import streamlit as st from streamlit_datalist import stDatalist import pandas as pd from utils import extract_from_url, get_model, calculate_memory import plotly.express as px import numpy as np import gc from huggingface_hub import login st.set_page_config(page_title='Can you run it? LLM version', layout="wide", initial_sidebar_state="expanded") model_list = [ "NousResearch/Meta-Llama-3-8B-Instruct", "NousResearch/Meta-Llama-3-70B-Instruct", "mistral-community/Mistral-7B-v0.2", # "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistral-community/Mixtral-8x22B-v0.1", "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1", # "CohereForAI/c4ai-command-r-plus", # "CohereForAI/c4ai-command-r-v01", "hpcai-tech/grok-1", "NexaAIDev/Octopus-v2", "HuggingFaceH4/zephyr-7b-gemma-v0.1", "HuggingFaceH4/starchat2-15b-v0.1", "deepseek-ai/deepseek-coder-6.7b-instruct", "deepseek-ai/deepseek-coder-1.3b-base", "microsoft/phi-2", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "codellama/CodeLlama-7b-hf", "codellama/CodeLlama-13b-hf", "codellama/CodeLlama-34b-hf", "Phind/Phind-CodeLlama-34B-v2", "tiiuae/falcon-40B", "tiiuae/falcon-40B-Instruct", "tiiuae/falcon-180B", "tiiuae/falcon-180B-Chat", ] st.title("Can you run it? LLM version") percentage_width_main = 80 st.markdown( f""" """, unsafe_allow_html=True, ) @st.cache_resource() def cache_model_list(): model_list_info = {} for model_name in model_list: if not "tiiuae/falcon" in model_name: # Exclude Falcon models model = get_model(model_name, library="transformers", access_token="") model_list_info[model_name] = calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"]) del model gc.collect() return model_list_info @st.cache_resource def get_gpu_specs(): return pd.read_csv("data/gpu_specs.csv") # @st.cache_resource # def get_mistralai_table(): # model = get_model("mistralai/Mistral-7B-v0.1", library="transformers", access_token="") # return calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"]) def show_gpu_info(info, trainable_params=0, vendor=""): for var in ['Inference', 'Full Training Adam', 'LoRa Fine-tuning']: _info = info.loc[var] if vendor != "Apple": if _info['Number of GPUs'] >= 3: func = st.error icon = "⛔" elif _info['Number of GPUs'] == 2: func = st.warning icon = "⚠️" else: func = st.success icon = "✅" msg = f"You require **{_info['Number of GPUs']}** GPUs for **{var}**" if var == 'LoRa Fine-tuning': msg += f" ({trainable_params}%)" else: if _info['Number of GPUs']==1: msg = f"You can run **{var}**" func = st.success icon = "✅" else: msg = f"You cannot run **{var}**" func = st.error icon = "⛔" func(msg, icon=icon) def get_name(index): row = gpu_specs.iloc[index] return f"{row['Product Name']} ({row['RAM (GB)']} GB, {row['Year']})" def custom_ceil(a, precision=0): return np.round(a + 0.5 * 10**(-precision), precision) gpu_specs = get_gpu_specs() model_list_info = cache_model_list() _, col, _ = st.columns([1,3,1]) with col.expander("Information", expanded=True): st.markdown("""- GPU information comes from [TechPowerUp GPU Specs](https://www.techpowerup.com/gpu-specs/) - Mainly based on [Model Memory Calculator by hf-accelerate](https://huggingface.co/spaces/hf-accelerate/model-memory-usage) using `transformers` library - Inference is calculated following [EleutherAI Transformer Math 101](https://blog.eleuther.ai/transformer-math/), where is estimated as """) st.latex(r"""\text{Memory}_\text{Inference} \approx \text{Model Size} \times 1.2""") st.markdown("""- For LoRa Fine-tuning, I'm asuming a **16-bit** dtype of trainable parameters. The formula (in terms of GB) is""") st.latex(r"\text{Memory}_\text{LoRa} \approx \left(\text{Model Size} + \text{ \# trainable Params}_\text{Billions}\times\frac{16}{8} \times 4\right) \times 1.2") access_token = st.sidebar.text_input("Access token") if access_token: login(token=access_token) #model_name = st.sidebar.text_input("Model