!14045 [Graph Kernel] add compare test case

From: @zengzitao
Reviewed-by: @gaoxiong1
Signed-off-by:

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc

@@ -587,12 +587,12 @@ std::vector<PrimitivePtr> GetFusibleOpList() {
     prim::kPrimCast,       prim::kPrimRealDiv,    prim::kPrimMatMul,  prim::kPrimAssign};
 #elif ENABLE_GPU
   std::vector<PrimitivePtr> fusible_basic_ops = {
-    prim::kPrimAbs,     prim::kPrimRound,      prim::kPrimNeg,       prim::kPrimExp,     prim::kPrimAdd,
+    prim::kPrimAbs,     prim::kPrimRound,      prim::kPrimNeg,       prim::kPrimExp,       prim::kPrimAdd,
-    prim::kPrimRealDiv, prim::kPrimMul,        prim::kPrimMinimum,   prim::kPrimMaximum, prim::kPrimLog,
+    prim::kPrimRealDiv, prim::kPrimMul,        prim::kPrimMinimum,   prim::kPrimMaximum,   prim::kPrimLog,
-    prim::kPrimPow,     prim::kPrimSub,        prim::kPrimRsqrt,     prim::kPrimSqrt,    prim::kPrimAddN,
+    prim::kPrimPow,     prim::kPrimSub,        prim::kPrimRsqrt,     prim::kPrimSqrt,      prim::kPrimAddN,
-    prim::kPrimEqual,   prim::kPrimReciprocal, prim::KPrimTransData, prim::kPrimSelect,  prim::kPrimGreater,
+    prim::kPrimEqual,   prim::kPrimReciprocal, prim::KPrimTransData, prim::kPrimSelect,    prim::kPrimGreater,
-    prim::kPrimCast,    prim::kPrimReduceSum,  prim::kPrimTanh,      prim::kPrimReshape, prim::kPrimTranspose,
+    prim::kPrimCast,    prim::kPrimReduceSum,  prim::kPrimTanh,      prim::kPrimReshape,   prim::kPrimTranspose,
-    prim::kPrimAssign,  prim::kPrimExpandDims};
+    prim::kPrimAssign,  prim::kPrimExpandDims, prim::kPrimLess,      prim::kPrimLessEqual, prim::kPrimGreaterEqual};
 #else
   std::vector<PrimitivePtr> fusible_basic_ops;
 #endif

