app.py

from dataclasses import dataclass
import argparse
from functools import partial


import gradio as gr 
from transformers import AutoConfig


PRECISION_TO_BYTES = {"float32": 4,
                      "fp32": 4,
                      "float16": 2,
                      "fp16": 2,
                      "bfloat16": 2,
                      "bf16": 2,
                      "int8": 1}

ZERO_STAGES = [0, 1, 2, 3]
BATCH_SIZES = [1, 2, 4, 8, 16, 32, 64]
OPTIMIZERS = ["adam", "adamw", "sgd"]
HUGGINGFACE_URL_CONFIG = "https://huggingface.co/{}/resolve/main/config.json"


@dataclass
class ModelConfig:
    model_size: float
    hidden_size: int
    sequence_length: int
    num_layers: int
    num_heads: int

    def overwrite_with_hf_config(self, config: dict):
        self.model_size = round(get_model_size_from_config(config) / 10**9, 2)
        self.hidden_size = config["hidden_size"]
        self.sequence_length = config["max_position_embeddings"]
        self.num_layers = config["num_hidden_layers"]
        self.num_heads = config["num_attention_heads"]

@dataclass
class TrainingConfig:
    micro_batch_size: int
    num_gpus: int
    optimizer: str
    zero_stage: int
    gradient_checkpointing: False
    mixed_precision: False


def parse_args():
    parser = argparse.ArgumentParser(description="Parser for VRAM estimator")

    parser.add_argument("--repo_id", type=str, default=None, help="HuggingFace repo id to automatically determine model settings")
    parser.add_argument("--model_size", type=float, default=7, help="Model size (in billion parameters)")
    parser.add_argument("--hidden_size", type=int, default=4096, help="Hidden size")
    parser.add_argument("--sequence_length", type=int, default=8192, help="Sequence length")
    parser.add_argument("--num_layers", type=int, default=32, help="Number of layers")
    parser.add_argument("--num_heads", type=int, default=32, help="Number of heads")
    parser.add_argument("--micro_batch_size", type=int, default=4, help="Micro batch size (batch size per device/GPU)")
    parser.add_argument("--zero_stage", type=int, default=0, choices=ZERO_STAGES, help="ZeRO optimization stage")
    parser.add_argument("--gradient_checkpointing", action="store_false", help="Enable gradient checkpointing")
    parser.add_argument("--mixed_precision", action="store_false", help="Enable mixed precision for model training")
    parser.add_argument("--optimizer", type=str, default="adamw", choices=OPTIMIZERS, help="Type of optimizer")
    parser.add_argument("--num_gpus", type=int, default=4, help="Number of GPUs. Necessary for estimating ZeRO stages")
    parser.add_argument("--cache_dir", type=str, default=None, help="HuggingFace cache directory to download config from")

    parser.add_argument("--no-app", action="store_true", help="Launch gradio app. Otherwise, commandline output")
    return parser

def get_model_size_from_config(config: dict):
    # Embedding parameters:
    embedding_params = config["vocab_size"] * config["hidden_size"]

    # Transformer layer parameters
    def transformer_layer_params(hidden_size, intermediate_size, num_key_value_heads):
            input_layernorm_params = hidden_size
            mlp_down_proj_params = hidden_size * intermediate_size
            mlp_gate_proj_params = intermediate_size * hidden_size
            mlp_up_proj_params = intermediate_size * hidden_size
            post_attention_layernorm_params = hidden_size
            self_attn_k_proj_params = (hidden_size // (num_key_value_heads // 2)) * hidden_size
            self_attn_o_proj_params = hidden_size * hidden_size
            self_attn_q_proj_params = hidden_size * hidden_size
            self_attn_v_proj_params = (hidden_size // (num_key_value_heads // 2)) * hidden_size
            
            total_layer_params = (
                input_layernorm_params + mlp_down_proj_params + mlp_gate_proj_params + mlp_up_proj_params +
                post_attention_layernorm_params + self_attn_k_proj_params + self_attn_o_proj_params +
                self_attn_q_proj_params + self_attn_v_proj_params
            )

            return total_layer_params
    
    # Total parameters for all transformer layers
    single_layer_params = transformer_layer_params(config["hidden_size"], config["intermediate_size"], config["num_key_value_heads"])
    total_transformer_params = config["num_hidden_layers"] * single_layer_params
    
