def transform_and_save_int8_model(original_path,
save_path,
ops_to_quantize='',
op_ids_to_skip='',
debug=False,
quant_model_filename='',
quant_params_filename='',
save_model_filename="__model__",
save_params_filename=None):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.executor.global_scope()
with fluid.scope_guard(inference_scope):
if not quant_model_filename:
if os.path.exists(os.path.join(original_path, '__model__')):
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(original_path, exe)
else:
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(original_path, exe, 'model',
'params')
else:
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(original_path, exe,
quant_model_filename,
quant_params_filename)
ops_to_quantize_set = set()
print(ops_to_quantize)
if len(ops_to_quantize) > 0:
ops_to_quantize_set = set(ops_to_quantize.split(','))
op_ids_to_skip_set = set([-1])
print(op_ids_to_skip)
if len(op_ids_to_skip) > 0:
op_ids_to_skip_set = set(map(int, op_ids_to_skip.split(',')))
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
if (debug):
graph.draw('.', 'quant_orig', graph.all_op_nodes())
transform_to_mkldnn_int8_pass = Quant2Int8MkldnnPass(
ops_to_quantize_set,
_op_ids_to_skip=op_ids_to_skip_set,
_scope=inference_scope,
_place=place,
_core=core,
_debug=debug)
graph = transform_to_mkldnn_int8_pass.apply(graph)
inference_program = graph.to_program()
with fluid.scope_guard(inference_scope):
fluid.io.save_inference_model(save_path,
feed_target_names,
fetch_targets,
exe,
inference_program,
model_filename=save_model_filename,
params_filename=save_params_filename)
print(
"Success! INT8 model obtained from the Quant model can be found at {}\n"
.format(save_path))
def residual_block_quant(self,
quantizable_op_type,
skip_pattern=None,
for_ci=True):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = quant_dequant_residual_block(2, skip_pattern)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
place = fluid.CPUPlace()
graph = IrGraph(core.Graph(main.desc), for_test=False)
add_quant_dequant_pass = AddQuantDequantPass(
scope=fluid.global_scope(),
place=place,
skip_pattern=skip_pattern,
quantizable_op_type=quantizable_op_type)
add_quant_dequant_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quant') > -1:
marked_nodes.add(op)
graph.draw('.', 'add_quant_dequant_graph', marked_nodes)
self.check_graph(graph, skip_pattern)
program = graph.to_program()
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
if not for_ci:
val_marked_nodes = set()
for op in val_graph.all_op_nodes():
if op.name().find('quant') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_add_quant_dequant_graph', val_marked_nodes)
def linear_fc_quant(self,
activation_quant_type,
weight_quantize_type,
for_ci=True):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = linear_fc(3)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
place = fluid.CPUPlace()
graph = IrGraph(core.Graph(main.desc), for_test=False)
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quantize_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_fc_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
if not for_ci:
val_marked_nodes = set()
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_fc_' + activation_quant_type,
val_marked_nodes)
def residual_block_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
graph = IrGraph(core.Graph(main.desc), for_test=False)
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
transform_pass.apply(graph)
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
val_marked_nodes = set()
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
def generate_dot_for_model(model_path, save_graph_dir, save_graph_name):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.executor.global_scope()
with fluid.scope_guard(inference_scope):
if os.path.exists(os.path.join(model_path, '__model__')):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
else:
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe,
'model', 'params')
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
if not os.path.exists(save_graph_dir):
os.makedirs(save_graph_dir)
model_name = os.path.basename(os.path.normpath(save_graph_dir))
if save_graph_name is '':
save_graph_name = model_name
graph.draw(save_graph_dir, save_graph_name, graph.all_op_nodes())
print(
"Success! Generated dot and pdf files for {0} model, that can be found at {1} named {2}.\n".
