chore: import upstream snapshot with attribution

tools/pytorch-quantization/tests/clip_test.py

@@ -0,0 +1,69 @@
+#
+# 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.
+#
+
+
+"""tests of Clip module."""
+
+import pytest
+import numpy as np
+
+import torch
+
+from pytorch_quantization.nn.modules import clip
+
+# make everything run on the GPU
+torch.set_default_tensor_type('torch.cuda.FloatTensor')
+
+np.random.seed(1234)
+torch.manual_seed(1234)
+
+# pylint:disable=missing-docstring, no-self-use
+
+
+class TestClip():
+
+    def test_simple_run(self):
+        x_np = np.random.rand(1023).astype(np.float32)
+        x_torch = torch.Tensor(x_np)
+        clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7))
+        clip_x_np = np.clip(x_np, 0.3, 0.7)
+        clip_x_torch = clip_op(x_torch)
+        np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np)
+
+    def test_raise(self):
+        with pytest.raises(ValueError, match="must be scalar"):
+            clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7), learn_min=True)
+
+    def test_backward(self):
+        x = torch.randn(3, 7, requires_grad=True)
+        x.retain_grad()
+
+        min_value = 0.3
+        max_value = 0.7
+        clip_op = clip.Clip(min_value, max_value, learn_min=True, learn_max=True)
+        clip_x = clip_op(x)
+        clip_x.retain_grad()
+
+        labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda()
+        criterion = torch.nn.CrossEntropyLoss()
+        loss = criterion(clip_x, labels)
+
+        loss.backward()
+
+        assert x.grad.cpu()[x.cpu() < min_value].sum() == 0
+        assert x.grad.cpu()[x.cpu() > max_value].sum() == 0
+        assert torch.equal(clip_x.grad[(x > min_value) & (x < max_value)], x.grad[(x > min_value) & (x < max_value)])