[Paddle] Refactor FP8 state (#350)

Refactor fp8 state
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

transformer_engine/paddle/fp8.py

@@ -3,6 +3,7 @@
 # See LICENSE for license information.
 """FP8 utilities for TransformerEngine"""
 
+import copy
 from contextlib import contextmanager
 from typing import Tuple, Optional, Dict, Any
 
@@ -15,10 +16,6 @@ from transformer_engine.common.recipe import DelayedScaling, Format
 # FP8 support
 _is_fp8_available = None
 _reason_for_no_fp8 = ""
-# FP8 status
-_FP8_ENABLED = False
-_FP8_CALIBRATION = False
-_FP8_RECIPE = None
 
 
 def _check_fp8_support() -> Tuple[bool, str]:
@@ -49,29 +46,42 @@ def is_fp8_available() -> Tuple[bool, str]:
     return _is_fp8_available, _reason_for_no_fp8
 
 
-# Functions used to access fp8 status
-def is_fp8_enabled() -> bool:
-    """Is FP8 enabled"""
-    return _FP8_ENABLED
+class FP8State:
+    """Stores FP8 state"""
 
+    def __init__(self):
+        self.fp8_enabled = False
+        self.fp8_calibration = False
+        self.fp8_recipe = None
 
-def is_fp8_calibration() -> bool:
-    """Is FP8 calibration"""
-    return _FP8_CALIBRATION
+    def is_fp8_enabled(self) -> bool:
+        """Is FP8 enabled"""
+        return self.fp8_enabled
 
+    def is_fp8_calibration(self) -> bool:
+        """Is FP8 calibration"""
+        return self.fp8_calibration
 
-def get_fp8_recipe() -> DelayedScaling:
+    def get_fp8_recipe(self) -> DelayedScaling:
         """Return the fp8 recipe"""
-    return _FP8_RECIPE
-
+        return self.fp8_recipe
 
-def get_default_fp8_recipe() -> DelayedScaling:
+    @staticmethod
+    def get_default_fp8_recipe() -> DelayedScaling:
         """FP8 recipe if not provided by user
         Margin = 0, interval = 1, E4M3
         """
         return DelayedScaling()
 
 
+_global_fp8_state = FP8State()
+
+
+def get_global_fp8_state() -> FP8State:
+    """Get global fp8 state"""
+    return _global_fp8_state
+
+
 @contextmanager
 def fp8_autocast(
     enabled: bool = False,
@@ -82,19 +92,20 @@ def fp8_autocast(
     Context manager for FP8 usage.
     """
 
-    global _FP8_ENABLED, _FP8_CALIBRATION, _FP8_RECIPE
-    fp8_state = (_FP8_ENABLED, _FP8_CALIBRATION, _FP8_RECIPE)
+    global _global_fp8_state
+    saved_fp8_state = copy.deepcopy(_global_fp8_state)
     try:
-        _FP8_ENABLED = enabled
-        _FP8_CALIBRATION = calibrating
-        _FP8_RECIPE = get_default_fp8_recipe() if fp8_recipe is None else fp8_recipe
+        _global_fp8_state.fp8_enabled = enabled
+        _global_fp8_state.fp8_calibration = calibrating
+        _global_fp8_state.fp8_recipe = FP8State.get_default_fp8_recipe(
+        ) if fp8_recipe is None else fp8_recipe
 
         if enabled:
             fp8_available, reason_for_no_fp8 = is_fp8_available()
             assert fp8_available, reason_for_no_fp8
         yield
     finally:
-        (_FP8_ENABLED, _FP8_CALIBRATION, _FP8_RECIPE) = fp8_state
+        _global_fp8_state = saved_fp8_state
 
 
 def get_fp8_te_dtype(fp8_recipe: DelayedScaling, fprop_tensor: bool = True) -> tex.DType:

transformer_engine/paddle/layer/base.py

@@ -17,13 +17,11 @@ from paddle.fluid.framework import _dygraph_tracer
 from ..constants import FP8BwdTensors
 from ..cpp_extensions import cast_transpose, cast_transpose_bgrad, cast_to_fp8
 from ..fp8 import (
-    get_fp8_recipe,
-    get_default_fp8_recipe,
-    is_fp8_enabled,
-    is_fp8_calibration,
+    FP8State,
+    FP8TensorMeta,
     amax_and_scale_update,
+    get_global_fp8_state,
     get_fp8_te_dtype,
-    FP8TensorMeta,
 )
 from ..profile import nvtx_range
 from ..utils import get_bias_dtype, cast_if_needed
@@ -63,7 +61,7 @@ class TransformerEngineBaseLayer(paddle.nn.Layer, ABC):
         self.fp8_calibration = False
         self.fp8_meta = {}
         self.fp8_meta["fp8_checkpoint"] = False
-        self.fp8_meta["recipe"] = get_default_fp8_recipe()
+        self.fp8_meta["recipe"] = FP8State.get_default_fp8_recipe()
         self.fp8_meta["scaling_fwd"] = FP8TensorMeta(is_forward=True)
         self.fp8_meta["scaling_bwd"] = FP8TensorMeta(is_forward=False)
 
@@ -104,17 +102,18 @@ class TransformerEngineBaseLayer(paddle.nn.Layer, ABC):
     # assume FP8 execution.
     def fp8_init(self, num_gemms: int = 1) -> None:
         """Initialize fp8 related metadata and tensors during fprop."""
-        self.fp8_enabled = is_fp8_enabled()
-        self.fp8_calibration = is_fp8_calibration()
+        state = get_global_fp8_state()
+        self.fp8_enabled = state.is_fp8_enabled()
+        self.fp8_calibration = state.is_fp8_calibration()
         self.fp8_meta["fp8_checkpoint"] = self.fp8_enabled or self.fp8_calibration
 
         if self.fp8_enabled or self.fp8_calibration:
             # FP8 init has already been run and recipe is the same, don't do anything.
-            if self.fp8_initialized and get_fp8_recipe() == self.fp8_meta["recipe"]:
+            if self.fp8_initialized and state.get_fp8_recipe() == self.fp8_meta["recipe"]:
                 return
 
             # Set FP8, recipe, and other FP8 metadata
-            self.fp8_meta["recipe"] = get_fp8_recipe()
+            self.fp8_meta["recipe"] = state.get_fp8_recipe()
 
             # Set FP8_MAX per tensor according to recipe
             self.fp8_meta["fp8_max_fwd"] = self.fp8_meta["recipe"].fp8_format.value.max_fwd