Fix test of FSDP2 by correcting init logic and applying autocast (#2105)

* Fix test of FSDP2 by correcting init logic and applying autocast

This fixes multiple issues in the FSDP2 test, namely
1. Previously fp8 init was performed when `args.fp8_init == False`. I have updated the logic to match what I presume was intended by leveraging the nullcontext context manager.
2. `te.fp8_autocast` was previously not called; the recipe was created but was unused. The autocast context manager now wraps the model's computation.
Signed-off-by: Neil Tenenholtz <ntenenz@users.noreply.github.com>

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* Fix typo
Signed-off-by: Neil Tenenholtz <ntenenz@users.noreply.github.com>

* Update tests/pytorch/distributed/run_fsdp2_model.py
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

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* Fix bug when constructing context for model init
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

---------
Signed-off-by: Neil Tenenholtz <ntenenz@users.noreply.github.com>
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

tests/pytorch/distributed/run_fsdp2_model.py

@@ -105,23 +105,19 @@ def _train(args):
     fp8_format = Format.HYBRID
     fp8_recipe = DelayedScaling(fp8_format=fp8_format, amax_history_len=16, amax_compute_algo="max")
 
-    if not args.fp8_init:
-        # Build model context (FP8 init)
-        build_model_context = nullcontext
-        build_model_context_args = {}
-
+    # Create build context manager
+    if args.fp8_init:
         from transformer_engine.pytorch import quantized_model_init
 
-        build_model_context = quantized_model_init
-        build_model_context_args["enabled"] = True
+        build_model_context = quantized_model_init()
+    else:
+        build_model_context = nullcontext()
 
     # Build the model with the specified context
-        with build_model_context(**build_model_context_args):
-            model = SimpleNet(args.input_size, args.hidden_size, args.output_size)
-    else:
+    with build_model_context:
         model = SimpleNet(args.input_size, args.hidden_size, args.output_size)
-    # Move the model to the correct device
 
+    # Move the model to the correct device
     model.to(device)
 
     if LOCAL_RANK == 0:
@@ -163,6 +159,7 @@ def _train(args):
         # Zero the parameter gradients
         optimizer.zero_grad()
         input_data = torch.randn(args.batch_size, args.input_size).to(device)
+        with te.autocast(enabled=True, recipe=fp8_recipe):
             output = model(input_data)
         target = torch.randn(args.batch_size, args.output_size).to(device)
         loss = F.mse_loss(output, target)