diff --git a/quanto-exps/benchmark.py b/quanto-exps/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..cf39c0b2142407f30a7c5e29c02c8ebd1fe5ae43 --- /dev/null +++ b/quanto-exps/benchmark.py @@ -0,0 +1,128 @@ +import argparse +from optimum.quanto import freeze, qfloat8, qint4, qint8, quantize +import torch +import json +import torch.utils.benchmark as benchmark +from diffusers import DiffusionPipeline +import gc + + +WARM_UP_ITERS = 5 +PROMPT = "ghibli style, a fantasy landscape with castles" + +TORCH_DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16} +QTYPES = {"fp8": qfloat8, "int8": qint8, "int4": qint4, "none": None} + +PREFIXES = { + "stabilityai/stable-diffusion-3-medium-diffusers": "sd3", + "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS": "pixart", + "fal/AuraFlow": "auraflow", +} + +def flush(): + """Wipes off memory.""" + gc.collect() + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() + torch.cuda.reset_peak_memory_stats() + +def load_pipeline(ckpt_id, torch_dtype, qtype=None, exclude_layers=None, qte=False): + pipe = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=torch_dtype).to("cuda") + + if qtype: + quantize(pipe.transformer, weights=qtype, exclude=exclude_layers) + freeze(pipe.transformer) + + if qte: + quantize(pipe.text_encoder, weights=qtype) + freeze(pipe.text_encoder) + if hasattr(pipe, "text_encoder_2"): + quantize(pipe.text_encoder_2, weights=qtype) + freeze(pipe.text_encoder_2) + if hasattr(pipe, "text_encoder_3"): + quantize(pipe.text_encoder_3, weights=qtype) + freeze(pipe.text_encoder_3) + + pipe.set_progress_bar_config(disable=True) + return pipe + + +def run_inference(pipe, batch_size=1): + _ = pipe( + prompt=PROMPT, + num_images_per_prompt=batch_size, + generator=torch.manual_seed(0), + ) + + +def benchmark_fn(f, *args, **kwargs): + t0 = benchmark.Timer(stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}) + return f"{(t0.blocked_autorange().mean):.3f}" + + +def bytes_to_giga_bytes(bytes): + return f"{(bytes / 1024 / 1024 / 1024):.3f}" + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--ckpt_id", + type=str, + default="stabilityai/stable-diffusion-3-medium-diffusers", + choices=list(PREFIXES.keys()), + ) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--torch_dtype", type=str, default="fp16", choices=list(TORCH_DTYPES.keys())) + parser.add_argument("--qtype", type=str, default="none", choices=list(QTYPES.keys())) + parser.add_argument("--qte", type=int, default=0, help="Quantize text encoder") + parser.add_argument("--exclude_layers", metavar="N", type=str, nargs="*", default=None) + args = parser.parse_args() + + flush() + + print( + f"Running with ckpt_id: {args.ckpt_id}, batch_size: {args.batch_size}, torch_dtype: {args.torch_dtype}, qtype: {args.qtype}, qte: {bool(args.qte)}" + ) + pipeline = load_pipeline( + ckpt_id=args.ckpt_id, + torch_dtype=TORCH_DTYPES[args.torch_dtype], + qtype=QTYPES[args.qtype], + exclude_layers=args.exclude_layers, + qte=args.qte, + ) + + for _ in range(WARM_UP_ITERS): + run_inference(pipeline, args.batch_size) + + time = benchmark_fn(run_inference, pipeline, args.batch_size) + memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs. + print( + f"ckpt: {args.ckpt_id} batch_size: {args.batch_size}, qte: {args.qte}, " + f"torch_dtype: {args.torch_dtype}, qtype: {args.qtype} in {time} seconds and {memory} GBs." + ) + + ckpt_id = PREFIXES[args.ckpt_id] + img_name = f"ckpt@{ckpt_id}-bs@{args.batch_size}-dtype@{args.torch_dtype}-qtype@{args.qtype}-qte@{args.qte}" + if args.exclude_layers: + exclude_layers = "_".join(args.exclude_layers) + img_name += f"-exclude@{exclude_layers}" + image = pipeline( + prompt=PROMPT, + num_images_per_prompt=args.batch_size, + generator=torch.manual_seed(0), + ).images[0] + image.save(f"{img_name}.png") + + info = dict( + batch_size=args.batch_size, + memory=memory, + time=time, + dtype=args.torch_dtype, + qtype=args.qtype, + qte=args.qte, + exclude_layers=args.exclude_layers, + ) + info_file = f"{img_name}_info.json" + with open(info_file, "w") as f: + json.dump(info, f) diff --git a/quanto-exps/benchmark_2.py b/quanto-exps/benchmark_2.py new file mode 100644 index 0000000000000000000000000000000000000000..f4e8ee2155b94aac6b751d0780a7b405f18eb084 --- /dev/null +++ b/quanto-exps/benchmark_2.py @@ -0,0 +1,147 @@ +import argparse +from optimum.quanto import freeze, qfloat8, qint4, qint8, quantize +import torch +import json +import torch.utils.benchmark as benchmark +from diffusers import DiffusionPipeline +import gc + + +WARM_UP_ITERS = 5 +PROMPT = "ghibli style, a fantasy landscape