    # Output layer parameters
    output_params = config["vocab_size"] * config["hidden_size"]
    
    # Total parameters
    total_params = embedding_params + total_transformer_params + output_params
    return total_params


def download_config_from_hub(repo_id: str, cache_dir: str):
    return AutoConfig.from_pretrained(pretrained_model_name_or_path=repo_id, cache_dir=cache_dir)

def scrape_config_from_hub(repo_id):
    import requests
    url = HUGGINGFACE_URL_CONFIG.format(repo_id)
    try:
        print(f"Fetching config.json from the following URL: {url}...")
        response = requests.get(url)
        response.raise_for_status()  # Raises a HTTPError if the status is 4xx, 5xx
        
        config = response.json()
        print(f"Fetched the config for model {repo_id} succesfully!")
    except requests.exceptions.HTTPError as errh:
        print(f"HTTP Error: {errh}")
    except requests.exceptions.ConnectionError as errc:
        print(f"Error Connecting: {errc}")
    except requests.exceptions.Timeout as errt:
        print(f"Timeout Error: {errt}")
    except requests.exceptions.RequestException as err:
        print(f"Something went wrong: {err}")
    except ValueError as e:
        print(f"Error decoding JSON: {e}")

    return config

def model_memory(parameters, precision = "bf16", mixed_precision = False):
    if mixed_precision:
        return parameters * (PRECISION_TO_BYTES["fp32"] + PRECISION_TO_BYTES["fp16"])
    return parameters * PRECISION_TO_BYTES[precision]
    

def gradients_memory(parameters, precision = "fp32"):
    return parameters * PRECISION_TO_BYTES[precision]

def optimizer_memory(parameters, optimizer= "adamw", precision = "fp32"):
    optimizer_choices = {"adam": 3,
                         "adamw": 2,
                         "sgd": 1}
    return optimizer_choices[optimizer] * parameters * PRECISION_TO_BYTES[precision]

def activations_memory(num_layers, sequence_length, micro_batch_size, hidden_size, num_heads):
    # Reference: https://arxiv.org/pdf/2205.05198
    # Activations assumed to be in 16-bit floating precision
    bytes_per_layer = sequence_length * micro_batch_size * hidden_size * (34 + 5 * (num_heads * sequence_length / hidden_size))
    bytes_model = bytes_per_layer * num_layers
    return round(bytes_model / 10**9, 2)

def vram_required(model_size, hidden_size, sequence_length, num_layers, num_heads, micro_batch_size, num_gpus, optimizer, zero_stage, gradient_checkpointing, mixed_precision):
    # Reference: https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/

    model_vram = model_memory(model_size, mixed_precision=mixed_precision)
    gradients_vram = gradients_memory(model_size)
    optimizer_vram = optimizer_memory(model_size, optimizer=optimizer)

    # Baseline
    if zero_stage == 0:
        pass
    # Optimizer state partitioning
    if zero_stage >= 1:
        optimizer_vram = optimizer_vram / num_gpus
    # Gradient + Optimzer state partitioning
    if zero_stage >= 2:
        gradients_vram = gradients_vram / num_gpus
    # Parameter partitioning + Gradient + Optimizer partitioning
    if zero_stage == 3:
        aggregated_vram = model_vram / num_gpus

    aggregated_vram = round(model_vram, 2) + gradients_vram + optimizer_vram

    activations_vram = activations_memory(num_layers, sequence_length, micro_batch_size, hidden_size, num_heads)
    if gradient_checkpointing:
        activations_vram = round(activations_vram ** 0.5, 2)
    
    total_vram = aggregated_vram + activations_vram
    return {"total": total_vram, "model": model_vram, "gradients": gradients_vram, "optimizer": optimizer_vram, "activations": activations_vram}

def build_interface(estimate_vram_fn):
    training_params = []
    with gr.Blocks() as app:
        
        option = gr.Radio(["Repo ID", "Model Parameters"], label="Select Input Type")
        
        repo_id = gr.Textbox(label="Repo ID", visible=False)



        with gr.Row(visible=False) as model_params_row:
            model_params = [gr.Slider(label="Model Size", minimum=0.1, maximum=400, step=0.1, value=7, info="Model size (in billion parameters)"),
                            gr.Slider(label="Hidden size", minimum=256, maximum=8192, step=128, value=4096, info="Hidden size"),
                            gr.Slider(label="Sequence length", minimum=256, maximum=128_000, step=256, value=8192, info="Sequence length"),
                            gr.Slider(label="Num layers", minimum=8, maximum=64, step=1, value=32, info="Number of layers"),
                            gr.Slider(label="Num heads", minimum=8, maximum=64, step=1, value=32, info="Number of attention heads")
            ]


