From d67d9c25ebefc4f716ab835c91db2025e62e2492 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 20 Nov 2024 11:35:12 +0000 Subject: [PATCH] 2024-11-20 nightly release (c8f5deb258213fe7c05b4c026d6abf18e1a98507) --- README.md | 3 +- .../decoders/benchmark_decoders_library.py | 99 ++++- .../decoders/benchmark_readme_chart.png | Bin 77135 -> 106334 bytes .../decoders/benchmark_readme_data.json | 369 +++++++++++++----- benchmarks/decoders/generate_readme_data.py | 64 +-- .../_samplers/video_clip_sampler.py | 2 +- 6 files changed, 408 insertions(+), 129 deletions(-) diff --git a/README.md b/README.md index 14b32452..8643e0e3 100644 --- a/README.md +++ b/README.md @@ -185,7 +185,8 @@ format you want. Refer to Nvidia's GPU support matrix for more details ## Benchmark Results -The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 56-core machine. +The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 22-core machine with an Nvidia A100 with +5 [NVDEC decoders](https://docs.nvidia.com/video-technologies/video-codec-sdk/12.1/nvdec-application-note/index.html#). ![benchmark_results](./benchmarks/decoders/benchmark_readme_chart.png) diff --git a/benchmarks/decoders/benchmark_decoders_library.py b/benchmarks/decoders/benchmark_decoders_library.py index 33d25c63..a5102fd3 100644 --- a/benchmarks/decoders/benchmark_decoders_library.py +++ b/benchmarks/decoders/benchmark_decoders_library.py @@ -38,6 +38,14 @@ def __init__(self): def get_frames_from_video(self, video_file, pts_list): pass + @abc.abstractmethod + def get_consecutive_frames_from_video(self, video_file, numFramesToDecode): + pass + + @abc.abstractmethod + def decode_and_transform(self, video_file, pts_list, height, width, device): + pass + class DecordAccurate(AbstractDecoder): def __init__(self): @@ -89,8 +97,10 @@ def __init__(self, backend): self._backend = backend self._print_each_iteration_time = False import torchvision # noqa: F401 + from torchvision.transforms import v2 as transforms_v2 self.torchvision = torchvision + self.transforms_v2 = transforms_v2 def get_frames_from_video(self, video_file, pts_list): self.torchvision.set_video_backend(self._backend) @@ -111,6 +121,20 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode): frames.append(frame["data"].permute(1, 2, 0)) return frames + def decode_and_transform(self, video_file, pts_list, height, width, device): + self.torchvision.set_video_backend(self._backend) + reader = self.torchvision.io.VideoReader(video_file, "video") + frames = [] + for pts in pts_list: + reader.seek(pts) + frame = next(reader) + frames.append(frame["data"].permute(1, 2, 0)) + frames = [ + self.transforms_v2.functional.resize(frame.to(device), (height, width)) + for frame in frames + ] + return frames + class TorchCodecCore(AbstractDecoder): def __init__(self, num_threads=None, color_conversion_library=None, device="cpu"): @@ -239,6 +263,10 @@ def __init__(self, num_ffmpeg_threads=None, device="cpu"): ) self._device = device + from torchvision.transforms import v2 as transforms_v2 + + self.transforms_v2 = transforms_v2 + def get_frames_from_video(self, video_file, pts_list): decoder = VideoDecoder( video_file, num_ffmpeg_threads=self._num_ffmpeg_threads, device=self._device @@ -258,6 +286,14 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode): break return frames + def decode_and_transform(self, video_file, pts_list, height, width, device): + decoder = VideoDecoder( + video_file, num_ffmpeg_threads=self._num_ffmpeg_threads, device=self._device + ) + frames = decoder.get_frames_played_at(pts_list) + frames = self.transforms_v2.functional.resize(frames.data, (height, width)) + return