import gradio as gr import math from transformers import AutoConfig # Required for Hugging Face integration # ---- Helper Functions ---- # def convert_params(params): if params == 0: return "0" size_name = ("", "K", "M", "B", "T", "P", "E", "Z", "Y") i = int(math.floor(math.log(params, 1000))) p = math.pow(1000, i) s = round(params / p, 2) return "%s %s" % (s, size_name[i]) # Set defaults for missing arguments def set_defaults(args, defaults): for key, value in defaults.items(): if getattr(args, key) is None: setattr(args, key, value) return args # Set value if it's None, else use the config value def set_if_none(args, key, config, config_key, defaults): if getattr(args, key) is None: setattr(args, key, config.get(config_key, defaults[key])) return args # Get Hugging Face model arguments def get_hf_model_args(args, defaults): if args.hf_model_name_or_path: try: config = AutoConfig.from_pretrained(args.hf_model_name_or_path, trust_remote_code=True).to_dict() except Exception as e: raise gr.Error(f"Error fetching Hugging Face model: {str(e)}") # Update arguments with Hugging Face model config values args.num_layers = config.get("num_hidden_layers", defaults["num_layers"]) args.hidden_size = config.get("hidden_size", defaults["hidden_size"]) args.num_attention_heads = config.get("num_attention_heads", defaults["num_attention_heads"]) args.vocab_size = config.get("vocab_size", defaults["vocab_size"]) args.sequence_length = config.get("max_position_embeddings", defaults["sequence_length"]) return set_defaults(args, defaults) # ---- Memory Calculation ---- # def calc_mem(hf_model_name_or_path, num_gpus, tensor_parallel_size, pipeline_parallel_size, batch_size_per_gpu, sequence_length, vocab_size, hidden_size, num_attention_heads, num_layers, ffn_expansion_factor, is_mixed_precision, misc_mem_gib): # Define defaults defaults = { "num_layers": 44, "hidden_size": 6144, "num_attention_heads": 64, "vocab_size": 51200, "sequence_length": 2048, "ffn_expansion_factor": 4, } # Create a simple args object to simulate parsed arguments class Args: def __init__(self, **kwargs): for key, value in kwargs.items(): setattr(self, key, value) args = Args(hf_model_name_or_path=hf_model_name_or_path, num_gpus=num_gpus, tensor_parallel_size=tensor_parallel_size, pipeline_parallel_size=pipeline_parallel_size, batch_size_per_gpu=batch_size_per_gpu, sequence_length=sequence_length, vocab_size=vocab_size, hidden_size=hidden_size, num_attention_heads=num_attention_heads, num_layers=num_layers, ffn_expansion_factor=ffn_expansion_factor, is_mixed_precision=is_mixed_precision, misc_mem_gib=misc_mem_gib) # Fetch Hugging Face model args if a model is provided args = get_hf_model_args(args, defaults) dp_degree = args.num_gpus / (args.tensor_parallel_size * args.pipeline_parallel_size) embed_params = 2 * args.vocab_size * args.hidden_size positional_params = args.hidden_size * args.sequence_length ln_params = 8 * args.hidden_size * args.num_layers + (2 * args.hidden_size) attention_params = int(2 * (1 + args.ffn_expansion_factor) * args.num_layers * args.hidden_size * args.hidden_size) mlp_params = args.ffn_expansion_factor * args.num_layers * args.hidden_size * args.hidden_size total_params = embed_params + positional_params + ln_params + attention_params + mlp_params bytes_per_param = 2 if args.is_mixed_precision else 4 model_mem = total_params * bytes_per_param per_gpu_mem_gib = (model_mem / (args.tensor_parallel_size * args.pipeline_parallel_size)) / 1024**3 + args.misc_mem_gib return f"Per-GPU Memory Required for Training: {per_gpu_mem_gib:.2f} GiB" # ---- Gradio Interface ---- # with gr.Blocks() as demo: with gr.Tabs(): with gr.TabItem("Parameter Calculation"): vocab_size = gr.Number(label="Vocab Size", value=51200) tied_embeddings = gr.Checkbox(label="Tied Embeddings", value=False) hidden_size = gr.Number(label="Hidden Size", value=6144) sequence_length = gr.Number(label="Sequence Length", value=2048) num_layers = gr.Number(label="Number of Layers", value=44) ffn_expansion_factor = gr.Number(label="FFN Expansion Factor", value=4) num_mlp_linears = gr.Number(label="Number of Linear Layers per MLP Block", value=2) kv_size_ratio = gr.Number(label="KV Size Ratio", value=1.0) with gr.Accordion("MoE Parameters", open=False): moe = gr.Checkbox(label="MoE", value=False) num_experts = gr.Number(label="Number of Experts", value=8) expert_interval = gr.Number(label="Expert Interval", value=1) topk = gr.Number(label="Top k Routing", value=1) result = gr.Textbox(label="Output", interactive=False) calculate_button = gr.Button("Calculate") calculate_button.click(calc_params, inputs=[vocab_size, tied_embeddings, hidden_size, sequence_length, num_layers, moe, num_experts, expert_interval, topk, ffn_expansion_factor, num_mlp_linears, kv_size_ratio], outputs=result) with gr.TabItem("Memory Calculation"): hf_model_name_or_path = gr.Textbox(label="HuggingFace Model Name or Path", value="") num_gpus = gr.Number(label="Number of GPUs", value=1) tensor_parallel_size = gr.Number(label="Tensor Parallel Size", value=1) pipeline_parallel_size = gr.Number(label="Pipeline Parallel Size", value=1) batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=8) sequence_length = gr.Number(label="Sequence Length", value=2048) vocab_size = gr.Number(label="Vocab Size", value=51200) hidden_size = gr.Number(label="Hidden Size", value=6144) num_attention_heads = gr.Number(label="Number of Attention Heads", value=64) num_layers = gr.Number(label="Number of Layers", value=44) ffn_expansion_factor = gr.Number(label="FFN Expansion Factor", value=4) is_mixed_precision = gr.Checkbox(label="Mixed Precision", value=True) misc_mem_gib = gr.Number(label="Misc Memory Overhead (GiB)", value=5) memory_result = gr.Textbox(label="Memory Calculation Result", interactive=False) calc_memory_button = gr.Button("Calculate Memory") calc_memory_button.click(calc_mem, inputs=[hf_model_name_or_path, num_gpus, tensor_parallel_size, pipeline_parallel_size, batch_size_per_gpu, sequence_length, vocab_size, hidden_size, num_attention_heads, num_layers, ffn_expansion_factor, is_mixed_precision, misc_mem_gib], outputs=memory_result) demo.launch()