gdb special command to print tensors (#54339)

Summary:
This is something which I wrote because it was useful during my debugging sessions, but I think it might be generally useful to other people as well so I took the liberty of proposing an official `pytorch-gdb` extension.

`pytorch-gdb` is a gdb script written in python. Currently, it contains only one command: `torch-tensor-repr`, which prints a human-readable repr of an `at::Tensor` object. Example:
```
Breakpoint 1, at::native::neg (self=...) at [...]/pytorch/aten/src/ATen/native/UnaryOps.cpp:520
520     Tensor neg(const Tensor& self) { return unary_op_impl(self, at::neg_out); }
(gdb) # the default repr of 'self' is not very useful
(gdb) p self
$1 = (const at::Tensor &) 0x7ffff72ed780: {impl_ = {target_ = 0x5555559df6e0}}
(gdb) torch-tensor-repr self
Python-level repr of self:
tensor([1., 2., 3., 4.], dtype=torch.float64)
```

The idea is that by having an official place where to put these things, `pytorch-gdb` will slowly grow other useful features and make the pytorch debugging experience nicer and faster.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/54339

Reviewed By: bdhirsh

Differential Revision: D27253674

Pulled By: ezyang

fbshipit-source-id: dba219e126cc2fe66b2d26740f3a8e3b886e56f5

tools/gdb/pytorch-gdb.py

@@ -0,0 +1,52 @@
+import gdb
+import textwrap
+
+class DisableBreakpoints:
+    """
+    Context-manager to temporarily disable all gdb breakpoints, useful if
+    there is a risk to hit one during the evaluation of one of our custom
+    commands
+    """
+
+    def __enter__(self):
+        self.disabled_breakpoints = []
+        for b in gdb.breakpoints():
+            if b.enabled:
+                b.enabled = False
+                self.disabled_breakpoints.append(b)
+
+    def __exit__(self, etype, evalue, tb):
+        for b in self.disabled_breakpoints:
+            b.enabled = True
+
+class TensorRepr(gdb.Command):
+    """
+    Print a human readable representation of the given at::Tensor.
+    Usage: torch-tensor-repr EXP
+
+    at::Tensor instances do not have a C++ implementation of a repr method: in
+    pytoch, this is done by pure-Python code. As such, torch-tensor-repr
+    internally creates a Python wrapper for the given tensor and call repr()
+    on it.
+    """
+    __doc__ = textwrap.dedent(__doc__).strip()
+
+    def __init__(self):
+        gdb.Command.__init__(self, 'torch-tensor-repr',
+                             gdb.COMMAND_USER, gdb.COMPLETE_EXPRESSION)
+
+    def invoke(self, args, from_tty):
+        args = gdb.string_to_argv(args)
+        if len(args) != 1:
+            print('Usage: torch-tensor-repr EXP')
+            return
+        name = args[0]
+        with DisableBreakpoints():
+            res = gdb.parse_and_eval('torch::gdb::tensor_repr(%s)' % name)
+            print('Python-level repr of %s:' % name)
+            print(res.string())
+            # torch::gdb::tensor_repr returns a malloc()ed buffer, let's free it
+            gdb.parse_and_eval('(void)free(%s)' % int(res))
+
+TensorRepr()
+