forked from mindspore-Ecosystem/mindspore
309 lines
12 KiB
Python
309 lines
12 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ============================================================================
|
|
"""test flatten tensors"""
|
|
import numpy as np
|
|
import mindspore as ms
|
|
import mindspore.common.initializer as init
|
|
from mindspore._c_expression import Tensor as Tensor_
|
|
from mindspore.common import Tensor, Parameter
|
|
from mindspore.nn import Cell
|
|
from mindspore import context
|
|
|
|
|
|
def test_flatten_tensors_basic():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Basic function for flatten tensors.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor(np.ones([2], np.float32))
|
|
t2 = Tensor(np.ones([2, 2], np.float32))
|
|
t3 = Tensor(np.ones([2, 2, 2], np.float32))
|
|
# Before flatten.
|
|
assert not Tensor._is_flattened([t1, t2, t3]) # pylint: disable=W0212
|
|
assert not Tensor._get_flattened_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
# Do flatten.
|
|
chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
# After flatten.
|
|
assert len(chunks) == 1
|
|
assert Tensor._is_flattened([t1, t2, t3]) # pylint: disable=W0212
|
|
assert chunks[0].dtype == ms.float32
|
|
assert chunks[0].shape == [14]
|
|
assert np.allclose(chunks[0].asnumpy(), np.ones([14], np.float32))
|
|
# Get flattened tensors.
|
|
chunks2 = Tensor._get_flattened_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
assert chunks == chunks2
|
|
fusion_size = Tensor._get_fusion_size(chunks2) # pylint: disable=W0212
|
|
assert fusion_size == 0
|
|
|
|
|
|
def test_flatten_tensors_order():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Test flatten tensors in order.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor([1], ms.float32)
|
|
t2 = Tensor([2], ms.float32)
|
|
t3 = Tensor([3], ms.float32)
|
|
chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
|
|
chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
|
|
|
|
|
|
def test_flatten_tensors_float16():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Test flatten tensors for float16.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor([1], ms.float16)
|
|
t2 = Tensor([2], ms.float16)
|
|
t3 = Tensor([3], ms.float16)
|
|
chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
|
|
chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
|
|
|
|
|
|
def test_flatten_tensors_scalar():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Test flatten tensors for scalar tensor.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor(1)
|
|
t2 = Tensor(2)
|
|
t3 = Tensor(3)
|
|
chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
|
|
chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
|
|
assert len(chunks) == 1
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
|
|
|
|
|
|
def test_flatten_tensors_dtypes():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten tensors group by data types.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor(np.ones([2], np.float32))
|
|
t2 = Tensor(np.ones([2, 2], np.float32))
|
|
t3 = Tensor(np.ones([2, 2, 2], np.float32))
|
|
t4 = Tensor(np.ones([3, 3], np.float64))
|
|
t5 = Tensor(np.ones([3, 3, 3], np.float64))
|
|
chunks = Tensor._flatten_tensors([t1, t2, t3, t4, t5]) # pylint: disable=W0212
|
|
assert len(chunks) == 2
|
|
assert chunks[0].dtype == ms.float32
|
|
assert chunks[0].shape == [14]
|
|
assert np.allclose(chunks[0].asnumpy(), np.ones([14], np.float32))
|
|
assert chunks[1].dtype == ms.float64
|
|
assert chunks[1].shape == [36]
|
|
assert np.allclose(chunks[1].asnumpy(), np.ones([36], np.float64))
|
|
# Different order.
|
|
chunks1 = Tensor._flatten_tensors([t4, t1, t2, t5, t3]) # pylint: disable=W0212
|
|
assert np.allclose(chunks[0].asnumpy(), chunks1[0].asnumpy())
|
|
|
|
|
|
def test_cell_flatten_weights():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten weights for Cell.
|
|
Expectation: Flatten weights works as expected.
