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add matmul & adamw unittest test=kunlun (#41186)

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python/paddle/fluid/tests/unittests/xpu/test_adamw_op_xpu.py 0 → 100644
浏览文件 @ 814315b4
# Copyright (c) 2022 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 sys
sys.path.append("..")
import unittest
import paddle
import random
import numpy as np
import paddle.fluid as fluid
from functools import partial
from paddle.framework import core
from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
def adamw_step(inputs, attributes):
param = inputs['Param']
grad = inputs['Grad']
moment1 = inputs['Moment1']
moment2 = inputs['Moment2']
lr = inputs['LearningRate']
beta1_pow = inputs['Beta1Pow']
beta2_pow = inputs['Beta2Pow']
epsilon = attributes['epsilon']
if 'lr_ratio' in attributes:
lr = lr * attributes['lr_ratio']
if attributes["with_decay"]:
coeff = attributes["coeff"]
decay = 1.0 - lr * coeff
param2 = param * decay
param = param2.copy()
if 'beta1' in attributes:
beta1 = attributes['beta1']
else:
beta1 = inputs['Beta1Tensor'][0]
if 'beta2' in attributes:
beta2 = attributes['beta2']
else:
beta2 = inputs['Beta2Tensor'][0]
moment1_out = beta1 * moment1 + (1 - beta1) * grad
moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
return param_out, moment1_out, moment2_out
def simple_lr_setting(param, decay_rate, n_layers):
if "fc_0" in param.name or "linear_1" in param.name:
depth = int(param.name.split("_")[2]) + 1
elif "fc_1" in param.name or "linear_2" in param.name:
depth = int(param.name.split("_")[2]) + 2
else:
depth = 0
return decay_rate**(n_layers + 2 - depth)
class XPUTestAdamwOp1(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'adamw'
self.use_dynamic_create_class = False
class TestAdamW(XPUOpTest):
def setUp(self):
#Test AdamW Op with supplied attributes
self.op_type = "adamw"
self.init_shape()
self.dtype = self.in_type_str
param = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
grad = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
moment1 = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
# The second moment is positive
moment2 = np.random.random(self.shape).astype(self.dtype)
learning_rate = 0.004
beta1 = 0.78
beta2 = 0.836
epsilon = 1e-4
beta1_pow = beta1**10
beta2_pow = beta2**10
self.inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype(self.dtype),
'Beta1Pow': np.array([beta1_pow]).astype(self.dtype),
'Beta2Pow': np.array([beta2_pow]).astype(self.dtype)
}
self.attrs = {
'epsilon': epsilon,
'beta1': beta1,
'beta2': beta2,
"coeff": 0.5,
"with_decay": True
}
param_out, moment1_out, \
moment2_out = adamw_step(self.inputs, self.attrs)
self.outputs = {
'Moment1Out': moment1_out,
'Moment2Out': moment2_out,
'ParamOut': param_out,
'Beta1PowOut': np.array([beta1_pow]).astype(self.dtype) * beta1,
'Beta2PowOut': np.array([beta2_pow]).astype(self.dtype) * beta2
}
def init_shape(self):
self.shape = [102, 105]
def test_check_output(self):
paddle.enable_static()
self.check_output_with_place(place=paddle.XPUPlace(0))
class TestAdamW2(TestAdamW):
def init_shape(self):
self.shape = [1000, ]
class TestAdamW3(TestAdamW):
def init_shape(self):
self.shape = [200, 3000]
class XPUTestAdamwOp2(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'adamw'
self.use_dynamic_create_class = False
class TestAdamWOp(unittest.TestCase):
def test_adamw_op_dygraph(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype(self.in_type_str)
a = paddle.to_tensor(value)
linear = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.AdamW(
learning_rate=0.01,
parameters=linear.parameters(),
apply_decay_param_fun=lambda name: True,
weight_decay=0.01)
for _ in range(2):
out = linear(a)
out.backward()
adam.step()
adam.clear_gradients()
def test_adamw_op_coverage(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype(self.in_type_str)
