wehub-resource-sync
499 lines
16 KiB
Python
499 lines
16 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import (
|
|
OpTest,
|
|
convert_float_to_uint16,
|
|
get_device,
|
|
get_device_place,
|
|
is_custom_device,
|
|
)
|
|
from utils import dygraph_guard
|
|
|
|
import paddle
|
|
from paddle.base import core, in_pir_mode
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
def rprop_wrapper(
|
|
param,
|
|
grad,
|
|
prev,
|
|
learning_rate,
|
|
master_param=None,
|
|
learning_rate_range=np.array((1e-5, 50)).astype("float32"),
|
|
etas=np.array((0.5, 1.2)).astype("float32"),
|
|
multi_precision=False,
|
|
):
|
|
paddle._C_ops.rprop_(
|
|
param,
|
|
grad,
|
|
prev,
|
|
learning_rate,
|
|
master_param,
|
|
learning_rate_range,
|
|
etas,
|
|
multi_precision,
|
|
)
|
|
|
|
|
|
class TestRpropOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "rprop"
|
|
self.python_api = rprop_wrapper
|
|
self.python_out_sig = ['Out']
|
|
self.conf()
|
|
params = np.random.random((self.h, self.w)).astype("float32")
|
|
grads = np.random.random((self.h, self.w)).astype("float32")
|
|
prevs = np.random.random((self.h, self.w)).astype("float32")
|
|
learning_rates = np.random.random((self.h, self.w)).astype("float32")
|
|
|
|
scale = 0.01
|
|
np.subtract(params, 0.5, out=params)
|
|
np.multiply(params, scale, out=params)
|
|
np.subtract(grads, 0.5, out=grads)
|
|
np.multiply(grads, scale, out=grads)
|
|
np.subtract(prevs, 0.5, out=prevs)
|
|
np.multiply(prevs, scale, out=prevs)
|
|
np.multiply(learning_rates, scale, out=learning_rates)
|
|
|
|
learning_rate_min = 0.1 * scale
|
|
learning_rate_max = 0.9 * scale
|
|
eta_negative = 0.5
|
|
eta_positive = 1.2
|
|
|
|
param_outs = params.copy()
|
|
prev_outs = prevs.copy()
|
|
learning_rate_outs = learning_rates.copy()
|
|
|
|
for i, param in enumerate(params):
|
|
grad = grads[i]
|
|
prev = prevs[i]
|
|
lr = learning_rate_outs[i]
|
|
param_out = param_outs[i]
|
|
prev_out = prev_outs[i]
|
|
|
|
sign = np.sign(np.multiply(grad, prev))
|
|
sign[np.greater(sign, 0)] = eta_positive
|
|
sign[np.less(sign, 0)] = eta_negative
|
|
sign[np.equal(sign, 0)] = 1
|
|
np.multiply(lr, sign, out=lr)
|
|
lr[np.less(lr, learning_rate_min)] = learning_rate_min
|
|
lr[np.greater(lr, learning_rate_max)] = learning_rate_max
|
|
|
|
grad = grad.copy()
|
|
grad[np.equal(sign, eta_negative)] = 0
|
|
|
|
learning_rate_outs[i] = lr
|
|
param_outs[i] = np.subtract(
|
|
param_out, np.multiply(np.sign(grad), lr)
|
|
)
|
|
prev_outs[i] = grad.copy()
|
|
|
|
self.inputs = {
|
|
"param": params,
|
|
"grad": grads,
|
|
"prev": prevs,
|
|
"learning_rate": learning_rates,
|
|
"learning_rate_range": np.array(
|
|
(learning_rate_min, learning_rate_max)
|
|
).astype("float32"),
|
|
"etas": np.array((0.5, 1.2)).astype("float32"),
|
|
}
|
|
|
|
self.outputs = {
|
|
"param_out": param_outs,
|
|
"prev_out": prev_outs,
|
|
"learning_rate_out": learning_rate_outs,
|
|
}
|
|
|
|
def conf(self):
|
|
self.h = 102
|
|
self.w = 105
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
|
|
class TestRpropOpCase8X(TestRpropOp):
|
|
def conf(self):
|
|
self.h = 10
|
|
self.w = 64
|
|
|
|
|
|
class TestRpropV2(unittest.TestCase):
|
|
def test_rprop_dygraph(self):
|
|
paddle.disable_static()
|
|
value = np.arange(26).reshape(1, 26).astype("float32")
