Introduce new precision layout in fabric (#16767)

docs/source-pytorch/fabric/api/fabric_args.rst

@@ -112,23 +112,27 @@ Learn more about :ref:`distributed multi-node training on clusters <Fabric Clust
 precision
 =========
 
-Fabric supports double precision (64), full precision (32), or half-precision (16) operation (including `bfloat16 <https://pytorch.org/docs/1.10.0/generated/torch.Tensor.bfloat16.html>`_).
+Fabric supports double precision (64 bit), full precision (32 bit), or half-precision (16 bit) floating point operation (including `bfloat16 <https://pytorch.org/docs/1.10.0/generated/torch.Tensor.bfloat16.html>`_).
 Half precision, or mixed precision, combines 32 and 16-bit floating points to reduce the memory footprint during model training.
+Automatic mixed precision settings are denoted by a ``"-mixed"`` suffix, while settings that only work in the specified precision have a ``"-true"`` suffix.
 This can result in improved performance, achieving significant speedups on modern GPUs.
 
 .. code-block:: python
 
     # Default used by the Fabric
-    fabric = Fabric(precision=32, devices=1)
+    fabric = Fabric(precision="32-true", devices=1)
+
+    # the same as:
+    fabric = Fabric(precision="32", devices=1)
 
     # 16-bit (mixed) precision
-    fabric = Fabric(precision=16, devices=1)
+    fabric = Fabric(precision="16-mixed", devices=1)
 
     # 16-bit bfloat precision
-    fabric = Fabric(precision="bf16", devices=1)
+    fabric = Fabric(precision="bf16-mixed", devices=1)
 
     # 64-bit (double) precision
-    fabric = Fabric(precision=64, devices=1)
+    fabric = Fabric(precision="64-true", devices=1)
 
 See also: :doc:`../fundamentals/precision`
 

docs/source-pytorch/fabric/fundamentals/launch.rst

@@ -68,9 +68,13 @@ This is essentially the same as running ``python path/to/your/script.py``, but i
       --main-port, --main_port INTEGER
                                       The main port to connect to the main
                                       machine.
-      --precision [64|32|16|bf16]     Double precision (``64``), full precision
-                                      (``32``), half precision (``16``) or
-                                      bfloat16 precision (``'bf16'``)
+      --precision [16-mixed|bf16-mixed|32-true|64-true|64|32|16|bf16]
+                                      Double precision (``64-true`` or ``64``),
+                                      full precision (``32-true`` or ``64``), half
+                                      precision (``16-mixed`` or ``16``) or
+                                      bfloat16 precision (``bf16-mixed`` or
+                                      ``bf16``)
+
       --help                          Show this message and exit.
 
 

docs/source-pytorch/fabric/fundamentals/precision.rst

@@ -24,26 +24,35 @@ This is how you select the precision in Fabric:
     from lightning.fabric import Fabric
 
     # This is the default
+    fabric = Fabric(precision="32-true")
+
+    # Also FP32
     fabric = Fabric(precision=32)
 
-    # FP16 mixed precision
-    fabric = Fabric(precision=16)
+    # FP32 as well
+    fabric = Fabric(precision="32")
 
-    # Precision values can also be set as a string
-    fabric = Fabric(precision="16")
+    # FP16 mixed precision
+    fabric = Fabric(precision="16-mixed)
 
     # BFloat16 precision (Volta GPUs and later)
-    fabric = Fabric(precision="bf16")
+    fabric = Fabric(precision="bf16-mixed")
 
     # Double precision
+    fabric = Fabric(precision="64-true")
+
+    # Or
+    fabric = Fabric(precision="64")
+
+    # Or
     fabric = Fabric(precision=64)
 
 
 The same values can also be set through the :doc:`command line interface <launch>`:
 
 .. code-block:: bash
 
-    lightning run model train.py --precision=bf16
+    lightning run model train.py --precision=bf16-mixed
 
 
 .. note::
@@ -70,14 +79,11 @@ This is how you enable FP16 in Fabric:
 .. code-block:: python
 
     # Select FP16 mixed precision
-    fabric = Fabric(precision=16)
-
-    # Or as a string
-    fabric = Fabric(precision="16")
+    fabric = Fabric(precision="16-mixed")
 
 .. note::
 
-    When using TPUs, setting ``precision=16`` will enable bfloat16, the only supported half-precision type on TPUs.
+    When using TPUs, setting ``precision="16-mixed"`` will enable bfloat16 based mixed precision, the only supported half-precision type on TPUs.
 
 
 ----
@@ -94,7 +100,7 @@ For more information, see `this TPU performance blog post <https://cloud.google.
 .. code-block:: python
 
     # Select BF16 precision
-    fabric = Fabric(precision="bf16")
+    fabric = Fabric(precision="bf16-mixed")
 
 
 Under the hood, we use `torch.autocast <https://pytorch.org/docs/stable/amp.html>`__ with the dtype set to ``bfloat16``, with no gradient scaling.
@@ -117,7 +123,7 @@ Fabric automatically casts the data type and operations in the ``forward`` of yo
 
 .. code-block:: python
 
-    fabric = Fabric(precision="bf16")
+    fabric = Fabric(precision="bf16-mixed")
 
     model = ...
     optimizer = ...

docs/source-pytorch/fabric/guide/multi_node/cloud.rst

@@ -50,7 +50,7 @@ Launch multi-node training in the cloud
         def run(self):
             # Set up Fabric
             # The `devices` and `num_nodes` gets set by Lightning automatically
-            fabric = L.Fabric(strategy="ddp", precision=16)
+            fabric = L.Fabric(strategy="ddp", precision="16-mixed")
 
             # Your training code
             model = ...

src/lightning/fabric/CHANGELOG.md

@@ -38,6 +38,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Renamed `strategy='tpu_spawn'` to `strategy='xla'` and `strategy='tpu_spawn_debug'` to `strategy='xla_debug'` ([#16781](https://github.com/Lightning-AI/lightning/pull/16781))
 
 
+- Changed arguments for precision settings (from [64|32|16|bf16] to ["64-true"|"32-true"|"16-mixed"|"bf16-mixed"]) ([#16767](https://github.com/Lightning-AI/lightning/pull/16767))
+
 ### Deprecated
 
 -

src/lightning/fabric/cli.py

@@ -18,8 +18,10 @@
 from typing import Any, List, Optional
 
 from lightning_utilities.core.imports import RequirementCache
+from typing_extensions import get_args
 
 from lightning.fabric.accelerators import CPUAccelerator, CUDAAccelerator, MPSAccelerator
+from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT_STR, _PRECISION_INPUT_STR_ALIAS
 from lightning.fabric.strategies import STRATEGY_REGISTRY
 from lightning.fabric.utilities.device_parser import _parse_gpu_ids
 
@@ -28,7 +30,6 @@
 _CLICK_AVAILABLE = RequirementCache("click")
 
 _SUPPORTED_ACCELERATORS = ("cpu", "gpu", "cuda", "mps", "tpu")
-_SUPPORTED_PRECISION = ("64", "32", "16", "bf16")
 
 
 def _get_supported_strategies() -> List[str]:
@@ -106,11 +107,11 @@ def _get_supported_strategies() -> List[str]:
     )
     @click.option(
         "--precision",
-        type=click.Choice(_SUPPORTED_PRECISION),
-        default="32",
+        type=click.Choice(get_args(_PRECISION_INPUT_STR) + get_args(_PRECISION_INPUT_STR_ALIAS)),
+        default="32-true",
         help=(
-            "Double precision (``64``), full precision (``32``), half precision (``16``) or bfloat16 precision"
-            " (``'bf16'``)"
+            "Double precision (``64-true`` or ``64``), full precision (``32-true`` or ``64``), "
+            "half precision (``16-mixed`` or ``16``) or bfloat16 precision (``bf16-mixed`` or ``bf16``)"
         ),
     )
     @click.argument("script_args", nargs=-1, type=click.UNPROCESSED)

src/lightning/fabric/connector.py

@@ -42,7 +42,13 @@
 )
 from lightning.fabric.plugins.precision.double import DoublePrecision
 from lightning.fabric.plugins.precision.fsdp import FSDPPrecision
-from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT, _PRECISION_INPUT_INT, _PRECISION_INPUT_STR
+from lightning.fabric.plugins.precision.precision import (
+    _PRECISION_INPUT,
+    _PRECISION_INPUT_INT,
+    _PRECISION_INPUT_STR,
+    _PRECISION_INPUT_STR_ALIAS,
+    _PRECISION_INPUT_STR_ALIAS_CONVERSION,
+)
 from lightning.fabric.strategies import (
     DeepSpeedStrategy,
     ParallelStrategy,
@@ -98,7 +104,7 @@ def __init__(
         strategy: Optional[Union[str, Strategy]] = None,
         devices: Optional[Union[List[int], str, int]] = None,
         num_nodes: int = 1,
-        precision: _PRECISION_INPUT = 32,
+        precision: _PRECISION_INPUT = "32-true",
         plugins: Optional[Union[_PLUGIN_INPUT, List[_PLUGIN_INPUT]]] = None,
     ) -> None:
 
@@ -107,7 +113,7 @@ def __init__(
         strategy = self._argument_from_env("strategy", strategy, default=None)
         devices = self._argument_from_env("devices", devices, default=None)
         num_nodes = self._argument_from_env("num_nodes", num_nodes, default=1)
-        precision = self._argument_from_env("precision", precision, default=32)
+        precision = self._argument_from_env("precision", precision, default="32-true")
 
         # 1. Parsing flags
         # Get registered strategies, built-in accelerators and precision plugins
@@ -119,7 +125,7 @@ def __init__(
         # For devices: Assign gpus, etc. to the accelerator flag and devices flag
         self._strategy_flag: Optional[Union[Strategy, str]] = None
         self._accelerator_flag: Optional[Union[Accelerator, str]] = None
-        self._precision_input: _PRECISION_INPUT_STR = "32"
+        self._precision_input: _PRECISION_INPUT_STR = "32-true"
         self._precision_instance: Optional[Precision] = None
         self._cluster_environment_flag: Optional[Union[ClusterEnvironment, str]] = None
         self._parallel_devices: List[Union[int, torch.device, str]] = []
@@ -220,10 +226,7 @@ def _check_config_and_set_final_flags(
 
         self._accelerator_flag = accelerator
 
-        supported_precision = get_args(_PRECISION_INPUT_STR) + get_args(_PRECISION_INPUT_INT)
-        if precision not in supported_precision:
-            raise ValueError(f"Precision {repr(precision)} is invalid. Allowed precision values: {supported_precision}")
-        self._precision_input = cast(_PRECISION_INPUT_STR, str(precision))
+        self._precision_input = _convert_precision_to_unified_args(precision)
 
         if plugins:
             plugins_flags_types: Dict[str, int] = Counter()
@@ -453,34 +456,34 @@ def _check_and_init_precision(self) -> Precision:
             return self._precision_instance
 
         if isinstance(self.accelerator, TPUAccelerator):
-            if self._precision_input == "32":
+            if self._precision_input == "32-true":
                 return TPUPrecision()
-            elif self._precision_input in ("16", "bf16"):
-                if self._precision_input == "16":
+            elif self._precision_input in ("16-mixed", "bf16-mixed"):
+                if self._precision_input == "16-mixed":
                     rank_zero_warn(
-                        "You passed `Fabric(accelerator='tpu', precision=16)` but AMP"
-                        " is not supported with TPUs. Using `precision='bf16'` instead."
+                        "You passed `Fabric(accelerator='tpu', precision='16-mixed')` but AMP with fp16"
+                        " is not supported with TPUs. Using `precision='bf16-mixed'` instead."
                     )
                 return TPUBf16Precision()
         if isinstance(self.strategy, DeepSpeedStrategy):
             return DeepSpeedPrecision(self._precision_input)  # type: ignore
 
-        if self._precision_input == "32":
+        if self._precision_input == "32-true":
             return Precision()
-        if self._precision_input == "64":
+        if self._precision_input == "64-true":
             return DoublePrecision()
 
-        if self._precision_input == "16" and self._accelerator_flag == "cpu":
+        if self._precision_input == "16-mixed" and self._accelerator_flag == "cpu":
             rank_zero_warn(
-                "You passed `Fabric(accelerator='cpu', precision=16)` but AMP is not supported on CPU."
-                " Using `precision='bf16'` instead."
+                "You passed `Fabric(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on "
+                "CPU. Using `precision='bf16-mixed'` instead."
             )
-            self._precision_input = "bf16"
+            self._precision_input = "bf16-mixed"
 
-        if self._precision_input in ("16", "bf16"):
+        if self._precision_input in ("16-mixed", "bf16-mixed"):
             rank_zero_info(
                 "Using 16-bit Automatic Mixed Precision (AMP)"
-                if self._precision_input == "16"
+                if self._precision_input == "16-mixed"
                 else "Using bfloat16 Automatic Mixed Precision (AMP)"
             )
             device = "cpu" if self._accelerator_flag == "cpu" else "cuda"
@@ -494,9 +497,9 @@ def _check_and_init_precision(self) -> Precision:
     def _validate_precision_choice(self) -> None:
         """Validate the combination of choices for precision, and accelerator."""
         if isinstance(self.accelerator, TPUAccelerator):
-            if self._precision_input == "64":
+            if self._precision_input == "64-true":
                 raise NotImplementedError(
-                    "`Fabric(accelerator='tpu', precision=64)` is not implemented."
+                    "`Fabric(accelerator='tpu', precision='64-true')` is not implemented."
                     " Please, open an issue in `https://github.com/Lightning-AI/lightning/issues`"
                     " requesting this feature."
                 )
@@ -561,3 +564,22 @@ def _argument_from_env(name: str, current: Any, default: Any) -> Any:
         if env_value is None:
             return current
         return env_value
+
+
+def _convert_precision_to_unified_args(precision: _PRECISION_INPUT) -> _PRECISION_INPUT_STR:
+    supported_precision = (
+        get_args(_PRECISION_INPUT_STR) + get_args(_PRECISION_INPUT_INT) + get_args(_PRECISION_INPUT_STR_ALIAS)
+    )
+    if precision not in supported_precision:
+        raise ValueError(f"Precision {repr(precision)} is invalid. Allowed precision values: {supported_precision}")
+
+    precision = str(precision)  # convert int flags to str here to enable the legacy-conversion below
+
+    if precision in get_args(_PRECISION_INPUT_STR_ALIAS):
+        if str(precision)[:2] not in ("32", "64"):
+            rank_zero_warn(
+                f"{precision} is supported for historical reasons but its usage is discouraged. "
+                f"Please set your precision to {_PRECISION_INPUT_STR_ALIAS_CONVERSION[precision]} instead!"
+            )
+        precision = _PRECISION_INPUT_STR_ALIAS_CONVERSION[precision]
+    return cast(_PRECISION_INPUT_STR, precision)

src/lightning/fabric/fabric.py

@@ -67,8 +67,8 @@ class Fabric:
         devices: Number of devices to train on (``int``), which GPUs to train on (``list`` or ``str``), or ``"auto"``.
             The value applies per node.
         num_nodes: Number of GPU nodes for distributed training.
-        precision: Double precision (``64``), full precision (``32``), half precision (``16``),
-            or bfloat16 precision (``"bf16"``).
+        precision: Double precision (``"64-true"``), full precision (``"32"``), half precision AMP (``"16-mixed"``),
+            or bfloat16 precision AMP (``"bf16-mixed"``).
         plugins: One or several custom plugins
         callbacks: A single callback or a list of callbacks. A callback can contain any arbitrary methods that
             can be invoked through :meth:`~lightning.fabric.fabric.Fabric.call` by the user.
@@ -82,7 +82,7 @@ def __init__(
         strategy: Optional[Union[str, Strategy]] = None,
         devices: Optional[Union[List[int], str, int]] = None,
         num_nodes: int = 1,
-        precision: _PRECISION_INPUT = 32,
+        precision: _PRECISION_INPUT = "32-true",
         plugins: Optional[Union[_PLUGIN_INPUT, List[_PLUGIN_INPUT]]] = None,
         callbacks: Optional[Union[List[Any], Any]] = None,
         loggers: Optional[Union[Logger, List[Logger]]] = None,

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

@@ -29,21 +29,24 @@ class MixedPrecision(Precision):
     """Plugin for Automatic Mixed Precision (AMP) training with ``torch.autocast``.
 
     Args:
-        precision: Whether to use ``torch.float16`` (``16``) or ``torch.bfloat16`` (``'bf16'``).
+        precision: Whether to use ``torch.float16`` (``'16-mixed'``) or ``torch.bfloat16`` (``'bf16-mixed'``).
         device: The device for ``torch.autocast``.
         scaler: An optional :class:`torch.cuda.amp.GradScaler` to use.
     """
 
     def __init__(
-        self, precision: Literal["16", 16, "bf16"], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None
+        self,
+        precision: Literal["16-mixed", "bf16-mixed"],
+        device: str,
+        scaler: Optional[torch.cuda.amp.GradScaler] = None,
     ) -> None:
-        self.precision = cast(Literal["16", "bf16"], str(precision))
-        if scaler is None and self.precision == "16":
+        self.precision = cast(Literal["16-mixed", "bf16-mixed"], str(precision))
+        if scaler is None and self.precision == "16-mixed":
             with _patch_cuda_is_available():
                 # if possible, we defer CUDA initialization to support strategies that will attempt forks
                 scaler = torch.cuda.amp.GradScaler()
-        if scaler is not None and self.precision == "bf16":
-            raise ValueError(f"`precision='bf16'` does not use a scaler, found {scaler}.")
+        if scaler is not None and self.precision == "bf16-mixed":
+            raise ValueError(f"`precision='bf16-mixed'` does not use a scaler, found {scaler}.")
         self.device = device
         self.scaler = scaler
 
@@ -53,7 +56,7 @@ def forward_context(self) -> Generator[None, None, None]:
             yield
 
     def convert_input(self, data: Tensor) -> Tensor:
-        precision_to_type = {"bf16": torch.bfloat16, "16": torch.float16}
+        precision_to_type = {"bf16-mixed": torch.bfloat16, "16-mixed": torch.float16}
         dst_type = precision_to_type[self.precision]
         return _convert_fp_tensor(data, dst_type)
 
@@ -89,4 +92,4 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
     def _autocast_context_manager(self) -> torch.autocast:
         # the dtype could be automatically inferred but we need to manually set it due to a bug upstream
         # https://github.com/pytorch/pytorch/issues/67233
-        return torch.autocast(self.device, dtype=torch.bfloat16 if self.precision == "bf16" else torch.half)
+        return torch.autocast(self.device, dtype=torch.bfloat16 if self.precision == "bf16-mixed" else torch.half)