a = paddle.to_tensor(value)
linear = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.AdamW(
learning_rate=0.0,
parameters=linear.parameters(),
apply_decay_param_fun=lambda name: True,
weight_decay=0.01)
assert (adam.__str__() is not None)
def test_adamw_op(self):
paddle.enable_static()
place = fluid.XPUPlace(0)
shape = [2, 3, 8, 8]
exe = fluid.Executor(place)
train_prog = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(train_prog, startup):
with fluid.unique_name.guard():
data = fluid.data(name="data", shape=shape)
conv = fluid.layers.conv2d(data, 8, 3)
loss = paddle.mean(conv)
beta1 = fluid.layers.create_global_var(
shape=[1],
value=0.85,
dtype=self.in_type_str,
persistable=True)
beta2 = fluid.layers.create_global_var(
shape=[1],
value=0.95,
dtype=self.in_type_str,
persistable=True)
betas = [beta1, beta2]
opt = paddle.optimizer.AdamW(
learning_rate=1e-5,
beta1=beta1,
beta2=beta2,
weight_decay=0.01,
epsilon=1e-8)
opt.minimize(loss)
exe.run(startup)
data_np = np.random.random(shape).astype(self.in_type_str)
rets = exe.run(train_prog,
feed={"data": data_np},
fetch_list=[loss])
assert rets[0] is not None
paddle.disable_static()
def test_adamw_op_invalid_input(self):
paddle.disable_static()
linear = paddle.nn.Linear(10, 10)
with self.assertRaises(ValueError):
adam = paddle.optimizer.AdamW(
0.1, beta1=-1, parameters=linear.parameters())
with self.assertRaises(ValueError):
adam = paddle.optimizer.AdamW(
0.1, beta2=-1, parameters=linear.parameters())
with self.assertRaises(ValueError):
adam = paddle.optimizer.AdamW(
0.1, epsilon=-1, parameters=linear.parameters())
class TestAdamWOpGroup(TestAdamWOp):
def test_adamw_op_dygraph(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype(self.in_type_str)
a = paddle.to_tensor(value)
linear_1 = paddle.nn.Linear(13, 5)
linear_2 = paddle.nn.Linear(5, 3)
adam = paddle.optimizer.AdamW(
learning_rate=0.01,
parameters=[{
'params': linear_1.parameters()
}, {
'params': linear_2.parameters(),
'weight_decay': 0.001
}],
apply_decay_param_fun=lambda name: True,
weight_decay=0.01)
for _ in range(2):
out = linear_1(a)
out = linear_2(out)
out.backward()
adam.step()
adam.clear_gradients()
class TestAdamWOpGroupWithLR(TestAdamWOp):
def test_adamw_op_dygraph(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype(self.in_type_str)
a = paddle.to_tensor(value)
linear_1 = paddle.nn.Linear(13, 5)
linear_2 = paddle.nn.Linear(5, 3)
adam = paddle.optimizer.AdamW(
learning_rate=paddle.optimizer.lr.PiecewiseDecay(
boundaries=[3, 6], values=[0.1, 0.2, 0.3]),
parameters=[{
'params': linear_1.parameters(),
'learning_rate': 0.1,
}, {
'params': linear_2.parameters(),
'weight_decay': 0.001,
}],
apply_decay_param_fun=lambda name: True,
weight_decay=0.01)
for _ in range(2):
out = linear_1(a)
out = linear_2(out)
out.backward()
adam.step()
adam.clear_gradients()
support_types = get_xpu_op_support_types('adamw')
for stype in support_types:
create_test_class(globals(), XPUTestAdamwOp1, stype)
create_test_class(globals(), XPUTestAdamwOp2, stype)
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_matmul_v2_op_xpu.py
浏览文件 @ 814315b4
......@@ -23,6 +23,9 @@ import paddle.fluid.core as core
import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
from paddle.fluid.framework import _test_eager_guard
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
......@@ -55,273 +58,239 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
return Out
class TestMatMulV2Op(XPUOpTest):
"""
case 1
"""
def config(self):
self.x_shape = (100, )
self.y_shape = (100, )
self.trans_x = False
self.trans_y = False
def init_kernel_type(self):
self.dtype = "float32"
def setUp(self):
self.use_xpu = True
self.init_kernel_type()
self.config()
self.op_type = "matmul_v2"
x = np.random.random(self.x_shape).astype(self.dtype)
y = np.random.random(self.y_shape).astype(self.dtype)
# -0.1 ~ 0.1
x = -0.1 + 0.2 * x
