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

271 lines
11 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
******************************************************************************/
//
// Created by raver119 on 29/10/17.
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_reshape)
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
// here iArgs is a vector with (optional) negative of order as first element:
// ({-order, dim1, dim2, dim3, ...})
CUSTOM_OP_IMPL(reshape, 1, 1, true, 0, -2) {
auto x = INPUT_VARIABLE(0);
if (block.width() == 1) {
auto arguments = block.getIArguments();
int argsSize = arguments->size();
//Special case: empty.reshape(<other empty shape>) -> return empty
if (x->isEmpty()) {
REQUIRE_TRUE(OUTPUT_VARIABLE(0)->isEmpty(), 0, "Reshape: when input is empty, output must also be empty");
return ND4J_STATUS_OK; //No op
}
int e = 1;
char order = (char) -(*arguments)[0];
if (order != 'c' && order != 'f') {
order = 'c'; //x->ordering();
e = 0;
}
REQUIRE_TRUE(argsSize - e >= 1, 0, "Reshape arguments should have at least 1 dimension");
std::vector<Nd4jLong> shapeNew;
int e2 = e;
for (; e < (int) arguments->size(); e++) {
if (arguments->at(e) == -1){
long shapeLength = 1;
for(; e2 < e; e2++){
shapeLength *= arguments->at(e2);
}
for(e2 = e + 1; e2 < arguments->size(); e2++){
shapeLength *= arguments->at(e2);
}
long realShape = x->lengthOf() / shapeLength;
shapeNew.push_back(realShape);
}
else{
shapeNew.push_back(arguments->at(e));
}
}
auto len = shape::prodLong(shapeNew.data(), shapeNew.size());
REQUIRE_TRUE(len == x->lengthOf(), 0, "Reshape: lengths before and after reshape should match, but got %i vs %i", x->lengthOf(), len);
if (Environment::getInstance()->isDebugAndVerbose()) {
nd4j_printv("Reshape: new shape", shapeNew);
}
if (block.isInplace()) {
if (x->reshapei(order, shapeNew)) {
STORE_RESULT(*x);
return ND4J_STATUS_OK;
}
} else {
auto ret = OUTPUT_VARIABLE(0);
auto xr = x->reshape(order, shapeNew);
ret->assign(xr);
STORE_RESULT(*ret);
delete xr;
return ND4J_STATUS_OK;
}
} else if (block.width() == 2) {
auto s = INPUT_VARIABLE(1);
//Special case: empty.reshape(-1) -> return empty
if (x->isEmpty()) {
//REQUIRE_TRUE(s->lengthOf() == 1 && s->e<Nd4jLong>(0) == -1, 0, "Reshape: when input is empty, shape must be [-1]");
REQUIRE_TRUE(OUTPUT_VARIABLE(0)->isEmpty(), 0, "Reshape: when input is empty, output must also be empty");
return Status::OK(); //No op
}
char order = 'c';
if (block.numI() > 0)
order = (char) -INT_ARG(0);
std::vector<Nd4jLong> shapeNew(s->lengthOf());
for (int e = 0; e < (int) s->lengthOf(); e++) {
auto dim = s->e<Nd4jLong >(e);
if (dim == -1){
long shapeLength = 1;
for(int e2 = 0; e2 < e; e2++){
shapeLength *= s->e<Nd4jLong>(e2);
}
for(int e2 = e + 1; e2 < (int) s->lengthOf(); e2++){
REQUIRE_TRUE(s->e<Nd4jLong>(e2) != -1, 0, "Reshape : Only one unknown dimension (-1) is allowed.");
shapeLength *=
s->e<Nd4jLong>(e2);
}
long realShape = x->lengthOf() / shapeLength;
shapeNew[e] = realShape;
}
else{
shapeNew[e] = dim;
}
}
if (Environment::getInstance()->isDebugAndVerbose()) {
nd4j_printv("Reshape: new shape", shapeNew);
}
if (block.isInplace()) {
