From 71f94a8a1c89577ec4482b3e5600fbcdfb3dd1a8 Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Mon, 23 Dec 2019 22:28:34 +0100 Subject: [PATCH] Remove unused variables in src. --- src/transformers/data/metrics/__init__.py | 2 +- src/transformers/modeling_albert.py | 5 ----- src/transformers/modeling_t5.py | 1 - src/transformers/modeling_tf_pytorch_utils.py | 7 +++---- src/transformers/modeling_tf_t5.py | 1 - src/transformers/modeling_tf_transfo_xl_utilities.py | 1 - src/transformers/modeling_tf_utils.py | 6 +++--- 7 files changed, 7 insertions(+), 16 deletions(-) diff --git a/src/transformers/data/metrics/__init__.py b/src/transformers/data/metrics/__init__.py index f65c76faeb..48cd3b99af 100644 --- a/src/transformers/data/metrics/__init__.py +++ b/src/transformers/data/metrics/__init__.py @@ -19,7 +19,7 @@ try: from sklearn.metrics import matthews_corrcoef, f1_score _has_sklearn = True -except (AttributeError, ImportError) as e: +except (AttributeError, ImportError): _has_sklearn = False diff --git a/src/transformers/modeling_albert.py b/src/transformers/modeling_albert.py index 5162a1d1de..c663b7b8ec 100644 --- a/src/transformers/modeling_albert.py +++ b/src/transformers/modeling_albert.py @@ -241,8 +241,6 @@ class AlbertAttention(BertSelfAttention): context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - reshaped_context_layer = context_layer.view(*new_context_layer_shape) # Should find a better way to do this w = ( @@ -334,9 +332,6 @@ class AlbertTransformer(nn.Module): # Index of the hidden group group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups)) - # Index of the layer inside the group - layer_idx = int(i - group_idx * layers_per_group) - layer_group_output = self.albert_layer_groups[group_idx]( hidden_states, attention_mask, diff --git a/src/transformers/modeling_t5.py b/src/transformers/modeling_t5.py index 9c169d5016..576eb89d88 100644 --- a/src/transformers/modeling_t5.py +++ b/src/transformers/modeling_t5.py @@ -629,7 +629,6 @@ class T5Stack(T5PreTrainedModel): all_attentions = all_attentions + (layer_outputs[1],) # We keep only self-attention weights for now hidden_states = self.final_layer_norm(hidden_states) - layer_output = self.dropout(hidden_states) # Add last layer if self.output_hidden_states: diff --git a/src/transformers/modeling_tf_pytorch_utils.py b/src/transformers/modeling_tf_pytorch_utils.py index 94d15ba74b..81290326c9 100644 --- a/src/transformers/modeling_tf_pytorch_utils.py +++ b/src/transformers/modeling_tf_pytorch_utils.py @@ -122,7 +122,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: - tfo = tf_model(tf_inputs, training=False) # Make sure model is built + tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state dict - TODO remove this and update the AWS weights files instead # Convert old format to new format if needed from a PyTorch state_dict @@ -187,7 +187,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a K.batch_set_value(weight_value_tuples) if tf_inputs is not None: - tfo = tf_model(tf_inputs, training=False) # Make sure restore ops are run + tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel)) @@ -218,7 +218,6 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs import transformers - tf_path = os.path.abspath(tf_checkpoint_path) logger.info("Loading TensorFlow weights from {}".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model @@ -230,7 +229,7 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: - tfo = tf_model(tf_inputs, training=False) # Make sure model is built + tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) diff --git a/src/transformers/modeling_tf_t5.py b/src/transformers/modeling_tf_t5.py index 5840407273..43f5517be9 100644 --- a/src/transformers/modeling_tf_t5.py +++ b/src/transformers/modeling_tf_t5.py @@ -491,7 +491,6 @@ class TFT5MainLayer(tf.keras.layers.Layer): all_attentions = all_attentions + (layer_outputs[1],) hidden_states = self.final_layer_norm(hidden_states) - layer_output = self.dropout(hidden_states, training=training) # Add last layer if self.output_hidden_states: diff --git a/src/transformers/modeling_tf_transfo_xl_utilities.py b/src/transformers/modeling_tf_transfo_xl_utilities.py index cd32d86390..23ffb639f7 100644 --- a/src/transformers/modeling_tf_transfo_xl_utilities.py +++ b/src/transformers/modeling_tf_transfo_xl_utilities.py @@ -118,7 +118,6 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer): hidden, target = inputs head_logprob = 0 if self.n_clusters == 0: - softmax_b = tf.get_variable("bias", [self.config.vocab_size], initializer=tf.zeros_initializer()) output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0]) if target is not None: loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output) diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py index bfb773e38a..b9c0adac38 100644 --- a/src/transformers/modeling_tf_utils.py +++ b/src/transformers/modeling_tf_utils.py @@ -320,7 +320,7 @@ class TFPreTrainedModel(tf.keras.Model): # Load from a PyTorch checkpoint return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True) - ret = model(model.dummy_inputs, training=False) # build the network with dummy inputs + model(model.dummy_inputs, training=False) # build the network with dummy inputs assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file) # 'by_name' allow us to do transfer learning by skipping/adding layers @@ -333,7 +333,7 @@ class TFPreTrainedModel(tf.keras.Model): "If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. " ) - ret = model(model.dummy_inputs, training=False) # Make sure restore ops are run + model(model.dummy_inputs, training=False) # Make sure restore ops are run # Check if the models are the same to output loading informations with h5py.File(resolved_archive_file, "r") as f: @@ -515,7 +515,7 @@ class TFSequenceSummary(tf.keras.layers.Layer): cls_index = inputs[1] if len(inputs) > 1 else None assert len(inputs) <= 2, "Too many inputs." else: - input_ids = inputs.get("input_ids") + hidden_states = inputs.get("hidden_states") cls_index = inputs.get("cls_index", None) if self.summary_type == "last":