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未验证 提交 b8814777 编写于 作者: W wangxiaoning 提交者： GitHub
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Fluid clean (#48841) 

* add index sample fp16 support

* remove fluid APIs in distributed_strategy.py and role_maker.py

* Revert "remove fluid APIs in distributed_strategy.py and role_maker.py"

This reverts commit 223bbee990d3bf69e252fc3c0f19e3873550a264.

* remove fluid APIs in distributed_strategy.py and role_maker.py

* remove index sample op changes

* remove fluid APIs under fleet.base

* remove fluid APIs under fleet.layers.mpu

* remove fluid APIs under fleet.meta_optimizers

* fix fluid error

* fix util_factory.py

* reset fluid.io.load_inference_model API

* remove dygraph.parallel.prepare_context

* remove fluid.dygraph.StaticModelRunner API

* remove split_lod_tensor merge_lod_tensor

* remove unittests
上级 1e0f8734
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Showing with 4 addition and 1282 deletion
python/paddle/fluid/dygraph/__init__.py
浏览文件 @ b8814777
......@@ -36,9 +36,6 @@ from .checkpoint import *
from . import learning_rate_scheduler
from .learning_rate_scheduler import *
from . import static_runner
from .static_runner import StaticModelRunner
from . import amp
from .amp import *
......
python/paddle/fluid/dygraph/parallel.py
浏览文件 @ b8814777
......@@ -37,51 +37,11 @@ from paddle.fluid.framework import (
in_dygraph_mode,
)
__all__ = ["prepare_context", "ParallelEnv", "DataParallel"]
__all__ = ["ParallelEnv", "DataParallel"]
ParallelStrategy = core.ParallelStrategy
@deprecated(since="2.0.0", update_to="paddle.distributed.init_parallel_env")
def prepare_context(strategy=None):
'''
:api_attr: imperative
'''
if strategy is None:
strategy = ParallelStrategy()
strategy.nranks = Env().nranks
strategy.local_rank = Env().local_rank
strategy.trainer_endpoints = Env().trainer_endpoints
strategy.current_endpoint = Env().current_endpoint
if strategy.nranks < 2:
return
assert (
framework._non_static_mode() is True
), "dygraph.prepare_context should be used with dygraph mode."
place = framework._current_expected_place()
assert (
place is not None
), "dygraph.prepare_context should be used in fluid.dygraph.guard(place) guard."
if not parallel_helper._is_parallel_ctx_initialized():
if isinstance(place, core.CUDAPlace):
parallel_helper._set_parallel_ctx(
core.NCCLParallelContext(strategy, place)
)
elif isinstance(place, core.XPUPlace):
parallel_helper._set_parallel_ctx(
core.BKCLParallelContext(strategy, place)
)
elif isinstance(place, core.NPUPlace):
parallel_helper._set_parallel_ctx(
core.HCCLParallelContext(strategy, place)
)
else:
# TODO(Yancey1989): add Gloo Parallel Context to support CPU parallel computation
assert "Only support CUDAPlace or XPUPlace or NPUPlace for now."
parallel_helper._init_parallel_ctx()
return strategy
class ParallelEnv:
"""
.. note::
......
python/paddle/fluid/dygraph/static_runner.py 已删除 100644 → 0
浏览文件 @ 1e0f8734
# 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.
from paddle.jit.api import _SaveLoadConfig
from paddle.jit.translated_layer import TranslatedLayer
# NOTE: This class will be deprecated later.
# It is kept here because PaddleHub is already using this API.
class StaticModelRunner:
"""
A Dynamic graph Layer for loading inference program and related parameters,
and then performing fine-tune training or inference.
.. note::
This is a temporary API, which will be deprecated later, please use
`fluid.dygraph.jit.load` to achieve the same function.
"""
def __new__(cls, model_dir, model_filename=None, params_filename=None):
configs = _SaveLoadConfig()
if model_filename is not None:
configs.model_filename = model_filename
if params_filename is not None:
configs.params_filename = params_filename
return TranslatedLayer._construct(model_dir, configs)
python/paddle/fluid/layers/control_flow.py
浏览文件 @ b8814777
......@@ -145,130 +145,6 @@ def select_input(inputs, mask):
return out
def split_lod_tensor(input, mask, level=0):
"""
This function takes in an input that contains the complete lod information,
and takes in a mask which is used to mask certain parts of the input.
The output is the true branch and the false branch with the mask applied to
the input at a certain level in the tensor. Mainly used in IfElse to split
data into two parts.
Args:
