未验证 提交 b7cac50b 编写于 作者: Y Yiqun Liu 提交者: GitHub

Implement a common python unittest to test the ir passes. (#22209)

* Implement a common python unittest to test the ir passes.
test=develop

* Save the results in np.array and support to startup on CPU.
test=develop

* Fix the unittest.
test=develop

* Add check_program to check whether the optimized program is different from the origin one.
test=develop

* Remove the inferface all_ops.
test=develop

* Add exception test in pass_test.
test=develop
上级 99f5907e
develop 2.0.1-rocm-post Ligoml-patch-1 OliverLPH-patch-1 OliverLPH-patch-2 PaddlePM-patch-1 PaddlePM-patch-2 ZHUI-patch-1 add_default_att add_model_benchmark_ci add_some_yaml_config addfile all_new_design_exec ascendrc ascendrelease cherry_undefined_var compile_windows delete_2.0.1-rocm-post delete_add_default_att delete_all_new_design_exec delete_ascendrc delete_compile_windows delete_delete_addfile delete_disable_iterable_dataset_unittest delete_fix_dataloader_memory_leak delete_fix_imperative_dygraph_error delete_fix_retry_ci delete_fix_undefined_var delete_improve_sccache delete_paralleltest delete_prv-disable-more-cache delete_revert-31068-fix_conv3d_windows delete_revert-31562-mean delete_revert-33630-bug-fix delete_revert-34159-add_npu_bce_logical_dev delete_revert-34910-spinlocks_for_allocator delete_revert-35069-revert-34910-spinlocks_for_allocator delete_revert-36057-dev/read_flags_in_ut dingjiaweiww-patch-1 disable_iterable_dataset_unittest dy2static enable_eager_model_test final_state_gen_python_c final_state_intermediate fix-numpy-issue fix_concat_slice fix_dataloader_memory_leak fix_imperative_dygraph_error fix_npu_ci fix_op_flops fix_retry_ci fix_rnn_docs fix_tensor_type fix_undefined_var fixiscan fixiscan1 fixiscan2 fixiscan3 github/fork/123malin/netifaces github/fork/123malin/tdm_abacus github/fork/AshburnLee/dev_unique github/fork/ForFishes/fix_memory_matmul github/fork/ForFishes/rm_fluid github/fork/LielinJiang/move-2.0-api github/fork/LielinJiang/visual-dl-cb github/fork/LiuChiachi/add-transformer-generate-square-subsequent-mask-api github/fork/LiuChiachi/fix-example-code-for-hapi-Model github/fork/LiuChiachi/remove-input-requirment-in-dygraph-Model github/fork/MrChengmo/fix_ps_profiler github/fork/MrChengmo/update_ps_heter github/fork/PWhiddy/patch-1 github/fork/Shixiaowei02/dev/save_load_upgrade github/fork/TCChenlong/fix_hapi github/fork/TCChenlong/fix_inden github/fork/Thunderbrook/xpu_slice github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var_2 github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var_3 github/fork/XieYunshen/timeout_20S_ut github/fork/ZeyuChen/remove-nltk github/fork/arlesniak/arlesniak/selective__mkldnn_flags github/fork/baiyfbupt/code_doc_mig github/fork/chalsliu/set_timeout github/fork/chen-zhiyu/develop github/fork/chenwhql/ci/try_to_find_test_buffer_shared_memory_reuse_pass_error github/fork/chenwhql/dygraph/remove_scale_loss_and_apply_collective_grads github/fork/chenwhql/saveload/add_get_inference_program github/fork/chenwhql/saveload/remove_save_load_config github/fork/cryoco/pass-compatibility-trt github/fork/danleifeng/isempty_api2.0 github/fork/frankwhzhang/api_transfer github/fork/hbwx24/error_msg/cuda_kernel_error_msg github/fork/heavengate/cherry_yolo_box github/fork/heavengate/update_yolo_box github/fork/iclementine/rnn_fix github/fork/iducn/testestse github/fork/jczaja/prv-25537-fix github/fork/jeff41404/release/1.8 github/fork/jiweibo/api_2.0 github/fork/jiweibo/fix_lite_resnet50_test github/fork/juncaipeng/fix_doc_1 github/fork/lfchener/sample_code github/fork/littletomatodonkey/fix_reg_doc github/fork/liym27/dy2stat_update_assign_to_rc20 github/fork/luotao1/profiler_ut github/fork/mapingshuo/add_wait github/fork/mapingshuo/doc_2.0 github/fork/mapingshuo/zero-0.5 github/fork/miraiwk/dev github/fork/pangyoki/add-Categorical-class-branch github/fork/pangyoki/add-multinomial-op-branch github/fork/pangyoki/fix-test_distritbution-CI github/fork/qjing666/doublegrad github/fork/qjing666/fix_hdfs_download github/fork/sandyhouse/add_gather_etc github/fork/sandyhouse/add_send_recv_alltoall_etc github/fork/sandyhouse/pipeline_exe_run github/fork/seiriosPlus/feature/large_scale_kv_save_delta github/fork/seiriosPlus/fix/paddle_errors_fix github/fork/seiriosPlus/fix/paddle_op_errors github/fork/shangzhizhou/fix_test_activation_op_random_bug github/fork/smallv0221/yxp0924 github/fork/smallv0221/yxp0925 github/fork/swtkiwi/del-matplotlib github/fork/tianshuo78520a/kunlun_test github/fork/tianshuo78520a/update_dockerfile github/fork/wanghaoshuang/bert_fuse github/fork/wanghaoshuang/label_smooth github/fork/wanghuancoder/develop_CUDASynchronize github/fork/wanghuancoder/develop_Layer_doc github/fork/wanghuancoder/develop_ParameterList_doc github/fork/wanghuancoder/develop_Sequential_doc github/fork/wanghuancoder/develop_bilinear_tensor_product github/fork/wanghuancoder/develop_coverage_build_sh github/fork/wanghuancoder/develop_in_dynamic_mode_doc github/fork/wanghuancoder/develop_unique_name_doc github/fork/wangxicoding/fleet_meta_combine github/fork/wawltor/error_message_fix_5 github/fork/willthefrog/remove_l2_norm github/fork/windstamp/momentum_op github/fork/windstamp/mv_op_5 github/fork/windstamp/normal_api github/fork/wojtuss/wojtuss/fusion_gru_quantization github/fork/wojtuss/wojtuss/quantization-with-shift github/fork/wzzju/fix_err_info github/fork/wzzju/pure_fp16 github/fork/xiemoyuan/op_error_message github/fork/xiemoyuan/optimize_error_message github/fork/yaoxuefeng6/fix_doc github/fork/yaoxuefeng6/mod_dataset_v2 github/fork/yongqiangma/lod github/fork/ysh329/fix-clip-by-norm-error github/fork/ysh329/fix-error-clip-by-value github/fork/yukavio/error_info github/fork/zhangting2020/conv_filter_grad github/fork/zhangting2020/is_compile_with_cuda github/fork/zhangting2020/place_doc github/fork/zhangting2020/program github/fork/zhhsplendid/fix_any github/fork/zhhsplendid/refine_api2 github/fork/zhhsplendid/refine_api2_test github/fork/zhhsplendid/refine_api_test_ptb_lm github/fork/zhhsplendid/refine_api_test_resnet github/fork/zhhsplendid/refine_api_test_simnet github/fork/zhiqiu/dev/refine_initializer github/fork/zhiqiu/dev/remove_inplace_argument github/fork/zlsh80826/nvinfer_plugin_var_len_cuda11 improve_sccache incubate/infrt inplace_addto make_flag_adding_easier move_embedding_to_phi move_histogram_to_pten move_sgd_to_phi move_slice_to_pten move_temporal_shift_to_phi move_yolo_box_to_phi npu_fix_alloc numel paralleltest preln_ernie prv-disable-more-cache prv-md-even-more prv-onednn-2.5 pten_tensor_refactor release/1.8 release/2.0 release/2.0-alpha release/2.0-beta release/2.0-rc release/2.0-rc1 release/2.1 release/2.2 release/2.3 release/2.3-fc-ernie-fix release/2.4 revert-24981-add_device_attr_for_regulization revert-26856-strategy_example2 revert-27520-disable_pr revert-31068-fix_conv3d_windows revert-31562-mean revert-32290-develop-hardlabel revert-33037-forci revert-33475-fix_cifar_label_dimension revert-33630-bug-fix revert-34159-add_npu_bce_logical_dev revert-34406-add_copy_from_tensor revert-34910-spinlocks_for_allocator revert-35069-revert-34910-spinlocks_for_allocator revert-36057-dev/read_flags_in_ut revert-36201-refine_fast_threaded_ssa_graph_executor revert-36985-add_license 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......@@ -165,4 +165,5 @@ int FCFusePass::ApplyFCPattern(Graph* graph, bool with_relu) const {
