Revert "Enable TensorFlow INT8/BF16 mix precision for small ops (#814)" (#849)

This reverts commit 61c275d

Author: chensuyue

neural_compressor/adaptor/tensorflow.py

@@ -103,7 +103,7 @@ def _pre_hook_for_hvd(self):
         import horovod.tensorflow as hvd
         self.hvd = hvd
         self.hvd.init()
-
+    
     @dump_elapsed_time(customized_msg="Model training")
     def train(self, model, dataloader, optimizer_tuple,
               criterion_tuple, hooks, postprocess, **kwargs):
@@ -142,7 +142,7 @@ def train(self, model, dataloader, optimizer_tuple,
                     for i in range(len(list_len_dataloader)-1):
                         if list_len_dataloader[i] != list_len_dataloader[i+1]:
                             raise AttributeError("The traning dataloader's iteration is"
-                                                "different between processes, please reset dataloader's batch_size.")
+                                                "different between processes, please reset dataloader's batch_size.") 
 
         def training_step(x, y, first_batch):
             with tf.GradientTape() as tape:
@@ -158,7 +158,7 @@ def training_step(x, y, first_batch):
                 self.hvd.broadcast_variables(input_model.variables, root_rank=0)
                 self.hvd.broadcast_variables(optimizer.variables(), root_rank=0)
             return loss_value
-
+        
         training_step = training_step if execution_mode=='eager' else tf.function(training_step)
         if start_epochs is not None and end_epochs is not None:
             epochs = end_epochs - start_epochs
@@ -599,18 +599,6 @@ def _dump_model_op_stats(self, model_graphdef):
                    header='Mixed Precision Statistics',
                    field_names=["Op Type", "Total", "INT8", "BF16", "FP32"]).print_stat()
 
-    def _query_bf16_ops(self, matched_nodes):
-        self.bf16_op_details = OrderedDict()
-
-        valid_precision = self.query_handler.get_mixed_precision_combination()
-        if ('bf16' in valid_precision and CpuInfo().bf16) or os.getenv('FORCE_BF16') == '1':
-            for details in matched_nodes:
-                node_op = details[-1][0]
-                node_name = details[0]
-
-                self.bf16_op_details[(node_name, node_op)] = {'weight': {'dtype': ['bf16']}, \
-                                                              'activation': {'dtype': ['bf16']}}
-
     def _query_quantizable_ops(self, matched_nodes):
         """Collect the op-wise configuration for quantization.
 
@@ -730,9 +718,7 @@ def query_fw_capability(self, model):
 
         self.exclude_node_names = self.pre_optimizer_handle.get_excluded_node_names()
         patterns = self.query_handler.generate_internal_patterns()
-        bf16_patterns = self.query_handler.get_bf16_patterns()
         matched_nodes = self.pre_optimizer_handle.get_matched_nodes(patterns)
-        matched_bf16_nodes = self.pre_optimizer_handle.get_matched_nodes(bf16_patterns)
         original_graph_node_name = [i.name for i in model.graph_def.node]
         matched_nodes = sorted(matched_nodes, reverse=True, key=lambda i: (
             original_graph_node_name.index(i[0]), len(i[-1])))
@@ -756,21 +742,11 @@ def check_match(patterns, input_pattern):
 
         del copied_matched_nodes
 
-        copied_matched_nodes = copy.deepcopy(matched_bf16_nodes)
-        for i in copied_matched_nodes:
-            for j in matched_nodes:
-                if i[0] in j and i in matched_bf16_nodes:
-                    matched_bf16_nodes.remove(i)
-
-        del copied_matched_nodes
-
         self._query_quantizable_ops(matched_nodes)
-        self._query_bf16_ops(matched_bf16_nodes)
         capability = {
             'optypewise': self.get_optype_wise_ability(),
         }
         capability['opwise'] = copy.deepcopy(self.quantizable_op_details)
-        capability['opwise'].update(self.bf16_op_details)
         logger.debug("Dump framework quantization capability:")
         logger.debug(capability)
 
@@ -1013,9 +989,6 @@ def get_optype_wise_ability(self):
                 res[op[1]] = {'activation': self.quantizable_op_details[op]['activation']}
                 if 'weight' in self.quantizable_op_details[op]:
                     res[op[1]]['weight'] = self.quantizable_op_details[op]['weight']
-        for op in self.bf16_op_details:
-            if op[1] not in res:
-                res[op[1]] = {'activation': {'dtype': ['bf16']}, 'weight': {'dtype': ['bf16']}}
         return res
 
     def _pre_eval_hook(self, model):
@@ -1318,15 +1291,6 @@ def get_mixed_precision_combination(self):
     def get_grappler_optimization_cfg(self):
         return self.cur_config['grappler_optimization']
 
