Add shortcut to find out if an AbstractArray is differentiable

pulser-core/pulser/math/abstract_array.py

@@ -71,6 +71,11 @@ def is_tensor(self) -> bool:
         """Whether the stored array is a tensor."""
         return self.has_torch() and isinstance(self._array, torch.Tensor)
 
+    @property
+    def is_differentiable(self) -> bool:
+        """Whether the stored array is a differentiable tensor."""
+        return self.is_tensor and cast(torch.Tensor, self._array).requires_grad
+
     def astype(self, dtype: DTypeLike) -> AbstractArray:
         """Casts the data type of the array contents."""
         if self.is_tensor:
@@ -271,10 +276,7 @@ def __setitem__(self, indices: Any, values: AbstractArrayLike) -> None:
                 self._process_indices(indices)
             ] = values  # type: ignore[assignment]
         except RuntimeError as e:
-            if (
-                self.is_tensor
-                and cast(torch.Tensor, self._array).requires_grad
-            ):
+            if self.is_differentiable:
                 raise RuntimeError(
                     "Failed to modify a tensor that requires grad in place."
                 ) from e

pulser-core/pulser/register/base_register.py

@@ -79,7 +79,6 @@ def __init__(
         )
         self._ids: tuple[QubitId, ...] = tuple(qubits.keys())
         if any(not isinstance(id, str) for id in self._ids):
-            warnings.simplefilter("always")
             warnings.warn(
                 "Usage of `int`s or any non-`str`types as `QubitId`s will be "
                 "deprecated. Define your `QubitId`s as `str`s, prefer setting "

tests/test_channels.py

@@ -290,8 +290,7 @@ def test_modulation(channel, tr, eom, side_buffer_len, requires_grad):
         tr,
         tr,
     )
-    if requires_grad:
-        assert out_.as_tensor().requires_grad
+    assert out_.is_differentiable == requires_grad
 
     wf2 = BlackmanWaveform(800, wf_vals[1])
     out_ = channel.modulate(wf2.samples, eom=eom)
@@ -300,8 +299,7 @@ def test_modulation(channel, tr, eom, side_buffer_len, requires_grad):
         side_buffer_len,
         side_buffer_len,
     )
-    if requires_grad:
-        assert out_.as_tensor().requires_grad
+    assert out_.is_differentiable == requires_grad
 
 
 @pytest.mark.parametrize(

tests/test_eom.py

@@ -190,8 +190,7 @@ def calc_offset(amp):
         ]
     )
     assert calculated_det_off == min(det_off_options, key=abs)
-    if requires_grad:
-        assert calculated_det_off.as_tensor().requires_grad
+    assert calculated_det_off.is_differentiable == requires_grad
 
     # Case where the EOM pulses are off-resonant
     detuning_on = detuning_on + 1.0
@@ -210,5 +209,4 @@ def calc_offset(amp):
         assert off_options[0] == eom_.calculate_detuning_off(
             amp, detuning_on, optimal_detuning_off=0.0
         )
-        if requires_grad:
-            assert off_options.as_tensor().requires_grad
+        assert off_options.is_differentiable == requires_grad

tests/test_math.py

@@ -39,8 +39,7 @@ def test_pad(cast_to, requires_grad):
         arr = torch.tensor(arr, requires_grad=requires_grad)
 
     def check_match(arr1: pm.AbstractArray, arr2):
-        if requires_grad:
-            assert arr1.as_tensor().requires_grad
+        assert arr1.is_differentiable == requires_grad
         np.testing.assert_array_equal(
             arr1.as_array(detach=requires_grad), arr2
         )
@@ -260,8 +259,7 @@ def test_items(self, use_tensor, requires_grad, indices):
             assert item == val[i]
             assert isinstance(item, pm.AbstractArray)
             assert item.is_tensor == use_tensor
-            if use_tensor:
-                assert item.as_tensor().requires_grad == requires_grad
+            assert item.is_differentiable == requires_grad
 
         # setitem
         if not requires_grad:
@@ -292,8 +290,8 @@ def test_items(self, use_tensor, requires_grad, indices):
             new_val[indices] = 0.0
             assert np.all(arr_np == new_val)
             assert arr_np.is_tensor
-            # The resulting tensor requires grad if the assing one did
-            assert arr_np.as_tensor().requires_grad == requires_grad
+            # The resulting tensor requires grad if the assigned one did
+            assert arr_np.is_differentiable == requires_grad
 
     @pytest.mark.parametrize("scalar", [False, True])
     @pytest.mark.parametrize(

tests/test_parametrized.py

@@ -105,8 +105,7 @@ def test_var_diff(a, b, requires_grad):
 
     for var in [a, b]:
         assert (
-            a.value is not None
-            and a.value.as_tensor().requires_grad == requires_grad
+            a.value is not None and a.value.is_differentiable == requires_grad
         )
 
