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talmolab / sleap

A deep learning framework for multi-animal pose tracking.
https://sleap.ai
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Use custom modulo implementation #1990

Closed roomrys closed 1 month ago

roomrys commented 1 month ago

Description

UPDATE: It seems this is actually caused by this error:

Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice

since that is where the JIT libraries are located. This could also explain the ~10X slowdown that was noted in the #1989 Windows test.

While testing the package build in #1989, I ran into a JIT error during inference, specifically on the line where we call tf.math.mod.

To avoid the following error:

    File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\peak_finding.py", line 224, in find_global_peaks_rough
      channel_subs = tf.math.mod(tf.range(total_peaks, dtype=tf.int64), channels)
Node: 'FloorMod'
2 root error(s) found.
  (0) UNKNOWN:  JIT compilation failed.
         [[{{node FloorMod}}]]
         [[top_down_inference_model/find_instance_peaks_1/RaggedFromValueRowIds_1/RowPartitionFromValueRowIds/ArithmeticOptimizer/ReorderCastLikeAndValuePreserving_int32_control_dependency/_434]]
  (1) UNKNOWN:  JIT compilation failed.
         [[{{node FloorMod}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_predict_function_34022]
INFO:sleap.nn.callbacks:Closing the reporter controller/context.
INFO:sleap.nn.callbacks:Closing the training controller socket/context.

this PR implements a custom modulo from the formula floor(x / y) * y + mod(x, y) = x.

The tests/nn/test_peak_finding.py::test_find_global_peaks_rough catches this locally. It is a bit strange that our CI tests did not notify us.

Full Traceback ``` 2024-10-09 13:51:03.242665: W tensorflow/core/framework/op_kernel.cc:1733] UNKNOWN: JIT compilation failed. Predicting... ---------------------------------------- 0% ETA: -:--:-- ? Traceback (most recent call last): File "\\?\C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\Scripts\sleap-train-script.py", line 33, in sys.exit(load_entry_point('sleap==1.4.1a3', 'console_scripts', 'sleap-train')()) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 2039, in main trainer.train() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 953, in train self.evaluate() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 961, in evaluate sleap.nn.evals.evaluate_model( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\evals.py", line 744, in evaluate_model labels_pr: Labels = predictor.predict(labels_gt, make_labels=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 527, in predict self._make_labeled_frames_from_generator(generator, data) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2645, in _make_labeled_frames_from_generator for ex in generator: File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 437, in _predict_generator ex = process_batch(ex) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 400, in process_batch preds = self.inference_model.predict_on_batch(ex, numpy=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 1070, in predict_on_batch outs = super().predict_on_batch(data, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 2230, in predict_on_batch outputs = self.predict_function(iterator) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.UnknownError: Graph execution error: Detected at node 'FloorMod' defined at (most recent call last): File "\\?\C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\Scripts\sleap-train-script.py", line 33, in sys.exit(load_entry_point('sleap==1.4.1a3', 'console_scripts', 'sleap-train')()) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 2039, in main trainer.train() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 953, in train self.evaluate() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 961, in evaluate sleap.nn.evals.evaluate_model( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\evals.py", line 744, in evaluate_model labels_pr: Labels = predictor.predict(labels_gt, make_labels=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 527, in predict self._make_labeled_frames_from_generator(generator, data) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2645, in _make_labeled_frames_from_generator for ex in generator: File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 437, in _predict_generator ex = process_batch(ex) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 400, in process_batch preds = self.inference_model.predict_on_batch(ex, numpy=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 1070, in predict_on_batch outs = super().predict_on_batch(data, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 2230, in predict_on_batch outputs = self.predict_function(iterator) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1845, in predict_function return step_function(self, iterator) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1834, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1823, in run_step outputs = model.predict_step(data) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1791, in predict_step return self(x, training=False) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 490, in __call__ return super().__call__(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2267, in call if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth): File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2276, in call peaks_output = self.instance_peaks(crop_output) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2112, in call if self.offsets_ind is None: File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2114, in call peak_points, peak_vals = sleap.nn.peak_finding.find_global_peaks( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\peak_finding.py", line 366, in find_global_peaks rough_peaks, peak_vals = find_global_peaks_rough( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\peak_finding.py", line 224, in find_global_peaks_rough channel_subs = tf.math.mod(tf.range(total_peaks, dtype=tf.int64), channels) Node: 'FloorMod' Detected at node 'FloorMod' defined at (most recent call last): File "\\?\C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\Scripts\sleap-train-script.py", line 33, in sys.exit(load_entry_point('sleap==1.4.1a3', 'console_scripts', 'sleap-train')()) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 2039, in main trainer.train() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 953, in train self.evaluate() File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\training.py", line 961, in evaluate sleap.nn.evals.evaluate_model( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\evals.py", line 744, in evaluate_model labels_pr: Labels = predictor.predict(labels_gt, make_labels=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 527, in predict self._make_labeled_frames_from_generator(generator, data) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2645, in _make_labeled_frames_from_generator for ex in generator: File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 437, in _predict_generator ex = process_batch(ex) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 400, in process_batch preds = self.inference_model.predict_on_batch(ex, numpy=True) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 1070, in predict_on_batch outs = super().predict_on_batch(data, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 2230, in predict_on_batch outputs = self.predict_function(iterator) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1845, in predict_function return step_function(self, iterator) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1834, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1823, in run_step outputs = model.predict_step(data) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 1791, in predict_step return self(x, training=False) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\training.py", line 490, in __call__ return super().__call__(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2267, in call if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth): File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2276, in call peaks_output = self.instance_peaks(crop_output) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler return fn(*args, **kwargs) File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2112, in call if self.offsets_ind is None: File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\inference.py", line 2114, in call peak_points, peak_vals = sleap.nn.peak_finding.find_global_peaks( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\peak_finding.py", line 366, in find_global_peaks rough_peaks, peak_vals = find_global_peaks_rough( File "C:\Users\TalmoLab\mambaforge\envs\sleap_1.4.1a3_py310\lib\site-packages\sleap\nn\peak_finding.py", line 224, in find_global_peaks_rough channel_subs = tf.math.mod(tf.range(total_peaks, dtype=tf.int64), channels) Node: 'FloorMod' 2 root error(s) found. (0) UNKNOWN: JIT compilation failed. [[{{node FloorMod}}]] [[top_down_inference_model/find_instance_peaks_1/RaggedFromValueRowIds_1/RowPartitionFromValueRowIds/ArithmeticOptimizer/ReorderCastLikeAndValuePreserving_int32_control_dependency/_434]] (1) UNKNOWN: JIT compilation failed. [[{{node FloorMod}}]] 0 successful operations. 0 derived errors ignored. [Op:__inference_predict_function_34022] INFO:sleap.nn.callbacks:Closing the reporter controller/context. INFO:sleap.nn.callbacks:Closing the training controller socket/context. ```