-    def get_bf16_patterns(self):
-        int8_op_types = self.get_op_types_by_precision('int8') + self.get_op_types_by_precision('uint8')
-        bf16_op_types = [i for i in self.get_op_types_by_precision('bf16') if i not in int8_op_types]
-        res = []
-        for i in bf16_op_types:
-            res.append([[i]])
-
-        return res
-
     def get_eightbit_patterns(self):
         """Get eightbit op wise sequences information.
 

neural_compressor/adaptor/tensorflow.yaml

@@ -25,30 +25,7 @@
   ops: &common_ops
     int8: ['Conv2D', 'MatMul', 'ConcatV2', 'MaxPool', 'AvgPool']
     uint8: ['Conv2D', 'DepthwiseConv2dNative', 'MatMul', 'ConcatV2', 'MaxPool', 'AvgPool']
-    bf16: ['Conv2D', 'Conv2DBackpropFilter', 'Conv2DBackpropInput', 'Conv3D', 'Conv3DBackpropFilterV2',
-           'Conv3DBackpropInputV2', 'DepthwiseConv2dNative', 'DepthwiseConv2dNativeBackpropFilter',
-           'DepthwiseConv2dNativeBackpropInput', 'MatMul', 'BatchMatMul', 'BatchMatMulV2', # allow_list
-           'Add', 'AddN', 'AddV2', 'AvgPool', 'AvgPool3D', 'AvgPool3DGrad', 'AvgPoolGrad', 'BiasAdd',
-           'BiasAddGrad', 'BiasAddV1', 'FusedBatchNormV2', 'FusedBatchNormGradV2', 'FusedBatchNormV3',
-           'FusedBatchNormGradV3', 'LeakyRelu', 'LeakyReluGrad', 'Mul', 'Sub', 'Elu', 'EluGrad', 'FloorDiv',
-           '_FusedBatchNormEx', 'Log', 'Log1p', 'LogSoftmax', 'Prod', 'RealDiv', 'Reciprocal', 'Selu',
-           'SeluGrad', 'Sigmoid', 'SigmoidGrad', 'Softmax', 'Softplus', 'SoftplusGrad', 'Softsign',
-           'SoftsignGrad', 'Sqrt', 'Tanh', 'TanhGrad', #infer_list
-           'Abs', 'ArgMax', 'ArgMin', 'BatchToSpace', 'BatchToSpaceND', 'BroadcastTo', 'Ceil', 'CheckNumerics',
-           'ClipByValue', 'Concat', 'ConcatV2', 'DepthToSpace', 'DynamicPartition', 'DynamicStitch',
-           'EnsureShape', 'Enter', #'Range',
-           'Equal', 'Exit', 'ExpandDims', 'Fill', 'Floor',
-            'Gather', 'GatherNd', 'GatherV2', 'Greater', 'GreaterEqual',
-           'Identity', 'IsFinite', 'IsInf', 'IsNan', 'Less', 'LessEqual',
-           'Max', 'Maximum', 'MaxPool', 'MaxPool3D', 'MaxPool3DGrad', 'MaxPoolGrad', 'MaxPoolGradGrad',
-           'MaxPoolGradGradV2', 'MaxPoolGradV2', 'MaxPoolV2', 'Merge', 'Min', 'Minimum', 'MirrorPad', 'MirrorPadGrad',
-           'Neg', 'NextIteration', 'NotEqual', 'OnesLike', 'Pack', 'Pad', 'PadV2', 'PreventGradient', 'Rank',
-           'Relu', 'Relu6', 'Relu6Grad', 'ReluGrad', 'Reshape',
-           'ResizeNearestNeighbor', 'ResizeNearestNeighborGrad',
-           'Reverse', 'ReverseSequence', 'ReverseV2', 'Round', 'Select', 'SelectV2', 'Shape', 'ShapeN', 'Sign',
-           'Slice', 'Snapshot', 'SpaceToBatch', 'SpaceToBatchND', 'SpaceToDepth', 'Split', 'SplitV', 'Squeeze',
-           'StopGradient', 'StridedSlice', 'StridedSliceGrad', #'Switch',
-           'Tile', 'TopK', 'TopKV2', 'Transpose', 'Where', 'Unpack', 'ZerosLike']  #clear_list
+    bf16: ['Conv2D']  #TODO need to add more bf16 op types here
     fp32: ['*'] # '*' means all op types
 
   capabilities: &common_capabilities