Support segformer fx (huggingface#19924)

* Support segformer fx

* Add fx_compatible attribute to test_modeling_segformer.py

* Update glpn model (fx support)

glpn model was copied from segformer.

* Update utils/fx.py | add semantic-segmentation

for SegformerForSemanticSegmentation model

* Fix minor import order(isort)

* Add random input generation for segformer fx

Co-authored-by: noelbird <[email protected]>

src/transformers/models/glpn/modeling_glpn.py

@@ -149,7 +149,7 @@ def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_
 
     def transpose_for_scores(self, hidden_states):
         new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        hidden_states = hidden_states.view(*new_shape)
+        hidden_states = hidden_states.view(new_shape)
         return hidden_states.permute(0, 2, 1, 3)
 
     def forward(
@@ -190,7 +190,7 @@ def forward(
 
         context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
         new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
+        context_layer = context_layer.view(new_context_layer_shape)
 
         outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
 

src/transformers/models/segformer/modeling_segformer.py

@@ -179,7 +179,7 @@ def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_
 
     def transpose_for_scores(self, hidden_states):
         new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        hidden_states = hidden_states.view(*new_shape)
+        hidden_states = hidden_states.view(new_shape)
         return hidden_states.permute(0, 2, 1, 3)
 
     def forward(
@@ -220,7 +220,7 @@ def forward(
 
         context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
         new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
+        context_layer = context_layer.view(new_context_layer_shape)
 
         outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
 

src/transformers/utils/fx.py

@@ -44,6 +44,7 @@
     MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
     MODEL_FOR_PRETRAINING_MAPPING_NAMES,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
+    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
     MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
@@ -80,6 +81,7 @@ def _generate_supported_model_class_names(
         "image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
         "ctc": MODEL_FOR_CTC_MAPPING_NAMES,
         "audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
+        "semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
     }
 
     if supported_tasks is None:
@@ -128,6 +130,7 @@ def _generate_supported_model_class_names(
     "plbart",
     "resnet",
     "roberta",
+    "segformer",
     "speech_to_text",
     "speech_to_text_2",
     "swin",
@@ -730,6 +733,7 @@ def _generate_dummy_input(
                 *get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
                 *get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
                 *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),
+                *get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES),
                 "GPT2DoubleHeadsModel",
             ]:
                 inputs_dict["labels"] = torch.zeros(shape, dtype=torch.long, device=device)

tests/models/segformer/test_modeling_segformer.py

@@ -161,6 +161,7 @@ class SegformerModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
 
+    fx_compatible = True
     test_head_masking = False
     test_pruning = False
     test_resize_embeddings = False