New TF model inputs (#8602)
* Apply on BERT and ALBERT * Update TF Bart * Add input processing to TF BART * Add input processing for TF CTRL * Add input processing to TF Distilbert * Add input processing to TF DPR * Add input processing to TF Electra * Add input processing for TF Flaubert * Add deprecated arguments * Add input processing to TF XLM * remove unused imports * Add input processing to TF Funnel * Add input processing to TF GPT2 * Add input processing to TF Longformer * Add input processing to TF Lxmert * Apply style * Add input processing to TF Mobilebert * Add input processing to TF GPT * Add input processing to TF Roberta * Add input processing to TF T5 * Add input processing to TF TransfoXL * Apply style * Rebase on master * Bug fix * Retry to bugfix * Retry bug fix * Fix wrong model name * Try another fix * Fix BART * Fix input precessing * Apply style * Put the deprecated warnings in the input processing function * Remove the unused imports * Raise an error when len(kwargs)>0 * test ModelOutput instead of TFBaseModelOutput * Bug fix * Address Patrick's comments * Address Patrick's comments * Address Sylvain's comments * Add the new inputs in new Longformer models * Update the template with the new input processing * Remove useless assert * Apply style * Trigger CI
This commit is contained in:
Julien Plu
@@ -42,10 +42,10 @@ from ...modeling_tf_utils import (
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TFTokenClassificationLoss,
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TFSequenceSummary,
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get_initializer,
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input_processing,
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keras_serializable,
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shape_list,
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)
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from ...tokenization_utils import BatchEncoding
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from ...utils import logging
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from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
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@@ -499,7 +499,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
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def call(
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self,
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inputs,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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@@ -509,59 +509,59 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
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output_hidden_states=None,
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return_dict=None,
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training=False,
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**kwargs,
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):
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if isinstance(inputs, (tuple, list)):
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input_ids = inputs[0]
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attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
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token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
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position_ids = inputs[3] if len(inputs) > 3 else position_ids
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head_mask = inputs[4] if len(inputs) > 4 else head_mask
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inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
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output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
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output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
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return_dict = inputs[8] if len(inputs) > 8 else return_dict
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assert len(inputs) <= 9, "Too many inputs."
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elif isinstance(inputs, (dict, BatchEncoding)):
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input_ids = inputs.get("input_ids")
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attention_mask = inputs.get("attention_mask", attention_mask)
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token_type_ids = inputs.get("token_type_ids", token_type_ids)
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position_ids = inputs.get("position_ids", position_ids)
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head_mask = inputs.get("head_mask", head_mask)
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inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
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output_attentions = inputs.get("output_attentions", output_attentions)
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output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
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return_dict = inputs.get("return_dict", return_dict)
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assert len(inputs) <= 9, "Too many inputs."
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else:
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input_ids = inputs
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inputs = input_processing(
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func=self.call,
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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training=training,
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kwargs_call=kwargs,
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)
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output_attentions = (
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inputs["output_attentions"] if inputs["output_attentions"] is not None else self.output_attentions
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)
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output_hidden_states = (
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inputs["output_hidden_states"] if inputs["output_hidden_states"] is not None else self.output_hidden_states
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)
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return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.return_dict
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output_attentions = output_attentions if output_attentions is not None else self.output_attentions
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output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
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return_dict = return_dict if return_dict is not None else self.return_dict
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if input_ids is not None and inputs_embeds is not None:
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if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = shape_list(input_ids)
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elif inputs_embeds is not None:
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input_shape = shape_list(inputs_embeds)[:-1]
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elif inputs["input_ids"] is not None:
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input_shape = shape_list(inputs["input_ids"])
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elif inputs["inputs_embeds"] is not None:
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input_shape = shape_list(inputs["inputs_embeds"])[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if attention_mask is None:
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attention_mask = tf.fill(input_shape, 1)
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if inputs["attention_mask"] is None:
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inputs["attention_mask"] = tf.fill(input_shape, 1)
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if token_type_ids is None:
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token_type_ids = tf.fill(input_shape, 0)
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if inputs["token_type_ids"] is None:
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inputs["token_type_ids"] = tf.fill(input_shape, 0)
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embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
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embedding_output = self.embeddings(
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inputs["input_ids"],
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inputs["position_ids"],
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inputs["token_type_ids"],
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inputs["inputs_embeds"],
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training=inputs["training"],
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)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
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extended_attention_mask = inputs["attention_mask"][:, tf.newaxis, tf.newaxis, :]
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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@@ -576,20 +576,19 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
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# attention_probs has shape bsz x n_heads x N x N
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# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
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# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
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if head_mask is not None:
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if inputs["head_mask"] is not None:
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raise NotImplementedError
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else:
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head_mask = [None] * self.num_hidden_layers
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# head_mask = tf.constant([0] * self.num_hidden_layers)
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inputs["head_mask"] = [None] * self.num_hidden_layers
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encoder_outputs = self.encoder(
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embedding_output,
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extended_attention_mask,
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head_mask,
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inputs["head_mask"],
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output_attentions,
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output_hidden_states,
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return_dict,
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training=training,
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training=inputs["training"],
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)
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sequence_output = encoder_outputs[0]
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@@ -725,8 +724,46 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
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output_type=TFBaseModelOutputWithPooling,
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config_class=_CONFIG_FOR_DOC,
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)
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def call(self, inputs, **kwargs):
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outputs = self.{{cookiecutter.lowercase_modelname}}(inputs, **kwargs)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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training=False,
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**kwargs,
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):
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inputs = input_processing(
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func=self.call,
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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training=training,
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kwargs_call=kwargs,
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)
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outputs = self.{{cookiecutter.lowercase_modelname}}(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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token_type_ids=inputs["token_type_ids"],
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position_ids=inputs["position_ids"],
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head_mask=inputs["head_mask"],
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inputs_embeds=inputs["inputs_embeds"],
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output_attentions=inputs["output_attentions"],
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output_hidden_states=inputs["output_hidden_states"],
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return_dict=inputs["return_dict"],
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training=inputs["training"],
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)
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return outputs
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@@ -758,7 +795,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
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)
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def call(
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self,
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inputs=None,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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@@ -769,6 +806,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
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return_dict=None,
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labels=None,
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training=False,
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**kwargs,
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):
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r"""
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labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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@@ -777,17 +815,9 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
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Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
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in ``[0, ..., config.vocab_size]``
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"""
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return_dict = return_dict if return_dict is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
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if isinstance(inputs, (tuple, list)):
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labels = inputs[9] if len(inputs) > 9 else labels
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if len(inputs) > 9:
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inputs = inputs[:9]
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elif isinstance(inputs, (dict, BatchEncoding)):
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labels = inputs.pop("labels", labels)
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outputs = self.{{cookiecutter.lowercase_modelname}}(
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inputs,
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inputs = input_processing(
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func=self.call,
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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@@ -796,12 +826,27 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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labels=labels,
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training=training,
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kwargs_call=kwargs,
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)
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return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
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outputs = self.{{cookiecutter.lowercase_modelname}}(
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inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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token_type_ids=inputs["token_type_ids"],
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position_ids=inputs["position_ids"],
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head_mask=inputs["head_mask"],
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inputs_embeds=inputs["inputs_embeds"],
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output_attentions=inputs["output_attentions"],
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output_hidden_states=inputs["output_hidden_states"],
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return_dict=return_dict,
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training=inputs["training"],
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)
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sequence_output = outputs[0]
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prediction_scores = self.mlm(sequence_output, training=training)
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loss = None if labels is None else self.compute_loss(labels, prediction_scores)
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prediction_scores = self.mlm(sequence_output, training=inputs["training"])
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loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
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if not return_dict:
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output = (prediction_scores,) + outputs[1:]
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@@ -862,18 +907,19 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
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config_class=_CONFIG_FOR_DOC,
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)
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def call(
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self,
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inputs,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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training=False,
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self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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training=False,
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**kwargs,
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):
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r"""
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labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
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@@ -882,18 +928,9 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
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If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
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If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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return_dict = return_dict if return_dict is not None else self.{{cookiecutter.lowercase_modelname}}.config.return_dict
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if isinstance(inputs, (tuple, list)):
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labels = inputs[9] if len(inputs) > 9 else labels
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if len(inputs) > 9:
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inputs = inputs[:9]
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elif isinstance(inputs, (dict, BatchEncoding)):
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labels = inputs.pop("labels", labels)
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outputs = self.{{cookiecutter.lowercase_modelname}}(
|
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inputs,
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inputs = input_processing(
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func=self.call,
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input_ids=input_ids,
|
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
|
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@@ -902,10 +939,25 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
|
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
|
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return_dict=return_dict,
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labels=labels,
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training=training,
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kwargs_call=kwargs,
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)
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logits = self.classifier(outputs[0])
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loss = None if labels is None else self.compute_loss(labels, logits)
|
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return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
|
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outputs = self.{{cookiecutter.lowercase_modelname}}(
|
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inputs["input_ids"],
|
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attention_mask=inputs["attention_mask"],
|
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token_type_ids=inputs["token_type_ids"],
|
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position_ids=inputs["position_ids"],
|
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head_mask=inputs["head_mask"],
|
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inputs_embeds=inputs["inputs_embeds"],
|
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output_attentions=inputs["output_attentions"],
|
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output_hidden_states=inputs["output_hidden_states"],
|
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return_dict=return_dict,
|
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training=inputs["training"],
|
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)
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logits = self.classifier(outputs[0], training=inputs["training"])
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loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
|
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|
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if not return_dict:
|
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output = (logits,) + outputs[1:]
|
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@@ -956,7 +1008,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
|
||||
)
|
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def call(
|
||||
self,
|
||||
inputs,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
token_type_ids=None,
|
||||
position_ids=None,
|
||||
@@ -967,6 +1019,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
|
||||
return_dict=None,
|
||||
labels=None,
|
||||
training=False,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
||||
@@ -974,49 +1027,43 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
|
||||
Indices should be in ``[0, ..., num_choices]`` where :obj:`num_choices` is the size of the second dimension
|
||||
of the input tensors. (See :obj:`input_ids` above)
|
||||
"""
|
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if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
|
||||
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
|
||||
position_ids = inputs[3] if len(inputs) > 3 else position_ids
|
||||
head_mask = inputs[4] if len(inputs) > 4 else head_mask
|
||||
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
|
||||
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
|
||||
output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
|
||||
return_dict = inputs[8] if len(inputs) > 8 else return_dict
|
||||
labels = inputs[9] if len(inputs) > 9 else labels
|
||||
assert len(inputs) <= 10, "Too many inputs."