} // namespace framework
} // namespace paddle
REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass);
REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass)
.RequirePassAttr("use_gpu");
......@@ -27,12 +27,15 @@ Graph* Pass::Apply(Graph* graph) const {
CheckPrevPass();
PADDLE_ENFORCE(graph, "graph passed to Pass::Apply() cannot be empty.");
for (const std::string& attr : required_pass_attrs_) {
PADDLE_ENFORCE(attrs_.find(attr) != attrs_.end(),
"Required pass atrribute %s not set.", attr);
PADDLE_ENFORCE_NE(
attrs_.find(attr), attrs_.end(),
platform::errors::InvalidArgument(
"Required atrribute %s for pass < %s > is not set.", attr, Type()));
}
for (const std::string& attr : required_graph_attrs_) {
PADDLE_ENFORCE(graph->Has(attr), "Required graph atrribute %s not set.",
attr);
PADDLE_ENFORCE_EQ(graph->Has(attr), true,
platform::errors::InvalidArgument(
"Required atrribute %s for graph is not set.", attr));
}
ApplyImpl(graph);
// TODO(panyx0718): Add more verifications.
......
......@@ -60,10 +60,25 @@ class Pass {
try {
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
} catch (boost::bad_any_cast &) {
PADDLE_THROW(
"Invalid attribute type of %s error, expected: %s, actual: %s",
attr_name, typeid(AttrType *).name(),
attrs_.at(attr_name).type().name());
auto TypeToString = [](const std::type_info &info) -> std::string {
if (std::type_index(info) == std::type_index(typeid(bool *))) {
return "bool";
} else if (std::type_index(info) == std::type_index(typeid(int *))) {
return "int";
} else if (std::type_index(info) ==
std::type_index(typeid(const int *))) {
return "const int";
} else if (std::type_index(info) ==
std::type_index(typeid(std::string *))) {
return "std::string";
}
return info.name();
};
PADDLE_THROW(platform::errors::InvalidArgument(
"Invalid type for attritube %s, expected: %s, actual: %s", attr_name,
TypeToString(typeid(AttrType *)),
TypeToString(attrs_.at(attr_name).type())));
}
}
......
......@@ -63,18 +63,38 @@ TEST(PassTest, TestPassAttrCheck) {
} catch (paddle::platform::EnforceNotMet& e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("test_pass_attr not set") != exception.npos);
ASSERT_TRUE(exception.find("Required atrribute test_pass_attr for pass < "
"test_pass > is not set") != exception.npos);
int val = 1;
graph.reset(new Graph(prog));
pass->SetNotOwned<int>("test_pass_attr", &val);
for (std::string try_type : {"bool", "const int", "std::string"}) {
try {
if (try_type == "bool") {
pass->Get<bool>("test_pass_attr");
} else if (try_type == "const int") {
pass->Get<const int>("test_pass_attr");
} else if (try_type == "std::string") {
pass->Get<std::string>("test_pass_attr");
}
} catch (paddle::platform::EnforceNotMet& e) {
exception = std::string(e.what());
}
std::string msg = "Invalid type for attritube test_pass_attr, expected: " +
try_type + ", actual: int";
ASSERT_TRUE(exception.find(msg) != exception.npos);
}
try {
graph.reset(pass->Apply(graph.release()));
} catch (paddle::platform::EnforceNotMet& e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("test_graph_attr not set") != exception.npos);
ASSERT_TRUE(exception.find(
"Required atrribute test_graph_attr for graph is not set") !=
exception.npos);
graph.reset(new Graph(prog));
graph->Set<int>("test_graph_attr", new int);
......
......@@ -1597,6 +1597,8 @@ All parameter, weight, gradient are variables in Paddle.
[](ir::Pass &self, const std::string &name, const std::string &attr) {
self.Set<std::string>(name, new std::string(attr));
})
.def("set", [](ir::Pass &self, const std::string &name,
bool val) { self.Set<bool>(name, new bool(val)); })
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("set",
......
