FlashPack

[hlky]

What does this PR do?

Adds support for FlashPack

FlashPack could be used as weight format only (see: load_flashpack_checkpoint) - keeping only weight format code would be a cleaner integration, the model loading is indeed faster[1] however part of the performance difference seems to be just due to the complexity of existing from_pretrained code, for example I noticed that _caching_allocator_warmup slows things down, also empty_device_cache is called in _load_pretrained_model whereas FlashPack's code doesn't thus the empty cache time is excluded from FlashPack's benchmark results.

• Pipeline
• Cleanup (is_flashpack_available check logging, etc)
• FlashPack arguments (silent, num_streams, use_distributed_loading etc.)

Benchmark

Changes from FlashPack's version:

• Use Diffusers
• Remove transformers related code
• Add SD v1.5

import csv
import gc
import os
import shutil
import tempfile
import time

import torch

from diffusers.models import AutoModel as DiffusersAutoModel
from huggingface_hub import snapshot_download


def test_model(
    repo_id: str,
    subfolder: str | None = None,
    accelerate_device: str | torch.device = "cuda",
    flashpack_device: str | torch.device = "cuda",
    dtype: torch.dtype | None = None,
    allow_pattern: str = "*",
) -> tuple[float, float, int]:
    """
    Test a model from a repository.
    """
    allow_patterns = [f"{subfolder}/{allow_pattern}"]
    if allow_pattern != "*":
        allow_patterns = [f"{subfolder}/{allow_pattern}", f"{subfolder}/config.json"]
    repo_dir = snapshot_download(
        repo_id, allow_patterns=None if subfolder is None else allow_patterns
    )
    model_dir = repo_dir if subfolder is None else os.path.join(repo_dir, subfolder)
    saved_flashpack_path = os.path.join(model_dir, "model.flashpack")
    saved_flashpack_config_path = os.path.join(model_dir, "flashpack_config.json")

    with tempfile.TemporaryDirectory() as tmpdir:
        # Make a new model directory with the model in it so it isn't cached
        temp_model_dir = os.path.join(tmpdir, "model")
        flashpack_dir = os.path.join(tmpdir, "flashpack")
        os.makedirs(flashpack_dir, exist_ok=True)
        print("Copying model to temporary directory")
        shutil.copytree(model_dir, temp_model_dir)

        # Load from the temporary model directory
        print("Loading model from temporary directory using from_pretrained")
        start_time = time.time()
        model = DiffusersAutoModel.from_pretrained(
            temp_model_dir,
            torch_dtype=dtype,
            device_map={"": accelerate_device},
            variant="fp16" if allow_pattern != "*" else None,
        )

        end_time = time.time()
        accelerate_time = end_time - start_time
        print(f"Time taken with from_pretrained: {accelerate_time} seconds")

        if os.path.exists(saved_flashpack_path) and os.path.exists(
            saved_flashpack_config_path
        ):
            print("Copying flashpack to temporary directory")
            shutil.copy(
                saved_flashpack_path, os.path.join(flashpack_dir, "model.flashpack")
            )
            shutil.copy(
                saved_flashpack_config_path, os.path.join(flashpack_dir, "config.json")
            )
        else:
            print("Packing model to flashpack")
            pack_start_time = time.time()
            model.save_pretrained(
                flashpack_dir,
                use_flashpack=True,
            )
            pack_end_time = time.time()
            print(
                f"Time taken with save_pretrained_flashpack: {pack_end_time - pack_start_time} seconds"
            )
            # Copy back to the original model directory
            shutil.copy(
                os.path.join(flashpack_dir, "model.flashpack"), saved_flashpack_path
            )
            shutil.copy(
                os.path.join(flashpack_dir, "config.json"), saved_flashpack_config_path
            )

        del model
        sync_and_flush()

        print("Loading model from flashpack directory using from_pretrained_flashpack")
        flashpack_start_time = time.time()
        flashpack_model = DiffusersAutoModel.from_pretrained(
            flashpack_dir,
            torch_dtype=dtype,
            device_map={"": flashpack_device},
            use_flashpack=True,
        )

        flashpack_end_time = time.time()
        flashpack_time = flashpack_end_time - flashpack_start_time
        print(f"Time taken with from_pretrained_flashpack: {flashpack_time} seconds")

