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Numpy Dot Large Tensor Fix (apache#18925)
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* fix np dot

* add test

* fix test

* tweak test

Co-authored-by: Zhu <zhaoqzhu@3c22fbbb4e1a.ant.amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-10-124.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-6-47.us-west-2.compute.internal>
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@Zha0q1
4 people committed Aug 19, 2020
1 parent 32994bb commit 8794a0a
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Showing 3 changed files with 37 additions and 24 deletions.
24 changes: 12 additions & 12 deletions 3rdparty/mshadow/mshadow/dot_engine-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -299,17 +299,17 @@ struct BLASEngine<cpu, float> {
}
inline static void gemm(Stream<cpu> *stream,
bool transa, bool transb,
int m, int n, int k, float alpha,
const float *A, int lda, const float *B, int ldb,
float beta, float *C, int ldc) {
index_t m, index_t n, index_t k, float alpha,
const float *A, index_t lda, const float *B, index_t ldb,
float beta, float *C, index_t ldc) {
cblas_sgemm(CblasColMajor, GetT(transa), GetT(transb),
m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
inline static void batched_gemm(Stream<cpu> *stream,
bool transa, bool transb,
int m, int n, int k, float alpha,
const float *A, int lda, const float *B, int ldb,
float beta, float *C, int ldc, int batch_count,
index_t m, index_t n, index_t k, float alpha,
const float *A, index_t lda, const float *B, index_t ldb,
float beta, float *C, index_t ldc, index_t batch_count,
float **workspace) {
#if (MSHADOW_USE_MKL && INTEL_MKL_VERSION >= 20160000)
// since same m/n/k is used for all single gemms, so we put all gemms into one group
Expand Down Expand Up @@ -408,17 +408,17 @@ struct BLASEngine<cpu, double> {
}
inline static void gemm(Stream<cpu> *stream,
bool transa, bool transb,
int m, int n, int k, double alpha,
const double *A, int lda, const double *B, int ldb,
double beta, double *C, int ldc) {
index_t m, index_t n, index_t k, double alpha,
const double *A, index_t lda, const double *B, index_t ldb,
double beta, double *C, index_t ldc) {
cblas_dgemm(CblasColMajor, GetT(transa), GetT(transb),
m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
inline static void batched_gemm(Stream<cpu> *stream,
bool transa, bool transb,
int m, int n, int k, double alpha,
const double *A, int lda, const double *B, int ldb,
double beta, double *C, int ldc, int batch_count,
index_t m, index_t n, index_t k, double alpha,
const double *A, index_t lda, const double *B, index_t ldb,
double beta, double *C, index_t ldc, index_t batch_count,
double **workspace) {
#if (MSHADOW_USE_MKL && INTEL_MKL_VERSION >= 20160000)
// since same m/n/k is used for all single gemms, so we put all gemms into one group
Expand Down
24 changes: 12 additions & 12 deletions src/operator/numpy/np_tensordot_op-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -60,10 +60,10 @@ inline void ShiftAxes(Tuple<int>* axes_summed, const int ndim) {
/**
* Gets matrix dimensions of a and b after transpose and reshape.
*/
inline void GetMatrixDimensions(int* ad1,
int* ad2,
int* bd1,
int* bd2,
inline void GetMatrixDimensions(index_t* ad1,
index_t* ad2,
index_t* bd1,
index_t* bd2,
const mxnet::Tuple<int>& a_axes_remained,
const mxnet::Tuple<int>& a_axes_summed,
const mxnet::Tuple<int>& b_axes_remained,
Expand Down Expand Up @@ -157,10 +157,10 @@ void MatrixDot(const OpContext& ctx,
const TBlob& b,
const TBlob& out,
const OpReqType req,
const int ad1,
const int ad2,
const int bd1,
const int bd2,
const index_t ad1,
const index_t ad2,
const index_t bd1,
const index_t bd2,
const bool aT = false,
const bool bT = false) {
using namespace mshadow;
Expand Down Expand Up @@ -266,7 +266,7 @@ void TensordotImpl(const Tuple<int>& a_axes_summed,
GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
&b_axes, a_shape, b_shape);

int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
index_t ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
b_axes_remained, b_axes_summed, a_shape, b_shape);

Expand Down Expand Up @@ -435,7 +435,7 @@ void TensordotBackwardImpl(const Tuple<int>& a_axes_summed,
GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
&b_axes, a_shape, b_shape);

int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
index_t ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
b_axes_remained, b_axes_summed, a_shape, b_shape);

Expand Down Expand Up @@ -653,7 +653,7 @@ void TensordotIntAxesImpl(const int axes,
GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
&b_axes, a_shape, b_shape);

int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
index_t ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
b_axes_remained, b_axes_summed, a_shape, b_shape);
MatrixDot<xpu>(ctx, a, b, out, req, ad1, ad2, bd1, bd2);
Expand Down Expand Up @@ -746,7 +746,7 @@ void TensordotIntAxesBackwardImpl(const int axes,
GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained,
&b_axes, a_shape, b_shape);

int ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
index_t ad1 = 1, ad2 = 1, bd1 = 1, bd2 = 1;
GetMatrixDimensions(&ad1, &ad2, &bd1, &bd2, a_axes_remained, a_axes_summed,
b_axes_remained, b_axes_summed, a_shape, b_shape);

Expand Down
13 changes: 13 additions & 0 deletions tests/nightly/test_np_large_array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
LARGE_X = 100000000
SMALL_X = 100
SMALL_Y = 50
INT_OVERFLOW = 2**31


@use_np
Expand Down Expand Up @@ -76,3 +77,15 @@ def test_softmax():
true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
output = npx.softmax(input_data, axis=axis)
assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5)

#@pytest.mark.skip(reason="CI hasn't switch to ILP64 OpenBLAS yet")
@use_np
def test_dot():
A = np.ones((1, INT_OVERFLOW), dtype='float32')
B = np.ones((INT_OVERFLOW, 1), dtype='float32')
A.attach_grad()
with mx.autograd.record():
C = np.dot(A, B)
assert_almost_equal(C.asnumpy(), [INT_OVERFLOW], rtol=1e-5, atol=1e-5)
C.backward()
assert A.grad.shape == (1, INT_OVERFLOW)

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