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Generate - update cookie cutters to not initialize cache with training and gradient checkpointing (#21759)

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Joao Gante 2023-02-24 11:21:00 +00:00 committed by GitHub
parent 087436c98e
commit 440f39754b
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1 changed files with 13 additions and 15 deletions
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28
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
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@ -184,7 +184,7 @@ class {{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
@ -525,11 +525,17 @@ class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
output_hidden_states=False,
return_dict=True,
):
if self.gradient_checkpointing and self.training and use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
@ -538,13 +544,6 @@ class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
@ -2024,7 +2023,7 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ({{cookiecutter.camelcase_modelname}}Decoder, {{cookiecutter.camelcase_modelname}}Encoder)):
module.gradient_checkpointing = value
@ -2525,6 +2524,10 @@ class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_model
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
if self.gradient_checkpointing and self.training and use_cache:
logger.warning("`use_cache = True` is incompatible with gradient checkpointing`. Setting `use_cache = False`...")
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
@ -2547,11 +2550,6 @@ class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_model
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning("`use_cache = True` is incompatible with gradient checkpointing`. Setting `use_cache = False`...")
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value