AutoAndroidController/python/Lib/site-packages/onnxruntime/transformers/large_model_exporter.py

# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation.  All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------

"""
Export LLM to onnx
"""

import argparse
import inspect
import math
import os
import tempfile
from pathlib import Path

import onnx
import torch
import transformers
from torch import nn


def disable_huggingface_init():
    """do not init model twice as it slow initialization"""

    torch.nn.init.kaiming_uniform_ = lambda x, *args, **kwargs: x
    torch.nn.init.uniform_ = lambda x, *args, **kwargs: x
    torch.nn.init.normal_ = lambda x, *args, **kwargs: x
    torch.nn.init.constant_ = lambda x, *args, **kwargs: x
    torch.nn.init.xavier_uniform_ = lambda x, *args, **kwargs: x
    torch.nn.init.xavier_normal_ = lambda x, *args, **kwargs: x
    torch.nn.init.kaiming_normal_ = lambda x, *args, **kwargs: x
    torch.nn.init.orthogonal_ = lambda x, *args, **kwargs: x


def get_model_parameter_size(model: nn.Module):
    """to calculate how much memory this model needs"""
    param_size = 0
    param_sum = 0
    for param in model.parameters():
        param_size += param.nelement() * param.element_size()
        param_sum += param.nelement()
    buffer_size = 0
    buffer_sum = 0
    for buffer in model.buffers():
        buffer_size += buffer.nelement() * buffer.element_size()
        buffer_sum += buffer.nelement()
    all_size = (param_size + buffer_size) / 1024 / 1024
    return all_size


def initialize_model_and_sample_inputs(hf_model: str, cache_dir: str | None, tokenizer=None):
    """
    get the pretrained torch model from hugginface,
    and sample model-inputs
    """

    disable_huggingface_init()

    model = transformers.AutoModelForCausalLM.from_pretrained(  # type: ignore
        hf_model, torch_dtype=torch.float16, cache_dir=cache_dir, trust_remote_code=True
    )
    if tokenizer is None:
        tokenizer = hf_model
    tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer)  # type: ignore

    sample_inputs = tuple(tokenizer("Hello, my dog is cute", return_tensors="pt").values())
    return model, sample_inputs


def auto_pipeline_parallel(model: nn.Module, gpulist: list, sample_inputs: tuple):
    """Make the model executable across multiple GPUs."""

    def input_gpu_device_hook(mod, inputs, kwargs):
        modifyed_inputs = []
        first_dev = None
        for layer_input in inputs:
            if type(layer_input) is not torch.Tensor:
                modifyed_inputs.append(layer_input)
            elif hasattr(mod, "weight"):
                modifyed_inputs.append(layer_input.to(mod.weight.device))
            elif hasattr(mod, "parameters"):
                device = next(mod.parameters(), layer_input).device
                modifyed_inputs.append(layer_input.to(device))
            elif hasattr(next(mod.children(), None), "weight"):
                modifyed_inputs.append(layer_input.to(next(mod.children()).weight.device))
            elif first_dev is not None and layer_input.device != first_dev:
                modifyed_inputs.append(layer_input.to(first_dev))
            else:
                modifyed_inputs.append(layer_input)
            if first_dev is None:
                first_dev = modifyed_inputs[0].device
        for key, value in kwargs.items():
            if type(value) is torch.Tensor:
                kwargs[key] = value.to(first_dev)

        return (tuple(modifyed_inputs), kwargs)

    def move_layer_to_device_rurc(mod, dev):
        mod.to(dev)
        for layer in mod.named_children():
            move_layer_to_device_rurc(layer[1], dev)

    model = model.half()
    all_hooks = []
    all_hooks.append(model.register_forward_pre_hook(input_gpu_device_hook, with_kwargs=True))
    pre_fix = next(iter(model.named_children()))[0]
    for top_name, top_module in model.named_children():
        for name, module in top_module.named_children():
            all_hooks.append(module.register_forward_pre_hook(input_gpu_device_hook, with_kwargs=True))
            if type(module) in [torch.nn.ModuleList]:
                num_layers_on_each_gpu = math.floor(len(module) / len(gpulist))
                for idx, attn_layer in enumerate(module):
                    all_hooks.append(attn_layer.register_forward_pre_hook(input_gpu_device_hook, with_kwargs=True))

                    to_dev = gpulist[min(idx // num_layers_on_each_gpu, len(gpulist))]
                    attn_layer.to(to_dev)
                    move_layer_to_device_rurc(attn_layer, to_dev)
                    print(f"move {pre_fix}.{name}.{idx} to {to_dev}")
            else:
                module.to(gpulist[0])
                print(f"move {pre_fix}.{name} to {gpulist[0]}")
        if len(list(top_module.named_children())) == 0:
            top_module.to(gpulist[0])
            print(f"move {top_name} to {gpulist[0]}")

    with torch.no_grad():
        model(sample_inputs[0], attention_mask=sample_inputs[1])
    return model


