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| # 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, get_model, classify_module, get_module_tensors | |
| 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, access_token): | |
| 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'] | |
| if 'parameter_count' in st.session_state: | |
| st.session_state.pop('parameter_count') | |
| 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("Fetching information failed! Maybe 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 | |
| try: | |
| model_config['model'] = get_model(model_id, None, access_token=access_token if len(access_token)>0 else None) | |
| module_tensors = get_module_tensors(model_config['model']) | |
| model_config['module_classes'] = classify_module(module_tensors) | |
| except Exception as e: | |
| st.warning(e) | |
| model_config['model'] = None | |
| model_config['module_classes'] = None | |
| st.session_state['model_config'] = model_config | |
| return model_config | |
| subtotal_parameters = [ | |
| 'embedding_weights', | |
| 'attention_weights', | |
| 'mlp_weights', | |
| ] | |
| subtotal_operations = [ | |
| 'embeddings', | |
| 'attention', | |
| 'mlp', | |
| 'total', | |
| ] | |
| col1, col2, col3, col4, col5 = st.columns([0.8, 2, 2.5, 2.5, 0.01]) | |
| inference_config = {} | |
| parameter_count = {} | |
| cached_parameter_count = {} | |
| prefilling_operation_count = {} | |
| generation_operation_count = {} | |
| prefilling_memory_count = {} | |
| generation_memory_count = {} | |
| gpu_config = {} | |
| inference_info = {} | |
| with col1: | |
| header4("Model") | |
| model_id = st.text_input("huggingface model id", 'ArthurZ/opt-13b') | |
| access_token = st.text_input("access token", '') | |
| model_config = load_model_config(model_id, access_token) | |
| 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") | |
| model_config['hidden_size_per_head'] = model_config['hidden_size']/model_config['num_attention_heads'] | |
| 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: | |
| if 'parameter_count' not in st.session_state: | |
| if model_config['model']: | |
| st.info("Model info fetcted!") | |
| parameter_count = calc_model_size_from_model(model_config, inference_config) | |
| else: | |
| st.info("Fail to fetch model info. Using estimation!") | |
| parameter_count = model_size_estimate(model_config, inference_config) | |
| st.session_state.parameter_count = parameter_count | |
| else: | |
| parameter_count = st.session_state.parameter_count | |
| 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)) | |
| model_total_size_in_byte = inference_config['byte_per_parameter'] * ( | |
| parameter_count['embedding_weights'] + | |
| parameter_count['attention_weights'] + | |
| parameter_count['mlp_weights'] + | |
| parameter_count['layernorm'] | |
| ) | |
| st.write(f'model_total_size (Byte): {model_total_size_in_byte:,}') | |
| # add parameter viewer | |
| if model_config['model']: | |
| header4("Parameters Viewer") | |
| weight_generic = st.selectbox('Select weight:', options=model_config['module_classes']) | |
| modules = {} | |
| for module in model_config['module_classes'][weight_generic]: | |
| modules.update(module) | |
| modules = {k: list(v) for k, v in modules.items()} | |
| modules = pd.DataFrame(list(modules.items()), columns=["Parameter", "Shape"]) | |
| st.markdown(create_table(modules)) | |
| with col3: # Prefilling | |
| prefilling_operation_count = prefilling_operation(model_config, inference_config) | |
| prefilling_activation_memory_count = prefilling_activation_memory(model_config, inference_config) | |
| inference_info['inference_prefilling_time'] = prefilling_operation_count['total'] / (gpu_config['TFLOP']*1024**4) | |
| inference_info['prefilling_memory_latency'] = prefilling_activation_memory_count['total'] / (gpu_config['memory_bandwidth']*1024**3) | |
| calc_prefilling_throughput(model_config, inference_config, inference_info) | |
| 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} | |
| prefilling_arithmetic_intensity = {key: "{:.3f}".format(prefilling_operation_count[key]/prefilling_activation_memory_count[key] if prefilling_activation_memory_count[key]>0 else float('inf')) for key in prefilling_activation_memory_count} | |
| prefilling_activation_memory_count = {key: "{:,}".format(int(value)) for key, value in prefilling_activation_memory_count.items()} | |
| ## 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"]) | |
| df_operation_count["Activation (Byte)"] = df_operation_count["Operation"].map(prefilling_activation_memory_count) | |
| df_operation_count["Arithmetic Intensity"] = df_operation_count["Operation"].map(prefilling_arithmetic_intensity) | |
| df_subtotal_operation_count["Activation (Byte)"] = df_subtotal_operation_count["Operation"].map(prefilling_activation_memory_count) | |
| df_subtotal_operation_count["Arithmetic Intensity"] = df_subtotal_operation_count["Operation"].map(prefilling_arithmetic_intensity) | |
| 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"FLOPS latency: {inference_info['inference_prefilling_time']}") | |
| st.write(f"Memory latency: {inference_info['prefilling_memory_latency']}") | |
| st.write(f"Prefillng throughput (tokens/s): {inference_info['prefilling_throughput']:.2f} ({inference_info['prefilling_bound_type']}-bound)") | |
| if inference_config['KV_cache']: | |
| st.write(f"kv cache (Byte): {cached_parameter_count['kv_cache']:,}") | |
| with col4: # Generation | |
| generation_operation_count = generation_operation(model_config, inference_config) | |
| generation_activation_memory_count = generation_activation_memory(model_config, inference_config) | |
| inference_info['inference_generation_time'] = generation_operation_count['total'] / (gpu_config['TFLOP']*1024**4) | |
| inference_info['generation_memory_latency'] = generation_activation_memory_count['total'] / (gpu_config['memory_bandwidth']*1024**3) | |
| calc_generation_throughput(model_config, inference_config, inference_info) | |
| 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} | |
| generation_arithmetic_intensity = {key: "{:.3f}".format(generation_operation_count[key]/generation_activation_memory_count[key] if generation_activation_memory_count[key]>0 else float('inf')) for key in generation_activation_memory_count} | |
| generation_activation_memory_count = {key: "{:,}".format(int(value)) for key, value in generation_activation_memory_count.items()} | |
| ## 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"]) | |
| df_operation_count["Activation (Byte)"] = df_operation_count["Operation"].map(generation_activation_memory_count) | |
| df_operation_count["Arithmetic Intensity"] = df_operation_count["Operation"].map(generation_arithmetic_intensity) | |
| df_subtotal_operation_count["Activation (Byte)"] = df_subtotal_operation_count["Operation"].map(generation_activation_memory_count) | |
| df_subtotal_operation_count["Arithmetic Intensity"] = df_subtotal_operation_count["Operation"].map(generation_arithmetic_intensity) | |
| 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}") | |
| st.write(f"FLOPS latency: {inference_info['inference_generation_time']}") | |
| st.write(f"Memory latency: {inference_info['generation_memory_latency']}") | |
| st.write(f"Generation-only throughput (tokens/s): {inference_info['generation_throughput']:.2f} ({inference_info['generation_bound_type']}-bound)") | |
| st.write(f"(Client) Generation throughput (tokens/s): {inference_info['client_generation_throughput']:.2f}") | |
| if inference_config['KV_cache']: | |
| st.write(f"kv cache (Byte): {cached_parameter_count['kv_cache']:,}") |