From ef99537f37f640d1cc945b8ffa0af948e31ece01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=81kos=20Hadnagy?= Date: Tue, 22 Jul 2025 14:07:10 +0200 Subject: [PATCH] Add AMD test expectations to DETR model (#39539) * Add AMD test expectations to DETR model * Fix baseline expectation * Address review comments * Make formatting a bit more consistent --- tests/models/detr/test_modeling_detr.py | 138 ++++++++++++++++++------ 1 file changed, 103 insertions(+), 35 deletions(-) diff --git a/tests/models/detr/test_modeling_detr.py b/tests/models/detr/test_modeling_detr.py index 2af2ca9211..38bc54b301 100644 --- a/tests/models/detr/test_modeling_detr.py +++ b/tests/models/detr/test_modeling_detr.py @@ -18,7 +18,7 @@ import math import unittest from transformers import DetrConfig, ResNetConfig, is_torch_available, is_vision_available -from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device +from transformers.testing_utils import Expectations, require_timm, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester @@ -585,13 +585,23 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase): expected_shape = torch.Size((1, 100, 256)) assert outputs.last_hidden_state.shape == expected_shape - expected_slice = torch.tensor( - [ - [0.0622, -0.5142, -0.4034], - [-0.7628, -0.4935, -1.7153], - [-0.4751, -0.6386, -0.7818], - ] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [0.0622, -0.5142, -0.4034], + [-0.7628, -0.4935, -1.7153], + [-0.4751, -0.6386, -0.7818], + ], + ("rocm", (9, 5)): + [ + [ 0.0616, -0.5146, -0.4032], + [-0.7629, -0.4934, -1.7153], + [-0.4768, -0.6403, -0.7826], + ], + } + ) # fmt: skip + expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) def test_inference_object_detection_head(self): @@ -609,13 +619,23 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase): # verify outputs expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) self.assertEqual(outputs.logits.shape, expected_shape_logits) - expected_slice_logits = torch.tensor( - [ - [-19.1211, -0.0881, -11.0188], - [-17.3641, -1.8045, -14.0229], - [-20.0415, -0.5833, -11.1005], - ] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [-19.1211, -0.0881, -11.0188], + [-17.3641, -1.8045, -14.0229], + [-20.0415, -0.5833, -11.1005], + ], + ("rocm", (9, 5)): + [ + [-19.1194, -0.0893, -11.0154], + [-17.3640, -1.8035, -14.0219], + [-20.0461, -0.5837, -11.1060], + ], + } + ) # fmt: skip + expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) @@ -657,27 +677,65 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase): # verify outputs expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) self.assertEqual(outputs.logits.shape, expected_shape_logits) - expected_slice_logits = torch.tensor( - [ - [-18.1523, -1.7592, -13.5019], - [-16.8866, -1.4139, -14.1025], - [-17.5735, -2.5090, -11.8666], - ] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [-18.1523, -1.7592, -13.5019], + [-16.8866, -1.4139, -14.1025], + [-17.5735, -2.5090, -11.8666], + ], + ("rocm", (9, 5)): + [ + [-18.1565, -1.7568, -13.5029], + [-16.8888, -1.4138, -14.1028], + [-17.5709, -2.5080, -11.8654], + ], + } + ) # fmt: skip + expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) - expected_slice_boxes = torch.tensor( - [[0.5344, 0.1790, 0.9284], [0.4421, 0.0571, 0.0875], [0.6632, 0.6886, 0.1015]] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [0.5344, 0.1790, 0.9284], + [0.4421, 0.0571, 0.0875], + [0.6632, 0.6886, 0.1015] + ], + ("rocm", (9, 5)): + [ + [0.5344, 0.1789, 0.9285], + [0.4420, 0.0572, 0.0875], + [0.6630, 0.6887, 0.1017], + ], + } + ) # fmt: skip + expected_slice_boxes = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=2e-4, atol=2e-4) expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267)) self.assertEqual(outputs.pred_masks.shape, expected_shape_masks) - expected_slice_masks = torch.tensor( - [[-7.8408, -11.0104, -12.1279], [-12.0299, -16.6498, -17.9806], [-14.8995, -19.9940, -20.5646]] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [-7.8408, -11.0104, -12.1279], + [-12.0299, -16.6498, -17.9806], + [-14.8995, -19.9940, -20.5646], + ], + ("rocm", (9, 5)): + [ + [ -7.7558, -10.8789, -11.9798], + [-11.8882, -16.4330, -17.7452], + [-14.7317, -19.7384, -20.3005], + ], + } + ) # fmt: skip + expected_slice_masks = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=2e-3, atol=2e-3) # verify postprocessing @@ -731,11 +789,21 @@ class DetrModelIntegrationTests(unittest.TestCase): expected_shape = torch.Size((1, 100, 256)) assert outputs.last_hidden_state.shape == expected_shape - expected_slice = torch.tensor( - [ - [0.0622, -0.5142, -0.4034], - [-0.7628, -0.4935, -1.7153], - [-0.4751, -0.6386, -0.7818], - ] - ).to(torch_device) + expected_slices = Expectations( + { + (None, None): + [ + [0.0622, -0.5142, -0.4034], + [-0.7628, -0.4935, -1.7153], + [-0.4751, -0.6386, -0.7818], + ], + ("rocm", (9, 5)): + [ + [ 0.0616, -0.5146, -0.4032], + [-0.7629, -0.4934, -1.7153], + [-0.4768, -0.6403, -0.7826] + ], + } + ) # fmt: skip + expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)