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// See docs in ../ops/math_ops.cc.
#define EIGEN_USE_THREADS
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/util/work_sharder.h"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
template <typename T>
void PrefetchBlockNTA(const T& tensor, int si, int ei, int sj, int ej) {
for (int i = si; i < ei; ++i) {
for (int j = sj; j < ej; j = j + 16) {
port::prefetch<port::PREFETCH_HINT_NTA>(&tensor(i, j));
}
}
}
template <typename T>
void PrefetchBlockT1(const T& tensor, int si, int ei, int sj, int ej) {
for (int i = si; i < ei; ++i) {
for (int j = sj; j < ej; j = j + 16) {
port::prefetch<port::PREFETCH_HINT_T1>(&tensor(i, j));
}
}
}
struct Block {
Block(int sm, int em, int sk, int ek, int sn, int en)
: startm(sm), endm(em), startk(sk), endk(ek), startn(sn), endn(en) {}
int startm;
int endm;
int startk;
int endk;
int startn;
int endn;
};
bool NextBlock(const int Bm, const int Bk, const int Bn, const int m_start,
const int m, const int k, const int n, const Block& b,
Block* next) {
*next = b;
if (b.endk < k) {
next->startk = b.endk;
next->endk = std::min(b.endk + Bk, k);
} else {
next->startk = 0;
next->endk = std::min(Bk, k);
if (b.endm < m) {
next->startm = b.endm;
next->endm = std::min(b.endm + Bm, m);
} else {
next->startm = m_start;
next->endm = std::min(m_start + Bm, m);
next->startn = b.endn;
next->endn = std::min(b.endn + Bn, n);
}
}
return next->startn == next->endn;
}
class SparseMatMulOp : public OpKernel {
public:
explicit SparseMatMulOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("a_is_sparse", &a_is_sparse_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("b_is_sparse", &b_is_sparse_));
}
void Compute(OpKernelContext* ctx) override {
const Tensor& a = ctx->input(0);
const Tensor& b = ctx->input(1);
OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a.shape()),
errors::InvalidArgument("a is not a matrix"));
OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b.shape()),
errors::InvalidArgument("b is not a matrix"));
auto left = a.matrix<float>();
auto right_mat = b.matrix<float>();
const int m = transpose_a_ ? left.dimension(1) : left.dimension(0);
const int k = transpose_a_ ? left.dimension(0) : left.dimension(1);
const int n =
transpose_b_ ? right_mat.dimension(0) : right_mat.dimension(1);
const int k2 =
transpose_b_ ? right_mat.dimension(1) : right_mat.dimension(0);
OP_REQUIRES(ctx, k == k2,
errors::InvalidArgument("Matrix size incompatible: a: ",
a.shape().DebugString(), ", b: ",
b.shape().DebugString()));
Tensor* output = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({m, n}), &output));
auto out = output->matrix<float>();
if (!a_is_sparse_) {
// Fallback to Eigen contract.
// Note that we currently don't optimize the case where only right is
// sparse. That can generally be handled by tranposing the order of the
// matmul.
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair;
dim_pair[0].first = transpose_a_ ? 0 : 1;
dim_pair[0].second = transpose_b_ ? 1 : 0;
out.device(ctx->template eigen_device<CPUDevice>()) =
left.contract(right_mat, dim_pair);
return;
}
typedef Eigen::Tensor<float, 2, Eigen::RowMajor> Matrix;
std::unique_ptr<Matrix> right_tr_mat;
std::unique_ptr<TTypes<float>::ConstMatrix> right_tr_map;
if (transpose_b_) {
right_tr_mat.reset(new Matrix(k, n));
Eigen::array<int, 2> perm({1, 0});
right_tr_mat->device(ctx->template eigen_device<CPUDevice>()) =
right_mat.shuffle(perm);
right_tr_map.reset(new TTypes<float>::ConstMatrix(
right_tr_mat->data(), right_tr_mat->dimensions()));
}
TTypes<float>::ConstMatrix& right =
transpose_b_ ? *right_tr_map : right_mat;
const bool transpose_a = transpose_a_;
typedef Eigen::TensorMap<Eigen::Tensor<float, 1, Eigen::RowMajor>,
Eigen::Unaligned> TensorMap;
typedef Eigen::TensorMap<Eigen::Tensor<const float, 1, Eigen::RowMajor>,
Eigen::Unaligned> ConstTensorMap;
typedef Eigen::DSizes<Eigen::DenseIndex, 1> DSizes;
const int Bm = 16;
const int Bk = 16;
const int Bn = 1024;
auto work_shard = [m, n, k, transpose_a, Bm, Bk, Bn, &left, &right, &out](
int64 start64, int64 end64) {
const int start = static_cast<int>(start64);
const int end = static_cast<int>(end64);
Block curr(start, std::min(start + Bm, end), 0, std::min(Bk, k), 0,
std::min(Bn, n));
Block next(curr);
bool done = false;
for (int i = start; i < end; ++i) {
out.chip<0>(i).setZero();
}
while (true) {
done = NextBlock(Bm, Bk, Bn, start, end, k, n, curr, &next);
PrefetchBlockT1(right, curr.startk, curr.endk, curr.startn, curr.endn);
// Process current block
for (int i = curr.startm; i < curr.endm; ++i) {
PrefetchBlockNTA(left, i, i + 1, curr.startk, curr.endk);
PrefetchBlockNTA(out, i, i + 1, curr.startn, curr.endn);
DSizes out_slice_shape(curr.endn - curr.startn);
TensorMap out_i(&out(i, curr.startn), out_slice_shape);
for (int j = curr.startk; j < curr.endk; ++j) {
const float l = transpose_a ? left(j, i) : left(i, j);
if (l == 0) continue;
ConstTensorMap right_j(&right(j, curr.startn), out_slice_shape);
out_i += right_j * l;
}
}
if (done) break;
curr = next;
}
};
auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
Shard(worker_threads.num_threads, worker_threads.workers, m, 2 * k * n,
work_shard);
}
private:
bool transpose_a_;
bool transpose_b_;
bool a_is_sparse_;
bool b_is_sparse_;
TF_DISALLOW_COPY_AND_ASSIGN(SparseMatMulOp);
};
REGISTER_KERNEL_BUILDER(Name("SparseMatMul").Device(DEVICE_CPU),
SparseMatMulOp);
} // end namespace tensorflow
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