format(model_name, save_graph_dir, save_graph_name))
def test_graph_functions(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
graph.draw('.', 'residual', marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
self.assertEqual(len(nodes), len(graph.all_op_nodes()))
nodes_map = graph.build_adjacency_list()
self.assertEqual(len(nodes_map), len(graph.all_op_nodes()))
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
def _predict(self,
test_reader=None,
model_path=None,
batch_size=1,
batch_num=1,
skip_batch_num=0,
transform_to_int8=False):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.executor.global_scope()
with fluid.scope_guard(inference_scope):
if os.path.exists(os.path.join(model_path, '__model__')):
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(model_path, exe)
else:
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(model_path, exe, 'model',
'params')
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
if (self._debug):
graph.draw('.', 'qat_orig', graph.all_op_nodes())
if (transform_to_int8):
transform_to_mkldnn_int8_pass = Qat2Int8MkldnnPass(
self._quantized_ops,
_scope=inference_scope,
_place=place,
_core=core,
_debug=self._debug)
graph = transform_to_mkldnn_int8_pass.apply(graph)
inference_program = graph.to_program()
total_correct = 0
total_samples = 0
batch_times = []
ppses = [] # predictions per second
iters = 0
infer_start_time = time.time()
for data in test_reader():
if batch_num > 0 and iters >= batch_num:
break
if iters == skip_batch_num:
total_samples = 0
infer_start_time = time.time()
input0 = np.array([x[0] for x in data]).astype('int64')
input1 = np.array([x[1] for x in data]).astype('int64')
labels = np.array([x[2] for x in data]).astype('int64')
start = time.time()
out = exe.run(inference_program,
feed={
feed_target_names[0]: input0,
feed_target_names[1]: input1
},
fetch_list=fetch_targets)
batch_time = (time.time() - start) * 1000 # in miliseconds
batch_times.append(batch_time)
batch_correct = self._get_batch_correct(out, labels)
batch_len = len(data)
total_samples += batch_len
total_correct += batch_correct
batch_acc = float(batch_correct) / float(batch_len)
pps = batch_len / batch_time * 1000
ppses.append(pps)
latency = batch_time / batch_len
iters += 1
appx = ' (warm-up)' if iters <= skip_batch_num else ''
_logger.info(
'batch {0}{4}, acc: {1:.4f}, latency: {2:.4f} ms, predictions per sec: {3:.2f}'
.format(iters, batch_acc, latency, pps, appx))
# Postprocess benchmark data
infer_total_time = time.time() - infer_start_time
batch_latencies = batch_times[skip_batch_num:]
batch_latency_avg = np.average(batch_latencies)
latency_avg = batch_latency_avg / batch_size
ppses = ppses[skip_batch_num:]
pps_avg = np.average(ppses)
acc_avg = float(np.sum(total_correct)) / float(total_samples)
_logger.info(
'Total inference run time: {:.2f} s'.format(infer_total_time))
return acc_avg, pps_avg, latency_avg
def freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=True,
quant_skip_pattern='skip_quant'):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label, quant_skip_pattern)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type,
skip_pattern=quant_skip_pattern)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
if not for_ci:
marked_nodes = set()
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name +
activation_quant_type + '_' +
weight_quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
quantized_test_program)
# Testing
with fluid.scope_guard(scope):
test_loss1, w_quant = exe.run(program=quantized_test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
server_program = test_graph.to_program()
with fluid.scope_guard(scope):
test_loss2, = exe.run(program=server_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
if not for_ci:
print(
'{}: {}'.format('test_loss1' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss1))
print(
'{}: {}'.format('test_loss2' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
if not for_ci:
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
convert_int8_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + activation_quant_type
+ '_' + weight_quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
if not for_ci:
print('{}: {}'.format('w_8bit' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
'mobile_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
mobile_program)
def _predict(self,
test_reader=None,
model_path=None,
batch_size=1,
batch_num=1,
skip_batch_num=0,
transform_to_int8=False):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.executor.global_scope()
with fluid.scope_guard(inference_scope):
if os.path.exists(os.path.join(model_path, '__model__')):
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(model_path, exe)
else:
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(model_path, exe, 'model',
'params')
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
