wehub-resource-sync
790 lines
30 KiB
Python
790 lines
30 KiB
Python
# Copyright (c) 2020 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 utils import static_guard
|
|
|
|
import paddle
|
|
from paddle import base, nn
|
|
from paddle.base import framework
|
|
from paddle.base.core import VarDesc
|
|
from paddle.nn import initializer
|
|
|
|
DELTA = 0.00001
|
|
|
|
|
|
def get_uniform_min_and_max(weight):
|
|
min_value = np.min(weight)
|
|
max_value = np.max(weight)
|
|
return min_value, max_value
|
|
|
|
|
|
def check_cast_op(op):
|
|
return (
|
|
op.type == 'cast'
|
|
and op.attr('in_dtype') == VarDesc.VarType.FP32
|
|
and op.attr('out_dtype') in [VarDesc.VarType.FP16, VarDesc.VarType.BF16]
|
|
)
|
|
|
|
|
|
class TestConstantInitializer(unittest.TestCase):
|
|
def static_test_constant_initializer_common(
|
|
self, init_inst, dtype="float32", value_target=0.0
|
|
):
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=init_inst,
|
|
)
|
|
num_ops = 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'fill_constant')
|
|
self.assertAlmostEqual(
|
|
init_op.attr('value'), value_target, delta=DELTA
|
|
)
|
|
paddle.disable_static()
|
|
return block
|
|
|
|
def test_constant_initializer_default_value_static(self, dtype="float32"):
|
|
"""Test the constant initializer with default value in static graph"""
|
|
block = self.static_test_constant_initializer_common(
|
|
init_inst=initializer.Constant(), dtype=dtype, value_target=0.0
|
|
)
|
|
return block
|
|
|
|
def test_constant_initializer_default_value_dygraph(self, dtype="float32"):
|
|
"""Test constant initializer with supplied value in dygraph"""
|
|
with base.dygraph.guard():
|
|
linear = nn.Linear(2, 4, weight_attr=nn.initializer.Constant())
|
|
mat_target = np.ones((2, 4), dtype=dtype) * 0.0
|
|
mat_linear = linear.weight.numpy()
|
|
mismatch = np.sum(
|
|
(mat_target - mat_linear) * (mat_target - mat_linear)
|
|
)
|
|
self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
|
|
|
|
def test_constant_initializer_static(self, dtype="float32"):
|
|
"""Test constant initializer with supplied value in static graph"""
|
|
block = self.static_test_constant_initializer_common(
|
|
init_inst=initializer.Constant(2.3), dtype=dtype, value_target=2.3
|
|
)
|
|
return block
|
|
|
|
def test_constant_initializer_dygraph(self, dtype="float32"):
|
|
"""Test constant initializer with supplied value in dygraph"""
|
|
with base.dygraph.guard():
|
|
linear = nn.Linear(
|
|
2, 4, weight_attr=nn.initializer.Constant(value=2.0)
|
|
)
|
|
mat_target = np.ones((2, 4), dtype=dtype) * 2.0
|
|
mat_linear = linear.weight.numpy()
|
|
mismatch = np.sum(
|
|
(mat_target - mat_linear) * (mat_target - mat_linear)
|
|
)
|
|
self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
|
|
|
|
def test_constant_initializer_fp16(self):
|
|
"""Test constant initializer with float16"""
|
|
block = self.test_constant_initializer_default_value_static("float16")
|
|
block = self.test_constant_initializer_static("float16")
|
|
self.test_constant_initializer_default_value_dygraph("float16")
|
|
self.test_constant_initializer_dygraph("float16")
|
|
|
|
def test_constant_initializer_bf16(self):
|
|
"""Test constant initializer with bfloat16
|
|
No cast operator has been added here
|
|
"""
|
|
self.test_constant_initializer_default_value_static(
|
|
"uint16"
|
|
) # bfloat16
|
|
self.test_constant_initializer_static("uint16") # bfloat16
|
|
|
|
|
|
class TestKaimingInitializer(unittest.TestCase):
|
|
def static_test_kaiming_initializer_common(
|
|
self, init_inst, dtype="float32", uniform=False, is_conv=False
|
|
):
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
shape_mat = [5, 10, 15, 20] if is_conv else [5, 10]
|
|
for _ in range(2):
|
|
param = block.create_parameter(
|
|
dtype="float32",
|
|
shape=shape_mat,
|
|
name="param",
|
|
initializer=init_inst,
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
if uniform:
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
if is_conv:
|
|
receptive_field_size = float(15 * 20)
|
|
limit = np.sqrt(
|
|
6.0 / (param.shape[1] * receptive_field_size)
|
|
)
|
|
else:
|
|
limit = np.sqrt(6.0 / param.shape[0])
|
|