with castles" + +TORCH_DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16} +QTYPES = {"fp8": qfloat8, "int8": qint8, "int4": qint4, "none": None} + +PREFIXES = { + "stabilityai/stable-diffusion-3-medium-diffusers": "sd3", +} + + +def flush(): + """Wipes off memory.""" + gc.collect() + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() + torch.cuda.reset_peak_memory_stats() + + +def load_pipeline( + ckpt_id, torch_dtype, qtype=None, exclude_layers=None, qte=False, first=False, second=False, third=False +): + pipe = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=torch_dtype).to("cuda") + + if qtype: + quantize(pipe.transformer, weights=qtype, exclude=exclude_layers) + freeze(pipe.transformer) + + if qte: + if first: + quantize(pipe.text_encoder, weights=qtype) + freeze(pipe.text_encoder) + if second: + quantize(pipe.text_encoder_2, weights=qtype) + freeze(pipe.text_encoder) + if third: + quantize(pipe.text_encoder_3, weights=qtype) + freeze(pipe.text_encoder_3) + + pipe.set_progress_bar_config(disable=True) + return pipe + + +def run_inference(pipe, batch_size=1): + _ = pipe( + prompt=PROMPT, + num_images_per_prompt=batch_size, + generator=torch.manual_seed(0), + ) + + +def benchmark_fn(f, *args, **kwargs): + t0 = benchmark.Timer(stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}) + return f"{(t0.blocked_autorange().mean):.3f}" + + +def bytes_to_giga_bytes(bytes): + return f"{(bytes / 1024 / 1024 / 1024):.3f}" + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--ckpt_id", + type=str, + default="stabilityai/stable-diffusion-3-medium-diffusers", + choices=list(PREFIXES.keys()), + ) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--torch_dtype", type=str, default="fp16", choices=list(TORCH_DTYPES.keys())) + parser.add_argument("--qtype", type=str, default="none", choices=list(QTYPES.keys())) + parser.add_argument("--qte", type=int, default=0, help="Quantize text encoder") + parser.add_argument("--first", type=int, default=0, help="Quantize first text encoder") + parser.add_argument("--second", type=int, default=0, help="Quantize second text encoder") + parser.add_argument("--third", type=int, default=0, help="Quantize third text encoder") + parser.add_argument("--exclude_layers", metavar="N", type=str, nargs="*", default=None) + args = parser.parse_args() + + flush() + + print( + f"Running with ckpt_id: {args.ckpt_id}, batch_size: {args.batch_size}, torch_dtype: {args.torch_dtype}, qtype: {args.qtype}, qte: {bool(args.qte)}" + ) + pipeline = load_pipeline( + ckpt_id=args.ckpt_id, + torch_dtype=TORCH_DTYPES[args.torch_dtype], + qtype=QTYPES[args.qtype], + exclude_layers=args.exclude_layers, + qte=args.qte, + first=args.first, + second=args.second, + third=args.third, + ) + + for _ in range(WARM_UP_ITERS): + run_inference(pipeline, args.batch_size) + + time = benchmark_fn(run_inference, pipeline, args.batch_size) + memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs. + print( + f"ckpt: {args.ckpt_id} batch_size: {args.batch_size}, qte: {args.qte}, " + f"torch_dtype: {args.torch_dtype}, qtype: {args.qtype} in {time} seconds and {memory} GBs." + ) + + ckpt_id = PREFIXES[args.ckpt_id] + img_name = f"ckpt@{ckpt_id}-bs@{args.batch_size}-dtype@{args.torch_dtype}-qtype@{args.qtype}-qte@{args.qte}" + if args.exclude_layers: + exclude_layers = "_".join(args.exclude_layers) + img_name += f"-exclude@{exclude_layers}" + if args.first: + img_name += f"-first@{args.first}" + if args.second: + img_name += f"-second@{args.second}" + if args.third: + img_name += f"-third@{args.third}" + + image = pipeline( + prompt=PROMPT, + num_images_per_prompt=args.batch_size, + generator=torch.manual_seed(0), + ).images[0] + image.save(f"{img_name}.png") + + info = dict( + batch_size=args.batch_size, + memory=memory, + time=time, + dtype=args.torch_dtype, + qtype=args.qtype, + qte=args.qte, + exclude_layers=args.exclude_layers, + first=args.first, + second=args.second, + third=args.third, + ) + info_file = f"{img_name}_info.json" + with open(info_file, "w") as f: + json.dump(info, f) diff --git a/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0.png b/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0.png new file mode 100644 index 0000000000000000000000000000000000000000..8d43be492395a20dae8106d85d77696edee42d6c --- /dev/null +++ b/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37db1ad1c4d8ca7c1f4c116c99a99a2c0880f8e5aa2ce74ba70ae5b5970805fb +size 2219214 diff --git a/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0_info.json b/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0_info.json