Repository: huggingface/transformers
Pull request: fix to avoid modifying a view in place

# What does this PR do?

When using `tp_auto` on `GptOssModel`, I got the following

```
RuntimeError: Output 0 of _ToTorchTensorBackward is a view and is being modified inplace. This view was created inside a custom Function (or because an input was returned as-is) and the autograd logic to handle view+inplace would override the custom backward associated with the custom Function, leading to incorrect gradients. This behavior is forbidden. You can fix this by cloning the output of the custom Function
```
The fix is to avoid the inplace modification

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface, @SunMarc and @qgallouedec
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: src/transformers/integrations/tensor_parallel.py
patch: @@ -677,7 +677,7 @@ def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_me
             outputs = outputs.redistribute(placements=output_layouts, async_op=True)
         outputs = outputs.to_local()  # otherwise the `+=` op will gather
         if hasattr(mod, "_bias"):
-            outputs += mod._bias
+            outputs = outputs + mod._bias
         # back to local tensor if use_local_output is True
         return outputs
 

filename: tests/models/gpt_oss/test_modeling_gpt_oss.py
patch: @@ -397,6 +397,51 @@ def test_model_outputs(self, quantized, model, kernels, attn_impl, mode):
     def test_model_outputs_distributed(self, quantized, model, kernels, attn_impl, mode):
         self.run_distributed_test(quantized, model, kernels, attn_impl, mode)
 
+    # ------------------------
+    # Training test
+    # ------------------------
+    @parameterized.expand(PARAMETERS)
+    @require_read_token
+    def test_training_step(self, quantized, model, kernels, attn_impl, mode):
+        if mode != "train":
+            self.skipTest("This test is only for training mode.")
+
+        if quantized:
+            self.skipTest("Training test for quantized models is not supported.")
+
+        model_id = f"/fsx/vb/new-oai/gpt-oss-{model}-trfs"
+
+        model_obj = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            attn_implementation=attn_impl,
+            use_kernels=kernels,
+        )
+        model_obj.train()
+
+        tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(model_obj.device)
+        inputs["labels"] = inputs["input_ids"].clone()
+
+        outputs = model_obj(**inputs)
+        loss = outputs.loss
+        self.assertIsNotNone(loss)
+
+        loss.backward()
+
+        # Check that gradients were computed for all parameters that have a grad field
+        for name, param in model_obj.named_parameters():
+            if param.requires_grad:
+                self.assertIsNotNone(param.grad, f"Parameter '{name}' did not receive a gradient.")
+                # Check that gradients are not all zero
+                self.assertTrue(
+                    torch.sum(torch.abs(param.grad)).item() > 0, f"Gradient for parameter '{name}' is all zeros."
+                )
+
     def test_model_matches_original_20b(self):
         input_text = "Roses are red, violets"
 

filename: tests/tensor_parallel/test_tensor_parallel.py
patch: @@ -126,6 +126,32 @@ def test_model_forward(self):
         )
         self.torchrun(script_to_run)
 
+    def test_model_backward_pass(self):
+        script_to_run = textwrap.dedent(
+            """
+            import torch
+            import os
+            from transformers import AutoModelForCausalLM
+            from torch import nn
+
+            model_id = "JackFram/llama-68m"
+
+            model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, tp_plan="auto")
+            torch.distributed.barrier()
+
+            # Dummy forward and backward pass
+            # Note that loss.backward() will fail if there is a bug in the TP implementation
+            inputs = torch.randint(0, model.config.vocab_size, (2, 10), device=model.device)
+            labels = torch.randint(0, model.config.vocab_size, (2, 10), device=model.device)
+            loss = model(inputs, labels=labels).loss
+            loss.backward()
+
+            torch.distributed.barrier()
+            torch.distributed.destroy_process_group()
+            """
+        )
+        self.torchrun(script_to_run)
+
     def test_model_generate(self):
         script_to_run = textwrap.dedent(
             """