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
|
|
else:
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
if is_conv:
|
|
receptive_field_size = float(15 * 20)
|
|
std = np.sqrt(2.0 / (param.shape[1] * receptive_field_size))
|
|
else:
|
|
std = np.sqrt(2.0 / param.shape[0])
|
|
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
|
|
paddle.disable_static()
|
|
|
|
def dygraph_test_kaiming_initializer_common(
|
|
self, init_inst, dtype="float32", uniform=False
|
|
):
|
|
linear = nn.Linear(40, 20, weight_attr=init_inst)
|
|
|
|
def test_kaiming_dygraph(self):
|
|
self.dygraph_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingUniform(),
|
|
dtype="float32",
|
|
uniform=True,
|
|
)
|
|
self.dygraph_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingNormal(),
|
|
dtype="float32",
|
|
uniform=False,
|
|
)
|
|
|
|
def test_kaiming_uniform_initializer_static(self):
|
|
"""Test Kaiming uniform initializer for matrix multiply."""
|
|
self.static_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingUniform(),
|
|
dtype="float32",
|
|
uniform=True,
|
|
is_conv=False,
|
|
)
|
|
|
|
def test_kaiming_uniform_initializer_conv_static(self):
|
|
"""Test Kaiming uniform initializer for convolutions."""
|
|
self.static_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingUniform(),
|
|
dtype="float32",
|
|
uniform=True,
|
|
is_conv=True,
|
|
)
|
|
|
|
def test_kaiming_normal_initializer_static(self):
|
|
"""Test Kaiming normal initializer for matrix multiply."""
|
|
self.static_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingNormal(),
|
|
dtype="float32",
|
|
uniform=False,
|
|
is_conv=False,
|
|
)
|
|
|
|
def test_kaiming_normal_initializer_conv_static(self):
|
|
"""Test Kaiming normal initializer for convolutions."""
|
|
self.static_test_kaiming_initializer_common(
|
|
init_inst=initializer.KaimingNormal(),
|
|
dtype="float32",
|
|
uniform=False,
|
|
is_conv=True,
|
|
)
|
|
|
|
|
|
class TestUniform(unittest.TestCase):
|
|
def test_uniform_common(self, dtype="float32", seed=0):
|
|
"""Test the uniform initializer with default value"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
program.random_seed = seed
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Uniform(),
|
|
)
|
|
num_ops = 2 if dtype == "float16" else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
self.assertAlmostEqual(init_op.attr('min'), -1.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), 1.0, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), seed)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_uniform_initializer_default_value(
|
|
self, dtype="float32", seed=0, min_value=-1.0, max_value=1.0
|
|
):
|
|
"""Test the uniform initializer with default value"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
program.random_seed = seed
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Uniform(),
|
|
)
|
|
num_ops = 2 if dtype == "float16" else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), max_value, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), seed)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_uniform_initializer(
|
|
self, dtype="float32", seed=0, min_value=-4.2, max_value=3.1
|
|
):
|
|
"""Test uniform initializer with supplied attributes"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
program.random_seed = seed
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Uniform(min_value, max_value),
|
|
)
|
|
num_ops = 2 if dtype == "float16" else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), max_value, delta=DELTA)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_uniform_initializer_two_op(
|
|
self, dtype="float32", seed=123, min_value=-4.2, max_value=0.0
|
|
):
|
|
"""Test uniform initializer with supplied attributes"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
program.random_seed = seed
|
|
block = program.global_block()
|
|
for i in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Uniform(min_value, float(i)),
|
|
)
|
|
num_ops = 2 if dtype == "float16" else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op0 = block.ops[0]
|
|
self.assertEqual(init_op0.type, 'uniform_random')
|
|
