From: Benoit Steiner <benoitsteiner@fb.com>
Date: Thu, 28 Mar 2019 15:52:01 +0000 (-0700)
Subject: Improved onnx export for 3 onnx ops. (#18512)
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~581
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=eee760dbd341127cd9e33f04813532fbf9e63316;p=platform%2Fupstream%2Fpytorch.git

Improved onnx export for 3 onnx ops. (#18512)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18512

Ceil and Floor have been supported since version 6 of ONNX: export them using the native onnx ops instead of an Aten op.
Similarly, support for the Where op has been added in version 9, so we don't need to wrap these op in an Aten op.

Reviewed By: houseroad

Differential Revision: D14635130

fbshipit-source-id: d54a2b6e295074a6214b5939b21051a6735c9958
---

diff --git a/caffe2/onnx/backend.cc b/caffe2/onnx/backend.cc
index e7c512a..3564ebe 100644
--- a/caffe2/onnx/backend.cc
+++ b/caffe2/onnx/backend.cc
@@ -362,7 +362,8 @@ Caffe2Backend::get_special_operators() const {
               {"Dropout", &Caffe2Backend::CreateDropout},
               {"LRN", &Caffe2Backend::CreateLRN},
               {"DynamicSlice", &Caffe2Backend::CreateDynamicSlice},
-              {"RandomNormal", &Caffe2Backend::CreateRandomNormal}};
+              {"RandomNormal", &Caffe2Backend::CreateRandomNormal},
+              {"Where", &Caffe2Backend::CreateWhereOp}};
   return kSpecialOperators;
 }
 
@@ -580,6 +581,21 @@ Caffe2Ops Caffe2Backend::CreateRandomNormal(
   return CommonOnnxNodeToCaffe2Ops(onnx_node, ctx);
 }
 
+Caffe2Ops Caffe2Backend::CreateWhereOp(
+    OnnxNode* onnx_node,
+    const ConversionContext& ctx) {
+  // The native Caffe2 op doesn't support broadcasting, so we defer the handling
+  // of this op to the ATen library that does.
+  onnx::NodeProto converted;
+  converted.CopyFrom(onnx_node->node);
+  converted.set_op_type("ATen");
+  onnx::AttributeProto* attr = converted.add_attribute();
+  attr->set_name("operator");
+  attr->set_s("where");
+  OnnxNode new_node(converted);
+  return CommonOnnxNodeToCaffe2Ops(&new_node, ctx);
+}
+
 Caffe2Ops Caffe2Backend::CreateReciprocal(
     OnnxNode* onnx_node,
     const ConversionContext& ctx) {
diff --git a/caffe2/onnx/backend.h b/caffe2/onnx/backend.h
index d61af29..8ee33ef 100644
--- a/caffe2/onnx/backend.h
+++ b/caffe2/onnx/backend.h
@@ -236,6 +236,8 @@ class CAFFE2_API Caffe2Backend {
       OnnxNode* onnx_node,
       const ConversionContext& ctx);
 
+  Caffe2Ops CreateWhereOp(OnnxNode* onnx_node, const ConversionContext& ctx);
+
   Caffe2Ops CreateBatchNormalization(
       OnnxNode* onnx_node,
       const ConversionContext& ctx);
diff --git a/caffe2/python/onnx/tests/onnx_backend_test.py b/caffe2/python/onnx/tests/onnx_backend_test.py
index 75d4b5a..f353e22 100644
--- a/caffe2/python/onnx/tests/onnx_backend_test.py
+++ b/caffe2/python/onnx/tests/onnx_backend_test.py
@@ -52,7 +52,6 @@ backend_test.exclude(r'(test_hardsigmoid'  # Does not support Hardsigmoid.
                      '|test_isnan.*'  # Needs implementation
                      '|test_scatter.*'  # Should be similar to ScatterAssign
                      '|test_constantofshape_int.*'  # Needs implementation
-                     '|test_where.*'  # Needs implementation
                      '|test_shrink.*'  # Needs implementation
                      '|test_strnorm.*'  # Needs implementation
                      '|test_nonzero.*'  # Needs implementation
diff --git a/torch/onnx/symbolic.py b/torch/onnx/symbolic.py
index fbb8d97..9a1911f 100644
--- a/torch/onnx/symbolic.py
+++ b/torch/onnx/symbolic.py
@@ -548,6 +548,14 @@ def relu(g, input):
     return g.op("Relu", input)
 
 
+def ceil(g, input):
+    return g.op("Ceil", input)
+
+
+def floor(g, input):
+    return g.op("Floor", input)
+
+
 @parse_args('v', 't', 't')
 def threshold(g, self, threshold, value):
     # See Note [Export inplace]
@@ -922,7 +930,7 @@ def le(g, input, other):
 
 
 def where(g, condition, self, other):
-    return g.op("ATen", condition, self, other, operator_s="where")
+    return g.op("Where", condition, self, other)
 
 
 @parse_args('v', 'i', 'i')