ONNX WOQ supports different dtypes (#1490)

ONNX WOQ supports different dtypes

Signed-off-by: Mengni Wang <mengni.wang@intel.com>
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

Author: mengniwang95

neural_compressor/adaptor/onnxrt.py

@@ -979,12 +979,10 @@ def _pre_optimize(self, model, level=1):
             sess_options.register_custom_ops_library(get_library_path())
 
         if not model.is_large_model:
-            sess = ort.InferenceSession(
-                model.model.SerializeToString(), sess_options, providers=["CPUExecutionProvider"]
-            )
+            sess = ort.InferenceSession(model.model.SerializeToString(), sess_options, providers=[self.backend])
         elif model.model_path is not None:  # pragma: no cover
             model.model = onnx.ModelProto()  # clean memory for large model
-            sess = ort.InferenceSession(model.model_path, sess_options, providers=["CPUExecutionProvider"])
+            sess = ort.InferenceSession(model.model_path, sess_options, providers=[self.backend])
         else:  # pragma: no cover
             logger.warning("Please use model path instead of onnx model object to quantize")
         del sess
@@ -1914,6 +1912,7 @@ def quantize(self, tune_cfg, model, data_loader, q_func=None):
                 mse=mse,
                 perchannel=perchannel,
                 accuracy_level=accuracy_level,
+                providers=[self.backend],
             )
         if "AWQ" in algos:
             from neural_compressor.adaptor.ox_utils.weight_only import awq_quantize
@@ -1931,6 +1930,7 @@ def quantize(self, tune_cfg, model, data_loader, q_func=None):
                 enable_auto_scale=enable_auto_scale,
                 enable_mse_search=enable_mse_search,
                 accuracy_level=accuracy_level,
+                providers=[self.backend],
             )
         elif "RTN" in algos:
             from neural_compressor.adaptor.ox_utils.weight_only import rtn_quantize
@@ -1940,6 +1940,7 @@ def quantize(self, tune_cfg, model, data_loader, q_func=None):
                 tmp_model,
                 quant_config,
                 accuracy_level=accuracy_level,
+                providers=[self.backend],
             )
         tmp_model.q_config = copy.deepcopy(quant_config)
         self._dump_model_op_stats(tmp_model, tune_cfg)

neural_compressor/adaptor/onnxrt_cuda.yaml

@@ -17,6 +17,20 @@
 -
   version:
     name: '1.6.0'
+  weight_only_integer: &cap_weight_only {
+    'MatMul': &cap_weight_only_matmul {
+        'weight': {
+                    'dtype': ['int'], # no need to care uint
+                    'bits': [4, 3, 8], # [1-8]
+                    'group_size': [32, -1, 1, 16, 64, 128, 256, 512, 1024], # [1-inf]
+                    'scheme': ['sym', 'asym'], # sym, no ZP
+                    'algorithm': ['RTN', 'AWQ', 'GPTQ']
+        },
+        'activation': {
+                    'dtype': ['fp32']
+        }
+    },
+  }
   int8: &ref_1_6 {
     'static': &ref_1_6_static {
       'Conv': {
@@ -114,6 +128,7 @@
 -
   version:
     name: '1.7.0'
+  weight_only_integer: *cap_weight_only
   int8: {
     'static': {
       'FusedConv': {
@@ -155,6 +170,7 @@
 -
   version:
     name: '1.8.0'
+  weight_only_integer: *cap_weight_only
   int8: {
     'static': {
       'FusedConv': {
@@ -224,6 +240,7 @@
 -
   version:
     name: '1.9.0'
+  weight_only_integer: *cap_weight_only
   int8: {
     'static': {
       'FusedConv': {
@@ -300,6 +317,7 @@
 -
   version:
     name: '1.10.0'
+  weight_only_integer: *cap_weight_only
   int8: {
     'static': {
       'FusedConv': {
@@ -356,6 +374,7 @@
 -
   version:
     name: '1.11.0'
+  weight_only_integer: *cap_weight_only
   int8: &ref_1_11 {
     'static': {
       'FusedConv': {
@@ -427,6 +446,7 @@
 -
   version:
     name: '1.12.0'
+  weight_only_integer: *cap_weight_only
   int8: *ref_1_11
   fp16: *common_fp16
   bf16: *common_bf16
@@ -436,6 +456,7 @@
 -
   version:
     name: 'default'
+  weight_only_integer: *cap_weight_only
   int8: *ref_1_6
   fp16: *common_fp16
   bf16: *common_bf16

neural_compressor/adaptor/ox_utils/util.py

@@ -57,6 +57,7 @@
 
 dtype_mapping = {
     "fp32": 1,
+    "float32": 1,
     "uint8": 2,
     "int8": 3,
     "uint16": 4,
@@ -66,12 +67,14 @@
     "string": 8,
     "bool": 9,
     "fp16": 10,
+    "float16": 10,
     "double": 11,
     "uint32": 12,
     "uint64": 13,
     "complex64": 14,
     "complex128": 15,
     "bf16": 16,
+    "bfloat16": 16,
 }
 
 PROVIDERS = {