Introduce code from Rust monorepo. This is a mess

2026-10-26 08:48:03 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
commit ba326bfcfd
1058 changed files with 192192 additions and 0 deletions
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+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+import pytest
+import torch
+
+from .loss_inputs import ALL_NUM_MEASUREMENTS_ARE_ZERO, NO_ZERO_NUM_MEASUREMENTS, SOME_NUM_MEASUREMENTS_ARE_ZERO
+from .perplexity_inputs import NO_PROBS_NO_LOGITS, ONLY_LOGITS1, ONLY_LOGITS100, ONLY_PROBS, PROBS_AND_LOGITS
+from .pl_utils import LossTester, PerplexityTester
+from nemo.collections.common.metrics.classification_accuracy import TopKClassificationAccuracy
+
+
+class TestCommonMetrics:
+    top_k_logits = torch.tensor([[0.1, 0.3, 0.2, 0.0], [0.9, 0.6, 0.2, 0.3], [0.2, 0.1, 0.4, 0.3]],)  # 1  # 0  # 2
+
+    @pytest.mark.unit
+    def test_top_1_accuracy(self):
+        labels = torch.tensor([0, 0, 2], dtype=torch.long)
+
+        accuracy = TopKClassificationAccuracy(top_k=None)
+        acc = accuracy(logits=self.top_k_logits, labels=labels)
+
+        assert accuracy.correct_counts_k.shape == torch.Size([1])
+        assert accuracy.total_counts_k.shape == torch.Size([1])
+        assert abs(acc[0] - 0.667) < 1e-3
+
+    @pytest.mark.unit
+    def test_top_1_2_accuracy(self):
+        labels = torch.tensor([0, 1, 0], dtype=torch.long)
+
+        accuracy = TopKClassificationAccuracy(top_k=[1, 2])
+        top1_acc, top2_acc = accuracy(logits=self.top_k_logits, labels=labels)
+
+        assert accuracy.correct_counts_k.shape == torch.Size([2])
+        assert accuracy.total_counts_k.shape == torch.Size([2])
+
+        assert abs(top1_acc - 0.0) < 1e-3
+        assert abs(top2_acc - 0.333) < 1e-3
+
+    @pytest.mark.unit
+    def test_top_1_accuracy_distributed(self):
+        # Simulate test on 2 process DDP execution
+        labels = torch.tensor([[0, 0, 2], [2, 0, 0]], dtype=torch.long)
+
+        accuracy = TopKClassificationAccuracy(top_k=None)
+        proc1_acc = accuracy(logits=self.top_k_logits, labels=labels[0])
+        correct1, total1 = accuracy.correct_counts_k, accuracy.total_counts_k
+
+        accuracy.reset()
+        proc2_acc = accuracy(logits=torch.flip(self.top_k_logits, dims=[1]), labels=labels[1])  # reverse logits
+        correct2, total2 = accuracy.correct_counts_k, accuracy.total_counts_k
+
+        correct = torch.stack([correct1, correct2])
+        total = torch.stack([total1, total2])
+
+        assert correct.shape == torch.Size([2, 1])
+        assert total.shape == torch.Size([2, 1])
+
+        assert abs(proc1_acc[0] - 0.667) < 1e-3  # 2/3
+        assert abs(proc2_acc[0] - 0.333) < 1e-3  # 1/3
+
+        accuracy.reset()
+        accuracy.correct_counts_k = torch.tensor([correct.sum()])
+        accuracy.total_counts_k = torch.tensor([total.sum()])
+        acc_topk = accuracy.compute()
+        acc_top1 = acc_topk[0]
+
+        assert abs(acc_top1 - 0.5) < 1e-3  # 3/6
+
+    @pytest.mark.unit
+    def test_top_1_accuracy_distributed_uneven_batch(self):
+        # Simulate test on 2 process DDP execution
+        accuracy = TopKClassificationAccuracy(top_k=None)
+
+        proc1_acc = accuracy(logits=self.top_k_logits, labels=torch.tensor([0, 0, 2]))
+        correct1, total1 = accuracy.correct_counts_k, accuracy.total_counts_k
+
+        proc2_acc = accuracy(
+            logits=torch.flip(self.top_k_logits, dims=[1])[:2, :],  # reverse logits, select first 2 samples
+            labels=torch.tensor([2, 0]),
+        )  # reduce number of labels
+        correct2, total2 = accuracy.correct_counts_k, accuracy.total_counts_k
+
+        correct = torch.stack([correct1, correct2])
+        total = torch.stack([total1, total2])
+
+        assert correct.shape == torch.Size([2, 1])
+        assert total.shape == torch.Size([2, 1])
+
+        assert abs(proc1_acc[0] - 0.667) < 1e-3  # 2/3
+        assert abs(proc2_acc[0] - 0.500) < 1e-3  # 1/2
+
+        accuracy.correct_counts_k = torch.tensor([correct.sum()])
+        accuracy.total_counts_k = torch.tensor([total.sum()])
+        acc_topk = accuracy.compute()
+        acc_top1 = acc_topk[0]
+
+        assert abs(acc_top1 - 0.6) < 1e-3  # 3/5
+
+
+@pytest.mark.parametrize("ddp", [True, False])
+@pytest.mark.parametrize("dist_sync_on_step", [True, False])
+@pytest.mark.parametrize(
+    "probs, logits",
+    [
+        (ONLY_PROBS.probs, ONLY_PROBS.logits),
+        (ONLY_LOGITS1.probs, ONLY_LOGITS1.logits),
+        (ONLY_LOGITS100.probs, ONLY_LOGITS100.logits),
+        (PROBS_AND_LOGITS.probs, PROBS_AND_LOGITS.logits),
+        (NO_PROBS_NO_LOGITS.probs, NO_PROBS_NO_LOGITS.logits),
+    ],
+)
+class TestPerplexity(PerplexityTester):
+    def test_perplexity(self, ddp, dist_sync_on_step, probs, logits):
+        self.run_class_perplexity_test(
+            ddp=ddp, probs=probs, logits=logits, dist_sync_on_step=dist_sync_on_step,
+        )
+
+
+@pytest.mark.parametrize("ddp", [True, False])
+@pytest.mark.parametrize("dist_sync_on_step", [True, False])
+@pytest.mark.parametrize("take_avg_loss", [True, False])
+@pytest.mark.parametrize(
+    "loss_sum_or_avg, num_measurements",
+    [
+        (NO_ZERO_NUM_MEASUREMENTS.loss_sum_or_avg, NO_ZERO_NUM_MEASUREMENTS.num_measurements),
+        (SOME_NUM_MEASUREMENTS_ARE_ZERO.loss_sum_or_avg, SOME_NUM_MEASUREMENTS_ARE_ZERO.num_measurements),
+        (ALL_NUM_MEASUREMENTS_ARE_ZERO.loss_sum_or_avg, ALL_NUM_MEASUREMENTS_ARE_ZERO.num_measurements),
+    ],
+)
+class TestLoss(LossTester):
+    def test_loss(self, ddp, dist_sync_on_step, loss_sum_or_avg, num_measurements, take_avg_loss):
+        self.run_class_loss_test(
+            ddp=ddp,
+            loss_sum_or_avg=loss_sum_or_avg,
+            num_measurements=num_measurements,
+            dist_sync_on_step=dist_sync_on_step,
+            take_avg_loss=take_avg_loss,
+        )