input(Variable|tuple|list|None): The input tensor that contains complete
lod information needed to construct the output.
mask(Variable|list): A bool column vector which masks the input.
level(int): The specific lod level to split.
Returns:
tuple(Variable, Variable):
The true branch of tensor as per the mask applied to input.
The false branch of tensor as per the mask applied to input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[1])
x.persistable = True
y = fluid.layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = fluid.layers.split_lod_tensor(
input=x, mask=y, level=level)
"""
check_type(
input,
'input',
(Variable, list, tuple, type(None)),
'fluid.layers.split_lod_tensor',
)
check_type(mask, 'mask', (Variable, list), 'fluid.layers.split_lod_tensor')
check_type(level, 'level', int, 'fluid.layers.split_lod_tensor')
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
'X': input,
'Mask': mask,
},
outputs={'OutTrue': out_true, 'OutFalse': out_false},
attrs={'level': level},
)
return out_true, out_false
def merge_lod_tensor(in_true, in_false, x, mask, level=0):
"""
**merge_lod_tensor**
This function takes in an input :math:`x`, the True branch, the False
branch and a binary :math:`mask`. Using this information, this function
merges the True and False branches of the tensor into a single tensor as
output at a certain lod level indicated by :math:`level`. Used in IfElse
to merge the output if True block and False Block.
Args:
in_true(Variable|tuple|list|None): The True branch to be merged.
in_false(Variable|tuple|list|None): The False branch to be merged.
x(Variable|tuple|list|None): The input tensor that contains complete
lod information needed to construct the output.
mask(Variable|list): A bool column vector which masks the input.
level(int): The specific lod level to merge.
Returns:
Variable: The merged output tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False)
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out = layers.merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
"""
helper = LayerHelper('merge_lod_tensor', **locals())
check_type(
x,
'x',
(Variable, list, tuple, type(None)),
'fluid.layers.merge_lod_tensor',
)
check_type(mask, 'mask', (Variable, list), 'fluid.layers.merge_lod_tensor')
check_type(
in_true,
'in_true',
(Variable, list, tuple, type(None)),
'fluid.layers.merge_lod_tensor',
)
check_type(
in_false,
'in_false',
(Variable, list, tuple, type(None)),
'fluid.layers.merge_lod_tensor',
)
out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x, 'Mask': mask, 'InTrue': in_true, 'InFalse': in_false},
outputs={'Out': out},
attrs={'level': level},
)
return out
@static_only
def Print(
input,
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ b8814777
......@@ -449,8 +449,6 @@ list(REMOVE_ITEM TEST_OPS test_basic_lstm_unit_op)
list(REMOVE_ITEM TEST_OPS test_fuse_all_reduce_pass)
list(REMOVE_ITEM TEST_OPS test_fuse_bn_act_pass)
list(REMOVE_ITEM TEST_OPS test_fuse_bn_add_act_pass)
list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_mnist)
list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_while)
# disable this unittest temporarily
list(REMOVE_ITEM TEST_OPS test_imperative_data_loader_exception)
......@@ -569,12 +567,6 @@ py_test_modules(
py_test_modules(test_install_check MODULES test_install_check ENVS
FLAGS_cudnn_deterministic=1)
set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST")
py_test_modules(
test_imperative_static_runner_mnist MODULES
test_imperative_static_runner_mnist ENVS FLAGS_cudnn_deterministic=1)
py_test_modules(
test_imperative_static_runner_while MODULES
test_imperative_static_runner_while ENVS FLAGS_cudnn_deterministic=1)
if((WITH_GPU) AND (CUDA_VERSION GREATER_EQUAL 11.6))
py_test_modules(test_fused_gemm_epilogue_op MODULES
......@@ -1084,7 +1076,6 @@ set_tests_properties(test_svd_op PROPERTIES TIMEOUT 80)
set_tests_properties(test_einsum_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_qr_op PROPERTIES TIMEOUT 60)
set_tests_properties(test_trilinear_interp_v2_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_imperative_static_runner_mnist PROPERTIES TIMEOUT 120)
set_tests_properties(test_masked_select_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_sigmoid_cross_entropy_with_logits_op
PROPERTIES TIMEOUT 120)
......@@ -1167,8 +1158,6 @@ if(APPLE)
PROPERTIES TIMEOUT 300)
