# Ref: Ouyang, A. (2023). Understanding the Performance of Transformer Inference (Doctoral dissertation, Massachusetts Institute of Technology). import streamlit as st import pandas as pd from model_util import fetch_dictionary_content, load_parameter from calc_util import * from render_util import create_table, header4, header5 st.set_page_config(layout='wide') if 'model_config' not in st.session_state: st.session_state['model_config'] = {} def load_model_config(model_id): if 'model_id' in st.session_state['model_config'] and st.session_state['model_config']['model_id'] == model_id: return st.session_state['model_config'] model_config = {} dictionary_content = fetch_dictionary_content(model_id) if dictionary_content: model_config['model_id'] = model_id model_config['hidden_size'] = dictionary_content['hidden_size'] model_config['num_attention_heads'] = dictionary_content['num_attention_heads'] model_config['num_hidden_layers'] = dictionary_content['num_hidden_layers'] model_config['intermediate_size'] = load_parameter(dictionary_content, ['intermediate_size', 'ffn_dim']) model_config['vocab_size'] = dictionary_content['vocab_size'] model_config['max_position_embeddings'] = dictionary_content['max_position_embeddings'] model_config['layernorm_operation'] = 2 else: st.warning("Model Info is not public!") model_config['model_id'] = 'opt-1.3b' model_config['hidden_size'] = 2048 model_config['num_attention_heads'] = 32 model_config['num_hidden_layers'] = 24 model_config['intermediate_size'] = 8192 model_config['vocab_size'] = 50272 model_config['max_position_embeddings'] = 2048 model_config['layernorm_operation'] = 2 st.session_state['model_config'] = model_config return model_config subtotal_parameters = [ 'embedding_weights', 'attention_weights', 'mlp_weights', 'model_total_size' ] subtotal_operations = [ 'embeddings', 'attention', 'mlp', 'total', ] col1, col2, col3, col4, col5 = st.columns(5) inference_config = {} parameter_count = {} cached_parameter_count = {} prefilling_operation_count = {} generation_operation_count = {} gpu_config = {} inference_info = {} with col1: header4("Model") model_id = st.text_input("huggingface model id", 'ArthurZ/opt-13b') model_config = load_model_config(model_id) model_config['hidden_size'] = st.number_input('hidden size', value=model_config['hidden_size'], format ="%d") model_config['num_attention_heads'] = st.number_input('num attention heads', value=model_config['num_attention_heads'], format ="%d") model_config['num_hidden_layers'] = st.number_input('num hidden layers', value=model_config['num_hidden_layers'], format ="%d") model_config['intermediate_size'] = st.number_input('intermediate size', value=model_config['intermediate_size'], format ="%d") model_config['vocab_size'] = st.number_input('vocab size', value= model_config['vocab_size'], format ="%d") model_config['max_position_embeddings'] = st.number_input('max position embeddings', value=model_config['max_position_embeddings'], format ="%d") header4("Inference Setting") inference_config['batchsize'] = st.number_input('batchsize', value=1, format ="%d") inference_config['input_seq_length'] = st.number_input('input seq length', value=1, format ="%d") inference_config['output_seq_length'] = st.number_input('output seq length', value=1, format ="%d") inference_config['byte_per_parameter'] = st.number_input('byte per parameter', value=2, format ="%d") inference_config['KV_cache'] = st.checkbox("Use KV cache", value=True) header4("GPU Setting") gpu_config['Name'] = st.text_input('GPU Type', value="A6000") gpu_config['TFLOP'] = st.number_input('TFLOP', value=38.7, format ="%2f") gpu_config['memory_bandwidth'] = st.number_input('memory bandwidth (GB/s)', value=768, format ="%2d") gpu_config['arithmetic_intensity'] = gpu_config['TFLOP']*10**12/gpu_config['memory_bandwidth']/1024**3 st.write(f"arithmetic_intensity: {gpu_config['arithmetic_intensity']:.3f}") with col2: parameter_count['word_embedding'] = model_config['vocab_size']*model_config['hidden_size'] parameter_count['positional_embedding'] = model_config['max_position_embeddings']*model_config['hidden_size'] parameter_count['attention_Q'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['hidden_size']/model_config['num_attention_heads']*model_config['num_attention_heads'] parameter_count['attention_K'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['hidden_size']/model_config['num_attention_heads']*model_config['num_attention_heads'] parameter_count['attention_V'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['hidden_size']/model_config['num_attention_heads']*model_config['num_attention_heads'] parameter_count['attention_out'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['hidden_size']/model_config['num_attention_heads']*model_config['num_attention_heads'] parameter_count['layernorm'] = 