未验证 提交 a3f9bcb2 编写于 作者: G Guanghua Yu 提交者: GitHub

Modify quantization use tempfile to place the temporary files (#43267)

上级 d5afc1ba
......@@ -20,6 +20,7 @@ import random
import unittest
import logging
import warnings
import tempfile
import paddle
import paddle.fluid as fluid
......@@ -111,6 +112,16 @@ class ImperativeLenet(fluid.dygraph.Layer):
class TestImperativeOutSclae(unittest.TestCase):
def setUp(self):
self.root_path = tempfile.TemporaryDirectory()
self.param_save_path = os.path.join(self.root_path.name,
"lenet.pdparams")
self.save_path = os.path.join(self.root_path.name,
"lenet_dynamic_outscale_infer_model")
def tearDown(self):
self.root_path.cleanup()
def func_out_scale_acc(self):
seed = 1000
lr = 0.001
......@@ -138,46 +149,16 @@ class TestImperativeOutSclae(unittest.TestCase):
loss_list = train_lenet(lenet, reader, adam)
lenet.eval()
param_save_path = "test_save_quantized_model/lenet.pdparams"
save_dict = lenet.state_dict()
paddle.save(save_dict, param_save_path)
save_path = "./dynamic_outscale_infer_model/lenet"
imperative_out_scale.save_quantized_model(
layer=lenet,
path=save_path,
input_spec=[
paddle.static.InputSpec(shape=[None, 1, 28, 28],
dtype='float32')
])
paddle.save(save_dict, self.param_save_path)
for i in range(len(loss_list) - 1):
self.assertTrue(loss_list[i] > loss_list[i + 1],
msg='Failed to do the imperative qat.')
def test_out_scale_acc(self):
with _test_eager_guard():
self.func_out_scale_acc()
self.func_out_scale_acc()
class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
def func_save_quantized_model(self):
lr = 0.001
load_param_path = "test_save_quantized_model/lenet.pdparams"
save_path = "./dynamic_outscale_infer_model_from_checkpoint/lenet"
weight_quantize_type = 'abs_max'
activation_quantize_type = 'moving_average_abs_max'
imperative_out_scale = ImperativeQuantAware(
weight_quantize_type=weight_quantize_type,
activation_quantize_type=activation_quantize_type)
with fluid.dygraph.guard():
lenet = ImperativeLenet()
load_dict = paddle.load(load_param_path)
load_dict = paddle.load(self.param_save_path)
imperative_out_scale.quantize(lenet)
lenet.set_dict(load_dict)
......@@ -191,7 +172,7 @@ class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
imperative_out_scale.save_quantized_model(
layer=lenet,
path=save_path,
path=self.save_path,
input_spec=[
paddle.static.InputSpec(shape=[None, 1, 28, 28],
dtype='float32')
......@@ -201,10 +182,10 @@ class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
self.assertTrue(loss_list[i] > loss_list[i + 1],
msg='Failed to do the imperative qat.')
def test_save_quantized_model(self):
def test_out_scale_acc(self):
with _test_eager_guard():
self.func_save_quantized_model()
self.func_save_quantized_model()
self.func_out_scale_acc()
self.func_out_scale_acc()
if __name__ == '__main__':
......
......@@ -22,6 +22,7 @@ import time
import unittest
import copy
import logging
import tempfile
import paddle.nn as nn
import paddle
......@@ -73,10 +74,6 @@ class TestImperativePTQ(unittest.TestCase):
@classmethod
def setUpClass(cls):
timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
cls.root_path = os.path.join(os.getcwd(), "imperative_ptq_" + timestamp)
cls.save_path = os.path.join(cls.root_path, "model")
cls.download_path = 'dygraph_int8/download'
cls.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
cls.download_path)
......@@ -89,14 +86,6 @@ class TestImperativePTQ(unittest.TestCase):
paddle.static.default_main_program().random_seed = seed
paddle.static.default_startup_program().random_seed = seed
@classmethod
def tearDownClass(cls):
try:
pass
# shutil.rmtree(cls.root_path)
except Exception as e:
print("Failed to delete {} due to {}".format(cls.root_path, str(e)))
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
cmd = 'mkdir {0} && tar xf {1} -C {0}'.format(
......@@ -217,16 +206,18 @@ class TestImperativePTQ(unittest.TestCase):
input_spec = [
paddle.static.InputSpec(shape=[None, 1, 28, 28], dtype='float32')
]
with tempfile.TemporaryDirectory(prefix="imperative_ptq_") as tmpdir:
save_path = os.path.join(tmpdir, "model")
self.ptq.save_quantized_model(model=quant_model,
path=self.save_path,
path=save_path,
input_spec=input_spec)
print('Quantized model saved in {%s}' % self.save_path)
print('Quantized model saved in {%s}' % save_path)
after_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
paddle.enable_static()
infer_acc_top1 = self.program_test(self.save_path, self.batch_num,
infer_acc_top1 = self.program_test(save_path, self.batch_num,
self.batch_size)
paddle.disable_static()
......@@ -278,16 +269,18 @@ class TestImperativePTQfuse(TestImperativePTQ):
input_spec = [
paddle.static.InputSpec(shape=[None, 1, 28, 28], dtype='float32')
]
with tempfile.TemporaryDirectory(prefix="imperative_ptq_") as tmpdir:
save_path = os.path.join(tmpdir, "model")
self.ptq.save_quantized_model(model=quant_model,
path=self.save_path,
path=save_path,
input_spec=input_spec)
print('Quantized model saved in {%s}' % self.save_path)
print('Quantized model saved in {%s}' % save_path)
after_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
paddle.enable_static()
infer_acc_top1 = self.program_test(self.save_path, self.batch_num,
infer_acc_top1 = self.program_test(save_path, self.batch_num,
self.batch_size)
paddle.disable_static()
......