name", value="mistralai/Mistral-7B-v0.1") with st.sidebar.container(): model_name = stDatalist("Model name (Press Enter to apply)", model_list, index=0) if not model_name: st.info("Please enter a model name") st.stop() model_name = extract_from_url(model_name) if model_name not in st.session_state: if 'actual_model' in st.session_state: del st.session_state[st.session_state['actual_model']] del st.session_state['actual_model'] gc.collect() if model_name in model_list_info.keys(): st.session_state[model_name] = model_list_info[model_name] else: model = get_model(model_name, library="transformers", access_token=access_token) st.session_state[model_name] = calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"]) del model gc.collect() st.session_state['actual_model'] = model_name gpu_vendor = st.sidebar.selectbox("GPU Vendor", ["NVIDIA", "AMD", "Intel", "Apple"]) # year = st.sidebar.selectbox("Filter by Release Year", list(range(2014, 2024))[::-1], index=None) gpu_info = gpu_specs[gpu_specs['Vendor'] == gpu_vendor].sort_values('Product Name') # if year: # gpu_info = gpu_info[gpu_info['Year'] == year] min_ram = gpu_info['RAM (GB)'].min() max_ram = gpu_info['RAM (GB)'].max() ram = st.sidebar.slider("Filter by RAM (GB)", min_ram, max_ram, (10.0, 40.0), step=0.5) gpu_info = gpu_info[gpu_info["RAM (GB)"].between(ram[0], ram[1])] if len(gpu_info) == 0: st.sidebar.error(f"**{gpu_vendor}** has no GPU in that RAM range") st.stop() gpu = st.sidebar.selectbox("GPU", gpu_info['Product Name'].index.tolist(), format_func=lambda x : gpu_specs.iloc[x]['Product Name']) gpu_spec = gpu_specs.iloc[gpu] gpu_spec.name = 'INFO' lora_pct = st.sidebar.slider("LoRa % trainable parameters", 0.1, 100.0, 2.0, step=0.1) st.sidebar.dataframe(gpu_spec.T.astype(str)) memory_table = pd.DataFrame(st.session_state[model_name]).set_index('dtype') memory_table['LoRA Fine-Tuning (GB)'] = (memory_table["Total Size (GB)"] + (memory_table["Parameters (Billion)"]* lora_pct/100 * (16/8)*4)) * 1.2 _memory_table = memory_table.copy() memory_table = memory_table.round(2).T _memory_table /= gpu_spec['RAM (GB)'] _memory_table = _memory_table.apply(np.ceil).astype(int).drop(columns=['Parameters (Billion)', 'Total Size (GB)']) _memory_table.columns = ['Inference', 'Full Training Adam', 'LoRa Fine-tuning'] _memory_table = _memory_table.stack().reset_index() _memory_table.columns = ['dtype', 'Variable', 'Number of GPUs'] col1, col2 = st.columns([1,1.3]) if gpu_vendor == "Apple": col.warning("""For M1/M2/M3 Apple chips, PyTorch uses [Metal Performance Shaders (MPS)](https://huggingface.co/docs/accelerate/usage_guides/mps) as backend.\\ Remember that Apple M1/M2/M3 chips share memory between CPU and GPU.""", icon="⚠️") with col1: st.write(f"#### [{model_name}](https://huggingface.co/{model_name}) ({custom_ceil(memory_table.iloc[3,0],1):.1f}B)") dtypes = memory_table.columns.tolist()[::-1] tabs = st.tabs(dtypes) for dtype, tab in zip(dtypes, tabs): with tab: if dtype in ["int4", "int8"]: _dtype = dtype.replace("int", "") st.markdown(f"`int{_dtype}` refers to models in `GPTQ-{_dtype}bit`, `AWQ-{_dtype}bit` or `Q{_dtype}_0 GGUF/GGML`") info = _memory_table[_memory_table['dtype'] == dtype].set_index('Variable') show_gpu_info(info, lora_pct, gpu_vendor) st.write(memory_table.iloc[[0, 1, 2, 4]]) with col2: extra = "" if gpu_vendor == "Apple": st.warning("This graph is irrelevant for M1/M2 chips as they can't run in parallel.", icon="⚠️") extra = "⚠️" num_colors= 4 colors = [px.colors.sequential.RdBu[int(i*(len(px.colors.sequential.RdBu)-1)/(num_colors-1))] for i in range(num_colors)] fig = px.bar(_memory_table, x='Variable', y='Number of GPUs', color='dtype', barmode='group', color_discrete_sequence=colors) fig.update_layout(title=dict(text=f"{extra} Number of GPUs required for
{get_name(gpu)}", font=dict(size=25)) , xaxis_tickfont_size=14, yaxis_tickfont_size=16, yaxis_dtick='1') st.plotly_chart(fig, use_container_width=True)