[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336315621
##
File path: tests/nightly/test_large_vector.py
##
@@ -708,6 +709,57 @@ def test_full():
assert a[-1] == 3
+def test_load_save():
+x = create_vector(size=LARGE_X)
+nd.save('large_vector', [x])
+y = nd.load('large_vector')
+y = y[0]
+assert x[0] == y[0]
+assert x[-1] == y[-1]
+os.remove('large_vector')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X)]
+y = nd.add_n(*x)
+assert y[0] == 1
+assert y[-1] == 1
+
+
+def test_modulo():
+x = mx.nd.ones(LARGE_X)*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0] == 2
+assert z[-1] == 2
+x = mx.nd.ones(LARGE_X)*5
+z = nd.modulo(x,y)
+assert z[0] == 1
+assert z[-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*4
+z = nd.maximum(x, y)
+assert z[0] == 4
+assert z[-1] == 4
+z = nd.maximum(x, 5)
+assert z[0] == 5
+assert z[-1] == 5
+
+
+def test_minimum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*2
+z = nd.minimum(x, y)
+assert z[0] == 2
+assert z[-1] == 2
+z = nd.minimum(x, 5)
+assert z[0] == 3
+assert z[-1] == 3
Review comment:
I have to write that. Currently we don't have any docs explaining how to use
large tensor support and caveats along with limitations and perf regressions.
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336134272
##
File path: tests/nightly/test_large_array.py
##
@@ -1199,6 +1200,57 @@ def test_full():
assert a[-1][-1] == 3
+def test_load_save():
+x = create_2d_tensor(SMALL_Y, LARGE_X)
+nd.save('large_tensor', [x])
+y = nd.load('large_tensor')
+y = y[0]
+assert x[0][0] == y[0][0]
+assert x[-1][-1]== y[-1][-1]
+os.remove('large_tensor')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
+y = nd.add_n(*x)
+assert y[0] == SMALL_Y
+assert y[-1] == SMALL_Y
+
+
+def test_modulo():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0][0] == 2
+assert z[-1][-1] == 2
+x = mx.nd.ones((SMALL_Y, LARGE_X))*5
+z = nd.modulo(x,y)
+assert z[0][0] == 1
+assert z[-1][-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*3
Review comment:
@ChaiBapchya Did you know this works or not?
https://mxnet.apache.org/api/python/docs/api/ndarray/ndarray.html#mxnet.ndarray.ones
gives the following error:
`
>>> x = mx.nd.ones(2, 1)*3
Traceback (most recent call last):
File "", line 1, in
File "/home/ubuntu/incubator-mxnet/python/mxnet/ndarray/ndarray.py", line
3162, in ones
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 36, in _ones
File "/home/ubuntu/incubator-mxnet/python/mxnet/_ctypes/ndarray.py", line
107, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/home/ubuntu/incubator-mxnet/python/mxnet/base.py", line 254, in
check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:31:04] include/mxnet/./base.h:526: Invalid
context string 1
Stack trace:
[bt] (0)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x34)
[0x7f3f4fe284c6]
[bt] (1)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Context::FromString(std::__cxx11::basic_string, std::allocator > const&)+0x4d9) [0x7f3f545699c9]
[bt] (2)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::GetContext(nnvm::NodeAttrs
const&, std::vector > const&,
std::vector > const&,
mxnet::Context const&)+0x414) [0x7f3f54569e1d]
[bt] (3)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context
const&, nnvm::NodeAttrs const&, std::vector > const&, std::vector > const&)+0x250) [0x7f3f5457030c]
[bt] (4)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeImpl(void*,
int, void**, int*, void***, int, char const**, char const**)+0x195)
[0x7f3f54eedf89]
[bt] (5)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeEx+0x87)
[0x7f3f54eee2fa]
[bt] (6)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c)
[0x7f3f5dedbec0]
[bt] (7)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d)
[0x7f3f5dedb87d]
[bt] (8)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce)
[0x7f3f5e0f0dee]
`
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336134697
##
File path: tests/nightly/test_large_vector.py
##
@@ -708,6 +709,57 @@ def test_full():
assert a[-1] == 3
+def test_load_save():
+x = create_vector(size=LARGE_X)
+nd.save('large_vector', [x])
+y = nd.load('large_vector')
+y = y[0]
+assert x[0] == y[0]
+assert x[-1] == y[-1]
+os.remove('large_vector')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X)]
+y = nd.add_n(*x)
+assert y[0] == 1
+assert y[-1] == 1
+
+
+def test_modulo():
+x = mx.nd.ones(LARGE_X)*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
Review comment:
readability ... it works fine without it too
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336134272
##
File path: tests/nightly/test_large_array.py
##
@@ -1199,6 +1200,57 @@ def test_full():
assert a[-1][-1] == 3
+def test_load_save():
+x = create_2d_tensor(SMALL_Y, LARGE_X)
+nd.save('large_tensor', [x])
+y = nd.load('large_tensor')
+y = y[0]
+assert x[0][0] == y[0][0]
+assert x[-1][-1]== y[-1][-1]
+os.remove('large_tensor')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
+y = nd.add_n(*x)
+assert y[0] == SMALL_Y
+assert y[-1] == SMALL_Y
+
+
+def test_modulo():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0][0] == 2
+assert z[-1][-1] == 2
+x = mx.nd.ones((SMALL_Y, LARGE_X))*5
+z = nd.modulo(x,y)
+assert z[0][0] == 1
+assert z[-1][-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*3
Review comment:
@ChaiBapchya Did you know this works or not?
