Add callback for enabling Tensorfloat32 (#885)

Summary:
Pull Request resolved: #885

This is something that can boost performance quite a bit with float32 training on CUDA, so I figured it'd make sense to package it up into a re-useable callback.

Reviewed By: diego-urgell

Differential Revision: D61608792

fbshipit-source-id: ccd0712c9022029bf59ee0730a71ad59feea60ae

tests/framework/callbacks/test_tensorfloat32.py

@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import contextlib
+import unittest
+from typing import Iterator
+
+import torch
+from torchtnt.framework._test_utils import (
+    DummyFitUnit,
+    DummyPredictUnit,
+    DummyTrainUnit,
+    generate_random_dataloader,
+)
+from torchtnt.framework.callback import Callback
+from torchtnt.framework.callbacks.tensorfloat32 import EnableTensorFloat32
+from torchtnt.framework.fit import fit
+from torchtnt.framework.predict import predict
+from torchtnt.framework.state import State
+from torchtnt.framework.train import train
+from torchtnt.framework.unit import TEvalUnit, TPredictUnit, TTrainUnit
+
+
+class _CheckTensorFloat32Enabled(Callback):
+    def __init__(self, testcase: unittest.TestCase) -> None:
+        self.testcase = testcase
+
+    def assert_enabled(self) -> None:
+        self.testcase.assertEqual(torch.get_float32_matmul_precision(), "high")
+        self.testcase.assertTrue(torch.backends.cudnn.allow_tf32)
+        self.testcase.assertTrue(torch.backends.cuda.matmul.allow_tf32)
+
+    def on_train_step_start(self, state: State, unit: TTrainUnit) -> None:
+        self.assert_enabled()
+
+    def on_eval_step_start(self, state: State, unit: TEvalUnit) -> None:
+        self.assert_enabled()
+
+    def on_predict_step_start(self, state: State, unit: TPredictUnit) -> None:
+        self.assert_enabled()
+
+
+class EnableTensorFloat32Test(unittest.TestCase):
+    @contextlib.contextmanager
+    def check_proper_restore(self) -> Iterator[EnableTensorFloat32]:
+        callback = EnableTensorFloat32()
+
+        # Disable TensorFloat32
+        torch.set_float32_matmul_precision("highest")
+        torch.backends.cudnn.allow_tf32 = False
+        torch.backends.cuda.matmul.allow_tf32 = False
+
+        yield callback
+
+        # Original Values are Restored
+        self.assertIsNone(callback.original_cuda_matmul)
+        self.assertIsNone(callback.original_cudnn)
+        self.assertIsNone(callback.original_float32_matmul_precision)
+
+        self.assertEqual(torch.get_float32_matmul_precision(), "highest")
+        self.assertFalse(torch.backends.cudnn.allow_tf32)
+        self.assertFalse(torch.backends.cuda.matmul.allow_tf32)
+
+    def test_tensorfloat32_callback_train(self) -> None:
+        input_dim = batch_size = max_epochs = 2
+        dataset_len = 5
+
+        unit = DummyTrainUnit(input_dim=input_dim)
+        dataloader = generate_random_dataloader(dataset_len, input_dim, batch_size)
+        with self.check_proper_restore() as callback:
+            callbacks: list[Callback] = [callback, _CheckTensorFloat32Enabled(self)]
+            train(unit, dataloader, max_epochs=max_epochs, callbacks=callbacks)
+
+    def test_tensorfloat32_callback_fit(self) -> None:
+        input_dim = batch_size = max_epochs = 2
+        dataset_len = 5
+
+        unit = DummyFitUnit(input_dim=input_dim)
+        dataloader = generate_random_dataloader(dataset_len, input_dim, batch_size)
+        with self.check_proper_restore() as callback:
+            callbacks: list[Callback] = [callback, _CheckTensorFloat32Enabled(self)]
+            fit(
+                unit,
+                dataloader,
+                dataloader,
+                max_epochs=max_epochs,
+                callbacks=callbacks,
+            )
+
+    def test_tensorfloat32_callback_predict(self) -> None:
+        input_dim = batch_size = 2
+        dataset_len = 5
+
+        unit = DummyPredictUnit(input_dim=input_dim)
+        dataloader = generate_random_dataloader(dataset_len, input_dim, batch_size)
+        with self.check_proper_restore() as callback:
+            callbacks: list[Callback] = [callback, _CheckTensorFloat32Enabled(self)]
+            predict(unit, dataloader, callbacks=callbacks)

torchtnt/framework/callbacks/__init__.py

@@ -22,6 +22,7 @@
 from .slow_rank_detector import SlowRankDetector
 from .system_resources_monitor import SystemResourcesMonitor
 from .tensorboard_parameter_monitor import TensorBoardParameterMonitor
+from .tensorfloat32 import EnableTensorFloat32
 from .throughput_logger import ThroughputLogger
 from .time_limit_interrupter import TimeLimitInterrupter
 from .time_wait_for_batch_logger import TimeWaitForBatchLogger
@@ -34,6 +35,7 @@
     "BaseCSVWriter",
     "EarlyStopping",
     "EmptyCudaCache",
+    "EnableTensorFloat32",
     "GarbageCollector",
     "IterationTimeLogger",
     "Lambda",

torchtnt/framework/callbacks/tensorfloat32.py

@@ -0,0 +1,87 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import logging
+from typing import Optional
+
+import torch
+from torchtnt.framework.callback import Callback
+from torchtnt.framework.state import EntryPoint, State
+from torchtnt.framework.unit import TEvalUnit, TPredictUnit, TTrainUnit
+from torchtnt.utils.rank_zero_log import rank_zero_info
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+class EnableTensorFloat32(Callback):
+    """
+    A callback that enables TensorFloat32 operations on CUDA.
+
+    Args:
+        float32_matmul_precision: precision to use for float32 matmul operations.
+            See `torch.set_float32_matmul_precision` for details.
+    """
+
+    def __init__(self, float32_matmul_precision: str = "high") -> None:
+        self.float32_matmul_precision = float32_matmul_precision
+
+        self.original_float32_matmul_precision: Optional[str] = None
+        self.original_cuda_matmul: Optional[bool] = None
+        self.original_cudnn: Optional[bool] = None
+
+    def _enable(self) -> None:
+        rank_zero_info("Enabling TensorFloat32 operations on CUDA", logger=logger)
+        assert self.original_float32_matmul_precision is None
+        assert self.original_cuda_matmul is None
+        assert self.original_cudnn is None
+
+        self.original_float32_matmul_precision = torch.get_float32_matmul_precision()
+        self.original_cuda_matmul = torch.backends.cuda.matmul.allow_tf32
+        self.original_cudnn = torch.backends.cudnn.allow_tf32
+
+        torch.set_float32_matmul_precision(self.float32_matmul_precision)
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+
+    def _reset(self) -> None:
+        rank_zero_info(
+            "Restoring original TensorFloat32 permissions on CUDA", logger=logger
+        )
+        if self.original_float32_matmul_precision is not None:
+            torch.set_float32_matmul_precision(self.original_float32_matmul_precision)
+            self.original_float32_matmul_precision = None
+
+        if self.original_cuda_matmul is not None:
+            torch.backends.cuda.matmul.allow_tf32 = self.original_cuda_matmul
+            self.original_cuda_matmul = None
+
+        if self.original_cudnn is not None:
+            torch.backends.cudnn.allow_tf32 = self.original_cudnn
+            self.original_cudnn = None
+
+    def on_train_start(self, state: State, unit: TTrainUnit) -> None:
+        self._enable()
+
+    def on_train_end(self, state: State, unit: TTrainUnit) -> None:
+        self._reset()
+
+    def on_eval_start(self, state: State, unit: TEvalUnit) -> None:
+        if state.entry_point == EntryPoint.FIT:
+            return  # if fitting, this is already handled in on_train_start
+        self._enable()
+
+    def on_eval_end(self, state: State, unit: TEvalUnit) -> None:
+        if state.entry_point == EntryPoint.FIT:
+            return  # if fitting, this is already handled in on_train_end
+        self._reset()
+
+    def on_predict_start(self, state: State, unit: TPredictUnit) -> None:
+        self._enable()
+
+    def on_predict_end(self, state: State, unit: TPredictUnit) -> None:
+        self._reset()