if (x->reshapei(order, shapeNew)) {
STORE_RESULT(*x);
return Status::OK();
}
} else {
auto ret = OUTPUT_VARIABLE(0);
if (s->isEmpty()) {
// just a scalar
ret->assign(x);
} else {
auto xr = x->reshape(order, shapeNew);
ret->assign(xr);
delete xr;
}
return Status::OK();
}
}
return ND4J_STATUS_BAD_INPUT;
}
DECLARE_TYPES(reshape) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_INTS})
->setSameMode(true);
}
DECLARE_SHAPE_FN(reshape) {
auto inp = inputShape->at(0);
// we can launch op using Int arguments
if (inputShape->size() == 1) {
std::vector<int> *arguments = block.getIArguments();
int e = 1;
char order = (char) -(*arguments)[0];
if (order != 'c' && order != 'f') {
order = shape::order(inp);
e = 0;
}
// //Special case: empty.reshape(-1) -> return empty
// if (INPUT_VARIABLE(0)->isEmpty()) {
// //
// auto newShape = ConstantShapeHelper::getInstance()->emptyShapeInfo(ArrayOptions::dataType(inp));
// return SHAPELIST(newShape);
// }
std::vector<Nd4jLong> shapeNew;
int e2 = e;
for (; e < (int) arguments->size(); e++) {
if ((int) arguments->at(e) == -1){
long shapeLength = 1;
for(; e2 < e; e2 ++){
shapeLength *= arguments->at(e2);
}
for(e2 = e + 1; e2 < arguments->size(); e2++){
REQUIRE_TRUE(arguments->at(e2) != -1, 0, "Reshape : Only one unknown dimension (-1) is allowed.");
shapeLength *= arguments->at(e2);
}
if(shapeLength == 0){
//Edge case for empty:
shapeNew.push_back(0);
} else {
//Standard case
long realShape = shape::length(inp) / shapeLength;
shapeNew.push_back(realShape);
}
}
else{
shapeNew.push_back(arguments->at(e));
}
}
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inp), order, shapeNew)));
} else {
// or, with second input "as shape"
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
// special case here
if (y->isEmpty()) {
REQUIRE_TRUE(x->lengthOf() == 1, 0, "Reshape: new length doesn't match existing array");
return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inp)));
}
//Special case: empty.reshape(-1) -> return empty
if (x->isEmpty()) {
//REQUIRE_TRUE(y->lengthOf() == 1 && y->e<Nd4jLong>(0) == -1, 0, "Reshape: when input is empty, shape must be [-1]");
auto shapeOf = y->getBufferAsVector<Nd4jLong>();
Nd4jLong prod = 1;
for (auto v:shapeOf)
prod *= v;
REQUIRE_TRUE(prod == 0, 0, "Reshape: in case of empty arrays reshape must return empty array as well");
auto newShape = ShapeBuilders::createShapeInfo(ArrayOptions::dataType(inp), shape::order(inp), y->lengthOf(), shapeOf.data());
return SHAPELIST(CONSTANT(newShape));
}
std::vector<Nd4jLong> shapeNew(y->lengthOf());
for (int e = 0; e < (int) y->lengthOf(); e++) {
auto dim = y->e<Nd4jLong>(e);
if (dim == -1){
long shapeLength = 1;
for(int e2 = 0; e2 < e; e2++){
shapeLength *= y->e<Nd4jLong>(e2);
}
for(int e2 = e + 1; e2 < (int)y->lengthOf(); e2++){
REQUIRE_TRUE(y->e<Nd4jLong>(e2) != -1, 0, "Reshape : Only one unknown dimension (-1) is allowed.");
shapeLength *= y->e<Nd4jLong>(e2);
}
if(shapeLength == 0){
//Edge case for empty:
shapeNew[e] = 0;
} else {
long realShape = shape::length(inp) / shapeLength;
shapeNew[e] = realShape;
}
}else {
shapeNew[e] = dim;
}
}
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inp), 'c', shapeNew));
}
}
}
}
#endif