def retrieve_onnx_inputs(model: nn.Module, sample_inputs: tuple, with_past: bool):
    """
    auto retrieve onnx inputs from torch model as we can't enumlate all possibilities
    for all models
    """
    user_inputs = []

    def hook_for_inputs(_, inputs, kwargs):
        user_inputs.append((inputs, kwargs))
        return user_inputs[0]

    hook_handle = model.register_forward_pre_hook(hook_for_inputs, with_kwargs=True)

    forward_params = inspect.signature(model.forward).parameters
    input_keys = list(forward_params.keys())
    default_values = [forward_params.get(key).default for key in input_keys]
    out = model(sample_inputs[0], attention_mask=sample_inputs[1])
    hook_handle.remove()
    user_inputs = user_inputs[0]
    onnx_inputs = default_values
    for idx, _val in enumerate(user_inputs[0]):
        onnx_inputs[idx] = user_inputs[0][idx]
    for key, value in user_inputs[1].items():
        idx = input_keys.index(key)
        onnx_inputs[idx] = value
    for idx, (key, value) in enumerate(zip(input_keys, onnx_inputs, strict=False)):
        if type(value) is torch.Tensor:
            value.to(model.device)
        if "use_cache" in key:
            onnx_inputs[idx] = with_past
            out = model(sample_inputs[0], attention_mask=sample_inputs[1], use_cache=with_past) if with_past else out

    return input_keys, onnx_inputs, out.past_key_values


def move_to_appropriate_device(model: nn.Module, sample_inputs_tp: tuple) -> nn.Module:
    """
    According to the model size, we will upload it to
    CPU if has no GPU or enough GPU memory,
    Single GPU if has only one GPU in local or model size is enough to fit one GPU
    Multiple GPU if there is more than one gpu in local and model is too large
    """
    total_mem_per_cpu = torch.cuda.get_device_properties(0).total_memory / 1024 / 1024

    print(f"Model_Size = {get_model_parameter_size(model) / 1024} GB")
    print(f"total_mem_per_cpu = {total_mem_per_cpu / 1024} GB")
    if get_model_parameter_size(model) > total_mem_per_cpu * 0.45:
        device_collection = [torch.device(i) for i in range(torch.cuda.device_count())]
        if len(device_collection) > 1:
            print(
                f"{len(device_collection)} GPUs are used to export onnx, \
                   Please set CUDA_VISIBLE_DEVICES to use specific GPU group"
            )
            model = auto_pipeline_parallel(model, device_collection, sample_inputs_tp)
        else:
            print("!!!! convert model to float and export onnx using CPU")
            model = model.cpu().float()
    else:
        print("Export model on a single GPU")
        model = model.cuda().half()
    return model


def adapt_inputs_to_device(sample_inputs: tuple, device: torch.device) -> tuple:
    """move inputs to device"""
    sample_inputs_ = []
    for sample_int in sample_inputs:
        if isinstance(sample_int, torch.Tensor):
            sample_inputs_.append(sample_int.to(device))
        else:
            sample_inputs_.append(sample_int)
    return tuple(sample_inputs_)


def fetch_onnx_inputs_outputs_name(
    model: nn.Module,
    onnx_inputs: list,
    torch_input_names: tuple,
    past_key_values: tuple,
    with_past: bool,
    input_with_past: bool,
):
    """fetch onnx inputs and outputs name"""
    num_of_past_key = 0
    kv_cache_axis = {0: "batch_size"}
    # try get num_of_past_key and shape of past_key_value
    if past_key_values is not None:
        num_of_past_key = len(past_key_values)
        seq_index = (torch.tensor(past_key_values[0][0].shape) == onnx_inputs[0].shape[-1]).nonzero().view(-1)
        assert seq_index.numel() == 1
        kv_cache_axis = {0: "batch_size", seq_index.item(): "seq_len"}

    if not num_of_past_key:
        num_of_past_key = model.config.num_hidden_layers

    # filter out constant inputs
    onnx_inp_names = tuple(
        [torch_input_names[i] for i in range(len(torch_input_names)) if isinstance(onnx_inputs[i], torch.Tensor)]
    )
    assert "input_ids" in onnx_inp_names and "attention_mask" in onnx_inp_names, (
        "input_ids and attention_mask must be existed in inputs"
    )
    onnx_out_names = ("logits",)
    onnx_dynamic_axes = {
        "input_ids": {0: "batch_size", 1: "seq_len"},
        "attention_mask": {0: "batch_size", 1: "seq_len"},
    }
    # add dyanmic dimensions for the unkonw inputs
    for idx, name in enumerate(onnx_inp_names):
        if name not in onnx_dynamic_axes:
            unknown_dims = {i: f"{idx}__unknown_dims__{i}" for i in range(onnx_inputs[idx].dim())}
            onnx_dynamic_axes[name] = unknown_dims
    if input_with_past:
        for i in range(num_of_past_key):
            onnx_inp_names += (f"past_key_values.{i}.key",)
            onnx_inp_names += (f"past_key_values.{i}.value",)

