Fix natten (huggingface#22229)

* Add kernel size to NATTEN's QK arguments.

The new NATTEN 0.14.5 supports PyTorch 2.0, but also adds an additional
argument to the QK operation to allow optional RPBs.

This ends up failing NATTEN tests.

This commit adds NATTEN back to circleci and adds the arguments to get
it working again.

* Force NATTEN >= 0.14.5

.circleci/create_circleci_config.py

@@ -374,8 +374,7 @@ def job_name(self):
         "pip install 'git+https://github.com/facebookresearch/detectron2.git'",
         "sudo apt install tesseract-ocr",
         "pip install pytesseract",
-        # wait until natten is ready for torch 2.0.0
-        # "pip install natten",
+        "pip install natten",
     ],
     tests_to_run=[
         "tests/models/*layoutlmv*",

setup.py

@@ -129,7 +129,7 @@
     "keras-nlp>=0.3.1",
     "librosa",
     "nltk",
-    "natten>=0.14.4",
+    "natten>=0.14.5",
     "numpy>=1.17",
     "onnxconverter-common",
     "onnxruntime-tools>=1.4.2",

src/transformers/dependency_versions_table.py

@@ -35,7 +35,7 @@
     "keras-nlp": "keras-nlp>=0.3.1",
     "librosa": "librosa",
     "nltk": "nltk",
-    "natten": "natten>=0.14.4",
+    "natten": "natten>=0.14.5",
     "numpy": "numpy>=1.17",
     "onnxconverter-common": "onnxconverter-common",
     "onnxruntime-tools": "onnxruntime-tools>=1.4.2",

src/transformers/models/dinat/modeling_dinat.py

@@ -347,7 +347,7 @@ def forward(
         query_layer = query_layer / math.sqrt(self.attention_head_size)
 
         # Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
-        attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.dilation)
+        attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, self.dilation)
 
         # Normalize the attention scores to probabilities.
         attention_probs = nn.functional.softmax(attention_scores, dim=-1)

src/transformers/models/nat/modeling_nat.py

@@ -339,7 +339,7 @@ def forward(
         query_layer = query_layer / math.sqrt(self.attention_head_size)
 
         # Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
-        attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, 1)
+        attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, 1)
 
         # Normalize the attention scores to probabilities.
         attention_probs = nn.functional.softmax(attention_scores, dim=-1)