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move private weight fields to public ones (#21982)

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* move private weight properties to public ones
test=develop

* revert changes to FC
test=develop

* fix unittest
test=develop

* fix unittest
test=develop

* fix coverage
test=develop

* fix merged dev
test=develop

* bug fix
test=develop
release/1.7
songyouwei 5 years ago committed by hong
parent b852ef7375
commit 56414c7daf
8 changed files with 129 additions and 280 deletions
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315
python/paddle/fluid/dygraph/nn.py
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66
python/paddle/fluid/tests/unittests/test_imperative_auto_prune.py
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@ -153,8 +153,8 @@ class TestImperativeAutoPrune(unittest.TestCase):
v2 = fluid.dygraph.to_variable(value2)
loss = case1(v1, v2)
loss.backward()
self.assertTrue(case1.fc2._w._grad_ivar() is not None)
self.assertTrue(case1.fc1._w._grad_ivar() is not None)
self.assertTrue(case1.fc2.weight._grad_ivar() is not None)
self.assertTrue(case1.fc1.weight._grad_ivar() is not None)
def test_auto_prune2(self):
with fluid.dygraph.guard():
@ -166,8 +166,8 @@ class TestImperativeAutoPrune(unittest.TestCase):
loss = case2(v1, v2)
loss.backward()
self.assertTrue(case2.fc2._w._grad_ivar() is None)
self.assertTrue(case2.fc1._w._grad_ivar() is not None)
self.assertTrue(case2.fc2.weight._grad_ivar() is None)
self.assertTrue(case2.fc1.weight._grad_ivar() is not None)
def test_auto_prune3(self):
with fluid.dygraph.guard():
@ -178,7 +178,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
v2 = fluid.dygraph.to_variable(value2)
loss, part2 = case3(v1, v2, 1)
loss.backward()
self.assertTrue(case3.fc._w._grad_ivar() is not None)
self.assertTrue(case3.fc.weight._grad_ivar() is not None)
self.assertTrue((part2.gradient() == 0).all())
def test_auto_prune4(self):
@ -190,7 +190,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
v2 = fluid.dygraph.to_variable(value2)
loss, part2 = case4(v1, v2, 1)
part2.backward()
self.assertTrue(case4.fc._w._grad_ivar() is not None)
self.assertTrue(case4.fc.weight._grad_ivar() is not None)
self.assertTrue((part2.gradient() == 1).all())
def test_auto_prune5(self):
@ -202,7 +202,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
v2 = fluid.dygraph.to_variable(value2)
loss, part1, part2 = case4(v1, v2, 2)
part1.backward()
self.assertTrue(case4.fc._w._grad_ivar() is not None)
self.assertTrue(case4.fc.weight._grad_ivar() is not None)
self.assertTrue((part2.gradient() == 0).all())
def test_auto_prune6(self):
@ -220,7 +220,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
out1.stop_gradient = True
out = fluid.layers.concat(input=[out1, out2, c], axis=1)
out.backward()
self.assertTrue((fc._w.gradient() == 0).all())
self.assertTrue((fc.weight.gradient() == 0).all())
self.assertTrue((out1.gradient() == 0).all())
def test_auto_prune7(self):
@ -239,7 +239,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
out = fluid.layers.concat(input=[out1, out2, c], axis=1)
backward_strategy = fluid.dygraph.BackwardStrategy()
out.backward(backward_strategy)
self.assertTrue((fc._w.gradient() == 0).all())
self.assertTrue((fc.weight.gradient() == 0).all())
self.assertTrue((out1.gradient() == 0).all())
def test_auto_prune8(self):
@ -253,17 +253,17 @@ class TestImperativeAutoPrune(unittest.TestCase):
b = fluid.dygraph.to_variable(value1)
c = fluid.dygraph.to_variable(value2)
out1 = fc(a)
fc_origin = fc._w.numpy()
fc_origin = fc.weight.numpy()
out2 = fc2(out1)
fc2_origin = fc2._w.numpy()
fc2._w.stop_gradient = True
fc2_origin = fc2.weight.numpy()
fc2.weight.stop_gradient = True
out2.backward()
optimizer = fluid.optimizer.SGD(
learning_rate=0.003,
parameter_list=(fc.parameters() + fc2.parameters()))
optimizer.minimize(out2)
