46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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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 numbers
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import torch
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from megatron.core import ModelParallelConfig
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from torch import nn
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from transformers import LlamaConfig
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from apex.normalization.fused_layer_norm import fused_rms_norm_affine
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from verl.utils.megatron import sequence_parallel as sp_utils
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class ParallelLlamaRMSNorm(nn.Module):
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def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig):
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"""
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LlamaRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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if isinstance(config.hidden_size, numbers.Integral):
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normalized_shape = (config.hidden_size,)
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self.normalized_shape = torch.Size(normalized_shape)
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self.weight = nn.Parameter(torch.ones(self.normalized_shape))
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self.variance_epsilon = config.rms_norm_eps
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if megatron_config.sequence_parallel:
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sp_utils.mark_parameter_as_sequence_parallel(self.weight)
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def forward(self, hidden_states):
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return fused_rms_norm_affine(input=hidden_states,
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weight=self.weight,
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normalized_shape=self.normalized_shape,
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eps=self.variance_epsilon,
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memory_efficient=True) |