Refactor model generation scripts (#6336)

* Refactor model generation scripts

* Fix codeql

* Fix relative path import

* Fix package structure

* Copy the gen_common file

* Add missing uint8

* Remove duplicate import

qa/common/gen_common.py

@@ -0,0 +1,158 @@
+# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# Common utilities for model generation scripts
+import numpy as np
+
+np_dtype_string = np.dtype(object)
+
+
+def np_to_onnx_dtype(np_dtype):
+    import onnx
+
+    if np_dtype == bool:
+        return onnx.TensorProto.BOOL
+    elif np_dtype == np.int8:
+        return onnx.TensorProto.INT8
+    elif np_dtype == np.int16:
+        return onnx.TensorProto.INT16
+    elif np_dtype == np.int32:
+        return onnx.TensorProto.INT32
+    elif np_dtype == np.int64:
+        return onnx.TensorProto.INT64
+    elif np_dtype == np.uint8:
+        return onnx.TensorProto.UINT8
+    elif np_dtype == np.uint16:
+        return onnx.TensorProto.UINT16
+    elif np_dtype == np.float16:
+        return onnx.TensorProto.FLOAT16
+    elif np_dtype == np.float32:
+        return onnx.TensorProto.FLOAT
+    elif np_dtype == np.float64:
+        return onnx.TensorProto.DOUBLE
+    elif np_dtype == np_dtype_string:
+        return onnx.TensorProto.STRING
+    return None
+
+
+def np_to_model_dtype(np_dtype):
+    if np_dtype == bool:
+        return "TYPE_BOOL"
+    elif np_dtype == np.int8:
+        return "TYPE_INT8"
+    elif np_dtype == np.int16:
+        return "TYPE_INT16"
+    elif np_dtype == np.int32:
+        return "TYPE_INT32"
+    elif np_dtype == np.int64:
+        return "TYPE_INT64"
+    elif np_dtype == np.uint8:
+        return "TYPE_UINT8"
+    elif np_dtype == np.uint16:
+        return "TYPE_UINT16"
+    elif np_dtype == np.float16:
+        return "TYPE_FP16"
+    elif np_dtype == np.float32:
+        return "TYPE_FP32"
+    elif np_dtype == np.float64:
+        return "TYPE_FP64"
+    elif np_dtype == np_dtype_string:
+        return "TYPE_STRING"
+    return None
+
+
+def np_to_trt_dtype(np_dtype):
+    import tensorrt as trt
+
+    if np_dtype == bool:
+        return trt.bool
+    elif np_dtype == np.int8:
+        return trt.int8
+    elif np_dtype == np.int32:
+        return trt.int32
+    elif np_dtype == np.uint8:
+        return trt.uint8
+    elif np_dtype == np.float16:
+        return trt.float16
+    elif np_dtype == np.float32:
+        return trt.float32
+    return None
+
+
+def np_to_tf_dtype(np_dtype):
+    import tensorflow as tf
+
+    if np_dtype == bool:
+        return tf.bool
+    elif np_dtype == np.int8:
+        return tf.int8
+    elif np_dtype == np.int16:
+        return tf.int16
+    elif np_dtype == np.int32:
+        return tf.int32
+    elif np_dtype == np.int64:
+        return tf.int64
+    elif np_dtype == np.uint8:
+        return tf.uint8
+    elif np_dtype == np.uint16:
+        return tf.uint16
+    elif np_dtype == np.float16:
+        return tf.float16
+    elif np_dtype == np.float32:
+        return tf.float32
+    elif np_dtype == np.float64:
+        return tf.float64
+    elif np_dtype == np_dtype_string:
+        return tf.string
+    return None
+
+
+def np_to_torch_dtype(np_dtype):
+    import torch
+
+    if np_dtype == bool:
+        return torch.bool
+    elif np_dtype == np.int8:
+        return torch.int8
+    elif np_dtype == np.int16:
+        return torch.int16
+    elif np_dtype == np.int32:
+        return torch.int
+    elif np_dtype == np.int64:
+        return torch.long
+    elif np_dtype == np.uint8:
+        return torch.uint8
+    elif np_dtype == np.uint16:
+        return None  # Not supported in Torch
+    elif np_dtype == np.float16:
+        return None
+    elif np_dtype == np.float32:
+        return torch.float
+    elif np_dtype == np.float64:
+        return torch.double
+    elif np_dtype == np_dtype_string:
+        return None  # Not supported in Torch
+    return None

qa/common/gen_ensemble_model_utils.py

@@ -30,38 +30,13 @@
 
 import numpy as np
 import test_util as tu
+from gen_common import np_to_model_dtype
 
 BASIC_ENSEMBLE_TYPES = ["simple", "sequence", "fan"]
 
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
 def fixed_to_variable_size(shape):
     return [-1] * len(shape)
 

qa/common/gen_qa_dyna_sequence_implicit_models.py

@@ -30,76 +30,12 @@
 import os
 
 import numpy as np
+from gen_common import np_to_model_dtype, np_to_onnx_dtype, np_to_trt_dtype
 
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-
-
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
 def create_onnx_modelfile(models_dir, model_version, max_batch, dtype, shape):
     if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
         return