chore: import upstream snapshot with attribution

tools/pytorch-quantization/pytorch_quantization/utils/__init__.py

@@ -0,0 +1,21 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""Main entry of all utils"""
+
+from .reduce_amax import reduce_amax

tools/pytorch-quantization/pytorch_quantization/utils/quant_logging.py

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+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""A WAR for codes that messes up logging format"""
+
+import logging
+
+def reset_logger_handler():
+    """Remove all handler in root logger"""
+    root_logger = logging.getLogger()
+    while root_logger.handlers:
+        root_logger.removeHandler(root_logger.handlers[0])

tools/pytorch-quantization/pytorch_quantization/utils/reduce_amax.py

@@ -0,0 +1,63 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""Function to get absolute maximum of a tensor
+Follow numpy fashion, which is more generic as pytorch's
+"""
+
+import torch
+
+def reduce_amax(input, axis=None, keepdims=True):
+    """Compute the absolute maximum value of a tensor.
+
+    Reduces input_tensor along the dimensions given in axis. Unless keepdims is true,
+    the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true,
+    the reduced dimensions are retained with length 1.
+
+    .. note::
+        Gradient computeation is disabled as this function is never meant learning reduces amax
+
+    Args:
+        input: Input tensor
+        axis: The dimensions to reduce. None or int or tuple of ints. If None (the default),
+            reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
+        keepdims: A boolean. If true, retains reduced dimensions with length 1. Default True
+        granularity: DEPRECTED. specifies if the statistic has to be calculated at tensor or channel granularity
+
+    Returns:
+        The reduced tensor.
+
+    Raises:
+        ValueError: Any axis which doesn't make sense or is not supported
+        ValueError: If unknown granularity is passed in.
+    """
+    with torch.no_grad():
+        output = input.abs()
+        if axis is None:
+            output = torch.max(output)
+        else:
+            if isinstance(axis, int):
+                output, _ = torch.max(output, dim=axis, keepdim=keepdims)
+            else:
+                if isinstance(axis, tuple) and len(axis) > input.dim():
+                    raise ValueError("Cannot reduce more axes than tensor's dim.")
+                for i in axis:
+                    output, _ = torch.max(output, dim=i, keepdim=True)
+                if not keepdims or output.numel() == 1:
+                    output.squeeze_()
+        return output