|
|
a = paddle.to_tensor(value)
|
|
linear = paddle.nn.Linear(26, 5)
|
|
|
|
rprop = paddle.optimizer.Rprop(
|
|
learning_rate=0.01,
|
|
parameters=linear.parameters(),
|
|
)
|
|
out = linear(a)
|
|
out.backward()
|
|
rprop.step()
|
|
rprop.clear_gradients()
|
|
|
|
def test_raise_error(self):
|
|
self.assertRaises(
|
|
ValueError, paddle.optimizer.Rprop, learning_rate=None
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
paddle.optimizer.Rprop,
|
|
learning_rate=1e-3,
|
|
learning_rate_range=np.array((1e-2, 1e-1)).astype("float32"),
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
paddle.optimizer.Rprop,
|
|
learning_rate=1e-3,
|
|
etas=np.array((-0.1, 1.1)).astype("float32"),
|
|
)
|
|
|
|
def test_rprop_group_dygraph(self):
|
|
paddle.disable_static()
|
|
value = np.arange(26).reshape(1, 26).astype("float32")
|
|
a = paddle.to_tensor(value)
|
|
linear_1 = paddle.nn.Linear(26, 5)
|
|
linear_2 = paddle.nn.Linear(5, 3)
|
|
rprop = paddle.optimizer.Rprop(
|
|
learning_rate=0.01,
|
|
parameters=[
|
|
{'params': linear_1.parameters()},
|
|
{
|
|
'params': linear_2.parameters(),
|
|
'learning_rate': 0.1,
|
|
},
|
|
],
|
|
)
|
|
out = linear_1(a)
|
|
out = linear_2(out)
|
|
out.backward()
|
|
rprop.step()
|
|
rprop.clear_gradients()
|
|
|
|
|
|
class TestRpropMultiPrecision2_0(unittest.TestCase):
|
|
def dygraph_rprop_mp(self, mp):
|
|
paddle.disable_static()
|
|
paddle.seed(10)
|
|
paddle.set_device(get_device())
|
|
input = paddle.randn((2, 2))
|
|
model = paddle.nn.Linear(2, 2)
|
|
optimizer = paddle.optimizer.Rprop(
|
|
parameters=model.parameters(), multi_precision=mp
|
|
)
|
|
if mp:
|
|
model = paddle.amp.decorate(models=model, level='O2')
|
|
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
|
|
for idx in range(5):
|
|
if mp:
|
|
with paddle.amp.auto_cast(level='O2'):
|
|
output = model(input)
|
|
loss = paddle.mean(output)
|
|
scaled = scaler.scale(loss)
|
|
scaled.backward()
|
|
scaler.minimize(optimizer, scaled)
|
|
optimizer.clear_grad()
|
|
else:
|
|
output = model(input)
|
|
loss = paddle.mean(output)
|
|
optimizer.step()
|
|
optimizer.clear_grad()
|
|
|
|
return output, model.parameters()
|
|
|
|
def static_rprop_mp(self, mp):
|
|
paddle.enable_static()
|
|
paddle.seed(10)
|
|
np.random.seed(10)
|
|
exe = paddle.static.Executor(get_device_place())
|
|
train_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(train_program, startup_program):
|
|
if in_pir_mode():
|
|
optimizer = paddle.optimizer.Rprop(multi_precision=mp)
|
|
linear = paddle.nn.Linear(2, 2)
|
|
|
|
if mp:
|
|
linear, optimizer = paddle.amp.decorate(
|
|
models=linear,
|
|
optimizers=optimizer,
|
|
level='O2',
|
|
dtype='float16',
|
|
)
|
|
else:
|
|
optimizer = paddle.optimizer.Rprop(multi_precision=mp)
|
|
linear = paddle.nn.Linear(2, 2)
|
|
|
|
if mp:
|
|
optimizer = paddle.static.amp.decorate(
|
|
optimizer,
|
|
init_loss_scaling=128.0,
|
|
use_dynamic_loss_scaling=True,
|
|
use_pure_fp16=True,
|
|
use_fp16_guard=False,
|
|
)
|
|
|
|
if mp:
|
|
data = paddle.static.data(
|
|
shape=[2, 2], name='X', dtype='float16'
|
|
)
|
|
else:
|
|
data = paddle.static.data(
|
|
shape=[2, 2], name='X', dtype='float32'
|
|
)
|
|
if in_pir_mode():
|
|
if mp:
|
|
with paddle.amp.auto_cast(
|
|
level='O2', dtype='float16', use_promote=True
|
|
):
|
|
hidden = linear(data)
|
|
else:
|
|
hidden = linear(data)
|
|
loss = paddle.mean(hidden)
|
|
optimizer.minimize(loss)
|
|
else:
|
|
hidden = paddle.static.nn.fc(x=data, size=10)
|
|
loss = paddle.mean(hidden)
|
|
optimizer.minimize(loss)
|
|
if mp:
|
|
optimizer.amp_init(
|
|
place=get_device_place(),
|
|
scope=paddle.static.global_scope(),
|
|
)
|
|
x = np.random.random(size=(2, 2)).astype('float16')
|
|
else:
|
|
x = np.random.random(size=(2, 2)).astype('float32')
|
|
|
|
if mp:
|
|
optimizer.amp_init(
|
|
place=get_device_place(), scope=paddle.static.global_scope()
|
|
)
|
|
x = np.random.random(size=(2, 2)).astype('float16')
|
|
else:
|
|
x = np.random.random(size=(2, 2)).astype('float32')
|
|
|
|
exe.run(startup_program)
|
|
out = []
|
|
for idx in range(5):
|
|
if in_pir_mode():
|
|
(loss_data,) = exe.run(
|
|
train_program, feed={"X": x}, fetch_list=[loss]
|
|
)
|
|
else:
|
|
(loss_data,) = exe.run(
|
|
train_program, feed={"X": x}, fetch_list=[loss.name]
|
|
)
|
|
out.append(loss_data)
|
|
return out
|
|
|
|
def test_main(self):
|
|
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
"Test dygraph mode"
|
|
output1_dy, params1_dy = self.dygraph_rprop_mp(mp=True)
|
|
output2_dy, params2_dy = self.dygraph_rprop_mp(mp=False)
|
|
np.testing.assert_allclose(
|
|
output1_dy.astype('float32').numpy(),
|
|
output2_dy.astype('float32').numpy(),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
for idx in range(len(params1_dy)):
|
|
np.testing.assert_allclose(
|
|
params1_dy[idx].astype('float32').numpy(),
|
|
params2_dy[idx].astype('float32').numpy(),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
"Test static graph mode"
|
|
output1_st = self.static_rprop_mp(mp=True)
|
|
output2_st = self.static_rprop_mp(mp=False)
|
|
for idx in range(len(output1_st)):
|
|
np.testing.assert_allclose(
|
|
output1_st[idx].astype('float32'),
|
|
output2_st[idx].astype('float32'),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
|
|
|
|
class TestRpropSimple(unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
self.data = np.random.random(size=(2, 2)).astype('float32')
|
|
|
|
def run_static(self):
|
|
with paddle.pir_utils.IrGuard():
|
|
paddle.seed(10)
|
|
np.random.seed(10)
|
|
|
|
exe = paddle.static.Executor(get_device_place())
|
|
train_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(train_program, startup_program):
|
|
input = paddle.static.data(
|
|
shape=[2, 2], name='input', dtype='float32'
|
|
)
|
|
model = paddle.nn.Linear(2, 2)
|
|
output = model(input)
|
|
loss = paddle.mean(output)
|
|
|
|
optimizer = paddle.optimizer.Rprop()
|
|
optimizer.minimize(loss)
|
|
|
|
exe.run(startup_program)
|
|
|
|
out = []
|
|
for _ in range(5):
|
|
(loss_data,) = exe.run(
|
|
train_program, feed={"input": self.data}, fetch_list=[loss]
|
|
)
|
|
out.append(loss_data)
|
|
return out
|
|
|
|
def run_dygraph(self):
|
|
with dygraph_guard():
|
|
paddle.seed(10)
|
|
np.random.seed(10)
|
|
|
|
out = []
|
|
model = paddle.nn.Linear(2, 2)
|
|
optimizer = paddle.optimizer.Rprop(parameters=model.parameters())
|
|
for _ in range(5):
|
|
output = model(paddle.to_tensor(self.data))
|
|
loss = paddle.mean(output)
|
|
out.append(loss.numpy())
|
|
loss.backward()
|
|
optimizer.step()
|
|
optimizer.clear_grad()
|
|
|
|