self.assertAlmostEqual(init_op0.attr('min'), min_value, delta=DELTA)
|
|
self.assertAlmostEqual(init_op0.attr('max'), 0.0, delta=DELTA)
|
|
self.assertEqual(init_op0.attr("seed"), seed)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_uniform_initializer_fp16(self):
|
|
"""Test uniform initializer with float16"""
|
|
block = self.test_uniform_initializer_default_value("float16")
|
|
self.assertTrue(check_cast_op(block.ops[1]))
|
|
block = self.test_uniform_initializer(dtype="float16")
|
|
self.assertTrue(check_cast_op(block.ops[1]))
|
|
block = self.test_uniform_initializer_two_op("float16")
|
|
self.assertTrue(check_cast_op(block.ops[1]))
|
|
|
|
def test_uniform_initializer_bf16(self):
|
|
"""Test uniform initializer with bfloat16"""
|
|
block = self.test_uniform_initializer_default_value(
|
|
"uint16"
|
|
) # bfloat16
|
|
block = self.test_uniform_initializer(dtype="uint16") # bfloat16
|
|
block = self.test_uniform_initializer_two_op("uint16") # bfloat16
|
|
|
|
def test_uniform_initializer_dygraph(self):
|
|
"""Test uniform initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5),
|
|
)
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
min_value, max_value = get_uniform_min_and_max(linear.weight.numpy())
|
|
self.assertTrue(
|
|
min_value >= -0.5, f'min value {min_value} should >= -0.5'
|
|
)
|
|
self.assertTrue(
|
|
max_value <= 0.5, f'max value {max_value} should <= 0.5'
|
|
)
|
|
|
|
|
|
class TestNormal(unittest.TestCase):
|
|
def test_normal_initializer_default_value(self):
|
|
"""Test the normal initializer with default value"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Normal(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
paddle.disable_static()
|
|
|
|
def test_normal_initializer(self, dtype="float32"):
|
|
"""Test normal initializer with supplied attributes"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Normal(2.3, 1.9),
|
|
)
|
|
num_ops = 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_normal_initializer_complex(self, dtype="complex64"):
|
|
"""Test normal initializer with complex dtype"""
|
|
with paddle.pir_utils.OldIrGuard():
|
|
with static_guard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.Normal(2.3 + 2.3j, 1.9),
|
|
)
|
|
num_ops = 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
|
|
|
|
return block
|
|
|
|
def test_normal_initializer_fp16(self):
|
|
"""Test normal initializer with float16"""
|
|
block = self.test_normal_initializer("float16")
|
|
|
|
def test_normal_initializer_bf16(self):
|
|
"""Test normal initializer with bfloat16"""
|
|
block = self.test_normal_initializer("uint16") # bfloat16
|
|
|
|
def test_normal_initializer_complex64(self):
|
|
"""Test normal initializer with complex64"""
|
|
block = self.test_normal_initializer_complex("complex64")
|
|
|
|
def test_normal_initializer_complex128(self):
|
|
"""Test normal initializer with complex128"""
|
|
block = self.test_normal_initializer_complex("complex128")
|
|
|
|
def test_normal_initializer_dygraph(self):
|
|
"""Test normal initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0),
|
|
)
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
def test_normal_initializer_complex_error(self):
|
|
with self.assertRaises(ValueError):
|
|
paddle.nn.initializer.Normal(mean=0.1 + 0.2j, std=2.0)
|
|
|
|
|
|
class TestTruncatedNormal(unittest.TestCase):
|
|
def test_truncated_normal_initializer_default_value(self):
|
|
"""Test the truncated normal initializer with default value"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.TruncatedNormal(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'truncated_gaussian_random')
|
|
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
self.assertAlmostEqual(init_op.attr('a'), -2.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('b'), 2.0, delta=DELTA)
|
|
|
|
paddle.disable_static()
|
|
|
|
def test_truncated_normal_initializer(self, dtype="float32"):
|
|
"""Test truncated normal initializer with supplied attributes"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=dtype,
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.TruncatedNormal(2.3, 1.9),
|
|
)
|
|
