Added test for Evaluating Timing Consistency in MNIST Workload Training for PyTorch and JAX and changed the int32 to uint32

algoperf/random_utils.py

@@ -18,30 +18,30 @@
 
 # Annoyingly, RandomState(seed) requires seed to be in [0, 2 ** 31 - 1] (an
 # unsigned int), while RandomState.randint only accepts and returns signed ints.
-MAX_INT32 = 2**31 - 1
-MIN_INT32 = 0
+MAX_UINT32 = 2**31 - 1
+MIN_UINT32 = 0
 
 SeedType = Union[int, list, np.ndarray]
 
 
 def _signed_to_unsigned(seed: SeedType) -> SeedType:
   if isinstance(seed, int):
-    return seed % MAX_INT32
+    return seed % MAX_UINT32
   if isinstance(seed, list):
-    return [s % MAX_INT32 for s in seed]
+    return [s % MAX_UINT32 for s in seed]
   if isinstance(seed, np.ndarray):
-    return np.array([s % MAX_INT32 for s in seed.tolist()])
+    return np.array([s % MAX_UINT32 for s in seed.tolist()])
 
 
 def _fold_in(seed: SeedType, data: Any) -> List[Union[SeedType, Any]]:
   rng = np.random.RandomState(seed=_signed_to_unsigned(seed))
-  new_seed = rng.randint(MIN_INT32, MAX_INT32, dtype=np.int32)
+  new_seed = rng.randint(MIN_UINT32, MAX_UINT32, dtype=np.uint32)
   return [new_seed, data]
 
 
 def _split(seed: SeedType, num: int = 2) -> SeedType:
   rng = np.random.RandomState(seed=_signed_to_unsigned(seed))
-  return rng.randint(MIN_INT32, MAX_INT32, dtype=np.int32, size=[num, 2])
+  return rng.randint(MIN_UINT32, MAX_UINT32, dtype=np.uint32, size=[num, 2])
 
 
 def _PRNGKey(seed: SeedType) -> SeedType:  # pylint: disable=invalid-name

tests/test_evals_time.py

@@ -0,0 +1,127 @@
+"""
+Module for evaluating timing consistency in MNIST workload training.
+
+This script runs timing consistency tests for PyTorch and JAX implementations of an MNIST training workload.
+It ensures that the total reported training time aligns with the sum of submission, evaluation, and logging times.
+"""
+
+import os
+import sys
+import copy
+from absl import flags
+from absl.testing import absltest
+from absl.testing import parameterized
+from absl import logging
+from collections import namedtuple
+from algoperf import halton
+from algoperf import random_utils as prng
+from algoperf.profiler import PassThroughProfiler
+from algoperf.workloads import workloads
+import submission_runner
+import reference_algorithms.development_algorithms.mnist.mnist_pytorch.submission as submission_pytorch
+import reference_algorithms.development_algorithms.mnist.mnist_jax.submission as submission_jax
+import jax.random as jax_rng
+
+FLAGS = flags.FLAGS
+FLAGS(sys.argv)
+
+class Hyperparameters:
+    """
+    Defines hyperparameters for training.
+    """
+    def __init__(self):
+        self.learning_rate = 0.0005
+        self.one_minus_beta_1 = 0.05
+        self.beta2 = 0.999
+        self.weight_decay = 0.01
+        self.epsilon = 1e-25
+        self.label_smoothing = 0.1
+        self.dropout_rate = 0.1
+
+class CheckTime(parameterized.TestCase):
+    """
+    Test class to verify timing consistency in MNIST workload training.
+
+    Ensures that submission time, evaluation time, and logging time sum up to approximately the total wall-clock time.
+    """
+    rng_seed = 0
+
+    @parameterized.named_parameters(
+        dict(
+            testcase_name='mnist_pytorch',
+            framework='pytorch',
+            init_optimizer_state=submission_pytorch.init_optimizer_state,
+            update_params=submission_pytorch.update_params,
+            data_selection=submission_pytorch.data_selection,
+            rng=prng.PRNGKey(rng_seed)
+        ),
+        dict(
+            testcase_name='mnist_jax',
+            framework='jax',
+            init_optimizer_state=submission_jax.init_optimizer_state,
+            update_params=submission_jax.update_params,
+            data_selection=submission_jax.data_selection,
+            rng=jax_rng.PRNGKey(rng_seed)
+        )
+    )
+    def test_train_once_time_consistency(self, framework, init_optimizer_state, update_params, data_selection, rng):
+        """
+        Tests the consistency of timing metrics in the training process.
+
+        Ensures that:
+        - The total logged time is approximately the sum of submission, evaluation, and logging times.
+        - The expected number of evaluations occurred within the training period.
+        """
+        workload_metadata = copy.deepcopy(workloads.WORKLOADS["mnist"])
+        workload_metadata['workload_path'] = os.path.join(
+            workloads.BASE_WORKLOADS_DIR,
+            workload_metadata['workload_path'] + '_' + framework,
+            'workload.py'
+        )
+        workload = workloads.import_workload(
+            workload_path=workload_metadata['workload_path'],
+            workload_class_name=workload_metadata['workload_class_name'],
+            workload_init_kwargs={}
+        )
+
+        Hp = namedtuple("Hp", ["dropout_rate", "learning_rate", "one_minus_beta_1", "weight_decay", "beta2", "warmup_factor", "epsilon"])
+        hp1 = Hp(0.1, 0.0017486387539278373, 0.06733926164, 0.9955159689799007, 0.08121616522670176, 0.02, 1e-25)
+
+        accumulated_submission_time, metrics = submission_runner.train_once(
+            workload=workload,
+            workload_name="mnist",
+            global_batch_size=32,
+            global_eval_batch_size=256,
+            data_dir='~/tensorflow_datasets',  # Dataset location
+            imagenet_v2_data_dir=None,
+            hyperparameters=hp1,
+            init_optimizer_state=init_optimizer_state,
+            update_params=update_params,
+            data_selection=data_selection,
+            rng=rng,
+            rng_seed=0,
+            profiler=PassThroughProfiler(),
+            max_global_steps=500,
+            prepare_for_eval=None
+        )
+
+        # Calculate total logged time
+        total_logged_time = (
+            metrics['eval_results'][-1][1]['total_duration']
+            - (accumulated_submission_time +
+               metrics['eval_results'][-1][1]['accumulated_logging_time'] +
+               metrics['eval_results'][-1][1]['accumulated_eval_time'])
+        )
+
+        # Set tolerance for floating-point precision errors
+        tolerance = 10
+        self.assertAlmostEqual(total_logged_time, 0, delta=tolerance,
+                               msg="Total wallclock time does not match the sum of submission, eval, and logging times.")
+
+        # Verify expected number of evaluations
+        expected_evals = int(accumulated_submission_time // workload.eval_period_time_sec)
+        self.assertTrue(expected_evals <= len(metrics['eval_results']) + 2,
+                        f"Number of evaluations {len(metrics['eval_results'])} exceeded the expected number {expected_evals + 2}.")
+
+if __name__ == '__main__':
+    absltest.main()