return out
|
|
|
|
def test_main(self):
|
|
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
out1 = self.run_dygraph()
|
|
out2 = self.run_static()
|
|
np.testing.assert_allclose(out1, out2)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not core.supports_bfloat16(), 'place does not support BF16 evaluation'
|
|
)
|
|
class TestRpropOpBF16(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "rprop"
|
|
self.dtype = np.uint16
|
|
self.use_onednn = True
|
|
self.conf()
|
|
params = np.random.random((self.h, self.w)).astype("float32")
|
|
grads = np.random.random((self.h, self.w)).astype("float32")
|
|
prevs = np.random.random((self.h, self.w)).astype("float32")
|
|
learning_rates = np.random.random((self.h, self.w)).astype("float32")
|
|
|
|
scale = 0.01
|
|
np.subtract(params, 0.5, out=params)
|
|
np.multiply(params, scale, out=params)
|
|
np.subtract(grads, 0.5, out=grads)
|
|
np.multiply(grads, scale, out=grads)
|
|
np.subtract(prevs, 0.5, out=prevs)
|
|
np.multiply(prevs, scale, out=prevs)
|
|
np.multiply(learning_rates, scale, out=learning_rates)
|
|
|
|
learning_rate_min = 0.1 * scale
|
|
learning_rate_max = 0.9 * scale
|
|
eta_negative = 0.5
|
|
eta_positive = 1.2
|
|
|
|
param_outs = params.copy()
|
|
prev_outs = prevs.copy()
|
|
learning_rate_outs = learning_rates.copy()
|
|
|
|
for i, param in enumerate(params):
|
|
grad = grads[i]
|
|
prev = prevs[i]
|
|
lr = learning_rate_outs[i]
|
|
param_out = param_outs[i]
|
|
prev_out = prev_outs[i]
|
|
|
|
sign = np.sign(np.multiply(grad, prev))
|
|
sign[np.greater(sign, 0)] = eta_positive
|
|
sign[np.less(sign, 0)] = eta_negative
|
|
sign[np.equal(sign, 0)] = 1
|
|
np.multiply(lr, sign, out=lr)
|
|
lr[np.less(lr, learning_rate_min)] = learning_rate_min
|
|
lr[np.greater(lr, learning_rate_max)] = learning_rate_max
|
|
|
|
grad = grad.copy()
|
|
grad[np.equal(sign, eta_negative)] = 0
|
|
|
|
learning_rate_outs[i] = lr
|
|
param_outs[i] = np.subtract(
|
|
param_out, np.multiply(np.sign(grad), lr)
|
|
)
|
|
prev_outs[i] = grad.copy()
|
|
|
|
learning_rate_range = np.array(
|
|
(learning_rate_min, learning_rate_max)
|
|
).astype("float32")
|
|
etas = np.array((0.5, 1.2)).astype("float32")
|
|
|
|
params_bf16 = convert_float_to_uint16(params)
|
|
grads_bf16 = convert_float_to_uint16(grads)
|
|
prevs_bf16 = convert_float_to_uint16(prevs)
|
|
learning_rates_bf16 = convert_float_to_uint16(learning_rates)
|
|
learning_rate_range_bf16 = convert_float_to_uint16(learning_rate_range)
|
|
etas_bf16 = convert_float_to_uint16(etas)
|
|
|
|
param_outs_bf16 = convert_float_to_uint16(param_outs)
|
|
prev_outs_bf16 = convert_float_to_uint16(prev_outs)
|
|
learning_rate_outs_bf16 = convert_float_to_uint16(learning_rate_outs)
|
|
|
|
self.inputs = {
|
|
"param": params_bf16,
|
|
"grad": grads_bf16,
|
|
"prev": prevs_bf16,
|
|
"learning_rate": learning_rates_bf16,
|
|
"learning_rate_range": learning_rate_range_bf16,
|
|
"etas": etas_bf16,
|
|
}
|
|
|
|
self.outputs = {
|
|
"param_out": param_outs_bf16,
|
|
"prev_out": prev_outs_bf16,
|
|
"learning_rate_out": learning_rate_outs_bf16,
|
|
}
|
|
|
|
def conf(self):
|
|
self.h = 102
|
|
self.w = 105
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(core.CPUPlace(), check_dygraph=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|