frames + @torch.compile(fullgraph=True, backend="eager") def compiled_seek_and_next(decoder, pts): @@ -299,7 +335,9 @@ def __init__(self): self.torchaudio = torchaudio - pass + from torchvision.transforms import v2 as transforms_v2 + + self.transforms_v2 = transforms_v2 def get_frames_from_video(self, video_file, pts_list): stream_reader = self.torchaudio.io.StreamReader(src=video_file) @@ -325,6 +363,21 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode): return frames + def decode_and_transform(self, video_file, pts_list, height, width, device): + stream_reader = self.torchaudio.io.StreamReader(src=video_file) + stream_reader.add_basic_video_stream(frames_per_chunk=1) + frames = [] + for pts in pts_list: + stream_reader.seek(pts) + stream_reader.fill_buffer() + clip = stream_reader.pop_chunks() + frames.append(clip[0][0]) + frames = [ + self.transforms_v2.functional.resize(frame.to(device), (height, width)) + for frame in frames + ] + return frames + def create_torchcodec_decoder_from_file(video_file): video_decoder = create_from_file(video_file) @@ -443,7 +496,7 @@ def plot_data(df_data, plot_path): # Set the title for the subplot base_video = Path(video).name.removesuffix(".mp4") - ax.set_title(f"{base_video}\n{vcount} x {vtype}", fontsize=11) + ax.set_title(f"{base_video}\n{vtype}", fontsize=11) # Plot bars with error bars ax.barh( @@ -486,6 +539,14 @@ class BatchParameters: batch_size: int +@dataclass +class DataLoaderInspiredWorkloadParameters: + batch_parameters: BatchParameters + resize_height: int + resize_width: int + resize_device: str + + def run_batch_using_threads( function, *args, @@ -525,6 +586,7 @@ def run_benchmarks( num_sequential_frames_from_start: list[int], min_runtime_seconds: float, benchmark_video_creation: bool, + dataloader_parameters: DataLoaderInspiredWorkloadParameters = None, batch_parameters: BatchParameters = None, ) -> list[dict[str, str | float | int]]: # Ensure that we have the same seed across benchmark runs. @@ -550,6 +612,39 @@ def run_benchmarks( for decoder_name, decoder in decoder_dict.items(): print(f"video={video_file_path}, decoder={decoder_name}") + if dataloader_parameters: + bp = dataloader_parameters.batch_parameters + dataloader_result = benchmark.Timer( + stmt="run_batch_using_threads(decoder.decode_and_transform, video_file, pts_list, height, width, device, batch_parameters=batch_parameters)", + globals={ + "video_file": str(video_file_path), + "pts_list": uniform_pts_list, + "decoder": decoder, + "run_batch_using_threads": run_batch_using_threads, + "batch_parameters": dataloader_parameters.batch_parameters, + "height": dataloader_parameters.resize_height, + "width": dataloader_parameters.resize_width, + "device": dataloader_parameters.resize_device, + }, + label=f"video={video_file_path} {metadata_label}", + sub_label=decoder_name, + description=f"dataloader[threads={bp.num_threads} batch_size={bp.batch_size}] {num_samples} decode_and_transform()", + ) + results.append( + dataloader_result.blocked_autorange( + min_run_time=min_runtime_seconds + ) + ) + df_data.append( + convert_result_to_df_item( + results[-1], + decoder_name, + video_file_path, + num_samples * dataloader_parameters.batch_parameters.batch_size, + f"dataloader[threads={bp.num_threads} batch_size={bp.batch_size}] {num_samples} x decode_and_transform()", + ) + ) + for kind, pts_list in [ ("uniform", uniform_pts_list), ("random", random_pts_list), diff --git a/benchmarks/decoders/benchmark_readme_chart.png b/benchmarks/decoders/benchmark_readme_chart.png index 28ceabe4adf8b0588f761d786164e3e289827079..f433f61991837910ba3baf75397cf4432b1efbde 100644 GIT binary patch literal 106334 zcmeEuhdXpa;w*K~|1G`J=mBv{Ln~efechMhAH5`&x*>R6s z-cR0GE{56o$niMu+q1cz!