if (self._debug):
graph.draw('.', 'quant_orig', graph.all_op_nodes())
if (transform_to_int8):
mkldnn_int8_pass = QuantInt8MkldnnPass(_scope=inference_scope,
_place=place)
graph = mkldnn_int8_pass.apply(graph)
else:
graph = self._prepare_for_fp32_mkldnn(graph)
inference_program = graph.to_program()
dshape = [3, 224, 224]
outputs = []
infer_accs1 = []
infer_accs5 = []
fpses = []
batch_times = []
total_samples = 0
iters = 0
infer_start_time = time.time()
for data in test_reader():
if batch_num > 0 and iters >= batch_num:
break
if iters == skip_batch_num:
total_samples = 0
infer_start_time = time.time()
if six.PY2:
images = map(lambda x: x[0].reshape(dshape), data)
if six.PY3:
images = list(map(lambda x: x[0].reshape(dshape), data))
images = np.array(images).astype('float32')
labels = np.array([x[1] for x in data]).astype('int64')
start = time.time()
out = exe.run(inference_program,
feed={feed_target_names[0]: images},
fetch_list=fetch_targets)
batch_time = (time.time() - start) * 1000 # in miliseconds
outputs.append(out[0])
batch_acc1, batch_acc5 = self._get_batch_accuracy(
out[0], labels)
infer_accs1.append(batch_acc1)
infer_accs5.append(batch_acc5)
samples = len(data)
total_samples += samples
batch_times.append(batch_time)
fps = samples / batch_time * 1000
fpses.append(fps)
iters += 1
appx = ' (warm-up)' if iters <= skip_batch_num else ''
_logger.info('batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, '
'latency: {3:.4f} ms, fps: {4:.2f}'.format(
iters, batch_acc1, batch_acc5,
batch_time / batch_size, fps, appx))
# Postprocess benchmark data
batch_latencies = batch_times[skip_batch_num:]
batch_latency_avg = np.average(batch_latencies)
latency_avg = batch_latency_avg / batch_size
fpses = fpses[skip_batch_num:]
fps_avg = np.average(fpses)
infer_total_time = time.time() - infer_start_time
acc1_avg = np.mean(infer_accs1)
acc5_avg = np.mean(infer_accs5)
_logger.info(
'Total inference run time: {:.2f} s'.format(infer_total_time))
return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg
def graph_apis(self, use_cuda=False, for_ci=True):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
feeds, loss = conv_block()
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
backup_graph = graph.clone()
self.assertEqual(len(graph.all_nodes()), len(backup_graph.all_nodes()))
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
origin_binary = fluid.CompiledProgram(graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
backup_binary = fluid.CompiledProgram(
backup_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
iters = 5
batch_size = 8
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
def _train(binary):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss.name])
if not for_ci:
print('{}: {}'.format('loss', loss_v))
_train(origin_binary)
_train(backup_binary)
checkponit_dir = "checkpoint_gpu" if use_cuda else "checkpoint_cpu"
def _set_zero(var_name, scope, place):
var = scope.find_var(var_name).get_tensor()
var_array = np.zeros(var._get_dims()).astype("float32")
var.set(var_array, place)
sum_before = np.sum(
np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
)))
fluid.io._save_persistable_nodes(exe, checkponit_dir, graph)
_set_zero('conv2d_1.w_0', fluid.global_scope(), place)
set_after = np.sum(
np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
)))
self.assertEqual(set_after, 0)
fluid.io._load_persistable_nodes(exe, checkponit_dir, graph)
sum_after = np.sum(
np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
)))
self.assertEqual(sum_before, sum_after)
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
if not for_ci:
graph.draw('.', 'residual', marked_nodes)
backup_marked_nodes = set()
for op in backup_graph.all_op_nodes():
if op.name().find('conv2d') > -1:
backup_marked_nodes.add(op)
backup_graph.draw('./origin', 'backup', backup_marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
self.assertEqual(len(nodes), len(graph.all_op_nodes()))
nodes_map = graph.build_adjacency_list()
self.assertEqual(len(nodes_map), len(graph.all_op_nodes()))
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
def quantization_scale(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = residual_block(img, label, 1)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
add_quant_dequant_pass.apply(test_graph)
scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
scale_training_pass.apply(main_graph)
dev_name = '_gpu' if use_cuda else '_cpu'
if not for_ci:
marked_nodes = set()
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main_scale' + dev_name, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_scale' + dev_name, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name, loss_v))
scale_inference_pass = OutScaleForInferencePass(scope=scope)
scale_inference_pass.apply(test_graph)
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
