pytorch/benchmarks/functional_autograd_benchmark/functional_autograd_benchmark.py

import time
from argparse import ArgumentParser
from collections import defaultdict
from typing import Any, Callable, List, NamedTuple

import torch
from torch.autograd import functional


try:
    import functorch as ft

    has_functorch = True
    print(f"Found functorch: {ft.__version__}")
except ImportError:
    has_functorch = False

import audio_text_models
import ppl_models
import vision_models

from utils import GetterType, InputsType, TimingResultType, to_markdown_table, VType


def get_task_func(task: str) -> Callable:
    def hessian_fwdrev(model, inp, strict=None):
        return functional.hessian(
            model,
            inp,
            strict=False,
            vectorize=True,
            outer_jacobian_strategy="forward-mode",
        )

    def hessian_revrev(model, inp, strict=None):
        return functional.hessian(model, inp, strict=False, vectorize=True)

    def jacfwd(model, inp, strict=None):
        return functional.jacobian(
            model, inp, strict=False, vectorize=True, strategy="forward-mode"
        )

    def jacrev(model, inp, strict=None):
        return functional.jacobian(model, inp, strict=False, vectorize=True)

    if task == "hessian_fwdrev":
        return hessian_fwdrev
    elif task == "hessian_revrev":
        return hessian_revrev
    elif task == "jacfwd":
        return jacfwd
    elif task == "jacrev":
        return jacrev
    else:
        return getattr(functional, task)


def get_task_functorch(task: str) -> Callable:
    @torch.no_grad()
    def vjp(model, inp, v=None, strict=None):
        assert v is not None
        out, vjpfunc = ft.vjp(model, *inp)
        return out, vjpfunc(v)

    @torch.no_grad()
    def jvp(model, inp, v=None, strict=None):
        assert v is not None
        return ft.jvp(model, inp, v)

    @torch.no_grad()
    def vhp(model, inp, v=None, strict=None):
        assert v is not None
        argnums = tuple(range(len(inp)))
        _, vjpfunc, aux = ft.vjp(ft.grad_and_value(model, argnums), *inp, has_aux=True)
        return aux, vjpfunc(v)

    @torch.no_grad()
    def hvp(model, inp, v=None, strict=None):
        assert v is not None
        argnums = tuple(range(len(inp)))
        _, hvp_out, aux = ft.jvp(
            ft.grad_and_value(model, argnums), inp, v, has_aux=True
        )
        return aux, hvp_out

    @torch.no_grad()
    def jacfwd(model, inp, v=None, strict=None):
        argnums = tuple(range(len(inp)))
        return ft.jacfwd(model, argnums)(*inp)

    @torch.no_grad()
    def jacrev(model, inp, v=None, strict=None):
        argnums = tuple(range(len(inp)))
        return ft.jacrev(model, argnums)(*inp)

    @torch.no_grad()
    def hessian(model, inp, v=None, strict=None):
        argnums = tuple(range(len(inp)))
        return ft.hessian(model, argnums=argnums)(*inp)

    @torch.no_grad()
    def hessian_fwdrev(model, inp, v=None, strict=None):
        argnums = tuple(range(len(inp)))
        return ft.jacfwd(ft.jacrev(model, argnums=argnums), argnums=argnums)(*inp)

    @torch.no_grad()
    def hessian_revrev(model, inp, v=None, strict=None):
        argnums = tuple(range(len(inp)))
        return ft.jacrev(ft.jacrev(model, argnums=argnums), argnums=argnums)(*inp)

    if task in locals():
        return locals()[task]
    elif task == "jacobian":
        raise RuntimeError(
            "functorch has no equivalent of autograd.functional.jacobian with vectorize=False yet"
        )
    else:
        raise RuntimeError(f"Unsupported task: {task}")


# Listing of the different tasks
FAST_TASKS_NO_DOUBLE_BACK = [
    "vjp",
]

FAST_TASKS = FAST_TASKS_NO_DOUBLE_BACK + [
    "vhp",
    "jvp",
]

ALL_TASKS_NON_VECTORIZED = FAST_TASKS + ["hvp", "jacobian", "hessian"]

DOUBLE_BACKWARD_TASKS = ["jvp", "hvp", "vhp", "hessian"]

VECTORIZED_TASKS = ["hessian_fwdrev", "hessian_revrev", "jacfwd", "jacrev"]

ALL_TASKS = ALL_TASKS_NON_VECTORIZED + VECTORIZED_TASKS


# Model definition which contains:
# - name: a string with the model name.
# - getter: a function to get the model. It takes as input the device on which the model
#     will run. It should return the forward function and the parameters (Tensors) used as
#     input for the forward function. Note that the forward must *not* have any side effect.
# - tasks: the list of recommended tasks that can run in a reasonable amount of time with this model.
# - unsupported: the list of tasks that this model cannot run.
class ModelDef(NamedTuple):
    name: str
    getter: GetterType
    tasks: List[str]
    unsupported: List[str]