......@@ -336,6 +336,8 @@ if (WITH_MKLDNN)
add_subdirectory(mkldnn)
endif()
add_subdirectory(ir)
if (WITH_TESTING)
set_property(TEST test_parallel_executor_mnist PROPERTY ENVIRONMENT GLOG_vmodule=all_reduce_deps_pass=10)
endif()
......
file(GLOB TEST_IR_PASSES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_IR_PASSES "${TEST_IR_PASSES}")
foreach(target ${TEST_IR_PASSES})
py_test_modules(${target} MODULES ${target})
endforeach()
# Copyright (c) 2019 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 __future__ import print_function
import os
import six
import random
import unittest
import warnings
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.framework import Program, Block
from paddle.fluid.backward import append_backward
class PassTest(unittest.TestCase):
@classmethod
def setUpClass(self):
self.main_program = fluid.Program()
self.startup_program = fluid.Program()
self.feeds = None
self.fetch_list = None
self.pass_names = None
self.pass_attrs = {}
self.fused_op_type = None
self.num_fused_ops = -1
np.random.seed(123)
random.seed(124)
def _get_places(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
return places
def check_output(self, startup_on_cpu=False, atol=1e-5):
'''
Check whether the fetched outputs of the origin program and the
optimized program are the same.
For inference model, the parameters are loaded to CPUPlace first,
after apply all specified passes, then copy the parameters to GPUPlace.
We can set startup_on_cpu to True to test inference pass.
'''
places = self._get_places()
for place in places:
self.check_output_with_place(place, startup_on_cpu, atol)
def _run_program(self, executor, program):
outs = executor.run(program=program,
feed=self.feeds,
fetch_list=self.fetch_list,
return_numpy=False)
outs_np = []
outs_lod = []
for out in outs:
outs_np.append(np.array(out))
outs_lod.append(out.lod())
return outs_np, outs_lod
def _apply_ir_passes(self):
graph = core.Graph(self.main_program.desc)
graph.set_not_owned("__param_scope__", fluid.global_scope())
if not isinstance(self.pass_names, list):
self.pass_names = [self.pass_names]
pass_builder = core.PassBuilder()
for name in self.pass_names:
ir_pass = pass_builder.append_pass(name)
# Set attr for pass
if self.pass_attrs.get(name, None) is not None:
attrs = self.pass_attrs[name]
for key in attrs:
ir_pass.set(key, attrs[key])
trans_pass = pass_builder.append_pass("graph_to_program_pass")
opt_program = fluid.Program()
trans_pass.set_not_owned("program", opt_program.desc)
for p in pass_builder.all_passes():
p.apply(graph)
opt_program.blocks = [
Block(opt_program, i)
for i in six.moves.range(opt_program.desc.num_blocks())
]
opt_program._sync_with_cpp()
return opt_program
def check_output_with_place(self, place, startup_on_cpu=False, atol=1e-5):
'''
Check whether the fetched outputs of the origin program and the
optimized program are the same.
For inference model, the parameters are loaded to CPUPlace first,
after apply all specified passes, then copy the parameters to GPUPlace.
We can set startup_on_cpu to True to test inference pass.
'''
executor = fluid.Executor(place)
if startup_on_cpu:
# Initialize parameters on CPU
cpu_executor = fluid.Executor(fluid.CPUPlace())
cpu_executor.run(self.startup_program)
outs, lods = self._run_program(cpu_executor, self.main_program)
else:
executor.run(self.startup_program)
outs, lods = self._run_program(executor, self.main_program)
self.assertTrue(
len(self.fetch_list) == len(outs),
"Checking the number of fetchs failed. Expected: {}, Received: {}".
format(len(self.fetch_list), len(outs)))
# Parameters may be changed in ir passes.
opt_program = self._apply_ir_passes()
self.check_program(opt_program)
if startup_on_cpu and not isinstance(place, fluid.CPUPlace):
warnings.warn(
"Parameters are on CPU, and will be transfered to GPU "
"automatically by data transform.")
outs_opt, lods_opt = self._run_program(executor, opt_program)
self.assertTrue(
len(self.fetch_list) == len(outs_opt),
"Checking the number of fetchs failed. Expected: {}, Received: {}".
format(len(self.fetch_list), len(outs_opt)))
for i in six.moves.xrange(len(self.fetch_list)):
self.assertTrue(
np.allclose(
outs_opt[i], outs[i], atol=atol),
"Output < {} > has diff at {}".format(self.fetch_list[i].name,
str(place)))
def _check_fused_ops(self, program):
'''
Check the number of specified fused op is equal to the the expected
number.
