Alex Black 68ea5f3688
Dev branch merge: dev_20190606 (#7904)
* correct logsoftmax looss (#2)

* Small SameDiff listener fix (#4)

* Various fixes (#6)

* #7839 Fix for asXMatrix and tests

* #7866 EmbeddingSequenceLayer dtype fix + test

* #7856 SameDiff save/load stream methods

* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration

* EvaluationBinary 3d/4d

* More evaluation 3d/4d tests

* #7847 Evaluation empty checks

* Small test ifx

* #7848 Fix median edge case

* Improve DL4J samediff layer tests

* [WIP] FastText wrapper implemented (#8)

* FastText implemented

* Some fixes

* Fix shapes for wordsNearest

* Validation of input vectors

* Fixes

* Fixed test

* Thread tagged

* Some tweaks

* setContextClassLoader for DeallocatorServiceThread

* Numpy format tests (#1)

* Various fixes (#11)

* #7852 SameDiff gather fix

* #7892 SameDiff placeholder to constant conversion

* #7890 validate input rank for MLN/CG init methods

* Fix broken permute shape calculation

* Permute and gather fixes

* Tests

* #7850 LogSumExp fix + test

* Handful of test fixes

* Empty arrays with non-scalar shapes (#10)

* minor rearrangements for lambdas

* empty tensors with non-scalar shapes

* numpy empty tensors with non-scalar shapes

* few more empty tweaks

* Small fixes

* conv3d signature update

* micro fix in batchnorm mkldnn

* Import fixes

* Fix

* MKL-DNN update

* Small fill fix

* fill with empty input + test

* Fixes

* Small error improvement

* Fix

* one special test

* couple of fixes for lstm

* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone

* Fixes

* FP16

* Unsigned

* BFloat16

* Fill op - empty tweaks

* - couple of fixes for empty arrays construction
- stack updated

* strided slice fix

* one transform test

* provide method for reducing shapeInfo in case of input array is empty

* Fixed reduceAlongDimensions to use empty input properly.

* couple of broadcast tests

* couple of tests broadcast tests + tweak to make them pass

* add check of non-empty to methods producing sub-arrays

* Fixed reshapeC with zeros in shape.

* complete empty check in reduce_... legacy ops

* Concat and cumsum/prod

* Tweak to empty shape inference on import

* add empty check to the rest of reduce legacy ops

* one more test

* correct typo in evalReduceShapeInfoEmpty

* Added tests for reduce_* ops to tests with zero shapes.

* few more tests for empty reductions

* Fixed strided_slice op with empty case and tests.

* one more empty reduction test

* Fixed strided_slice test.

* add empty check to NDArray::reshapei

* infOrMax

* empty min/max with infinity tests

* made unstack working correctly with empty arrays

* few IndexReduce tests + tweaks for empty shapes

* add test for empty concat

* few tests fixed

* Validation fix for reductions on empty shapes

* Reverse fix

* Reduction shape calc fixes

* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs

* Range fix

* - NDArray constructor updated for scalars/empty arrays
- few tests fixed

* More fixes

* Empty creator fixes

* concat fix

* concat fix

* TF import tests: allow 'both all NaN' and 'both all inf' to pass

* Slice, zero fraction, and reshape fixes

* transpose, gather

* Zero fraction

* scalar cast fix

* Empty reduction axis support

* few more tests fixed

* Fixed input checks conforming with TF for concat op and tests.

* few tests fixed

* matmul scalar shape fix

* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.

