Update app.py

app.py

@@ -1,132 +1,63 @@
 import gradio as gr
 import argparse
-from tabulate import tabulate
 
 def main():
-    parser = argparse.ArgumentParser(description='Estimate LLM Memory Footprint')
-    parser.add_argument('--num_gpu', type=int, default=1, help='Number of GPUs')
-    parser.add_argument('--prompt_sz', type=int, default=4096, help='Prompt size in tokens')
-    parser.add_argument('--response_sz', type=int, default=256, help='Response size in tokens')
-    parser.add_argument('--n_concurrent_req', type=int, default=10, help='Number of concurrent requests')
-    parser.add_argument('--ctx_window', type=int, default=1024, help='Average context window')
+    parser = argparse.ArgumentParser(description='Estimare capacità e latenza di un modello LLM')
+    parser.add_argument('--gpu', type=str, default='A100 80GB', help='Tipo di GPU')
+    parser.add_argument('--model', type=str, default='Llama-3-70B', help='Nome del modello')
+    parser.add_argument('--prompt_size', type=int, default=4096, help='Dimensione della promessa in token')
+    parser.add_argument('--response_size', type=int, default=256, help='Dimensione della risposta in token')
+    parser.add_argument('--concurrent_requests', type=int, default=10, help='Numero di richieste concorrenti')
 
     args = parser.parse_args()
 
-    num_gpu = args.num_gpu
-    prompt_size = args.prompt_sz
-    response_size = args.response_sz
-    n_concurrent_request = args.n_concurrent_req
-    avg_context_window = args.ctx_window
+    gpu_specs = {
+        'A100 80GB': {'tflops': 312, 'memory_gb': 80, 'bandwidth': 1935},
+        'H100 SXM': {'tflops': 1979, 'memory_gb': 80, 'bandwidth': 3350},
+    }
 
-    # Print input
-    print(f" num_gpu = {num_gpu}, prompt_size = {prompt_size} tokens, response_size = {response_size} tokens")
-    print(f" n_concurrent_request = {n_concurrent_request}, avg_context_window = {avg_context_window} tokens")
+    model_specs = {
+        'Llama-3-70B': {'params_billion': 70, 'd_model': 8192, 'n_layers': 80},
+        'Llama-3-8B': {'params_billion': 8, 'd_model': 4096, 'n_layers': 32},
+    }
 
-    # Define variables
-    gpu_specs = [
-        {"name": "A10", "fp16_tflops": 125, "memory_gb": 24, "memory_bandwidth_gbps": 600},
-        {"name": "A30", "fp16_tflops": 330, "memory_gb": 24, "memory_bandwidth_gbps": 933},
-        {"name": "L40", "fp16_tflops": 181, "memory_gb": 48, "memory_bandwidth_gbps": 864},
-        {"name": "L40s", "fp16_tflops": 362, "memory_gb": 48, "memory_bandwidth_gbps": 864},
-        {"name": "A100 40 GB", "fp16_tflops": 312, "memory_gb": 40, "memory_bandwidth_gbps": 1555},
-        {"name": "A100 40 GB SXM", "fp16_tflops": 312, "memory_gb": 40, "memory_bandwidth_gbps": 1555},
-        {"name": "A100 80 GB PCIe", "fp16_tflops": 312, "memory_gb": 80, "memory_bandwidth_gbps": 1935},
-        {"name": "A100 80 GB SXM", "fp16_tflops": 312, "memory_gb": 80, "memory_bandwidth_gbps": 2039},
-        {"name": "H100 PCIe", "fp16_tflops": 1513, "memory_gb": 80, "memory_bandwidth_gbps": 2000},
-        {"name": "H100 SXM", "fp16_tflops": 1979, "memory_gb": 80, "memory_bandwidth_gbps": 3350},
-        {"name": "H100 NVL", "fp16_tflops": 3958, "memory_gb": 188, "memory_bandwidth_gbps": 7800}
-        # Add or comment out GPU types as needed
-    ]
+    def estimate_llm_capacity(model_name, gpu_name, prompt_size, response_size, concurrent_requests):
+        gpu = gpu_specs[gpu_name]
+        model = model_specs[model_name]
 
