Merge branch 'lwawrzyniak/fix-typestr-for-bytes' into 'main'

Fixed array interface type strings

See merge request omniverse/warp!756

CHANGELOG.md

@@ -57,6 +57,7 @@
 - Fix a crash when kernel functions are not found in CPU modules.
 - Fix conditions not being evaluated as expected in `while` statements.
 - Fix printing Boolean and 8-bit integer values.
+- Fix array interface type strings used for Boolean and 8-bit integer values.
 
 ## [1.3.3] - 2024-09-04
 

warp/tests/test_array.py

@@ -2590,6 +2590,25 @@ def test_array_from_int64_domain(test, device):
     wp.zeros(np.array([1504, 1080, 520], dtype=np.int64), dtype=wp.float32, device=device)
 
 
+def test_numpy_array_interface(test, device):
+    # We should be able to convert between NumPy and Warp arrays using __array_interface__ on CPU.
+    # This tests all scalar types supported by both.
+
+    n = 10
+
+    scalar_types = wp.types.scalar_types
+
+    for dtype in scalar_types:
+        # test round trip
+        a1 = wp.zeros(n, dtype=dtype, device="cpu")
+        na = np.array(a1)
+        a2 = wp.array(na, device="cpu")
+
+        assert a1.dtype == a2.dtype
+        assert a1.shape == a2.shape
+        assert a1.strides == a2.strides
+
+
 devices = get_test_devices()
 
 
@@ -2648,6 +2667,7 @@ def test_array_new_del(self):
 add_function_test(TestArray, "test_array_of_structs_roundtrip", test_array_of_structs_roundtrip, devices=devices)
 add_function_test(TestArray, "test_array_from_numpy", test_array_from_numpy, devices=devices)
 add_function_test(TestArray, "test_array_aliasing_from_numpy", test_array_aliasing_from_numpy, devices=["cpu"])
+add_function_test(TestArray, "test_numpy_array_interface", test_numpy_array_interface, devices=["cpu"])
 
 add_function_test(TestArray, "test_array_inplace_ops", test_array_inplace_ops, devices=devices)
 add_function_test(TestArray, "test_direct_from_numpy", test_direct_from_numpy, devices=["cpu"])

warp/tests/test_torch.py

@@ -382,6 +382,27 @@ def wrap_vec_tensor_with_warp_grad(vec_dtype):
     wrap_vec_tensor_with_warp_grad(wp.transform)
 
 
+def test_cuda_array_interface(test, device):
+    # We should be able to construct Torch tensors from Warp arrays via __cuda_array_interface__ on GPU.
+    # Note that Torch does not support __array_interface__ on CPU.
+
+    torch_device = wp.device_to_torch(device)
+    n = 10
+
+    # test the types supported by both Warp and Torch
+    scalar_types = [wp.float16, wp.float32, wp.float64, wp.int8, wp.int16, wp.int32, wp.int64, wp.uint8]
+
+    for dtype in scalar_types:
+        # test round trip
+        a1 = wp.zeros(n, dtype=dtype, device=device)
+        t = torch.tensor(a1, device=torch_device)
+        a2 = wp.array(t, device=device)
+
+        assert a1.dtype == a2.dtype
+        assert a1.shape == a2.shape
+        assert a1.strides == a2.strides
+
+
 def test_to_torch(test, device):
     import torch
 
@@ -918,6 +939,9 @@ class TestTorch(unittest.TestCase):
             test_warp_graph_torch_stream,
             devices=torch_compatible_cuda_devices,
         )
+        add_function_test(
+            TestTorch, "test_cuda_array_interface", test_cuda_array_interface, devices=torch_compatible_cuda_devices
+        )
 
     # multi-GPU tests
     if len(torch_compatible_cuda_devices) > 1:

warp/types.py

@@ -1302,17 +1302,17 @@ def type_to_warp(dtype):
 
 def type_typestr(dtype):
     if dtype == bool:
-        return "?"
+        return "|b1"
     elif dtype == float16:
         return "<f2"
     elif dtype == float32:
         return "<f4"
     elif dtype == float64:
         return "<f8"
     elif dtype == int8:
-        return "b"
+        return "|i1"
     elif dtype == uint8:
-        return "B"
+        return "|u1"
     elif dtype == int16:
         return "<i2"
     elif dtype == uint16: