added compilation readme

Author: Chillee

COMPILE_README.md

@@ -0,0 +1,62 @@
+# An introduction to some of the (prototype) compilation features in Functorch
+
+The primary compilation API we provide is something called AOTAutograd.
+
+This is currently a prototype feature.
+
+For example, here are some examples of how to use it.
+```
+from functorch.compile import aot_function, aot_module, draw_graph
+import torch.fx as fx
+import torch
+
+# This simply prints out the FX graph of the forwards and the backwards
+def print_graph(name):
+    def f(fx_g: fx.GraphModule, inps):
+        print(name)
+        print(fx_g.code)
+        return fx_g
+    return f
+
+def f(x):
+    return x.cos().cos()
+
+nf = aot_function(f, fw_compiler=print_graph("forward"), bw_compiler=print_graph("backward"))
+nf(torch.randn(3, requires_grad=True))
+
+# You can do whatever you want before and after, and you can still backprop through the function.
+inp = torch.randn(3, requires_grad=True)
+inp = inp.cos()
+out = nf(inp)
+out = out.sin().sum().backward()
+
+def f(x):
+    return x.cos().cos()
+
+# This draws out the forwards and the backwards graphs as svg files
+def graph_drawer(name):
+    def f(fx_g: fx.GraphModule, inps):
+        draw_graph(fx_g, name)
+        return fx_g
+    return f
+
+aot_function(f, fw_compiler=graph_drawer("forward"), bw_compiler=graph_drawer("backward"))(torch.randn(3, requires_grad=True))
+
+# We also have a convenience API for applying AOTAutograd to modules
+from torchvision.models import resnet18
+aot_module(resnet18(), print_graph("forward"), print_graph("backward"))(torch.randn(1,3,200,200))
+# output elided since it's very long
+
+# In practice, you might want to speed it up by sending it to Torchscript. You might also lower it to Torchscript before passing it to another compiler
+
+def f(x):
+    return x.cos().cos()
+
+def ts_compiler(fx_g: fx.GraphModule, inps):
+    f = torch.jit.script(fx_g)
+    print(f.graph)
+    f = torch.jit.freeze(f.eval()) # Note: This eval() works fine *even* though we're using this for training
+    return f
+
+aot_function(f, ts_compiler, ts_compiler)(torch.randn(3, requires_grad=True))
+```