y = -0.1 + 0.2 * y
result = reference_matmul(x, y, self.trans_x, self.trans_y)
result = result.astype(self.dtype)
self.inputs = {
'X': x,
'Y': y,
}
self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
self.outputs = {'Out': result}
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X', 'Y'], 'Out')
class TestMatMulOp2(TestMatMulV2Op):
"""
case 2
"""
def config(self):
self.x_shape = (100)
self.y_shape = (100, 3)
self.trans_x = False
self.trans_y = False
class TestMatMulOp3(TestMatMulV2Op):
"""
case 3
"""
def config(self):
self.x_shape = (100, )
self.y_shape = (1, 1, 100, 2)
self.trans_x = False
self.trans_y = False
class TestMatMulOp4(TestMatMulV2Op):
"""
case 4
"""
def config(self):
self.x_shape = (1, 1, 100, 1)
self.y_shape = (1, 100)
self.trans_x = False
self.trans_y = False
class TestMatMulOp5(TestMatMulV2Op):
"""
case 5
"""
def config(self):
self.x_shape = (1, 1, 100, 1)
self.y_shape = (100, )
self.trans_x = True
self.trans_y = False
class TestMatMulOp6(TestMatMulV2Op):
"""
case 6
"""
def config(self):
self.x_shape = (1, 2, 102, 10)
self.y_shape = (2, 10, 111)
self.trans_x = False
self.trans_y = False
class TestMatMulOp7(TestMatMulV2Op):
"""
case 7
"""
def config(self):
self.x_shape = (1, 2, 100, 1)
self.y_shape = (2, 100, 12)
self.trans_x = True
self.trans_y = False
class TestMatMulOp8(TestMatMulV2Op):
"""
case 8
"""
def config(self):
self.x_shape = (1, 1, 2, 100)
self.y_shape = (1, 1, 100, 2)
self.trans_x = False
self.trans_y = False
class TestMatMulOp9(TestMatMulV2Op):
"""
case 9
"""
def config(self):
self.x_shape = (100, 20, 100)
self.y_shape = (100, 100, 100)
self.trans_x = False
self.trans_y = True
class TestMatMulOp10(TestMatMulV2Op):
"""
case 10
"""
def config(self):
self.x_shape = (100, 20, 100)
self.y_shape = (100, 20, 100)
self.trans_x = True
self.trans_y = False
class TestMatMulOp11(TestMatMulV2Op):
"""
case 11
"""
def config(self):
self.x_shape = (2, 20, 100)
self.y_shape = (100, 30)
self.trans_x = False
self.trans_y = False
class TestMatMulOp12(TestMatMulV2Op):
"""
case 12
"""
def config(self):
self.x_shape = (1, 20, 100)
self.y_shape = (100, )
self.trans_x = False
self.trans_y = False
class TestMatMulOp13(TestMatMulV2Op):
"""
case 13
"""
def config(self):
self.x_shape = (2, 2, 10, 10)
self.y_shape = (2, 2, 10, 10)
self.trans_x = True
self.trans_y = False
class TestMatMulOp14(TestMatMulV2Op):
"""
case 14_1
"""
def config(self):
self.x_shape = (100, 2, 100, 10)
self.y_shape = (100, 2, 10, 90)
self.trans_x = False
self.trans_y = False
class TestMatMulOp15(TestMatMulV2Op):
"""
case 14_2
"""
def config(self):
self.x_shape = (100, 2, 100, 10)
self.y_shape = (100, 2, 100, 10)
self.trans_x = False
self.trans_y = True
class TestMatMulOp16(TestMatMulV2Op):
"""
case 16 : to check the big data
"""
def config(self):
self.x_shape = (1000, 2, 100, 100)
self.y_shape = (1000, 2, 100, 900)
self.trans_x = False
self.trans_y = False
class TestMatMulOp17(TestMatMulV2Op):
"""
case 17 : to check the gradient for special case
"""
def config(self):
self.x_shape = (2, 1, 100)
self.y_shape = (100)
self.trans_x = False
self.trans_y = False
class TestMatMulOp18(TestMatMulV2Op):
"""
case 18 : for ppyoloe model
"""
def config(self):
self.x_shape = (8, 111, 4, 17)
self.y_shape = (17)
self.trans_x = False
self.trans_y = False
# class TestMatMulOpBroadcast1(TestMatMulV2Op):
# """
# case 14_3
# """
# def config(self):
# self.x_shape = (3, 1, 10, 10)
# self.y_shape = (1, 2, 10, 10)
# self.trans_x = True
# self.trans_y = True
# class TestMatMulOpBroadcast2(TestMatMulV2Op):
# """
# case 14_4
# """
# def config(self):
# self.x_shape = (3, 1, 10, 10)
# self.y_shape = (1, 2, 10, 10)
# self.trans_x = False
# self.trans_y = True
class XPUTestMatmulV2Op(XPUOpTestWrapper):
def __init__(self):
self.op_name = "matmul_v2"
self.use_dynamic_create_class = False
class TestMatMulV2Op(XPUOpTest):
"""
case 1
"""
