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# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch.nn as nn
from .imports import is_fp8_available
if is_fp8_available():
import transformer_engine.pytorch as te
def convert_model(model, to_transformer_engine=True, _convert_linear=True, _convert_ln=True):
"""
Recursively converts the linear and layernorm layers of a model to their `transformers_engine` counterpart.
"""
if not is_fp8_available():
raise ImportError("Using `convert_model` requires transformer_engine to be installed.")
for name, module in model.named_children():
if isinstance(module, nn.Linear) and to_transformer_engine and _convert_linear:
# Return early if the linear layer weights are not multiples of 16
if any(p % 16 != 0 for p in module.weight.shape):
return
has_bias = module.bias is not None
te_module = te.Linear(
module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
)
te_module.weight.data = module.weight.data.clone()
if has_bias:
te_module.bias.data = module.bias.data.clone()
setattr(model, name, te_module)
elif isinstance(module, nn.LayerNorm) and to_transformer_engine and _convert_ln:
te_module = te.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
te_module.weight.data = module.weight.data.clone()
te_module.bias.data = module.bias.data.clone()
setattr(model, name, te_module)
elif isinstance(module, te.Linear) and not to_transformer_engine and _convert_linear:
has_bias = module.bias is not None
new_module = nn.Linear(
module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
)
new_module.weight.data = module.weight.data.clone()
if has_bias:
new_module.bias.data = module.bias.data.clone()
setattr(model, name, new_module)
elif isinstance(module, te.LayerNorm) and not to_transformer_engine and _convert_ln:
new_module = nn.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
new_module.weight.data = module.weight.data.clone()
new_module.bias.data = module.bias.data.clone()
setattr(model, name, new_module)
else:
convert_model(
module,
to_transformer_engine=to_transformer_engine,
_convert_linear=_convert_linear,
_convert_ln=_convert_ln,
)
def has_transformer_engine_layers(model):
"""
Returns whether a given model has some `transformer_engine` layer or not.
"""
if not is_fp8_available():
raise ImportError("Using `has_transformer_engine_layers` requires transformer_engine to be installed.")
for m in model.modules():
if isinstance(m, (te.LayerNorm, te.Linear)):
return True
return False
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