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matrix_op-inl.h
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matrix_op-inl.h
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://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.
*/
/*!
* Copyright (c) 2015 by Contributors
* \file matrix_op-inl.h
* \brief Function definition of matrix related operators
*/
#ifndef MXNET_OPERATOR_TENSOR_MATRIX_OP_INL_H_
#define MXNET_OPERATOR_TENSOR_MATRIX_OP_INL_H_
#include <mxnet/operator_util.h>
#include <vector>
#include <algorithm>
#include <utility>
#include <type_traits>
#include "../mshadow_op.h"
#include "../elemwise_op_common.h"
#include "../channel_op_common.h"
#include "../mxnet_op.h"
#include "broadcast_reduce_op.h"
#include "./init_op.h"
#include "../../common/static_array.h"
#if MXNET_USE_CUDA
#include <thrust/device_vector.h>
#endif
namespace mxnet {
namespace op {
struct ReshapeParam : public dmlc::Parameter<ReshapeParam> {
TShape target_shape;
bool keep_highest;
nnvm::Tuple<int> shape;
bool reverse;
DMLC_DECLARE_PARAMETER(ReshapeParam) {
DMLC_DECLARE_FIELD(shape)
.set_default(nnvm::Tuple<int>())
.describe("The target shape");
DMLC_DECLARE_FIELD(reverse)
.set_default(false)
.describe("If true then the special values are inferred from right to left");
DMLC_DECLARE_FIELD(target_shape)
.set_default(TShape())
.describe("(Deprecated! Use ``shape`` instead.) "
"Target new shape. One and only one dim can be 0, "
"in which case it will be inferred from the rest of dims");
DMLC_DECLARE_FIELD(keep_highest).set_default(false)
.describe("(Deprecated! Use ``shape`` instead.) Whether keep the highest dim unchanged."
"If set to true, then the first dim in target_shape is ignored,"
"and always fixed as input");
}
};
inline bool ReshapeShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
const ReshapeParam& param_ = nnvm::get<ReshapeParam>(attrs.parsed);
CHECK_EQ(in_attrs->size(), 1U) << "Input: [data]";
CHECK_EQ(out_attrs->size(), 1U);
const TShape &dshape = (*in_attrs)[0];
if (dshape.ndim() == 0) return false;
if (param_.shape.ndim() != 0) {
std::vector<int> dshape_vec;
std::vector<int> param_shape_vec(param_.shape.begin(), param_.shape.end());
for (index_t i = 0; i < dshape.ndim(); ++i) {
dshape_vec.push_back(dshape[i]);
}
std::vector<int> tmp;
size_t src_idx = 0;
int inf_idx = -1;
if (param_.reverse) {
std::reverse(dshape_vec.begin(), dshape_vec.end());
std::reverse(param_shape_vec.begin(), param_shape_vec.end());
}
auto dshape_len = dshape_vec.size();
auto params_len = param_shape_vec.size();
for (index_t i = 0; i < params_len; ++i) {
int proposed_dim = param_shape_vec[i];
if (proposed_dim == 0) {
// keep same
CHECK_LT(src_idx, dshape_len);
tmp.push_back(dshape_vec[src_idx++]);
} else if (proposed_dim == -1) {
// infer
CHECK_LT(inf_idx, 0) << "One and only one dim can be inferred";
inf_idx = i;
tmp.push_back(1);
src_idx++;
} else if (proposed_dim == -2) {
// copy all remaining dims from source
while (src_idx < dshape_len) {
size_t dn = dshape_vec[src_idx++];
tmp.push_back(dn);
}
} else if (proposed_dim == -3) {
// merge two dims from source
CHECK_LT(src_idx, dshape_len-1);
size_t d1 = dshape_vec[src_idx++];
size_t d2 = dshape_vec[src_idx++];
size_t dn = d1 * d2;
tmp.push_back(dn);
} else if (proposed_dim == -4) {
// split the source dim s into two dims
// read the left dim and then the right dim (either can be -1)
CHECK_LT(i + 2, params_len);
CHECK_LT(src_idx, dshape_len);
size_t d0 = dshape_vec[src_idx++];
int d1 = param_shape_vec[++i];
int d2 = param_shape_vec[++i];
CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
if (d1 == -1) d1 = d0 / d2;
if (d2 == -1) d2 = d0 / d1;
CHECK_EQ(d1 * d2, static_cast<int>(d0)) <<
"Split dims " << d1 << ", " << d2 << " do not divide original dim " << d0;
tmp.push_back(d1);
tmp.push_back(d2);
} else {
// greater than 0, new shape
tmp.push_back(proposed_dim);
src_idx++;
