chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,441 @@
|
||||
/* ******************************************************************************
|
||||
*
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://www.apache.org/licenses/LICENSE-2.0.
|
||||
*
|
||||
* See the NOTICE file distributed with this work for additional
|
||||
* information regarding copyright ownership.
|
||||
* 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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
|
||||
//
|
||||
// @author Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017
|
||||
//
|
||||
|
||||
#include <system/op_boilerplate.h>
|
||||
#if NOT_EXCLUDED(OP_sigm_cross_entropy_loss)
|
||||
|
||||
#include <ops/declarable/CustomOperations.h>
|
||||
#include <ops/declarable/helpers/legacy_helpers.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
CUSTOM_OP_IMPL(sigm_cross_entropy_loss, 3, 1, false, 1, 1) {
|
||||
auto logits = INPUT_VARIABLE(0);
|
||||
auto weights = INPUT_VARIABLE(1);
|
||||
auto labels = INPUT_VARIABLE(2);
|
||||
auto output = OUTPUT_VARIABLE(0);
|
||||
|
||||
int reductionMode =
|
||||
INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
|
||||
auto labelsSmoothing = T_ARG(0);
|
||||
|
||||
// input validation
|
||||
REQUIRE_TRUE(labels->isSameShape(logits), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s "
|
||||
"correspondingly!",
|
||||
ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
|
||||
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
|
||||
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
|
||||
"got %i and %i correspondingly!",
|
||||
weights->rankOf(), labels->rankOf());
|
||||
// check whether broadcast operation is possible for weights array
|
||||
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got "
|
||||
"weights = %s and labels = %s instead!",
|
||||
ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
|
||||
// only 4 possible reduction modes exist
|
||||
REQUIRE_TRUE(reductionMode == 0 || reductionMode == 1 || reductionMode == 2 || reductionMode == 3, 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, "
|
||||
"but got %i instead!",
|
||||
reductionMode);
|
||||
|
||||
// perform weights broadcasting/tile to labels if needed
|
||||
auto weightsBroad = weights;
|
||||
if (!weights->isScalar() && !weights->isSameShape(logits))
|
||||
weightsBroad = new NDArray(weights->tileToShape(logits->shapeInfo()));
|
||||
|
||||
// If labelsSmoothing is nonzero, smooth the labels towards 1/2:
|
||||
auto newLabels = labels;
|
||||
if (labelsSmoothing != 0.) {
|
||||
newLabels = new NDArray(*labels);
|
||||
newLabels->applyScalar(scalar::SXELogitsSmoother, labelsSmoothing, newLabels);
|
||||
}
|
||||
|
||||
NDArray E(labels, false, block.launchContext());
|
||||
|
||||
// logits - labels * logits + log(1 + exp(-logits)) -> take into account numerical stability at large logits
|
||||
helpers::sigmCrossEntropy(block.launchContext(), logits, newLabels, &E);
|
||||
|
||||
// multiply E on weights
|
||||
NDArray* EWeighted = E * (*weightsBroad);
|
||||
|
||||
switch (reductionMode) {
|
||||
case 0: // 0 - "none", un-reduced weighted losses with the same shape as labels.