tests/st/ops/graph_kernel/test_comare.py

@@ -0,0 +1,217 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import pytest
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+class LessNet(nn.Cell):
+    def __init__(self):
+        super(LessNet, self).__init__()
+        self.ops = P.Less()
+
+    def construct(self, x, y):
+        return self.ops(x, y)
+
+
+class GreaterNet(nn.Cell):
+    def __init__(self):
+        super(GreaterNet, self).__init__()
+        self.ops = P.Greater()
+
+    def construct(self, x, y):
+        return self.ops(x, y)
+
+
+class LessEqualNet(nn.Cell):
+    def __init__(self):
+        super(LessEqualNet, self).__init__()
+        self.ops = P.LessEqual()
+
+    def construct(self, x, y):
+        return self.ops(x, y)
+
+
+class GreaterEqualNet(nn.Cell):
+    def __init__(self):
+        super(GreaterEqualNet, self).__init__()
+        self.ops = P.GreaterEqual()
+
+    def construct(self, x, y):
+        return self.ops(x, y)
+
+
+def gen_data():
+    # Generate data which contains broadcast scene and two inputs are expr.
+    np.random.seed(0)
+    x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
+    y0_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
+    x1_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float16)
+    y1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
+    x2_np = np.random.randint(1, 5, 1).astype(np.int32)
+    y2_np = np.random.randint(1, 5, 1).astype(np.int32)
+    x3_np = np.array(768).astype(np.float32)
+    y3_np = np.array(3072.5).astype(np.float32)
+
+    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)
+    return x0, y0, x1, y1, x2, y2, x3, y3
+
+
+def get_less_net_output(x0, y0, x1, y1, x2, y2, x3, y3, enable_graph_kernel=False):
+    context.set_context(enable_graph_kernel=enable_graph_kernel)
+    net_less = LessNet()
+    less_output_0 = net_less(x0, y0).asnumpy()
+    less_output_1 = net_less(x1, y1).asnumpy()
+    less_output_2 = net_less(x2, y2).asnumpy()
+    less_output_3 = net_less(x3, y3).asnumpy()
+    return less_output_0, less_output_1, less_output_2, less_output_3
+
+
+def get_greater_net_output(x0, y0, x1, y1, x2, y2, x3, y3, enable_graph_kernel=False):
+    context.set_context(enable_graph_kernel=enable_graph_kernel)
+    net_greater = GreaterNet()
+    greater_output_0 = net_greater(x0, y0).asnumpy()
+    greater_output_1 = net_greater(x1, y1).asnumpy()
+    greater_output_2 = net_greater(x2, y2).asnumpy()
+    greater_output_3 = net_greater(x3, y3).asnumpy()
+    return greater_output_0, greater_output_1, greater_output_2, greater_output_3
+
+
+def get_less_equal_net_output(x0, y0, x1, y1, x2, y2, x3, y3, enable_graph_kernel=False):
+    context.set_context(enable_graph_kernel=enable_graph_kernel)
+    net_less_equal = LessEqualNet()
+    less_equal_output_0 = net_less_equal(x0, y0).asnumpy()
+    less_equal_output_1 = net_less_equal(x1, y1).asnumpy()
+    less_equal_output_2 = net_less_equal(x2, y2).asnumpy()
+    less_equal_output_3 = net_less_equal(x3, y3).asnumpy()
+    return less_equal_output_0, less_equal_output_1, less_equal_output_2, less_equal_output_3
+
+
+def get_greater_equal_net_output(x0, y0, x1, y1, x2, y2, x3, y3, enable_graph_kernel=False):
+    context.set_context(enable_graph_kernel=enable_graph_kernel)
+    net_greater_equal = GreaterEqualNet()
+    greter_equal_output_0 = net_greater_equal(x0, y0).asnumpy()
+    greter_equal_output_1 = net_greater_equal(x1, y1).asnumpy()
+    greter_equal_output_2 = net_greater_equal(x2, y2).asnumpy()
+    greter_equal_output_3 = net_greater_equal(x3, y3).asnumpy()
+    return greter_equal_output_0, greter_equal_output_1, greter_equal_output_2, greter_equal_output_3
+
+
+def test_less_net():
+    x0, y0, x1, y1, x2, y2, x3, y3 = gen_data()
+    out_gk_on_0, out_gk_on_1, out_gk_on_2, out_gk_on_3 = get_less_net_output(x0, y0, x1, y1, x2, y2, x3, y3, True)
+    out_gk_off_0, out_gk_off_1, out_gk_off_2, out_gk_off_3 = get_less_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, False)
+
+    assert np.all(out_gk_on_0 == out_gk_off_0)
+    assert out_gk_on_0.shape == out_gk_off_0.shape
+    assert np.all(out_gk_on_1 == out_gk_off_1)
+    assert out_gk_on_1.shape == out_gk_off_1.shape
+    assert np.all(out_gk_on_2 == out_gk_off_2)
+    assert out_gk_on_2.shape == out_gk_off_2.shape
+    assert np.all(out_gk_on_3 == out_gk_off_3)
+    assert out_gk_on_3.shape == out_gk_off_3.shape
+
+
+def test_greater_net():
+    x0, y0, x1, y1, x2, y2, x3, y3 = gen_data()
+    out_gk_on_0, out_gk_on_1, out_gk_on_2, out_gk_on_3 = get_greater_net_output(x0, y0, x1, y1, x2, y2, x3, y3, True)
+    out_gk_off_0, out_gk_off_1, out_gk_off_2, out_gk_off_3 = get_greater_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, False)
+
+    assert np.all(out_gk_on_0 == out_gk_off_0)
+    assert out_gk_on_0.shape == out_gk_off_0.shape
+    assert np.all(out_gk_on_1 == out_gk_off_1)
+    assert out_gk_on_1.shape == out_gk_off_1.shape
+    assert np.all(out_gk_on_2 == out_gk_off_2)
+    assert out_gk_on_2.shape == out_gk_off_2.shape
+    assert np.all(out_gk_on_3 == out_gk_off_3)
+    assert out_gk_on_3.shape == out_gk_off_3.shape
+
+
+def test_less_equal_net():
+    x0, y0, x1, y1, x2, y2, x3, y3 = gen_data()
+    out_gk_on_0, out_gk_on_1, out_gk_on_2, out_gk_on_3 = get_less_equal_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, True)
+    out_gk_off_0, out_gk_off_1, out_gk_off_2, out_gk_off_3 = get_less_equal_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, False)
+
+    assert np.all(out_gk_on_0 == out_gk_off_0)
+    assert out_gk_on_0.shape == out_gk_off_0.shape
+    assert np.all(out_gk_on_1 == out_gk_off_1)
+    assert out_gk_on_1.shape == out_gk_off_1.shape
+    assert np.all(out_gk_on_2 == out_gk_off_2)
+    assert out_gk_on_2.shape == out_gk_off_2.shape
+    assert np.all(out_gk_on_3 == out_gk_off_3)
+    assert out_gk_on_3.shape == out_gk_off_3.shape
+
+
+def test_greater_equal_net():
+    x0, y0, x1, y1, x2, y2, x3, y3 = gen_data()
+    out_gk_on_0, out_gk_on_1, out_gk_on_2, out_gk_on_3 = get_greater_equal_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, True)
+    out_gk_off_0, out_gk_off_1, out_gk_off_2, out_gk_off_3 = get_greater_equal_net_output(
+        x0, y0, x1, y1, x2, y2, x3, y3, False)
+
+    assert np.all(out_gk_on_0 == out_gk_off_0)
+    assert out_gk_on_0.shape == out_gk_off_0.shape
+    assert np.all(out_gk_on_1 == out_gk_off_1)
+    assert out_gk_on_1.shape == out_gk_off_1.shape
+    assert np.all(out_gk_on_2 == out_gk_off_2)
+    assert out_gk_on_2.shape == out_gk_off_2.shape
+    assert np.all(out_gk_on_3 == out_gk_off_3)
+    assert out_gk_on_3.shape == out_gk_off_3.shape
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_less_gpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    test_less_net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_greater_gpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    test_greater_net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_less_equal_gpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    test_less_equal_net()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_greater_equal_gpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    test_greater_equal_net()