        def update_visibility(selected_option):
            if selected_option == "Repo ID":
                return gr.update(visible=True), gr.update(visible=False),
            elif selected_option == "Model Parameters":
                return gr.update(visible=False), gr.update(visible=True)

        option.change(
                fn=update_visibility,
                inputs=[option],
                outputs=[repo_id, model_params_row]
            )
        
        
        with gr.Row(equal_height=True):
            training_params = [gr.Dropdown(label="Micro batch size", choices=BATCH_SIZES, value=4, info="Micro batch size (batch size per device/GPU)"),
                               gr.Dropdown(label="ZeRO stage", choices=ZERO_STAGES, value=0, info="ZeRO optimization stage"),
                               gr.Dropdown(label="Gradient checkpointing", choices=[True, False], value=True, info="Enable gradient checkpointing"),
                               gr.Dropdown(label="Mixed precision", choices=[False, True], value=False, info="Enable mixed precision for model training"),
                               gr.Dropdown(label="Optimizer", choices=OPTIMIZERS, value="adamw", info="Type of optimizer"),
                               gr.Slider(label="Num GPUs", minimum=1, maximum=64, step=1, value=4, info="Number of GPUs. Necessary for estimating ZeRO stages"),
                               gr.Textbox(label="Cache dir", value=None, placeholder=".huggingface_configs", info="HuggingFace cache directory to download config from")
            ]

        submit_btn = gr.Button("Estimate!")
        
        output = gr.Textbox(label="Total estimated VRAM per device/GPU (in GB)")

        submit_btn.click(
            fn=estimate_vram_fn,
            inputs=[repo_id, *model_params, *training_params],
            outputs=[output]
        )

    return app
        

def estimate_vram(arg_keys, *args):
    params = dict(zip(arg_keys, args))
    print("Parameters: ", params)

    model_config = ModelConfig(params["model_size"], params["hidden_size"], params["sequence_length"], params["num_layers"], params["num_heads"])
    training_config = TrainingConfig(params["micro_batch_size"], params["num_gpus"], params["optimizer"], params["zero_stage"], params["gradient_checkpointing"], params["mixed_precision"])
    if not params["repo_id"]:
        return "No model selected!"
    # If cache directory set, then download config
    if params["cache_dir"]:
        config = scrape_config_from_hub(params["repo_id"])
        model_config.overwrite_with_hf_config(config)
    # By default, scrape config.json from hub
    else:
        config = download_config_from_hub(params["repo_id"], params["cache_dir"])
        model_config.overwrite_with_hf_config(config.to_dict())

    total_vram_dict = vram_required(**vars(model_config), **vars(training_config))
    output_str = f"Total {total_vram_dict['total']}GB = {total_vram_dict['model']}GB (model) + {total_vram_dict['gradients']}GB (gradients) + {total_vram_dict['optimizer']}GB (optimizer) + {total_vram_dict['activations']}GB activations"
    return output_str

if __name__ == "__main__":
    parser = parse_args()
    args = parser.parse_args()
    
    # Launch gradio interface
    if not args.no_app:
        import gradio as gr
        arg_keys = list(vars(args).keys())
        estimate_vram_fn = partial(estimate_vram, arg_keys)
        interface = build_interface(estimate_vram_fn)
        interface.launch()
    # Command line interface
    else:
        model_config = ModelConfig(args.model_size, args.hidden_size, args.sequence_length, args.num_layers, args.num_heads)
        training_config = TrainingConfig(args.micro_batch_size, args.num_gpus, args.optimizer, args.zero_stage, args.gradient_checkpointing, args.mixed_precision)
        if args.repo_id:
            # If cache directory set, then download config
            if args.cache_dir:
                config = download_config_from_hub(args.repo_id, args.cache_dir).to_dict()
            # By default, scrape config.json from hub
            else:
                config = scrape_config_from_hub(args.repo_id)
            model_config.overwrite_with_hf_config(config)

        total_vram_dict = vram_required(**vars(model_config), **vars(training_config))
        print(f"Total {total_vram_dict['total']}GB = {total_vram_dict['model']}GB (model) + {total_vram_dict['gradients']}GB (gradients) + {total_vram_dict['optimizer']}GB (optimizer) + {total_vram_dict['activations']}GB activations")