Update Tensor & CUDA graph section

Author: orion160

recipes_source/recipes/tuning_guide.py

@@ -323,21 +323,15 @@ def gelu(x):
 # Enable Tensor cores
 # ~~~~~~~~~~~~~~~~~~~~~~~
 # Tensor cores are specialized hardware designed to compute matrix-matrix multiplication
-# operations, which neural network operations can take advantage of.
+# operations, primarily utilized in deep learning and AI workloads. Tensor cores have
+# specific precision requirements which can be adjusted manually or via the Automatic
+# Mixed Precision API.
 #
-# Hardware tensor core operations tend to use a different floating point format
-# which sacrifices precision at expense of speed gains.
-# Prior to PyTorch 1.12 this functionality was enabled by default but since this version
-# it must be explicitly set as it can conflict with some operations which do not
-# benefit from Tensor core computations.
-
-## Tensor computation can be enabled "manually" modifying the matrix multiplication precision
-## The default precision is "highest" which will perform the operation according to the dtype
-
-# precision "high" and "medium" can be hardware accelerated via tensor cores
-
-# Carefully consider the tradeoff between speed and precision at the moment of evaluating your models!
-torch.set_float32_matmul_precision("high")
+# In particular, tensor operations take advantage of lower precision workloads.
+# Which can be controlled via ``torch.set_float32_matmul_precision``.
+# The default format is set to 'highest,' which utilizes the tensor data type. 
+# However, PyTorch offers alternative precision settings: 'high' and 'medium.'
+# These options prioritize computational speed over numerical precision."
 
 ###############################################################################
 # Use CUDA Graphs
@@ -353,7 +347,8 @@ def gelu(x):
 torch.compile(m, "max-autotune")
 
 ###############################################################################
-# Special care must be present when using cuda graphs as it can lead to increased memory consumption and some models might not compile.
+# Support for CUDA graph is in development, and its usage can incur in increased
+# device memory consumption and some models might not compile.
 
 ###############################################################################
 # Enable cuDNN auto-tuner