fix ptq hpo (#1021)

demo/quant/quant_post_hpo/quant_post_hpo.py

@@ -55,7 +55,7 @@ def quantize(args):
         save_model_filename='__model__',
         save_params_filename='__params__',
         quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
-        weight_quantize_type='channel_wise_abs_max',
+        weight_quantize_type=['channel_wise_abs_max'],
         runcount_limit=args.max_model_quant_count)
 
 def main():

paddleslim/quant/quant_post_hpo.py

@@ -189,10 +189,10 @@ def eval_quant_model():
     valid_data_num = 0
     max_eval_data_num = 200
     if g_quant_config.eval_sample_generator is not None:
-        feed_dict=False
+        feed_dict = False
         eval_dataloader = g_quant_config.eval_sample_generator
     else:
-        feed_dict=True
+        feed_dict = True
         eval_dataloader = g_quant_config.eval_dataloader
     for i, data in enumerate(eval_dataloader()):
         with paddle.static.scope_guard(float_scope):
@@ -236,12 +236,20 @@ def eval_quant_model():
 def quantize(cfg):
     """model quantize job"""
     algo = cfg["algo"] if 'algo' in cfg else g_quant_config.algo[0][0]
-    hist_percent = cfg["hist_percent"] if "hist_percent" in cfg else g_quant_config.hist_percent[0][0]
-    bias_correct = cfg["bias_correct"] if "bias_correct" in cfg else g_quant_config.bias_correct[0][0]
-    batch_size = cfg["batch_size"] if "batch_size" in cfg else g_quant_config.batch_size[0][0]
-    batch_num = cfg["batch_num"] if "batch_num" in cfg else g_quant_config.batch_num[0][0]
-    weight_quantize_type = cfg["weight_quantize_type"] if "weight_quantize_type" in cfg else g_quant_config.weight_quantize_type[0]
-    print(hist_percent, bias_correct, batch_size, batch_num, weight_quantize_type)
+    hist_percent = cfg[
+        "hist_percent"] if "hist_percent" in cfg else g_quant_config.hist_percent[
+            0][0]
+    bias_correct = cfg[
+        "bias_correct"] if "bias_correct" in cfg else g_quant_config.bias_correct[
+            0][0]
+    batch_size = cfg[
+        "batch_size"] if "batch_size" in cfg else g_quant_config.batch_size[0][
+            0]
+    batch_num = cfg[
+        "batch_num"] if "batch_num" in cfg else g_quant_config.batch_num[0][0]
+    weight_quantize_type = cfg[
+        "weight_quantize_type"] if "weight_quantize_type" in cfg else g_quant_config.weight_quantize_type[
+            0]
 
     quant_post( \
         executor=g_quant_config.executor, \
@@ -279,34 +287,35 @@ def quantize(cfg):
     return emd_loss
 
 
-def quant_post_hpo(executor,
-                   place,
-                   model_dir,
-                   quantize_model_path,
-                   train_sample_generator=None,
-                   eval_sample_generator=None,
-                   train_dataloader=None,
-                   eval_dataloader=None,
-                   eval_function=None,
-                   model_filename=None,
-                   params_filename=None,
-                   save_model_filename='__model__',
-                   save_params_filename='__params__',
-                   scope=None,
-                   quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
-                   is_full_quantize=False,
-                   weight_bits=8,
-                   activation_bits=8,
-                   weight_quantize_type=['channel_wise_abs_max'],
-                   algo=["KL", "hist", "avg", "mse"],
-                   bias_correct=[True, False],
-                   hist_percent=[0.98, 0.999], ### uniform sample in list.
-                   batch_size=[10, 30], ### uniform sample in list.
-                   batch_num=[10, 30], ### uniform sample in list.
-                   optimize_model=False,
-                   is_use_cache_file=False,
-                   cache_dir="./temp_post_training",
-                   runcount_limit=30):
+def quant_post_hpo(
+        executor,
+        place,
+        model_dir,
+        quantize_model_path,
+        train_sample_generator=None,
+        eval_sample_generator=None,
+        train_dataloader=None,
+        eval_dataloader=None,
+        eval_function=None,
+        model_filename=None,
+        params_filename=None,
+        save_model_filename='__model__',
+        save_params_filename='__params__',
+        scope=None,
+        quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
+        is_full_quantize=False,
+        weight_bits=8,
+        activation_bits=8,
+        weight_quantize_type=['channel_wise_abs_max'],
+        algo=["KL", "hist", "avg", "mse"],
+        bias_correct=[True, False],
+        hist_percent=[0.98, 0.999],  ### uniform sample in list.
+        batch_size=[10, 30],  ### uniform sample in list.
+        batch_num=[10, 30],  ### uniform sample in list.
+        optimize_model=False,
+        is_use_cache_file=False,
+        cache_dir="./temp_post_training",
+        runcount_limit=30):
     """
     The function utilizes static post training quantization method to
     quantize the fp32 model. It uses calibrate data to calculate the
@@ -360,25 +369,27 @@ def quant_post_hpo(executor,
 
     global g_quant_config
     g_quant_config = QuantConfig(
-        executor, place, model_dir, quantize_model_path, algo, hist_percent, 
+        executor, place, model_dir, quantize_model_path, algo, hist_percent,
         bias_correct, batch_size, batch_num, train_sample_generator,
         eval_sample_generator, train_dataloader, eval_dataloader, eval_function,
-        model_filename, params_filename,
-        save_model_filename, save_params_filename, scope, quantizable_op_type,
-        is_full_quantize, weight_bits, activation_bits, weight_quantize_type,
-        optimize_model, is_use_cache_file, cache_dir)
+        model_filename, params_filename, save_model_filename,
+        save_params_filename, scope, quantizable_op_type, is_full_quantize,
+        weight_bits, activation_bits, weight_quantize_type, optimize_model,
+        is_use_cache_file, cache_dir)
     cs = ConfigurationSpace()
 
     hyper_params = []
 
     if 'hist' in algo:
         hist_percent = UniformFloatHyperparameter(
-            "hist_percent", hist_percent[0], hist_percent[1], default_value=hist_percent[0])
+            "hist_percent",
+            hist_percent[0],
+            hist_percent[1],
+            default_value=hist_percent[0])
         hyper_params.append(hist_percent)
 
     if len(algo) > 1:
-        algo = CategoricalHyperparameter(
-            "algo", algo, default_value=algo[0])
+        algo = CategoricalHyperparameter("algo", algo, default_value=algo[0])
         hyper_params.append(algo)
     else:
         algo = algo[0]
@@ -397,7 +408,10 @@ def quant_post_hpo(executor,
         weight_quantize_type = weight_quantize_type[0]
     if len(batch_size) > 1:
         batch_size = UniformIntegerHyperparameter(
-            "batch_size", batch_size[0], batch_size[1], default_value=batch_size[0])
+            "batch_size",
+            batch_size[0],
+            batch_size[1],
+            default_value=batch_size[0])
         hyper_params.append(batch_size)
     else:
         batch_size = batch_size[0]
@@ -407,7 +421,7 @@ def quant_post_hpo(executor,
             "batch_num", batch_num[0], batch_num[1], default_value=batch_num[0])
         hyper_params.append(batch_num)
     else:
-       batch_num = batch_num[0]
+        batch_num = batch_num[0]
 
     if len(hyper_params) == 0:
         quant_post( \