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Adding TensorBoard logging for inference metrics #283

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Merged
entrpn merged 8 commits into from
Nov 18, 2025
Merged

Adding TensorBoard logging for inference metrics #283

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fd36989
Added tensorboard logging for inference metrics
prishajain1 Nov 16, 2025
cf9cdd6
Removed config.tensorboard_dir
prishajain1 Nov 16, 2025
6a7e4a2
Added tokamax as a requirement in requirements_with_jax_ai_image.txt
prishajain1 Nov 16, 2025
22367e2
Added logging for model details
prishajain1 Nov 17, 2025
dfcd0c0
Added logging for model details
prishajain1 Nov 17, 2025
4e4d799
Adding Tensorboard logging for inference metrics
prishajain1 Nov 18, 2025
448ee98
Adding Tensorboard logging for inference metrics
prishajain1 Nov 18, 2025
5f458fd
Adding Tensorboard logging for inference metrics
prishajain1 Nov 18, 2025
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1 change: 1 addition & 0 deletions requirements_with_jax_ai_image.txt
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Expand Up @@ -30,6 +30,7 @@ orbax-checkpoint
tokenizers==0.21.0
huggingface_hub>=0.30.2
transformers==4.48.1
tokamax
einops==0.8.0
sentencepiece
aqtp
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40 changes: 36 additions & 4 deletions src/maxdiffusion/generate_wan.py
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Expand Up @@ -141,6 +141,11 @@ def inference_generate_video(config, pipeline, filename_prefix=""):
def run(config, pipeline=None, filename_prefix=""):
print("seed: ", config.seed)
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model_key = config.model_name
tensorboard_dir = os.path.join(config.output_dir, "tensorboard")
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# Initialize TensorBoard writer
writer = max_utils.initialize_summary_writer(config)
if jax.process_index() == 0 and writer:
max_logging.log(f"TensorBoard logs will be written to: {tensorboard_dir}")

checkpointer_lib = get_checkpointer(model_key)
WanCheckpointer = checkpointer_lib.WanCheckpointer
Expand All @@ -163,8 +168,19 @@ def run(config, pipeline=None, filename_prefix=""):
)

videos = call_pipeline(config, pipeline, prompt, negative_prompt)

print("compile time: ", (time.perf_counter() - s0))
print("===================== Model details =======================")
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print("model name: ", config.model_name)
print("model path: ", config.pretrained_model_name_or_path)
print("model type: t2v")
print("hardware: ", jax.devices()[0].platform)
print("number of devices: ", jax.device_count())
print("per_device_batch_size: ", config.per_device_batch_size)
print("============================================================")

compile_time = time.perf_counter() - s0
print("compile_time: ", compile_time)
if writer and jax.process_index() == 0:
writer.add_scalar("inference/compile_time", compile_time, global_step=0)
saved_video_path = []
for i in range(len(videos)):
video_path = f"{filename_prefix}wan_output_{config.seed}_{i}.mp4"
Expand All @@ -175,14 +191,30 @@ def run(config, pipeline=None, filename_prefix=""):

s0 = time.perf_counter()
videos = call_pipeline(config, pipeline, prompt, negative_prompt)
print("generation time: ", (time.perf_counter() - s0))
generation_time = time.perf_counter() - s0
print("generation_time: ", generation_time)
if writer and jax.process_index() == 0:
writer.add_scalar("inference/generation_time", generation_time, global_step=0)
num_devices = jax.device_count()
num_videos = num_devices * config.per_device_batch_size
if num_videos > 0:
generation_time_per_video = generation_time / num_videos
writer.add_scalar("inference/generation_time_per_video", generation_time_per_video, global_step=0)
print(f"generation time per video: {generation_time_per_video}")
else:
max_logging.log("Warning: Number of videos is zero, cannot calculate generation_time_per_video.")


s0 = time.perf_counter()
if config.enable_profiler:
max_utils.activate_profiler(config)
videos = call_pipeline(config, pipeline, prompt, negative_prompt)
max_utils.deactivate_profiler(config)
print("generation time: ", (time.perf_counter() - s0))
generation_time_with_profiler = time.perf_counter() - s0
print("generation_time_with_profiler: ", generation_time_with_profiler)
if writer and jax.process_index() == 0:
writer.add_scalar("inference/generation_time_with_profiler", generation_time_with_profiler, global_step=0)

return saved_video_path


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