num_ops = 2 if dtype in ["float16", "uint16"] else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'truncated_gaussian_random')
|
|
self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
|
|
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_truncated_normal_initializer_fp16(self):
|
|
"""Test truncated normal initializer with float16"""
|
|
paddle.enable_static()
|
|
|
|
block = self.test_truncated_normal_initializer("float16")
|
|
self.assertTrue(check_cast_op(block.ops[1]))
|
|
|
|
def test_truncated_normal_initializer_bf16(self):
|
|
"""Test truncated normal initializer with bfloat16"""
|
|
paddle.enable_static()
|
|
|
|
block = self.test_truncated_normal_initializer("uint16") # bfloat16
|
|
self.assertTrue(check_cast_op(block.ops[1]))
|
|
|
|
def test_truncated_normal_initializer_fp64(self):
|
|
"""Test truncated normal initializer with float64"""
|
|
with static_guard():
|
|
# Only test whether float64 data can be generated without error
|
|
_ = self.test_truncated_normal_initializer("float64") # float64
|
|
|
|
def test_truncated_normal_initializer_dygraph(self):
|
|
"""Test truncated normal initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.TruncatedNormal(
|
|
mean=0.0, std=2.0
|
|
),
|
|
)
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
def test_truncated_normal_initializer_cutoff(self):
|
|
"""Test truncated normal initializer with cutoff"""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.TruncatedNormal(
|
|
mean=0.0, std=1.0, a=-10.0, b=10.0
|
|
),
|
|
)
|
|
linear = paddle.nn.Linear(100, 500, weight_attr=weight_attr)
|
|
result = (linear.weight >= -10.0).all() * (linear.weight <= 10.0).all()
|
|
np.testing.assert_equal(result.numpy(), np.array(True))
|
|
|
|
def test_truncated_normal_initializer_cutoff2(self):
|
|
"""Test truncated normal initializer with cutoff"""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight2",
|
|
initializer=paddle.nn.initializer.TruncatedNormal(
|
|
mean=0.0, std=1.0, a=-1.0, b=1.0
|
|
),
|
|
)
|
|
linear = paddle.nn.Linear(100, 500, weight_attr=weight_attr)
|
|
result = (linear.weight >= -1.0).all() * (linear.weight <= 1.0).all()
|
|
np.testing.assert_equal(result.numpy(), np.array(True))
|
|
|
|
|
|
class TestXavierUniform(unittest.TestCase):
|
|
def test_xavier_uniform_initializer(self):
|
|
"""Test Xavier initializer with uniform distribution on
|
|
for matrix multiply.
|
|
"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
param = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.XavierUniform(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
limit = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
|
|
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
paddle.disable_static()
|
|
|
|
def test_xavier_uniform_initializer_conv(self):
|
|
"""Test Xavier initializer with uniform distribution on
|
|
for convolutions.
|
|
"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
param = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10, 15, 20],
|
|
name="param",
|
|
initializer=initializer.XavierUniform(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'uniform_random')
|
|
receptive_field_size = float(15 * 20)
|
|
limit = np.sqrt(
|
|
6.0 / ((param.shape[0] + param.shape[1]) * receptive_field_size)
|
|
)
|
|
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
def test_xavier_uniform_initializer_dygraph(self):
|
|
"""Test xavier uniform initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.XavierUniform(),
|
|
)
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
|
|
class TestXavierNormal(unittest.TestCase):
|
|
def test_xavier_normal_initializer(self):
|
|
"""Test Xavier initializer with normal distribution on
|
|
for matrix multiply.
|
|
"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
param = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
name="param",
|
|
initializer=initializer.XavierNormal(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
std = np.sqrt(2.0 / (param.shape[0] + param.shape[1]))
|
|
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
paddle.disable_static()
|
|
|
|
def test_xavier_normal_initializer_conv(self):
|
|
"""Test Xavier initializer with normal distribution on
|
|
for convolutions.