|
||||
output->assign(EWeighted);
|
||||
break;
|
||||
|
||||
case 1: { // 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
|
||||
auto* sumResult = EWeighted->reduceNumber(reduce::Sum);
|
||||
output->assign(sumResult);
|
||||
delete sumResult;
|
||||
break;
|
||||
}
|
||||
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of
|
||||
// all elements of weightsBroad array
|
||||
NDArray* sum;
|
||||
if (weights->isScalar()) {
|
||||
sum = (*weights) * EWeighted->lengthOf();
|
||||
} else {
|
||||
sum = weightsBroad->reduceNumber(reduce::Sum);
|
||||
}
|
||||
|
||||
if (sum->e<double>(0) == 0.) {
|
||||
*output = 0.;
|
||||
} else {
|
||||
auto* sumE = EWeighted->reduceNumber(reduce::Sum);
|
||||
auto* outputTemp = (*sumE) / (*sum);
|
||||
output->assign(outputTemp);
|
||||
delete outputTemp;
|
||||
delete sumE;
|
||||
}
|
||||
delete sum;
|
||||
break;
|
||||
}
|
||||
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E
|
||||
// array divided by number of non-zero weights
|
||||
LongType numOfNonZeroWeights = 0;
|
||||
if (weights->isScalar()) {
|
||||
if (weights->e<double>(0) != 0.) numOfNonZeroWeights = EWeighted->lengthOf();
|
||||
} else {
|
||||
auto* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
|
||||
numOfNonZeroWeights = countResult->e<LongType>(0);
|
||||
delete countResult;
|
||||
}
|
||||
|
||||
if (numOfNonZeroWeights == 0) {
|
||||
(*output) = 0.;
|
||||
} else {
|
||||
auto* sumE = EWeighted->reduceNumber(reduce::Sum);
|
||||
auto* outputTemp = (*sumE) / double(numOfNonZeroWeights);
|
||||
output->assign(outputTemp);
|
||||
delete outputTemp;
|
||||
delete sumE;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
delete EWeighted;
|
||||
if (weightsBroad != weights) delete weightsBroad;
|
||||
if (newLabels != labels) delete newLabels;
|
||||
|
||||
return Status::OK;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
DECLARE_TYPES(sigm_cross_entropy_loss) {
|
||||
getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
DECLARE_SHAPE_FN(sigm_cross_entropy_loss) {
|
||||
auto logitsShapeInfo = inputShape->at(0);
|
||||
auto weightsShapeInfo = inputShape->at(1);
|
||||
auto labelsShapeInfo = inputShape->at(2);
|
||||
|
||||
// labels and logits must have the same shapes
|
||||
REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, logitsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s "
|
||||
"correspondingly !",
|
||||
ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
|
||||
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
|
||||
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
|
||||
"got %i and %i correspondingly!",
|
||||
shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
|
||||
// check whether broadcast operation is possible for weights array
|
||||
REQUIRE_TRUE(
|
||||
shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s "
|
||||
"and labels = %s instead!",
|
||||
ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
|
||||
|
||||
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
|
||||
LongType* outShapeInfo = nullptr;
|
||||
|
||||
if (INT_ARG(0) != 0) // in this case output is scalar
|
||||
outShapeInfo = ConstantShapeHelper::getInstance().scalarShapeInfo(outType);
|
||||
else { // in this case output has the same shape as labels and logits
|
||||
outShapeInfo = ConstantShapeHelper::getInstance().bufferForShapeInfo(outType, shape::order(labelsShapeInfo),
|
||||
shape::rank(labelsShapeInfo),
|
||||
shape::shapeOf(labelsShapeInfo))->primary();
|
||||
}
|
||||
return SHAPELIST(outShapeInfo);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
CUSTOM_OP_IMPL(sigm_cross_entropy_loss_grad, 3, 3, false, 1, 1) {
|
||||
auto logits = INPUT_VARIABLE(0);
|
||||
auto weights = INPUT_VARIABLE(1);
|
||||
auto labels = INPUT_VARIABLE(2);
|
||||
|
||||
auto dLdp = OUTPUT_VARIABLE(0); // dL/dlogits
|
||||
auto dLdw = OUTPUT_VARIABLE(1); // dL/dweights
|
||||
auto dLdl = OUTPUT_VARIABLE(2); // dL/dlabels
|
||||
|
||||
NDArray *labelsSmoothing = NDArrayFactory::create(logits->dataType(), T_ARG(0), block.launchContext());
|
||||
|
||||
int reductionMode =
|
||||
INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
|
||||
// take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
|
||||
if (reductionMode == 0) reductionMode = 1;
|
||||
|
||||
// input validation
|
||||
