Update what's new with 2.0 tutorials (#2255)

* Update what's new with 2.0 tutorials
* Add notes on running in colab to the SDPA tutorial

Author: Svetlana Karslioglu

index.rst

@@ -3,10 +3,15 @@ Welcome to PyTorch Tutorials
 
 What's new in PyTorch tutorials?
 
-* `PyTorch Distributed Series <https://pytorch.org/tutorials/beginner/ddp_series_intro.html?utm_source=whats_new_tutorials&utm_medium=ddp_series_intro>`__
-* `Fast Transformer Inference with Better Transformer <https://pytorch.org/tutorials/beginner/bettertransformer_tutorial.html?utm_source=whats_new_tutorials&utm_medium=bettertransformer>`__
-* `Advanced model training with Fully Sharded Data Parallel (FSDP) <https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html?utm_source=whats_new_tutorials&utm_medium=FSDP_advanced>`__
-* `Grokking PyTorch Intel CPU Performance from First Principles <https://pytorch.org/tutorials/intermediate/torchserve_with_ipex?utm_source=whats_new_tutorials&utm_medium=torchserve_ipex>`__
+* `Implementing High Performance Transformers with Scaled Dot Product Attention <https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html?utm_source=whats_new_tutorials&utm_medium=scaled_dot_product_attention_tutorial>`__
+* `torch.compile Tutorial <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html?utm_source=whats_new_tutorials&utm_medium=torch_compile>`__
+* `Per Sample Gradients <https://pytorch.org/tutorials/intermediate/per_sample_grads.html?utm_source=whats_new_tutorials&utm_medium=per_sample_grads>`__
+* `Jacobians, Hessians, hvp, vhp, and more: composing function transforms <https://pytorch.org/tutorials/intermediate/jacobians_hessians.html?utm_source=whats_new_tutorials&utm_medium=jacobians_hessians>`__
+* `Model Ensembling <https://pytorch.org/tutorials/intermediate/ensembling.html?utm_source=whats_new_tutorials&utm_medium=ensembling>`__
+* `Neural Tangent Kernels <https://pytorch.org/tutorials/intermediate/neural_tangent_kernels.html?utm_source=whats_new_tutorials&utm_medium=neural_tangent_kernels>`__
+* `Reinforcement Learning (PPO) with TorchRL Tutorial <https://pytorch.org/tutorials/intermediate/reinforcement_ppo.html?utm_source=whats_new_tutorials&utm_medium=reinforcement_ppo>`__
+* `Changing Default Device <https://pytorch.org/tutorials/recipes/recipes/changing_default_device.html?utm_source=whats_new_tutorials&utm_medium=changing_default_device>`__
+
 
 .. raw:: html
 

intermediate_source/scaled_dot_product_attention_tutorial.py

@@ -1,9 +1,10 @@
 """
-Implementing High-Performance Transformers with SCALED DOT PRODUCT ATTENTION
-================================================================================
+(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)
+==========================================================================================
 
-"""
 
+**Author:** `Driss Guessous <https://github.com/drisspg>`_
+"""
 
 ######################################################################
 # Summary
@@ -34,6 +35,8 @@
 # * `Memory-Efficient Attention <https://github.com/facebookresearch/xformers>`__
 # * A PyTorch implementation defined in C++
 #
+# .. literalinclude:: ../beginner_source/new-release-colab.rst
+#    :language: rst
 
 import torch
 import torch.nn as nn
@@ -334,4 +337,4 @@ def generate_rand_batch(
 # compilable. In the process we have shown how to the profiling tools can
 # be used to explore the performance characteristics of a user defined
 # module.
-#
+#