set default scope None test=develop

paddleslim/quant/quant_embedding.py

@@ -19,6 +19,7 @@ import logging
 import copy
 import numpy as np
 
+import paddle.fluid as fluid
 from paddle.fluid.framework import IrGraph
 from paddle.fluid import core
 
@@ -216,7 +217,7 @@ def _quant_embedding_abs_max(graph, scope, place, config):
     scope.var(_get_scale_var_name(var_name))
     #set var by tensor array or scale
     _restore_var(_get_quant_var_name(var_name), quanted_tensor, scope, place)
-    _restore_var(_get_scale_var_name(var_name), scale, scope, place)
+    _restore_var(_get_scale_var_name(var_name), np.array(scale), scope, place)
 
     # insert dequantize_abs_max op
     for op_node in embedding_node.outputs:
@@ -231,12 +232,12 @@ def _quant_embedding_abs_max(graph, scope, place, config):
     graph.safe_remove_nodes(embedding_node)
 
 
-def quant_embedding(program, scope, place, config):
+def quant_embedding(program, place, config, scope=None):
     """
     quant lookup_table op parameters
     Args:
         program(fluid.Program): infer program
-        scope(fluid.Scope): the scope to store var, usually is fluid.global_scope()
+        scope(fluid.Scope): the scope to store var, when is None will use fluid.global_scope()
         place(fluid.CPUPlace or fluid.CUDAPlace): place
         config(dict): config to quant. The keys are 'params_name', 'quantize_type', \
                 'quantize_bits', 'dtype', 'threshold'. \
@@ -249,6 +250,7 @@ def quant_embedding(program, scope, place, config):
     """
     assert isinstance(config, dict), "config must be dict"
     config = _merge_config(copy.deepcopy(default_config), config)
+    scope = fluid.global_scope() if scope is None else scope
 
     graph = IrGraph(core.Graph(program.desc), for_test=True)
     if config['quantize_type'] == 'abs_max':