fix bug of supported data type for LessEqual on CPU

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h

@@ -247,41 +247,16 @@ MS_REG_CPU_KERNEL(
   GreaterEqual,
   KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
   ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
 MS_REG_CPU_KERNEL(
   LessEqual, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
   ArithmeticCPUKernel);
 MS_REG_CPU_KERNEL(
-  LessEqual, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual,
-  KernelAttr().AddInputAttr(kNumberTypeUInt16).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual,
-  KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual,
-  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
+  LessEqual, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
   ArithmeticCPUKernel);
 MS_REG_CPU_KERNEL(
   LessEqual,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
   ArithmeticCPUKernel);
-MS_REG_CPU_KERNEL(
-  LessEqual,
-  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
-  ArithmeticCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

tests/st/ops/cpu/test_less_equal_op.py

@@ -32,7 +32,7 @@ class Net(nn.Cell):
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu_training
 @pytest.mark.env_onecard
-def test_net():
+def test_net_fp32():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
     y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
@@ -81,3 +81,129 @@ def test_net():
     expect = x4_np <= y4_np
     assert np.all(out == expect)
     assert out.shape == expect.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_net_fp16():
+    x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
+    y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
+    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
+    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float16)
+    x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float16)
+    y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
+    x3_np = np.random.randint(1, 5, 1).astype(np.float16)
+    y3_np = np.random.randint(1, 5, 1).astype(np.float16)
+    x4_np = np.array(768).astype(np.float16)
+    y4_np = np.array(3072.5).astype(np.float16)
+
+    x0 = Tensor(x0_np)
+    y0 = Tensor(y0_np)
+    x1 = Tensor(x1_np)
+    y1 = Tensor(y1_np)
+    x2 = Tensor(x2_np)
+    y2 = Tensor(y2_np)
+    x3 = Tensor(x3_np)
+    y3 = Tensor(y3_np)
+    x4 = Tensor(x4_np)
+    y4 = Tensor(y4_np)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    net = Net()
+    out = net(x0, y0).asnumpy()
+    expect = x0_np <= y0_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+    out = net(x1, y1).asnumpy()
+    expect = x1_np <= y1_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+    out = net(x2, y2).asnumpy()
+    expect = x2_np <= y2_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+    out = net(x3, y3).asnumpy()
+    expect = x3_np <= y3_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+    out = net(x4, y4).asnumpy()
+    expect = x4_np <= y4_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_net_int32():
+    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
+    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int32)
+    x1 = Tensor(x1_np)
+    y1 = Tensor(y1_np)
+
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    net = Net()
+    out = net(x1, y1).asnumpy()
+    expect = x1_np <= y1_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_net_int64():
+    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int64)
+    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int64)
+    x1 = Tensor(x1_np)
+    y1 = Tensor(y1_np)
+
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    net = Net()
+    out = net(x1, y1).asnumpy()
+    expect = x1_np <= y1_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_net_float64():
+    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
+    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float64)
+    x1 = Tensor(x1_np)
+    y1 = Tensor(y1_np)
+
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    net = Net()
+    out = net(x1, y1).asnumpy()
+    expect = x1_np <= y1_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_net_int16():
+    x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int16)
+    y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int16)
+    x1 = Tensor(x1_np)
+    y1 = Tensor(y1_np)
+
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    net = Net()
+    out = net(x1, y1).asnumpy()
+    expect = x1_np <= y1_np
+    assert np.all(out == expect)
+    assert out.shape == expect.shape