        total_numel = 0
        for param in flashpack_model.parameters():
            total_numel += param.numel()

        total_bytes = total_numel * dtype.itemsize

        del flashpack_model
        sync_and_flush()

        return accelerate_time, flashpack_time, total_bytes


def test_wan_small_transformer() -> tuple[float, float, int]:
    return test_model(
        repo_id="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
        subfolder="transformer",
        accelerate_device="cuda:0" if torch.cuda.is_available() else "cpu",
        flashpack_device="cuda:0" if torch.cuda.is_available() else "cpu",
        dtype=torch.bfloat16,
    )


def test_wan_large_transformer() -> tuple[float, float, int]:
    return test_model(
        repo_id="Wan-AI/Wan2.1-T2V-14B-Diffusers",
        subfolder="transformer",
        accelerate_device="cuda:0" if torch.cuda.is_available() else "cpu",
        flashpack_device="cuda:0" if torch.cuda.is_available() else "cpu",
        dtype=torch.bfloat16,
    )

def test_stable_diffusion_v1_5() -> tuple[float, float, int]:
    return test_model(
        repo_id="stable-diffusion-v1-5/stable-diffusion-v1-5",
        subfolder="unet",
        accelerate_device="cuda:0" if torch.cuda.is_available() else "cpu",
        flashpack_device="cuda:0" if torch.cuda.is_available() else "cpu",
        dtype=torch.float16,
        allow_pattern="*.fp16.safetensors",
    )

def test_flux_transformer() -> tuple[float, float, int]:
    return test_model(
        repo_id="black-forest-labs/FLUX.1-dev",
        subfolder="transformer",
        accelerate_device="cuda:0" if torch.cuda.is_available() else "cpu",
        flashpack_device="cuda:0" if torch.cuda.is_available() else "cpu",
        dtype=torch.bfloat16,
    )


def print_test_result(
    model_name: str,
    accelerate_time: float,
    flashpack_time: float,
    total_bytes: int,
) -> None:
    print(f"{model_name}: Accelerate time: {accelerate_time} seconds")
    print(f"{model_name}: Flashpack time: {flashpack_time} seconds")
    accelerate_gbps = (total_bytes / 1000**3) / accelerate_time
    flashpack_gbps = (total_bytes / 1000**3) / flashpack_time
    print(f"{model_name}: Accelerate GB/s: {accelerate_gbps} GB/s")
    print(f"{model_name}: Flashpack GB/s: {flashpack_gbps} GB/s")


def sync_and_flush() -> None:
    torch.cuda.empty_cache()
    gc.collect()
    os.system("sync")
    if os.geteuid() == 0:
        os.system("echo 3 | tee /proc/sys/vm/drop_caches")


if __name__ == "__main__":
    with open("benchmark_results.csv", "a") as f:
        writer = csv.writer(f)
        writer.writerow(["model", "accelerate_time", "flashpack_time", "total_bytes"])
        for i in range(10):
            for test_model_name, test_func in [
                ("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", test_wan_small_transformer),
                ("stable-diffusion-v1-5/stable-diffusion-v1-5", test_stable_diffusion_v1_5),
                # ("black-forest-labs/FLUX.1-dev", test_flux_transformer),
            ]:
                accelerate_time, flashpack_time, total_bytes = test_func()
                writer.writerow(
                    [test_model_name, accelerate_time, flashpack_time, total_bytes]
                )
                print_test_result(
                    test_model_name, accelerate_time, flashpack_time, total_bytes
                )

======================================================================
SUMMARY STATISTICS
======================================================================

Model                                    Size (GB)    Accel (s)    Flash (s)    Speedup   
----------------------------------------------------------------------
Stable Diffusion v1.5  (fp16)                        1.60       0.250       0.263      0.95x
Wan2.1 1.3B DiT                                2.64       1.315       0.600      2.19x

======================================================================

Model                                    Accel GB/s      Flash GB/s     
----------------------------------------------------------------------
Stable Diffusion v1.5  (fp16)                           6.41           6.13
Wan2.1 1.3B DiT                                   2.33           4.44
======================================================================

[1] For bfloat16 - with float16 existing code appears to be faster

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