REQUIRE_TRUE(labels->isSameShape(logits), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s "
|
||||
"correspondingly!",
|
||||
ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
|
||||
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
|
||||
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, "
|
||||
"but got %i and %i correspondingly!",
|
||||
weights->rankOf(), labels->rankOf());
|
||||
// check whether broadcast operation is possible for weights array
|
||||
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got "
|
||||
"weights = %s and labels = %s instead!",
|
||||
ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
|
||||
// only 4 possible reduction modes exist
|
||||
REQUIRE_TRUE(reductionMode == 0 || reductionMode == 1 || reductionMode == 2 || reductionMode == 3, 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, "
|
||||
"3, but got %i instead!",
|
||||
reductionMode);
|
||||
|
||||
// perform weights broadcasting/tile to labels if needed
|
||||
auto weightsBroad = weights;
|
||||
if (!weights->isScalar() && !weights->isSameShape(logits))
|
||||
weightsBroad = new NDArray(weights->tileToShape(logits->shapeInfo()));
|
||||
|
||||
// If labelsSmoothing is nonzero, smooth the labels towards 1/2:
|
||||
auto newLabels = labels;
|
||||
if (labelsSmoothing->e<float>(0) != 0.f) {
|
||||
newLabels = new NDArray(*labels);
|
||||
newLabels->applyScalar(scalar::SXELogitsSmoother, labelsSmoothing->e<float>(0), newLabels);
|
||||
}
|
||||
|
||||
NDArray E(labels, false, block.launchContext());
|
||||
|
||||
// logits - labels * logits + log(1 + exp(-logits)) -> take into account numerical stability at large logits
|
||||
helpers::sigmCrossEntropy(block.launchContext(), logits, newLabels, &E);
|
||||
|
||||
// dLdp = 1 - labels - 1 / (1 + exp(logits))
|
||||
helpers::sigmCrossEntropyGrad(block.launchContext(), logits, newLabels, dLdp);
|
||||
|
||||
// dLdl = -logits
|
||||
*labelsSmoothing -= 1.f;
|
||||
NDArray* dLdlTemp = (*logits) * (*labelsSmoothing);
|
||||
dLdl->assign(dLdlTemp);
|
||||
delete dLdlTemp;
|
||||
|
||||
switch (reductionMode) {
|
||||
case 1: { // 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array
|
||||
|
||||
NDArray* dLdpWeighted = (*dLdp) * (*weightsBroad);
|
||||
dLdp->assign(dLdpWeighted);
|
||||
delete dLdpWeighted;
|
||||
|
||||
NDArray* dLdlWeighted = (*dLdl) * (*weightsBroad);
|
||||
dLdl->assign(dLdlWeighted);
|
||||
delete dLdlWeighted;
|
||||
|
||||
if (weights->isScalar()) {
|
||||
auto* sumE = E.reduceNumber(reduce::Sum);
|
||||
dLdw->assign(sumE);
|
||||
delete sumE;
|
||||
} else if (weights != weightsBroad) {
|
||||
std::vector<LongType> axesToReduceAlong =
|
||||
ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
||||
E.reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
|
||||
} else {
|
||||
dLdw->assign(&E);
|
||||
}
|
||||
break;
|
||||
}
|
||||
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of
|
||||
// all elements of weightsBroad array
|
||||
NDArray* sum;
|
||||
if (weights->isScalar()) {
|
||||
sum = (*weights) * E.lengthOf();
|
||||
} else {
|
||||
sum = weightsBroad->reduceNumber(reduce::Sum);
|
||||
}
|
||||
|
||||
if (sum->e<double>(0) == 0.) {
|
||||
*dLdp = 0.;
|
||||
*dLdl = 0.;
|
||||
*dLdw = 0.;
|
||||
} else {
|
||||
NDArray* temp = (*weightsBroad) / (*sum);
|
||||
NDArray* dLdpResult = (*dLdp) * (*temp);
|
||||
dLdp->assign(dLdpResult);
|
||||
delete dLdpResult;
|
||||
|
||||
NDArray* dLdlResult = (*dLdl) * (*temp);
|
||||
dLdl->assign(dLdlResult);
|
||||
delete dLdlResult;
|
||||
delete temp;
|
||||
|
||||
if (weights->isScalar()) {
|
||||
*dLdw = 0.;
|
||||
} else if (weights != weightsBroad) {
|
||||
std::vector<LongType> axesToReduceAlong =
|
||||
ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
||||
NDArray* EWeighted = E * (*weightsBroad);
|
||||
NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
|
||||
delete EWeighted;
|
||||
NDArray* ESum = E * (*sum);
|
||||
NDArray* numerator = (*ESum) - (*EWeightedSum);
|
||||
delete ESum;
|
||||
delete EWeightedSum;
|
||||
NDArray* sumSquared = (*sum) * (*sum);
|
||||
NDArray* gradTemp = (*numerator) / (*sumSquared);
|
||||
delete numerator;
|
||||
delete sumSquared;
|
||||
gradTemp->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
|
||||
delete gradTemp;
|
||||
} else {
|
||||
NDArray* EWeighted = E * (*weightsBroad);
