FSDP grad accum fix (#34645)

* add gradient accumulation steps tests for fsdp

* invert no_sync context to fix training for fsdp

src/transformers/trainer.py

@@ -2474,7 +2474,7 @@ def _inner_training_loop(
                     # We explicitly want to avoid relying on `accelerator.accumulate` for generation training
                     context = (
                         functools.partial(self.accelerator.no_sync, model=model)
-                        if i == len(batch_samples) - 1
+                        if i != len(batch_samples) - 1
                         else contextlib.nullcontext
                     )
                     with context():

tests/fsdp/test_fsdp.py

@@ -224,6 +224,18 @@ def test_basic_run(self, sharding_strategy, dtype):
         cmd = launcher + script + args + fsdp_args
         execute_subprocess_async(cmd, env=self.get_env())
 
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    @require_torch_multi_accelerator
+    @slow
+    def test_basic_run_with_gradient_accumulation(self, sharding_strategy, dtype):
+        launcher = get_launcher(distributed=True, use_accelerate=False)
+        output_dir = self.get_auto_remove_tmp_dir()
+        args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--gradient_accumulation_steps", "2"]
+        fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        cmd = launcher + script + args + fsdp_args
+        execute_subprocess_async(cmd, env=self.get_env())
+
     @parameterized.expand(dtypes)
     @require_torch_multi_accelerator
     @slow