[CI] [GHA] Use OV_CACHE in the WWB tests

.github/workflows/linux.yml

@@ -26,6 +26,8 @@ env:
   SCCACHE_AZURE_KEY_PREFIX: genai/ubuntu/22_04/x64
   HF_HOME: /mount/caches/huggingface/lin
   OV_CACHE: /mount/caches/huggingface/.ov_cache/lin
+  HF_HUB_VERBOSITY: debug
+  TRANSFORMERS_VERBOSITY: debug
   OPENVINO_LOG_LEVEL: 5
   GENAI_ARCHIVE_NAME: genai.tar.gz
   GENAI_SAMPLES_NAME: genai_samples.tar.gz

.github/workflows/windows.yml

@@ -24,6 +24,8 @@ env:
   CCACHE_MAXSIZE: 500Mi
   HF_HOME: C:/mount/caches/huggingface/win
   OV_CACHE: C:/mount/caches/huggingface/.ov_cache/win/
+  HF_HUB_VERBOSITY: debug
+  TRANSFORMERS_VERBOSITY: debug
   OPENVINO_LOG_LEVEL: 5
   ARTIFACTS_SHARE: '/mount/build-artifacts'
   BASE_PRODUCT_TYPE: public_windows_vs2022

tools/who_what_benchmark/tests/constants.py

@@ -0,0 +1,7 @@
+from pathlib import Path
+import os
+import tempfile
+
+
+WWB_CACHE_PATH = Path(os.path.join(os.environ.get('OV_CACHE', tempfile.TemporaryDirectory().name), 'wwb_cache'))
+SHOULD_CLEANUP = os.environ.get('CLEANUP_CACHE', '').lower() in ('1', 'true', 'yes')

tools/who_what_benchmark/tests/test_cli_image.py

@@ -5,14 +5,14 @@
 import shutil
 import pytest
 import logging
-import tempfile
 import re
+from constants import WWB_CACHE_PATH, SHOULD_CLEANUP
 
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-MODEL_CACHE = tempfile.mkdtemp()
+MODEL_CACHE = WWB_CACHE_PATH
 OV_IMAGE_MODELS = ["echarlaix/tiny-random-stable-diffusion-xl",
                    "yujiepan/stable-diffusion-3-tiny-random",
                    "katuni4ka/tiny-random-flux",
@@ -38,13 +38,14 @@ def run_wwb(args):
 
 def setup_module():
     for model_id in OV_IMAGE_MODELS:
-        MODEL_PATH = os.path.join(MODEL_CACHE, model_id.replace("/", "--"))
+        MODEL_PATH = MODEL_CACHE.joinpath(model_id.replace("/", "--"))
         subprocess.run(["optimum-cli", "export", "openvino", "--model", model_id, MODEL_PATH], capture_output=True, text=True)
 
 
 def teardown_module():
-    logger.info("Remove models")
-    shutil.rmtree(MODEL_CACHE)
+    if SHOULD_CLEANUP:
+        logger.info("Removing models")
+        shutil.rmtree(MODEL_CACHE)
 
 
 def get_similarity(output: str) -> float:
@@ -121,11 +122,12 @@ def test_image_model_genai(model_id, model_type, tmp_path):
         pytest.xfail("Ticket 173169")
 
     GT_FILE = tmp_path / "gt.csv"
-    MODEL_PATH = os.path.join(MODEL_CACHE, model_id.replace("/", "--"))
+    MODEL_PATH = MODEL_CACHE.joinpath(model_id.replace("/", "--"))
+    MODEL_PATH = MODEL_PATH if MODEL_PATH.exists() else model_id
 
     run_wwb([
         "--base-model",
-        model_id,
+        MODEL_PATH,
         "--num-samples",
         "1",
         "--gt-data",

tools/who_what_benchmark/tests/test_cli_text.py

@@ -1,12 +1,13 @@
 import os
 import shutil
-import tempfile
 import pandas as pd
 import pytest
 import logging
 import json
 import sys
 
+from constants import WWB_CACHE_PATH, SHOULD_CLEANUP
+
 from transformers import AutoTokenizer
 from optimum.intel.openvino import OVModelForCausalLM, OVWeightQuantizationConfig
 
@@ -18,9 +19,9 @@
 
 
 model_id = "facebook/opt-125m"
-tmp_dir = tempfile.mkdtemp()
-base_model_path = os.path.join(tmp_dir, "opt125m")
-target_model_path = os.path.join(tmp_dir, "opt125m_int8")
+cache_dir = WWB_CACHE_PATH
+base_model_path = os.path.join(cache_dir, "opt125m")
+target_model_path = os.path.join(cache_dir, "opt125m_int8")
 
 gptq_model_id = "ybelkada/opt-125m-gptq-4bit"
 awq_model_id = "TitanML/tiny-mixtral-AWQ-4bit"
@@ -29,24 +30,27 @@
 def setup_module():
     from optimum.exporters.openvino.convert import export_tokenizer
 
