Remove the last few TF serving sigs (#23738)
Remove some more serving methods that (I think?) turned up while this PR was open
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@ -1147,12 +1147,8 @@ class TFSamPreTrainedModel(TFPreTrainedModel):
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@property
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def dummy_inputs(self) -> Dict[str, tf.Tensor]:
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"""
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Dummy inputs to build the network.
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Returns:
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`Dict[str, tf.Tensor]`: The dummy inputs.
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"""
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# We override the default dummy inputs here because SAM has some really explosive memory usage in the
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# attention layers, so we want to pass the smallest possible batches
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VISION_DUMMY_INPUTS = tf.random.uniform(
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shape=(
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1,
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@ -1164,25 +1160,6 @@ class TFSamPreTrainedModel(TFPreTrainedModel):
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)
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return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
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@tf.function(
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input_signature=[
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{
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"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
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}
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]
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)
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def serving(self, inputs):
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"""
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Method used for serving the model.
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Args:
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inputs (`Dict[str, tf.Tensor]`):
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The input of the saved model as a dictionary of tensors.
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"""
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output = self.call(inputs)
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return self.serving_output(output)
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SAM_START_DOCSTRING = r"""
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This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
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@ -1938,34 +1938,6 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
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config_class = {{cookiecutter.camelcase_modelname}}Config
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base_model_prefix = "model"
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@property
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def dummy_inputs(self):
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pad_token = 1
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input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
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decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
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dummy_inputs = {
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": tf.math.not_equal(input_ids, pad_token),
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"input_ids": input_ids,
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}
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return dummy_inputs
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@tf.function(
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input_signature=[
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{
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"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
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"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
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"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
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"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
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}
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]
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)
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# Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving
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def serving(self, inputs):
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output = self.call(inputs)
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return self.serving_output(output)
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{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
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This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the
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