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未验证 提交 c6f49f0b 编写于 作者: Z zhaoyingli 提交者： GitHub
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fix adamw unittest (#42593) 

* fix adamw unittest

* tiny fix

* fix param name
上级 30234dd7
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Showing with 185 addition and 36 deletion
python/paddle/fluid/tests/unittests/test_adamw_op.py
浏览文件 @ c6f49f0b
...@@ -54,8 +54,8 @@ def adamw_step(inputs, attributes): ...@@ -54,8 +54,8 @@ def adamw_step(inputs, attributes):
moment1_out = beta1 * moment1 + (1 - beta1) * grad moment1_out = beta1 * moment1 + (1 - beta1) * grad
moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) denom = (np.sqrt(moment2_out) / np.sqrt(1.0 - beta2_pow)) + epsilon
param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon)) param_out = param + ((moment1_out / denom) * (-(lr / (1.0 - beta1_pow))))
return param_out, moment1_out, moment2_out return param_out, moment1_out, moment2_out
...@@ -314,16 +314,16 @@ def simple_lr_setting(param, decay_rate, n_layers): ...@@ -314,16 +314,16 @@ def simple_lr_setting(param, decay_rate, n_layers):
"core is not compiled with CUDA") "core is not compiled with CUDA")
class TestAdamWOpLayerwiseLR(TestAdamWOp): class TestAdamWOpLayerwiseLR(TestAdamWOp):
def setUp(self): def setUp(self):
random.seed(2021) random.seed(2022)
np.random.seed(2021) np.random.seed(2022)
paddle.seed(2021) paddle.seed(2022)
def test_adamw_op_dygraph(self): def test_adamw_op_dygraph(self):
paddle.disable_static() paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype("float32") linear1 = paddle.nn.Linear(
a = paddle.to_tensor(value) 13, 8, bias_attr=paddle.nn.initializer.Constant(value=1.0))
linear1 = paddle.nn.Linear(13, 8) linear2 = paddle.nn.Linear(
linear2 = paddle.nn.Linear(8, 5) 8, 5, bias_attr=paddle.nn.initializer.Constant(value=1.0))
# fix the linear name, simple_lr_setting function will use the name # fix the linear name, simple_lr_setting function will use the name
linear1.weight.name = "linear_1.w_0" linear1.weight.name = "linear_1.w_0"
...@@ -331,33 +331,103 @@ class TestAdamWOpLayerwiseLR(TestAdamWOp): ...@@ -331,33 +331,103 @@ class TestAdamWOpLayerwiseLR(TestAdamWOp):
linear2.weight.name = "linear_2.w_0" linear2.weight.name = "linear_2.w_0"
linear2.bias.name = "linear_2.b_0" linear2.bias.name = "linear_2.b_0"
fc1_w = np.array(linear1.weight)
fc1_w_mon1 = np.zeros_like(fc1_w)
fc1_w_mon2 = np.zeros_like(fc1_w)
fc1_b = np.array(linear1.bias)
fc1_b_mon1 = np.zeros_like(fc1_b)
fc1_b_mon2 = np.zeros_like(fc1_b)
fc2_w = np.array(linear2.weight)
fc2_w_mon1 = np.zeros_like(fc2_w)
fc2_w_mon2 = np.zeros_like(fc2_w)
fc2_b = np.array(linear2.bias)
fc2_b_mon1 = np.zeros_like(fc2_b)
fc2_b_mon2 = np.zeros_like(fc2_b)
simple_lr_fun = partial(simple_lr_setting, decay_rate=0.8, n_layers=2) simple_lr_fun = partial(simple_lr_setting, decay_rate=0.8, n_layers=2)
learning_rate = 0.001
weight_decay = 0.01
beta1 = 0.9
beta2 = 0.999
adam = paddle.optimizer.AdamW( opt = paddle.optimizer.AdamW(
learning_rate=0.01, learning_rate=learning_rate,
parameters=[{ parameters=[{
'params': linear1.parameters() 'params': linear1.parameters()
}, { }, {
'params': linear2.parameters(), 'params': linear2.parameters(),
}], }],
apply_decay_param_fun=lambda name: True, apply_decay_param_fun=lambda name: True,
weight_decay=0.01, weight_decay=weight_decay,
lr_ratio=simple_lr_fun) lr_ratio=simple_lr_fun)
loss_ref = np.array( def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t):
[-1.7267396, -2.81524, -3.9250019, -5.05954, -6.2272625]) np_inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype("float32"),