gives the following error:
`
>>> x = mx.nd.ones(2, 1)*3
Traceback (most recent call last):
File "", line 1, in
File "/home/ubuntu/incubator-mxnet/python/mxnet/ndarray/ndarray.py", line
3162, in ones
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 36, in _ones
File "/home/ubuntu/incubator-mxnet/python/mxnet/_ctypes/ndarray.py", line
107, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/home/ubuntu/incubator-mxnet/python/mxnet/base.py", line 254, in
check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:31:04] include/mxnet/./base.h:526: Invalid
context string 1
Stack trace:
[bt] (0)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x34)
[0x7f3f4fe284c6]
[bt] (1)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Context::FromString(std::__cxx11::basic_string, std::allocator > const&)+0x4d9) [0x7f3f545699c9]
[bt] (2)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::GetContext(nnvm::NodeAttrs
const&, std::vector > const&,
std::vector > const&,
mxnet::Context const&)+0x414) [0x7f3f54569e1d]
[bt] (3)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context
const&, nnvm::NodeAttrs const&, std::vector > const&, std::vector > const&)+0x250) [0x7f3f5457030c]
[bt] (4)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeImpl(void*,
int, void**, int*, void***, int, char const**, char const**)+0x195)
[0x7f3f54eedf89]
[bt] (5)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeEx+0x87)
[0x7f3f54eee2fa]
[bt] (6)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c)
[0x7f3f5dedbec0]
[bt] (7)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d)
[0x7f3f5dedb87d]
[bt] (8)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce)
[0x7f3f5e0f0dee]
`
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336134272
##
File path: tests/nightly/test_large_array.py
##
@@ -1199,6 +1200,57 @@ def test_full():
assert a[-1][-1] == 3
+def test_load_save():
+x = create_2d_tensor(SMALL_Y, LARGE_X)
+nd.save('large_tensor', [x])
+y = nd.load('large_tensor')
+y = y[0]
+assert x[0][0] == y[0][0]
+assert x[-1][-1]== y[-1][-1]
+os.remove('large_tensor')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X) for j in range(SMALL_Y)]
+y = nd.add_n(*x)
+assert y[0] == SMALL_Y
+assert y[-1] == SMALL_Y
+
+
+def test_modulo():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0][0] == 2
+assert z[-1][-1] == 2
+x = mx.nd.ones((SMALL_Y, LARGE_X))*5
+z = nd.modulo(x,y)
+assert z[0][0] == 1
+assert z[-1][-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones((SMALL_Y, LARGE_X))*3
Review comment:
@ChaiBapchya Did you know this wroks or not?
