fix(gpt2): Resolve NaN/Inf issue in lm_head on Python 3.13 with tied weights

[JokeYoonic]

Problem:

• On macOS ARM64 + Python 3.13 + transformers 5.x, GPT-2 model's lm_head forward pass produces NaN/Inf values during inference
• Root cause: lm_head.weight is tied to transformer.wte.weight, and the shared memory reference causes numerical instability in Python 3.13

Solution:

• Clone the lm_head weight before passing to F.linear in GPT2LMHeadModel and GPT2DoubleHeadsModel forward methods
• This breaks the memory sharing and resolves the NaN issue

Changes:

• src/transformers/models/gpt2/modeling_gpt2.py: Modified GPT2LMHeadModel.forward() and GPT2DoubleHeadsModel.forward() to use self.lm_head.weight.clone()

Testing:

• Verified fix with gpt2-medium model on Python 3.13.5 + PyTorch 2.6.0
• All existing GPT-2 model tests pass

Reproduction Code

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("gpt2-medium")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
inputs = tokenizer("Hello world", return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

print(f"Has NaN: {torch.isnan(logits).any().item()}")  # Should be False after fix
print(f"Has Inf: {torch.isinf(logits).any().item()}")  # Should be False after fix

What does this PR do?

Fixes # (issue)

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[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: gpt2

[Rocketknight1]

Hi, "shared memory reference causes numerical instability" doesn't make any sense to me, because "numerical instability" usually refers to errors in the least significant bits caused by floating point precision. Is the tied weight just not being handled correctly and we're getting randomly-initialized values instead?