......@@ -21,6 +21,7 @@ import shutil
import time
import unittest
import logging
import tempfile
import paddle
import paddle.fluid as fluid
......@@ -46,10 +47,9 @@ class TestImperativeQatAmp(unittest.TestCase):
@classmethod
def setUpClass(cls):
timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
cls.root_path = os.path.join(os.getcwd(),
"imperative_qat_amp_" + timestamp)
cls.save_path = os.path.join(cls.root_path, "model")
cls.root_path = tempfile.TemporaryDirectory(
prefix="imperative_qat_amp_")
cls.save_path = os.path.join(cls.root_path.name, "model")
cls.download_path = 'dygraph_int8/download'
cls.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
......@@ -65,10 +65,7 @@ class TestImperativeQatAmp(unittest.TestCase):
@classmethod
def tearDownClass(cls):
try:
shutil.rmtree(cls.root_path)
except Exception as e:
print("Failed to delete {} due to {}".format(cls.root_path, str(e)))
cls.root_path.cleanup()
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......
......@@ -20,6 +20,7 @@ import math
import functools
import contextlib
import struct
import tempfile
import numpy as np
import paddle
import paddle.fluid as fluid
......@@ -38,9 +39,9 @@ class TestPostTrainingQuantization(unittest.TestCase):
self.download_path = 'int8/download'
self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
self.download_path)
self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
self.int8_model_path = os.path.join(os.getcwd(),
"post_training_" + self.timestamp)
self.root_path = tempfile.TemporaryDirectory()
self.int8_model_path = os.path.join(self.root_path.name,
"post_training_quantization")
try:
os.system("mkdir -p " + self.int8_model_path)
except Exception as e:
......@@ -49,11 +50,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
sys.exit(-1)
def tearDown(self):
try:
os.system("rm -rf {}".format(self.int8_model_path))
except Exception as e:
print("Failed to delete {} due to {}".format(
self.int8_model_path, str(e)))
self.root_path.cleanup()
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......
......@@ -18,6 +18,7 @@ import sys
import random
import math
import functools
import tempfile
import contextlib
import numpy as np
import paddle
......@@ -34,12 +35,12 @@ np.random.seed(0)
class TestPostTrainingQuantization(unittest.TestCase):
def setUp(self):
self.root_path = tempfile.TemporaryDirectory()
self.int8_model_path = os.path.join(self.root_path.name,
"post_training_quantization")
self.download_path = 'int8/download'
self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
self.download_path)
self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
self.int8_model_path = os.path.join(os.getcwd(),
"post_training_" + self.timestamp)
try:
os.system("mkdir -p " + self.int8_model_path)
except Exception as e:
......@@ -48,11 +49,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
sys.exit(-1)
def tearDown(self):
try:
os.system("rm -rf {}".format(self.int8_model_path))
except Exception as e:
print("Failed to delete {} due to {}".format(
self.int8_model_path, str(e)))
self.root_path.cleanup()
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......@@ -123,7 +120,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.global_scope()
val_reader = paddle.dataset.mnist.train()
ptq = PostTrainingQuantization(executor=exe,
......
......@@ -19,6 +19,7 @@ import random
import math
import functools
import contextlib
import tempfile
import numpy as np
from PIL import Image, ImageEnhance
import paddle
......@@ -150,16 +151,12 @@ class TestPostTrainingQuantization(unittest.TestCase):
self.infer_iterations = 50000 if os.environ.get(
'DATASET') == 'full' else 2
self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
self.int8_model = os.path.join(os.getcwd(),
"post_training_" + self.timestamp)
self.root_path = tempfile.TemporaryDirectory()
self.int8_model = os.path.join(self.root_path.name,
"post_training_quantization")
def tearDown(self):
try:
os.system("rm -rf {}".format(self.int8_model))
except Exception as e:
print("Failed to delete {} due to {}".format(
self.int8_model, str(e)))
self.root_path.cleanup()
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......
