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Posted to commits@mxnet.apache.org by li...@apache.org on 2020/07/25 23:21:03 UTC
[incubator-mxnet] branch master updated: add support for np.ndarray
in autograd.function (#18790)
This is an automated email from the ASF dual-hosted git repository.
liuyizhi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 98b3f73 add support for np.ndarray in autograd.function (#18790)
98b3f73 is described below
commit 98b3f73bd0f30034e3f6848eb75d38c30c8b60b4
Author: Sheng Zha <sz...@users.noreply.github.com>
AuthorDate: Sat Jul 25 16:19:36 2020 -0700
add support for np.ndarray in autograd.function (#18790)
---
python/mxnet/autograd.py | 14 +++++---
tests/python/unittest/test_autograd.py | 61 ++++++++++++++++++++++++++++++++++
2 files changed, 71 insertions(+), 4 deletions(-)
diff --git a/python/mxnet/autograd.py b/python/mxnet/autograd.py
index f968275..aac7cbc 100644
--- a/python/mxnet/autograd.py
+++ b/python/mxnet/autograd.py
@@ -28,6 +28,7 @@ from .base import NDArrayHandle, c_array, c_handle_array, c_array_buf, MXCallbac
from .ndarray import NDArray, _ndarray_cls
from .ndarray import _GRAD_REQ_MAP
from .symbol import Symbol
+from .util import is_np_array
def set_recording(is_recording): #pylint: disable=redefined-outer-name
@@ -448,25 +449,30 @@ class Function(object):
outputs = (outputs,)
key = Function._registry.inc()
+ if is_np_array():
+ from .numpy import ndarray
+ array_cls = ndarray
+ else:
+ array_cls = NDArray
def backward_entry(num_ograds, num_igrads, ptrs, reqs, is_train, _):
"""entry point for backward."""
# pylint: disable=W0613
try:
- output_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=False) \
+ output_grads = [array_cls(ctypes.cast(i, NDArrayHandle), writable=False) \
for i in ptrs[:num_ograds]]
- input_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=True) \
+ input_grads = [array_cls(ctypes.cast(i, NDArrayHandle), writable=True) \
for i in ptrs[num_ograds:num_ograds+num_igrads]]
reqs = [reqs[i] for i in range(num_igrads)]
rets = self.backward(*output_grads)
- if isinstance(rets, NDArray):
+ if isinstance(rets, array_cls):
rets = (rets,)
assert len(rets) == len(input_grads), \
"%s.backward must return exactly the same number " \
"of NDArrays as the number of NDArrays arguments to forward." \
"Expecting %d got %d"%(self.__class__.name, len(input_grads), len(rets))
for igrad, ret, req in zip(input_grads, rets, reqs):
- assert isinstance(ret, NDArray), \
+ assert isinstance(ret, array_cls), \
"autograd.Function.backward must return NDArrays, not %s"%type(ret)
if req == 0: # null
return True
diff --git a/tests/python/unittest/test_autograd.py b/tests/python/unittest/test_autograd.py
index 6a75eed..f9a7ecc 100644
--- a/tests/python/unittest/test_autograd.py
+++ b/tests/python/unittest/test_autograd.py
@@ -407,6 +407,67 @@ def test_function1():
@with_seed()
@pytest.mark.garbage_expected
+@use_np
+def test_np_function():
+ class func(Function):
+ def forward(self, x, y):
+ m = x / y
+ n = x * y
+ self.save_for_backward(x, y)
+ return m, n
+
+ def backward(self, dm, dn):
+ x, y = self.saved_tensors
+ dx = dm/y + dn*y
+ dy = dn*x - dm * x / y / y
+ return dx, dy
+
+ f = func()
+ x = mx.np.random.uniform(size=(10,))
+ x.attach_grad()
+ y = mx.np.random.uniform(size=(10,))
+ y.attach_grad()
+ with record():
+ m, n = f(x, y)
+ backward([m, n])
+
+ dx1 = x.grad.asnumpy()
+ dy1 = y.grad.asnumpy()
+
+ with record():
+ backward([x/y, x*y])
+
+ # Non-zero atol required, as exposed by seed 630179191
+ atol = 1e-6
+ assert_almost_equal(x.grad.asnumpy(), dx1, atol=atol)
+ assert_almost_equal(y.grad.asnumpy(), dy1, atol=atol)
+
+
+@with_seed()
+@pytest.mark.garbage_expected
+@use_np
+def test_np_function1():
+ class Foo(mx.autograd.Function):
+ def __init__(self):
+ super(Foo, self).__init__()
+
+ def forward(self, X):
+ return X + 1;
+
+ def backward(self, dY):
+ return dY
+
+ with mx.autograd.record():
+ X = mx.np.zeros((3, 4))
+ #X.attach_grad() # uncommenting this line works
+ for i in range(5):
+ f = Foo()
+ X = f(X)
+ X.wait_to_read()
+
+
+@with_seed()
+@pytest.mark.garbage_expected
def test_get_symbol():
x = mx.nd.ones((1,))
x.attach_grad()