Self-study:

How PyTorch complies code -    • Code RoPE, How GPU Processes Tensors, How ...  

Why modifying operations in place will cause issues for torch backpropagation - chatgpt.com/share/6831dd7a-33a0-8002-a906-b04f0992…

Torch compile, detach, zeroes, etc (good to know about compiler) - chatgpt.com/share/6831ddfe-f7fc-8002-978f-cdf4ef5e…

github - github.com/vukrosic/gpt-lab

Code DeepSeek V3 From Scratch Full Course -    • Understand & Code DeepSeek V3 From Scratch...  

Main lesson from the video (copy this into AI chatbot so you can study about it):


🚫 Why You Should Avoid In-Place Operations in PyTorch Autograd

🔧 What Are In-Place Operations?

In PyTorch, *in-place operations* modify the content of a tensor **without making a copy**. They are typically denoted by a trailing underscore (`_`), e.g.:

```python
x.add_(1) # in-place
x += 1 # also in-place for tensors
x = x + 1 # NOT in-place (creates a new tensor)
```

---

⚠️ Why In-Place Ops Can Break Autograd

PyTorch uses *dynamic computation graphs**. When you perform operations on tensors that require gradients, PyTorch **builds a graph of those operations**. During `.backward()`, it **traverses this graph in reverse* to compute gradients.

🔥 In-place modifications can destroy intermediate values needed for backpropagation!

Example 1 — The Problem:

```python
import torch

x = torch.tensor([2.0], requires_grad=True)
y = x ** 2 # y = x^2, so y = 4.0
x += 1 # in-place change to x!
z = y * 3 # using the old y, not updated x

z.backward()
```

💥 Error:

```text
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
```

Why? Because `x += 1` modifies `x` in-place *after* it was used to compute `y`. Autograd saved the original `x` to compute gradients, but that value got overwritten.

---

✅ How to Avoid This

1. *Avoid modifying tensors with `_` operations* unless you’re certain it’s safe.

❌ Don’t:

```python
x.relu_()
x.add_(5)
```

✅ Do:

```python
x = x.relu()
x = x + 5
```

2. *Avoid in-place operations on tensors that require gradients* or are involved in gradient computations.

Even this can fail:

```python
x = torch.randn(3, requires_grad=True)
x[0] = 0 # in-place indexing operation
```

This can cause errors during `.backward()`.

---

🔁 Example 2 — Safe vs Unsafe Code

❌ Unsafe Version:

```python
x = torch.tensor([2.0], requires_grad=True)
y = x ** 2
x.add_(1) # modifies x in-place
z = y * 3
z.backward() # RuntimeError
```

✅ Safe Version:

```python
x = torch.tensor([2.0], requires_grad=True)
y = x ** 2
x2 = x + 1 # creates a new tensor
z = y * 3
z.backward() # works!
```

---

🔍 Exception: Some in-place ops are safe if…

The tensor is *not* used elsewhere in the computation graph
You *know for sure* it won't be needed for gradient calculation

But unless you're optimizing for memory or speed, **just use out-of-place ops**.

---

🧠 Debugging Tips

1. Turn on anomaly detection

```python
torch.autograd.set_detect_anomaly(True)
```

This will help pinpoint where the in-place operation corrupted the graph.

2. Wrap your code in small blocks and test `.backward()` often

---

🧪 Final Test Case

```python
torch.autograd.set_detect_anomaly(True)

x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = x * 2
x[0] = 100 # in-place modification

z = y.sum()
z.backward() # will raise an error
```

---

✅ Summary

| ✅ Do | ❌ Don’t |
| ------------------------------- | ---------------------------------------- |
| Use out-of-place operations | Use `tensor +=`, `tensor.add_()`, etc. |
| Clone tensor before modifying | Modify tensor after using in computation |
| Check `requires_grad` and graph | Assume in-place is safe |




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