[PyTorch] Fix autocast deprecation warnings (#1277)

* Fix autocast deprecation warnings
Signed-off-by: Xin Yao <xiny@nvidia.com>

* merge main
Signed-off-by: Xin Yao <xiny@nvidia.com>

* update
Signed-off-by: Xin Yao <xiny@nvidia.com>

* resolve comments
Signed-off-by: Xin Yao <xiny@nvidia.com>

---------
Signed-off-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

tests/pytorch/test_fused_optimizer.py

@@ -16,6 +16,7 @@ from transformer_engine.pytorch.attention.multi_head_attention import MultiheadA
 from transformer_engine.pytorch import fp8_model_init
 from transformer_engine.pytorch.utils import is_bf16_compatible
 from transformer_engine.pytorch.fp8 import FP8GlobalStateManager
+from transformer_engine.pytorch.utils import gpu_autocast_ctx
 
 # Check if FP8 is supported
 fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
@@ -596,7 +597,7 @@ class AdamTest:
             gt_ = gt.clone()
 
             # Reference
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model(x)
                 loss = ((gt - y) ** 2).mean()
 
@@ -605,7 +606,7 @@ class AdamTest:
             scaler.update()
 
             # DUT
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model_(x)
                 loss_ = ((gt_ - y) ** 2).mean()
 
@@ -647,7 +648,7 @@ class AdamTest:
             gt_ = gt.clone()
 
             # Reference
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model(x)
                 loss = ((gt - y) ** 2).mean()
 
@@ -656,7 +657,7 @@ class AdamTest:
             scaler.update()
 
             # DUT
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model_(x)
                 loss_ = ((gt_ - y) ** 2).mean()
 
@@ -705,7 +706,7 @@ class AdamTest:
             gt_ = gt.clone()
 
             # Reference
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model(x)
                 loss = ((gt - y) ** 2).mean()
 
@@ -714,7 +715,7 @@ class AdamTest:
             scaler.update()
 
             # DUT
-            with torch.cuda.amp.autocast(enabled=True):
+            with gpu_autocast_ctx(enabled=True):
                 y = self.model_(x)
                 loss_ = ((gt_ - y) ** 2).mean()
 

transformer_engine/pytorch/distributed.py

@@ -19,6 +19,7 @@ from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp._common_utils import _get_module_fsdp_state
 from torch.distributed.fsdp._traversal_utils import _get_fsdp_states_with_modules
 
+from . import torch_version
 from .utils import (
     is_non_tn_fp8_gemm_supported,
     safely_set_viewless_tensor_data,
@@ -271,17 +272,36 @@ def _get_active_autocast_contexts():
     """
     autocast_cached = torch.is_autocast_cache_enabled()
 
-    gpu_autocast_enabled = torch.is_autocast_enabled()
-    gpu_autocast_dtype = torch.get_autocast_gpu_dtype()
-    gpu_autocast_ctx = torch.cuda.amp.autocast(
-        gpu_autocast_enabled, gpu_autocast_dtype, autocast_cached
-    )
+    if torch_version() >= (2, 4, 0):
+        gpu_autocast_enabled = torch.is_autocast_enabled("cuda")
+        gpu_autocast_dtype = torch.get_autocast_dtype("cuda")
+        gpu_autocast_ctx = torch.amp.autocast(
+            "cuda",
+            enabled=gpu_autocast_enabled,
+            dtype=gpu_autocast_dtype,
+            cache_enabled=autocast_cached,
+        )
 
-    cpu_autocast_enabled = torch.is_autocast_cpu_enabled()
-    cpu_autocast_dtype = torch.get_autocast_cpu_dtype()
-    cpu_autocast_ctx = torch.cpu.amp.autocast(
-        cpu_autocast_enabled, cpu_autocast_dtype, autocast_cached
-    )
+        cpu_autocast_enabled = torch.is_autocast_enabled("cpu")
+        cpu_autocast_dtype = torch.get_autocast_dtype("cpu")
+        cpu_autocast_ctx = torch.amp.autocast(
+            "cpu",
+            enabled=cpu_autocast_enabled,
+            dtype=cpu_autocast_dtype,
+            cache_enabled=autocast_cached,
+        )
+    else:
+        gpu_autocast_enabled = torch.is_autocast_enabled()
+        gpu_autocast_dtype = torch.get_autocast_gpu_dtype()
+        gpu_autocast_ctx = torch.cuda.amp.autocast(
+            gpu_autocast_enabled, gpu_autocast_dtype, autocast_cached
+        )
+
+        cpu_autocast_enabled = torch.is_autocast_cpu_enabled()
+        cpu_autocast_dtype = torch.get_autocast_cpu_dtype()
+        cpu_autocast_ctx = torch.cpu.amp.autocast(
+            cpu_autocast_enabled, cpu_autocast_dtype, autocast_cached
+        )
 
     return gpu_autocast_ctx, cpu_autocast_ctx
 

transformer_engine/pytorch/jit.py

@@ -9,6 +9,9 @@ from typing import Callable, Optional, Tuple
 
 import torch
 
+from . import torch_version
+from .utils import gpu_autocast_ctx
+
 # pylint: disable=unnecessary-lambda-assignment
 
 
@@ -31,13 +34,13 @@ def lazy_compile(func):
 
 
 jit_fuser = lambda func: func
-if torch.__version__ >= "2" and bool(int(os.getenv("NVTE_TORCH_COMPILE", "1"))):
+if torch_version() >= (2, 0, 0) and bool(int(os.getenv("NVTE_TORCH_COMPILE", "1"))):
     jit_fuser = lazy_compile
 
 
 # See: https://github.com/NVIDIA/TransformerEngine/issues/597
 dropout_fuser = torch.jit.script
-if torch.__version__ >= "2.2" and bool(int(os.getenv("NVTE_TORCH_COMPILE", "1"))):
+if torch_version() >= (2, 2, 0) and bool(int(os.getenv("NVTE_TORCH_COMPILE", "1"))):
     dropout_fuser = lazy_compile
 
 
@@ -49,11 +52,9 @@ no_torch_dynamo = lambda recursive=True: lambda f: torch._dynamo.disable(f, recu
 def set_jit_fusion_options() -> None:
     """Set PyTorch JIT layer fusion options."""
     # flags required to enable jit fusion kernels
-    TORCH_MAJOR = int(torch.__version__.split(".")[0])
-    TORCH_MINOR = int(torch.__version__.split(".")[1])
-    if TORCH_MAJOR == 2 and TORCH_MINOR >= 2:
+    if torch_version() >= (2, 2, 0):
         pass
-    elif (TORCH_MAJOR == 2) or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10):
+    elif torch_version() >= (1, 10, 0):
         # nvfuser
         torch._C._jit_set_profiling_executor(True)
         torch._C._jit_set_profiling_mode(True)
@@ -122,7 +123,7 @@ def dgelu_fused_(grad_output: torch.Tensor, inp: torch.Tensor) -> torch.Tensor:
 
 def bias_gelu_fused(inp: torch.Tensor, bias: torch.Tensor) -> torch.Tensor:
     """Disable native AMP for bias_gelu_fused_"""
-    with torch.cuda.amp.autocast(enabled=False):
+    with gpu_autocast_ctx(enabled=False):
         if bias is not None and bias.numel() != 0:
             return bias_gelu_fused_(inp, bias)
         return gelu_fused_(inp)
@@ -132,7 +133,7 @@ def bgrad_dgelu_fused(
     grad_output: torch.Tensor, inp: torch.Tensor, bias: torch.Tensor
 ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
     """Disable native AMP for `bgrad_dgelu_fused_`"""
-    with torch.cuda.amp.autocast(enabled=False):
+    with gpu_autocast_ctx(enabled=False):
         if bias is not None and bias.numel() != 0:
             return bgrad_dgelu_fused_(grad_output, inp, bias)
         return None, dgelu_fused_(grad_output, inp)
@@ -173,7 +174,7 @@ def bias_dropout_add_fused_train(
 ) -> torch.Tensor:
     """Disable native AMP and enable grad for BDA"""
     with torch.enable_grad():
-        with torch.cuda.amp.autocast(enabled=False):
+        with gpu_autocast_ctx(enabled=False):
             return bias_dropout_add_fused_train_(x, bias, residual, prob)
 
 
@@ -189,7 +190,7 @@ def bias_dropout_add_fused_inference(
     x: torch.Tensor, bias: torch.Tensor, residual: torch.Tensor, prob: float
 ) -> torch.Tensor:
     """Disable native AMP for BDA"""
-    with torch.cuda.amp.autocast(enabled=False):
+    with gpu_autocast_ctx(enabled=False):
         return bias_dropout_add_fused_inference_(x, bias, residual, prob)
 
 

transformer_engine/pytorch/module/base.py

@@ -39,6 +39,7 @@ from ..tensor import QuantizedTensor, Quantizer
 from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
 from ..tensor._internal.float8_tensor_base import Float8TensorBase
 from ..tensor._internal.mxfp8_tensor_base import MXFP8TensorBase
+from ..utils import torch_get_autocast_gpu_dtype
 from ..tensor._internal.float8_blockwise_tensor_base import Float8BlockwiseQTensorBase
 from ...common.recipe import Recipe
 from ...debug.pytorch.debug_state import TEDebugState
@@ -700,7 +701,7 @@ class TransformerEngineBaseModule(torch.nn.Module, ABC):
         """Get activation data type for AMP."""
         # Native AMP (`torch.autocast`) gets highest priority
         if torch.is_autocast_enabled():
-            self.activation_dtype = torch.get_autocast_gpu_dtype()
+            self.activation_dtype = torch_get_autocast_gpu_dtype()
             return
 
         # All checks after this have already been performed once, thus skip

transformer_engine/pytorch/transformer.py

@@ -10,6 +10,7 @@ from typing import Callable, List, Optional, Tuple, Union
 
 import torch
 
+from transformer_engine.pytorch import torch_version
 from transformer_engine.pytorch.module import LayerNormMLP, LayerNorm, RMSNorm
 from transformer_engine.debug.pytorch.debug_state import TEDebugState
 from transformer_engine.pytorch.attention.multi_head_attention import MultiheadAttention
@@ -24,6 +25,7 @@ from transformer_engine.pytorch.jit import (
 from transformer_engine.pytorch.utils import (
     cast_if_needed,
     get_default_init_method,
+    torch_get_autocast_gpu_dtype,
 )
 from transformer_engine.pytorch.constants import (
     AttnMaskTypes,
@@ -435,9 +437,7 @@ class TransformerLayer(torch.nn.Module):
         self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else None
 
         # Set bias+dropout+add fusion grad_enable execution handler.
-        TORCH_MAJOR = int(torch.__version__.split(".")[0])
-        TORCH_MINOR = int(torch.__version__.split(".")[1])
-        use_nvfuser = TORCH_MAJOR > 1 or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10)
+        use_nvfuser = torch_version() >= (1, 10, 0) and torch_version() < (2, 2, 0)
         self.bias_dropout_add_exec_handler = nullcontext if use_nvfuser else torch.enable_grad
 
         if self.bias_dropout_fusion:
@@ -687,7 +687,7 @@ class TransformerLayer(torch.nn.Module):
 
         # For AMP
         if torch.is_autocast_enabled():
-            hidden_states = cast_if_needed(hidden_states, torch.get_autocast_gpu_dtype())
+            hidden_states = cast_if_needed(hidden_states, torch_get_autocast_gpu_dtype())
 
         # Self attention.
         self_attention_outputs = self.self_attention(

transformer_engine/pytorch/utils.py

@@ -9,9 +9,10 @@ import math
 import os
 from typing import Any, Callable, List, Optional, Tuple, Union
 import numpy as np
-
 import torch
+
 import transformer_engine.pytorch.cpp_extensions as ext
+from . import torch_version
 from ..debug.pytorch.debug_quantization import DebugQuantizedTensor
 
 
@@ -596,3 +597,16 @@ def canonicalize_process_group(
     if group is None:
         return torch.distributed.distributed_c10d._get_default_group()
     return group
+
+
+def torch_get_autocast_gpu_dtype() -> torch.dtype:
+    """Get PyTorch autocast GPU dtype."""
+    if torch_version() >= (2, 4, 0):
+        return torch.get_autocast_dtype("cuda")
+    return torch.get_autocast_gpu_dtype()
+
+
+if torch_version() >= (2, 4, 0):
+    gpu_autocast_ctx = functools.partial(torch.amp.autocast, device_type="cuda")
+else:
+    gpu_autocast_ctx = torch.cuda.amp.autocast