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support loss format corresponding to this format.
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"""
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# TODO(guosheng): merge_labels if loss_cls not
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ori_fwd = loss_cls.forward
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if merge_labels:
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fwd_params = inspect.signature(ori_fwd).parameters
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if len(fwd_params.keys()) > 3: # merge_fwd_labels has not done
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loss_cls = merge_fwd_labels(loss_cls)
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# forward(self, predict, label1, label2, ...)
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loss_arg_names = list(inspect.signature(loss_cls.__init__).parameters.keys())[2:]
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if isinstance(extra_args, dict):
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loss_kwargs = dict([(name, extra_args[name]) for name in loss_arg_names if name in extra_args])
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else:
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# create from TrainingArguments
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loss_kwargs = dict([(name, getattr(extra_args, name)) for name in loss_arg_names if hasattr(extra_args, name)])
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loss = loss_cls(config, **loss_kwargs)
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return loss
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@paddle.no_grad()
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def make_position_ids(attention_mask, source=None):
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if len(attention_mask.shape) == 4: # causal mask
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position_ids_p1 = attention_mask.cast(paddle.int64).sum(-1)
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position_ids = position_ids_p1 - 1
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position_ids = paddle.where(position_ids == -1, position_ids_p1, position_ids)
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return position_ids[:, 0, :]
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assert len(attention_mask.shape) == 2 # padding mask
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attention_mask_bool = attention_mask
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attention_mask = attention_mask.cast(paddle.int64)
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position_ids = attention_mask.cumsum(-1) - 1
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# Make padding positions in source be 0, since reward model use position_ids
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# plus with padding size (number of 0s) in source to calculate end offsets.
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# It does not matter when source is left padding and target is right padding
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# which is the output of non-FuseMT generation, while when using FuseMT whose
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# output is right padding source and right padding target, we have to set
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# padding positions in source be 0 to make compatible.
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if source is not None:
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src_len = position_ids[:, source.shape[-1] - 1].unsqueeze(-1)
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position_ids = paddle.where(
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paddle.logical_and(paddle.logical_not(attention_mask_bool), position_ids <= src_len),
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attention_mask,
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position_ids,
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)
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return position_ids
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position_ids = paddle.where(position_ids == -1, attention_mask, position_ids)
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return position_ids
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@paddle.no_grad()
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def make_attention_mask(input_ids, pad_id, unk_id=None, past_key_values_length=0, causal_mask=True):
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attention_mask = input_ids != pad_id
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if unk_id is not None and pad_id != unk_id:
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attention_mask = paddle.logical_and(attention_mask, input_ids != unk_id)
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if not causal_mask:
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return attention_mask
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batch_size, target_length = input_ids.shape # target_length: seq_len
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mask = paddle.tril(paddle.ones((target_length, target_length), dtype="bool"))
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if past_key_values_length > 0:
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# [tgt_len, tgt_len + past_len]
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mask = paddle.concat([paddle.ones([target_length, past_key_values_length], dtype="bool"), mask], axis=-1)
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# [bs, 1, tgt_len, tgt_len + past_len]
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causal_mask = mask[None, None, :, :].expand([batch_size, 1, target_length, target_length + past_key_values_length])
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attention_mask = attention_mask[:, None, None, :]
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expanded_attn_mask = attention_mask & causal_mask
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return expanded_attn_mask
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def gather_log_probabilities(logits: paddle.Tensor, labels: paddle.Tensor) -> paddle.Tensor:
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"""Gather log probabilities of the given labels from the logits."""
