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未验证 提交 393a91f1 编写于 作者: G guofei 提交者： GitHub
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Quantization supports 2.0 APIs (#30036) (#30257) 

* Quantization supports 2.0 APIs

* Fix the error of save_quantized_model
上级 e283dc6f
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Showing with 85 addition and 50 deletion
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @ 393a91f1
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import logging
import numpy as np
import sys
......@@ -20,8 +21,8 @@ import paddle
from paddle.fluid import dygraph, core, framework
from paddle.fluid.executor import Executor
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
from paddle.nn import Linear, Conv2D
from paddle.fluid.dygraph.nn import BatchNorm, Pool2D, Conv2DTranspose
from paddle.nn import Linear, Conv2D, Conv2DTranspose, MaxPool2D, MaxPool1D, BatchNorm1D, BatchNorm2D, BatchNorm3D
from paddle.fluid.dygraph.nn import BatchNorm, Pool2D
from paddle.fluid.io import load_inference_model, save_inference_model
from paddle.nn.layer.activation import ReLU, LeakyReLU, Sigmoid, ReLU6, Tanh, Softmax, PReLU, Swish
from paddle.fluid.log_helper import get_logger
......@@ -263,6 +264,7 @@ class ImperativeQuantAware(object):
parent = obj
quant_layer = self._get_quantized_counterpart(layer)
setattr(quant_layer, "layer_name", layer.full_name())
setattr(obj, target, quant_layer)
self._out_scale.calc_out_scale(model)
......@@ -306,10 +308,11 @@ class ImperativeCalcOutScale(object):
super(ImperativeCalcOutScale, self).__init__()
self._moving_rate = moving_rate
self._out_scale_layer_type_list = (
BatchNorm, Conv2D, Conv2DTranspose, LeakyReLU, Linear, PReLU,
Pool2D, ReLU, ReLU6, Sigmoid, Softmax, Tanh, Swish)
BatchNorm, BatchNorm1D, BatchNorm2D, BatchNorm3D, Conv2D,
Conv2DTranspose, LeakyReLU, Linear, PReLU, Pool2D, MaxPool1D,
MaxPool2D, ReLU, ReLU6, Sigmoid, Softmax, Tanh, Swish)
self._register_hook_handle_list = []
self._out_scale_dict = {}
self._out_scale_dict = collections.OrderedDict()
def calc_out_scale(self, model):
"""
......@@ -325,6 +328,7 @@ class ImperativeCalcOutScale(object):
model, dygraph.Layer), "model must be the instance of dygraph.Layer"
for _, layer in model.named_sublayers():
if not isinstance(layer, self._out_scale_layer_type_list):
if 'quantized_' not in layer.full_name():
continue
forward_post_hook_handle = layer.register_forward_post_hook(
self._forward_post_hook)
......@@ -364,12 +368,12 @@ class ImperativeCalcOutScale(object):
self._out_scale_dict[key] = float(self._out_scale_dict[key]
.numpy())
paddle.jit.save(layer=layer, path=path, input_spec=input_spec, **config)
if paddle.in_dynamic_mode():
is_dynamic_mode = True
paddle.enable_static()
paddle.jit.save(layer=layer, path=path, input_spec=input_spec, **config)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
......@@ -391,9 +395,22 @@ class ImperativeCalcOutScale(object):
# Traverse all ops in the program and find out the op matching
# the Layer in the dynamic graph.
layer_var_dict = {}
ops_list = [key for key, _ in self._out_scale_dict.items()]
op_count = 0
for block in inference_program.blocks:
for op in block.ops:
if op.type in _op_real_in_out_name:
if op.type in ["batch_norm", "pool2d"]:
if op.type == "pool2d" and op.attr(
"pooling_type") != "max":
continue
op_count = self.op_match(op, ops_list, op_count)
if op_count >= len(ops_list):
continue
op._set_attr('out_threshold',
self._out_scale_dict[ops_list[op_count]])
op_count += 1
else:
output_var_names = quantization_pass._get_op_output_var_names(
op)
for output_var_name in output_var_names:
......@@ -423,8 +440,9 @@ class ImperativeCalcOutScale(object):
var_name_suffix, op
]
else:
layer_var_dict[
dynamic_layer_name] = [var_name_suffix, op]
layer_var_dict[dynamic_layer_name] = [
var_name_suffix, op
]
