Rebase to the latest main branch and added test cases.

Makefile

@@ -41,6 +41,11 @@ fast_tests_diffusers:
  python -m pip install .[tests]
  python -m pytest tests/test_diffusers.py
 
+# Run single-card non-regression tests on image classification models
+fast_tests_image_classifications:
+ pip install timm
+ python -m pytest tests/test_image_classification.py
+
 # Run single-card non-regression tests
 slow_tests_1x: test_installs
  python -m pytest tests/test_examples.py -v -s -k "single_card"

tests/test_image_classification.py

@@ -0,0 +1,120 @@
+#
+# 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 time
+from unittest import TestCase
+
+import habana_frameworks.torch as ht
+import numpy as np
+import requests
+import timm
+import torch
+from PIL import Image
+
+from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+
+
+adapt_transformers_to_gaudi()
+
+# For Gaudi 2
+LATENCY_FastViT_BF16_GRAPH_BASELINE = 2.5270626640319824
+
+
+class GaudiFastViTTester(TestCase):
+ """
+ Tests for FastViT model
+ """
+
+ def prepare_model_and_processor(self):
+ model = timm.create_model("timm/fastvit_t8.apple_in1k", pretrained=True)
+ model.to("hpu")
+ model = model.eval()
+ data_config = timm.data.resolve_model_data_config(model)
+ processor = timm.data.create_transform(**data_config, is_training=False)
+ return model, processor
+
+ def prepare_data(self):
+ url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
+ image = Image.open(requests.get(url, stream=True).raw)
+ return image
+
+ def test_inference_default(self):
+ model, processor = self.prepare_model_and_processor()
+ image = self.prepare_data()
+ inputs = processor(image).unsqueeze(0).to("hpu")
+ outputs = model(inputs)
+ top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+ top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+ top1_class_indices = top1_class_indices.to("cpu").numpy()
+ expected_scores = np.array([21.406523]) # from CPU
+ expected_class = np.array([960])
+ self.assertEqual(top1_class_indices, expected_class)
+ self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+ def test_inference_autocast(self):
+ model, processor = self.prepare_model_and_processor()
+ image = self.prepare_data()
+ inputs = processor(image).unsqueeze(0).to("hpu")
+
+ with torch.autocast(device_type="hpu", dtype=torch.bfloat16): # Autocast BF16
+ outputs = model(inputs)
+ top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+ top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+ top1_class_indices = top1_class_indices.to("cpu").numpy()
+ expected_scores = np.array([21.406523]) # from CPU
+ expected_class = np.array([960])
+ self.assertEqual(top1_class_indices, expected_class)
+ self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+ def test_inference_hpu_graphs(self):
+ model, processor = self.prepare_model_and_processor()
+ image = self.prepare_data()
+ inputs = processor(image).unsqueeze(0).to("hpu")
+
+ model = ht.hpu.wrap_in_hpu_graph(model) # Apply graph
+
+ outputs = model(inputs)
+ top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+ top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+ top1_class_indices = top1_class_indices.to("cpu").numpy()
+ expected_scores = np.array([21.406523]) # from CPU
+ expected_class = np.array([960])
+ self.assertEqual(top1_class_indices, expected_class)
+ self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+ def test_no_latency_regression_autocast(self):
+ warmup = 3
+ iterations = 20
+
+ model, processor = self.prepare_model_and_processor()
+ image = self.prepare_data()
+
+ model = ht.hpu.wrap_in_hpu_graph(model)
+
+ with torch.no_grad(), torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=True):
+ for i in range(warmup):
+ inputs = processor(image).unsqueeze(0).to("hpu")
+ _ = model(inputs)
+ torch.hpu.synchronize()
+
+ total_model_time = 0
+ for i in range(iterations):
+ inputs = processor(image).unsqueeze(0).to("hpu")
+ model_start_time = time.time()
+ _ = model(inputs)
+ torch.hpu.synchronize()
+ model_end_time = time.time()
+ total_model_time = total_model_time + (model_end_time - model_start_time)
+
+ latency = total_model_time * 1000 / iterations # in terms of ms
+ self.assertLessEqual(latency, 1.05 * LATENCY_FastViT_BF16_GRAPH_BASELINE)