Test large vector mean operator and fix a few bugs #16079

apeforest · 2019-09-03T05:59:15Z

Description

Add test for mean operator with large vector.
Fixed a few bugs that may affect other large array operators.
Fixed a few tests in test_large_vector to make it more memory efficient.

access2rohit · 2019-09-03T06:34:25Z

src/operator/tensor/broadcast_reduce-inl.h

+
+  index_t s = 1;
  #pragma unroll
-  for (int i = ndim-1, j = mdim, s = 1; i >= 0; --i) {


nice catch ! :)

src/common/tensor_inspector.h

access2rohit · 2019-09-03T06:37:24Z

tests/nightly/test_large_vector.py


+
+def test_mean():
+    a = nd.arange(-LARGE_X / 2, LARGE_X / 2 + 1, dtype=np.int64)


nit: even though LARGE_X is divisible by 2 but can we use integer division here LARGE_X//2

…y-mean

ChaiBapchya

LGTM!

access2rohit · 2019-09-04T20:16:15Z

tests/nightly/test_large_vector.py

    a = create_vector(LARGE_X)
    res = nd.clip(a, a_min=100, a_max=1000)
-    assert np.sum(res[-1].asnumpy() == 1000) == 1
+    assert res[-1] == 1000


good ... previous one was bad

access2rohit · 2019-09-04T20:17:59Z

Nice work !
Removal of all numpy functions should speedup the processes

apeforest · 2019-09-04T23:19:21Z

@access2rohit My test test_mean runs through. However, test_sequence_last seems to run out of memory on an instance with 480G memory.

test_large_vector.test_slice ... ok
test_large_vector.test_ndarray_zeros ... ok
test_large_vector.test_ndarray_ones ... ok
test_large_vector.test_ndarray_random_uniform ... ok
test_large_vector.test_ndarray_random_randint ... ok
test_large_vector.test_ndarray_empty ... ok
test_large_vector.test_elementwise ... ok
test_large_vector.test_clip ... ok
test_large_vector.test_argmin ... ok
test_large_vector.test_take ... ok
test_large_vector.test_slice_assign ... ok
test_large_vector.test_expand_dims ... ok
test_large_vector.test_squeeze ... ok
test_large_vector.test_broadcast_div ... ok
test_large_vector.test_Dense ... ok
test_large_vector.test_argsort ... ok
test_large_vector.test_sort ... ok
test_large_vector.test_topk ... ok
test_large_vector.test_mean ... ok
test_large_vector.test_ndarray_random_exponential ... ok
test_large_vector.test_ndarray_random_gamma ... ok
test_large_vector.test_ndarray_random_generalized_negative_binomial ... ok
test_large_vector.test_ndarray_random_multinomial ... ok
test_large_vector.test_ndarray_random_negative_binomial ... ok
test_large_vector.test_ndarray_random_normal ... ok
test_large_vector.test_ndarray_random_poisson ... ok
test_large_vector.test_ndarray_random_randn ... ok
test_large_vector.test_ndarray_random_shuffle ... ok
test_large_vector.test_exponent_logarithm_operators ... ok
test_large_vector.test_power_operators ... ok
test_large_vector.test_sequence_mask ... ok
test_large_vector.test_sequence_reverse ... ok
test_large_vector.test_sequence_last ...
Segmentation fault: 11

Segmentation fault: 11

Stack trace:
[bt] (0) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(+0x8f96a9) [0x7fc345d596a9]
[bt] (1) /lib/x86_64-linux-gnu/libc.so.6(+0x354b0) [0x7fc366cc94b0]
[bt] (2) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(+0x1909334) [0x7fc346d69334]
[bt] (3) /usr/lib/x86_64-linux-gnu/libgomp.so.1(+0xf43e) [0x7fc352a0543e]
[bt] (4) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7fc3670656ba]
[bt] (5) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7fc366d9b41d]
Stack trace:
[bt] (0) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(+0x8f96a9) [0x7fc345d596a9]
[bt] (1) /lib/x86_64-linux-gnu/libc.so.6(+0x354b0) [0x7fc366cc94b0]
[bt] (2) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(+0x1909334) [0x7fc346d69334]
[bt] (3) /usr/lib/x86_64-linux-gnu/libgomp.so.1(GOMP_parallel+0x3f) [0x7fc352a01cbf]
[bt] (4) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(mxnet::op::SequenceLastOp<mshadow::cpu, float, float>::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocatormxnet::TBlob > const&, std::vector<mxnet::OpReqType, std::allocatormxnet::OpReqType > const&, std::vector<mxnet::TBlob, std::allocatormxnet::TBlob > const&, std::vector<mxnet::TBlob, std::allocatormxnet::TBlob > const&)+0x50b) [0x7fc346d8838b]
[bt] (5) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(mxnet::op::OperatorState::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocatormxnet::TBlob > const&, std::vector<mxnet::OpReqType, std::allocatormxnet::OpReqType > const&, std::vector<mxnet::TBlob, std::allocatormxnet::TBlob > const&)+0xab0) [0x7fc345eca990]
[bt] (6) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(mxnet::imperative::PushOperator(mxnet::OpStatePtr const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocatormxnet::engine::Var* > const&, std::vector<mxnet::engine::Var*, std::allocatormxnet::engine::Var* > const&, std::vector<mxnet::Resource, std::allocatormxnet::Resource > const&, std::vector<mxnet::NDArray*, std::allocatormxnet::NDArray* > const&, std::vector<mxnet::NDArray*, std::allocatormxnet::NDArray* > const&, std::vector<unsigned int, std::allocator > const&, std::vector<mxnet::OpReqType, std::allocatormxnet::OpReqType > const&, mxnet::DispatchMode)::{lambda(mxnet::RunContext, mxnet::engine::CallbackOnComplete)#3}::operator()(mxnet::RunContext, mxnet::engine::CallbackOnComplete) const+0x2aa) [0x7fc345d5241a]
[bt] (7) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(std::_Function_handler<void (mxnet::RunContext), mxnet::imperative::PushOperator(mxnet::OpStatePtr const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocatormxnet::engine::Var* > const&, std::vector<mxnet::engine::Var*, std::allocatormxnet::engine::Var* > const&, std::vector<mxnet::Resource, std::allocatormxnet::Resource > const&, std::vector<mxnet::NDArray*, std::allocatormxnet::NDArray* > const&, std::vector<mxnet::NDArray*, std::allocatormxnet::NDArray* > const&, std::vector<unsigned int, std::allocator > const&, std::vector<mxnet::OpReqType, std::allocatormxnet::OpReqType > const&, mxnet::DispatchMode)::{lambda(mxnet::RunContext)#4}>::_M_invoke(std::_Any_data const&, mxnet::RunContext&&)+0x1d) [0x7fc345d533ed]
[bt] (8) /home/ubuntu/src/mxnet/python/mxnet/../../build/libmxnet.so(+0x84c3de) [0x7fc345cac3de]

apeforest · 2019-09-05T04:34:09Z

@wkcn Can you help to review and merge this for me?

wkcn

Thank you for the update!

In the unittest test_ndarray_random_randint,
it will be specific to use 2 ** 32 rather than 4294967296.

tests/nightly/test_large_vector.py

…y-mean

wkcn

LGTM. I will merge it after the CI pass.

* add test_mean and fix a few potential bugs * address reviewer commnet * fix and refactor test * fix topk test * address reviewer comment

apeforest added 2 commits September 2, 2019 22:56

add test_mean and fix a few potential bugs

04fab9c

Merge branch 'master' into bugfix/large-array-mean

32f9800

access2rohit reviewed Sep 3, 2019

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src/common/tensor_inspector.h Show resolved Hide resolved

access2rohit reviewed Sep 3, 2019

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address reviewer commnet

0cdf325

access2rohit approved these changes Sep 3, 2019

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apeforest added 2 commits September 4, 2019 19:30

fix and refactor test

a30fb91

Merge remote-tracking branch 'upstream/master' into bugfix/large-arra…

d4208b2

…y-mean

ChaiBapchya approved these changes Sep 4, 2019

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access2rohit reviewed Sep 4, 2019

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access2rohit approved these changes Sep 4, 2019

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fix topk test

58dda55

apeforest added the pr-awaiting-merge Review and CI is complete. Ready to Merge label Sep 5, 2019

wkcn reviewed Sep 5, 2019

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tests/nightly/test_large_vector.py Outdated Show resolved Hide resolved

tests/nightly/test_large_vector.py Outdated Show resolved Hide resolved

apeforest added 2 commits September 4, 2019 22:28

Merge remote-tracking branch 'upstream/master' into bugfix/large-arra…

4fdba4a

…y-mean

address reviewer comment

aed27ea

wkcn approved these changes Sep 5, 2019

View reviewed changes

wkcn merged commit d0fa8c0 into apache:master Sep 5, 2019

apeforest deleted the bugfix/large-array-mean branch September 5, 2019 09:13

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Test large vector mean operator and fix a few bugs #16079

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		def test_mean():
		a = nd.arange(-LARGE_X / 2, LARGE_X / 2 + 1, dtype=np.int64)

Test large vector mean operator and fix a few bugs #16079

Test large vector mean operator and fix a few bugs #16079

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