Replace mentions of `.type_as()` in our docs

📚 Documentation

As a follow up to #2585, we should consider removing mentions of the .type_as() syntax in our docs and replace it with best practices for device placement and type conversion.

---

If you enjoy Lightning, check out our other projects! ⚡

• Metrics: Machine learning metrics for distributed, scalable PyTorch applications.

• Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

• Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

• Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

• Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging PyTorch Lightning, Transformers, and Hydra.

cc @borda @rohitgr7 @Felonious-Spellfire

[nsarang]

Tensor.to can be used as a safe replacement for type_as. It also accepts another tensor as input.

From the documentation:

torch.to(other, non_blocking=False, copy=False) → Tensor Returns a Tensor with same torch.dtype and torch.device as the Tensor other. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.

[yipliu]

For me, I often use the following methods

# As the LightningModule, self.deivce is known
torch.zeros(B, S, device=self.device)