set_tests_properties(test_multiclass_nms_op PROPERTIES TIMEOUT 300)
set_tests_properties(test_weight_decay PROPERTIES TIMEOUT 300)
set_tests_properties(test_imperative_static_runner_mnist PROPERTIES TIMEOUT
300)
endif()
if((WITH_ROCM OR WITH_GPU) AND NOT WIN32)
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ b8814777
......@@ -676,7 +676,6 @@ class TestParallelDyGraphRunnerBase:
type(self).__name__,
"begin to prepare context in dygraph with nccl2",
)
dygraph.parallel.prepare_context(strategy)
if not args.find_unused_parameters:
model = dygraph.parallel.DataParallel(
model, strategy, find_unused_parameters=False
......
python/paddle/fluid/tests/unittests/test_imperative_data_parallel.py
浏览文件 @ b8814777
......@@ -19,6 +19,7 @@ import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.dygraph as dygraph
from paddle.distributed import init_parallel_env
from paddle.nn import Linear
......@@ -38,9 +39,9 @@ class MLP(fluid.Layer):
class TestDataParallelStateDict(unittest.TestCase):
def test_data_parallel_state_dict(self):
with fluid.dygraph.guard():
strategy = dygraph.parallel.prepare_context()
init_parallel_env()
mlp = MLP()
parallel_mlp = dygraph.parallel.DataParallel(mlp, strategy)
parallel_mlp = dygraph.parallel.DataParallel(mlp)
single_state = mlp.state_dict()
parallel_state = parallel_mlp.state_dict()
......
python/paddle/fluid/tests/unittests/test_imperative_static_runner_mnist.py 已删除 100644 → 0
浏览文件 @ 1e0f8734
# 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 test_imperative_base import new_program_scope
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
LOADED_VAR_SUFFIX = ".load_0"
def convolutional_neural_network(img):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu",
)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu",
)
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return prediction
def static_train_net(img, label):
prediction = convolutional_neural_network(img)
loss = paddle.nn.functional.cross_entropy(
input=prediction, label=label, reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimizer.minimize(avg_loss)
return prediction, avg_loss
class TestImperativeStaticModelRunnerMnist(unittest.TestCase):
def setUp(self):
self.seed = 90
self.epoch_num = 1
self.batch_size = 128
self.batch_num = 50
def reader_decorator(self, reader):
def _reader_impl():
for item in reader():
image = np.array(item[0]).reshape(1, 28, 28)
label = np.array(item[1]).astype('int64').reshape(1)
yield image, label
return _reader_impl
def train_and_save_model(self):
with new_program_scope():
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
img = fluid.data(
name='img', shape=[None, 1, 28, 28], dtype='float32'
)
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
prediction, avg_loss = static_train_net(img, label)
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(startup_program)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=100
),
batch_size=self.batch_size,
)
for _ in range(0, self.epoch_num):
for batch_id, data in enumerate(train_reader()):
exe.run(
main_program,
feed=feeder.feed(data),
fetch_list=[avg_loss],
)
if batch_id > self.batch_num:
break
fluid.io.save_inference_model(
self.save_dirname,
["img"],
[prediction],
exe,
model_filename=self.model_filename,
params_filename=self.params_filename,
clip_extra=False,
)
def load_and_train_dygraph(self):
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
with fluid.dygraph.guard(place):
fluid.default_startup_program().random_seed = self.seed
fluid.default_main_program().random_seed = self.seed
fluid.set_flags({'FLAGS_sort_sum_gradient': True})
mnist = fluid.dygraph.static_runner.StaticModelRunner(
model_dir=self.save_dirname,
model_filename=self.model_filename,
params_filename=self.params_filename,
)
suffix_varname_dict = mnist._program_holder_dict[
'forward'
]._suffix_varname_dict
dict_old_new = {v: k for k, v in suffix_varname_dict.items()}
dy_param_init_value = {}
for param in mnist.parameters():
dy_param_init_value[param.name] = param.numpy()
sgd = fluid.optimizer.SGD(
learning_rate=0.001, parameter_list=mnist.parameters()
)
train_reader = paddle.batch(
self.reader_decorator(paddle.dataset.mnist.train()),
batch_size=self.batch_size,
drop_last=True,
)
train_loader = fluid.io.DataLoader.from_generator(capacity=10)
train_loader.set_sample_list_generator(train_reader, places=place)
