Support true 16-bit precision with deepspeed in Trainer

requirements/_integrations/accelerators.txt

@@ -1,3 +1,3 @@
 # validation HPU connectors
 lightning-habana >=0.1.0
-lightning-graphcore >=0.1.0.rc3
+lightning-graphcore >=0.1.0.rc4

src/lightning/fabric/plugins/precision/deepspeed.py

@@ -70,8 +70,12 @@ def convert_module(self, module: Module) -> Module:
 
     @contextmanager
     def init_context(self) -> Generator[None, None, None]:
+        if "true" not in self.precision:
+            yield
+            return
+
         default_dtype = torch.get_default_dtype()
-        torch.set_default_dtype(self._desired_dtype if "true" in self.precision else default_dtype)
+        torch.set_default_dtype(self._desired_dtype)
         yield
         torch.set_default_dtype(default_dtype)
 

src/lightning/pytorch/CHANGELOG.md

@@ -91,7 +91,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Allowed accessing rank information in the main process before processes are launched when using the `XLAStrategy` ([#18194](https://github.com/Lightning-AI/lightning/pull/18194))
 
 
-- Added support for true half-precision training via `Trainer(precision="16-true"|"bf16-true")` ([#18193](https://github.com/Lightning-AI/lightning/pull/18193))
+- Added support for true half-precision training via `Trainer(precision="16-true"|"bf16-true")` ([#18193](https://github.com/Lightning-AI/lightning/pull/18193), [#18217](https://github.com/Lightning-AI/lightning/pull/18217))
 
 
 - Added automatic process cleanup to avoid zombie child processes and stalls when exceptions are raised ([#18218](https://github.com/Lightning-AI/lightning/pull/18218))

src/lightning/pytorch/plugins/precision/deepspeed.py

@@ -11,13 +11,19 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, cast, Literal, Optional, TYPE_CHECKING, Union
+from contextlib import contextmanager
+from typing import Any, Callable, Generator, Optional, TYPE_CHECKING, Union
 
+import torch
+from lightning_utilities import apply_to_collection
 from torch import Tensor
+from torch.nn import Module
 from torch.optim import LBFGS, Optimizer
 from typing_extensions import get_args
 
 import lightning.pytorch as pl
+from lightning.fabric.plugins.precision.deepspeed import _PRECISION_INPUT
+from lightning.fabric.plugins.precision.utils import _convert_fp_tensor
 from lightning.fabric.strategies.deepspeed import _DEEPSPEED_AVAILABLE
 from lightning.fabric.utilities.types import Steppable
 from lightning.pytorch.plugins.precision.precision_plugin import PrecisionPlugin
@@ -31,16 +37,16 @@
 
 warning_cache = WarningCache()
 
-_PRECISION_INPUT = Literal["32-true", "16-mixed", "bf16-mixed"]
-
 
 class DeepSpeedPrecisionPlugin(PrecisionPlugin):
     """Precision plugin for DeepSpeed integration.
 
     .. warning::  This is an :ref:`experimental <versioning:Experimental API>` feature.
 
     Args:
-        precision: Full precision (32), half precision (16) or bfloat16 precision (bf16).
+        precision: Full precision (32-true), half precision (16-true, bf16-true) or
+            mixed precision (16-mixed, bf16-mixed).
+
     Raises:
         ValueError:
             If unsupported ``precision`` is provided.
@@ -53,7 +59,34 @@ def __init__(self, precision: _PRECISION_INPUT) -> None:
                 f"`Trainer(strategy='deepspeed', precision={precision!r})` is not supported."
                 f" `precision` must be one of: {supported_precision}."
             )
-        self.precision = cast(_PRECISION_INPUT, str(precision))
+        self.precision = precision
+        precision_to_type = {
+            "bf16-mixed": torch.bfloat16,
+            "16-mixed": torch.float16,
+            "bf16-true": torch.bfloat16,
+            "16-true": torch.float16,
+            "32-true": torch.float32,
+        }
+        self._desired_dtype = precision_to_type[self.precision]
+
+    def convert_module(self, module: Module) -> Module:
+        if "true" in self.precision:
+            return module.to(dtype=self._desired_dtype)
+        return module
+
+    def convert_input(self, data: Any) -> Any:
+        return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=self._desired_dtype)
+
+    @contextmanager
+    def init_context(self) -> Generator[None, None, None]:
+        if "true" not in self.precision:
+            yield
+            return
+
+        default_dtype = torch.get_default_dtype()
+        torch.set_default_dtype(self._desired_dtype)
+        yield
+        torch.set_default_dtype(default_dtype)
 
     def backward(  # type: ignore[override]
         self,

src/lightning/pytorch/strategies/deepspeed.py

@@ -22,8 +22,6 @@
 from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, TYPE_CHECKING, Union
 
 import torch
-from lightning_utilities.core.apply_func import apply_to_collection
-from torch import Tensor
 from torch.nn import Module
 from torch.optim import Optimizer
 
@@ -43,7 +41,6 @@
 from lightning.pytorch.core.optimizer import _init_optimizers_and_lr_schedulers
 from lightning.pytorch.plugins.precision import PrecisionPlugin
 from lightning.pytorch.strategies.ddp import DDPStrategy
-from lightning.pytorch.strategies.utils import _fp_to_half
 from lightning.pytorch.trainer.states import TrainerFn
 from lightning.pytorch.utilities import GradClipAlgorithmType
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
@@ -894,5 +891,8 @@ def register_strategies(cls, strategy_registry: _StrategyRegistry) -> None:
         )
 
     def batch_to_device(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0) -> Any:
-        batch = apply_to_collection(batch, Tensor, function=_fp_to_half, precision=self.precision_plugin.precision)
+        # The strategy casts the input before moving to the device
+        # In all other strategies, the input gets converted in the `Strategy.*_step` methods
+        # TODO: standardize this for all strategies
+        batch = self.precision_plugin.convert_input(batch)
         return super().batch_to_device(batch, device, dataloader_idx)

src/lightning/pytorch/strategies/utils.py

@@ -13,12 +13,7 @@
 # limitations under the License.
 import importlib
 from inspect import getmembers, isclass
-from typing import Literal
 
-import torch
-from torch import Tensor
-
-from lightning.fabric.plugins.precision.utils import _convert_fp_tensor
 from lightning.fabric.strategies import _StrategyRegistry
 from lightning.fabric.utilities.registry import _is_register_method_overridden
 from lightning.pytorch.strategies.strategy import Strategy
@@ -30,19 +25,3 @@ def _call_register_strategies(registry: _StrategyRegistry, base_module: str) ->
     for _, mod in getmembers(module, isclass):
         if issubclass(mod, Strategy) and _is_register_method_overridden(mod, Strategy, "register_strategies"):
             mod.register_strategies(registry)
-
-
-def _fp_to_half(
-    tensor: Tensor,
-    precision: Literal[
-        "64-true",
-        "32-true",
-        "16-mixed",
-        "bf16-mixed",
-    ],
-) -> Tensor:
-    if str(precision) == "16-mixed":
-        return _convert_fp_tensor(tensor, torch.half)
-    if precision == "bf16-mixed":
-        return _convert_fp_tensor(tensor, torch.bfloat16)
-    return tensor

tests/tests_pytorch/plugins/precision/test_deepspeed_precision.py

@@ -13,10 +13,63 @@
 # limitations under the License.
 
 import pytest
+import torch
 
 from lightning.pytorch.plugins.precision.deepspeed import DeepSpeedPrecisionPlugin
 
 
 def test_invalid_precision_with_deepspeed_precision():
     with pytest.raises(ValueError, match="is not supported. `precision` must be one of"):
         DeepSpeedPrecisionPlugin(precision="64-true")
+
+
+@pytest.mark.parametrize(
+    ("precision", "expected_dtype"),
+    [
+        ("32-true", torch.float32),
+        ("bf16-mixed", torch.bfloat16),
+        ("16-mixed", torch.float16),
+        ("bf16-true", torch.bfloat16),
+        ("16-true", torch.float16),
+    ],
+)
+def test_selected_dtype(precision, expected_dtype):
+    plugin = DeepSpeedPrecisionPlugin(precision=precision)
+    assert plugin.precision == precision
+    assert plugin._desired_dtype == expected_dtype
+
+
+@pytest.mark.parametrize(
+    ("precision", "expected_dtype"),
+    [
+        ("32-true", torch.float32),
+        ("bf16-mixed", torch.float32),
+        ("16-mixed", torch.float32),
+        ("bf16-true", torch.bfloat16),
+        ("16-true", torch.float16),
+    ],
+)
+def test_module_init_context(precision, expected_dtype):
+    plugin = DeepSpeedPrecisionPlugin(precision=precision)
+    with plugin.init_context():
+        model = torch.nn.Linear(2, 2)
+        assert torch.get_default_dtype() == expected_dtype
+    assert model.weight.dtype == expected_dtype
+
+
+@pytest.mark.parametrize(
+    ("precision", "expected_dtype"),
+    [
+        ("32-true", torch.float32),
+        ("bf16-mixed", torch.float32),
+        ("16-mixed", torch.float32),
+        ("bf16-true", torch.bfloat16),
+        ("16-true", torch.float16),
+    ],
+)
+def test_convert_module(precision, expected_dtype):
+    precision = DeepSpeedPrecisionPlugin(precision=precision)
+    module = torch.nn.Linear(2, 2)
+    assert module.weight.dtype == module.bias.dtype == torch.float32
+    module = precision.convert_module(module)
+    assert module.weight.dtype == module.bias.dtype == expected_dtype

tests/tests_pytorch/strategies/test_deepspeed_strategy.py

@@ -1266,7 +1266,7 @@ def transfer_batch_to_device(self, batch, *args, **kwargs):
     model = CustomBoringModel()
     trainer = Trainer(strategy="deepspeed", devices=1, accelerator="cuda", precision="16-mixed")
     trainer.strategy.connect(model)
-    batch = torch.zeros((1), dtype=torch.float32)
+    batch = torch.zeros(1, dtype=torch.float32)
     batch = trainer.strategy.batch_to_device(batch)
     assert batch.is_cuda
     assert batch.dtype is torch.float16

tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py

@@ -36,6 +36,7 @@
 from lightning.pytorch.plugins.io import TorchCheckpointIO
 from lightning.pytorch.plugins.layer_sync import LayerSync, TorchSyncBatchNorm
 from lightning.pytorch.plugins.precision import (
+    DeepSpeedPrecisionPlugin,
     DoublePrecisionPlugin,
     HalfPrecisionPlugin,
     MixedPrecisionPlugin,
@@ -1015,16 +1016,21 @@ def test_connector_sets_num_nodes(strategy, cuda_count_2):
 
 
 @pytest.mark.parametrize(
-    ("precision_str", "precision_cls"),
+    ("precision_str", "strategy_str", "expected_precision_cls"),
     [
-        ("64-true", DoublePrecisionPlugin),
-        ("32-true", PrecisionPlugin),
-        ("16-true", HalfPrecisionPlugin),
-        ("bf16-true", HalfPrecisionPlugin),
-        ("16-mixed", MixedPrecisionPlugin),
-        ("bf16-mixed", MixedPrecisionPlugin),
+        ("64-true", "auto", DoublePrecisionPlugin),
+        ("32-true", "auto", PrecisionPlugin),
+        ("16-true", "auto", HalfPrecisionPlugin),
+        ("bf16-true", "auto", HalfPrecisionPlugin),
+        ("16-mixed", "auto", MixedPrecisionPlugin),
+        ("bf16-mixed", "auto", MixedPrecisionPlugin),
+        pytest.param("32-true", "deepspeed", DeepSpeedPrecisionPlugin, marks=RunIf(deepspeed=True, mps=False)),
+        pytest.param("16-true", "deepspeed", DeepSpeedPrecisionPlugin, marks=RunIf(deepspeed=True, mps=False)),
+        pytest.param("bf16-true", "deepspeed", DeepSpeedPrecisionPlugin, marks=RunIf(deepspeed=True, mps=False)),
+        pytest.param("16-mixed", "deepspeed", DeepSpeedPrecisionPlugin, marks=RunIf(deepspeed=True, mps=False)),
+        pytest.param("bf16-mixed", "deepspeed", DeepSpeedPrecisionPlugin, marks=RunIf(deepspeed=True, mps=False)),
     ],
 )
-def test_precision_selection(precision_str, precision_cls):
-    connector = _AcceleratorConnector(precision=precision_str)
-    assert isinstance(connector.precision_plugin, precision_cls)
+def test_precision_selection(precision_str, strategy_str, expected_precision_cls):
+    connector = _AcceleratorConnector(precision=precision_str, strategy=strategy_str)
+    assert isinstance(connector.precision_plugin, expected_precision_cls)