Solve the bug of distributed training. (#1122)

paddleslim/auto_compression/utils/predict.py

@@ -26,6 +26,11 @@ def predict_compressed_model(model_dir,
     Returns:
         latency_dict(dict): The latency latency of the model under various compression strategies.
     """
+    local_rank = paddle.distributed.get_rank()
+    quant_model_path = f'quant_model/rank_{local_rank}'
+    prune_model_path = f'prune_model/rank_{local_rank}'
+    sparse_model_path = f'sparse_model/rank_{local_rank}'
+
     latency_dict = {}
 
     model_file = os.path.join(model_dir, model_filename)
@@ -43,13 +48,13 @@ def predict_compressed_model(model_dir,
         model_dir=model_dir,
         model_filename=model_filename,
         params_filename=params_filename,
-        save_model_path='quant_model',
+        save_model_path=quant_model_path,
         quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
         is_full_quantize=False,
         activation_bits=8,
         weight_bits=8)
-    quant_model_file = os.path.join('quant_model', model_filename)
-    quant_param_file = os.path.join('quant_model', params_filename)
+    quant_model_file = os.path.join(quant_model_path, model_filename)
+    quant_param_file = os.path.join(quant_model_path, params_filename)
 
     latency = predictor.predict(
         model_file=quant_model_file,
@@ -62,9 +67,9 @@ def predict_compressed_model(model_dir,
             model_file=model_file,
             param_file=param_file,
             ratio=prune_ratio,
-            save_path='prune_model')
-        prune_model_file = os.path.join('prune_model', model_filename)
-        prune_param_file = os.path.join('prune_model', params_filename)
+            save_path=prune_model_path)
+        prune_model_file = os.path.join(prune_model_path, model_filename)
+        prune_param_file = os.path.join(prune_model_path, params_filename)
 
         latency = predictor.predict(
             model_file=prune_model_file,
@@ -74,16 +79,16 @@ def predict_compressed_model(model_dir,
 
         post_quant_fake(
             exe,
-            model_dir='prune_model',
+            model_dir=prune_model_path,
             model_filename=model_filename,
             params_filename=params_filename,
-            save_model_path='quant_model',
+            save_model_path=quant_model_path,
             quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
             is_full_quantize=False,
             activation_bits=8,
             weight_bits=8)
-        quant_model_file = os.path.join('quant_model', model_filename)
-        quant_param_file = os.path.join('quant_model', params_filename)
+        quant_model_file = os.path.join(quant_model_path, model_filename)
+        quant_param_file = os.path.join(quant_model_path, params_filename)
 
         latency = predictor.predict(
             model_file=quant_model_file,
@@ -96,9 +101,9 @@ def predict_compressed_model(model_dir,
             model_file=model_file,
             param_file=param_file,
             ratio=sparse_ratio,
-            save_path='sparse_model')
-        sparse_model_file = os.path.join('sparse_model', model_filename)
-        sparse_param_file = os.path.join('sparse_model', params_filename)
+            save_path=sparse_model_path)
+        sparse_model_file = os.path.join(sparse_model_path, model_filename)
+        sparse_param_file = os.path.join(sparse_model_path, params_filename)
 
         latency = predictor.predict(
             model_file=sparse_model_file,
@@ -108,7 +113,7 @@ def predict_compressed_model(model_dir,
 
         post_quant_fake(
             exe,
-            model_dir='sparse_model',
+            model_dir=sparse_model_path,
             model_filename=model_filename,
             params_filename=params_filename,
             save_model_path='quant_model',
@@ -116,8 +121,8 @@ def predict_compressed_model(model_dir,
             is_full_quantize=False,
             activation_bits=8,
             weight_bits=8)
-        quant_model_file = os.path.join('quant_model', model_filename)
-        quant_param_file = os.path.join('quant_model', params_filename)
+        quant_model_file = os.path.join(quant_model_path, model_filename)
+        quant_param_file = os.path.join(quant_model_path, params_filename)
 
         latency = predictor.predict(
             model_file=quant_model_file,
@@ -125,8 +130,11 @@ def predict_compressed_model(model_dir,
             data_type='int8')
         latency_dict.update({f'sparse_{sparse_ratio}_int8': latency})
 
-    # Delete temporary model files
-    shutil.rmtree('./quant_model')
-    shutil.rmtree('./prune_model')
-    shutil.rmtree('./sparse_model')
+    # NOTE: Delete temporary model files
+    if os.path.exists('quant_model'):
+        shutil.rmtree('quant_model', ignore_errors=True)
+    if os.path.exists('prune_model'):
+        shutil.rmtree('prune_model', ignore_errors=True)
+    if os.path.exists('sparse_model'):
+        shutil.rmtree('sparse_model', ignore_errors=True)
     return latency_dict