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log_probs = F.log_softmax(logits, axis=-1)
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log_probs_labels = paddle.take_along_axis(log_probs, axis=-1, indices=labels.unsqueeze(axis=-1))
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return log_probs_labels.squeeze(axis=-1)
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class RLHFPPOLoss(nn.Layer):
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def __init__(self, config, clip_range_ratio=0.2):
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super().__init__()
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self.clip_range_ratio = clip_range_ratio
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self.config = config
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def actor_loss_fn(
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self, log_probs: paddle.Tensor, old_log_probs: paddle.Tensor, advantages: paddle.Tensor, mask: paddle.Tensor
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) -> paddle.Tensor:
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# policy gradient loss
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ratio = paddle.exp(log_probs - old_log_probs)
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pg_loss1 = -advantages * ratio
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pg_loss2 = -advantages * paddle.clip(
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ratio,
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1.0 - self.clip_range_ratio,
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1.0 + self.clip_range_ratio,
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)
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return paddle.sum(paddle.maximum(pg_loss1, pg_loss2) * mask) / mask.sum()
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def forward(self, logits, input_ids, old_log_probs, reward_advantages, sequence_mask):
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log_probs = gather_log_probabilities(logits[:, :-1], input_ids[:, 1:])
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if log_probs.shape[1] == old_log_probs.shape[1]:
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# labels (old_log_probs, reward_advantages, sequence_mask) has
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# src+tgt-1 length, valid length is determined by sequence_mask
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pass
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elif log_probs.shape[1] < old_log_probs.shape[1]:
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# labels (old_log_probs, reward_advantages, sequence_mask) has
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# src+tgt length and the last one is a padding to be consistent
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# with input_ids
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assert log_probs.shape[1] == old_log_probs.shape[1] - 1
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log_probs = paddle.concat([log_probs, paddle.zeros([log_probs.shape[0], 1], dtype=log_probs.dtype)], -1)
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else:
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# labels (old_log_probs, reward_advantages, sequence_mask) has tgt length
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log_probs = log_probs[:, -old_log_probs.shape[1] :]
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actor_loss = self.actor_loss_fn(
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log_probs,
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old_log_probs,
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reward_advantages,
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sequence_mask,
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)
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return actor_loss
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@merge_fwd_labels
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class RLHFPPOMixedLoss(nn.Layer):
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"""provide two losses, one for PPO loss, the other for SFT loss."""
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def __init__(self, config, ptx_coeff=16, clip_range_ratio=0.2):
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super(RLHFPPOMixedLoss, self).__init__()
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self.ptx_coeff = ptx_coeff
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self.ppo_criterion = RLHFPPOLoss(config, clip_range_ratio)
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self.sft_criterion = PretrainingCriterion(config)
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def forward(self, logits, labels, input_ids, old_log_probs, reward_advantages, sequence_mask):
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logits = logits if isinstance(logits, paddle.Tensor) else logits[0]
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loss = None
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# sft, pt loss
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if labels is not None:
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loss = self.ptx_coeff * self.sft_criterion(logits, labels)
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# ppo loss
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if reward_advantages is not None:
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loss = self.ppo_criterion(logits, input_ids, old_log_probs, reward_advantages, sequence_mask)
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return loss
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@merge_fwd_labels
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class RLHFValueLoss(nn.Layer):
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def __init__(self, config, clip_range_value=5.0):
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super().__init__()
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self.clip_range_value = clip_range_value
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self.config = config
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def critic_loss_fn(
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self,
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values: paddle.Tensor,
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old_values: paddle.Tensor,
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returns: paddle.Tensor,
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mask: paddle.Tensor,
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) -> paddle.Tensor:
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"""Compute critic loss."""
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# TODO(guosheng): use paddle.clip when its min/max can support more than
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# 0D Tensor
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values_clipped = paddle.minimum(
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paddle.maximum(values, old_values - self.clip_range_value), old_values + self.clip_range_value
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)
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vf_loss1 = paddle.square(values - returns)
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vf_loss2 = paddle.square(values_clipped - returns)
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return 0.5 * paddle.sum(paddle.maximum(vf_loss1, vf_loss2) * mask) / mask.sum()
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def forward(self, reward_values, old_reward_values, reward_returns, sequence_mask):
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reward_values = reward_values if isinstance(reward_values, paddle.Tensor) else reward_values[0]
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reward_values = reward_values.squeeze(axis=-1)[:, :-1]
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if reward_values.shape[1] == old_reward_values.shape[1]:
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# labels (old_reward_values, reward_returns, sequence_mask) has
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# src+tgt-1 length, valid length is determined by sequence_mask
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pass
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elif reward_values.shape[1] < old_reward_values.shape[1]:
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# labels (old_reward_values, reward_returns, sequence_mask) has
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# src+tgt length and the last one is a padding to be consistent
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# with input_ids
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assert reward_values.shape[1] == old_reward_values.shape[1] - 1
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reward_values = paddle.concat(
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[reward_values, paddle.zeros([reward_values.shape[0], 1], dtype=reward_values.dtype)], -1
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)
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else:
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# labels (old_reward_values, reward_returns, sequence_mask) has
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# tgt length
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reward_values = reward_values[:, -old_reward_values.shape[1] :]
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reward_critic_loss = self.critic_loss_fn(
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reward_values,
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old_reward_values,
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reward_returns,
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sequence_mask,
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)
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return reward_critic_loss
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