Types of changes

Does this address any currently open issues?

Outside contributors checklist

Thank you for contributing to SLEAP!

:heart:

Summary by CodeRabbit

codecov[bot] commented 1 month ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 75.51%. Comparing base (7ed1229) to head (3869d5d). Report is 56 commits behind head on develop.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## develop #1990 +/- ## =========================================== + Coverage 73.30% 75.51% +2.20% =========================================== Files 134 133 -1 Lines 24087 24636 +549 =========================================== + Hits 17658 18604 +946 + Misses 6429 6032 -397 ```

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.

coderabbitai[bot] commented 1 month ago

Walkthrough

The changes in this pull request focus on the find_global_peaks_rough function within the sleap/nn/peak_finding.py module. The primary modification involves replacing the use of tf.math.mod with a custom modulo calculation using integer division to avoid JIT compilation errors. This adjustment affects the computation of channel indices for identifying global peaks in confidence maps. Additionally, minor formatting changes were made for consistency, but they do not impact the code's functionality.

Changes

File Change Summary
sleap/nn/peak_finding.py Modified find_global_peaks_rough to implement a custom modulo operation for JIT compatibility; minor formatting adjustments made.

Possibly related PRs

Poem

In the code where peaks do rise,
A modulo fix, a clever surprise!
With JIT now free from its old plight,
Channels align, all feels just right.
So hop along, let errors flee,
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roomrys commented 1 month ago

It seems this is actually caused by this error:

Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice

since that is where the JIT libraries are located. This could also explain the ~10X slowdown that was noted in the #1989 Windows test.

Setting the following environment variable in (9d42b) solved the JIT compilation error, but not the slowdown:

set XLA_FLAGS=--xla_gpu_cuda_data_dir=%CONDA_PREFIX%

. Testing on Linux to see if there is also a slowdown there. There was no slowdown on Linux, but we also discovered that a slower model was being trained on Windows. Linux is still generally faster, but not such a terrible slowdown.