Remove inteltensorflow adaptor (#1313)

examples/tensorflow/nlp/transformer_lt_mlperf/quantization/ptq/README.md

@@ -119,7 +119,7 @@ In examples directory, there is a transformer_lt_mlperf.yaml. We could remove mo
 ```yaml
 model:
   name: transformer_lt_mlperf
-  framework: inteltensorflow
+  framework: tensorflow
   inputs: input_tokens
   outputs: model/Transformer/strided_slice_15
 

examples/tensorflow/nlp/transformer_lt_mlperf/quantization/ptq/transformer_lt_mlperf.yaml

@@ -17,7 +17,7 @@ version: 1.0
 
 model:
   name: transformer_lt_mlperf
-  framework: inteltensorflow
+  framework: tensorflow
   inputs: input_tokens
   outputs: model/Transformer/strided_slice_15
 

examples/tensorflow/oob_models/quantization/ptq/config.yaml

@@ -15,7 +15,7 @@
 
 model:                                               # mandatory. used to specify model specific information.
   name: oob_models
-  framework: inteltensorflow                              # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension.
+  framework: tensorflow                              # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension.
   inputs: input
   outputs: output
 

neural_compressor/adaptor/tensorflow.py

@@ -76,16 +76,18 @@ def __init__(self, framework_specific_info):
         self.query_handler = TensorflowQuery(local_config_file=os.path.join(
             os.path.dirname(__file__), cfg_yaml_name), performance_only=self.performance_only)
         self.itex_mode = cfg_yaml_name == 'tensorflow_itex.yaml'
-        self.qdq_enabled = cfg_yaml_name == 'inteltensorflow.yaml' or \
-                                   cfg_yaml_name == 'tensorflow_itex.yaml'
+
+        from pkg_resources import parse_version
+        import tensorflow as tf
+        self.new_api = True if parse_version(tf.version.VERSION) == parse_version('2.11.0202242') else False
+        self.qdq_enabled = cfg_yaml_name == 'tensorflow_itex.yaml' or self.new_api
         self.op_wise_sequences = self.query_handler.get_eightbit_patterns(self.qdq_enabled)
         self.optimization = self.query_handler.get_grappler_optimization_cfg()
 
         self.fp32_results = []
         self.fp32_preds_as_label = False
         self.benchmark = (GLOBAL_STATE.STATE == MODE.BENCHMARK)
         self.callbacks = []
-        self.new_api = False
 
     def log_histogram(self, writer, tag, values, step=0, bins=1000):
         import tensorflow as tf
@@ -1438,9 +1440,6 @@ def diagnosis_helper(self, fp32_model, quan_model, tune_cfg, save_path):
 class Tensorflow_ITEXAdaptor(TensorFlowAdaptor):
     def __init__(self, framework_specific_info):
         super().__init__(framework_specific_info)
-        from pkg_resources import parse_version
-        import tensorflow as tf
-        self.new_api = True if parse_version(tf.version.VERSION) >= parse_version('2.10.0') else False
 
     @dump_elapsed_time("Pass quantize model")
     def quantize(self, tune_cfg, model, data_loader, q_func=None):
@@ -1789,16 +1788,3 @@ def _generate_pattern(data):
                 final_out.append(_generate_pattern(similar_sequences))
 
         return final_out
-
-@adaptor_registry
-class IntelTensorFlowAdaptor(TensorFlowAdaptor):
-    def __init__(self, framework_specific_info):
-        super().__init__(framework_specific_info)
-        from pkg_resources import parse_version
-        import tensorflow as tf
-        self.new_api = True if parse_version(tf.version.VERSION) >= parse_version('2.10.0') else False
-        # not enable qdq mode for old api
-        if not self.new_api:
-            self.qdq_enabled = False
-            self.op_wise_sequences = self.query_handler.get_eightbit_patterns()
-

neural_compressor/adaptor/tf_utils/graph_converter.py

@@ -24,24 +24,20 @@
 
 from collections import OrderedDict, UserDict
 from tensorflow.core.framework import graph_pb2
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.platform import gfile
 from neural_compressor.utils.utility import get_all_fp32_data
 from neural_compressor.utils.utility import get_tensor_histogram
 from neural_compressor.utils.utility import combine_histogram
 from neural_compressor.utils.utility import CaptureOutputToFile
-from neural_compressor.utils.utility import str2array
-from neural_compressor.utils.utility import Dequantize, DequantizeWeight