|
||||
NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
|
||||
delete EWeighted;
|
||||
NDArray* ESum = E * (*sum);
|
||||
NDArray* numerator = (*ESum) - (*EWeightedSum);
|
||||
delete ESum;
|
||||
delete EWeightedSum;
|
||||
NDArray* sumSquared = (*sum) * (*sum);
|
||||
NDArray* dLdwTemp1 = (*numerator) / (*sumSquared);
|
||||
delete numerator;
|
||||
delete sumSquared;
|
||||
dLdw->assign(dLdwTemp1);
|
||||
delete dLdwTemp1;
|
||||
}
|
||||
}
|
||||
delete sum;
|
||||
break;
|
||||
}
|
||||
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E
|
||||
// array divided by number of non-zero weights
|
||||
LongType numOfNonZeroWeights = 0;
|
||||
if (weights->isScalar()) {
|
||||
if (weights->e<double>(0) != 0.) numOfNonZeroWeights = E.lengthOf();
|
||||
} else {
|
||||
auto* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
|
||||
numOfNonZeroWeights = countResult->e<LongType>(0);
|
||||
delete countResult;
|
||||
}
|
||||
|
||||
if (numOfNonZeroWeights == 0) {
|
||||
*dLdp = 0.;
|
||||
*dLdl = 0.;
|
||||
*dLdw = 0.;
|
||||
} else {
|
||||
auto numOfNonZeroWeightsScalar =
|
||||
NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());
|
||||
|
||||
if (weights->isScalar()) {
|
||||
auto* sumE = E.reduceNumber(reduce::Sum);
|
||||
auto* dLdwTemp2 = (*sumE) / (*numOfNonZeroWeightsScalar);
|
||||
dLdw->assign(dLdwTemp2);
|
||||
delete dLdwTemp2;
|
||||
delete sumE;
|
||||
}
|
||||
else if (weights != weightsBroad) {
|
||||
std::vector<LongType> axesToReduceAlong =
|
||||
ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
||||
E.reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
|
||||
NDArray* dLdwResult = (*dLdw) / (*numOfNonZeroWeightsScalar);
|
||||
dLdw->assign(dLdwResult);
|
||||
delete dLdwResult;
|
||||
} else {
|
||||
auto* sumE = E.reduceNumber(reduce::Sum);
|
||||
auto* dLdwTemp2 = (*sumE) / (*numOfNonZeroWeightsScalar);
|
||||
dLdw->assign(dLdwTemp2);
|
||||
delete dLdwTemp2;
|
||||
delete sumE;
|
||||
}
|
||||
|
||||
NDArray* temp = (*weightsBroad) / (*numOfNonZeroWeightsScalar);
|
||||
NDArray* dLdpResult = (*dLdp) * (*temp);
|
||||
dLdp->assign(dLdpResult);
|
||||
delete dLdpResult;
|
||||
|
||||
NDArray* dLdlResult = (*dLdl) * (*temp);
|
||||
dLdl->assign(dLdlResult);
|
||||
delete dLdlResult;
|
||||
delete temp;
|
||||
|
||||
delete numOfNonZeroWeightsScalar;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
delete labelsSmoothing;
|
||||
if (weightsBroad != weights) delete weightsBroad;
|
||||
if (newLabels != labels) delete newLabels;
|
||||
|
||||
return Status::OK;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
DECLARE_TYPES(sigm_cross_entropy_loss_grad) {
|
||||
getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
DECLARE_SHAPE_FN(sigm_cross_entropy_loss_grad) {
|
||||
auto logitsShapeInfo = inputShape->at(0);
|
||||
auto weightsShapeInfo = inputShape->at(1);
|
||||
auto labelsShapeInfo = inputShape->at(2);
|
||||
|
||||
// labels and logits must have the same shapes
|
||||
REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, logitsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s "
|
||||
"correspondingly !",
|
||||
ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
|
||||
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
|
||||
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, "
|
||||
"but got %i and %i correspondingly!",
|
||||
shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
|
||||
// check whether broadcast operation is possible for weights array
|
||||
REQUIRE_TRUE(
|
||||
shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
|
||||
"SIGM_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = "
|
||||
"%s and labels = %s instead!",
|
||||
ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
|
||||
|
||||
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
|
||||
|
||||
auto dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(logitsShapeInfo, outType, false, block.getWorkspace());
|
||||
auto dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
|
||||
auto dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());
|
||||
|
||||
return SHAPELIST(CONSTANT(dLdpShapeInfo), CONSTANT(dLdwShapeInfo), CONSTANT(dLdlShapeInfo));
|
||||
}
|
||||
|
||||
} // namespace ops
|
||||
} // namespace sd
|
||||
|
||||
#endif
|
||||
Reference in New Issue
Block a user