未验证 提交 bd22453f 编写于 作者: T Tao Luo 提交者: GitHub

Revert "Add LeakyRelu MKLDNN support (#18656)" (#18723)

test=develop
上级 58469186
......@@ -363,13 +363,6 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Out", "Output of LeakyRelu operator");
AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>("is_test",
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true.")
.SetDefault(false);
AddComment(R"DOC(
LeakyRelu Activation Operator.
......@@ -702,8 +695,6 @@ class LeakyReluDoubleGradMaker
op->SetType("leaky_relu_grad_grad");
// input1: X
op->SetInput("X", Input("X"));
// input2: Out
op->SetInput("Out", Input("Out"));
// X@GRAD@GRAD: ddx
op->SetInput("DDX", OutputGrad(framework::GradVarName("X")));
op->SetAttrMap(Attrs());
......
......@@ -1001,7 +1001,7 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
dx.device(d) = dout * (temp1 + temp2).template cast<T>();
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepXOut; }
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
};
template <typename T>
......
......@@ -77,7 +77,8 @@ class MKLDNNActivationGradKernel
template <typename T>
void eltwise_forward(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm) {
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
......@@ -89,9 +90,6 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
const T alpha = ctx.op().HasAttr("alpha") ? ctx.Attr<T>("alpha") : 0;
const T beta = ctx.op().HasAttr("beta") ? ctx.Attr<T>("beta") : 0;
PADDLE_ENFORCE(
x->dims().size() == 2 || x->dims().size() == 3 || x->dims().size() == 4,
"Input dim must be with 2, 3 or 4");
......@@ -103,9 +101,10 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
bool is_test = ctx.Attr<bool>("is_test");
// TODO(jczaja): When adding leaky-relu , swish , elu make sure to extend key
// with alpha, beta
std::string key = platform::MKLDNNHandler::GetHash(
src_tz, std::to_string(algorithm) + std::to_string(alpha) +
std::to_string(beta) + ctx.op().Output("Out"));
src_tz, std::to_string(algorithm) + ctx.op().Output("Out"));
// TODO(jczaja): Make it Thread safe
// save input data and layout to be referred in backward path
......@@ -154,7 +153,8 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
template <typename T>
void eltwise_grad(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm) {
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto &mkldnn_engine = dev_ctx.GetEngine();
......@@ -164,9 +164,6 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
const T *diff_y_data = diff_y->data<T>();
T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
const T alpha = ctx.op().HasAttr("alpha") ? ctx.Attr<T>("alpha") : 0;
const T beta = ctx.op().HasAttr("beta") ? ctx.Attr<T>("beta") : 0;
std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());
auto diff_y_format =
......@@ -176,8 +173,7 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
std::string key = platform::MKLDNNHandler::GetHash(
diff_dst_tz, std::to_string(algorithm) + std::to_string(alpha) +
std::to_string(beta) + ctx.op().Input("Out"));
diff_dst_tz, std::to_string(algorithm) + ctx.op().Input("Out"));
const std::string key_src_data = key + "@eltwise_fwd_src_data";
const std::string key_src_layout = key + "@eltwise_fwd_src_layout";
......@@ -279,7 +275,6 @@ namespace ops = paddle::operators;
#define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \
__macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
__macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
__macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradFunctor); \
__macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradFunctor); \
__macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);
......
......@@ -18,7 +18,7 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs, TestLeakyRelu
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
......@@ -29,13 +29,6 @@ class TestMKLDNNReluDim2(TestRelu):
self.attrs = {"use_mkldnn": True}
class TestMKLDNNLeakyReluDim2(TestLeakyRelu):
def setUp(self):
super(TestMKLDNNLeakyReluDim2, self).setUp()
self.attrs = {"use_mkldnn": True}
class TestMKLDNNTanhDim2(TestTanh):
def setUp(self):
super(TestMKLDNNTanhDim2, self).setUp()
......@@ -70,20 +63,6 @@ class TestMKLDNNReluDim4(TestRelu):
self.attrs = {"use_mkldnn": True}
class TestMKLDNNLeakyReluDim4(TestLeakyRelu):
def setUp(self):
super(TestMKLDNNLeakyReluDim4, self).setUp()
x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = np.maximum(x, 0.02 * x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
self.attrs = {"use_mkldnn": True}
class TestMKLDNNTanhDim4(TestTanh):
def setUp(self):
super(TestMKLDNNTanhDim4, self).setUp()
......
......@@ -367,25 +367,6 @@ class TestRelu(TestActivation):
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestLeakyRelu(TestActivation):
def setUp(self):
self.op_type = "leaky_relu"
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = np.maximum(x, 0.02 * x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestGelu(TestActivation):
def setUp(self):
self.op_type = "gelu"
......
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