new file mode 100644 index 0000000000000000000000000000000000000000..af1e01732ac7a757a95e4ca5d91fc6e607ac77c2 --- /dev/null +++ b/quanto-exps/ckpt@auraflow-bs@1-dtype@bf16-qtype@fp8-qte@0_info.json @@ -0,0 +1 @@ +{"batch_size": 1, "memory": "15.647", "time": "14.063", "dtype": "bf16", 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https://git-lfs.github.com/spec/v1 +oid sha256:348a05a5bd7cfdabbb27d5c38dd1026b4cb47bf7e8fca5c137465b609591aa16 +size 1489682 diff --git a/quanto-exps/ckpt@sd3-bs@4-dtype@fp16-qtype@none-qte@1_info.json b/quanto-exps/ckpt@sd3-bs@4-dtype@fp16-qtype@none-qte@1_info.json new file mode 100644 index 0000000000000000000000000000000000000000..5c738623fe5164f0aebbd371cf3b2a9e41c4a31d --- /dev/null +++ b/quanto-exps/ckpt@sd3-bs@4-dtype@fp16-qtype@none-qte@1_info.json @@ -0,0 +1 @@ +{"batch_size": 4, "memory": "24.477", "time": "7.377", "dtype": "fp16", "qtype": "none", "qte": 1, "exclude_layers": null} \ No newline at end of file diff --git a/quanto-exps/launch_exps.sh b/quanto-exps/launch_exps.sh new file mode 100644 index 0000000000000000000000000000000000000000..2b79a8de7f9ddd0b6ed30b4425ef12f153d178e7 --- /dev/null +++ b/quanto-exps/launch_exps.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# Possible values for each argument +ckpt_ids=("stabilityai/stable-diffusion-3-medium-diffusers" "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS" "fal/AuraFlow") +batch_sizes=(1 4) +torch_dtypes=("fp16" "bf16") +qtypes=("fp8" "int8" "none") +qte=(0 1) + +# Loop over all combinations +for ckpt_id in "${ckpt_ids[@]}"; do + for batch_size in "${batch_sizes[@]}"; do + for torch_dtype in "${torch_dtypes[@]}"; do + for qtype in "${qtypes[@]}"; do + for te in "${qte[@]}"; do + # Adjust the qtype argument if it's "None" + if [ "$qtype" == "none" ]; then + q_arg="" + else + q_arg="--qtype $qtype" + fi + + # Run the Python script with the current combination of arguments + python3 benchmark.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te + done + done + done + done +done diff --git a/quanto-exps/launch_exps_2.sh b/quanto-exps/launch_exps_2.sh new file mode 100644 index 0000000000000000000000000000000000000000..62132f7fa91018407df6fff12e115b864f24a3ba --- /dev/null +++ b/quanto-exps/launch_exps_2.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# Possible values for each argument +ckpt_ids=("stabilityai/stable-diffusion-3-medium-diffusers" "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS" "fal/AuraFlow") +batch_sizes=(1 4) +torch_dtypes=("fp16" "bf16") +qtypes=("int4") +qte=(0 1) + +# Loop over all combinations +for ckpt_id in "${ckpt_ids[@]}"; do + for batch_size in "${batch_sizes[@]}"; do + for torch_dtype in "${torch_dtypes[@]}"; do + for qtype in "${qtypes[@]}"; do + for te in "${qte[@]}"; do + # Adjust the qtype argument if it's "None" + if [ "$qtype" == "none" ]; then + q_arg="" + else + q_arg="--qtype $qtype" + fi + + # Run the Python script with the current combination of arguments + python3 benchmark.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --exclude_layers=proj_out + done + done + done + done +done diff --git a/quanto-exps/launch_exps_3.sh b/quanto-exps/launch_exps_3.sh new file mode 100644 index 0000000000000000000000000000000000000000..31997ba93b5e040c626587252ea09cb223199909 --- /dev/null +++ b/quanto-exps/launch_exps_3.sh @@ -0,0 +1,34 @@ +#!/bin/bash + +# Possible values for each argument +ckpt_ids=("stabilityai/stable-diffusion-3-medium-diffusers") +batch_sizes=(1 4) +torch_dtypes=("fp16" "bf16") +qtypes=("fp8" "int8") +qte=(1) + +# Loop over all combinations +for ckpt_id in "${ckpt_ids[@]}"; do + for batch_size in "${batch_sizes[@]}"; do + for torch_dtype in "${torch_dtypes[@]}"; do + for qtype in "${qtypes[@]}"; do + for te in "${qte[@]}"; do + # Adjust the qtype argument if it's "None" + if [ "$qtype" == "none" ]; then + q_arg="" + else + q_arg="--qtype $qtype" + fi + + # Run the Python script with the current combination of arguments + python3 benchmark.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te + python3 benchmark_2.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --first 1 + python3 benchmark_2.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --second 1 + python3 benchmark_2.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --third 1 + python3 benchmark_2.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --first 1 --second 1 + python3 benchmark_2.py --ckpt_id $ckpt_id --batch_size $batch_size --torch_dtype $torch_dtype $q_arg --qte $te --first 1 --second 1 --third 1 + done + done + done + done +done