Some minor edits

_posts/2018-05-02-road-to-1.0.md

@@ -88,15 +88,15 @@ def rnn_loop(x):
 
 Regardless of whether you use tracing or `@script`, the result is a python-free representation of your model, which can be used to optimize the model or to export the model from python for use in production environments.
 
-Extracting bigger segments of the model into an intermediate representation makes it possible to do sophisticated whole-program optimizations and to offload computation to specialized AI accelerators which operator on graphs of computation. We have already been developing the beginnings of these optimizations, including passes that fuse GPU operations together to improve the performance of smaller RNN models.
+Extracting bigger segments of the model into an intermediate representation makes it possible to do sophisticated whole-program optimizations and to offload computation to specialized AI accelerators which operate on graphs of computation. We have already been developing the beginnings of these optimizations, including passes that fuse GPU operations together to improve the performance of smaller RNN models.
 
 It also allows us to use existing high-performance backends available in Caffe2 today to run the model efficiently. Additionally, @script functions (and modules!) can be fully exported to ONNX in a way that retains their dynamic nature, such that you can easily run them in a Python-free environment using the model executors from Caffe2 or by transferring the model to any other framework supporting ONNX.
 
 ### Usability
 
 **We care deeply about maintaining our current level of usability and we know that execution of the code not directly in Python leads to harder debugging, but this is something that we think about a lot, and we're making sure that you're not getting locked in to a completely different programming language.**
 
-First, we follow the principle of pay for what you use — if you don't need to optimize or export your model, you do not have to use these new features and won't see any downsides. Furthermore, use of traced or @script modules/functions can be done incrementally. For instance, all of these behaviors are allowed: You can trace part of your model and use the trace in a larger non-traced model. You can use tracing for 90% of your model, and use @script for the one sub-module that actually has some control flow in it. You can write a function using @script and have it call a native python function. If something appears incorrect in an @script function, you can remove the annotation and the code will execute in native python where it is easy to debug using your favorite tools and methods..  Think of tracing and @script like type annotations using MyPy or TypeScript — each additional annotation can be tested incrementally, and none are required until you want to optimize or productionize.
+First, we follow the principle of pay for what you use — if you don't need to optimize or export your model, you do not have to use these new features and won't see any downsides. Furthermore, use of traced or @script modules/functions can be done incrementally. For instance, all of these behaviors are allowed: You can trace part of your model and use the trace in a larger non-traced model. You can use tracing for 90% of your model, and use @script for the one sub-module that actually has some control flow in it. You can write a function using @script and have it call a native python function. If something appears incorrect in an @script function, you can remove the annotation and the code will execute in native python where it is easy to debug using your favorite tools and methods. Think of tracing and @script like type annotations using MyPy or TypeScript — each additional annotation can be tested incrementally, and none are required until you want to optimize or productionize.
 
 Most importantly, these modes will be built into the core of PyTorch so that mixing and matching them with your existing code can happen seamlessly.