# Because the naming styles of static and dynamic graph are different,
# in order to avoid mistakes, we unify the name here.
......@@ -451,6 +469,14 @@ class ImperativeCalcOutScale(object):
if is_dynamic_mode:
paddle.disable_static()
def op_match(self, op, ops_list, op_count):
while op_count < len(ops_list) and op.type not in ops_list[op_count]:
op_count += 1
while op_count < len(ops_list) and op.type is "pool2d" and op.attr(
"pooling_type") != "max":
op_count += 1
return op_count
def _forward_post_hook(self, layer, input, output):
assert isinstance(
output, (core.VarBase, framework.Variable)
......@@ -463,4 +489,8 @@ class ImperativeCalcOutScale(object):
layer._out_scale = quant_nn.MovingAverageAbsMaxScale(
output.name, self._moving_rate, output.dtype)
scale_out = layer._out_scale(output)
self._out_scale_dict[layer.full_name()] = scale_out
if hasattr(layer, 'layer_name'):
layer_name = layer.layer_name
else:
layer_name = layer.full_name()
self._out_scale_dict[layer_name] = scale_out
python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py
浏览文件 @ 393a91f1
......@@ -30,9 +30,10 @@ from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass, OutS
from paddle.fluid.dygraph.container import Sequential
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
from paddle.nn.layer import ReLU, LeakyReLU, Sigmoid, Softmax, ReLU6
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
from paddle.fluid.dygraph.nn import Pool2D
from paddle.fluid.log_helper import get_logger
from paddle.fluid.dygraph import nn
paddle.enable_static()
......@@ -50,7 +51,6 @@ def StaticLenet(data, num_classes=10, classifier_activation='softmax'):
fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
......@@ -62,7 +62,7 @@ def StaticLenet(data, num_classes=10, classifier_activation='softmax'):
stride=1,
padding=1,
param_attr=conv2d_w1_attr,
bias_attr=conv2d_b1_attr)
bias_attr=False)
batch_norm1 = layers.batch_norm(conv1)
relu1 = layers.relu(batch_norm1)
pool1 = fluid.layers.pool2d(
......@@ -99,14 +99,13 @@ def StaticLenet(data, num_classes=10, classifier_activation='softmax'):
class ImperativeLenet(fluid.dygraph.Layer):
def __init__(self, num_classes=10, classifier_activation='softmax'):
def __init__(self, num_classes=10):
super(ImperativeLenet, self).__init__()
conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
......@@ -119,8 +118,8 @@ class ImperativeLenet(fluid.dygraph.Layer):
stride=1,
padding=1,
weight_attr=conv2d_w1_attr,
bias_attr=conv2d_b1_attr),
BatchNorm(6),
bias_attr=False),
BatchNorm2D(6),
ReLU(),
Pool2D(
pool_size=2, pool_type='max', pool_stride=2),
......@@ -132,10 +131,10 @@ class ImperativeLenet(fluid.dygraph.Layer):
padding=0,
weight_attr=conv2d_w2_attr,
bias_attr=conv2d_b2_attr),
BatchNorm(16),
BatchNorm2D(16),
ReLU6(),
Pool2D(
pool_size=2, pool_type='max', pool_stride=2))
MaxPool2D(
kernel_size=2, stride=2))
self.fc = Sequential(
Linear(
......@@ -188,10 +187,10 @@ class TestImperativeOutSclae(unittest.TestCase):
reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=32, drop_last=True)
weight_quantize_type = 'abs_max'
activation_quant_type = 'moving_average_abs_max'
activation_quantize_type = 'moving_average_abs_max'
param_init_map = {}
seed = 1000
lr = 0.1
lr = 0.001
dynamic_out_scale_list = []
static_out_scale_list = []
......@@ -199,7 +198,9 @@ class TestImperativeOutSclae(unittest.TestCase):
_logger.info(
"--------------------------dynamic graph qat--------------------------"
)
imperative_out_scale = ImperativeQuantAware()
imperative_out_scale = ImperativeQuantAware(
weight_quantize_type=weight_quantize_type,
activation_quantize_type=activation_quantize_type)
with fluid.dygraph.guard():
np.random.seed(seed)
......@@ -282,14 +283,18 @@ class TestImperativeOutSclae(unittest.TestCase):
with fluid.scope_guard(scope):
exe.run(startup)
for param in main.all_parameters():
if "batch_norm" in param.name:
param_name = param.name.replace("norm", "norm2d")
else:
param_name = param.name
param_tensor = scope.var(param.name).get_tensor()
param_tensor.set(param_init_map[param.name], place)
param_tensor.set(param_init_map[param_name], place)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
infer_graph = IrGraph(core.Graph(infer.desc), for_test=True)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
activation_quantize_type=activation_quantize_type,
weight_quantize_type=weight_quantize_type,
quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul'])
transform_pass.apply(main_graph)
......
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