mnist.train()
for epoch in range(self.epoch_num):
for batch_id, data in enumerate(train_loader()):
img = data[0]
label = data[1]
label.stop_gradient = True
cost = mnist(img)
loss = paddle.nn.functional.cross_entropy(
cost, label, reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
avg_loss.backward()
sgd.minimize(avg_loss)
mnist.clear_gradients()
if batch_id >= self.batch_num:
break
dy_x_data = img.numpy()
dy_out = avg_loss.numpy()
dy_param_value = {}
for param in mnist.parameters():
dy_param_value[param.name] = param.numpy()
return (
dy_x_data,
dy_out,
dy_param_init_value,
dy_param_value,
dict_old_new,
)
def load_and_train_static(self):
with new_program_scope():
fluid.default_startup_program().random_seed = self.seed
fluid.default_main_program().random_seed = self.seed
img = fluid.data(
name='img', shape=[None, 1, 28, 28], dtype='float32'
)
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
prediction, avg_loss = static_train_net(img, label)
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid.io.load_params(
exe,
self.save_dirname,
main_program=fluid.default_main_program(),
filename=self.params_filename,
)
static_param_init_value = {}
static_param_name_list = []
for param in fluid.default_main_program().all_parameters():
static_param_name_list.append(param.name)
static_param_init_value[param.name] = fluid.executor._fetch_var(
param.name
)
train_reader = paddle.batch(
self.reader_decorator(paddle.dataset.mnist.train()),
batch_size=self.batch_size,
drop_last=True,
)
for epoch in range(self.epoch_num):
for batch_id, data in enumerate(train_reader()):
static_x_data = np.array([x[0] for x in data])
y_data = np.array([x[1] for x in data]).reshape(
[self.batch_size, 1]
)
fetch_list = [avg_loss.name]
fetch_list.extend(static_param_name_list)
out = exe.run(
fluid.default_main_program(),
feed={"img": static_x_data, "label": y_data},
fetch_list=fetch_list,
)
if batch_id >= self.batch_num:
break
static_param_value = {}
static_out = out[0]
for i in range(1, len(out)):
static_param_value[static_param_name_list[i - 1]] = out[i]
return (
static_x_data,
static_out,
static_param_init_value,
static_param_value,
)
def load_and_infer_dygraph(self):
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
with fluid.dygraph.guard(place):
fluid.default_main_program().random_seed = self.seed
mnist = fluid.dygraph.static_runner.StaticModelRunner(
model_dir=self.save_dirname, model_filename=self.model_filename
)
train_reader = paddle.batch(
self.reader_decorator(paddle.dataset.mnist.test()),
batch_size=self.batch_size,
drop_last=True,
)
train_loader = fluid.io.DataLoader.from_generator(capacity=10)
train_loader.set_sample_list_generator(train_reader, places=place)
mnist.eval()
for batch_id, data in enumerate(train_loader()):
img = data[0]
cost = mnist(img)
if batch_id >= 1:
break
dy_x_data = img.numpy()
dy_out = cost.numpy()
return dy_x_data, dy_out
def load_and_infer_static(self):
with new_program_scope():
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
[
infer_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(self.save_dirname, exe)
infer_program.random_seed = self.seed
train_reader = paddle.batch(
self.reader_decorator(paddle.dataset.mnist.test()),
batch_size=self.batch_size,
drop_last=True,
)
for batch_id, data in enumerate(train_reader()):
static_x_data = np.array([x[0] for x in data])
out = exe.run(
infer_program,
feed={feed_target_names[0]: static_x_data},
fetch_list=fetch_targets,
)
if batch_id >= 1:
break
static_param_value = {}
static_out = out[0]
return static_x_data, static_out
def test_mnist_train_no_params_filename(self):
self.save_dirname = "mnist.inference.model.noname"
self.model_filename = None
self.params_filename = None
# Phase 1. run and save static model
self.train_and_save_model()
# Phase 2. load model & train dygraph
(
dy_x_data,
dy_out,
dy_param_init_value,
dy_param_value,
dict_old_new_init,
) = self.load_and_train_dygraph()
(
static_x_data,
static_out,
static_param_init_value,
static_param_value,
) = self.load_and_train_static()
# Phase 3. compare
np.testing.assert_array_equal(static_x_data, dy_x_data)
for key, value in static_param_init_value.items():
key = dict_old_new_init[key]
np.testing.assert_array_equal(value, dy_param_init_value[key])
# np.testing.assert_array_almost_equal(static_out, dy_out)
np.testing.assert_allclose(static_out, dy_out, rtol=1e-05, atol=1e-4)
