Modified the prompt inputs

examples/offline_inference_granite.py

@@ -15,18 +15,18 @@
 from vllm.sampling_params import SamplingParams
 
 
-def generate_random_coding_question():
+def generate_random_coding_question(output_len):
     questions = [
-        "How do you reverse a string in Python?",
-        "Write a Python function to check if a number is prime.",
-        "Explain the difference between a list and a tuple in Python.",
-        "Write a Python script to merge two dictionaries.",
-        "What is the use of the 'with' statement in Python?",
-        "Write a Python program to find the factorial of a number using recursion.",
-        "How do you handle exceptions in Python?",
-        "Write a Python class to implement a basic calculator.",
-        "Explain the concept of decorators in Python.",
-        "Write a Python function to sort a list of tuples based on the second element."
+        "list all the imports available in python"
+        # "Write a Python function to check if a number is prime.",
+        # "Explain the difference between a list and a tuple in Python.",
+        # "Write a Python script to merge two dictionaries.",
+        # "What is the use of the 'with' statement in Python?",
+        # "Write a Python program to find the factorial of a number using recursion.",
+        # "How do you handle exceptions in Python?",
+        # "Write a Python class to implement a basic calculator.",
+        # "Explain the concept of decorators in Python.",
+        # "Write a Python function to sort a list of tuples based on the second element."
     ]
     return random.choice(questions)
 
@@ -40,11 +40,25 @@ def str2bool(v):
     else:
         raise argparse.ArgumentTypeError('Boolean value expected.')
 
-def get_conversation():
+def get_conversation(num_words,output_len):
     assistant_adjectives = ['an enthusiastic', 'a knowledgeable', 'a curious', 'a patient', 'an insightful', 'a clever']
     assistants = ['coder', 'developer', 'programmer', 'software engineer', 'tech enthusiast', 'Python expert']
+
     sys_message = ConversationMessage(role='system', content=f'You are {random.choice(assistant_adjectives)} {random.choice(assistants)}. You enjoy sharing your knowledge about Python programming.')
-    user_message = ConversationMessage(role='user', content=generate_random_coding_question())
+    user_message_content = []
+    word_count = 0
+
+    while word_count < num_words:
+        question = generate_random_coding_question(num_words)
+        words_in_question = question.split()
+        if word_count + len(words_in_question) > num_words:
+            # Add only the number of words needed to reach the exact word count
+            user_message_content.extend(words_in_question[:num_words - word_count])
+        else:
+            user_message_content.extend(words_in_question)
+        word_count = len(user_message_content)
+
+    user_message = ConversationMessage(role='user', content=' '.join(user_message_content))
     return [sys_message, user_message]
 
 def main():
@@ -65,6 +79,7 @@ def main():
     parser.add_argument('--warmup', type=int, default=0, help='Number of warmup iterations to skip')
     parser.add_argument('--fp8', type=str2bool, nargs='?', const=True, default=False, help='Boolean flag to enable fp8')
     parser.add_argument('--measure', type=str2bool, nargs='?', const=True, default=False, help='Boolean flag to enable fp8 measurements')
+    parser.add_argument('--input-seq-len', type=int, default=256, help='Maximum input sequence length')
 
     args = parser.parse_args()
 
@@ -108,18 +123,14 @@ def main():
 
     chat_template = load_chat_template(provided_chat_template)
     tokenizer = llm.llm_engine.get_tokenizer()
-    conversations = [get_conversation() for _ in range(batch_size)]
+    conversations = [get_conversation(args.input_seq_len,args.max_tokens) for _ in range(batch_size)]
     prompts = [tokenizer.apply_chat_template(
                 conversation=conversation,
                 tokenize=False,
                 add_generation_prompt=True,
                 chat_template=chat_template,
             ) for conversation in conversations]
 
-    # Warmup iterations
-    for _ in range(warmup):
-        _ = llm.generate(prompts, sampling_params)
-
     # Measure performance for the final iteration
     start = time.time()
     profile_ctx = contextlib.nullcontext()