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New Feature: #12686 SoftmaxCrossEntropyWithLogits gradient function #20763

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Jul 24, 2018
Merged

New Feature: #12686 SoftmaxCrossEntropyWithLogits gradient function #20763

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94 changes: 84 additions & 10 deletions tensorflow/cc/gradients/nn_grad.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,81 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op,
}
REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad);

bool IsZero(const Scope& scope, Output grad) {
std::array<std::string, 2> zero_op_type_names{{"ZerosLike", "Zeros"}};
string op_type_name = grad.op().node()->type_string();
for (auto& zero_op_type_name: zero_op_type_names) {
if (op_type_name == zero_op_type_name) {
return true;
}
}
// the Operation we were provided is not named something obvious
// we need to actually look at its contents.
// the original python code did this by calling a utility function called
// tensor_util.constant_value. When you dig into tensor_tuil.constant_value
// it is a large number of 'if' statements that measure certain edge cases
// where it is possible to get the value of the tensor without actually
// evaluating it. There are many kinds of tensors that can not have this
// done.
// There is no C++ equivalent to tensor_util.constant_value so we do nothing
// for the moment.
return false;
}

Output BroadcastMul(const Scope& scope, Output vec, Output mat) {
/* Multiply after broadcasting vec to match dimensions of mat.
Args:
vec: A 1-D tensor of dimension [D0]
mat: A 2-D tensor of dimesnion [D0, D1]

Returns:
A tensor of dimension [D0, D1], the result fo vec * mat
we use an element for element multiply here.
*/
auto reshaped = ExpandDims(scope, vec, -1);
return Multiply(scope, reshaped, mat);
}

Status SoftmaxCrossEntropyWithLogitsGrad(const Scope& scope,
const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
// Softmax gradient with cross entropy logits function
// We multiply the backprop for cost with the gradients - op.output[1]
// There is no gradient for labels
auto logits =
op.input(0); // the outputs of the network are at
// input index 0. The "truth" labels are at index 1.
auto softmax_grad = op.output(1);

// The documentation for ops::SoftmaxCrossEntropyWithLogits says
// loss is the output at index 0, and backprop is the output at index 1
auto grad_loss = grad_inputs[0];
auto grad_grad = grad_inputs[1];

auto grad = BroadcastMul(scope, grad_loss, softmax_grad);
if (!IsZero(scope, grad_grad)) {
std::vector<int> axis;
auto logitsSoftmax = Softmax(scope, logits);

auto grad_gradExpand = ExpandDims(scope, grad_grad, 1);
auto logitsSoftMaxExpand = ExpandDims(scope, logitsSoftmax, 2);
auto matMulResult =
BatchMatMul(scope, grad_gradExpand, logitsSoftMaxExpand);
axis.push_back(1);
auto squeezeResult = Squeeze(scope, matMulResult, Squeeze::Axis(axis));
auto subtractionResult = Subtract(scope, grad_grad, squeezeResult);
auto multiplyResult = Multiply(scope, subtractionResult, logitsSoftmax);
grad = Add(scope, grad, multiplyResult);
}
auto minusLogSoftmax = Multiply(scope, LogSoftmax(scope, logits), -1.0f);
grad_outputs->push_back(grad);
grad_outputs->push_back(BroadcastMul(scope, grad_loss, minusLogSoftmax));
return scope.status();
}
REGISTER_GRADIENT_OP("SoftmaxCrossEntropyWithLogits",
SoftmaxCrossEntropyWithLogitsGrad);

Status LogSoftmaxGrad(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
Expand Down Expand Up @@ -195,9 +270,9 @@ Status MaxPool3DGradHelper(const Scope& scope, const Operation& op,
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
MaxPool3DGrad::Attrs grad_attrs;
auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0],
ksize, strides, padding,
grad_attrs.DataFormat(data_format));
auto dx =
MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], ksize,
strides, padding, grad_attrs.DataFormat(data_format));
grad_outputs->push_back(dx);
return scope.status();
}
Expand All @@ -216,10 +291,9 @@ Status AvgPoolGradHelper(const Scope& scope, const Operation& op,
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
internal::AvgPoolGrad::Attrs grad_attrs;
auto dx =
internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
ksize, strides, padding,
grad_attrs.DataFormat(data_format));
auto dx = internal::AvgPoolGrad(scope, Shape(scope, op.input(0)),
grad_inputs[0], ksize, strides, padding,
grad_attrs.DataFormat(data_format));
grad_outputs->push_back(dx);
return scope.status();
}
Expand All @@ -238,9 +312,9 @@ Status AvgPool3DGradHelper(const Scope& scope, const Operation& op,
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
AvgPool3DGrad::Attrs grad_attrs;
auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
ksize, strides, padding,
grad_attrs.DataFormat(data_format));
auto dx =
AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], ksize,
strides, padding, grad_attrs.DataFormat(data_format));
grad_outputs->push_back(dx);
return scope.status();
}
Expand Down
29 changes: 25 additions & 4 deletions tensorflow/cc/gradients/nn_grad_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,8 @@ limitations under the License.
namespace tensorflow {
namespace {

using ops::AvgPool;
using ops::AvgPool3D;
using ops::BiasAdd;
using ops::Conv2D;
using ops::Elu;
Expand All @@ -33,11 +35,9 @@ using ops::FractionalMaxPool;
using ops::L2Loss;
using ops::LogSoftmax;
using ops::LRN;
using ops::AvgPool;
using ops::AvgPool3D;
using ops::MaxPool;
using ops::MaxPoolV2;
using ops::MaxPool3D;
using ops::MaxPoolV2;
using ops::Placeholder;
using ops::Relu;
using ops::Relu6;
Expand Down Expand Up @@ -111,6 +111,27 @@ TEST_F(NNGradTest, SoftmaxGrad) {
RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, SoftmaxCrossEntropyWithLogitsGrad) {
TensorShape logitsShape(
{5, 3}); // batch size of 5,3 possible labels (classes),
// logits is what is produced by a network
// they are compared to labels which are the truth
TensorShape lossShape(
{5}); // batch size of 5, 1 value for each entry in the batch
// loss is the difference between logits and labels

auto logits = Placeholder(scope_, DT_FLOAT,
Placeholder::Shape(logitsShape)); // estimation
auto labels =
Placeholder(scope_, DT_FLOAT, Placeholder::Shape(logitsShape)); // truth
auto y =
tensorflow::ops::SoftmaxCrossEntropyWithLogits(scope_, logits, labels);
// Please note the reversal of the backprop and loss orders. A separate issue
// #18734 has been opened for this.
RunTest({logits, labels}, {logitsShape, logitsShape}, {y.backprop, y.loss},
{logitsShape, lossShape});
}

TEST_F(NNGradTest, LogSoftmaxGrad) {
TensorShape shape({5, 3});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
Expand Down Expand Up @@ -253,7 +274,7 @@ TEST_F(NNGradTest, AvgPool3DGradHelper) {
RunTest(x, x_shape, y, y_shape);
}

TEST_F(NNGradTest, LRN){
TEST_F(NNGradTest, LRN) {
TensorShape x_shape({1, 1, 2, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
auto y = LRN(scope_, x);
Expand Down