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@XGZhang11
XGZhang11 committed Apr 13, 2021
1 parent 216e946 commit 724e56c
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41 changes: 19 additions & 22 deletions python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -265,7 +265,9 @@ def __init__(self,
'range_abs_max', 'moving_average_abs_max', 'abs_max'
]
self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
self._support_algo_type = ['KL', 'hist', 'avg', 'mse', 'abs_max', 'min_max']
self._support_algo_type = [
'KL', 'hist', 'avg', 'mse', 'abs_max', 'min_max'
]
self._dynamic_quantize_op_type = ['lstm']
self._support_quantize_op_type = \
list(set(QuantizationTransformPass._supported_quantizable_op_type +
Expand Down Expand Up @@ -389,11 +391,8 @@ def quantize(self):
batch_id += 1
if self._batch_nums and batch_id >= self._batch_nums:
break

_logger.info("Finish sampling stage, all batch: " + str(batch_id))

self._reset_activation_persistable()

if self._algo == 'avg':
for var_name in self._quantized_act_var_name:
self._quantized_threshold[var_name] = \
Expand Down Expand Up @@ -573,8 +572,6 @@ def _sample_mse(self):
abs_max_value.append(
float(np.max(np.abs(var_tensor[i]))))
self._quantized_threshold[var_name] = abs_max_value

#Search for the best threshold for activations
_logger.info("MSE searching stage ...")
for var_name in self._quantized_act_var_name:
var_tensor = _load_variable_data(self._scope, var_name)
Expand All @@ -586,13 +583,15 @@ def _sample_mse(self):
while s <= 1.0:
scale = s * abs_max_value
s += 0.02
bins = 2 ** (self._activation_bits -1) - 1
quant_dequant_var = np.round(np.clip(var_tensor, 0.0, scale) / scale * bins) / bins * scale
mse_loss = ((var_tensor - quant_dequant_var) ** 2).mean()
bins = 2**(self._activation_bits - 1) - 1
quant_dequant_var = np.round(
np.clip(var_tensor, 0.0, scale) / scale *
bins) / bins * scale
mse_loss = ((var_tensor - quant_dequant_var)**2).mean()
if mse_loss <= self._best_mse_loss[var_name]:
self._best_mse_loss[var_name] = mse_loss
self._quantized_threshold[var_name] = scale

def _sample_avg(self):
if self._quantized_threshold == {}:
for var_name in self._quantized_weight_var_name:
Expand All @@ -611,7 +610,7 @@ def _sample_avg(self):
abs_max_value.append(
float(np.max(np.abs(var_tensor[i]))))
self._quantized_threshold[var_name] = abs_max_value

for var_name in self._quantized_act_var_name:
var_tensor = _load_variable_data(self._scope, var_name)
abs_max_value = float(np.max(np.abs(var_tensor)))
Expand All @@ -621,8 +620,8 @@ def _sample_avg(self):
np.abs(var_tensor.reshape(var_tensor.shape[0], -1)), axis=(1))))
self._quantized_var_avg[var_name].append(abs_avg_value)
continue
def _sample_abs_max(self):

def _sample_abs_max(self):
if self._quantized_threshold == {}:
for var_name in self._quantized_weight_var_name:
var_tensor = _load_variable_data(self._scope, var_name)
Expand All @@ -640,7 +639,7 @@ def _sample_abs_max(self):
abs_max_value.append(
float(np.max(np.abs(var_tensor[i]))))
self._quantized_threshold[var_name] = abs_max_value