 
@@ -167,7 +166,7 @@ def test_paramobj(bwf, t, a, b):
 def test_opsupport(a, b, with_diff_tensor):
     def check_var_grad(var):
         if with_diff_tensor:
-            assert var.build().as_tensor().requires_grad
+            assert var.build().is_differentiable
 
     a._assign(-2.0)
     if with_diff_tensor:

tests/test_pulse.py

@@ -234,9 +234,9 @@ def test_eq():
 
 
 def _assert_pulse_requires_grad(pulse: Pulse, invert: bool = False) -> None:
-    assert pulse.amplitude.samples.as_tensor().requires_grad == (not invert)
-    assert pulse.detuning.samples.as_tensor().requires_grad == (not invert)
-    assert pulse.phase.as_tensor().requires_grad == (not invert)
+    assert pulse.amplitude.samples.is_differentiable == (not invert)
+    assert pulse.detuning.samples.is_differentiable == (not invert)
+    assert pulse.phase.is_differentiable == (not invert)
 
 
 @pytest.mark.parametrize("requires_grad", [True, False])

tests/test_register.py

@@ -508,9 +508,9 @@ def _assert_reg_requires_grad(
 ) -> None:
     for coords in reg.qubits.values():
         if invert:
-            assert not coords.as_tensor().requires_grad
+            assert not coords.is_differentiable
         else:
-            assert coords.is_tensor and coords.as_tensor().requires_grad
+            assert coords.is_tensor and coords.is_differentiable
 
 
 @pytest.mark.parametrize(

tests/test_sequence.py

@@ -2870,12 +2870,12 @@ def test_sequence_diff(device, parametrized, with_modulation, with_eom):
 
     seq_samples = sample(seq, modulation=with_modulation)
     ryd_ch_samples = seq_samples.channel_samples["ryd_global"]
-    assert ryd_ch_samples.amp.as_tensor().requires_grad
-    assert ryd_ch_samples.det.as_tensor().requires_grad
-    assert ryd_ch_samples.phase.as_tensor().requires_grad
+    assert ryd_ch_samples.amp.is_differentiable
+    assert ryd_ch_samples.det.is_differentiable
+    assert ryd_ch_samples.phase.is_differentiable
     if "dmm_0" in seq_samples.channel_samples:
         dmm_ch_samples = seq_samples.channel_samples["dmm_0"]
         # Only detuning is modulated
-        assert not dmm_ch_samples.amp.as_tensor().requires_grad
-        assert dmm_ch_samples.det.as_tensor().requires_grad
-        assert not dmm_ch_samples.phase.as_tensor().requires_grad
+        assert not dmm_ch_samples.amp.is_differentiable
+        assert dmm_ch_samples.det.is_differentiable
+        assert not dmm_ch_samples.phase.is_differentiable

tests/test_sequence_sampler.py

@@ -503,11 +503,11 @@ def test_phase_modulation(off_center, with_diff):
     seq_samples = sample(seq).channel_samples["rydberg_global"]
 
     if with_diff:
-        assert full_phase.samples.as_tensor().requires_grad
-        assert not seq_samples.amp.as_tensor().requires_grad
-        assert seq_samples.det.as_tensor().requires_grad
-        assert seq_samples.phase.as_tensor().requires_grad
-        assert seq_samples.phase_modulation.as_tensor().requires_grad
+        assert full_phase.samples.is_differentiable
+        assert not seq_samples.amp.is_differentiable
+        assert seq_samples.det.is_differentiable
+        assert seq_samples.phase.is_differentiable
+        assert seq_samples.phase_modulation.is_differentiable
 
     np.testing.assert_allclose(
         seq_samples.phase_modulation.as_array(detach=with_diff)

tests/test_waveforms.py

@@ -491,8 +491,7 @@ def test_waveform_diff(
     samples_tensor = wf.samples.as_tensor()
     assert samples_tensor.requires_grad == requires_grad
     assert (
-        wf.modulated_samples(rydberg_global).as_tensor().requires_grad
-        == requires_grad
+        wf.modulated_samples(rydberg_global).is_differentiable == requires_grad
     )
     wfx2_tensor = (-wf * 2).samples.as_tensor()
     assert torch.equal(wfx2_tensor, samples_tensor * -2.0)
@@ -501,14 +500,13 @@ def test_waveform_diff(
     wfdiv2 = wf / torch.tensor(2.0, requires_grad=True)
     assert torch.equal(wfdiv2.samples.as_tensor(), samples_tensor / 2.0)
     # Should always be true because it was divided by diff tensor
-    assert wfdiv2.samples.as_tensor().requires_grad
+    assert wfdiv2.samples.is_differentiable
 
-    assert wf[-1].as_tensor().requires_grad == requires_grad
+    assert wf[-1].is_differentiable == requires_grad
 
     try:
         assert (
-            wf.change_duration(1000).samples.as_tensor().requires_grad
-            == requires_grad
+            wf.change_duration(1000).samples.is_differentiable == requires_grad
         )
     except NotImplementedError:
         pass