!3957 Gpu StridedSlice dims exceeds

Merge pull request !3957 from chenweifeng/strided_slice_dims_exceeds

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h

@@ -59,6 +59,7 @@ class StridedSliceGpuKernel : public GpuKernel {
     ParseMasks(kernel_node);
     FillOutputDim();
     null_output_ = IsNullOutput();
+
     InitSizeLists();
     return true;
   }
@@ -86,14 +87,15 @@ class StridedSliceGpuKernel : public GpuKernel {
 
     for (size_t i = 0; i < MAX_DIMS; i++) {
       if (i < begin_.size()) {
-        begin_[i] =
-          std::min(begin_[i] < 0 ? SizeToInt(begin_[i] + input_shape_[i]) : begin_[i], SizeToInt(input_shape_[i] - 1));
+        int dim = SizeToInt(input_shape_[i]);
+        begin_[i] = std::min(begin_[i] < 0 ? std::max(begin_[i] + dim, 0) : begin_[i], dim - 1);
       } else {
         begin_.push_back(0);
       }
 
       if (i < end_.size()) {
-        end_[i] = std::max(end_[i] < 0 ? end_[i] + SizeToInt(input_shape_[i]) : end_[i], -1);
+        int dim = SizeToInt(input_shape_[i]);
+        end_[i] = std::max(end_[i] < 0 ? end_[i] + dim : std::min(end_[i], dim), -1);
       } else {
         end_.push_back(i < input_shape_.size() ? input_shape_[i] : 1);
       }

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h

@@ -87,14 +87,15 @@ class StridedSliceGradGpuKernel : public GpuKernel {
 
     for (size_t i = 0; i < MAX_DIMS; i++) {
       if (i < begin_.size()) {
-        begin_[i] =
-          std::min(begin_[i] < 0 ? SizeToInt(begin_[i] + input_shape_[i]) : begin_[i], SizeToInt(input_shape_[i] - 1));
+        int dim = SizeToInt(input_shape_[i]);
+        begin_[i] = std::min(begin_[i] < 0 ? std::max(begin_[i] + dim, 0) : begin_[i], dim - 1);
       } else {
         begin_.push_back(0);
       }
 
       if (i < end_.size()) {
-        end_[i] = std::max(end_[i] < 0 ? end_[i] + SizeToInt(input_shape_[i]) : end_[i], -1);
+        int dim = SizeToInt(input_shape_[i]);
+        end_[i] = std::max(end_[i] < 0 ? end_[i] + dim : std::min(end_[i], dim), -1);
       } else {
         end_.push_back(i < input_shape_.size() ? input_shape_[i] : 1);
       }

tests/st/ops/gpu/test_stridedslice_grad_op.py

@@ -150,73 +150,6 @@ def strided_slice_grad(nptype):
                          [0., 0., 0., 0., 0.]]]]).astype(nptype)
     assert np.allclose(dx[0].asnumpy(), expect)
 
-    # ME infer fault
-    # y = GradData()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
-    # expect = np.array([[[[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]]],
-
-
-    #                    [[[0., 0., 0., 0., 0.],
-    #                      [0., 1., 0., 1., 0.],
-    #                      [0., 1., 0., 1., 0.],
-    #                      [0., 1., 0., 1., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 1., 0., 1., 0.],
-    #                      [0., 1., 0., 1., 0.],
-    #                      [0., 1., 0., 1., 0.]],begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]]]])
-    # assert np.allclose(y.asnumpy(), expect)
-
-    # y = Grad(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
-    # expect = np.array([[[[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]]],
-
-
-    #                    [[[0., 0., 1., 1., 0.],
-    #                      [0., 0., 1., 1., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 1., 1., 0.],
-    #                      [0., 0., 1., 1., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]],
-
-    #                     [[0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.],
-    #                      [0., 0., 0., 0., 0.]]]])
-    # assert np.allclose(y.asnumpy(), expect)
-
 
     net = StridedSliceNet((1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1),
                           begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100)

tests/st/ops/gpu/test_stridedslice_op.py

@@ -45,23 +45,23 @@ def strided_slice(nptype):
                          [89, 88, 87]]]]).astype(nptype)
     assert np.allclose(y.asnumpy(), expect)
 
-    # ME infer fault
-    # y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
-    # expect = np.array([[[[78, 76],
-    #                      [73, 71],
-    #                      [68, 66]],
-    #                     [[98, 96],
-    #                      [93, 91],
-    #                      [88, 86]]]])
-    # assert np.allclose(y.asnumpy(), expect)
+    y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
+    expect = np.array([[[[78, 76],
+                         [73, 71],
+                         [68, 66]],
+                        [[98, 96],
+                         [93, 91],
+                         [88, 86]]]]).astype(nptype)
+    assert np.allclose(y.asnumpy(), expect)
 
+    # ME Infer fault
     # y = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
-    # expect = np.array([[[[ 62,  63],
-    #                      [ 67,  68]],
-    #                     [[ 82,  83],
-    #                      [ 87,  88]],
+    # expect = np.array([[[[62, 63],
+    #                      [67, 68]],
+    #                     [[82, 83],
+    #                      [87, 88]],
     #                     [[102, 103],
-    #                      [107, 108]]]])
+    #                      [107, 108]]]]).astype(nptype)
     # assert np.allclose(y.asnumpy(), expect)
 
     op = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100)
@@ -125,3 +125,25 @@ def test_strided_slice_uint8():
 @pytest.mark.env_onecard
 def test_strided_slice_bool():
     strided_slice(np.bool)
+    x = Tensor(np.arange(0, 4*4*4).reshape(4, 4, 4).astype(np.float32))
+    y = x[-8:, :8]
+    expect = np.array([[[0., 1., 2., 3.],
+                        [4., 5., 6., 7.],
+                        [8., 9., 10., 11.],
+                        [12., 13., 14., 15.]],
+
+                       [[16., 17., 18., 19.],
+                        [20., 21., 22., 23.],
+                        [24., 25., 26., 27.],
+                        [28., 29., 30., 31.]],
+
+                       [[32., 33., 34., 35.],
+                        [36., 37., 38., 39.],
+                        [40., 41., 42., 43.],
+                        [44., 45., 46., 47.]],
+
+                       [[48., 49., 50., 51.],
+                        [52., 53., 54., 55.],
+                        [56., 57., 58., 59.],
+                        [60., 61., 62., 63.]]])
+    assert np.allclose(y.asnumpy(), expect)