}
}
if (inf_idx >= 0) {
if (dshape.Size() > 0) {
int new_size = 1;
for (int x : tmp) new_size *= x;
tmp[inf_idx] = dshape.Size() / new_size;
} else {
tmp[inf_idx] = 0;
}
}
if (param_.reverse) {
std::reverse(param_shape_vec.begin(), param_shape_vec.end());
std::reverse(dshape_vec.begin(), dshape_vec.end());
std::reverse(tmp.begin(), tmp.end());
}
TShape oshape(tmp.begin(), tmp.end());
CHECK_EQ(oshape.Size(), dshape.Size())
<< "Target shape size is different to source. "
<< "Target: " << oshape
<< "\nSource: " << dshape;
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
} else if (param_.target_shape.ndim()) {
LOG(INFO) << "Using target_shape will be deprecated.";
TShape oshape = param_.target_shape;
int neg_count = 0;
index_t inf_idx = 0;
index_t start_idx = param_.keep_highest ? 1 : 0;
if (param_.keep_highest) {
oshape[0] = dshape[0];
}
for (index_t i = start_idx; i < oshape.ndim(); ++i) {
if (oshape[i] == 0) {
neg_count++;
inf_idx = i;
}
}
if (neg_count == 1) {
oshape[inf_idx] = 1;
oshape[inf_idx] = dshape.Size() / oshape.Size();
}
CHECK(oshape.Size() == dshape.Size())
<< "Target shape size is different to source. "
<< "Target: " << param_.target_shape.Size()
<< "\nSource: " << dshape.Size();
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
} else {
return (*out_attrs)[0].ndim();
}
return true;
}
inline bool FlattenShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
CHECK_EQ(in_attrs->size(), 1U) << "Input: [data]";
CHECK_EQ(out_attrs->size(), 1U);
const TShape &dshape = (*in_attrs)[0];
if (dshape.ndim() == 0) return false;
uint32_t target_dim = 1;
for (uint32_t i = 1; i < dshape.ndim(); ++i) {
target_dim *= dshape[i];
}
SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape2(dshape[0], target_dim));
return true;
}
struct TransposeParam : public dmlc::Parameter<TransposeParam> {
TShape axes;
DMLC_DECLARE_PARAMETER(TransposeParam) {
DMLC_DECLARE_FIELD(axes).set_default(TShape())
.describe("Target axis order. By default the axes will be inverted.");
}
};
template<typename xpu>
void TransposeImpl(RunContext ctx,
const TBlob& src,
const TBlob& ret,
const TShape& axes) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(src.type_flag_, ret.type_flag_);
Stream<xpu> *s = ctx.get_stream<xpu>();
MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, {
switch (axes.ndim()) {
case 0:
break;
case 1: {
Tensor<xpu, 1, DType> in = src.get<xpu, 1, DType>(s);
Tensor<xpu, 1, DType> out = ret.get<xpu, 1, DType>(s);
Copy(out, in, s);
break;
}
case 2: {
mshadow::Tensor<xpu, 2, DType> in = src.FlatTo2D<xpu, DType>(s);
mshadow::Tensor<xpu, 2, DType> out = ret.FlatTo2D<xpu, DType>(s);
if (axes[0] == 1 && axes[1] == 0) {
out = in.T();
} else {
Copy(out, in, s);
}
break;
}
case 3: {
Tensor<xpu, 3, DType> in = src.get<xpu, 3, DType>(s);
Tensor<xpu, 3, DType> out = ret.get<xpu, 3, DType>(s);
out = transpose(in, axes.get<3>());
break;
}
case 4: {
Tensor<xpu, 4, DType> in = src.get<xpu, 4, DType>(s);
Tensor<xpu, 4, DType> out = ret.get<xpu, 4, DType>(s);
out = transpose(in, axes.get<4>());
break;
}
case 5: {
Tensor<xpu, 5, DType> in = src.get<xpu, 5, DType>(s);
Tensor<xpu, 5, DType> out = ret.get<xpu, 5, DType>(s);
out = transpose(in, axes.get<5>());
break;
}
case 6: {
Tensor<xpu, 6, DType> in = src.get<xpu, 6, DType>(s);
Tensor<xpu, 6, DType> out = ret.get<xpu, 6, DType>(s);
out = transpose(in, axes.get<6>());
break;
}
default:
LOG(FATAL) << "Transpose support at most 6 dimensions";
break;
}
});
}
// matrix transpose
template<typename xpu>
void Transpose(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
if (param.axes.ndim() == 0) {
TShape axes = TShape(inputs[0].ndim());
for (index_t i = 0; i < axes.ndim(); ++i) {
axes[i] = axes.ndim() - 1 - i;
}
TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], axes);
} else {
TransposeImpl<xpu>(ctx.run_ctx, inputs[0], outputs[0], param.axes);
}
}