server_program = test_graph.to_program()
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'quant_scale' + dev_name, marked_nodes)
with open('quant_scale_model' + dev_name + '.txt', '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], exe,
server_program)
def mkldnn_based_freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
quant_perf=False,
for_ci=False):
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = self.build_program(main, startup, False, seed)
self.build_program(test_program, startup, True, seed)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
# Apply the QuantizationTransformPass
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
# Training the model to get the weights value
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
# Transform quantized graph for MKL-DNN INT8 inference
mkldnn_int8_pass = QuantInt8MkldnnPass(_scope=scope, _place=place)
mkldnn_int8_pass.apply(test_graph)
dev_name = '_cpu_'
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw(
'.', 'test_mkldnn' + dev_name + activation_quant_type + '_' +
weight_quant_type, marked_nodes)
mkldnn_program = test_graph.to_program()
# Check the transformation weights of conv2d and mul
conv_w_mkldnn = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
mul_w_mkldnn = np.array(scope.find_var('fc_0.w_0').get_tensor())
# Check if weights are still integer
self.assertFalse(self.isinteger(np.sum(conv_w_mkldnn)))
self.assertFalse(self.isinteger(np.sum(mul_w_mkldnn)))
# Check if the conv2d output and mul output are correctly linked to fake_dequantize's
# output
self.check_program(mkldnn_program)
if not for_ci:
print('{}: {}'.format(
'w_mkldnn' + dev_name + activation_quant_type + '_' +
weight_quant_type, np.sum(w_mkldnn)))
def quantize_program(self,
use_cuda,
seed,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
# 1 Define program
train_program = fluid.Program()
startup_program = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(train_program, startup_program, False)
build_program(test_program, startup_program, True)
test_program = test_program.clone(for_test=True)
if not for_ci:
train_graph = IrGraph(core.Graph(train_program.desc),
for_test=False)
train_graph.draw('.', 'train_program_1')
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
test_graph.draw('.', 'test_program_1')
# 2 Apply quantization
qt = QuantizeTranspilerV2(
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
qt.apply(train_program, startup_program, is_test=False)
qt.apply(test_program, startup_program, is_test=True)
# 3 Train
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup_program)
if not for_ci:
train_graph = IrGraph(core.Graph(train_program.desc),
for_test=False)
train_graph.draw('.', 'train_program_2')
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
test_graph.draw('.', 'test_program_2')
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(train_program).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for idx in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci and idx % 20 == 0:
print('{}: {}'.format('loss', np.mean(loss_v)))
print('{}: {}'.format('loss', np.mean(loss_v)))
# 4 Convert
qt.convert(test_program, scope)
if not for_ci:
with fluid.scope_guard(scope):
fluid.io.save_inference_model('./infer_model',
['image', 'label'], [loss],
exe,
test_program,
clip_extra=True)
def quant_aware(program,
place,
config=None,
scope=None,
for_test=False,
weight_quantize_func=None,
act_quantize_func=None,
weight_preprocess_func=None,
act_preprocess_func=None,
optimizer_func=None,
executor=None,
onnx_format=False,
return_program=False,
draw_graph=False):
"""Add quantization and dequantization operators to "program"
for quantization training or testing.
Args:
program(paddle.static.Program): training or testing ``program``.
place(paddle.CPUPlace or paddle.CUDAPlace): This parameter represents
the executor run on which device.
config(dict, optional): configs for quantization. if None, will use default config.
Default: None.
scope(paddle.static.Scope): Scope records the mapping between variable names and variables,
similar to brackets in programming languages. Usually users can use
`paddle.static.global_scope <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_. When ``None`` will use `paddle.static.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_ . Default: ``None``.
for_test(bool): If the 'program' parameter is a test program, this parameter should be set to ``True``.
Otherwise, set to ``False``.Default: False
weight_quantize_func(function): Function that defines how to quantize weight. Using this
can quickly test if user's quantization method works or not. In this function, user should
both define quantization function and dequantization function, that is, the function's input
is non-quantized weight and function returns dequantized weight. If None, will use
quantization op defined by 'weight_quantize_type'.
