wehub-resource-sync
365 lines
14 KiB
Python
365 lines
14 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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from typing import OrderedDict
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import paddle
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import paddle.distributed.fleet as fleet
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import paddle.nn as nn
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from paddle.distributed.fleet.meta_parallel import (
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LayerDesc,
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PipelineLayer,
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SharedLayerDesc,
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)
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from paddle.distributed.fleet.recompute.recompute import recompute
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from paddlenlp.transformers.refined_recompute import get_skip_recompute_ops
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from paddlenlp.transformers.refined_recompute import recompute as rr_recompute
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from ...utils.tools import get_env_device
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from ..dpo_criterion import DPOCriterion
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from ..model_utils import PipelinePretrainedModel
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from .modeling import (
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Qwen2Config,
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Qwen2DecoderLayer,
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Qwen2LMHead,
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Qwen2Model,
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Qwen2PretrainedModel,
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Qwen2PretrainingCriterion,
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Qwen2RMSNorm,
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)
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__all__ = [
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"Qwen2ForCausalLMPipe",
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]
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def parse_args(args):
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if isinstance(args, tuple):
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if len(args) == 4:
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hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = args
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elif len(args) == 3:
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hidden_states, attention_mask, attn_mask_startend_row_indices = args
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position_ids = None
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elif len(args) == 2:
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hidden_states, attention_mask = args
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attn_mask_startend_row_indices, position_ids = None, None
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else:
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hidden_states = args
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attention_mask, attn_mask_startend_row_indices, position_ids = None, None, None
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if position_ids is not None:
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position_ids.stop_gradient = True
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if attention_mask is not None:
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attention_mask.stop_gradient = True
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if attn_mask_startend_row_indices is not None:
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attn_mask_startend_row_indices.stop_gradient = True
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return hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids
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def return_args(hidden_states, attention_mask=None, attn_mask_startend_row_indices=None, position_ids=None):
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ret = (hidden_states,)
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if attention_mask is not None:
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ret += (attention_mask.clone(),)
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if attn_mask_startend_row_indices is not None:
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ret += (attn_mask_startend_row_indices.clone(),)
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if position_ids is not None:
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ret += (position_ids.clone(),)
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if len(ret) == 1:
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ret = ret[0]
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return ret
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def get_attr(layer, name):
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if getattr(layer, name, None) is not None:
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return getattr(layer, name, None)
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else:
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return get_attr(layer._layer, name)
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class Qwen2EmbeddingPipe(nn.Layer):
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"""Extends QWenEmbeddings to forward attention_mask through the pipeline."""
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def __init__(self, config: Qwen2Config):
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super(Qwen2EmbeddingPipe, self).__init__()
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self.config = config
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self.sequence_parallel = config.sequence_parallel
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self.hidden_size = config.hidden_size
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if config.tensor_parallel_degree > 1 and config.vocab_size % config.tensor_parallel_degree == 0:
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self.embed_tokens = fleet.meta_parallel.VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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weight_attr=paddle.ParamAttr(initializer=nn.initializer.XavierNormal()),
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)
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else:
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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@property
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def embedding_weight(self):
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return get_attr(self.embed_tokens, "weight")
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def forward(self, args):
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"""_summary_
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Args:
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input (_type_): _description_
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Returns:
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_type_: _description_
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"""
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input_ids, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args)
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input_embeds = self.embed_tokens(input_ids)
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if self.config.sequence_parallel:
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from paddlenlp.transformers import ScatterOp
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# [bs, seq_len, num_head * head_dim] -> [bs * seq_len, num_head * head_dim]
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bs, seq_len, hidden_size = input_embeds.shape
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input_embeds = paddle.reshape_(input_embeds, [bs * seq_len, hidden_size])
