未验证 提交 075b5502 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #15856 from kbinias/kbinias/activation-mkldnn-ut

MKL-DNN: Add UT to check whether primitives already exist in backward
......@@ -52,11 +52,6 @@ class MKLDNNActivationKernel
"Wrong layout/format set for Input x tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
......@@ -76,11 +71,6 @@ class MKLDNNActivationGradKernel
"is_test attribute should be set to False in training phase.");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
......
......@@ -18,8 +18,8 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from scipy.special import expit
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
import paddle.fluid as fluid
class TestMKLDNNReluDim2(TestRelu):
......@@ -97,5 +97,64 @@ class TestMKLDNNAbsDim4(TestAbs):
self.attrs = {"use_mkldnn": True}
# Check if primitives already exist in backward
class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
def test_check_forward_backward(self):
place = core.CPUPlace()
np.random.seed(123)
x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32)
out = np.abs(x)
out_grad = np.random.random_sample(x.shape).astype(np.float32)
x_grad = out_grad * np.sign(x) # Abs grad calculation
var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad}
var_names = list(var_dict.keys())
ground_truth = {name: var_dict[name] for name in var_names}
program = fluid.Program()
with fluid.program_guard(program):
block = program.global_block()
for name in ground_truth:
block.create_var(
name=name, dtype='float32', shape=ground_truth[name].shape)
relu_op = block.append_op(
type="abs",
inputs={"X": block.var('x'), },
outputs={"Out": block.var('out')},
attrs={"use_mkldnn": True})
# Generate backward op_desc
grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
relu_op.desc, set(), [])
grad_op_desc = grad_op_desc_list[0]
new_op_desc = block.desc.append_op()
new_op_desc.copy_from(grad_op_desc)
for var_name in grad_op_desc.output_arg_names():
block.desc.var(var_name.encode("ascii"))
grad_op_desc.infer_var_type(block.desc)
grad_op_desc.infer_shape(block.desc)
for arg in grad_op_desc.output_arg_names():
grad_var = block.desc.find_var(arg.encode("ascii"))
grad_var.set_dtype(core.VarDesc.VarType.FP32)
exe = fluid.Executor(place)
# Do at least 2 iterations
for i in range(2):
out = exe.run(
program,
feed={name: var_dict[name]
for name in ['x', 'out@GRAD']},
fetch_list=['x@GRAD'])
self.__assert_close(x_grad, out[0], "x@GRAD")
if __name__ == '__main__':
unittest.main()
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