wehub-resource-sync
257 lines
12 KiB
Python
257 lines
12 KiB
Python
# Copyright (c) 2023 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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import paddle
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import paddle.nn as nn
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from paddle.distributed.fleet.meta_parallel import LayerDesc
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from ...transformers import LlamaForCausalLM, LlamaForCausalLMPipe
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from ...transformers.llama.modeling import LlamaDecoderLayer
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from ...transformers.llama.modeling_pp import LlamaRMSNormPipe, parse_args, return_args
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from .pp_model_utils import fwd_args_to_dict, get_expected_keys, pad_batches_inputs
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from .ppo_model_utils import (
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RLHFPPOMixedLoss,
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RLHFValueLoss,
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create_loss,
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make_position_ids,
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)
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from .score_model_utils import ScoreModelMixin
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# patches for base pipe model
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# non-pipe model class, can be used to parse and convert forward args
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# mainly used for generation with PipelienParallel model
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LlamaForCausalLMPipe._non_pipe_model_class = LlamaForCausalLM
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LlamaForCausalLMPipe._non_pipe_decoder_layer_class = LlamaDecoderLayer
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class LlamaPolicyPipe(LlamaForCausalLMPipe):
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# TODO(guosheng): maybe make a Mixin is better
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@fwd_args_to_dict
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def _prepare_pipeline_inputs_func(self, inputs):
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# first_stage_keys = ["input_ids", "attention_mask"]
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first_stage_keys = ["input_ids", "attention_mask", "position_ids"]
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# last_stage_keys = [
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# "labels", "input_ids", "log_probs", "advantages", "sequence_mask"
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# ]
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# TODO(guosheng): make input keys same with model arg names, maybe we
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# can use inspect and set as global var which can then be used here and
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# in PPOTrainer.
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last_stage_keys = ["labels", "input_ids", "old_log_probs", "reward_advantages", "sequence_mask"]
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if type(inputs) is dict:
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# for left padding, position_ids is necessary
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if "position_ids" not in inputs:
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inputs["position_ids"] = make_position_ids(inputs["attention_mask"])
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# ppo-loss and ptx-loss need different labels, and data iter provides
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# corresponding data, thus add the not provided fields here.
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# policy train and infer has different inputs, infer uses position_ids.
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# for key in last_stage_keys:
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for key in first_stage_keys + last_stage_keys:
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if key not in inputs:
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inputs[key] = None
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return [
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get_expected_keys(inputs, first_stage_keys),
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get_expected_keys(inputs, last_stage_keys),
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]
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for data in inputs:
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# for key in last_stage_keys:
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for key in first_stage_keys + last_stage_keys:
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if key not in data:
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if key == "position_ids":
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data[key] = make_position_ids(data["attention_mask"])
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continue
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data[key] = None
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# keys = list(inputs[0].keys())
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inputs_batch = {key: [data.get(key) for data in inputs] for key in first_stage_keys + last_stage_keys}
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# NOTE(guosheng): PipelineParallel requires send/recv tensors among
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# micro-batches/accu-steps have the same shape. Thus pad here, maybe
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# should make data collator do padding and pad optionally here, since
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# padding strategy may not be clear here.
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# 1. For input_ids/attention_mask/labels (prompt+target) padding:
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# Some data fields, such as input_ids/attention_mask/labels, should
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# have same shape after padding, and each of them cannot pad only
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# according to its own max length which might be different since the
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# filed value is None for different batches/tasks.
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src_tgt_keys = ["input_ids", "attention_mask", "labels", "position_ids"]
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max_len = max([x.shape[-1] for x in inputs_batch["input_ids"]])
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pad_len = [max_len - x.shape[-1] for x in inputs_batch["input_ids"]]
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for key in src_tgt_keys:
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# Do not pad position_ids with 0 since 0s in position_ids has special
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# usage in reward model. We use 1 to pad.
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padding_value = self._ignore_index if key == "labels" else 1 if key == "position_ids" else 0
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inputs_batch[key] = pad_batches_inputs(inputs_batch[key], padding_value, pad_len=pad_len)
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# 2. For old_log_probs/reward_advantages/sequence_mask (target) padding:
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# hard to pad across batches, think in some cases one batch might have the
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# longest prompt+target length but the shortest target length, which might
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# cause mismatch between inputs with prompt+target length and labels with
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# target length. NOTE: however trick can be used here, label fields with
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# target length such as old_log_probs/reward_advantages/sequence_mask do
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# not need to join comm and thus there is no need to keep same shape among
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# batches of accumulation steps, they just need to pad as prompt+target
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# fields such as input_ids.
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tgt_keys = ["old_log_probs", "reward_advantages", "sequence_mask"]
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for key in tgt_keys:
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padding_value = 0
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inputs_batch[key] = pad_batches_inputs(inputs_batch[key], padding_value, pad_len=pad_len)
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# for key, value in inputs_batch.items():
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# padding_value = self._ignore_index if key == "labels" else 0
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# max_len = max_len if key in [
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# "input_ids", "attention_mask", "labels"
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# ] else None
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# inputs_batch[key] = pad_batches_inputs(value, padding_value, max_len)
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return [
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get_expected_keys(inputs_batch, first_stage_keys),
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get_expected_keys(inputs_batch, last_stage_keys),
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]
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def __init__(self, config, **kwargs):
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# NOTE: make _sequential_layers/_single_to_pp_mapping/_pp_to_single_mapping
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# instance attrs instead of class attrs to support more than one pipeline
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# models. Maybe make all sequential_layers add once.