for key, value in static_param_value.items():
key = dict_old_new_init[key]
np.testing.assert_allclose(
value, dy_param_value[key], rtol=1e-05, atol=1e-4
)
def test_mnist_train_with_params_filename(self):
self.save_dirname = "mnist.inference.model"
self.model_filename = "mnist.model"
self.params_filename = "mnist.params"
# Phase 1. run and save static model
self.train_and_save_model()
# Phase 2. load model & train dygraph
(
dy_x_data,
dy_out,
dy_param_init_value,
dy_param_value,
dict_old_new_init,
) = self.load_and_train_dygraph()
(
static_x_data,
static_out,
static_param_init_value,
static_param_value,
) = self.load_and_train_static()
# Phase 3. compare
np.testing.assert_array_equal(static_x_data, dy_x_data)
for key, value in static_param_init_value.items():
key = dict_old_new_init[key]
np.testing.assert_array_equal(value, dy_param_init_value[key])
# np.testing.assert_array_almost_equal(static_out, dy_out)
np.testing.assert_allclose(static_out, dy_out, rtol=1e-05, atol=1e-4)
for key, value in static_param_value.items():
key = dict_old_new_init[key]
np.testing.assert_allclose(
value, dy_param_value[key], rtol=1e-05, atol=1e-4
)
def test_mnist_infer_no_params_filename(self):
self.save_dirname = "mnist.inference.model.noname"
self.model_filename = None
self.params_filename = None
# Phase 1. run and save static model
self.train_and_save_model()
# Phase 2. load model & train dygraph
dy_x_data, dy_out = self.load_and_infer_dygraph()
static_x_data, static_out = self.load_and_infer_static()
# Phase 3. compare
np.testing.assert_array_equal(static_x_data, dy_x_data)
np.testing.assert_array_almost_equal(static_out, dy_out)
np.testing.assert_allclose(static_out, dy_out, rtol=1e-05, atol=1e-4)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative_static_runner_while.py 已删除 100644 → 0
浏览文件 @ 1e0f8734
# 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 jit_load_rename_var import rename_var_with_generator
from test_imperative_base import new_program_scope
import paddle
import paddle.fluid as fluid
from paddle.fluid import core, unique_name
LOADED_VAR_SUFFIX = ".load_0"
paddle.enable_static()
def while_softmax_regression(img):
def cond(i, times, pred):
return i < times
def body(i, times, pred):
pred = fluid.layers.fc(input=pred, size=10, act='softmax')
i = i + 1
return [i, times, pred]
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
times = fluid.layers.fill_constant(shape=[1], dtype='int64', value=5)
pred = fluid.layers.fc(input=img, size=10, act='softmax')
i, times, pred = paddle.static.nn.while_loop(
cond=cond, body=body, loop_vars=[i, times, pred]
)
return pred
class TestImperativeStaticModelRunnerWhile(unittest.TestCase):
def setUp(self):
self.seed = 90
self.batch_size = 32
self.batch_num = 50
self.save_dirname = "while.inference.model"
self.model_filename = None
self.params_filename = None
def _random_batch_reader(self):
def _get_random_images_and_labels(image_shape, label_shape):
image = np.random.random(size=image_shape).astype('float32')
label = np.random.random(size=label_shape).astype('int64')
return image, label
def __reader__():
for _ in range(self.batch_num):
batch_image, batch_label = _get_random_images_and_labels(
[self.batch_size, 784], [self.batch_size, 1]
)
yield batch_image, batch_label
return __reader__
def train_and_save_model(self):
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
img = fluid.data(name='img', shape=[None, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
pred = while_softmax_regression(img)
loss = paddle.nn.functional.cross_entropy(
input=pred, label=label, reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
exe.run(startup_program)
loader = fluid.io.DataLoader.from_generator(
feed_list=[img, label], capacity=5, iterable=True
)
loader.set_batch_generator(self._random_batch_reader(), places=place)
for data in loader():
exe.run(main_program, feed=data, fetch_list=[avg_loss])
fluid.io.save_inference_model(
self.save_dirname,
["img"],
[pred],
exe,
model_filename=self.model_filename,
params_filename=self.params_filename,
clip_extra=False,
)
def load_and_train_dygraph(self):
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
with fluid.dygraph.guard(place):
fluid.default_startup_program().random_seed = self.seed
fluid.default_main_program().random_seed = self.seed
