Format debug output
@@ -12,7 +12,7 @@ if DEBUG:
debug_times = collections.defaultdict(float)
def print_debug_exit():
for name, _ in sorted(debug_times.items(), key=lambda x: -x[1]):
- print("%20s : %3d %10.2f ms" % (name, debug_counts[name], debug_times[name]))
+ print(f"{name:>20} : {debug_counts[name]:>6} {debug_times[name]:>10.2f} ms")
atexit.register(print_debug_exit)
class ProfileOp:
@@ -26,7 +26,7 @@ class ProfileOp:
et = (time.time()-self.st)*1000.
debug_counts[self.name] += 1
debug_times[self.name] += et
- print("%20s : %7.2f ms %s" % (self.name, et, [y.shape for y in self.x]))
+ print(f"{self.name:>20} : {et:>7.2f} ms {[y.shape for y in self.x]}")
# **** GPU functions ****
@@ -46,7 +46,7 @@ class GPUBuffer:
hostbuf=hostbuf.astype(np.float32).ravel() if hostbuf is not None else None)
def __repr__(self):
- return "<GPUBuffer with shape %r>" % (self.shape,)
+ return f"<GPUBuffer with shape {self.shape!r}>"
# **** start with two base classes, Tensor and Function ****
@@ -64,12 +64,12 @@ class Tensor:
elif GPU and isinstance(data, GPUBuffer):
self.gpu = True
elif not isinstance(data, np.ndarray):
- raise TypeError("Error constructing tensor with %r" % data)
+ raise TypeError(f"Error constructing tensor with {data!r}")
if isinstance(data, np.ndarray):
if data.dtype != np.float32 and not Tensor.did_float_warning:
# warning? float64 is actually needed for numerical jacobian
- print("warning, %r isn't float32" % (data.shape,))
+ print(f"warning, {data.shape!r} isn't float32")
Tensor.did_float_warning = True
self.gpu = False
@@ -90,7 +90,7 @@ class Tensor:
Tensor.allocated -= 1
def __repr__(self):
- return "Tensor %r with grad %r" % (self.data, self.grad.data if self.grad else None)
+ return f"Tensor {self.data!r} with grad {(self.grad.data if self.grad else None)!r}"
def assign(self, x):
self.data = x.data
@@ -155,7 +155,7 @@ class Tensor:
if g is None:
continue
assert g.shape == t.shape, \
- "grad shape must match tensor shape in %r, %r != %r" % (self._ctx, g.shape, t.shape)
+ f"grad shape must match tensor shape in {self._ctx!r}, {g.shape!r} != {t.shape!r}"
gt = Tensor(g, requires_grad=False)
t.grad = gt if t.grad is None else (t.grad + gt)
@@ -250,8 +250,8 @@ def register(name, fxn, gpu=False):
return f.apply(f, *x, **kwargs)
setattr(Tensor, name, dispatch)
if name in ['add', 'sub', 'mul', 'div', 'pow']:
- setattr(Tensor, "__%s__" % name, dispatch)
- setattr(Tensor, "__i%s__" % name, lambda self,x: self.assign(dispatch(self,x)))
+ setattr(Tensor, f"__{name}__", dispatch)
+ setattr(Tensor, f"__i{name}__", lambda self,x: self.assign(dispatch(self,x)))
# this registers all the operations
import tinygrad.ops
