Merge branch 'main' into videollama3

Author: lkhl

Makefile

@@ -3,7 +3,7 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := examples tests src utils
+check_dirs := examples tests src utils scripts benchmark benchmark_v2
 
 exclude_folders :=  ""
 

benchmark/benches/llama.py

@@ -11,25 +11,28 @@
 # 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.
-from logging import Logger
 import os
+import sys
+from logging import Logger
 from threading import Event, Thread
 from time import perf_counter, sleep
 from typing import Optional
-import sys
+
 
 # Add the parent directory to Python path to import benchmarks_entrypoint
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from benchmarks_entrypoint import MetricsRecorder
-
 import gpustat
 import psutil
 import psycopg2
+from benchmarks_entrypoint import MetricsRecorder
+
 
 # Optional heavy ML dependencies - only required when actually running the benchmark
 try:
     import torch
+
     from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
+
     TRANSFORMERS_AVAILABLE = True
 except ImportError:
     TRANSFORMERS_AVAILABLE = False
@@ -63,19 +66,25 @@ def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder):
 
 
 def run_benchmark(
-    logger: Logger, repository: str, branch: str, commit_id: str, commit_msg: str, metrics_recorder=None, num_tokens_to_generate=100
+    logger: Logger,
+    repository: str,
+    branch: str,
+    commit_id: str,
+    commit_msg: str,
+    metrics_recorder=None,
+    num_tokens_to_generate=100,
 ):
     # Check if required ML dependencies are available
     if not TRANSFORMERS_AVAILABLE:
         logger.error("Transformers and torch are required to run the LLaMA benchmark. Please install them with:")
         logger.error("pip install torch transformers")
         logger.error("Skipping LLaMA benchmark due to missing dependencies.")
         return
-    
+
     continue_metric_collection = Event()
     metrics_thread = None
     model_id = "meta-llama/Llama-2-7b-hf"
-    
+
     # If no metrics_recorder is provided, create one for backward compatibility
     if metrics_recorder is None:
         try:
@@ -154,7 +163,7 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
         # First eager forward pass
         logger.info("running first eager forward pass")
         start = perf_counter()
-        outputs = model(**inputs)
+        _ = model(**inputs)
         torch.cuda.synchronize()
         end = perf_counter()
         first_eager_fwd_pass_time = end - start
@@ -163,7 +172,7 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
         # Second eager forward pass (should be faster)
         logger.info("running second eager forward pass")
         start = perf_counter()
-        outputs = model(**inputs)
+        _ = model(**inputs)
         torch.cuda.synchronize()
         end = perf_counter()
         second_eager_fwd_pass_time = end - start
@@ -339,7 +348,7 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
     continue_metric_collection.set()
     if metrics_thread is not None:
         metrics_thread.join()
-    
+
     # Only close the recorder if we created it locally
     if should_close_recorder:
-        metrics_recorder.close() 
+        metrics_recorder.close()

benchmark/benchmark.py

@@ -31,9 +31,7 @@
 from pathlib import Path
 
 from git import Repo
-
 from huggingface_hub import HfApi
-
 from optimum_benchmark import Benchmark
 from optimum_benchmark_wrapper import main