......@@ -17,6 +17,7 @@ import unittest
import random
import numpy as np
import six
import tempfile
import paddle.fluid as fluid
import paddle
from paddle.fluid.framework import IrGraph
......@@ -166,15 +167,19 @@ class TestQuantizationScalePass(unittest.TestCase):
marked_nodes.add(op)
test_graph.draw('.', 'quant_scale' + dev_name, marked_nodes)
with open('quant_scale_model' + dev_name + '.txt', 'w') as f:
tempdir = tempfile.TemporaryDirectory()
mapping_table_path = os.path.join(
tempdir.name, 'quant_scale_model' + dev_name + '.txt')
save_path = os.path.join(tempdir.name, 'quant_scale_model' + dev_name)
with open(mapping_table_path, 'w') as f:
f.write(str(server_program))
with fluid.scope_guard(scope):
fluid.io.save_inference_model('quant_scale_model' + dev_name,
['image', 'label'], [loss],
fluid.io.save_inference_model(save_path, ['image', 'label'], [loss],
exe,
server_program,
clip_extra=True)
tempdir.cleanup()
def test_quant_scale_cuda(self):
if fluid.core.is_compiled_with_cuda():
......
......@@ -18,6 +18,7 @@ import json
import random
import numpy as np
import six
import tempfile
import paddle.fluid as fluid
import paddle
from paddle.fluid.framework import IrGraph
......@@ -110,18 +111,20 @@ class TestUserDefinedQuantization(unittest.TestCase):
def get_optimizer():
return fluid.optimizer.MomentumOptimizer(0.0001, 0.9)
def load_dict():
with open('mapping_table_for_saving_inference_model', 'r') as file:
def load_dict(mapping_table_path):
with open(mapping_table_path, 'r') as file:
data = file.read()
data = json.loads(data)
return data
def save_dict(Dict):
with open('mapping_table_for_saving_inference_model', 'w') as file:
def save_dict(Dict, mapping_table_path):
with open(mapping_table_path, 'w') as file:
file.write(json.dumps(Dict))
random.seed(0)
np.random.seed(0)
tempdir = tempfile.TemporaryDirectory()
mapping_table_path = os.path.join(tempdir.name, 'inference')
main = fluid.Program()
startup = fluid.Program()
......@@ -162,7 +165,7 @@ class TestUserDefinedQuantization(unittest.TestCase):
executor=exe)
test_transform_pass.apply(test_graph)
save_dict(test_graph.out_node_mapping_table)
save_dict(test_graph.out_node_mapping_table, mapping_table_path)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
......@@ -203,10 +206,11 @@ class TestUserDefinedQuantization(unittest.TestCase):
activation_bits=8,
weight_quantize_type=weight_quant_type)
mapping_table = load_dict()
mapping_table = load_dict(mapping_table_path)
test_graph.out_node_mapping_table = mapping_table
if act_quantize_func == None and weight_quantize_func == None:
freeze_pass.apply(test_graph)
tempdir.cleanup()
def test_act_preprocess_cuda(self):
if fluid.core.is_compiled_with_cuda():
......