self.assertTrue(np.array_equal(fc2_origin, fc2._w.numpy()))
self.assertFalse(np.array_equal(fc_origin, fc._w.numpy()))
self.assertTrue(np.array_equal(fc2_origin, fc2.weight.numpy()))
self.assertFalse(np.array_equal(fc_origin, fc.weight.numpy()))
def test_auto_prune9(self):
with fluid.dygraph.guard():
@ -276,19 +276,19 @@ class TestImperativeAutoPrune(unittest.TestCase):
b = fluid.dygraph.to_variable(value1)
c = fluid.dygraph.to_variable(value2)
out1 = fc(a)
fc_origin = fc._w.numpy()
fc_origin = fc.weight.numpy()
out2 = fc2(out1)
fc2_origin = fc2._w.numpy()
fc2_origin = fc2.weight.numpy()
out2.stop_gradient = True
out2.backward()
optimizer = fluid.optimizer.SGD(
learning_rate=0.003,
parameter_list=(fc.parameters() + fc2.parameters()))
optimizer.minimize(out2)
self.assertTrue(np.array_equal(fc2_origin, fc2._w.numpy()))
self.assertTrue(np.array_equal(fc_origin, fc._w.numpy()))
self.assertTrue(np.array_equal(fc2_origin, fc2.weight.numpy()))
self.assertTrue(np.array_equal(fc_origin, fc.weight.numpy()))
try:
fc2._w.gradient()
fc2.weight.gradient()
except ValueError as e:
assert type(e) == ValueError
@ -309,7 +309,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
backward_strategy = fluid.dygraph.BackwardStrategy()
backward_strategy.sort_sum_gradient = True
out.backward(backward_strategy)
self.assertTrue((fc._w.gradient() == 0).all())
self.assertTrue((fc.weight.gradient() == 0).all())
self.assertTrue((out1.gradient() == 0).all())
def test_auto_prune_with_optimizer(self):
@ -336,10 +336,10 @@ class TestImperativeAutoPrune(unittest.TestCase):
loss.backward()
_, params_grads = optimizer.minimize(loss, grad_clip=grad_clip)
for items in params_grads:
assert items[0].name is not model.embed1._w.name
assert items[0].name is not model.fc1._w.name
assert model.embed1._w._grad_ivar() is None
assert model.fc1._w._grad_ivar() is None
assert items[0].name is not model.embed1.weight.name
assert items[0].name is not model.fc1.weight.name
assert model.embed1.weight._grad_ivar() is None
assert model.fc1.weight._grad_ivar() is None
with fluid.dygraph.guard(place):
model = MyLayer2("mylayer", vocab_size, size)
@ -355,10 +355,10 @@ class TestImperativeAutoPrune(unittest.TestCase):
loss.backward()
optimizer.minimize(loss, grad_clip=grad_clip)
for items in params_grads:
assert items[0].name is not model.embed1._w.name
assert items[0].name is not model.fc1._w.name
assert model.embed1._w._grad_ivar() is None
assert model.fc1._w._grad_ivar() is None
assert items[0].name is not model.embed1.weight.name
assert items[0].name is not model.fc1.weight.name
assert model.embed1.weight._grad_ivar() is None
assert model.fc1.weight._grad_ivar() is None
def test_case2_prune_no_grad_branch(self):
with fluid.dygraph.guard():
@ -369,8 +369,8 @@ class TestImperativeAutoPrune(unittest.TestCase):
case3 = AutoPruneLayer2("l2")
loss = case3(v1, v2)
loss.backward()
self.assertTrue(case3.fc2._w._grad_ivar() is None)
self.assertTrue(case3.fc._w._grad_ivar() is not None)
self.assertTrue(case3.fc2.weight._grad_ivar() is None)
self.assertTrue(case3.fc.weight._grad_ivar() is not None)
def test_case2_prune_no_grad_branch(self):
with fluid.dygraph.guard():
@ -381,8 +381,8 @@ class TestImperativeAutoPrune(unittest.TestCase):
case3 = AutoPruneLayer2("l2")
loss = case3(v1, v2)
loss.backward()
self.assertTrue(case3.fc2._w._grad_ivar() is None)
self.assertTrue(case3.fc._w._grad_ivar() is not None)
self.assertTrue(case3.fc2.weight._grad_ivar() is None)
self.assertTrue(case3.fc.weight._grad_ivar() is not None)
def test_case3_prune_no_grad_branch2(self):
with fluid.dygraph.guard():
@ -395,7 +395,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
out = fluid.layers.one_hot(input=label, depth=100)
loss = fluid.layers.mean(out)
loss.backward()
self.assertTrue(fc._w._grad_ivar() is None)
self.assertTrue(fc.weight._grad_ivar() is None)
def test_case4_with_no_grad_op_maker(self):
with fluid.dygraph.guard():