++hh9a%O<8)eJX6+5JwRXr0ob2N<*d2d$zQcg}*?tlDn 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zY)|-9S=*G#A#6+=N?*kY-&}K=abGO`yZSX68-*6kV`S_D#8m`EnXf@=qRU|ojnUsr z2)+Y=sE=ked3h7UK`2q*q&yvdb!AHz&VuEO8D!)2q|HPXmtOSxGtYq=pYAui7#P9c z^$7mEP?57ZG>nH6>oI2R5kMUwDHDoUoVF@Lm9ZUI-%uNT#7YR?1>CnCA+rqBN&r#O z#3p8NVM{P@^W$spvmyOay+rz`2TKpi4EP5ZYYJ3jiX#<#L>J9j&CW~@W0q1TbO9su z)<2&p{&PmX5{+#T8Ocfk$4pH5GQqDIOMO26Qo+~{IHt+V5_a!Y=0LrfdVT|+=PQDT zaNv4ApaNh7zoc#f^N<xpe80EqmX-eI$Z(g{-0s%12+GKsWLdK5oFLFWLP(p$cqHy&y None: """Benchmarks the performance of a few video decoders on synthetic videos""" videos_dir_path = "/tmp/torchcodec_benchmarking_videos" - shutil.rmtree(videos_dir_path, ignore_errors=True) - os.makedirs(videos_dir_path) + if not os.path.exists(videos_dir_path): + shutil.rmtree(videos_dir_path, ignore_errors=True) + os.makedirs(videos_dir_path) - resolutions = ["1280x720"] - encodings = ["libx264"] - patterns = ["mandelbrot"] - fpses = [60] - gop_sizes = [600] - durations = [120] - pix_fmts = ["yuv420p"] - ffmpeg_path = "ffmpeg" - generate_videos( - resolutions, - encodings, - patterns, - fpses, - gop_sizes, - durations, - pix_fmts, - ffmpeg_path, - videos_dir_path, - ) + resolutions = ["1920x1080"] + encodings = ["libx264"] + patterns = ["mandelbrot"] + fpses = [60] + gop_sizes = [600] + durations = [120] + pix_fmts = ["yuv420p"] + ffmpeg_path = "ffmpeg" + generate_videos( + resolutions, + encodings, + patterns, + fpses, + gop_sizes, + durations, + pix_fmts, + ffmpeg_path, + videos_dir_path, + ) - urls_and_dest_paths = [ - (NASA_URL, f"{videos_dir_path}/nasa_960x540_206s_30fps_yuv420p.mp4") - ] - retrieve_videos(urls_and_dest_paths) + urls_and_dest_paths = [ + (NASA_URL, f"{videos_dir_path}/nasa_960x540_206s_30fps_yuv420p.mp4") + ] + retrieve_videos(urls_and_dest_paths) decoder_dict = {} decoder_dict["torchcodec"] = TorchCodecPublic() + decoder_dict["torchcodec[cuda]"] = TorchCodecPublic(device="cuda") decoder_dict["torchvision[video_reader]"] = TorchVision("video_reader") decoder_dict["torchaudio"] = TorchAudioDecoder() # These are the number of uniform seeks we do in the seek+decode benchmark. num_samples = 10 video_files_paths = list(Path(videos_dir_path).glob("*.mp4")) + assert len(video_files_paths) == 2, "Expected exactly 2 videos" df_data = run_benchmarks( decoder_dict, video_files_paths, @@ -69,6 +76,12 @@ def main() -> None: num_sequential_frames_from_start=[100], min_runtime_seconds=30, benchmark_video_creation=False, + dataloader_parameters=DataLoaderInspiredWorkloadParameters( + batch_parameters=BatchParameters(batch_size=64, num_threads=8), + resize_height=256, + resize_width=256, + resize_device="cuda", + ), ) df_data.append( { @@ -77,6 +90,7 @@ def main() -> None: "machine": platform.machine(), "processor": platform.processor(), "python_version": str(platform.python_version()), + "is_cuda_available": str(torch.cuda.is_available()), } ) diff --git a/src/torchcodec/_samplers/video_clip_sampler.py b/src/torchcodec/_samplers/video_clip_sampler.py index 4900be53..bab8589c 100644 --- a/src/torchcodec/_samplers/video_clip_sampler.py +++ b/src/torchcodec/_samplers/video_clip_sampler.py @@ -212,7 +212,7 @@ def _get_clips_for_index_based_sampling( sample_start_index = max(0, index_based_sampler_args.sample_start_index) sample_end_index = ( min( - index_based_sampler_args.sample_end_index, + index_based_sampler_args.sample_end_index + 1, metadata_json["numFrames"], ) - index_based_sampler_args.video_frame_dilation