inline bool TransposeShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
TShape& shp = (*in_attrs)[0];
CHECK_LE(shp.ndim(), 6U) << "Transpose support at most 6 dimensions";
TShape ret(shp.ndim());
if (param.axes.ndim() == 0) {
for (index_t i = 0; i < shp.ndim(); ++i) {
ret[i] = shp[shp.ndim()-1-i];
}
} else {
CHECK_EQ(shp.ndim(), param.axes.ndim());
for (size_t i = 0; i < shp.ndim(); ++i) {
CHECK(param.axes[i] < static_cast<int64_t>(shp.ndim()));
ret[i] = shp[param.axes[i]];
}
}
SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
return true;
}
struct ExpandDimParam : public dmlc::Parameter<ExpandDimParam> {
int axis;
DMLC_DECLARE_PARAMETER(ExpandDimParam) {
DMLC_DECLARE_FIELD(axis)
.describe("Position where new axis is to be inserted. Suppose that "
"the input `NDArray`'s dimension is `ndim`, the range of "
"the inserted axis is `[-ndim, ndim]`");
}
};
inline bool ExpandDimShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
const ExpandDimParam& param = nnvm::get<ExpandDimParam>(attrs.parsed);
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
if (in_attrs->at(0).ndim() == 0U && out_attrs->at(0).ndim() == 0U) {
return false;
}
TShape& ishape = (*in_attrs)[0];
TShape& oshape = (*out_attrs)[0];
int indim = ishape.ndim();
bool unknown_ishape = false;
if (0 == indim) {
indim = oshape.ndim() - 1;
unknown_ishape = true;
}
int axis = param.axis;
if (axis < 0) {
axis += indim + 1;
}
CHECK(axis >= 0 && axis <= indim)
<< "axis must be in the range [" << -indim << ", " << indim << "] ("
<< param.axis << " provided)";
TShape ret(indim + 1);
for (int i = 0; i < axis; ++i) {
ret[i] = (unknown_ishape? 0 : ishape[i]);
}
ret[axis] = 1;
for (int i = axis+1; i < indim+1; ++i) {
ret[i] = (unknown_ishape? 0 : ishape[i-1]);
}
SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
ret = TShape(indim);
for (int i = 0; i < axis; ++i) ret[i] = oshape[i];
for (int i = axis+1; i < indim+1; ++i) ret[i-1] = oshape[i];
SHAPE_ASSIGN_CHECK(*in_attrs, 0, ret);
return true;
}
struct SliceParam : public dmlc::Parameter<SliceParam> {
nnvm::Tuple<dmlc::optional<int>> begin, end;
nnvm::Tuple<dmlc::optional<int>> step;
DMLC_DECLARE_PARAMETER(SliceParam) {
DMLC_DECLARE_FIELD(begin)
.describe("starting indices for the slice operation, supports negative indices.");
DMLC_DECLARE_FIELD(end)
.describe("ending indices for the slice operation, supports negative indices.");
DMLC_DECLARE_FIELD(step)
.set_default(nnvm::Tuple<dmlc::optional<int>>())
.describe("step for the slice operation, supports negative values.");
}
};
inline bool SliceForwardInferStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
const auto& in_stype = in_attrs->at(0);
auto& out_stype = out_attrs->at(0);
bool dispatched = false;
const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
const auto dispatch_ex = invalid_ctx ? DispatchMode::kFComputeFallback :
DispatchMode::kFComputeEx;
// If step = 1, no need to fallback; otherwise fallback to dense
bool trivial_step = false;
if (param.step.ndim() == 0U) {
trivial_step = true;
} else if (param.step.ndim() == 1U
&& (!param.step[0].has_value() || param.step[0].value() == 1)) {
trivial_step = true;
}
if (!dispatched && in_stype == kDefaultStorage) {
dispatched = storage_type_assign(&out_stype, kDefaultStorage,
dispatch_mode, DispatchMode::kFCompute);
}
if (!dispatched && in_stype == kCSRStorage && trivial_step) {
dispatched = storage_type_assign(&out_stype, kCSRStorage,
dispatch_mode, dispatch_ex);
}
if (!dispatched) {
dispatch_fallback(out_attrs, dispatch_mode);
}
if (*dispatch_mode == DispatchMode::kFComputeFallback) {
LogStorageFallback(attrs, dev_mask, in_attrs, out_attrs);
}
return true;
}
// slice the indptr of a csr
struct SliceCsrIndPtr {
template<typename IType>
MSHADOW_XINLINE static void Map(int i, IType* out, const IType* in, const IType* base) {
KERNEL_ASSIGN(out[i], kWriteTo, in[i] - *base);
}
};
/*
* a wrapper to launch SliceCsrIndPtr kernel.