np.random.seed(self.seed)
fluid.set_flags({'FLAGS_sort_sum_gradient': True})
while_net = fluid.dygraph.static_runner.StaticModelRunner(
self.save_dirname
)
dy_param_init_value = {}
for param in while_net.parameters():
dy_param_init_value[param.name] = param.numpy()
sgd = fluid.optimizer.SGD(
learning_rate=0.001, parameter_list=while_net.parameters()
)
train_loader = fluid.io.DataLoader.from_generator(capacity=10)
train_loader.set_batch_generator(
self._random_batch_reader(), places=place
)
while_net.train()
for data in train_loader():
img = data[0]
label = data[1]
label.stop_gradient = True
cost = while_net(img)
loss = paddle.nn.functional.cross_entropy(
cost, label, reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
avg_loss.backward()
sgd.minimize(avg_loss)
while_net.clear_gradients()
dy_out = avg_loss.numpy()
dy_param_value = {}
for param in while_net.parameters():
dy_param_value[param.name] = param.numpy()
return dy_out, dy_param_init_value, dy_param_value
def load_and_train_static(self):
with new_program_scope():
fluid.default_startup_program().random_seed = self.seed
fluid.default_main_program().random_seed = self.seed
np.random.seed(self.seed)
img = fluid.data(name='img', shape=[None, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
pred = while_softmax_regression(img)
loss = paddle.nn.functional.cross_entropy(
input=pred, label=label, reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = (
fluid.CUDAPlace(0)
if core.is_compiled_with_cuda()
else fluid.CPUPlace()
)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid.io.load_params(
exe,
self.save_dirname,
main_program=fluid.default_main_program(),
filename=self.params_filename,
)
static_param_init_value = {}
static_param_name_list = []
for param in fluid.default_main_program().all_parameters():
static_param_name_list.append(param.name)
static_param_init_value[param.name] = fluid.executor._fetch_var(
param.name
)
loader = fluid.io.DataLoader.from_generator(
feed_list=[img, label], capacity=5, iterable=True
)
loader.set_batch_generator(
self._random_batch_reader(), places=place
)
for data in loader():
fetch_list = [avg_loss.name]
fetch_list.extend(static_param_name_list)
out = exe.run(
fluid.default_main_program(),
feed=data,
fetch_list=[avg_loss],
)
static_param_value = {}
static_out = out[0]
for i in range(1, len(out)):
static_param_value[static_param_name_list[i - 1]] = out[i]
return static_out, static_param_init_value, static_param_value
def test_while_no_params_filename(self):
# Phase 1. run and save static model
self.train_and_save_model()
# # Phase 2. load model & train dygraph
with unique_name.guard():
(
dy_out,
dy_param_init_value,
dy_param_value,
) = self.load_and_train_dygraph()
with unique_name.guard():
(
static_out,
static_param_init_value,
static_param_value,
) = self.load_and_train_static()
# Phase 3. compare
with unique_name.guard():
dict_old_new_init = rename_var_with_generator(
static_param_init_value.keys()
)
for key, value in static_param_init_value.items():
key = dict_old_new_init[key]
np.testing.assert_array_equal(value, dy_param_init_value[key])
np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)
for key, value in static_param_value.items():
key += LOADED_VAR_SUFFIX
np.testing.assert_allclose(
value, dy_param_value[key], rtol=1e-05, atol=1e-05
)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_split_and_merge_lod_tensor_op.py 已删除 100644 → 0
浏览文件 @ 1e0f8734
# 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
import paddle
import paddle.fluid.core as core
import paddle.fluid.layers as layers
from paddle.fluid import Program, program_guard
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.control_flow import merge_lod_tensor, split_lod_tensor
class TestCPULoDTensorArrayOps(unittest.TestCase):
def place(self):
return core.CPUPlace()
def test_split_and_merge_lod_tensor_no_lod(self):
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('int32'), self.place())
mask_np = np.array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, self.place())
expect_true_tensor = np.array([2, 3, 4, 5]).astype('int32')
expect_true_tensor = np.expand_dims(expect_true_tensor, axis=1)
expect_true = core.LoDTensor()
expect_true.set(expect_true_tensor, self.place())
expect_false_tensor = np.array([0, 1, 6, 7, 8, 9]).astype('int32')