|
|
"""
|
|
paddle.enable_static()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
for _ in range(2):
|
|
param = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10, 15, 20],
|
|
name="param",
|
|
initializer=initializer.XavierNormal(),
|
|
)
|
|
self.assertEqual(len(block.ops), 1)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'gaussian_random')
|
|
receptive_field_size = float(15 * 20)
|
|
std = np.sqrt(
|
|
2.0 / ((param.shape[0] + param.shape[1]) * receptive_field_size)
|
|
)
|
|
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
|
|
self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
|
|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
paddle.disable_static()
|
|
|
|
def test_xavier_normal_initializer_dygraph(self):
|
|
"""Test xavier normal initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
|
|
initializer=paddle.nn.initializer.XavierNormal(),
|
|
)
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
|
|
class TestAssign(unittest.TestCase):
|
|
def test_assign_initializer(self, dtype="float32"):
|
|
"""Test the numpy array initializer with supplied arguments"""
|
|
paddle.enable_static()
|
|
|
|
import numpy
|
|
|
|
with paddle.pir_utils.OldIrGuard():
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
np_array = numpy.random.random(10000).astype(dtype)
|
|
for _ in range(2):
|
|
block.create_parameter(
|
|
dtype=np_array.dtype,
|
|
shape=np_array.shape,
|
|
name="param",
|
|
initializer=initializer.Assign(np_array),
|
|
)
|
|
num_ops = 2 if dtype in ["float16", "uint16"] else 1
|
|
self.assertEqual(len(block.ops), num_ops)
|
|
init_op = block.ops[0]
|
|
self.assertEqual(init_op.type, 'assign_value')
|
|
values = framework.extract_plain_list(init_op.attr('values'))
|
|
assert values == np_array.ravel().tolist()
|
|
paddle.disable_static()
|
|
|
|
return block
|
|
|
|
def test_assign_initializer_fp16(self):
|
|
"""Test the numpy array initializer with float16"""
|
|
block = self.test_assign_initializer("float16")
|
|
self.assertTrue(block.ops[1])
|
|
|
|
def test_assign_initializer_bf16(self):
|
|
"""Test the numpy array initializer with bfloat16"""
|
|
block = self.test_assign_initializer("uint16") # bfloat16
|
|
self.assertTrue(block.ops[1])
|
|
|
|
def test_assign_initializer_dygraph_1(self):
|
|
"""Test assign initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr_1 = paddle.framework.ParamAttr(
|
|
name="linear_weight_1",
|
|
initializer=paddle.nn.initializer.Assign(np.array([2, 2])),
|
|
)
|
|
linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1)
|
|
|
|
self.assertTrue((linear_1.weight.numpy() == [2.0, 2.0]).all(), '')
|
|
|
|
def test_assign_initializer_dygraph_2(self):
|
|
"""Test assign initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr_2 = paddle.framework.ParamAttr(
|
|
name="linear_weight_2",
|
|
initializer=paddle.nn.initializer.Assign([2, 2]),
|
|
)
|
|
linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2)
|
|
|
|
self.assertTrue((linear_2.weight.numpy() == [2.0, 2.0]).all(), '')
|
|
|
|
def test_assign_initializer_dygraph_3(self):
|
|
"""Test assign initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr_3 = paddle.framework.ParamAttr(
|
|
name="linear_weight_3",
|
|
initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)),
|
|
)
|
|
linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3)
|
|
|
|
self.assertTrue((linear_3.weight.numpy() == [2.0, 2.0]).all(), '')
|
|
|
|
def test_assign_initializer_dygraph_4(self):
|
|
"""Test assign initializer in dygraph model."""
|
|
paddle.disable_static()
|
|
|
|
weight_attr_4 = paddle.framework.ParamAttr(
|
|
name="linear_weight_4",
|
|
initializer=paddle.nn.initializer.Assign((2, 2)),
|
|
)
|
|
linear_4 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_4)
|
|
|
|
self.assertTrue((linear_4.weight.numpy() == [2.0, 2.0]).all(), '')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|