wehub-resource-sync
84 lines
3.6 KiB
Python
84 lines
3.6 KiB
Python
# Copyright 2022 The HuggingFace 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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import torch.nn as nn
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from accelerate.utils.imports import is_fp8_available
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if is_fp8_available():
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import transformer_engine.pytorch as te
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def convert_model(model, to_transformer_engine=True, _convert_linear=True, _convert_ln=True):
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"""
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Recursively converts the linear and layernorm layers of a model to their `transformers_engine` counterpart.
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"""
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if not is_fp8_available():
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raise ImportError("Using `convert_model` requires transformer_engine to be installed.")
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for name, module in model.named_children():
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if isinstance(module, nn.Linear) and to_transformer_engine and _convert_linear:
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# Return early if the linear layer weights are not multiples of 16
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if any(p % 16 != 0 for p in module.weight.shape):
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return
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has_bias = module.bias is not None
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te_module = te.Linear(
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module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
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)
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te_module.weight.data = module.weight.data.clone()
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if has_bias:
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te_module.bias.data = module.bias.data.clone()
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setattr(model, name, te_module)
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elif isinstance(module, nn.LayerNorm) and to_transformer_engine and _convert_ln:
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te_module = te.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
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te_module.weight.data = module.weight.data.clone()
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te_module.bias.data = module.bias.data.clone()
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setattr(model, name, te_module)
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elif isinstance(module, te.Linear) and not to_transformer_engine and _convert_linear:
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has_bias = module.bias is not None
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new_module = nn.Linear(
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module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
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)
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new_module.weight.data = module.weight.data.clone()
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if has_bias:
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new_module.bias.data = module.bias.data.clone()
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setattr(model, name, new_module)
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elif isinstance(module, te.LayerNorm) and not to_transformer_engine and _convert_ln:
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new_module = nn.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
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new_module.weight.data = module.weight.data.clone()
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new_module.bias.data = module.bias.data.clone()
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setattr(model, name, new_module)
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else:
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convert_model(
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module,
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to_transformer_engine=to_transformer_engine,
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_convert_linear=_convert_linear,
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_convert_ln=_convert_ln,
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)
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def has_transformer_engine_layers(model):
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"""
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Returns whether a given model has some `transformer_engine` layer or not.
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"""
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if not is_fp8_available():
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raise ImportError("Using `has_transformer_engine_layers` requires transformer_engine to be installed.")
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for m in model.modules():
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if isinstance(m, (te.LayerNorm, te.Linear)):
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return True
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return False
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