* slice [src[begin] .. src[end]) and store in dst[0, end - begin)
*/
template<typename xpu, typename IType>
void SliceCsrIndPtrImpl(const int begin, const int end, RunContext ctx,
const IType* src, IType* dst) {
using namespace mshadow;
using namespace mxnet_op;
Stream<xpu> *s = ctx.get_stream<xpu>();
int indptr_len = end - begin + 1;
Kernel<SliceCsrIndPtr, xpu>::Launch(s, indptr_len, dst, src + begin, src + begin);
}
/*
* Slice a CSR NDArray for first dimension
* Only implemented for CPU
*/
template<typename xpu>
void SliceDimOneCsrImpl(const TShape &begin, const TShape &end, const OpContext& ctx,
const NDArray &in, const NDArray &out) {
using namespace mshadow;
using namespace mxnet_op;
using namespace csr;
CHECK((std::is_same<xpu, cpu>::value)) << "SliceDimOneCsrImpl is only implemented for CPU";
nnvm::dim_t begin_row = begin[0];
nnvm::dim_t end_row = end[0];
nnvm::dim_t indptr_len = end_row - begin_row + 1;
out.CheckAndAllocAuxData(kIndPtr, Shape1(indptr_len));
// assume idx indptr share the same type
MSHADOW_IDX_TYPE_SWITCH(in.aux_type(kIndPtr), RType, {
MSHADOW_IDX_TYPE_SWITCH(in.aux_type(kIdx), IType, {
MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
RType* in_indptr = in.aux_data(kIndPtr).dptr<RType>();
RType* out_indptr = out.aux_data(kIndPtr).dptr<RType>();
SliceCsrIndPtrImpl<cpu, RType>(begin_row, end_row, ctx.run_ctx, in_indptr, out_indptr);
// retrieve nnz (CPU implementation)
int nnz = out_indptr[indptr_len - 1];
// return csr zeros if nnz = 0
if (nnz == 0) {
out.set_aux_shape(kIdx, Shape1(0));
return;
}
// copy indices and values
out.CheckAndAllocAuxData(kIdx, Shape1(nnz));
out.CheckAndAllocData(Shape1(nnz));
IType* in_idx = in.aux_data(kIdx).dptr<IType>();
IType* out_idx = out.aux_data(kIdx).dptr<IType>();
DType* in_data = in.data().dptr<DType>();
DType* out_data = out.data().dptr<DType>();
int offset = in_indptr[begin_row];
// this is also a CPU-only implementation
memcpy(out_idx, in_idx + offset, nnz * sizeof(IType));
memcpy(out_data, in_data + offset, nnz * sizeof(DType));
});
});
});
}
/*!
* \brief slice a CSRNDArray for two dimensions
*/
struct SliceDimTwoCsrAssign {
/*!