|
|
"""
|
|
class MyCell(Cell):
|
|
def __init__(self):
|
|
super(MyCell, self).__init__()
|
|
self.para1 = Parameter(Tensor([1, 2], ms.float32))
|
|
self.para2 = Parameter(Tensor([3, 4, 5], ms.float32))
|
|
self.para3 = Parameter(Tensor([6], ms.float32))
|
|
|
|
def construct(self, x):
|
|
return x
|
|
|
|
net = MyCell()
|
|
assert not Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
net.flatten_weights()
|
|
assert Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
chunks = Parameter._get_flattened_tensors(net.trainable_params()) # pylint: disable=W0212
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3, 4, 5, 6]))
|
|
|
|
|
|
def test_cell_flatten_weights_with_init():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten weights for Cell with parameter initializer.
|
|
Expectation: Flatten weights works as expected.
|
|
"""
|
|
class MyCell(Cell):
|
|
def __init__(self):
|
|
super(MyCell, self).__init__()
|
|
self.para1 = Parameter(Tensor([1, 2], ms.float32))
|
|
self.para2 = Parameter(init.initializer('ones', [3], ms.float32))
|
|
self.para3 = Parameter(Tensor([6], ms.float32))
|
|
|
|
def construct(self, x):
|
|
return x
|
|
|
|
net = MyCell()
|
|
assert not Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
net.flatten_weights()
|
|
assert Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
chunks = Parameter._get_flattened_tensors(net.trainable_params()) # pylint: disable=W0212
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 1, 1, 1, 6]))
|
|
|
|
|
|
def test_flatten_tensors_with_fusion_size_1():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten f32 tensors with fusion size.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor(np.ones([1], np.float32))
|
|
t2 = Tensor(np.ones([2], np.float32))
|
|
t3 = Tensor(np.ones([3], np.float32))
|
|
t4 = Tensor(np.ones([4], np.float32))
|
|
tensor_list = [t1, t2, t3, t4]
|
|
# Do flatten.
|
|
chunks = Tensor._flatten_tensors(tensor_list, 4 * 4) # pylint: disable=W0212
|
|
# After flatten.
|
|
assert len(chunks) == 3
|
|
assert Tensor._is_flattened(tensor_list) # pylint: disable=W0212
|
|
assert chunks[0].dtype == ms.float32
|
|
assert chunks[0].shape == [3]
|
|
assert chunks[1].shape == [3]
|
|
assert chunks[2].shape == [4]
|
|
assert np.allclose(chunks[0].asnumpy(), np.ones([3], np.float32))
|
|
assert np.allclose(chunks[1].asnumpy(), np.ones([3], np.float32))
|
|
assert np.allclose(chunks[2].asnumpy(), np.ones([4], np.float32))
|
|
# Get flattened tensors.
|
|
chunks2 = Tensor._get_flattened_tensors(tensor_list) # pylint: disable=W0212
|
|
assert chunks == chunks2
|
|
# Get fusion size from flattened tensors.
|
|
fusion_size = Tensor._get_fusion_size(chunks2) # pylint: disable=W0212
|
|
assert fusion_size == (4 * 4)
|
|
|
|
|
|
def test_flatten_tensors_with_fusion_size_2():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten f32 and f16 tensors with fusion size.
|
|
Expectation: Flatten tensor works as expected.
|
|
"""
|
|
t1 = Tensor(np.ones([1], np.float32))
|
|
t2 = Tensor(np.ones([2], np.float32))
|
|
t3 = Tensor(np.ones([3], np.float32))
|
|
t4 = Tensor(np.ones([4], np.float32))
|
|
t10 = Tensor(np.ones([1], np.float16))
|
|
t20 = Tensor(np.ones([2], np.float16))
|
|
t30 = Tensor(np.ones([3], np.float16))
|
|
t40 = Tensor(np.ones([4], np.float16))
|
|
tensor_list = [t1, t2, t3, t4, t10, t20, t30, t40]