2*model_config['layernorm_operation']*model_config['num_hidden_layers']*model_config['hidden_size'] parameter_count['mlp1'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['intermediate_size'] parameter_count['mlp2'] = model_config['num_hidden_layers']*model_config['hidden_size']*model_config['intermediate_size'] parameter_count['embedding_weights'] = parameter_count['word_embedding'] + parameter_count['positional_embedding'] parameter_count['attention_weights'] = parameter_count['attention_out'] + parameter_count['attention_Q'] + parameter_count['attention_K'] + parameter_count['attention_V'] parameter_count['mlp_weights'] = parameter_count['mlp1'] + parameter_count['mlp2'] parameter_count['model_total_size'] = inference_config['byte_per_parameter'] * ( parameter_count['embedding_weights'] + parameter_count['attention_weights'] + parameter_count['mlp_weights'] + parameter_count['layernorm']) parameters_items = {key: "{:,}".format(int(parameter_count[key])) for key in parameter_count if key not in subtotal_parameters} subtotal_parameters_items = {key: "{:,}".format(int(parameter_count[key])) for key in parameter_count if key in subtotal_parameters} # Convert dictionaries to pandas dataframes for table display df_parameters_items = pd.DataFrame(list(parameters_items.items()), columns=["Parameter", "Count"]) df_subtotal_parameters_items = pd.DataFrame(list(subtotal_parameters_items.items()), columns=["Parameter", "Count"]) header4("Model Parameters") st.markdown(create_table(df_parameters_items)) header4("Parameters Summary") st.markdown(create_table(df_subtotal_parameters_items)) with col3: # Prefilling prefilling_operation_count = prefilling_operation(model_config, inference_config) inference_info['inference_prefilling_time'] = prefilling_operation_count['total'] / (gpu_config['TFLOP']*10**12) inference_info['inference_prefilling_throughput'] = inference_config['input_seq_length']/inference_info['inference_prefilling_time'] cached_parameter_count['kv_cache'] = 2 * (inference_config['batchsize'] * (model_config['hidden_size'] * model_config['num_hidden_layers'] * inference_config['input_seq_length'])) operation_items = {key: "{:,}".format(int(prefilling_operation_count[key])) for key in prefilling_operation_count if key not in subtotal_operations} subtotal_operation_items = {key: "{:,}".format(int(prefilling_operation_count[key])) for key in prefilling_operation_count if key in subtotal_operations} ## Convert dictionaries to pandas dataframes for table display df_operation_count = pd.DataFrame(list(operation_items.items()), columns=["Operation", "FLOPS"]) df_subtotal_operation_count = pd.DataFrame(list(subtotal_operation_items.items()), columns=["Operation", "FLOPS"]) header4("Inference Ops: Prefilling") st.markdown(create_table(df_operation_count)) header5("Summary: Prefilling") st.markdown(create_table(df_subtotal_operation_count)) st.write(f"Prefillng throughput (tokens/s): {inference_info['inference_prefilling_throughput']:.2f}") if inference_config['KV_cache']: st.write(f"kv cache (Byte): {cached_parameter_count['kv_cache']:,}") with col4: # Prefilling generation_operation_count = generation_operation(model_config, inference_config) inference_info['inference_generation_time'] = generation_operation_count['total'] / (gpu_config['TFLOP']*10**12) inference_info['inference_generation_throughput'] = inference_config['output_seq_length']/inference_info['inference_generation_time'] inference_info['inference_client_generation_throughput'] = inference_config['output_seq_length'] / (inference_info['inference_prefilling_time'] + inference_info['inference_generation_time']) cached_parameter_count['kv_cache'] = 2 * (inference_config['batchsize'] * (model_config['hidden_size'] * model_config['num_hidden_layers'] * (inference_config['input_seq_length']+inference_config['output_seq_length']))) operation_items = {key: "{:,}".format(int(generation_operation_count[key])) for key in generation_operation_count if key not in subtotal_operations} subtotal_operation_items = {key: "{:,}".format(int(generation_operation_count[key])) for key in generation_operation_count if key in subtotal_operations} ## Convert dictionaries to pandas dataframes for table display df_operation_count = pd.DataFrame(list(operation_items.items()), columns=["Operation", "FLOPS"]) df_subtotal_operation_count = pd.DataFrame(list(subtotal_operation_items.items()), columns=["Operation", "FLOPS"]) header4("Inference Ops: Generation") st.markdown(create_table(df_operation_count)) header5("Summary: Generation") st.markdown(create_table(df_subtotal_operation_count)) st.write(f"Generation-only throughput (tokens/s): {inference_info['inference_generation_throughput']:.2f}") st.write(f"(Client) Generation throughput (tokens/s): {inference_info['inference_client_generation_throughput']:.2f}") if inference_config['KV_cache']: st.write(f"kv cache (Byte): {cached_parameter_count['kv_cache']:,}")