fix vlm running without image

tools/llm_bench/llm_bench_utils/model_utils.py

@@ -37,10 +37,10 @@ def get_param_from_file(args, input_key):
             if args["use_case"] != "vlm":
                 raise RuntimeError("Multiple sources for benchmarking supported only for Visual Language Models")
             data_dict = {}
-            if args["media"] is None and args["image"] is None:
+            if args["media"] is None and args["images"] is None:
                 log.warn("Input image is not provided. Only text generation part will be evaluated")
             else:
-                data_dict["media"] = args["media"] if args["media"] is not None else args["image"]
+                data_dict["media"] = args["media"] if args["media"] is not None else args["images"]
             if args["prompt"] is None:
                 data_dict["prompt"] = "What is OpenVINO?" if data_dict["media"] is None else "Describe image"
             else:

tools/llm_bench/task/text_generation.py

@@ -209,15 +209,17 @@ def run_text_generation_genai(input_text, num, model, tokenizer, args, iter_data
 
     enable_prompt_permutations = not args.get("disable_prompt_permutation", False)
     if enable_prompt_permutations:
-        log.warning("Enabled input prompt permutations. It means that generation results can be vary on different steps. If it does not expected please specify --disable_prompr_permutation in your benchmarking command to disable this behaviour")
+        log.warning(
+            "Enabled input prompt permutations. It means that generation results can be vary on different steps. "
+            "If it does not expected please specify --disable_prompr_permutation in your benchmarking command to disable this behavior"
+        )
         from openvino_genai import TokenizedInputs
         import openvino as ov
 
         input_ids = input_data.input_ids.data
         input_ids[:, 0] = num + 1
         attention_mask = input_data.attention_mask
         input_data = TokenizedInputs(input_ids=ov.Tensor(input_ids), attention_mask=attention_mask)
-
     num_input_tokens = input_data.input_ids.shape[1]
     if args['batch_size'] > 1:
         out_str = '[warm-up]' if num == 0 else '[{}]'.format(num)
@@ -379,7 +381,10 @@ def run_text_generation_genai_with_stream(input_text, num, model, tokenizer, arg
     gen_config.do_sample = False
     enable_prompt_permutations = not args.get("disable_prompt_permutation", False)
     if enable_prompt_permutations:
-        log.warning("Enabled input prompt permutations. It means that generation results can be vary on different steps. If it does not expected please specify --disable_prompr_permutation in your benchmarking command to disable this behaviour")
+        log.warning(
+            "Enabled input prompt permutations. It means that generation results can be vary on different steps. "
+            "If it does not expected please specify --disable_prompr_permutation in your benchmarking command to disable this behavior"
+        )
         from openvino_genai import TokenizedInputs
         import openvino as ov
 

tools/llm_bench/task/visual_language_generation.py

@@ -44,7 +44,7 @@ def run_visual_language_generation_optimum(
         for bs_index, in_text in enumerate(prompts):
             llm_bench_utils.output_file.output_input_text(in_text, args, model_precision, prompt_index, bs_index, proc_id)
     tok_encode_start = time.perf_counter()
-    input_data = model.preprocess_inputs(text=prompts[0], image=images[0], **processor)
+    input_data = model.preprocess_inputs(text=prompts[0], image=images[0] if images else None, **processor)
     tok_encode_end = time.perf_counter()
     tok_encode_time = (tok_encode_end - tok_encode_start) * 1000
     # Remove `token_type_ids` from inputs
@@ -211,8 +211,11 @@ def run_visual_language_generation_genai(
     gen_config.max_new_tokens = max_gen_tokens
     gen_config.num_beams = args["num_beams"]
     gen_config.do_sample = False
+    kwargs = {}
+    if len(images) >= 1:
+        kwargs["images"] = images[0]
     start = time.perf_counter()
-    generation_result = model.generate(prompts[0], images=images[0], generation_config=gen_config)
+    generation_result = model.generate(prompts[0], generation_config=gen_config)
     end = time.perf_counter()
     generated_text = generation_result.texts
     perf_metrics = generation_result.perf_metrics