..

Author: harneet862

tests/test_evals_time.py

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+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
+import json
+import jax
+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
+# try:
+#   import jax.random as jax_rng
+# except (ImportError, ModuleNotFoundError):
+#   logging.warning(
+#       'Could not import jax.random for the submission runner, falling back to '
+#       'numpy random_utils.')
+#   jax_rng = None
+
+FLAGS = flags.FLAGS
+FLAGS(sys.argv)
+
+class Hyperparameters:
+    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):
+    """Tests to check if submission_time + eval_time + logging_time ~ total _wallclock_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.random.PRNGKey(rng_seed),),
+            rng = prng.PRNGKey(rng_seed),),
+        ]
+    )
+    def test_train_once_time_consistency(self, framework, init_optimizer_state, update_params, data_selection, rng):
+        """Test to check the consistency of timing metrics."""
+        rng_seed = 0
+        #rng = jax.random.PRNGKey(rng_seed)
+        #rng, _ = prng.split(rng, 2)
+        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)
+        # HPARAMS = {
+        #     "dropout_rate": 0.1,
+        #     "learning_rate": 0.0017486387539278373,
+        #     "one_minus_beta_1": 0.06733926164,
+        #     "beta2": 0.9955159689799007,
+        #     "weight_decay": 0.08121616522670176,
+        #     "warmup_factor": 0.02,
+        #     "epsilon" : 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', # not sure
+            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)
+        
+    
+        # Example: Check if total time roughly equals to submission_time + eval_time + logging_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']))
+
+        # Use a tolerance for floating-point arithmetic
+        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.")
+
+        # Check if the expected number of evaluations occurred
+        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()