|
|
# Do flatten.
|
|
chunks = Tensor._flatten_tensors(tensor_list, 20) # pylint: disable=W0212
|
|
# After flatten.
|
|
assert len(chunks) == 4
|
|
assert Tensor._is_flattened(tensor_list) # pylint: disable=W0212
|
|
|
|
assert chunks[0].dtype == ms.float16
|
|
assert chunks[1].dtype == ms.float32
|
|
assert chunks[2].dtype == ms.float32
|
|
assert chunks[3].dtype == ms.float32
|
|
|
|
assert chunks[0].shape == [10] # f16: 1 + 2 + 3 + 4
|
|
assert chunks[1].shape == [3] # f32: 1 + 2
|
|
assert chunks[2].shape == [3] # f32: 3
|
|
assert chunks[3].shape == [4] # f32: 4
|
|
|
|
assert np.allclose(chunks[0].asnumpy(), np.ones([10], np.float16))
|
|
assert np.allclose(chunks[1].asnumpy(), np.ones([3], np.float32))
|
|
assert np.allclose(chunks[2].asnumpy(), np.ones([3], np.float32))
|
|
assert np.allclose(chunks[3].asnumpy(), np.ones([4], np.float32))
|
|
|
|
# Get flattened tensors.
|
|
chunks2 = Tensor._get_flattened_tensors(tensor_list) # pylint: disable=W0212
|
|
assert chunks == chunks2
|
|
# Get fusion size from flattened tensors.
|
|
fusion_size = Tensor._get_fusion_size(chunks2) # pylint: disable=W0212
|
|
assert fusion_size == (20)
|
|
|
|
|
|
def test_cell_flatten_weights_with_fusion_size():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Flatten weights for Cell with fusion size.
|
|
Expectation: Flatten weights works as expected.
|
|
"""
|
|
class MyCell(Cell):
|
|
def __init__(self):
|
|
super(MyCell, self).__init__()
|
|
self.para1 = Parameter(Tensor([1, 2], ms.float32))
|
|
self.para2 = Parameter(Tensor([3, 4, 5], ms.float32))
|
|
self.para3 = Parameter(Tensor([6], ms.float32))
|
|
self.para4 = Parameter(Tensor([7], ms.float32))
|
|
self.para5 = Parameter(Tensor([8], ms.float32))
|
|
|
|
def construct(self, x):
|
|
return x
|
|
|
|
net = MyCell()
|
|
assert not Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
net.flatten_weights(fusion_size=12)
|
|
assert Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
|
|
chunks = Parameter._get_flattened_tensors(net.trainable_params()) # pylint: disable=W0212
|
|
assert len(chunks) == 3
|
|
assert np.allclose(chunks[0].asnumpy(), np.array([1, 2]))
|
|
assert np.allclose(chunks[1].asnumpy(), np.array([3, 4, 5]))
|
|
assert np.allclose(chunks[2].asnumpy(), np.array([6, 7, 8]))
|
|
fusion_size = Parameter._get_fusion_size(chunks) # pylint: disable=W0212
|
|
assert fusion_size == 12
|
|
|
|
|
|
def test_init_data_after_flatten_weights():
|
|
"""
|
|
Feature: Flatten tensors.
|
|
Description: Init tensor data after flatten weights.
|
|
Expectation: Tensor data initialized as expected.
|
|
"""
|
|
class MyCell(Cell):
|
|
def __init__(self):
|
|
super(MyCell, self).__init__()
|
|
self.para1 = Parameter(Tensor([1, 2], ms.float32))
|
|
self.para2 = Parameter(init.initializer('ones', [3], ms.float32))
|
|
self.para3 = Parameter(Tensor([6], ms.float32))
|
|
|
|
def construct(self, x):
|
|
return x
|
|
|
|
# Set 'auto_parallel' to enable tensor data lazy initialization.
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
net = MyCell()
|
|
net.flatten_weights()
|
|
assert net.para2.init is not None
|
|
data = Tensor_.asnumpy(net.para2)
|
|
data.fill(2)
|
|
assert np.allclose(data, 2)
|
|
net.para2.init_data()
|
|
data = Tensor_.asnumpy(net.para2)
|
|
assert np.allclose(data, 1)
|
|
context.set_auto_parallel_context(parallel_mode="stand_alone")
|