expect_false_tensor = np.expand_dims(expect_false_tensor, axis=1)
expect_false = core.LoDTensor()
expect_false.set(expect_false_tensor, self.place())
self.main(
tensor=tensor,
mask=mask,
expect_true=expect_true,
expect_false=expect_false,
expect_out=tensor,
)
def split_and_merge_lod_tensor_level_0(self, use_merge_lod_infer=False):
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('int32'), self.place())
tensor.set_recursive_sequence_lengths([[3, 6, 1]])
mask_np = np.array([0, 1, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, self.place())
expect_true_tensor = np.array([3, 4, 5, 6, 7, 8]).astype('int32')
expect_true_tensor = np.expand_dims(expect_true_tensor, axis=1)
expect_true = core.LoDTensor()
expect_true.set(expect_true_tensor, self.place())
expect_true.set_recursive_sequence_lengths([[6]])
expect_false_tensor = np.array([0, 1, 2, 9]).astype('int32')
expect_false_tensor = np.expand_dims(expect_false_tensor, axis=1)
expect_false_lod = [[3, 1]]
expect_false = core.LoDTensor()
expect_false.set(expect_false_tensor, self.place())
expect_false.set_recursive_sequence_lengths(expect_false_lod)
self.main(
tensor=tensor,
mask=mask,
expect_true=expect_true,
expect_false=expect_false,
expect_out=tensor,
use_merge_lod_infer=use_merge_lod_infer,
)
def test_split_and_merge_lod_tensor_1(self):
self.split_and_merge_lod_tensor_level_0()
def test_split_and_merge_lod_tensor_2(self):
self.split_and_merge_lod_tensor_level_0(True)
def main(
self,
tensor,
mask,
expect_true,
expect_false,
expect_out,
level=0,
use_merge_lod_infer=False,
):
place = self.place()
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[1])
x.persistable = True
y = layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = split_lod_tensor(input=x, mask=y, level=level)
out_true.persistable = True
out_false.persistable = True
if use_merge_lod_infer:
input_dict = {
'X': x,
'Mask': mask,
'InTrue': out_true,
'InFalse': out_false,
'level': level,
}
helper = LayerHelper('merge_lod_tensor_infer')
out = helper.create_variable_for_type_inference(
dtype=out_true.dtype
)
helper.append_op(
type='merge_lod_tensor_infer',
inputs={
'X': x,
'Mask': y,
'InTrue': out_true,
'InFalse': out_false,
},
outputs={'Out': out},
attrs={'level': level},
)
out.persistable = True
else:
out = merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level,
)
out.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(
program,
feed={'x': tensor, 'y': mask},
scope=scope,
return_numpy=False,
)
var_true = scope.find_var(out_true.name).get_tensor()
var_false = scope.find_var(out_false.name).get_tensor()
var_out = scope.find_var(out.name).get_tensor()
if not use_merge_lod_infer:
self.check_tensor_same(var_true, expect_true)
self.check_tensor_same(var_false, expect_false)
self.check_tensor_same(var_out, expect_out)
def check_tensor_same(self, actual, expect):
np.testing.assert_allclose(
np.array(actual), np.array(expect), rtol=1e-05
)
self.assertEqual(
actual.recursive_sequence_lengths(),
expect.recursive_sequence_lengths(),
)
class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
def test_grad(self):
place = core.CPUPlace()
program = Program()
with program_guard(program):
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False
)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False
)
level = 0
out_true, out_false = split_lod_tensor(input=x, mask=y, level=level)
out = merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level
)
mean = paddle.mean(out)
append_backward(mean)
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('float32'), place)
tensor.set_recursive_sequence_lengths([[3, 6, 1]])
mask_np = np.array([0, 1, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, place)
exe = Executor(place)
scope = core.Scope()
g_vars = program.global_block().var(x.name + "@GRAD")
g_out = [
item.sum()
for item in map(
np.array,
exe.run(
program,
feed={'x': tensor, 'y': mask},
fetch_list=[g_vars],
scope=scope,
return_numpy=False,
),
)
]
g_out_sum = np.array(g_out).sum()
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
class TestMergeLodTensorOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
input_data = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False
)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False
)
x_true = layers.data(
name='x_true', shape=[1], dtype='float32', stop_gradient=False
)
x_false = layers.data(