-    logger.info("Create models")
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    base_model = OVModelForCausalLM.from_pretrained(model_id)
-    base_model.save_pretrained(base_model_path)
-    tokenizer.save_pretrained(base_model_path)
-    export_tokenizer(tokenizer, base_model_path)
+    if not os.path.exists(base_model_path):
+        logger.info("Create models")
+        tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=WWB_CACHE_PATH)
+        base_model = OVModelForCausalLM.from_pretrained(model_id, cache_dir=WWB_CACHE_PATH)
+        base_model.save_pretrained(base_model_path)
+        tokenizer.save_pretrained(base_model_path)
+        export_tokenizer(tokenizer, base_model_path)
 
-    target_model = OVModelForCausalLM.from_pretrained(
-        model_id, quantization_config=OVWeightQuantizationConfig(bits=8)
-    )
-    target_model.save_pretrained(target_model_path)
-    tokenizer.save_pretrained(target_model_path)
-    export_tokenizer(tokenizer, target_model_path)
+    if not os.path.exists(target_model_path):
+        target_model = OVModelForCausalLM.from_pretrained(
+            model_id, quantization_config=OVWeightQuantizationConfig(bits=8), cache_dir=WWB_CACHE_PATH
+        )
+        target_model.save_pretrained(target_model_path)
+        tokenizer.save_pretrained(target_model_path)
+        export_tokenizer(tokenizer, target_model_path)
 
 
 def teardown_module():
-    logger.info("Remove models")
-    shutil.rmtree(tmp_dir)
+    if SHOULD_CLEANUP:
+        logger.info("Removing models")
+        shutil.rmtree(cache_dir)
 
 
 @pytest.mark.skipif((sys.platform == "darwin"), reason='173169')
@@ -145,7 +149,7 @@ def test_text_language(tmp_path):
     temp_file_name = tmp_path / "gt.csv"
     run_wwb([
         "--base-model",
-        "Qwen/Qwen2-0.5B",
+        'Qwen/Qwen2-0.5B',
         "--gt-data",
         temp_file_name,
         "--num-samples",

tools/who_what_benchmark/tests/test_cli_vlm.py

@@ -3,6 +3,7 @@
 import logging
 import sys
 from test_cli_image import run_wwb
+from constants import WWB_CACHE_PATH
 
 
 logging.basicConfig(level=logging.INFO)
@@ -15,26 +16,27 @@
         ("katuni4ka/tiny-random-llava", "visual-text"),
     ],
 )
-def test_vlm_basic(model_id, model_type, tmp_path):
+def test_vlm_basic(model_id, model_type):
     if sys.platform == 'darwin':
         pytest.xfail("Ticket 173169")
-    GT_FILE = tmp_path / "gt.csv"
-    MODEL_PATH = tmp_path / model_id.replace("/", "--")
+    GT_FILE = WWB_CACHE_PATH / "gt.csv"
+    MODEL_PATH = WWB_CACHE_PATH.joinpath(model_id.replace("/", "--"))
 
-    result = subprocess.run(["optimum-cli", "export",
-                             "openvino", "-m", model_id,
-                             MODEL_PATH, "--task",
-                             "image-text-to-text",
-                             "--trust-remote-code"],
-                            capture_output=True,
-                            text=True,
-                            )
-    assert result.returncode == 0
+    if not MODEL_PATH.exists():
+        result = subprocess.run(["optimum-cli", "export",
+                                 "openvino", "-m", model_id,
+                                 MODEL_PATH, "--task",
+                                 "image-text-to-text",
+                                 "--trust-remote-code"],
+                                capture_output=True,
+                                text=True,
+                                )
+        assert result.returncode == 0
 
     # Collect reference with HF model
     run_wwb([
         "--base-model",
-        model_id,
+        MODEL_PATH,
         "--num-samples",
         "1",
         "--gt-data",
@@ -74,13 +76,13 @@ def test_vlm_basic(model_id, model_type, tmp_path):
         model_type,
         "--genai",
         "--output",
-        tmp_path,
+        WWB_CACHE_PATH,
     ])
 
     # test w/o models
     run_wwb([
         "--target-data",
-        tmp_path / "target.csv",
+        WWB_CACHE_PATH / "target.csv",
         "--num-samples",
         "1",
         "--gt-data",