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# [seq_len * bs / n, num_head * head_dim] (n is mp parallelism)
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input_embeds = ScatterOp.apply(input_embeds)
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batch_size, seq_length = input_ids.shape
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if attention_mask is not None:
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assert (
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attn_mask_startend_row_indices is None
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), "attention_mask and attn_mask_startend_row_indices can not be set at same time"
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attention_mask = Qwen2Model._prepare_decoder_attention_mask(
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attention_mask, (batch_size, seq_length), 0, input_embeds.dtype
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)
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attention_mask.stop_gradient = True
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if get_env_device() == "npu":
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attention_mask = attention_mask.astype("bool")
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elif get_env_device() == "npu":
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attention_mask = paddle.tril(paddle.ones((seq_length, seq_length), dtype="bool"))
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attention_mask.stop_gradient = True
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return return_args(input_embeds, attention_mask, attn_mask_startend_row_indices, position_ids)
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class Qwen2DecoderLayerPipe(Qwen2DecoderLayer):
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def forward(self, args):
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hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args)
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has_gradient = not hidden_states.stop_gradient
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if attention_mask is not None and attention_mask.dtype == paddle.int32:
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attention_mask, attn_mask_startend_row_indices, position_ids = (
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None,
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attention_mask,
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attn_mask_startend_row_indices,
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)
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elif attention_mask is not None and attention_mask.dtype == paddle.int64:
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attention_mask, attn_mask_startend_row_indices, position_ids = None, None, attention_mask
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elif attn_mask_startend_row_indices is not None and attn_mask_startend_row_indices.dtype == paddle.int64:
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attn_mask_startend_row_indices, position_ids = None, attn_mask_startend_row_indices
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if self.enable_recompute and self.config.recompute_granularity == "full" and has_gradient:
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recompute_fn = rr_recompute if any(self.skip_recompute_ops.values()) else recompute
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if attention_mask is not None or attn_mask_startend_row_indices is not None:
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hidden_states = recompute_fn(
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super().forward,
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hidden_states,
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position_ids=position_ids,
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attention_mask=attention_mask,
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attn_mask_startend_row_indices=attn_mask_startend_row_indices,
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use_reentrant=False,
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)
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else:
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# for pretrain
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hidden_states = recompute_fn(
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super().forward,
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hidden_states,
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position_ids=position_ids,
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attn_mask_startend_row_indices=attn_mask_startend_row_indices,
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use_reentrant=self.config.recompute_use_reentrant,
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)
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else:
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hidden_states = super().forward(
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hidden_states,
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position_ids=position_ids,
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attention_mask=attention_mask,
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attn_mask_startend_row_indices=attn_mask_startend_row_indices,
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)
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return return_args(hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids)
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class Qwen2RMSNormPipe(nn.Layer):
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def __init__(self, config):
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super().__init__()
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self.norm = Qwen2RMSNorm(config)
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def forward(self, args):
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hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args)
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return self.norm(hidden_states)
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class Qwen2LMHeadPipe(Qwen2LMHead):
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def __init__(self, config, transpose_y=False):
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super(Qwen2LMHeadPipe, self).__init__(config, transpose_y=transpose_y)
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@property
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def embedding_weight(self):
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return get_attr(self, "weight")
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class Qwen2ForCausalLMPipe(PipelinePretrainedModel, PipelineLayer):
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"""QWenForPretraining adapted for pipeline parallelism.
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The largest change is flattening the QWenModel class so we can express it as a
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sequence of layers including embedding, transformer layers, and output.
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"""
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config_class = Qwen2Config
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_get_tensor_parallel_mappings = Qwen2PretrainedModel._get_tensor_parallel_mappings
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_init_weights = Qwen2PretrainedModel._init_weights
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_keys_to_ignore_on_load_unexpected = Qwen2PretrainedModel._keys_to_ignore_on_load_unexpected
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_get_model_flops = Qwen2PretrainedModel._get_model_flops
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_get_hardware_flops = Qwen2PretrainedModel._get_hardware_flops
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_tied_weights_keys = ["lm_head.weight"]
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# DONOT Add base_model_prefix !!!!