for var_name in self._quantized_act_var_name:
var_tensor = _load_variable_data(self._scope, var_name)
abs_max_value = float(np.max(np.abs(var_tensor)))
Expand Down Expand Up @@ -856,18 +855,16 @@ def analysis_and_save_info(op_node, out_var_name):
out_var_name + " is not the output of the op"
if self._algo == "KL":
# For compatibility, we save output threshold by two methods.
save_info(op_node, out_var_name,
self._quantized_var_threshold, "out_threshold",
"post_kl")
save_info(op_node, out_var_name, self._quantized_var_threshold,
"out_threshold", "post_kl")
save_info(
op_node, out_var_name, self._quantized_var_threshold,
argname_index[0] + str(argname_index[1]) + "_threshold",
"post_kl")
elif self._algo == "hist":
# For compatibility, we save output threshold by two methods.
save_info(op_node, out_var_name,
self._quantized_var_threshold, "out_threshold",
"post_hist")
save_info(op_node, out_var_name, self._quantized_var_threshold,
"out_threshold", "post_hist")
save_info(
op_node, out_var_name, self._quantized_var_threshold,
argname_index[0] + str(argname_index[1]) + "_threshold",
Expand Down Expand Up @@ -926,7 +923,7 @@ def _get_hist_scaling_factor(self, hist, hist_edges):
'''
Using the hist method to get the scaling factor.
'''
threshold_rate = self._hist_percent
threshold_rate = self._hist_percent
hist = hist / float(sum(hist))
hist_sum = 0
hist_index = 0
Expand All @@ -942,7 +939,7 @@ def _get_kl_scaling_factor(self, hist, hist_edeges):
'''
Using the KL-divergenc method to get the more precise scaling factor.
'''
num_quantized_bins = 2 ** (self._activation_bits - 1) - 1
num_quantized_bins = 2**(self._activation_bits - 1) - 1
ending_iter = self._histogram_bins - 1
starting_iter = int(ending_iter * 0.7)
bin_width = hist_edeges[1] - hist_edeges[0]
Expand Down
11 changes: 8 additions & 3 deletions python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1372,6 +1372,7 @@ def _is_float(self, v):
def _quant(self, x, scale, num_bits, quant_axis):
assert quant_axis in [0, 1], 'quant_axis should be 0 or 1 for now.'
bnt = (1 << (num_bits - 1)) - 1

def _clip(x, scale):
x[x > scale] = scale
x[x < -scale] = -scale
Expand Down Expand Up @@ -1412,9 +1413,12 @@ def _bias_correction_w(self, x, x_quant, scale_v, quant_axis):
for i, s in enumerate(scale_v):
x_dequant[:, i] = x_quant[:, i] * s / bnt
quant_bias = x - x_dequant
mean_bias = np.array([quant_bias[:, i].mean() for i in range(quant_bias.shape[1])])
mean_bias = np.array([
quant_bias[:, i].mean() for i in range(quant_bias.shape[1])
])
std_orig = np.array([x[:, i].std() for i in range(x.shape[1])])
std_quant = np.array([x_dequant[:, i].std() for i in range(x_dequant.shape[1])])
std_quant = np.array(
[x_dequant[:, i].std() for i in range(x_dequant.shape[1])])
std_bias = std_orig / (std_quant + eps)
else:
x_dequant = x_quant * scale_v / bnt
Expand All @@ -1425,7 +1429,8 @@ def _bias_correction_w(self, x, x_quant, scale_v, quant_axis):
mean_bias = np.resize(mean_bias, x.shape)

x_dequant = (mean_bias + x_dequant) * std_bias
quantized_param_v = self._quant(x_dequant, scale_v, self._weight_bits, quant_axis)
quantized_param_v = self._quant(x_dequant, scale_v, self._weight_bits,
quant_axis)
return quantized_param_v


Expand Down
22 changes: 2 additions & 20 deletions python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -327,6 +327,7 @@ def test_post_training_kl_mobilenetv1(self):
is_full_quantize, is_use_cache_file, is_optimize_model,
diff_threshold)


class TestPostTrainingavgForMobilenetv1(TestPostTrainingQuantization):
def test_post_training_avg_mobilenetv1(self):
model = "MobileNet-V1"
Expand All @@ -348,6 +349,7 @@ def test_post_training_avg_mobilenetv1(self):
is_full_quantize, is_use_cache_file, is_optimize_model,
diff_threshold)


class TestPostTraininghistForMobilenetv1(TestPostTrainingQuantization):
def test_post_training_hist_mobilenetv1(self):
model = "MobileNet-V1"
Expand All @@ -369,26 +371,6 @@ def test_post_training_hist_mobilenetv1(self):
is_full_quantize, is_use_cache_file, is_optimize_model,
diff_threshold)

class TestPostTrainingmseForMobilenetv1(TestPostTrainingQuantization):
def test_post_training_mse_mobilenetv1(self):
model = "MobileNet-V1"
algo = "mse"
data_urls = [
'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
]
data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
quantizable_op_type = [
"conv2d",
"depthwise_conv2d",
"mul",
]
is_full_quantize = False
is_use_cache_file = False
is_optimize_model = True
diff_threshold = 0.025
self.run_test(model, algo, data_urls, data_md5s, quantizable_op_type,
is_full_quantize, is_use_cache_file, is_optimize_model,
diff_threshold)

class TestPostTrainingAbsMaxForMobilenetv1(TestPostTrainingQuantization):
def test_post_training_abs_max_mobilenetv1(self):
Expand Down

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