name='x_false', shape=[1], dtype='float32', stop_gradient=False
)
level = 0
def test_x():
out = merge_lod_tensor(
int_true=x_true,
in_false=x_false,
x=set(),
mask=y,
level=level,
)
self.assertRaises(TypeError, test_x)
def test_mask():
out = merge_lod_tensor(
int_true=x_true,
in_false=x_false,
x=input_data,
mask=set(),
level=level,
)
self.assertRaises(TypeError, test_mask)
def test_xtrue():
out = merge_lod_tensor(
int_true=set(),
in_false=x_false,
x=input_data,
mask=y,
level=level,
)
self.assertRaises(TypeError, test_xtrue)
def test_xfalse():
out = merge_lod_tensor(
int_true=x_true,
in_false=set(),
x=input_data,
mask=y,
level=level,
)
self.assertRaises(TypeError, test_xfalse)
class TestSplitLodTensorWithError(unittest.TestCase):
def test_error(self):
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False
)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False
)
level = 0
with self.assertRaises(TypeError):
split_lod_tensor(input=set(), mask=y, level=level)
with self.assertRaises(TypeError):
split_lod_tensor(input=x, mask=set(), level=level)
with self.assertRaises(TypeError):
split_lod_tensor(input=x, mask=set(), level=None)
if __name__ == '__main__':
unittest.main()
tools/parallel_UT_rule.py
浏览文件 @ b8814777
......@@ -72,7 +72,6 @@ HIGH_PARALLEL_JOB_NEW = [
'test_fleet_rolemaker_init',
'test_pybind_interface',
'test_io_save_load',
'test_split_and_merge_lod_tensor_op',
'test_fusion_lstm_int8_mkldnn_op',
'test_benchmark',
'test_protobuf',
......@@ -1164,7 +1163,6 @@ FOURTH_HIGH_PARALLEL_JOB_NEW = [
'test_proximal_gd_op',
'test_mul_nn_grad',
'test_full_like_op',
'test_imperative_static_runner_while',
'trt_instance_norm_test',
'test_elementwise_mod_op',
'test_grad_clip_minimize',
......@@ -1245,7 +1243,6 @@ FOURTH_HIGH_PARALLEL_JOB_NEW = [
'test_adam_op',
'test_elementwise_floordiv_op',
'test_diagonal_op',
'test_imperative_static_runner_mnist',
'test_nearest_interp_op',
'test_diag_embed',
'test_merge_selectedrows_op',
......@@ -1714,7 +1711,6 @@ CPU_PARALLEL_JOB = [
'test_static_shape_inferrence_for_shape_tensor',
'test_static_analysis',
'test_squared_mat_sub_fuse_pass',
'test_split_and_merge_lod_tensor_op',
'test_spawn_and_init_parallel_env',
'test_slice_var',
'test_skip_layernorm_fuse_pass',
......@@ -2370,7 +2366,6 @@ TETRAD_PARALLEL_JOB = [
'test_scatter_op',
'test_parallel_executor_pg',
'test_mix_precision_all_reduce_fuse',
'test_imperative_static_runner_mnist',
'test_tensorrt_engine_op',
'test_zeropad2d',
'test_isclose_op',
......@@ -2811,7 +2806,6 @@ TWO_PARALLEL_JOB = [
'test_lstm_op',
'test_margin_rank_loss_op',
'test_index_sample_op',
'test_imperative_static_runner_while',
'test_imperative_save_load',
'test_imperative_ptb_rnn_sorted_gradient',
'test_mul_op',
......
tools/static_mode_white_list.py
浏览文件 @ b8814777
......@@ -478,7 +478,6 @@ STATIC_MODE_TESTING_LIST = [
'test_smooth_l1_loss_op',
'test_softmax_with_cross_entropy_op',
'test_spectral_norm_op',
'test_split_and_merge_lod_tensor_op',
'test_split_ids_op',
'test_split_op',
'test_split_mkldnn_op',
......@@ -535,8 +534,6 @@ STATIC_MODE_TESTING_LIST = [
'test_imperative_mnist_sorted_gradient',
'test_imperative_se_resnext',
'test_imperative_ocr_attention_model',
'test_imperative_static_runner_mnist',
'test_imperative_static_runner_while',
'test_recv_save_op',
'test_transpiler_ops',
'test_communicator_sync',
......
tools/windows/run_unittests.sh
浏览文件 @ b8814777
......@@ -39,7 +39,6 @@ disable_wingpu_test="^test_model$|\
^test_reader_reset$|\
^test_imperative_se_resnext$|\
^test_sync_batch_norm_op$|\
^test_imperative_static_runner_while$|\
^test_dataloader_keep_order$|\
^test_dataloader_unkeep_order$|\
^test_multiprocess_dataloader_iterable_dataset_static$|\
......@@ -158,7 +157,6 @@ disable_win_inference_test="^trt_quant_int8_yolov3_r50_test$|\
^test_decoupled_py_reader$|\
^test_generator_dataloader$|\
^test_py_reader_using_executor$|\
^test_imperative_static_runner_while$|\
^test_dataloader_keep_order$|\
^test_dataloader_unkeep_order$|\
^test_sync_batch_norm_op$|\
......@@ -195,7 +193,6 @@ disable_wincpu_test="^jit_kernel_test$|\
^test_imperative_resnet$|\
^test_imperative_resnet_sorted_gradient$|\
^test_imperative_se_resnext$|\
^test_imperative_static_runner_mnist$|\
^test_bmn$|\
^test_mobile_net$|\
^test_resnet_v2$|\
......
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