'Beta1Pow': np.array([beta1**t]).astype("float32"),
'Beta2Pow': np.array([beta2**t]).astype("float32")
}
np_attrs = {
'epsilon': 1e-8,
'beta1': beta1,
'beta2': beta2,
"lr_ratio": lr_ratio,
"coeff": weight_decay,
"with_decay": True
}
param_out, moment1_out, moment2_out = adamw_step(np_inputs,
np_attrs)
return param_out, moment1_out, moment2_out
for i in range(5): for i in range(5):
a = paddle.to_tensor(
np.random.uniform(-1, 1, (2, 13)).astype("float32"))
a1 = linear1(a) a1 = linear1(a)
out = linear2(a1) out = linear2(a1)
out = paddle.mean(out) out = paddle.mean(out)
out.backward() out.backward()
adam.step()
adam.clear_gradients() fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output(
np.testing.assert_allclose(out[0].numpy(), loss_ref[i], rtol=1e-6) fc1_w,
np.array(linear1.weight.grad), fc1_w_mon1, fc1_w_mon2,
simple_lr_fun(linear1.weight), i + 1)
fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output(
fc1_b,
np.array(linear1.bias.grad), fc1_b_mon1, fc1_b_mon2,
simple_lr_fun(linear1.bias), i + 1)
fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output(
fc2_w,
np.array(linear2.weight.grad), fc2_w_mon1, fc2_w_mon2,
simple_lr_fun(linear2.weight), i + 1)
fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output(
fc2_b,
np.array(linear2.bias.grad), fc2_b_mon1, fc2_b_mon2,
simple_lr_fun(linear2.bias), i + 1)
opt.step()
opt.clear_gradients()
np.testing.assert_allclose(linear1.weight.numpy(), fc1_w, rtol=1e-6)
np.testing.assert_allclose(linear1.bias.numpy(), fc1_b, rtol=1e-6)
np.testing.assert_allclose(linear2.weight.numpy(), fc2_w, rtol=1e-6)
np.testing.assert_allclose(linear2.bias.numpy(), fc2_b, rtol=1e-6)
def test_adamw_op(self): def test_adamw_op(self):
paddle.enable_static() paddle.enable_static()
place = fluid.CUDAPlace(0) place = fluid.CUDAPlace(0)
learning_rate = 0.0001
beta1 = 0.85
beta2 = 0.95
weight_decay = 0.01
epsilon = 1e-8
train_prog = fluid.Program() train_prog = fluid.Program()
startup = fluid.Program() startup = fluid.Program()
with fluid.program_guard(train_prog, startup): with fluid.program_guard(train_prog, startup):
...@@ -365,42 +435,121 @@ class TestAdamWOpLayerwiseLR(TestAdamWOp): ...@@ -365,42 +435,121 @@ class TestAdamWOpLayerwiseLR(TestAdamWOp):
x = fluid.data(name='x', shape=[None, 10], dtype='float32') x = fluid.data(name='x', shape=[None, 10], dtype='float32')
y = fluid.data(name='y', shape=[None, 1], dtype='float32') y = fluid.data(name='y', shape=[None, 1], dtype='float32')
fc1 = fluid.layers.fc(input=x, size=32, act=None) weight_attr1 = paddle.framework.ParamAttr(name="linear_0.w_0")
prediction = fluid.layers.fc(input=fc1, size=1, act=None) bias_attr1 = paddle.framework.ParamAttr(
cost = fluid.layers.square_error_cost(input=prediction, label=y) name="linear_0.b_0",
initializer=paddle.nn.initializer.Constant(value=1.0))
weight_attr2 = paddle.framework.ParamAttr(name="linear_1.w_0")
bias_attr2 = paddle.framework.ParamAttr(
name="linear_1.b_0",
initializer=paddle.nn.initializer.Constant(value=1.0))
linear1 = paddle.nn.Linear(
10, 32, weight_attr=weight_attr1, bias_attr=bias_attr1)
linear2 = paddle.nn.Linear(
32, 1, weight_attr=weight_attr2, bias_attr=bias_attr2)
out = linear1(x)
out = linear2(out)
fc1_w_mon1 = np.zeros((linear1.weight.shape)).astype("float32")
fc1_w_mon2 = np.zeros((linear1.weight.shape)).astype("float32")
fc1_b_mon1 = np.zeros((linear1.bias.shape)).astype("float32")
fc1_b_mon2 = np.zeros((linear1.bias.shape)).astype("float32")
fc2_w_mon1 = np.zeros((linear2.weight.shape)).astype("float32")
fc2_w_mon2 = np.zeros((linear2.weight.shape)).astype("float32")