6
python/paddle/fluid/tests/unittests/test_imperative_basic.py
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@ -342,7 +342,7 @@ class TestImperative(unittest.TestCase):
out = mlp(var_inp)
dy_out = out.numpy()
out.backward()
dy_grad = mlp._fc1._w.gradient()
dy_grad = mlp._fc1.weight.gradient()
with fluid.dygraph.guard():
var_inp2 = fluid.dygraph.base.to_variable(np_inp)
@ -352,7 +352,7 @@ class TestImperative(unittest.TestCase):
backward_strategy = fluid.dygraph.BackwardStrategy()
backward_strategy.sort_sum_gradient = True
out2.backward(backward_strategy)
dy_grad2 = mlp2._fc1._w.gradient()
dy_grad2 = mlp2._fc1.weight.gradient()
with new_program_scope():
inp = fluid.layers.data(
@ -360,7 +360,7 @@ class TestImperative(unittest.TestCase):
mlp = MLP("mlp")
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
out, parameter_list=[mlp._fc1.weight.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
exe.run(fluid.default_startup_program())

2
python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py
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@ -59,7 +59,7 @@ class SimpleNet(fluid.Layer):
x_emb = self.embedding(input)
projection = fluid.layers.matmul(
x_emb, fluid.layers.transpose(
self.embedding._w, perm=[1, 0]))
self.embedding.weight, perm=[1, 0]))
projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
projection = fluid.layers.reshape(
projection, shape=[-1, self.vocab_size])

12
python/paddle/fluid/tests/unittests/test_imperative_selected_rows.py
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@ -61,7 +61,7 @@ class TestSimpleNet(unittest.TestCase):
input_emb, emb = simplenet(input)
try:
emb._w.gradient()
emb.weight.gradient()
except ValueError as e:
assert "has no grad, Please set Variable.stop_gradient=False, or check if this is the first and only variable need grad, if so, please set its pre-Variable's stop_gradient=False, to make sure it has gradient" in str(
e)
@ -73,11 +73,11 @@ class TestSimpleNet(unittest.TestCase):
input_emb.backward(backward_strategy)
adam.minimize(input_emb) # grad_clip=grad_clip
emb._w.gradient()
emb.weight.gradient()
emb.clear_gradients()
try:
emb._w.gradient()
emb.weight.gradient()
except ValueError as e:
assert "has no grad, Please set Variable.stop_gradient=False, or check if this is the first and only variable need grad, if so, please set its pre-Variable's stop_gradient=False, to make sure it has gradient" in str(
e)
@ -108,7 +108,7 @@ class TestSimpleNet(unittest.TestCase):
input_emb, emb = simplenet(input)
try:
emb._w.gradient()
emb.weight.gradient()
except ValueError as e:
assert "has no grad, Please set Variable.stop_gradient=False, or check if this is the first and only variable need grad, if so, please set its pre-Variable's stop_gradient=False, to make sure it has gradient" in str(
e)
@ -120,11 +120,11 @@ class TestSimpleNet(unittest.TestCase):
input_emb.backward(backward_strategy)
adam.minimize(input_emb, grad_clip=grad_clip)
emb._w.gradient()
emb.weight.gradient()
emb.clear_gradients()
try:
emb._w.gradient()
emb.weight.gradient()
except ValueError as e:
assert "has no grad, Please set Variable.stop_gradient=False, or check if this is the first and only variable need grad, if so, please set its pre-Variable's stop_gradient=False, to make sure it has gradient" in str(
e)

2
python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py
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@ -66,7 +66,7 @@ class SimpleNet(fluid.Layer):
fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
projection = fluid.layers.matmul(
fc, fluid.layers.transpose(
self.embedding._w, perm=[1, 0]))
self.embedding.weight, perm=[1, 0]))
projection = fluid.layers.reshape(
projection, shape=[-1, self.vocab_size])
loss = fluid.layers.softmax_with_cross_entropy(

4
python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py
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@ -843,7 +843,7 @@ class WrapDecoderLayer(Layer):
if self._weight_sharing:
predict = fluid.layers.matmul(
x=dec_output_reshape,
y=self._prepare_decoder_layer._input_emb._w,
y=self._prepare_decoder_layer._input_emb.weight,
transpose_y=True)
else:
predict = self._fc(dec_output_reshape)
@ -917,7 +917,7 @@ class TransFormer(Layer):
is_sparse=is_sparse)
if weight_sharing:
self._wrap_decoder_layer._prepare_decoder_layer._input_emb._w = self._wrap_encoder_layer._prepare_encoder_layer._input_emb._w
self._wrap_decoder_layer._prepare_decoder_layer._input_emb.weight = self._wrap_encoder_layer._prepare_encoder_layer._input_emb.weight
def forward(self, enc_inputs, dec_inputs, label, weights):
enc_output = self._wrap_encoder_layer(enc_inputs)

2
python/paddle/fluid/tests/unittests/test_layers.py
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@ -368,7 +368,7 @@ class TestLayer(LayerTest):
filter_size=[2, 2],
bias_attr=False)
dy_ret = conv2d(base.to_variable(images))
self.assertTrue(conv2d._bias_param is None)
self.assertTrue(conv2d.bias is None)
self.assertTrue(np.allclose(static_ret, dy_ret_value))
self.assertTrue(np.allclose(static_ret, static_ret2))

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