def config(self):
self.x_shape = (100, )
self.y_shape = (100, )
self.trans_x = False
self.trans_y = False
def setUp(self):
self.dtype = self.in_type
self.config()
self.op_type = "matmul_v2"
x = np.random.random(self.x_shape).astype(self.dtype)
y = np.random.random(self.y_shape).astype(self.dtype)
# -0.1 ~ 0.1
x = -0.1 + 0.2 * x
y = -0.1 + 0.2 * y
result = reference_matmul(x, y, self.trans_x, self.trans_y)
result = result.astype(self.dtype)
self.inputs = {
'X': x,
'Y': y,
}
self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
self.outputs = {'Out': result}
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X', 'Y'], 'Out')
class TestMatMulOp2(TestMatMulV2Op):
"""
case 2
"""
def config(self):
self.x_shape = (100)
self.y_shape = (100, 3)
self.trans_x = False
self.trans_y = False
class TestMatMulOp3(TestMatMulV2Op):
"""
case 3
"""
def config(self):
self.x_shape = (100, )
self.y_shape = (1, 1, 100, 2)
self.trans_x = False
self.trans_y = False
class TestMatMulOp4(TestMatMulV2Op):
"""
case 4
"""
def config(self):
self.x_shape = (1, 1, 100, 1)
self.y_shape = (1, 100)
self.trans_x = False
self.trans_y = False
class TestMatMulOp5(TestMatMulV2Op):
"""
case 5
"""
def config(self):
self.x_shape = (1, 1, 100, 1)
self.y_shape = (100, )
self.trans_x = True
self.trans_y = False
class TestMatMulOp6(TestMatMulV2Op):
"""
case 6
"""
def config(self):
self.x_shape = (1, 2, 102, 10)
self.y_shape = (2, 10, 111)
self.trans_x = False
self.trans_y = False
class TestMatMulOp7(TestMatMulV2Op):
"""
case 7
"""
def config(self):
self.x_shape = (1, 2, 100, 1)
self.y_shape = (2, 100, 12)
self.trans_x = True
self.trans_y = False
class TestMatMulOp8(TestMatMulV2Op):
"""
case 8
"""
def config(self):
self.x_shape = (1, 1, 2, 100)
self.y_shape = (1, 1, 100, 2)
self.trans_x = False
self.trans_y = False
class TestMatMulOp9(TestMatMulV2Op):
"""
case 9
"""
def config(self):
self.x_shape = (100, 20, 100)
self.y_shape = (100, 100, 100)
self.trans_x = False
self.trans_y = True
class TestMatMulOp10(TestMatMulV2Op):
"""
case 10
"""
def config(self):
self.x_shape = (100, 20, 100)
self.y_shape = (100, 20, 100)
self.trans_x = True
self.trans_y = False
class TestMatMulOp11(TestMatMulV2Op):
"""
case 11
"""
def config(self):
self.x_shape = (2, 20, 100)
self.y_shape = (100, 30)
self.trans_x = False
self.trans_y = False
class TestMatMulOp12(TestMatMulV2Op):
"""
case 12
"""
def config(self):
self.x_shape = (1, 20, 100)
self.y_shape = (100, )
self.trans_x = False
self.trans_y = False
class TestMatMulOp13(TestMatMulV2Op):
"""
case 13
"""
def config(self):
self.x_shape = (2, 2, 10, 10)
self.y_shape = (2, 2, 10, 10)
self.trans_x = True
self.trans_y = False
class TestMatMulOp14(TestMatMulV2Op):
"""
case 14_1
"""
def config(self):
self.x_shape = (100, 2, 100, 10)
self.y_shape = (100, 2, 10, 90)
self.trans_x = False
self.trans_y = False
class TestMatMulOp15(TestMatMulV2Op):
"""
case 14_2
"""
def config(self):
self.x_shape = (100, 2, 100, 10)
self.y_shape = (100, 2, 100, 10)
self.trans_x = False
self.trans_y = True
class TestMatMulOp16(TestMatMulV2Op):
"""
case 16 : to check the big data
"""
def config(self):
self.x_shape = (1000, 2, 100, 100)
self.y_shape = (1000, 2, 100, 900)
self.trans_x = False
self.trans_y = False
class TestMatMulOp17(TestMatMulV2Op):
"""
case 17 : to check the gradient for special case
"""
def config(self):
self.x_shape = (2, 1, 100)
self.y_shape = (100)
self.trans_x = False
self.trans_y = False
class TestMatMulOp18(TestMatMulV2Op):
"""
case 18 : for ppyoloe model
"""
def config(self):
self.x_shape = (8, 111, 4, 17)
self.y_shape = (17)
self.trans_x = False
self.trans_y = False
support_types = get_xpu_op_support_types('matmul_v2')
for stype in support_types:
create_test_class(globals(), XPUTestMatmulV2Op, stype)
if __name__ == "__main__":
paddle.enable_static()
......
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