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28 changes: 19 additions & 9 deletions src/transformers/models/gptj/modeling_gptj.py
Original file line number Diff line number Diff line change
Expand Up @@ -771,7 +771,7 @@ def get_output_embeddings(self):
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings

def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values:
Expand All @@ -790,14 +790,24 @@ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwarg
position_ids = position_ids[:, -1].unsqueeze(-1)
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}

# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}

model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
)

return model_inputs

@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
Expand Down