* \brief This function slices a CSRNDArray on axis one between begin_col and end_col
* \param i loop index
* \param out_idx output csr ndarray column indices
* \param out_data output csr ndarray data
* \param out_indptr output csr ndarray row index pointer
* \param in_idx input csr ndarray column indices
* \param in_data input csr ndarray data
* \param in_indptr input csr ndarray row index pointer
* \param begin_col begin column indice
* \param end_col end column indice
*/
template<typename IType, typename RType, typename DType>
MSHADOW_XINLINE static void Map(int i,
IType* out_idx, DType* out_data,
const RType* out_indptr,
const IType* in_idx, const DType* in_data,
const RType* in_indptr,
const int begin_col, const int end_col) {
RType ind = out_indptr[i];
for (RType j = in_indptr[i]; j < in_indptr[i+1]; j++) {
// indices of CSRNDArray are in ascending order per row
if (in_idx[j] >= end_col) {
break;
} else if (in_idx[j] >= begin_col) {
out_idx[ind] = in_idx[j] - begin_col;
out_data[ind] = in_data[j];
ind++;
}
}
}
};
/*
* Slice a CSR NDArray for two dimensions
* Only implemented for CPU
*/
template<typename xpu>
void SliceDimTwoCsrImpl(const TShape &begin, const TShape &end, const OpContext& ctx,
const NDArray &in, const NDArray &out) {
using namespace mshadow;
using namespace mxnet_op;
using namespace csr;
CHECK((std::is_same<xpu, cpu>::value)) << "SliceDimTwoCsrImpl is only implemented for CPU";
nnvm::dim_t begin_row = begin[0], end_row = end[0];
nnvm::dim_t begin_col = begin[1], end_col = end[1];
nnvm::dim_t indptr_len = end_row - begin_row + 1;
out.CheckAndAllocAuxData(kIndPtr, Shape1(indptr_len));
// assume idx indptr share the same type
MSHADOW_IDX_TYPE_SWITCH(in.aux_type(kIndPtr), RType, {
MSHADOW_IDX_TYPE_SWITCH(in.aux_type(kIdx), IType, {
MSHADOW_TYPE_SWITCH(in.dtype(), DType, {
RType *in_indptr = in.aux_data(kIndPtr).dptr<RType>();
IType *in_idx = in.aux_data(kIdx).dptr<IType>();
DType *in_data = in.data().dptr<DType>();
// retrieve nnz (CPU implementation)
RType *out_indptr = out.aux_data(kIndPtr).dptr<RType>();
int nnz = 0;
out_indptr[0] = 0;
// loop through indptr array and corresponding indices to count for nnz
for (nnvm::dim_t i = 0; i < indptr_len - 1; i++) {
out_indptr[i+1] = out_indptr[i];
for (RType j = in_indptr[i + begin_row];
j < in_indptr[i + begin_row + 1]; j++) {
// indices of CSRNDArray are in ascending order per row
if (in_idx[j] >= end_col) {
break;
} else if (in_idx[j] >= begin_col) {
out_indptr[i+1]++;
nnz++;
}
}
}
// returns zeros in csr format if nnz = 0
if (nnz == 0) {
out.set_aux_shape(kIdx, Shape1(0));
return;
}
out.CheckAndAllocAuxData(kIdx, Shape1(nnz));
out.CheckAndAllocData(Shape1(nnz));
IType *out_idx = out.aux_data(kIdx).dptr<IType>();
DType *out_data = out.data().dptr<DType>();
Stream<xpu> *s = ctx.get_stream<xpu>();
Kernel<SliceDimTwoCsrAssign, xpu>::Launch(s, indptr_len - 1, out_idx, out_data,
out_indptr, in_idx, in_data,
in_indptr + begin_row,
begin_col, end_col);
});
});
});
}
template<typename xpu>
void SliceCsrImpl(const SliceParam ¶m, const OpContext& ctx,
const NDArray &in, OpReqType req, const NDArray &out) {
CHECK((std::is_same<xpu, cpu>::value)) << "Slice for CSR input only implemented for CPU";
if (req == kNullOp) return;
CHECK_NE(req, kAddTo) << "kAddTo for Slice on CSR input is not supported";
CHECK_NE(req, kWriteInplace) << "kWriteInplace for Slice on CSR input is not supported";
const TShape ishape = in.shape();
const TShape oshape = out.shape();
uint32_t N = ishape.ndim();
TShape begin(N), end(N);
for (uint32_t i = 0; i < N; ++i) {
int s = 0;
if (param.begin[i]) {
s = *param.begin[i];
if (s < 0) s += ishape[i];
}
begin[i] = s;
end[i] = s + oshape[i];
}
switch (N) {
case 1: {
SliceDimOneCsrImpl<xpu>(begin, end, ctx, in, out);
break;
}