            onnx_dynamic_axes[onnx_inp_names[-1]] = kv_cache_axis
            onnx_dynamic_axes[onnx_inp_names[-2]] = kv_cache_axis

    if with_past or input_with_past:
        for i in range(num_of_past_key):
            onnx_out_names += (f"present.{i}.key",)
            onnx_out_names += (f"present.{i}.value",)

    for idx, name in enumerate(torch_input_names):
        if input_with_past:
            if name == "past_key_values":
                onnx_inputs[idx] = past_key_values
            elif name == "attention_mask":
                attn_mask = onnx_inputs[idx]
                onnx_inputs[idx] = torch.cat(
                    (attn_mask, torch.ones((attn_mask.shape[0], 1), device=attn_mask.device, dtype=attn_mask.dtype)),
                    dim=1,
                )
            elif name == "input_ids":
                input_ids = onnx_inputs[idx]
                onnx_inputs[idx] = input_ids[:, -1:]

    return onnx_inp_names, onnx_out_names, onnx_dynamic_axes


def do_export_internal(model: nn.Module, onnx_io_tuple: tuple, onnx_inputs: tuple, onnx_path: Path, opset: int):
    """do export with torch.onnx.export"""
    onnx_model_name = onnx_path.name
    onnx_inp_names, onnx_out_names, onnx_dynamic_axes = onnx_io_tuple
    # two step to export onnx
    # 1. export onnx with lots of pieces of weights
    # 2. save all weights to external data
    with tempfile.TemporaryDirectory() as tmpdirname:
        tmp_onnx = os.path.join(tmpdirname, "tmp.onnx")

        torch.onnx.export(
            model=model,
            args=tuple(onnx_inputs),
            f=tmp_onnx,
            verbose=False,
            opset_version=opset,
            input_names=onnx_inp_names,
            output_names=onnx_out_names,
            dynamic_axes=onnx_dynamic_axes,
        )

        onnx_path.unlink(missing_ok=True)
        (onnx_path.parent / f"{onnx_model_name}_ext.data").unlink(missing_ok=True)

        onnx_model = onnx.load(str(tmp_onnx))
        onnx.save_model(
            onnx_model,
            str(onnx_path),
            save_as_external_data=(len(os.listdir(tmpdirname)) > 1),
            all_tensors_to_one_file=True,
            location=f"{onnx_model_name}_ext.data",
            size_threshold=1024,
            convert_attribute=False,
        )


@torch.no_grad()
def export_onnx(hf_model: str, cache_dir: str | None, onnx_path_str: str, with_past: bool, opset: int):
    """
    do export
    model: torch model
    onnx_path: where the onnx model saved to
    sample_inputs_tp: inputs for torch model
    """
    model, sample_inputs_tp = initialize_model_and_sample_inputs(hf_model, cache_dir)

    model = move_to_appropriate_device(model, sample_inputs_tp)

    sample_inputs = adapt_inputs_to_device(sample_inputs_tp, next(model.parameters()).device)

    # input_keys would be usesful if the model has some special inputs
    input_keys, onnx_inputs, past_key_value = retrieve_onnx_inputs(model, sample_inputs, with_past)

    onnx_io_tuple = fetch_onnx_inputs_outputs_name(model, onnx_inputs, input_keys, past_key_value, with_past, False)

    onnx_model_name = "model.onnx"
    onnx_path: Path = Path(onnx_path_str).absolute()
    if onnx_path.suffix != ".onnx":
        onnx_path = onnx_path / onnx_model_name

    do_export_internal(model, onnx_io_tuple, onnx_inputs, onnx_path, opset)
    if not with_past:
        return

    onnx_io_tuple = fetch_onnx_inputs_outputs_name(model, onnx_inputs, input_keys, past_key_value, with_past, True)

    onnx_model_name = "model_with_past.onnx"
    onnx_path = onnx_path.parent / onnx_model_name

    do_export_internal(model, onnx_io_tuple, onnx_inputs, onnx_path, opset)


def parse_arguments():
    """arguments parsing."""
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "-m",
        "--model",
        required=True,
        type=str,
        default=["meta-llama/Llama-2-70b-hf"],
        help="Pre-trained models in huggingface model hub",
    )
    parser.add_argument(
        "-s",
        "--saved_path",
        required=False,
        type=str,
        default="./onnx_models/",
        help="where the onnx model will be saved",
    )
    parser.add_argument(
        "--cache_dir",
        required=False,
        type=str,
        default=None,
        help=("cache directly of huggingface, by setting this to avoid useless downloading if you have one"),
    )
    parser.add_argument(
        "--with_past",
        action="store_true",
        default=False,
        help=("The tool will export onnx without past-key-value by default"),
    )
    parser.add_argument(
        "--opset",
        required=False,
        type=int,
        default=17,
        help=(
            "the opset to save onnx model, \
              try to increase it if this opset doens't have new features you want"
        ),
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_arguments()

    export_onnx(args.model, args.cache_dir, args.saved_path, args.with_past, args.opset)