!14045 [Graph Kernel] add compare test case

From: @zengzitao
Reviewed-by: @gaoxiong1
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-03-29 09:15:57 +08:00 committed by Gitee
commit 7149e8c2c9
2 changed files with 223 additions and 6 deletions

View File

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

View File

@ -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()