case 2: {
SliceDimTwoCsrImpl<xpu>(begin, end, ctx, in, out);
break;
}
default:
LOG(FATAL) << "CSR is only for 2-D shape";
break;
}
}
template<typename xpu>
void SliceEx(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CHECK_EQ(inputs.size(), 1);
CHECK_EQ(outputs.size(), 1);
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
auto in_stype = inputs[0].storage_type();
if (in_stype == kCSRStorage) {
SliceCsrImpl<xpu>(param, ctx, inputs[0], req[0], outputs[0]);
} else {
LOG(FATAL) << "Slice not implemented for storage type" << in_stype;
}
}
template<int ndim>
inline void GetIndexRange(const TShape& dshape,
const nnvm::Tuple<dmlc::optional<int>>& param_begin,
const nnvm::Tuple<dmlc::optional<int>>& param_end,
const nnvm::Tuple<dmlc::optional<int>>& param_step,
common::StaticArray<int, ndim>* begin,
common::StaticArray<int, ndim>* end,
common::StaticArray<int, ndim>* step) {
CHECK_NE(dshape.ndim(), 0U);
CHECK_NE(dshape.Size(), 0U);
CHECK_LE(param_begin.ndim(), dshape.ndim())
<< "Slicing axis exceeds data dimensions";
CHECK_LE(param_end.ndim(), dshape.ndim())
<< "Slicing axis exceeds data dimensions";
CHECK_EQ(param_begin.ndim(), param_end.ndim())
<< "begin and end must have the same length";
CHECK_EQ(ndim, dshape.ndim())
<< "Static array size=" << ndim
<< " is not equal to data shape ndim=" << dshape.ndim();
if (param_step.ndim() != 0U) {
CHECK_EQ(param_step.ndim(), param_begin.ndim())
<< "step and begin must have the same length";
}
for (index_t i = 0; i < param_begin.ndim(); ++i) {
int b = 0, e = dshape[i], s = 1;
const int len = dshape[i];
if (param_step.ndim() != 0U) {
const auto& opt_step_val = param_step[i];
if (opt_step_val.has_value()) {
s = opt_step_val.value();
CHECK_NE(s, 0) << "slice op step[" << i << "] cannot be 0";
}
}
if (param_begin[i].has_value()) {
b = param_begin[i].value();
if (b < 0) {
b += len;
CHECK_GE(b, 0) << "slicing with begin[" << i << "]="
<< b - len << " exceeds limit of " << len;
}
} else if (s < 0) {
b = len - 1;
}
CHECK_LT(b, len) << "slicing with begin[" << i << "]="
<< b << " exceends limit of " << len;
if (param_end[i].has_value()) {
e = param_end[i].value();
if (e < 0) {
e += len;
CHECK_GE(e, 0) << "slicing with end[" << i << "]="
<< e - len << " exceeds limit of " << len;
}
} else if (s < 0) {
e = -1;
}
CHECK_LE(e, len) << "slicing with end[" << i << "]="
<< e << " exceeds limit of " << len;
(*begin)[i] = b;
(*end)[i] = e;
(*step)[i] = s;
}
for (index_t i = param_begin.ndim(); i < dshape.ndim(); ++i) {
(*begin)[i] = 0;
(*end)[i] = dshape[i];
(*step)[i] = 1;
}
}
inline void SetSliceOpOutputDimSize(const index_t i, const int b,
const int e, const int s,
TShape* oshape) {
if (s > 0) {
CHECK_LT(b, e) << "slicing with begin=[" << i << "]=" << b << ", end[" << i << "]="
<< e << ", and step[" << i << "]=" << s << " is invalid";
(*oshape)[i] = (e - b - 1) / s + 1;
} else {
CHECK_LT(e, b) << "slicing with begin=[" << i << "]=" << b << ", end[" << i << "]="
<< e << ", and step[" << i << "]=" << s << " is invalid";
(*oshape)[i] = (b - e - 1) / (-s) + 1;
}
}
inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape>* in_attrs,
std::vector<TShape>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
const TShape& dshape = (*in_attrs)[0];
if (dshape.ndim() == 0 || dshape.Size() == 0) return false;
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
TShape oshape = dshape;
MXNET_NDIM_SWITCH(dshape.ndim(), ndim, {
common::StaticArray<int, ndim> begin, end, step;
GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
for (index_t i = 0; i < param.begin.ndim(); ++i) {
const int b = begin[i], e = end[i], s = step[i];
SetSliceOpOutputDimSize(i, b, e, s, &oshape);
}
});
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
return oshape.ndim() != 0 && oshape.Size() != 0;
