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| Original file line number | Diff line number | Diff line change |
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| [submodule "3rdparty/tokenizers-wasm"] | ||
| path = 3rdparty/tokenizers-wasm | ||
| url = https://github.com/mithril-security/tokenizers-wasm |
Submodule tokenizers-wasm
added at
a2602d
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| from typing import Dict, List, Tuple | ||
|
|
||
| import os | ||
| import argparse | ||
| import pickle | ||
| import web_stable_diffusion.trace as trace | ||
| import web_stable_diffusion.utils as utils | ||
|
|
||
| import tvm | ||
| from tvm import relax | ||
| from tvm.contrib import tvmjs | ||
|
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|
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| def _parse_args(): | ||
| args = argparse.ArgumentParser() | ||
| args.add_argument("--target", type=str, default="apple/m2-gpu") | ||
| args.add_argument("--db-path", type=str, default="log_db/") | ||
| args.add_argument("--from-checkpt", type=str, choices=["deploy"], default="") | ||
| args.add_argument("--artifact-path", type=str, default="dist") | ||
| args.add_argument( | ||
| "--use-cache", | ||
| type=int, | ||
| default=1, | ||
| help="Whether to use previously pickled IRModule and skip trace.", | ||
| ) | ||
|
|
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| args.add_argument("--show-build-stage", action="store_true", default=False) | ||
| parsed = args.parse_args() | ||
|
|
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| if parsed.target == "webgpu": | ||
| parsed.target = tvm.target.Target( | ||
| "webgpu", host="llvm -mtriple=wasm32-unknown-unknown-wasm" | ||
| ) | ||
| else: | ||
| parsed.target = tvm.target.Target(parsed.target, host="llvm") | ||
| return parsed | ||
|
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|
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| def trace_models( | ||
| device_str: str, | ||
| ) -> Tuple[tvm.IRModule, Dict[str, List[tvm.nd.NDArray]]]: | ||
| from diffusers import StableDiffusionPipeline | ||
|
|
||
| pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") | ||
| clip = trace.clip_to_text_embeddings(pipe) | ||
| unet = trace.unet_latents_to_noise_pred(pipe, device_str) | ||
| vae = trace.vae_to_image(pipe) | ||
| concat_embeddings = trace.concat_embeddings() | ||
| image_to_rgba = trace.image_to_rgba() | ||
| scheduler_steps = trace.scheduler_steps() | ||
|
|
||
| mod = utils.merge_irmodules( | ||
| clip, unet, vae, concat_embeddings, image_to_rgba, scheduler_steps | ||
| ) | ||
| return relax.frontend.detach_params(mod) | ||
|
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|
|
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| def legalize_and_lift_params( | ||
| mod: tvm.IRModule, model_params: Dict[str, List[tvm.nd.NDArray]], args: Dict | ||
| ) -> tvm.IRModule: | ||
| """First-stage: Legalize ops and trace""" | ||
| model_names = ["clip", "unet", "vae"] | ||
| scheduler_func_names = [f"scheduler_step_{i}" for i in range(5)] | ||
| entry_funcs = ( | ||
| model_names + scheduler_func_names + ["image_to_rgba", "concat_embeddings"] | ||
| ) | ||
|
|
||
| mod = relax.pipeline.get_pipeline()(mod) | ||
| mod = relax.transform.RemoveUnusedFunctions(entry_funcs)(mod) | ||
| mod = relax.transform.LiftTransformParams()(mod) | ||
| if args.show_build_stage: | ||
| mod.show() | ||
| mod_transform, mod_deploy = utils.split_transform_deploy_mod( | ||
| mod, model_names, entry_funcs | ||
| ) | ||
|
|
||
| trace.compute_save_scheduler_consts(args.artifact_path) | ||
| new_params = utils.transform_params(mod_transform, model_params) | ||
| utils.save_params(new_params, args.artifact_path) | ||
| return mod_deploy | ||
|
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|
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| def build(mod: tvm.IRModule, args: Dict) -> None: | ||
| from tvm import meta_schedule as ms | ||
|
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| db = ms.database.create(work_dir=args.db_path) | ||
| with args.target, db, tvm.transform.PassContext(opt_level=3): | ||
| mod_deploy = relax.transform.MetaScheduleApplyDatabase()(mod) | ||
| ex = relax.build(mod_deploy, args.target) | ||
|
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| target_kind = args.target.kind.default_keys[0] | ||
|
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| if target_kind == "webgpu": | ||
| output_filename = f"stable_diffusion_{target_kind}.wasm" | ||
| tvmjs.export_runtime(f"{args.artifact_path}") | ||
| else: | ||
| output_filename = f"stable_diffusion_{target_kind}.so" | ||
| ex.export_library(os.path.join(args.artifact_path, output_filename)) | ||
|
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| if __name__ == "__main__": | ||
| ARGS = _parse_args() | ||
| os.makedirs(ARGS.artifact_path, exist_ok=True) | ||
| torch_dev_key = utils.detect_available_torch_device() | ||
| cache_path = os.path.join(ARGS.artifact_path, "mod_cache_before_build.pkl") | ||
| use_cache = ARGS.use_cache and os.path.isfile(cache_path) | ||
| if not use_cache: | ||
| mod, params = trace_models(torch_dev_key) | ||
| mod = legalize_and_lift_params(mod, params, ARGS) | ||
| with open(cache_path, "wb") as outfile: | ||
| pickle.dump(mod, outfile) | ||
| print(f"Save a cached module to {cache_path}.") | ||
| else: | ||
| print( | ||
| f"Load cached module from {cache_path} and skip tracing. " | ||
| "You can use --use-cache=0 to retrace" | ||
| ) | ||
| mod = pickle.load(open(cache_path, "rb")) | ||
| build(mod, ARGS) |
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