'''
if self.fused_op_type is None or self.num_fused_ops < 0:
return
if program is None or program == self.main_program:
program = self._apply_ir_passes()
acctual_num_fused_ops = 0
# Ir passes can only be applyed to block 0.
for op in program.block(0).ops:
if op.type == self.fused_op_type:
acctual_num_fused_ops += 1
self.assertTrue(
self.num_fused_ops == acctual_num_fused_ops,
"Checking of the number of fused operator < {} > failed. "
"Expected: {}, Received: {}".format(
self.fused_op_type, self.num_fused_ops, acctual_num_fused_ops))
def check_program(self, program=None):
'''
Check whether the optimized program is different from the origin
program.
'''
if program is None or program == self.main_program:
program = self._apply_ir_passes()
self._check_fused_ops(program)
self.assertTrue(
self.main_program.desc != program.desc,
"The optimized program and the origin main_program hold the same "
"desc.")
self.assertTrue(
self.main_program.num_blocks == program.num_blocks,
"The number of blocks of the origin program and the optimized "
"program are different ({} vs {}).".format(
self.main_program.num_blocks, program.num_blocks))
is_different = False
for i in six.moves.xrange(program.num_blocks):
if len(self.main_program.block(i).ops) != len(program.block(i).ops):
# The number of ops in the block i of the origin program and
# the optimized program is different.
is_different = True
break
# If there are different ops between the origin and optimized program.
for op in self.main_program.block(i).ops:
if not self._find_op(op, program, i):
is_different = True
break
if len(self.main_program.block(i).vars) != len(
program.block(i).vars):
# The number of vars in the block i of the origin program and
# the optimized program is different.
is_different = True
break
# If there are different vars between the origin and optimized program.
for name in self.main_program.block(i).vars:
var = self.main_program.block(i).var(name)
if not self._find_var(var, program, i):
is_different = True
break
self.assertTrue(
is_different,
"The optimized program is logically the same with the origin "
"program.")
def _find_op(self, specified_op, program, block_id):
is_find = False
for op in program.block(block_id).ops:
if specified_op.type == op.type:
for name in op.input_names:
if op.input(name) != specified_op.input(name):
break
for name in op.output_names:
if op.output(name) != specified_op.output(name):
break
for name in op.attr_names:
if op.attr(name) != specified_op.attr(name):
break
is_find = True
break
return is_find
def _find_var(self, specified_var, program, block_id):
if not program.block(block_id).has_var(specified_var.name):
return False
var = program.block(block_id).var(specified_var.name)
if var.type != specified_var.type:
return False
if var.dtype != specified_var.dtype:
return False
if var.lod_level != specified_var.lod_level:
return False
if var.shape != specified_var.shape:
return False
if var.persistable != specified_var.persistable:
return False
return True
# Copyright (c) 2019 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 pass_test import PassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
class FCFusePassTest(PassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[32, 128], dtype="float32", lod_level=0)
tmp_0 = fluid.layers.fc(input=data,
size=128,
num_flatten_dims=1,
act="relu")
tmp_1 = fluid.layers.fc(input=tmp_0, size=32, num_flatten_dims=1)
tmp_2 = fluid.layers.softmax(input=tmp_1)
self.feeds = {"data": np.random.random((32, 128)).astype("float32")}
self.fetch_list = [tmp_0, tmp_1, tmp_2]
self.pass_names = "fc_fuse_pass"
self.fused_op_type = "fc"
self.num_fused_ops = 2
def test_check_output(self):
use_gpu_set = [False]
if core.is_compiled_with_cuda():
use_gpu_set.append(True)
for use_gpu in use_gpu_set:
self.pass_attrs = {"fc_fuse_pass": {"use_gpu": use_gpu}}
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
self.check_output_with_place(place, startup_on_cpu=True)
if __name__ == "__main__":
unittest.main()
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