......@@ -18,6 +18,7 @@ import unittest
import os
import six
import tempfile
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
......@@ -46,8 +47,9 @@ class InferModel(object):
class TestBook(unittest.TestCase):
def test_fit_line_inference_model(self):
MODEL_DIR = "./tmp/inference_model"
UNI_MODEL_DIR = "./tmp/inference_model1"
root_path = tempfile.TemporaryDirectory()
MODEL_DIR = os.path.join(root_path.name, "inference_model")
UNI_MODEL_DIR = os.path.join(root_path.name, "inference_model1")
init_program = Program()
program = Program()
......@@ -118,6 +120,8 @@ class TestBook(unittest.TestCase):
print("fetch %s" % str(model.fetch_vars[0]))
self.assertEqual(expected, actual)
root_path.cleanup()
self.assertRaises(ValueError, fluid.io.load_inference_model, None, exe,
model_str, None)
......@@ -125,7 +129,8 @@ class TestBook(unittest.TestCase):
class TestSaveInferenceModel(unittest.TestCase):
def test_save_inference_model(self):
MODEL_DIR = "./tmp/inference_model2"
root_path = tempfile.TemporaryDirectory()
MODEL_DIR = os.path.join(root_path.name, "inference_model2")
init_program = Program()
program = Program()
......@@ -144,9 +149,11 @@ class TestSaveInferenceModel(unittest.TestCase):
exe.run(init_program, feed={}, fetch_list=[])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
root_path.cleanup()
def test_save_inference_model_with_auc(self):
MODEL_DIR = "./tmp/inference_model4"
root_path = tempfile.TemporaryDirectory()
MODEL_DIR = os.path.join(root_path.name, "inference_model4")
init_program = Program()
program = Program()
......@@ -168,6 +175,7 @@ class TestSaveInferenceModel(unittest.TestCase):
warnings.simplefilter("always")
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe,
program)
root_path.cleanup()
expected_warn = "please ensure that you have set the auc states to zeros before saving inference model"
self.assertTrue(len(w) > 0)
self.assertTrue(expected_warn == str(w[0].message))
......@@ -176,7 +184,8 @@ class TestSaveInferenceModel(unittest.TestCase):
class TestInstance(unittest.TestCase):
def test_save_inference_model(self):
MODEL_DIR = "./tmp/inference_model3"
root_path = tempfile.TemporaryDirectory()
MODEL_DIR = os.path.join(root_path.name, "inference_model3")
init_program = Program()
program = Program()
......@@ -202,12 +211,14 @@ class TestInstance(unittest.TestCase):
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, cp_prog)
self.assertRaises(TypeError, save_inference_model,
[MODEL_DIR, ["x", "y"], [avg_cost], [], cp_prog])
root_path.cleanup()
class TestSaveInferenceModelNew(unittest.TestCase):
def test_save_and_load_inference_model(self):
MODEL_DIR = "./tmp/inference_model5"
root_path = tempfile.TemporaryDirectory()
MODEL_DIR = os.path.join(root_path.name, "inference_model5")
init_program = fluid.default_startup_program()
program = fluid.default_main_program()
......@@ -303,6 +314,7 @@ class TestSaveInferenceModelNew(unittest.TestCase):
model = InferModel(paddle.static.io.load_inference_model(
MODEL_DIR, exe))
root_path.cleanup()
outs = exe.run(model.program,
feed={
......
......@@ -17,6 +17,7 @@ from __future__ import print_function
import os
import unittest
import numpy as np
import tempfile
import paddle
import paddle.fluid as fluid
......@@ -93,54 +94,60 @@ class TestConditionalOp(unittest.TestCase):
paddle.static.InputSpec(
shape=[1, 3, 8, 8], dtype='float32')
])
paddle.jit.save(net, './while_net')
root_path = tempfile.TemporaryDirectory()
model_file = os.path.join(root_path.name, "while_net")
paddle.jit.save(net, model_file)
right_pdmodel = set([
"uniform_random", "shape", "slice", "not_equal", "while",
"elementwise_add"
])
paddle.enable_static()
pdmodel = getModelOp("while_net.pdmodel")
#print(len(right_pdmodel.difference(pdmodel)))
pdmodel = getModelOp(model_file + ".pdmodel")
self.assertTrue(
len(right_pdmodel.difference(pdmodel)) == 0,
"The while op is pruned by mistake.")
root_path.cleanup()
def test_for_op(self):
paddle.disable_static()
net = ForNet()
net = paddle.jit.to_static(
net, input_spec=[paddle.static.InputSpec(shape=[1], dtype='int32')])
paddle.jit.save(net, './for_net')
root_path = tempfile.TemporaryDirectory()
model_file = os.path.join(root_path.name, "for_net")
paddle.jit.save(net, model_file)
right_pdmodel = set([
"randint", "fill_constant", "cast", "less_than", "while",
"elementwise_add"
])
paddle.enable_static()
pdmodel = getModelOp("for_net.pdmodel")
#print(len(right_pdmodel.difference(pdmodel)))
pdmodel = getModelOp(model_file + ".pdmodel")
self.assertTrue(
len(right_pdmodel.difference(pdmodel)) == 0,
"The for op is pruned by mistake.")
root_path.cleanup()
def test_if_op(self):
paddle.disable_static()
net = IfElseNet()
net = paddle.jit.to_static(
net, input_spec=[paddle.static.InputSpec(shape=[1], dtype='int32')])
paddle.jit.save(net, './if_net')
root_path = tempfile.TemporaryDirectory()
model_file = os.path.join(root_path.name, "if_net")
paddle.jit.save(net, model_file)
right_pdmodel = set([
"assign_value", "greater_than", "cast", "conditional_block",
"logical_not", "select_input"
])
paddle.enable_static()
pdmodel = getModelOp("if_net.pdmodel")
#print(len(right_pdmodel.difference(pdmodel)))
pdmodel = getModelOp(model_file + ".pdmodel")
self.assertTrue(
len(right_pdmodel.difference(pdmodel)) == 0,
"The if op is pruned by mistake.")
root_path.cleanup()
if __name__ == '__main__':
......
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