-    model_specs = [
-        {"name": "Llama-3-8B", "params_billion": 8, "d_model": 4096, "n_heads": 32, "n_layers": 32, "max_context_window": 8192, "d_head": 128},
-        {"name": "Llama-3-70B", "params_billion": 70, "d_model": 8192, "n_heads": 64, "n_layers": 80, "max_context_window": 8192, "d_head": 128},
-        {"name": "Llama-3.1-8B", "params_billion": 8, "d_model": 4096, "n_heads": 32, "n_layers": 32, "max_context_window": 131072, "d_head": 128},
-        {"name": "Llama-3.1-70B", "params_billion": 70, "d_model": 8192, "n_heads": 64, "n_layers": 80, "max_context_window": 131072, "d_head": 128},
-        {"name": "Mistral-7B-v0.3", "params_billion": 7, "d_model": 4096, "n_heads": 32, "n_layers": 32, "max_context_window": 32768, "d_head": 128},
-        {"name": "Falcon-7B", "params_billion": 7, "d_model": 4544, "n_heads": 71, "n_layers": 32, "max_context_window": 2048, "d_head": 64},
-        {"name": "Falcon-40B", "params_billion": 40, "d_model": 8192, "n_heads": 128, "n_layers": 60, "max_context_window": 2048, "d_head": 64},
-        {"name": "Falcon-180B", "params_billion": 180, "d_model": 14848, "n_heads": 232, "n_layers": 80, "max_context_window": 2048, "d_head": 64}
-        # Add or comment out model specifications as needed
-    ]
+        kv_cache_tokens = (gpu['tflops'] * concurrent_requests) // (model['params_billion'] * 2)
+        prefill_time_ms = (model['params_billion'] * 2) / (gpu['tflops'] * concurrent_requests) * 1000
+        generation_time_ms = (model['params_billion'] * 2) / (gpu['bandwidth'] * concurrent_requests) * 1000
+        estimated_response_time = (prompt_size * prefill_time_ms + response_size * generation_time_ms) / 1000
 
-    BYTES_IN_GB = 1_073_741_824  # 1 GB = 1,073,741,824 bytes
-
-    def estimate_llm_capacity_and_latency(model_name, gpu_name, num_gpu, prompt_size, response_size, n_concurrent_request, avg_context_window):
-        def calc_kv_cache_size_per_token(n_layers, d_model):
-            return 2 * 2 * n_layers * d_model / BYTES_IN_GB  # GB/token
-    
-        def calc_memory_footprint(model_spec, n_concurrent_request, avg_context_window):
-            kv_cache_size_per_token = calc_kv_cache_size_per_token(model_spec["n_layers"], model_spec["d_model"])
-            target_gpu_mem = kv_cache_size_per_token * avg_context_window * n_concurrent_request + model_spec["params_billion"] * 2
-            return target_gpu_mem
-    
-        print(f"\n******************** Estimate LLM Memory Footprint ********************")
-        memory_footprint_table = []
-        for model_spec in model_specs:
-            kv_cache_size_per_token = calc_kv_cache_size_per_token(model_spec["n_layers"], model_spec["d_model"])
-            memory_footprint = calc_memory_footprint(model_spec, n_concurrent_request, avg_context_window)
-            memory_footprint_table.append([model_spec['name'], f"{kv_cache_size_per_token:.6f} GiB/token", f"{memory_footprint:.2f} GB"])
-        print(tabulate(memory_footprint_table, headers=['Model', 'KV Cache Size per Token', 'Memory Footprint'], tablefmt='orgtbl'))
-    
-        def calc_kv_cache_tokens(num_gpu, gpu_memory_gb, model_params_billion, kv_cache_size):
-            result = (num_gpu * gpu_memory_gb - 2 * model_params_billion) / kv_cache_size
-            return result if result >= 0 else "OOM"
-    
-        def calc_prefill_time_per_token(num_gpu, model_params_billion, fp16_tflops):
-            result = (2 * model_params_billion / num_gpu) / fp16_tflops
-            return result if result >= 0 else "OOM"
-    
-        def calc_generation_time_per_token(num_gpu, model_params_billion, memory_bandwidth_gbps):
-            result = (2 * model_params_billion / num_gpu) / memory_bandwidth_gbps * 1000
-            return result if result >= 0 else "OOM"
-    
-        def calc_estimated_response_time(prefill_time, generation_time, prompt_size, response_size):
-            if isinstance(prefill_time, str) or isinstance(generation_time, str):  # Check if any are "NA"
-                return "OOM"
-            return (prompt_size * prefill_time + response_size * generation_time) / 1000  # convert ms to seconds
-    
-        print(f"\n******************** Estimate LLM Capacity and Latency ******************** ")
-        capacity_latency_table = []
-        for model in model_specs:
-            # print(f"Model: {model['name']} ({model['params_billion']}B parameters)")
-            kv_cache_size = calc_kv_cache_size_per_token(model['n_layers'], model['d_model'])
-            for gpu in gpu_specs:
-                kv_cache_tokens = calc_kv_cache_tokens(num_gpu, gpu['memory_gb'], model['params_billion'], kv_cache_size)
-                prefill_time_per_token = calc_prefill_time_per_token(num_gpu, model['params_billion'], gpu['fp16_tflops'])
-                generation_time_per_token = calc_generation_time_per_token(num_gpu, model['params_billion'], gpu['memory_bandwidth_gbps'])
-                estimated_response_time = calc_estimated_response_time(prefill_time_per_token, generation_time_per_token, prompt_size, response_size)
-                capacity_latency_table.append([model['name'], gpu['name'], f"{kv_cache_tokens}", f"{prefill_time_per_token:.3f} ms", f"{generation_time_per_token:.3f} ms", f"{estimated_response_time:.1f} s"])
-        print(tabulate(capacity_latency_table, headers=['Model', 'GPU', 'KV Cache Tokens', 'Prefill Time', 'Generation Time', 'Estimated Response Time'], tablefmt='orgtbl'))
-
-    def generate_output(model_name, gpu_name, kv_cache_tokens, prefill_time, generation_time, estimated_response_time):
-        return {
-            "Model": model_name,
-            "GPU": gpu_name,
-            "KV Cache Tokens": str(kv_cache_tokens),
-            "Prefill Time (ms)": f"{prefill_time:.3f}",
-            "Generation Time (ms)": f"{generation_time:.3f}",
-            "Estimated Response Time (s)": f"{estimated_response_time:.1f}"
-        }
+        return f"""
+Modello: {model_name}
+GPU: {gpu_name}
+KV Cache Tokens: {kv_cache_tokens:.0f}
+Prefill Time: {prefill_time_ms:.2f} ms
+Generation Time: {generation_time_ms:.2f} ms
+Estimated Response Time: {estimated_response_time:.2f} s
+"""
 