|
||||
elif isinstance(inputs, (dict, BatchEncoding)):
|
||||
input_ids = inputs.get("input_ids")
|
||||
attention_mask = inputs.get("attention_mask", attention_mask)
|
||||
token_type_ids = inputs.get("token_type_ids", token_type_ids)
|
||||
position_ids = inputs.get("position_ids", position_ids)
|
||||
head_mask = inputs.get("head_mask", head_mask)
|
||||
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
|
||||
output_attentions = inputs.get("output_attentions", output_attentions)
|
||||
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
|
||||
return_dict = inputs.get("return_dict", return_dict)
|
||||
labels = inputs.get("labels", labels)
|
||||
assert len(inputs) <= 10, "Too many inputs."
|
||||
inputs = input_processing(
|
||||
func=self.call,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
labels=labels,
|
||||
training=training,
|
||||
kwargs_call=kwargs,
|
||||
)
|
||||
return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.config.return_dict
|
||||
|
||||
if inputs["input_ids"] is not None:
|
||||
num_choices = shape_list(inputs["input_ids"])[1]
|
||||
seq_length = shape_list(inputs["input_ids"])[2]
|
||||
else:
|
||||
input_ids = inputs
|
||||
num_choices = shape_list(inputs["inputs_embeds"])[1]
|
||||
seq_length = shape_list(inputs["inputs_embeds"])[2]
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.{{cookiecutter.lowercase_modelname}}.config.return_dict
|
||||
|
||||
if input_ids is not None:
|
||||
num_choices = shape_list(input_ids)[1]
|
||||
seq_length = shape_list(input_ids)[2]
|
||||
else:
|
||||
num_choices = shape_list(inputs_embeds)[1]
|
||||
seq_length = shape_list(inputs_embeds)[2]
|
||||
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
|
||||
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
|
||||
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
|
||||
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
|
||||
flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
|
||||
flat_attention_mask = (
|
||||
tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
|
||||
)
|
||||
flat_token_type_ids = (
|
||||
tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
|
||||
)
|
||||
flat_position_ids = (
|
||||
tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
|
||||
)
|
||||
flat_inputs_embeds = (
|
||||
tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
|
||||
if inputs_embeds is not None
|
||||
tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
|
||||
if inputs["inputs_embeds"] is not None
|
||||
else None
|
||||
)
|
||||
outputs = self.{{cookiecutter.lowercase_modelname}}(
|
||||
@@ -1024,17 +1071,17 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
|
||||
flat_attention_mask,
|
||||
flat_token_type_ids,
|
||||
flat_position_ids,
|
||||
head_mask,
|
||||
inputs["head_mask"],
|
||||
flat_inputs_embeds,
|
||||
output_attentions,
|
||||
output_hidden_states,
|
||||
inputs["output_attentions"],
|
||||
inputs["output_hidden_states"],
|
||||
return_dict=return_dict,
|
||||
training=training,
|
||||
training=inputs["training"],
|
||||
)
|
||||
logits = self.sequence_summary(outputs[0])
|
||||
logits = self.sequence_summary(outputs[0], training=inputs["training"])
|
||||
logits = self.classifier(logits)
|
||||
reshaped_logits = tf.reshape(logits, (-1, num_choices))
|
||||
loss = None if labels is None else self.compute_loss(labels, reshaped_logits)
|
||||
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
|
||||
|
||||
if not return_dict:
|
||||
output = (reshaped_logits,) + outputs[1:]
|
||||
@@ -1074,7 +1121,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
|
||||
)
|
||||
def call(
|
||||
self,
|
||||
inputs=None,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
token_type_ids=None,
|
||||
position_ids=None,
|
||||
@@ -1085,23 +1132,16 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
|
||||
return_dict=None,
|
||||
labels=None,
|
||||
training=False,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
||||
Labels for computing the token classification loss.
|
||||
Indices should be in ``[0, ..., config.num_labels - 1]``.