Default is None.
act_quantize_func(function): Function that defines how to quantize activation. Using this
can quickly test if user's quantization method works or not. In this function, user should
both define quantization and dequantization process, that is, the function's input
is non-quantized activation and function returns dequantized activation. If None, will use
quantization op defined by 'activation_quantize_type'.
Default is None.
weight_preprocess_func(function): Function that defines how to preprocess weight before quantization. Using this
can quickly test if user's preprocess method works or not. The function's input
is non-quantized weight and function returns processed weight to be quantized. If None, the weight will
be quantized directly.
Default is None.
act_preprocess_func(function): Function that defines how to preprocess activation before quantization. Using this
can quickly test if user's preprocess method works or not. The function's input
is non-quantized activation and function returns processed activation to be quantized. If None, the activation will
be quantized directly.
Default is None.
optimizer_func(function): Fuction return a optimizer. When 'is_test' is False and user want to use self-defined
quantization function and preprocess function, this function must be set. Default is None.
exe(paddle.static.Executor): If user want to use self-defined quantization function and preprocess function, exe must be set for
initialization. Default is None.
return_program(bool): If user want return value is a Program rather than Compiled Program, This argument should be set True.
Default is False.
draw_graph(bool): whether to draw graph when quantization is initialized. In order to prevent cycle,
the ERNIE model needs to be set to True. Default is False.
Returns:
paddle.static.CompiledProgram | paddle.static.Program: Program with quantization and dequantization ``operators``
"""
scope = paddle.static.global_scope() if not scope else scope
if config is None:
config = _quant_config_default
else:
assert isinstance(config, dict), "config must be dict"
config = _parse_configs(config)
_logger.info("quant_aware config {}".format(config))
main_graph = IrGraph(core.Graph(program.desc), for_test=for_test)
transform_pass_ops = []
quant_dequant_ops = []
for op_type in config['quantize_op_types']:
if op_type in TRANSFORM_PASS_OP_TYPES:
transform_pass_ops.append(op_type)
elif op_type in QUANT_DEQUANT_PASS_OP_TYPES:
quant_dequant_ops.append(op_type)
if len(transform_pass_ops) > 0:
trannsform_func = 'QuantizationTransformPassV2' if onnx_format else 'QuantizationTransformPass'
transform_pass = eval(trannsform_func)(
scope=scope,
place=place,
weight_bits=config['weight_bits'],
activation_bits=config['activation_bits'],
activation_quantize_type=config['activation_quantize_type'],
weight_quantize_type=config['weight_quantize_type'],
window_size=config['window_size'],
moving_rate=config['moving_rate'],
quantizable_op_type=transform_pass_ops,
skip_pattern=config['not_quant_pattern'],
weight_quantize_func=weight_quantize_func,
act_quantize_func=act_quantize_func,
weight_preprocess_func=weight_preprocess_func,
act_preprocess_func=act_preprocess_func,
optimizer_func=optimizer_func,
executor=executor)
transform_pass.apply(main_graph)
if len(quant_dequant_ops) > 0:
qdq_func = 'AddQuantDequantPassV2' if onnx_format else 'AddQuantDequantPass'
quant_dequant_pass = eval(qdq_func)(
scope=scope,
place=place,
moving_rate=config['moving_rate'],
quant_bits=config['activation_bits'],
skip_pattern=config['not_quant_pattern'],
quantizable_op_type=quant_dequant_ops)
quant_dequant_pass.apply(main_graph)
out_scale_training_pass = OutScaleForTrainingPass(
scope=scope, place=place, moving_rate=config['moving_rate'])
out_scale_training_pass.apply(main_graph)
if (weight_preprocess_func is not None
or act_preprocess_func is not None) and not for_test:
_logger.info(
"When a preprocess_func is used in quant_aware, Need to save a mapping table to match variable names in the convert phase."
)
_logger.info(
"The mapping table is saved as '{}'.".format(VARS_MAPPING_TABLE))
save_dict(main_graph.out_node_mapping_table)
# TDOD: remove it.
if draw_graph:
main_graph.draw('./', 'graph.pdf')
if for_test or return_program:
quant_program = main_graph.to_program()
else:
quant_program = paddle.static.CompiledProgram(main_graph.graph)
return quant_program