     with gr.Blocks() as demo:
-        gr.Markdown("# Estimate LLM Capacity and Latency")
+        gr.Markdown("# Estimare Capacità e Latenza LLM")
         
-        num_gpu = gr.Number(label="Number of GPUs", value=1)
-        prompt_size = gr.Slider(minimum=1, maximum=8192, label="Prompt Size (tokens)", value=4096)
-        response_size = gr.Slider(minimum=1, maximum=8192, label="Response Size (tokens)", value=256)
-        n_concurrent_request = gr.Slider(minimum=1, maximum=100, label="Concurrent Requests", value=10)
-        avg_context_window = gr.Slider(minimum=1, maximum=131072, label="Average Context Window", value=1024)
+        gpu_dropdown = gr.Dropdown(choices=['A100 80GB', 'H100 SXM'], label="Tipo di GPU", value='A100 80GB')
+        model_dropdown = gr.Dropdown(choices=['Llama-3-70B', 'Llama-3-8B'], label="Nome del Modello", value='Llama-3-70B')
+        prompt_size = gr.Slider(minimum=1, maximum=8192, label="Dimensione della Promessa", value=4096)
+        response_size = gr.Slider(minimum=1, maximum=8192, label="Dimensione della Risposta", value=256)
+        concurrent_requests = gr.Slider(minimum=1, maximum=100, label="Richieste Concorrenti", value=10)
 
-        submit_button = gr.Button("Estimate")
+        table = gr.Textbox()
 
-        table = gr.Table()
+        submit_button = gr.Button("Estimare")
 
         submit_button.click(
-            fn=lambda num_gpu=num_gpu, prompt_size=prompt_size, response_size=response_size, 
-            n_concurrent_request=n_concurrent_request, avg_context_window=avg_context_window:
-                estimate_llm_capacity_and_latency(None, None, num_gpu, prompt_size, response_size, n_concurrent_request, avg_context_window),
-            inputs=[num_gpu, prompt_size, response_size, n_concurrent_request, avg_context_window],
+            fn=lambda gpu=gpu_dropdown.value, model=model_dropdown.value, 
+            prompt_size=prompt_size.value, response_size=response_size.value, 
+            concurrent_requests=concurrent_requests.value:
+                estimate_llm_capacity(model, gpu, prompt_size, response_size, concurrent_requests),
+            inputs=[gpu_dropdown, model_dropdown, prompt_size, response_size, concurrent_requests],
             outputs=[table]
         )