}
template<int ndim>
struct slice_forward {
// i is the i-th row after flattening out into 2D tensor
template<typename DType>
MSHADOW_XINLINE static void Map(int i, DType* out, const DType* data,
const OpReqType req,
const mshadow::Shape<ndim> dshape,
const mshadow::Shape<ndim> oshape,
const common::StaticArray<int, ndim> begin,
const common::StaticArray<int, ndim> step) {
const int data_last_dim_size = dshape[ndim-1];
const int out_last_dim_size = oshape[ndim-1];
const int step_last_dim = step[ndim-1];
const int begin_last_dim = begin[ndim-1];
int out_offset = i * out_last_dim_size;
for (int j = 0; j < out_last_dim_size; ++j) {
int irow = 0; // row id of flattend 2D data
int stride = 1;
int idx = i;
#pragma unroll
for (int k = ndim - 2; k >= 0; --k) {
irow += stride * ((idx % oshape[k]) * step[k] + begin[k]);
idx /= oshape[k];
stride *= dshape[k];
}
KERNEL_ASSIGN(out[out_offset++], req,
data[irow * data_last_dim_size + j * step_last_dim + begin_last_dim]);
}
}
};
template<typename xpu>
void SliceOpForward(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
CHECK_EQ(inputs.size(), 1U);
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
if (req[0] == kNullOp) return;
using namespace mshadow;
Stream<xpu>* s = ctx.get_stream<xpu>();
const TBlob& data = inputs[0];
const TBlob& out = outputs[0];
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
MXNET_NDIM_SWITCH(data.ndim(), ndim, {
common::StaticArray<int, ndim> begin, end, step;
GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
mxnet_op::Kernel<slice_forward<ndim>, xpu>::Launch(s, out.shape_.FlatTo2D()[0],
out.dptr<DType>(), data.dptr<DType>(), req[0],
data.shape_.get<ndim>(), out.shape_.get<ndim>(), begin, step);
})
})
}
template<int ndim>
struct slice_assign {
// i is the i-th row after flattening out into 2D tensor
template<typename DType>
MSHADOW_XINLINE static void Map(int i, DType* out, const DType* val,
const OpReqType req,
const mshadow::Shape<ndim> oshape,
const mshadow::Shape<ndim> vshape,
const common::StaticArray<int, ndim> begin,
const common::StaticArray<int, ndim> step) {
const int data_last_dim_size = oshape[ndim-1];
const int out_last_dim_size = vshape[ndim-1];
const int step_last_dim = step[ndim-1];
const int begin_last_dim = begin[ndim-1];
int offset = i * out_last_dim_size;
for (int j = 0; j < out_last_dim_size; ++j) {
int irow = 0; // row id of flattend 2D out
int stride = 1;
int idx = i;
#pragma unroll
for (int k = ndim - 2; k >= 0; --k) {
irow += stride * ((idx % vshape[k]) * step[k] + begin[k]);
idx /= vshape[k];
stride *= oshape[k];
}
KERNEL_ASSIGN(out[irow * data_last_dim_size + j * step_last_dim + begin_last_dim],
req, val[offset++]);
}
}
};
template<typename xpu>
void SliceOpBackward(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
CHECK_EQ(inputs.size(), 1U);
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
if (req[0] == kNullOp) return;
using namespace mshadow;
Stream<xpu>* s = ctx.get_stream<xpu>();
const TBlob& ograd = inputs[0];
const TBlob& igrad = outputs[0];
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
if (req[0] == kWriteTo) {
Fill(s, igrad, req[0], 0);
} else if (req[0] == kWriteInplace) {
LOG(FATAL) << "_slice_backward does not support kWriteInplace";
}
MXNET_NDIM_SWITCH(ograd.ndim(), ndim, {
common::StaticArray<int, ndim> begin, end, step;
GetIndexRange(igrad.shape_, param.begin, param.end, param.step, &begin, &end, &step);
MSHADOW_TYPE_SWITCH(ograd.type_flag_, DType, {
mxnet_op::Kernel<slice_assign<ndim>, xpu>::Launch(s, ograd.shape_.FlatTo2D()[0],
igrad.dptr<DType>(), ograd.dptr<DType>(), req[0],
igrad.shape_.get<ndim>(), ograd.shape_.get<ndim>(), begin, step);
})
})
}
inline bool SliceAssignOpShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
CHECK_EQ(in_attrs->size(), 2U);
