[ONNX] ONNX dev branch merge 01-06-2021 (#50163)

Summary: [ONNX] ONNX dev branch merge 01-06-2021 - [ONNX] Support onnx if/loop sequence output in opset 13 - (#49270) - Symbolic function for torch.square (#49446) - [ONNX] Add checks in ONNXSetDynamicInputShape (#49783) … - [ONNX] Enable export af aten::__derive_index (#49514) … - [ONNX] Update symbolic for unfold (#49378) … - [ONNX] Update the sequence of initializers in exported graph so that it is as same as inputs. (#49798) - [ONNX] Enable opset 13 ops (#49612) … - [ONNX] Improve error message for supported model input types in ONNX export API. (#50119) - [ONNX] Add a post-pass for If folding (#49410) Pull Request resolved: #50163 Reviewed By: pbelevich Differential Revision: D25821059 Pulled By: SplitInfinity fbshipit-source-id: 9f511a93d9d5812d0ab0a49d61ed0fa5f8066948
pytorch · Jan 13, 2021 · aeefe2c · aeefe2c
1 parent 30a8ba9
commit aeefe2c
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diff --git a/.jenkins/caffe2/test.sh b/.jenkins/caffe2/test.sh
@@ -165,7 +165,7 @@ if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
   # JIT C++ extensions require ninja, so put it into PATH.
   export PATH="/var/lib/jenkins/.local/bin:$PATH"
   if [[ "$BUILD_ENVIRONMENT" == *py3* ]]; then
-    pip install -q --user onnxruntime==1.5.2
+    pip install -q --user onnxruntime==1.6.0
   fi
   "$ROOT_DIR/scripts/onnx/test.sh"
 fi
diff --git a/aten/src/ATen/core/interned_strings.h b/aten/src/ATen/core/interned_strings.h
@@ -346,6 +346,7 @@ namespace c10 {
   _(onnx, ReduceL2)                  \
   _(onnx, Conv)                      \
   _(onnx, BatchNormalization)        \
+  _(onnx, ReduceProd)                \
   FORALL_ATTR_BASE_SYMBOLS(_)        \
   _(attr, Subgraph)                  \
   _(attr, ReverseSubgraph)           \

diff --git a/scripts/onnx/test.sh b/scripts/onnx/test.sh
@@ -72,7 +72,7 @@ if [[ "$BUILD_ENVIRONMENT" == *ort_test1* ]]; then
 fi
 if [[ "$BUILD_ENVIRONMENT" == *ort_test2* ]]; then
   # Update the loop for new opsets
-  for i in $(seq 10 12); do
+  for i in $(seq 10 13); do
     pytest "${args[@]}" \
       "$top_dir/test/onnx/test_pytorch_onnx_onnxruntime.py::TestONNXRuntime_opset$i"
   done

diff --git a/test/onnx/expect/TestOperators.test_batchnorm.expect b/test/onnx/expect/TestOperators.test_batchnorm.expect
@@ -26,25 +26,25 @@ graph {
   initializer {
     dims: 2
     data_type: 1
-    name: "bias"
-    raw_data: "\000\000\000\000\000\000\000\000"
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_mean"
+    name: "bias"
     raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_var"
-    raw_data: "\000\000\200?\000\000\200?"
+    name: "running_mean"
+    raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "weight"
+    name: "running_var"
     raw_data: "\000\000\200?\000\000\200?"
   }
   input {

diff --git a/test/onnx/expect/TestOperators.test_batchnorm_1d.expect b/test/onnx/expect/TestOperators.test_batchnorm_1d.expect
@@ -26,25 +26,25 @@ graph {
   initializer {
     dims: 2
     data_type: 1
-    name: "bias"
-    raw_data: "\000\000\000\000\000\000\000\000"
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_mean"
+    name: "bias"
     raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_var"
-    raw_data: "\000\000\200?\000\000\200?"
+    name: "running_mean"
+    raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "weight"
+    name: "running_var"
     raw_data: "\000\000\200?\000\000\200?"
   }
   input {

diff --git a/test/onnx/expect/TestOperators.test_batchnorm_onnx_irv4.expect b/test/onnx/expect/TestOperators.test_batchnorm_onnx_irv4.expect
@@ -26,25 +26,25 @@ graph {
   initializer {
     dims: 2
     data_type: 1
-    name: "bias"
-    raw_data: "\000\000\000\000\000\000\000\000"
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_mean"
+    name: "bias"
     raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "running_var"
-    raw_data: "\000\000\200?\000\000\200?"
+    name: "running_mean"
+    raw_data: "\000\000\000\000\000\000\000\000"
   }
   initializer {
     dims: 2
     data_type: 1
-    name: "weight"
+    name: "running_var"
     raw_data: "\000\000\200?\000\000\200?"
   }
   input {

diff --git a/test/onnx/expect/TestOperators.test_batchnorm_training.expect b/test/onnx/expect/TestOperators.test_batchnorm_training.expect
@@ -27,6 +27,12 @@ graph {
     }
   }
   name: "torch-jit-export"
+  initializer {
+    dims: 2
+    data_type: 1
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?"
+  }
   initializer {
     dims: 2
     data_type: 1
@@ -45,12 +51,6 @@ graph {
     name: "running_var"
     raw_data: "fff?fff?"
   }
-  initializer {
-    dims: 2
-    data_type: 1
-    name: "weight"
-    raw_data: "\000\000\200?\000\000\200?"
-  }
   input {
     name: "input"
     type {

diff --git a/test/onnx/expect/TestOperators.test_layer_norm_aten.expect b/test/onnx/expect/TestOperators.test_layer_norm_aten.expect
@@ -35,15 +35,15 @@ graph {
     dims: 10
     dims: 10
     data_type: 1
-    name: "bias"
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+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?"
   }
   initializer {
     dims: 10
     dims: 10
     data_type: 1
-    name: "weight"
-    raw_data: "\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?"
+    name: "bias"
+    raw_data: "\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000"
   }
   input {
     name: "input"

diff --git a/test/onnx/expect/TestOperators.test_linear.expect b/test/onnx/expect/TestOperators.test_linear.expect
@@ -28,16 +28,16 @@ graph {
   name: "torch-jit-export"
   initializer {
     dims: 5
+    dims: 4
     data_type: 1
-    name: "bias"
-    raw_data: "\324BO\276@\245T>\350\377\245\275\374u\336\276&\212\304>"
+    name: "weight"
+    raw_data: "\212\332\356>@\265u>p\303E\275 \320\306\274\354\201\221>\004\354\261\276\2746*>8\247)\276\340\035\224>\024\2446\276\200\211\312<\224\344,>D\356\257>\320\202\226\275\364\213\351>z\226\330\276\310\250\266\275\352F\377\276\000\250)=\244K\021>"
   }
   initializer {
     dims: 5
-    dims: 4
     data_type: 1
-    name: "weight"
-    raw_data: "\212\332\356>@\265u>p\303E\275 \320\306\274\354\201\221>\004\354\261\276\2746*>8\247)\276\340\035\224>\024\2446\276\200\211\312<\224\344,>D\356\257>\320\202\226\275\364\213\351>z\226\330\276\310\250\266\275\352F\377\276\000\250)=\244K\021>"
+    name: "bias"
+    raw_data: "\324BO\276@\245T>\350\377\245\275\374u\336\276&\212\304>"
   }
   input {
     name: "input"