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@classmethod
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def _prepare_pipeline_inputs_func(cls, inputs):
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first_stage_keys = ["input_ids", "attention_mask", "attn_mask_startend_row_indices", "position_ids"]
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last_stage_keys = ["labels"]
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def get_expected_keys(inputs, keys):
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ret = tuple([inputs.pop(k) if k in inputs else None for k in keys])
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if len(ret) == 1:
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ret = ret[0]
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return ret
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if type(inputs) is dict or type(inputs) is OrderedDict:
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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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keys = list(inputs[0].keys())
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inputs_batch = {key: [data.pop(key) for data in inputs] for key in keys}
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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: Qwen2Config):
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self.config = config
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# Note that we will actually perform a recompute only if both enable_recompute and layerwise_recompute are set to True
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# Enable_recompute defaults to False and is controlled by Trainer
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self.enable_recompute = False
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self.recompute_granularity = self.config.recompute_granularity
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self.pp_recompute_interval = self.config.pp_recompute_interval
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self.no_recompute_layers = config.no_recompute_layers if config.no_recompute_layers is not None else []
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if self.recompute_granularity == "full":
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assert len(self.no_recompute_layers) == 0, "for pp with full recompute, no_recompute_layers is not support"
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virtual_pp_degree = getattr(self.config, "virtual_pp_degree", 1)
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def get_hcg():
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return fleet.get_hybrid_communicate_group()
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hcg = get_hcg()
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tensor_parallel_degree = max(hcg.get_model_parallel_world_size(), 1)
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tensor_parallel_rank = max(hcg.get_model_parallel_rank(), 0)
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# TODO: fix tensor_parallel_degree rewrite in here
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config.tensor_parallel_degree = tensor_parallel_degree
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config.tensor_parallel_rank = tensor_parallel_rank
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if config.tie_word_embeddings:
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self.add_sequential_layer(
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SharedLayerDesc(
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"qwen2_shared_weight", Qwen2EmbeddingPipe, shared_weight_attr="embedding_weight", config=config
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),
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"qwen2",
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)
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else:
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self.add_sequential_layer(LayerDesc(Qwen2EmbeddingPipe, config=config), "qwen2")
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for i in range(config.num_hidden_layers):
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self.add_sequential_layer(
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LayerDesc(
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Qwen2DecoderLayerPipe,
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config=config,
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layerwise_recompute=i not in self.no_recompute_layers,
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skip_recompute_ops=get_skip_recompute_ops(config, i),
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),
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f"qwen2.layers.{i}",
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)
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self.add_sequential_layer(LayerDesc(Qwen2RMSNormPipe, config=config), "qwen2")
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if config.tie_word_embeddings:
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self.add_sequential_layer(
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SharedLayerDesc(
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"qwen2_shared_weight",
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Qwen2LMHeadPipe,
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shared_weight_attr="embedding_weight",
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config=config,
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**{"transpose_y": True},
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),
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"lm_head",
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)
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else:
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self.add_sequential_layer(LayerDesc(Qwen2LMHeadPipe, config=config), "lm_head")
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recompute_interval = 0
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if self.enable_recompute and self.recompute_granularity == "full":
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assert self.config.pp_recompute_interval <= config.num_hidden_layers // (
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virtual_pp_degree * get_hcg().topology().get_dim_size("pipe")
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), "pp recompute interval should smaller than num layers of each pp chunk"
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recompute_interval = self.config.pp_recompute_interval
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seg_method = "layer:Qwen2DecoderLayer"
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if config.num_hidden_layers % get_hcg().topology().get_dim_size("pipe") != 0:
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seg_method = "uniform"
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PipelineLayer.__init__(
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self,
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layers=self.get_sequential_layers(),
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loss_fn=self.get_loss_fn(config),
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topology=get_hcg().topology(),
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seg_method=seg_method,
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recompute_interval=recompute_interval,
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recompute_ctx={
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"mp_group": get_hcg().get_model_parallel_group(),
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"offload": False,
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"partition": False,
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},
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num_virtual_pipeline_stages=virtual_pp_degree,
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)
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# You should call init here, since there is a diamond inheritance problem
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self.apply(self._init_weights)
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# DON'T init PipelinePretrainedModel
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# PipelinePretrainedModel.__init__(self.super(), config=config)
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def get_loss_fn(self, config):
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if config.dpo_config is not None:
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return DPOCriterion(config, use_infohub=True)
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else:
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return Qwen2PretrainingCriterion(config)
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