Equal op dynamic shape

mindspore/core/abstract/prim_maths.cc

@@ -80,24 +80,31 @@ AbstractBasePtr InferImplEqual(const AnalysisEnginePtr &, const PrimitivePtr &pr
                                const AbstractBasePtrList &args_spec_list) {
   const std::string op_name = primitive->name();
   CheckArgsSize(op_name, args_spec_list, 2);
-  auto input_x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
-  MS_EXCEPTION_IF_NULL(input_x);
-  MS_EXCEPTION_IF_NULL(input_x->shape());
+  auto x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
+  MS_EXCEPTION_IF_NULL(x);
+  MS_EXCEPTION_IF_NULL(x->shape());
+  ShapeVector x_shape = x->shape()->shape();
+  ShapeVector x_shape_min = x->shape()->min_shape().empty() ? x_shape : x->shape()->min_shape();
+  ShapeVector x_shape_max = x->shape()->max_shape().empty() ? x_shape : x->shape()->max_shape();
+
+  auto y = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
+  MS_EXCEPTION_IF_NULL(y);
+  MS_EXCEPTION_IF_NULL(y->shape());
+  ShapeVector y_shape = y->shape()->shape();
+  ShapeVector y_shape_min = y->shape()->min_shape().empty() ? y_shape : y->shape()->min_shape();
+  ShapeVector y_shape_max = y->shape()->max_shape().empty() ? y_shape : y->shape()->max_shape();
 
-  auto input_y = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
-  MS_EXCEPTION_IF_NULL(input_y);
-  MS_EXCEPTION_IF_NULL(input_y->shape());
-
-  auto x_shape = input_x->shape()->shape();
-  auto y_shape = input_y->shape()->shape();
   auto out_shape = BroadcastShape(x_shape, y_shape);
   if (out_shape.empty()) {
     MS_LOG(EXCEPTION) << "BroadcastShape fail: " << args_spec_list[0]->ToString() << ","
                       << args_spec_list[1]->ToString();
   }
+  auto out_shape_min = BroadcastShape(x_shape_min, y_shape_min);
+  auto out_shape_max = BroadcastShape(x_shape_max, y_shape_max);
 
   auto output_type = std::make_shared<Bool>();
-  auto ret = std::make_shared<AbstractTensor>(output_type, out_shape);
+  auto ret =
+    std::make_shared<AbstractTensor>(output_type, std::make_shared<Shape>(out_shape, out_shape_min, out_shape_max));
   return ret;
 }
 

tests/st/ops/gpu/test_equal_op.py

@@ -20,6 +20,7 @@ import mindspore.context as context
 from mindspore.common.tensor import Tensor
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _inner_ops as inner
 
 
 class NetEqual(Cell):
@@ -30,6 +31,17 @@ class NetEqual(Cell):
     def construct(self, x, y):
         return self.Equal(x, y)
 
+class NetEqualDynamic(Cell):
+    def __init__(self):
+        super(NetEqualDynamic, self).__init__()
+        self.conv = inner.GpuConvertToDynamicShape()
+        self.Equal = P.Equal()
+
+    def construct(self, x, y):
+        x_conv = self.conv(x)
+        y_conv = self.conv(y)
+        return self.Equal(x_conv, y_conv)
+
 class NetNotEqual(Cell):
     def __init__(self):
         super(NetNotEqual, self).__init__()
@@ -211,3 +223,20 @@ def test_greaterqual():
     output2 = gequal(x2, y2)
     assert np.all(output2.asnumpy() == expect2)
     assert output2.shape == expect2.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_equal_dynamic_shape():
+    x0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
+    x0 = Tensor(x0_np)
+    y0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
+    y0 = Tensor(y0_np)
+    expect0 = np.equal(x0_np, y0_np)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    equal = NetEqualDynamic()
+    output0 = equal(x0, y0)
+    assert np.all(output0.asnumpy() == expect0)
+    assert output0.shape == expect0.shape