MODELS = [
    ModelDef("resnet18", vision_models.get_resnet18, FAST_TASKS, []),
    ModelDef("fcn_resnet", vision_models.get_fcn_resnet, FAST_TASKS, []),
    ModelDef("detr", vision_models.get_detr, FAST_TASKS, []),
    ModelDef("ppl_simple_reg", ppl_models.get_simple_regression, ALL_TASKS, []),
    ModelDef("ppl_robust_reg", ppl_models.get_robust_regression, ALL_TASKS, []),
    ModelDef("wav2letter", audio_text_models.get_wav2letter, FAST_TASKS, []),
    ModelDef(
        "deepspeech",
        audio_text_models.get_deepspeech,
        FAST_TASKS_NO_DOUBLE_BACK,
        DOUBLE_BACKWARD_TASKS,
    ),
    ModelDef("transformer", audio_text_models.get_transformer, FAST_TASKS, []),
    ModelDef("multiheadattn", audio_text_models.get_multiheadattn, FAST_TASKS, []),
]


def get_v_for(model: Callable, inp: InputsType, task: str) -> VType:
    v: VType

    if task in ["vjp"]:
        out = model(*inp)
        v = torch.rand_like(out)
    elif task in ["jvp", "hvp", "vhp"]:
        if isinstance(inp, tuple):
            v = tuple(torch.rand_like(i) for i in inp)
        else:
            v = torch.rand_like(inp)
    else:
        v = None

    return v


def run_once(model: Callable, inp: InputsType, task: str, v: VType, **kwargs) -> None:
    func = get_task_func(task)

    if v is not None:
        res = func(model, inp, v=v, strict=True)
    else:
        res = func(model, inp, strict=True)


def run_once_functorch(
    model: Callable, inp: InputsType, task: str, v: VType, maybe_check_consistency=False
) -> None:
    func = get_task_functorch(task)

    if v is not None:
        res = func(model, inp, v=v, strict=True)
    else:
        res = func(model, inp, strict=True)

    if maybe_check_consistency:
        af_func = get_task_func(task)
        if v is not None:
            expected = af_func(model, inp, v=v, strict=True)
        else:
            expected = af_func(model, inp, strict=True)
        atol = 1e-2 if task == "vhp" else 5e-3
        torch.testing.assert_close(
            res,
            expected,
            rtol=1e-5,
            atol=atol,
            msg=f"Consistency fail for task '{task}'",
        )


def run_model(
    model_getter: GetterType, args: Any, task: str, run_once_fn: Callable = run_once
) -> List[float]:
    if args.gpu == -1:
        device = torch.device("cpu")

        def noop():
            pass

        do_sync = noop
    else:
        device = torch.device(f"cuda:{args.gpu}")
        do_sync = torch.cuda.synchronize

    model, inp = model_getter(device)

    v = get_v_for(model, inp, task)

    # Warmup
    # maybe_check_consistency=True checks for consistency between
    # functorch vs autograd.functional and is done in run_once_functorch only
    run_once_fn(model, inp, task, v, maybe_check_consistency=True)

    elapsed = []
    for it in range(args.num_iters):
        do_sync()
        start = time.time()
        run_once_fn(model, inp, task, v)
        do_sync()
        elapsed.append(time.time() - start)

    return elapsed


def main():
    parser = ArgumentParser("Main script to benchmark functional API of the autograd.")
    parser.add_argument(
        "--output", type=str, default="", help="Text file where to write the output"
    )
    parser.add_argument("--num-iters", type=int, default=10)
    parser.add_argument(
        "--gpu",
        type=int,
        default=-2,
        help="GPU to use, -1 for CPU and -2 for auto-detect",
    )
    parser.add_argument(
        "--run-slow-tasks", action="store_true", help="Run even the slow tasks"
    )
    parser.add_argument(
        "--model-filter",
        type=str,
        default="",
        help="Only run the models in this filter",
    )
    parser.add_argument(
        "--task-filter", type=str, default="", help="Only run the tasks in this filter"
    )
    parser.add_argument(
        "--num-threads",
        type=int,
        default=10,
        help="Number of concurrent threads to use when running on cpu",
    )
    parser.add_argument("--seed", type=int, default=0, help="The random seed to use.")
    args = parser.parse_args()

    results: TimingResultType = defaultdict(defaultdict)
    torch.set_num_threads(args.num_threads)
    torch.set_num_interop_threads(args.num_threads)

    # This automatically seed cuda if it is available
    torch.manual_seed(args.seed)

    if args.gpu == -2:
        args.gpu = 0 if torch.cuda.is_available() else -1

    for name, model_getter, recommended_tasks, unsupported_tasks in MODELS:
        if args.model_filter and name not in args.model_filter:
            continue
        tasks = ALL_TASKS if args.run_slow_tasks else recommended_tasks
        for task in tasks:
            if task in unsupported_tasks:
                continue
            if args.task_filter and task not in args.task_filter:
                continue
            runtimes = run_model(model_getter, args, task)

            runtimes = torch.tensor(runtimes)
            mean, var = runtimes.mean(), runtimes.var()
            results[name][task] = (mean.item(), var.item())
            print(f"Results for model {name} on task {task}: {mean}s (var: {var})")

            if has_functorch:
                try:
                    runtimes = run_model(
                        model_getter, args, task, run_once_fn=run_once_functorch
                    )
                except RuntimeError as e:
                    print(
                        f"Failed model using Functorch: {name}, task: {task}, Error message: \n\t",
                        e,
                    )
                    continue

                runtimes = torch.tensor(runtimes)
                mean, var = runtimes.mean(), runtimes.var()
                results[name][f"functorch {task}"] = (mean.item(), var.item())
                print(
                    f"Results for model {name} on task {task} using Functorch: {mean}s (var: {var})"
                )

    if args.output:
        with open(args.output, "w") as f:
            f.write(to_markdown_table(results))


if __name__ == "__main__":
    main()