shard and to should preserve requires_grad (tinygrad#3224)

dtypes are inferred from underlying lazydata, requires_grad needs to be passed explicitly

test/test_tensor.py

@@ -3,8 +3,12 @@
 import unittest, copy
 import mmap
 from tinygrad import Tensor, Device, dtypes
-from tinygrad.helpers import temp
+from tinygrad.helpers import temp, CI
 from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
+from hypothesis import given, settings, strategies as strat
+
+settings.register_profile("my_profile", max_examples=200, deadline=None)
+settings.load_profile("my_profile")
 
 x_init = np.random.randn(1,3).astype(np.float32)
 U_init = np.random.randn(3,3).astype(np.float32)
@@ -315,6 +319,26 @@ def test_item_to_tensor_to_item(self):
       assert type(reshaped_item) == type(a), a
       np.testing.assert_allclose(reshaped_item, a), a
 
+@unittest.skipIf(CI and Device.DEFAULT in {"GPU", "CUDA", "METAL"}, "no GPU CI")
+class TestMoveTensor(unittest.TestCase):
+  d0, d1 = f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"
+  @given(strat.sampled_from([d0, d1]), strat.sampled_from([d0, d1]),
+         strat.sampled_from([dtypes.float16, dtypes.float32]), strat.sampled_from([True, False, None]))
+  def test_to_preserves(self, src, dest, dtype, requires_grad):
+    s = Tensor([1, 2, 3], device=src, dtype=dtype, requires_grad=requires_grad)
+    t = s.to(dest)
+    np.testing.assert_equal(s.numpy(), t.numpy())
+    assert s.dtype == t.dtype
+    assert s.requires_grad == t.requires_grad
+
+  @given(strat.sampled_from([dtypes.float16, dtypes.float32]), strat.sampled_from([True, False, None]))
+  def test_shard_preserves(self, dtype, requires_grad):
+    s = Tensor([1, 2, 3], dtype=dtype, requires_grad=requires_grad)
+    t = s.shard((f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"))
+    np.testing.assert_equal(s.numpy(), t.numpy())
+    assert s.dtype == t.dtype
+    assert s.requires_grad == t.requires_grad
+
 class TestZeroShapeTensor(unittest.TestCase):
   def test_shape_stride(self):
     t = Tensor.rand(3, 2, 0)

tinygrad/tensor.py

@@ -159,7 +159,7 @@ def numpy(self) -> np.ndarray:
   def to(self, device:Optional[Union[str, Tuple[str, ...]]]) -> Tensor:
     if device is None or device == self.device: return self
     if not isinstance(device, str): return self.shard(device)
-    ret = Tensor(self.lazydata, device)
+    ret = Tensor(self.lazydata, device, requires_grad=self.requires_grad)
     if self.grad: ret.grad = self.grad.to(device)
     return ret
 
@@ -173,7 +173,7 @@ def shard(self, devices:Tuple[str, ...], axis:Optional[int]=None) -> Tensor:
     assert isinstance(self.lazydata, LazyBuffer), "can't shard a MultiLazyBuffer"
     canonical_devices = tuple(Device.canonicalize(x) for x in devices)
     if axis is not None and axis < 0: axis += len(self.shape)
-    return Tensor(MultiLazyBuffer.from_sharded(self.lazydata, canonical_devices, axis), device=canonical_devices)
+    return Tensor(MultiLazyBuffer.from_sharded(self.lazydata, canonical_devices, axis), device=canonical_devices, requires_grad=self.requires_grad)
 
   def shard_(self, devices:Tuple[str, ...], axis:Optional[int]=None):
     self.lazydata = self.shard(devices, axis).lazydata