* broadcast bool fix

* few more tests

* few more tests

* correct evalReduceShapeInfoEmpty

* argmax/argmin + tests

* one more empty edge case + one more test

* argmax/argmin/realdiv_bp tweaks

* empty reshape test + fix

* Helper fixes

* Small fixes

* Gather test fix

* Gather test fix

* Small fixes

* reduce scalar zero values

* scalar mean workaround

* Remove debug code

* along dim mean workaround

* one more test

* - equalsTo() tweak for empty arrays
- one more test

* broadcast tweaks
2019-06-15 21:34:34 +10:00

276 lines
9.2 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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 raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_range)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/range.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(range, -2, 1, false, -2, -2) {
auto output = OUTPUT_VARIABLE(0);
const int numInArrs = block.width();
const int numTArgs = block.getTArguments()->size();
const int numIArgs = block.getIArguments()->size();
NDArray *s = nullptr;
NDArray *d = nullptr;
bool localS = false;
bool localD = false;
// FIXME: this op should be fully moved to helpers
if (output->isEmpty())
return Status::OK();
if (numInArrs > 0) {
if(numInArrs == 1) {
//limit = (*INPUT_VARIABLE(0))(0.);
if (output->isR()) {
s = NDArrayFactory::create_(0.0f, block.launchContext());
d = NDArrayFactory::create_(1.0f, block.launchContext());
} else {
s = NDArrayFactory::create_(0, block.launchContext());
d = NDArrayFactory::create_(1, block.launchContext());
}
localS = true;
localD = true;
} else if(numInArrs == 2) {
s = INPUT_VARIABLE(0);
//limit = (*INPUT_VARIABLE(1))(0.);
if (output->isR()) {
d = NDArrayFactory::create_(1.0f, block.launchContext());
} else {
d = NDArrayFactory::create_(1, block.launchContext());
}
localD = true;
} else {
s = INPUT_VARIABLE(0);
//limit = (*INPUT_VARIABLE(1))(0.);
d = INPUT_VARIABLE(2);
}
} else if (numIArgs > 0) {
if(numIArgs == 1) {
// limit = INT_ARG(0);
} else if(numIArgs == 2) {
s = NDArrayFactory::create_(INT_ARG(0), block.launchContext());
//limit = INT_ARG(1);
d = NDArrayFactory::create_(1, block.launchContext());
}
else {
s = NDArrayFactory::create_(INT_ARG(0), block.launchContext());
//limit = INT_ARG(1);
d = NDArrayFactory::create_(INT_ARG(2), block.launchContext());
}
localS = true;
localD = true;
}
else if (numTArgs > 0) {
if(numTArgs == 1) {
//limit = T_ARG(0);
s = NDArrayFactory::create_(0.0f, block.launchContext());
d = NDArrayFactory::create_(1.0f, block.launchContext());
} else if(numTArgs == 2) {
s = NDArrayFactory::create_(T_ARG(0), block.launchContext());
//limit = T_ARG(1);
d = NDArrayFactory::create_(1.0f, block.launchContext());
}
else {
s = NDArrayFactory::create_(T_ARG(0), block.launchContext());
//limit = T_ARG(1);
d = NDArrayFactory::create_(T_ARG(2), block.launchContext());
}
localS = true;
localD = true;
} else {
REQUIRE_TRUE(false, 0, "CUSTOM RANGE OP: op should have inputs defined in any possible way: T_args, INT_args, or INPUT variables!");
}
helpers::range(block.launchContext(), *s, *d, *output);
if (localS)
delete s;
if (localD)
delete d;
return Status::OK();
}
DECLARE_SHAPE_FN(range) {
const int numInArrs = block.width();
const int numTArgs = block.getTArguments()->size();
const int numIArgs = block.getIArguments()->size();
Nd4jLong steps = 0;
nd4j::DataType dataType = nd4j::DataType::INHERIT;
if (numInArrs > 0) {
auto isR = INPUT_VARIABLE(0)->isR();
auto isZ = INPUT_VARIABLE(0)->isZ();
auto dtype = INPUT_VARIABLE(0)->dataType();