 from neural_compressor.conf.dotdict import deep_get
 from neural_compressor.experimental.common import Model
 from .transform_graph.insert_logging import InsertLogging
 from .transform_graph.rerange_quantized_concat import RerangeQuantizedConcat
 from .transform_graph.bias_correction import BiasCorrection
-from .util import iterator_sess_run,version1_gt_version2,version1_eq_version2,version1_lt_version2
-from .util import version1_gte_version2,version1_lte_version2
+from .util import iterator_sess_run,version1_gt_version2,version1_eq_version2
+from .util import version1_gte_version2,version1_lte_version2,version1_lt_version2
 from .quantize_graph.quantize_graph_for_intel_cpu import QuantizeGraphForIntel
 from .quantize_graph_common import QuantizeGraphHelper
-from .quantize_graph.quantize_graph_conv import FuseNodeStartWithConv2d
 from .quantize_graph.qdq.optimize_qdq import OptimizeQDQGraph
 
 from .graph_util import GraphAnalyzer
@@ -236,6 +232,7 @@ def check_shape(tensor, data):
 
     def _check_tf_version(self):
         is_supported_version = False
+        is_sprbase_version = False
         try:
             from tensorflow import python
             if (hasattr(python, "pywrap_tensorflow")
@@ -253,35 +250,37 @@ def _check_tf_version(self):
 
             if version1_gte_version2(tf.version.VERSION, '2.9.0'):
                 is_supported_version = True
-                 
+
             if version1_eq_version2(tf.version.VERSION, '1.15.0-up3'):
                 is_supported_version = True
-
+
+            if version1_eq_version2(tf.version.VERSION, '2.11.0202242'):
+                is_supported_version = True
+                is_sprbase_version = True
+
         except Exception as e:
             raise ValueError(e)
         finally:
-            if version1_gt_version2(tf.version.VERSION, TF_SUPPORTED_MAX_VERSION):
+            if version1_gt_version2(tf.version.VERSION, TF_SUPPORTED_MAX_VERSION) and not is_sprbase_version:
                 logger.warning(
-                    str('Please note the {} version of Intel® Optimizations for '
-                        'TensorFlow is not fully verified! '
-                        'Suggest to use the versions '
-                        'between {} and {} if meet problem.').format(tf.version.VERSION,
-                                                                     TF_SUPPORTED_MIN_VERSION,
-                                                                     TF_SUPPORTED_MAX_VERSION))
+                    str('Please note the {} version of TensorFlow is not fully verified! '
+                        'Suggest to use the versions between {} and {} if meet problem.')
+                        .format(tf.version.VERSION, TF_SUPPORTED_MIN_VERSION, TF_SUPPORTED_MAX_VERSION))
+
             if version1_eq_version2(tf.version.VERSION, '2.5.0') and os.getenv('TF_ENABLE_MKL_NATIVE_FORMAT') != '0':
                 logger.fatal("Please set environment variable TF_ENABLE_MKL_NATIVE_FORMAT=0 "
                              "when TensorFlow 2.5.0 installed.")
 
-            if version1_gte_version2(tf.version.VERSION, '2.6.0') and os.getenv('TF_ENABLE_ONEDNN_OPTS') != '1':
+            if version1_gte_version2(tf.version.VERSION, '2.6.0') and \
+               version1_lt_version2(tf.version.VERSION, '2.9.0') and \
+               os.getenv('TF_ENABLE_ONEDNN_OPTS') != '1':
                 logger.fatal("Please set environment variable TF_ENABLE_ONEDNN_OPTS=1 "
                              "when TensorFlow >= 2.6.0 and < 2.9.0 installed.")
 
             if not is_supported_version:
                 raise ValueError(
-                    str('Please install Intel® Optimizations for TensorFlow '
-                        'or MKL enabled TensorFlow from source code '
-                        'within version >={} and <={}.').format(TF_SUPPORTED_MIN_VERSION,
-                                                                TF_SUPPORTED_MAX_VERSION))
+                    str('Please install TensorFlow within version >={} and <={}.')
+                    .format(TF_SUPPORTED_MIN_VERSION, TF_SUPPORTED_MAX_VERSION))
 
     def _check_args(self):
         if self.model.workspace_path and not os.path.isdir(self.model.workspace_path) \