gives the following error:
`
>>> x = mx.nd.ones(2, 1)*3
Traceback (most recent call last):
File "", line 1, in
File "/home/ubuntu/incubator-mxnet/python/mxnet/ndarray/ndarray.py", line
3162, in ones
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 36, in _ones
File "/home/ubuntu/incubator-mxnet/python/mxnet/_ctypes/ndarray.py", line
107, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/home/ubuntu/incubator-mxnet/python/mxnet/base.py", line 254, in
check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:31:04] include/mxnet/./base.h:526: Invalid
context string 1
Stack trace:
[bt] (0)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x34)
[0x7f3f4fe284c6]
[bt] (1)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Context::FromString(std::__cxx11::basic_string, std::allocator > const&)+0x4d9) [0x7f3f545699c9]
[bt] (2)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::GetContext(nnvm::NodeAttrs
const&, std::vector > const&,
std::vector > const&,
mxnet::Context const&)+0x414) [0x7f3f54569e1d]
[bt] (3)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context
const&, nnvm::NodeAttrs const&, std::vector > const&, std::vector > const&)+0x250) [0x7f3f5457030c]
[bt] (4)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeImpl(void*,
int, void**, int*, void***, int, char const**, char const**)+0x195)
[0x7f3f54eedf89]
[bt] (5)
/home/ubuntu/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(MXImperativeInvokeEx+0x87)
[0x7f3f54eee2fa]
[bt] (6)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c)
[0x7f3f5dedbec0]
[bt] (7)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d)
[0x7f3f5dedb87d]
[bt] (8)
/home/ubuntu/anaconda3/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce)
[0x7f3f5e0f0dee]
`
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r335790069
##
File path: tests/nightly/test_large_array.py
##
@@ -1199,6 +1200,57 @@ def test_full():
assert a[-1][-1] == 3
+def test_load_save():
+x = create_2d_tensor(SMALL_Y, LARGE_X)
+nd.save('large_tensor', [x])
+y = nd.load('large_tensor')
+y = y[0]
+assert x[0][0] == y[0][0]
+assert x[-1][-1]== y[-1][-1]
+os.remove('large_tensor')
Review comment:
done
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r335789956
##
File path: tests/nightly/test_large_vector.py
##
@@ -708,6 +709,57 @@ def test_full():
assert a[-1] == 3
+def test_load_save():
+x = create_vector(size=LARGE_X)
+nd.save('large_vector', [x])
+y = nd.load('large_vector')
+y = y[0]
+assert x[0] == y[0]
+assert x[-1] == y[-1]
+os.remove('large_vector')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X)]
+y = nd.add_n(*x)
+assert y[0] == 1
+assert y[-1] == 1
+
+
+def test_modulo():
+x = mx.nd.ones(LARGE_X)*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0] == 2
+assert z[-1] == 2
+x = mx.nd.ones(LARGE_X)*5
+z = nd.modulo(x,y)
+assert z[0] == 1
+assert z[-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*4
+z = nd.maximum(x, y)
+assert z[0] == 4
+assert z[-1] == 4
+z = nd.maximum(x, 5)
+assert z[0] == 5
+assert z[-1] == 5
+
+
+def test_minimum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*2
+z = nd.minimum(x, y)
+assert z[0] == 2
+assert z[-1] == 2
+z = nd.minimum(x, 5)
+assert z[0] == 3
+assert z[-1] == 3
Review comment:
Only forward passes are required by DGL. So only forward passes are tested.
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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r335789995 ## File path: src/operator/tensor/elemwise_sum.h ## @@ -64,7 +64,7 @@ void ElementWiseSumCompute_(const nnvm::NodeAttrs& attrs, size_t size = in_data.size(); Stream *s = ctx.get_stream(); DType* out_dptr = out_data[0].dptr(); - int out_size = static_cast((out_data[0].Size() + DataType::kLanes - 1) + index_t out_size = static_cast((out_data[0].Size() + DataType::kLanes - 1) Review comment: yes This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #16476: added large tensor support for add_n and tests for more ops
access2rohit commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r335789956
##
File path: tests/nightly/test_large_vector.py
##
@@ -708,6 +709,57 @@ def test_full():
assert a[-1] == 3
+def test_load_save():
+x = create_vector(size=LARGE_X)
+nd.save('large_vector', [x])
+y = nd.load('large_vector')
+y = y[0]
+assert x[0] == y[0]
+assert x[-1] == y[-1]
+os.remove('large_vector')
+
+
+def test_add_n():
+x = [nd.ones(LARGE_X)]
+y = nd.add_n(*x)
+assert y[0] == 1
+assert y[-1] == 1
+
+
+def test_modulo():
+x = mx.nd.ones(LARGE_X)*6
+y = mx.nd.ones(LARGE_X)*4
+z = (x%y)
+assert z[0] == 2
+assert z[-1] == 2
+x = mx.nd.ones(LARGE_X)*5
+z = nd.modulo(x,y)
+assert z[0] == 1
+assert z[-1] == 1
+
+
+def test_maximum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*4
+z = nd.maximum(x, y)
+assert z[0] == 4
+assert z[-1] == 4
+z = nd.maximum(x, 5)
+assert z[0] == 5
+assert z[-1] == 5
+
+
+def test_minimum():
+x = mx.nd.ones(LARGE_X)*3
+y = mx.nd.ones(LARGE_X)*2
+z = nd.minimum(x, y)
+assert z[0] == 2
+assert z[-1] == 2
+z = nd.minimum(x, 5)
+assert z[0] == 3
+assert z[-1] == 3
Review comment:
Only forward passes are required by DGL
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