CHECK_EQ(out_attrs->size(), 1U);
const TShape& dshape = (*in_attrs)[0];
if (dshape.ndim() == 0U || dshape.Size() == 0U) return false;
TShape vshape = dshape; // vshape is the value shape on the right hand side
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
MXNET_NDIM_SWITCH(dshape.ndim(), ndim, {
common::StaticArray<int, ndim> begin, end, step;
GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
for (index_t i = 0; i < param.begin.ndim(); ++i) {
const int b = begin[i], e = end[i], s = step[i];
SetSliceOpOutputDimSize(i, b, e, s, &vshape);
}
});
SHAPE_ASSIGN_CHECK(*in_attrs, 1, vshape);
SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
return true;
}
template<typename xpu>
void SliceAssignOpForward(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
using namespace mshadow;
CHECK_EQ(inputs.size(), 2U); // data[index] = val, data and val are two inputs
CHECK_EQ(outputs.size(), 1U);
if (req[0] == kNullOp) return;
Stream<xpu> *s = ctx.get_stream<xpu>();
const TBlob& data = inputs[0];
const TBlob& val = inputs[1];
const TBlob& out = outputs[0];
if (req[0] == kWriteTo) {
MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
Tensor<xpu, 1, DType> in = inputs[0].FlatTo1D<xpu, DType>(s);
Tensor<xpu, 1, DType> out = outputs[0].FlatTo1D<xpu, DType>(s);
Copy(out, in, s);
});
} else if (req[0] != kWriteInplace) {
LOG(FATAL) << "_slice_assign only supports kWriteTo and kWriteInplace";
}
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
MXNET_NDIM_SWITCH(data.ndim(), ndim, {
common::StaticArray<int, ndim> begin, end, step;
GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
mxnet_op::Kernel<slice_assign<ndim>, xpu>::Launch(s, val.shape_.FlatTo2D()[0],
out.dptr<DType>(), val.dptr<DType>(), req[0],
out.shape_.get<ndim>(), val.shape_.get<ndim>(), begin, step);
})
})
}
struct SliceAssignScalarParam : public dmlc::Parameter<SliceAssignScalarParam> {
real_t scalar;
nnvm::Tuple<dmlc::optional<int>> begin, end;
nnvm::Tuple<dmlc::optional<int>> step;
DMLC_DECLARE_PARAMETER(SliceAssignScalarParam) {
DMLC_DECLARE_FIELD(scalar)
.set_default(0)
.describe("The scalar value for assignment.");
DMLC_DECLARE_FIELD(begin)
.describe("starting indices for the slice operation, supports negative indices.");
DMLC_DECLARE_FIELD(end)
.describe("ending indices for the slice operation, supports negative indices.");
DMLC_DECLARE_FIELD(step)
.set_default(nnvm::Tuple<dmlc::optional<int>>())
.describe("step for the slice operation, supports negative values.");
}
};
inline bool SliceAssignScalarOpShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
const TShape& dshape = (*in_attrs)[0];
if (dshape.ndim() == 0U || dshape.Size() == 0U) return false;
SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
return true;
}
template<int ndim>
struct slice_assign_scalar {
// i is the i-th row after flattening out into 2D tensor
template<typename DType>
MSHADOW_XINLINE static void Map(int i, DType* out, const DType val,
const OpReqType req,
const mshadow::Shape<ndim> oshape,
const mshadow::Shape<ndim> vshape,
const common::StaticArray<int, ndim> begin,
const common::StaticArray<int, ndim> step) {
const int data_last_dim_size = oshape[ndim-1];
const int out_last_dim_size = vshape[ndim-1];
const int step_last_dim = step[ndim-1];
const int begin_last_dim = begin[ndim-1];
for (int j = 0; j < out_last_dim_size; ++j) {
int irow = 0; // row id of flattend 2D out
int stride = 1;
int idx = i;
#pragma unroll
for (int k = ndim - 2; k >= 0; --k) {
irow += stride * ((idx % vshape[k]) * step[k] + begin[k]);
idx /= vshape[k];
stride *= oshape[k];
}
KERNEL_ASSIGN(out[irow * data_last_dim_size + j * step_last_dim + begin_last_dim], req, val);
}
}
};
template<typename xpu>
void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {