make torch.amp.autocast more generic

ghstack-source-id: 1cbc46833c8bcb946e0eff23f4311e766631d9c2
Pull Request resolved: #125103

benchmarks/dynamo/common.py

@@ -2041,31 +2041,28 @@ def setup_amp(self, current_device=None):
 
         devices = [current_device] if current_device else self.args.devices
         if self.args.amp:
-            if devices == ["cuda"]:
-                # AMP training can lead to small loss values which can undeflow
-                # gradient values returning in zero gradients. To solve this
-                # problem, PyTorch introduces GradScaler. GradScaler is a stateful
-                # structure, that scales the loss values to prevent underflow. Loss
-                # values are big at the beginning of training (therefore not
-                # requiring scaling), while loss value tends to be small as network
-                # starts getting better (requiring scaling). GradScaler manages all
-                # of this fine tuning, checking the gradients are turning to inf,
-                # discarding such batches.
-
-                # Since we are not running a long iteration, default value of
-                # init_scale 65536 is going to turn all gradients to inf. Therefore,
-                # we just use a init_scale of 2.0 for benchmarking purpose.
-
-                # Disabling Gradscaler because
-                #  1) Benchmark setup runs 2 iterations of fwd-bwd. So, not useful.
-                #  2) Current setup shares grad_scaler for eager and dynamo model,
-                #  which is bad as Gradscaler has state and can adjust the scaling
-                #  factor between eager and dynamo run, making accuracy check
-                #  harder.
-                # self.grad_scaler = torch.cuda.amp.GradScaler(init_scale=2.0)
-                self.autocast = torch.cuda.amp.autocast
-            if devices == ["cpu"]:
-                self.autocast = torch.cpu.amp.autocast
+            # AMP training can lead to small loss values which can undeflow
+            # gradient values returning in zero gradients. To solve this
+            # problem, PyTorch introduces GradScaler. GradScaler is a stateful
+            # structure, that scales the loss values to prevent underflow. Loss
+            # values are big at the beginning of training (therefore not
+            # requiring scaling), while loss value tends to be small as network
+            # starts getting better (requiring scaling). GradScaler manages all
+            # of this fine tuning, checking the gradients are turning to inf,
+            # discarding such batches.
+
+            # Since we are not running a long iteration, default value of
+            # init_scale 65536 is going to turn all gradients to inf. Therefore,
+            # we just use a init_scale of 2.0 for benchmarking purpose.
+
+            # Disabling Gradscaler because
+            #  1) Benchmark setup runs 2 iterations of fwd-bwd. So, not useful.
+            #  2) Current setup shares grad_scaler for eager and dynamo model,
+            #  which is bad as Gradscaler has state and can adjust the scaling
+            #  factor between eager and dynamo run, making accuracy check
+            #  harder.
+            # self.grad_scaler = torch.cuda.amp.GradScaler(init_scale=2.0)
+            self.autocast = functools.partial(torch.amp.autocast, device_type=devices[0])
             if self.args.amp_dtype:
                 amp_dtype = (
                     torch.float16

torch/_dynamo/output_graph.py

@@ -598,28 +598,20 @@ def save_global_state(self, out=None):
         )
         global_state["grad_enabled"] = (torch.set_grad_enabled, torch.is_grad_enabled())
 
-        def autocast_specific_backend(
-            device_type: str, func: Callable[[str, Any], None]
-        ):
-            def decorator(value):
-                return func(device_type, value)
-
-            return decorator
-
         global_state["autocast_enabled"] = (
-            autocast_specific_backend("cuda", torch.set_autocast_enabled),
+            functools.partial(torch.set_autocast_enabled, "cuda"),
             torch.is_autocast_enabled("cuda"),
         )
         global_state["autocast_cpu_enabled"] = (
-            autocast_specific_backend("cpu", torch.set_autocast_enabled),
+            functools.partial(torch.set_autocast_enabled, "cpu"),
             torch.is_autocast_enabled("cpu"),
         )
         global_state["autocast_gpu_dtype"] = (
-            autocast_specific_backend("cuda", torch.set_autocast_dtype),
+            functools.partial(torch.set_autocast_dtype, "cuda"),
             torch.get_autocast_dtype("cuda"),
         )
         global_state["autocast_cpu_dtype"] = (
-            autocast_specific_backend("cpu", torch.set_autocast_dtype),
+            functools.partial(torch.set_autocast_dtype, "cpu"),
             torch.get_autocast_dtype("cpu"),
         )
         global_state["autocast_cache_enabled"] = (

torch/amp/autocast_mode.py

@@ -203,6 +203,8 @@ def __init__(
         enabled: bool = True,
         cache_enabled: Optional[bool] = None,
     ):
+        if dtype is None:
+            dtype = torch.get_autocast_dtype(device_type)
         if torch._jit_internal.is_scripting():
             self._enabled = enabled
             self.device = device_type

torch/utils/checkpoint.py

@@ -287,11 +287,10 @@ def backward(ctx, *args):
                     set_device_states(ctx.fwd_devices, ctx.fwd_device_states)
             detached_inputs = detach_variable(tuple(inputs))
 
-            device_autocast_ctx = device_module.amp.autocast(
-                **ctx.device_autocast_kwargs
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=ctx.device, **ctx.device_autocast_kwargs
             ) if torch.amp.is_autocast_available(ctx.device) else contextlib.nullcontext()
-            with torch.enable_grad(), device_autocast_ctx, \
-                 torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
+            with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
                 outputs = ctx.run_function(*detached_inputs)
 
         if isinstance(outputs, torch.Tensor):
@@ -1394,11 +1393,10 @@ def recompute_fn(*inputs):
                 if had_device_in_fwd:
                     set_device_states(fwd_devices, fwd_device_states)
 
-            device_autocast_ctx = device_module.amp.autocast(
-                **device_autocast_kwargs
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=device, **device_autocast_kwargs
             ) if torch.amp.is_autocast_available(device) else contextlib.nullcontext()
-            with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), \
-                 recompute_context:
+            with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
                 fn(*args, **kwargs)
 
     new_frame = _CheckpointFrame(