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fix: tests on cpu #38

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merged 3 commits into from
Feb 19, 2024
Merged

fix: tests on cpu #38

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f4e3c56
fix: training and evaluation on CPU (for testing)
flxst Jan 30, 2024
dfbefcb
fix: get rid of reduce mocking (for testing)
flxst Jan 30, 2024
76e0518
chore: moved if statement into torch.device
lllAlexanderlll Feb 19, 2024
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14 changes: 6 additions & 8 deletions src/modalities/evaluator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,11 +45,11 @@ def evaluate(
) -> Dict[str, EvaluationResultBatch]:
result_dict: Dict[str, EvaluationResultBatch] = {}
model.eval()

device = torch.device(self.local_rank) if torch.cuda.is_available() else "cpu"
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for data_loader in data_loaders:
if torch.cuda.is_available():
cummulated_loss = torch.zeros(3).to(torch.device(self.local_rank))
else:
cummulated_loss = torch.zeros(3).to("cpu")
cummulated_loss = torch.zeros(3).to(device)

Evaluator._publish_progress(
batch_progress_publisher=self.batch_progress_publisher,
Expand All @@ -68,7 +68,7 @@ def evaluate(

cummulated_loss[0] += batch_loss.item() # sum up batch loss
cummulated_loss[1] += len(batch)
batch_length_tensor = torch.tensor(len(batch)).to(torch.device(self.local_rank))
batch_length_tensor = torch.tensor(len(batch)).to(device)
thoughput_aggregator.add_value(key=ThroughputAggregationKeys.NUM_SAMPLES, value=batch_length_tensor)

local_dataset_sample_id = Evaluator._get_local_sample_id(
Expand All @@ -90,9 +90,7 @@ def evaluate(
post_processing_fun=lambda t: t[0] / t[1],
)

foward_backward_time = torch.tensor(forward_backward_timer_recorder.delta_t).to(
torch.device(self.local_rank)
)
foward_backward_time = torch.tensor(forward_backward_timer_recorder.delta_t).to(device)
thoughput_aggregator.add_value(
key=ThroughputAggregationKeys.FORWARD_BACKWARD_TIME, value=foward_backward_time
)
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8 changes: 4 additions & 4 deletions src/modalities/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,8 @@ def train(
cummulated_loss = self._reset_loss()
thoughput_aggregator = Aggregator[ThroughputAggregationKeys]()

device = torch.device(self.local_rank) if torch.cuda.is_available() else "cpu"
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# batch loop
batch: DatasetBatch
# TODO: why do we need a barrier here?
Expand All @@ -88,7 +90,7 @@ def train(
# Save the batch loss
cummulated_loss[0] += batch_loss.item()
cummulated_loss[1] += len(batch)
batch_length_tensor = torch.tensor(len(batch)).to(torch.device(self.local_rank))
batch_length_tensor = torch.tensor(len(batch)).to(device)
thoughput_aggregator.add_value(key=ThroughputAggregationKeys.NUM_SAMPLES, value=batch_length_tensor)
self._publish_progress(
batch_progress_publisher=self.batch_progress_publisher,
Expand All @@ -101,9 +103,7 @@ def train(
# Check, if model should be evaluated
if (local_train_batch_id + 1) % callback_interval_in_batches == 0:
if local_train_batch_id > 0:
foward_backward_time = torch.tensor(forward_backward_time_recorder.delta_t).to(
torch.device(self.local_rank)
)
foward_backward_time = torch.tensor(forward_backward_time_recorder.delta_t).to(device)
forward_backward_time_recorder.reset()

thoughput_aggregator.add_value(
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The previously used mocking of the "reduce" operation led to problems below. More concretely, line 112 and line 115 both returned None (the return_value), which caused an error in line 118.

Expand Down
25 changes: 11 additions & 14 deletions tests/test_gym.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,9 @@
from unittest.mock import call, patch
from unittest.mock import call

import torch

from modalities.batch import DatasetBatch
from modalities.gym import Gym
from modalities.running_env.fsdp.reducer import Reducer


def test_run_cpu_only(
Expand Down Expand Up @@ -37,15 +36,13 @@ def test_run_cpu_only(
llm_data_loader_mock.__len__ = lambda _: num_batches

gym = Gym(trainer=trainer, evaluator=evaluator_mock, loss_fun=loss_mock, num_ranks=num_ranks)
with patch.object(Reducer, "reduce", return_value=None) as reduce_mock:
gym.run(
model=nn_model_mock,
optimizer=optimizer_mock,
callback_interval_in_batches=int(num_batches),
train_data_loader=llm_data_loader_mock,
evaluation_data_loaders=[],
checkpointing=checkpointing_mock,
)
nn_model_mock.forward.assert_has_calls([call(b.samples) for b in batches])
optimizer_mock.step.assert_called()
reduce_mock.assert_called()
gym.run(
model=nn_model_mock,
optimizer=optimizer_mock,
callback_interval_in_batches=int(num_batches),
train_data_loader=llm_data_loader_mock,
evaluation_data_loaders=[],
checkpointing=checkpointing_mock,
)
nn_model_mock.forward.assert_has_calls([call(b.samples) for b in batches])
optimizer_mock.step.assert_called()
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