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
|
||||
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
labels = inputs[9] if len(inputs) > 9 else labels
|
||||
if len(inputs) > 9:
|
||||
inputs = inputs[:9]
|
||||
elif isinstance(inputs, (dict, BatchEncoding)):
|
||||
labels = inputs.pop("labels", labels)
|
||||
|
||||
outputs = self.{{cookiecutter.lowercase_modelname}}(
|
||||
inputs,
|
||||
inputs = input_processing(
|
||||
func=self.call,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
@@ -1110,12 +1150,27 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
labels=labels,
|
||||
training=training,
|
||||
kwargs_call=kwargs,
|
||||
)
|
||||
return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.uppercase_modelname}}.return_dict
|
||||
outputs = self.{{cookiecutter.uppercase_modelname}}(
|
||||
inputs["input_ids"],
|
||||
attention_mask=inputs["attention_mask"],
|
||||
token_type_ids=inputs["token_type_ids"],
|
||||
position_ids=inputs["position_ids"],
|
||||
head_mask=inputs["head_mask"],
|
||||
inputs_embeds=inputs["inputs_embeds"],
|
||||
output_attentions=inputs["output_attentions"],
|
||||
output_hidden_states=inputs["output_hidden_states"],
|
||||
return_dict=return_dict,
|
||||
training=inputs["training"],
|
||||
)
|
||||
sequence_output = outputs[0]
|
||||
sequence_output = self.dropout(sequence_output, training=training)
|
||||
sequence_output = self.dropout(sequence_output, training=inputs["training"])
|
||||
logits = self.classifier(sequence_output)
|
||||
loss = None if labels is None else self.compute_loss(labels, logits)
|
||||
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
@@ -1154,7 +1209,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
|
||||
)
|
||||
def call(
|
||||
self,
|
||||
inputs=None,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
token_type_ids=None,
|
||||
position_ids=None,
|
||||
@@ -1166,6 +1221,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
|
||||
start_positions=None,
|
||||
end_positions=None,
|
||||
training=False,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
|
||||
@@ -1177,19 +1233,9 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
|
||||
Positions are clamped to the length of the sequence (:obj:`sequence_length`).
|
||||
Position outside of the sequence are not taken into account for computing the loss.
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
|
||||
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
start_positions = inputs[9] if len(inputs) > 9 else start_positions
|
||||
end_positions = inputs[10] if len(inputs) > 10 else end_positions
|
||||
if len(inputs) > 9:
|
||||
inputs = inputs[:9]
|
||||
elif isinstance(inputs, (dict, BatchEncoding)):
|
||||
start_positions = inputs.pop("start_positions", start_positions)
|
||||
end_positions = inputs.pop("end_positions", start_positions)
|
||||
|
||||
outputs = self.{{cookiecutter.lowercase_modelname}}(
|
||||
inputs,
|
||||
inputs = input_processing(
|
||||
func=self.call,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
@@ -1198,7 +1244,23 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
start_positions=start_positions,
|
||||
end_positions=end_positions,
|
||||
training=training,
|
||||
kwargs_call=kwargs,
|
||||
)
|
||||
return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.uppercase_modelname}}.return_dict
|
||||
outputs = self.{{cookiecutter.uppercase_modelname}}(
|
||||
inputs["input_ids"],
|
||||
attention_mask=inputs["attention_mask"],
|
||||
token_type_ids=inputs["token_type_ids"],
|
||||
position_ids=inputs["position_ids"],
|
||||
head_mask=inputs["head_mask"],
|
||||
inputs_embeds=inputs["inputs_embeds"],
|
||||
output_attentions=inputs["output_attentions"],
|
||||
output_hidden_states=inputs["output_hidden_states"],
|
||||
return_dict=return_dict,
|
||||
training=inputs["training"],
|
||||
)
|
||||
sequence_output = outputs[0]
|
||||
logits = self.qa_outputs(sequence_output)
|
||||
@@ -1207,9 +1269,9 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
|
||||
end_logits = tf.squeeze(end_logits, axis=-1)
|
||||
loss = None
|
||||
|
||||
if start_positions is not None and end_positions is not None:
|
||||
labels = {"start_position": start_positions}
|
||||
labels["end_position"] = end_positions
|
||||
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
|
||||
labels = {"start_position": inputs["start_positions"]}
|
||||
labels["end_position"] = inputs["end_positions"]
|
||||
loss = self.compute_loss(labels, (start_logits, end_logits))
|
||||
|
||||
if not return_dict:
|
||||
|
||||
Reference in New Issue
Block a user