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autobatching bug fixed. #1666

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autobatching bug fixed. #1666

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7748c5e
autobatching bug fixed.
cydur Oct 24, 2023
a24fc16
Merge branch 'clab:master' into master
cydur Sep 10, 2024
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9 changes: 6 additions & 3 deletions dynet/exec.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -566,8 +566,11 @@ const Tensor& BatchedExecutionEngine::incremental_forward_no_update(
*(active_un_end++) = j;
}
}
for (size_t j = 0; j < (size_t)sigmap.size(); ++j)
prof2avg[j] /= prof2cnt[j];
for (size_t j = 0; j < (size_t)sigmap.size(); ++j) // -prh sigmap.size() == 40
{
if(std::abs(prof2cnt[j]) > 1e-8)
prof2avg[j] /= prof2cnt[j];
}

// 2) Travel through and do active nodes
while (node_id != (VariableIndex)uptop1) {
Expand Down Expand Up @@ -1060,7 +1063,7 @@ void BatchedExecutionEngine::backward(VariableIndex from_where, bool full) {
// No concatenation whatsoever
if (my_batch.concat[ai] == 0) {
if (needs_derivative[node2batch[arg]]) {
node->backward(xs, my_batch.nfx, batched_ndEdfs[i], ai, batched_ndEdfs[node2batch[arg]]);
node->backward(xs, my_batch.nfx, batched_ndEdfs[i], ai, ndEdfs[arg]);
// cerr << "batched backward[" << i << "](" << ai << ")->" << node2batch[arg] << " == " << print_vec(as_vector(batched_ndEdfs[node2batch[arg]])) << endl;
}
// Needs concatenation
Expand Down
9 changes: 9 additions & 0 deletions dynet/matrix-multiply.h
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Expand Up @@ -162,6 +162,11 @@ inline void MatrixTranspMultiplyAcc(const dynet::Device_CPU & dev, const dynet::
inline void MatrixMultiplyTranspAcc(const dynet::Device_GPU & dev, const dynet::Tensor& l, const dynet::Tensor& r, dynet::Tensor& y) {
int max_b = std::max(l.d.bd, r.d.bd);
if(y.d.bd == 1 && (l.d.bd == r.d.bd)) {
DYNET_ARG_CHECK(l.d.rows() == y.d.rows(), "MatrixMultiplyTranspAcc: l.d.rows() != y.d.rows()");
DYNET_ARG_CHECK(r.d.rows() == y.d.cols(), "MatrixMultiplyTranspAcc: r.d.rows() != y.d.cols()");
DYNET_ARG_CHECK(l.d.cols() == r.d.cols(), "MatrixMultiplyTranspAcc: l.d.cols() != r.d.cols()");
DYNET_ARG_CHECK(l.d.batch_elems() == r.d.batch_elems(), "MatrixMultiplyTranspAcc: l.d.batch_elems() != r.d.batch_elems()");

CUBLAS_CHECK(cublasSgemm(dev.cublas_handle, CUBLAS_OP_N, CUBLAS_OP_T,
y.d.rows(), y.d.cols(), l.d.cols() * l.d.batch_elems(),
dev.kSCALAR_ONE,
Expand All @@ -183,6 +188,10 @@ inline void MatrixMultiplyTranspAcc(const dynet::Device_GPU & dev, const dynet::
inline void MatrixMultiplyTranspAcc(const dynet::Device_CPU & dev, const dynet::Tensor& l, const dynet::Tensor& r, dynet::Tensor& y) {
int max_b = std::max(l.d.bd, r.d.bd);
if(y.d.bd == 1 && (l.d.bd == r.d.bd)) {
DYNET_ARG_CHECK(l.d.rows() == y.d.rows(), "MatrixMultiplyTranspAcc [CPU]: l.d.rows() != y.d.rows()");
DYNET_ARG_CHECK(r.d.rows() == y.d.cols(), "MatrixMultiplyTranspAcc [CPU]: r.d.rows() != y.d.cols()");
DYNET_ARG_CHECK(l.d.cols() == r.d.cols(), "MatrixMultiplyTranspAcc [CPU]: l.d.cols() != r.d.cols()");
DYNET_ARG_CHECK(l.d.batch_elems() == r.d.batch_elems(), "MatrixMultiplyTranspAcc [CPU]: l.d.batch_elems() != r.d.batch_elems()");
mat(y).noalias() += colbatch_matrix(l) * colbatch_matrix(r).transpose();
} else {
#ifdef __INTEL_MKL__
Expand Down
1 change: 1 addition & 0 deletions dynet/nodes-matrixmultiply.cc
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Expand Up @@ -51,6 +51,7 @@ template<class MyDevice>
void MatrixMultiply::forward_dev_impl(const MyDevice & dev, const vector<const Tensor*>& xs, Tensor& fx) const {
DYNET_ASSERT(xs.size() == 2, "Failed dimension check in MatrixMultiply::forward");
DYNET_ARG_CHECK(fx.d.bd == max(xs[0]->d.bd, xs[1]->d.bd), "Failed dimension check in MatrixMultiply::forward");
DYNET_ARG_CHECK(fx.d.batch_size() == dim_forward({xs[0]->d, xs[1]->d}).batch_size(), "Failed result dimension check in MatrixMultiply::forward");
// fx = mat(fx0) + xs[0] * xs[1]
dynet::MatrixMultiply(dev, *xs[0], *xs[1], fx, dev.kSCALAR_ZERO);
}
Expand Down