if (isR) {
double start(0), limit, delta(1);
if (numInArrs == 1)
limit = INPUT_VARIABLE(0)->e<double>(0);
else if (numInArrs == 2) {
start = INPUT_VARIABLE(0)->e<double>(0);
limit = INPUT_VARIABLE(1)->e<double>(0);
} else {
start = INPUT_VARIABLE(0)->e<double>(0);
limit = INPUT_VARIABLE(1)->e<double>(0);
delta = INPUT_VARIABLE(2)->e<double>(0);
}
if (limit == start){
//Return [0] to match TF
return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, dtype));
}
REQUIRE_TRUE(delta != 0, 0, "CUSTOM RANGE OP: delta should not be equal to zero !");
steps = static_cast<Nd4jLong >((limit - start) / delta);
dataType = INPUT_VARIABLE(0)->dataType();
if(math::nd4j_abs<double>(start + steps * delta) < math::nd4j_abs<double >(limit))
++steps;
} else if (isZ) {
Nd4jLong start(0), limit, delta(1);
if (numInArrs == 1)
limit = INPUT_VARIABLE(0)->e<Nd4jLong>(0);
else if (numInArrs == 2) {
start = INPUT_VARIABLE(0)->e<Nd4jLong>(0);
limit = INPUT_VARIABLE(1)->e<Nd4jLong>(0);
} else {
start = INPUT_VARIABLE(0)->e<Nd4jLong>(0);
limit = INPUT_VARIABLE(1)->e<Nd4jLong>(0);
delta = INPUT_VARIABLE(2)->e<Nd4jLong>(0);
}
//nd4j_printf("Start: [%lld]; Limit: [%lld]; Delta: [%lld];\n", start, limit, delta)
if (limit == start){
//Return [0] to match TF
return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, dtype));
}
REQUIRE_TRUE(delta != 0, 0, "CUSTOM RANGE OP: delta should not be equal to zero !");
steps = static_cast<Nd4jLong >((limit - start) / delta);
dataType = INPUT_VARIABLE(0)->dataType();
if(math::nd4j_abs<double>(start + steps * delta) < math::nd4j_abs<double >(limit))
++steps;
}
} else if (numIArgs > 0) {
Nd4jLong start(0), limit, delta(1);
if(numIArgs == 1)
limit = INT_ARG(0);
else if(numIArgs == 2) {
start = INT_ARG(0);
limit = INT_ARG(1);
}
else {
start = INT_ARG(0);
limit = INT_ARG(1);
delta = INT_ARG(2);
}
if (limit == start){
//Return [0] to match TF
return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, nd4j::DataType::INT32));
}
REQUIRE_TRUE(delta != 0, 0, "CUSTOM RANGE OP: delta should not be equal to zero !");
if (limit > DataTypeUtils::max<int>())
dataType = nd4j::DataType::INT64;
else
dataType = nd4j::DataType::INT32;
steps = (limit - start) / delta;
if(math::nd4j_abs<Nd4jLong>(start + steps * delta) < math::nd4j_abs<Nd4jLong>(limit))
++steps;
}
else if (numTArgs > 0) {
double start(0), limit, delta(1);
if(numTArgs == 1)
limit = T_ARG(0);
else if(numTArgs == 2) {
start = T_ARG(0);
limit = T_ARG(1);
}
else {
start = T_ARG(0);
limit = T_ARG(1);
delta = T_ARG(2);
}
if (limit == start){
//Return [0] to match TF
return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, Environment::getInstance()->defaultFloatDataType()));
}
REQUIRE_TRUE(delta != 0, 0, "CUSTOM RANGE OP: delta should not be equal to zero !");
steps = static_cast<Nd4jLong >((limit - start) / delta);
if (Environment::getInstance()->precisionBoostAllowed())
dataType = nd4j::DataType::DOUBLE;
else
dataType = Environment::getInstance()->defaultFloatDataType();
if(math::nd4j_abs<double>(start + steps * delta) < math::nd4j_abs<double >(limit))
++steps;
} else {
REQUIRE_TRUE(false, 0, "CUSTOM RANGE OP: op should have inputs defined in any possible way: T_args, INT_args, or INPUT variables!");
}
REQUIRE_TRUE(steps > 0, 0, "CUSTOM RANGE OP: value of (limit-start)/delta should be positive !");
return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(steps, dataType));
}
DECLARE_TYPES(range) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS, ALL_INTS});
}
}
}
#endif