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self._sequential_layers = []
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self._single_to_pp_mapping = None
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self._pp_to_single_mapping = None
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# To be consistent with score model init and allow hyper-param be passed
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# using __init__/from_pretrained
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self._init_kwargs = kwargs
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super().__init__(config)
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self._ignore_index = self._loss_fn.sft_criterion.ignore_index
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def get_loss_fn(self, config):
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return create_loss(RLHFPPOMixedLoss, config, self._init_kwargs)
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@property
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def head_out_meta(self):
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"""mainly for eval/generation with PipelineParallel"""
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# None means to use actual data info
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return paddle.static.InputSpec(shape=[None, None, self.config.vocab_size], dtype=None)
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class _LlamaRMSNormPipe(LlamaRMSNormPipe):
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"""
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We need position_ids for reward model, so wrap LlamaRMSNormPipe to pass position_ids
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"""
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def __init__(self, config):
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super().__init__(config)
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def forward(self, args):
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hidden_states, attention_mask, position_ids, alibi = parse_args(args)
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return return_args(self.norm(hidden_states), attention_mask, position_ids)
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# LayerDesc of PipelineParallel requires head to be a nn.Layer
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class ValueHead(nn.Layer, ScoreModelMixin):
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def __init__(self, config, **kwargs):
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super().__init__()
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self.config = config
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self.init_score_head(config, hidden_size=config.hidden_size, **kwargs)
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def forward(self, args):
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# attention_mask passed from pre-stage is shaped (bs, 1, seq_len, seq_len)
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hidden_state, attention_mask, position_ids, alibi = parse_args(args)
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outputs = self.get_score(
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hidden_state, attention_mask=attention_mask, position_ids=position_ids, return_dict=True
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)
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return outputs
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class LlamaValuePipe(LlamaForCausalLMPipe):
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# TODO(guosheng): maybe make a Mixin is better
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@fwd_args_to_dict
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def _prepare_pipeline_inputs_func(self, inputs):
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# ValueHead/get_score needs original attention_mask or position_ids,
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# while attention_mask passed from pre-stage is not the original, thus
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# hack for position_ids here.
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# Maybe add position_ids into inputs later and use position_ids instead
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# of attention_mask to get score not only for pipeline parallel.
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first_stage_keys = ["input_ids", "attention_mask", "position_ids"]
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# TODO(guosheng): make input keys same with model arg names, maybe we
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# can use inspect and set as global var which can then be used here and
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# in PPOTrainer.
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last_stage_keys = ["old_reward_values", "reward_returns", "sequence_mask"]
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if type(inputs) is dict:
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if "position_ids" not in inputs:
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inputs["position_ids"] = make_position_ids(inputs["attention_mask"])
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return [
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get_expected_keys(inputs, first_stage_keys),
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get_expected_keys(inputs, last_stage_keys),
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]
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for data in inputs:
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if "position_ids" not in data:
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data["position_ids"] = make_position_ids(data["attention_mask"])
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# keys = list(inputs[0].keys())
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inputs_batch = {key: [data.get(key) for data in inputs] for key in first_stage_keys + last_stage_keys}
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# 1. For input_ids/attention_mask (prompt+target) padding:
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# src_tgt_keys = ["input_ids", "attention_mask"]
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src_tgt_keys = ["input_ids", "attention_mask", "position_ids"]
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max_len = max([x.shape[-1] for x in inputs_batch["input_ids"]])
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pad_len = [max_len - x.shape[-1] for x in inputs_batch["input_ids"]]
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for key in src_tgt_keys:
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# Do not pad position_ids with 0 since 0s in position_ids has special
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# usage in reward model. We use 1 to pad.
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padding_value = self._ignore_index if key == "labels" else 1 if key == "position_ids" else 0
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inputs_batch[key] = pad_batches_inputs(inputs_batch[key], padding_value, pad_len=pad_len)
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# 2. For old_reward_values/reward_returns/sequence_mask (target) padding:
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tgt_keys = ["old_reward_values", "reward_returns", "sequence_mask"]
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for key in tgt_keys:
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padding_value = 0
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inputs_batch[key] = pad_batches_inputs(inputs_batch[key], padding_value, pad_len=pad_len)
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# for key, value in inputs_batch.items():
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# inputs_batch[key] = pad_batches_inputs(value, padding_value=0)
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# if "position_ids" not in inputs[0]:
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# inputs_batch["position_ids"] = [
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# make_position_ids(attention_mask) for attention_mask in inputs_batch["attention_mask"]
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# ]
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return [
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get_expected_keys(inputs_batch, first_stage_keys),
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get_expected_keys(inputs_batch, last_stage_keys),
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]
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def __init__(self, config, **kwargs):
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# NOTE: make _sequential_layers/_single_to_pp_mapping/_pp_to_single_mapping
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# instance attrs instead of class attrs to support more than one pipeline
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# models. Maybe make all sequential_layers add once.
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self._sequential_layers = []
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self._single_to_pp_mapping = None
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self._pp_to_single_mapping = None
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# To be consistent with score model init and allow hyper-param be passed
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# using __init__/from_pretrained
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self._init_kwargs = kwargs
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super().__init__(config)
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def add_head(self, config):
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init_kwargs = self._init_kwargs
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# hack to replace original RMSNormPipe to support ValueHead inputs
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norm_prefix = self._sequential_layers.pop(-1)["name_prefix"]
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self.add_sequential_layer(LayerDesc(_LlamaRMSNormPipe, config=config), norm_prefix)
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self.add_sequential_layer(LayerDesc(ValueHead, config, **init_kwargs), "")
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def get_loss_fn(self, config):
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return create_loss(RLHFValueLoss, config, self._init_kwargs)
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@property
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def head_out_meta(self):
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# None means to use actual data info
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return (
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paddle.static.InputSpec(shape=[None, None, 1], dtype=None),
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paddle.static.InputSpec(shape=[None, 1], dtype=None),
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)
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