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Merged
merged 7 commits into from
Sep 25, 2023
Merged

Refactor model generation scripts #6336

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d10f85b
Refactor model generation scripts
Tabrizian Sep 20, 2023
c6c8a8b
Fix codeql
Tabrizian Sep 20, 2023
5d44a31
Fix relative path import
Tabrizian Sep 21, 2023
5726204
Fix package structure
Tabrizian Sep 21, 2023
56a22ac
Copy the gen_common file
Tabrizian Sep 21, 2023
250f4f6
Add missing uint8
Tabrizian Sep 21, 2023
760da92
Remove duplicate import
Tabrizian Sep 25, 2023
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145 changes: 145 additions & 0 deletions qa/common/gen_common.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,145 @@
# 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


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
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):
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 np_to_tf_dtype(np_dtype):
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):
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
28 changes: 2 additions & 26 deletions qa/common/gen_ensemble_model_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,37 +31,13 @@
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)

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67 changes: 2 additions & 65 deletions qa/common/gen_qa_dyna_sequence_implicit_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,75 +31,12 @@

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
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125 changes: 8 additions & 117 deletions qa/common/gen_qa_dyna_sequence_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,127 +31,18 @@

import numpy as np

from .gen_common import (
np_to_model_dtype,
np_to_onnx_dtype,
np_to_tf_dtype,
np_to_torch_dtype,
np_to_trt_dtype,
)

FLAGS = None
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 np_to_tf_dtype(np_dtype):
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_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 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_torch_dtype(np_dtype):
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


def create_tf_modelfile(
create_savedmodel, models_dir, model_version, max_batch, dtype, shape
):
Expand Down
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