fc2_b_mon1 = np.zeros((linear2.bias.shape)).astype("float32")
fc2_b_mon2 = np.zeros((linear2.bias.shape)).astype("float32")
cost = fluid.layers.square_error_cost(input=out, label=y)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
simple_lr_fun = partial( simple_lr_fun = partial(
simple_lr_setting, decay_rate=0.8, n_layers=2) simple_lr_setting, decay_rate=0.8, n_layers=2)
beta1 = fluid.layers.create_global_var(
shape=[1], value=0.85, dtype='float32', persistable=True)
beta2 = fluid.layers.create_global_var(
shape=[1], value=0.95, dtype='float32', persistable=True)
betas = [beta1, beta2]
opt = paddle.optimizer.AdamW( opt = paddle.optimizer.AdamW(
learning_rate=1e-5, learning_rate=learning_rate,
beta1=beta1, beta1=beta1,
beta2=beta2, beta2=beta2,
weight_decay=0.01, weight_decay=weight_decay,
epsilon=1e-8, epsilon=epsilon,
lr_ratio=simple_lr_fun) lr_ratio=simple_lr_fun)
opt.minimize(avg_cost) opt.minimize(avg_cost)
def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t):
np_inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype("float32"),
'Beta1Pow': np.array([beta1**t]).astype("float32"),
'Beta2Pow': np.array([beta2**t]).astype("float32")
}
np_attrs = {
'epsilon': epsilon,
'beta1': beta1,
'beta2': beta2,
"lr_ratio": lr_ratio,
"coeff": weight_decay,
"with_decay": True
}
param_out, moment1_out, moment2_out = adamw_step(np_inputs,
np_attrs)
return param_out, moment1_out, moment2_out
fetch_list1 = [
"linear_0.w_0", "linear_0.b_0", "linear_1.w_0", "linear_1.b_0"
]
fetch_list2 = [
"linear_0.w_0", "linear_0.w_0@GRAD", "linear_0.b_0",
"linear_0.b_0@GRAD", "linear_1.w_0", "linear_1.w_0@GRAD",
"linear_1.b_0", "linear_1.b_0@GRAD"
]
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(startup) exe.run(startup)
test_prog = train_prog.clone(for_test=True)
loss_ref = np.array(
[0.33895183, 0.3159437, 0.19472016, 0.17764759, 0.1520702])
for i in range(5): for i in range(5):
inputs = np.random.random(size=[8, 10]).astype('float32') inputs = np.random.random(size=[8, 10]).astype('float32')
outputs = np.random.random(size=[8, 1]).astype('float32') outputs = np.random.random(size=[8, 1]).astype('float32')
rets = exe.run(train_prog,
param = exe.run(test_prog,
feed={"x": inputs, feed={"x": inputs,
"y": outputs}, "y": outputs},
fetch_list=[avg_cost]) fetch_list=fetch_list1)
assert rets[0] is not None params_and_gras = exe.run(train_prog,
np.testing.assert_allclose(rets[0], loss_ref[i], rtol=1e-6) feed={"x": inputs,
"y": outputs},
fetch_list=fetch_list2)
fc1_w = param[0]
fc1_w_grad = params_and_gras[1]
fc1_b = param[1]
fc1_b_grad = params_and_gras[3]
fc2_w = param[2]
fc2_w_grad = params_and_gras[5]
fc2_b = param[3]
fc2_b_grad = params_and_gras[7]
fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output(
fc1_w, fc1_w_grad, fc1_w_mon1, fc1_w_mon2,
simple_lr_fun(linear1.weight), i + 1)
fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output(
fc1_b, fc1_b_grad, fc1_b_mon1, fc1_b_mon2,
simple_lr_fun(linear1.bias), i + 1)
fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output(
fc2_w, fc2_w_grad, fc2_w_mon1, fc2_w_mon2,
simple_lr_fun(linear2.weight), i + 1)
fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output(
fc2_b, fc2_b_grad, fc2_b_mon1, fc2_b_mon2,
simple_lr_fun(linear2.bias), i + 1)
np.testing.assert_allclose(params_and_gras[0], fc1_w, rtol=1e-6)
np.testing.assert_allclose(params_and_gras[2], fc1_b, rtol=1e-6)
np.testing.assert_allclose(params_and_gras[4], fc2_w, rtol=1e-6)
np.testing.assert_allclose(params_and_gras[6], fc2_b, rtol=1e-6)
paddle.disable_static() paddle.disable_static()
......
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