mixtral uni test to float16 instead of fp8 (worker 8.9 or higher for …

…fp8)

tests/benchmarks/model_benchmarks/test_pytorch_mixtral.py

@@ -16,11 +16,11 @@
 @decorator.cuda_test
 @decorator.pytorch_test
 def test_pytorch_mixtral_8x7b():
-    """Test pytorch-mixtral-8x7b benchmark for fp8 train and inference."""
+    """Test pytorch-mixtral-8x7b benchmark for float16 train and inference."""
     context = BenchmarkRegistry.create_benchmark_context(
         'mixtral-8x7b',
         platform=Platform.CUDA,
-        parameters='--batch_size 1 --seq_len 32 --num_warmup 1 --num_steps 2 --precision fp8_e4m3 \
+        parameters='--batch_size 1 --seq_len 32 --num_warmup 1 --num_steps 2 --precision float16 \
             --hidden_size 1024 --max_position_embeddings 2048 --intermediate_size 3584 \
             --model_action train inference',
         framework=Framework.PYTORCH
@@ -59,7 +59,9 @@ def test_pytorch_mixtral_8x7b():
     assert (benchmark.run_count == 1)
     assert (benchmark.return_code == ReturnCode.SUCCESS)
 
-    for metric in ['fp8_e4m3_inference_step_time', 'fp8_e4m3_inference_throughput']:
+    for metric in [
+        'fp16_train_step_time', 'fp16_train_throughput', 'fp16_inference_step_time', 'fp16_inference_throughput'
+    ]:
         assert (len(benchmark.raw_data[metric]) == benchmark.run_count)
         assert (len(benchmark.raw_data[metric][0]) == benchmark._args.num_steps)
         assert (len(benchmark.result[metric]) == benchmark.run_count)