added validation

fix ci

fix ci

fixed test cases and ci

fixed test cases and ci

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/dynamic_range_impl.cu

@@ -45,6 +45,15 @@ __global__ void ValidateInputAndInferShape(const T *range_start, const T *range_
 
   if (*error_code == DynamicRangeErrorCode::kOk) {
     int64_t real_output_shape = static_cast<int64_t>(ceil(static_cast<double>(end - start) / delta));
+
+    // verification in case of precision error during calculation of real_output_shape. one multiplication followed by
+    // one addition is much more precise than the division that occurs when calculating real_output_shape.
+    double last_value = start + (delta * (real_output_shape - 1));
+    double epsilon = 1e-6;
+    if ((end > start && last_value > end) || (start > end && last_value < end) || fabsf(last_value - end) < epsilon) {
+      real_output_shape--;
+    }
+
     if (real_output_shape > max_output_size) {
         *error_code = DynamicRangeErrorCode::kMaxSizeExceeded;
     }

tests/st/ops/gpu/test_range_op.py

@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.ops import operations as P
 
 class RangeNet(nn.Cell):
-    def __init__(self, maxlen=10000):
+    def __init__(self, maxlen=50):
         super(RangeNet, self).__init__()
         self.range = P.Range(maxlen)
 
@@ -30,6 +30,40 @@ class RangeNet(nn.Cell):
         return self.range(start, limit, delta)
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_range_precision_end_equals_last_element():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    range_net = RangeNet(100)
+    ms_out = range_net(Tensor(1000.04, mstype.float32),
+                       Tensor(1001.04, mstype.float32),
+                       Tensor(0.01, mstype.float32)).asnumpy()
+    np_expected = np.arange(1000.04, 1001.04, 0.01, dtype=np.float32)
+    np.testing.assert_allclose(ms_out, np_expected, rtol=1e-5)
+
+    range_net = RangeNet(1000)
+    ms_out = range_net(Tensor(100, mstype.float32),
+                       Tensor(101, mstype.float32),
+                       Tensor(0.001, mstype.float32)).asnumpy()
+    np_expected = np.arange(100, 101, 0.001, dtype=np.float32)
+    np.testing.assert_allclose(ms_out, np_expected, rtol=1e-5)
+
+    range_net = RangeNet(799900)
+    ms_out = range_net(Tensor(1, mstype.float32),
+                       Tensor(8000, mstype.float32),
+                       Tensor(0.01, mstype.float32)).asnumpy()
+    np_expected = np.arange(1, 8000, 0.01, dtype=np.float32)
+    np.testing.assert_allclose(ms_out, np_expected, rtol=1e-5)
+
+    range_net = RangeNet(53)
+    ms_out = range_net(Tensor(-12000, mstype.float32),
+                       Tensor(-12053, mstype.float32),
+                       Tensor(-1, mstype.float32)).asnumpy()
+    np_expected = np.arange(-12000, -12053, -1, dtype=np.float32)
+    np.testing.assert_allclose(ms_out, np_expected, rtol=1e-5)
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -97,7 +131,7 @@ def test_range_invalid_max_output_length():
 @pytest.mark.env_onecard
 def test_range_invalid_input():
     with pytest.raises(RuntimeError) as info:
-        range_net = RangeNet(3500)
+        range_net = RangeNet()
         _ = range_net(Tensor(0, mstype.int32), Tensor(5, mstype.int32), Tensor(0, mstype.int32)).asnumpy()
     assert "delta cannot be equal to zero" in str(info.value)
 
@@ -107,11 +141,11 @@ def test_range_invalid_input():
     assert "number of elements in the output exceeds maxlen" in str(info.value)
 
     with pytest.raises(RuntimeError) as info:
-        range_net = RangeNet(3500)
+        range_net = RangeNet()
         _ = range_net(Tensor(20, mstype.int32), Tensor(5, mstype.int32), Tensor(1, mstype.int32)).asnumpy()
     assert "delta cannot be positive when limit < start" in str(info.value)
 
     with pytest.raises(RuntimeError) as info:
-        range_net = RangeNet(3500)
+        range_net = RangeNet()
         _ = range_net(Tensor(2, mstype.int32), Tensor(5, mstype.int32), Tensor(-4, mstype.int32)).asnumpy()
     assert "delta cannot be negative when limit > start" in str(info.value)