Merge branch 'IntelPython:master' into feature-sparse-linalg-solvers

Author: abagusetty

.github/workflows/build-sphinx.yml

@@ -224,7 +224,7 @@ jobs:
         if: env.GH_EVENT_OPEN_PR_UPSTREAM == 'true'
         env:
           PR_NUM: ${{ github.event.number }}
-        uses: mshick/add-pr-comment@ffd016c7e151d97d69d21a843022fd4cd5b96fe5 # v3.9.0.8.3.9.0
+        uses: mshick/add-pr-comment@64b8e914979889d746c99dea15a76e77ef64580a # v3.10.0.8.3.10.0
         with:
           message-id: url_to_docs
           message: |
@@ -268,7 +268,7 @@ jobs:
           git push tokened_docs gh-pages
 
       - name: Modify the comment with URL to official documentation
-        uses: mshick/add-pr-comment@ffd016c7e151d97d69d21a843022fd4cd5b96fe5 # v3.9.0.8.3.9.0
+        uses: mshick/add-pr-comment@64b8e914979889d746c99dea15a76e77ef64580a # v3.10.0.8.3.10.0
         with:
           message-id: url_to_docs
           find: |

.github/workflows/conda-package.yml

@@ -654,7 +654,7 @@ jobs:
 
       - name: Post result to PR
         if: ${{ github.event.pull_request && !github.event.pull_request.head.repo.fork }}
-        uses: mshick/add-pr-comment@ffd016c7e151d97d69d21a843022fd4cd5b96fe5 # v3.9.0.8.3.9.0
+        uses: mshick/add-pr-comment@64b8e914979889d746c99dea15a76e77ef64580a # v3.10.0.8.3.10.0
         with:
           message-id: array_api_results
           message: |

.pre-commit-config.yaml

@@ -124,7 +124,7 @@ repos:
     -   id: pretty-format-toml
         args: [--autofix]
 -   repo: https://github.com/rhysd/actionlint
-    rev: v1.7.11
+    rev: v1.7.12
     hooks:
     -   id: actionlint
 -   repo: https://github.com/BlankSpruce/gersemi

CHANGELOG.md

@@ -83,6 +83,7 @@ Also, that release drops support for Python 3.9, making Python 3.10 the minimum
 * Fixed `.data.ptr` property on array views to correctly return the pointer to the view's data location instead of the base allocation pointer [#2812](https://github.com/IntelPython/dpnp/pull/2812)
 * Resolved an issue with strides calculation in `dpnp.diagonal` to return correct values for empty diagonals [#2814](https://github.com/IntelPython/dpnp/pull/2814)
 * Fixed test tolerance issues for float16 intermediate precision that became visible when testing against conda-forge's NumPy [#2828](https://github.com/IntelPython/dpnp/pull/2828)
+* Ensured device aware dtype handling in `dpnp.identity` and `dpnp.gradient` [#2835](https://github.com/IntelPython/dpnp/pull/2835)
 
 ### Security
 

dpnp/dpnp_iface_arraycreation.py

@@ -2664,10 +2664,9 @@ def identity(
 
     dpnp.check_limitations(like=like)
 
-    _dtype = dpnp.default_float_type() if dtype is None else dtype
     return dpnp.eye(
         n,
-        dtype=_dtype,
+        dtype=dtype,
         device=device,
         usm_type=usm_type,
         sycl_queue=sycl_queue,

dpnp/dpnp_iface_mathematical.py

@@ -141,7 +141,9 @@ def _gradient_build_dx(f, axes, *varargs):
             if dpnp.issubdtype(distances.dtype, dpnp.integer):
                 # Convert integer types to default float type to avoid modular
                 # arithmetic in dpnp.diff(distances).
-                distances = distances.astype(dpnp.default_float_type())
+                distances = distances.astype(
+                    dpnp.default_float_type(sycl_queue=f.sycl_queue)
+                )
             diffx = dpnp.diff(distances)
 
             # if distances are constant reduce to the scalar case
@@ -2707,9 +2709,9 @@ def gradient(f, *varargs, axis=None, edge_order=1):
         # All other types convert to floating point.
         # First check if f is a dpnp integer type; if so, convert f to default
         # float type to avoid modular arithmetic when computing changes in f.
-        if dpnp.issubdtype(otype, dpnp.integer):
-            f = f.astype(dpnp.default_float_type())
-        otype = dpnp.default_float_type()
+        otype = dpnp.default_float_type(sycl_queue=f.sycl_queue)
+        if dpnp.issubdtype(f.dtype, dpnp.integer):
+            f = f.astype(otype)
 
     for axis_, ax_dx in zip(axes, dx):
         if f.shape[axis_] < edge_order + 1:

dpnp/tests/test_sycl_queue.py

@@ -54,6 +54,23 @@ def assert_sycl_queue_equal(result, expected):
     assert exec_queue is not None
 
 
+def get_all_dev_dtypes(no_float16=True, no_none=True):
+    """
+    Build a list of (device, dtype) combinations for each device's
+    supported dtype.
+
+    """
+
+    device_dtype_pairs = []
+    for device in valid_dev:
+        dtypes = get_all_dtypes(
+            no_float16=no_float16, no_none=no_none, device=device
+        )
+        for dtype in dtypes:
+            device_dtype_pairs.append((device, dtype))
+    return device_dtype_pairs
+
+
 @pytest.mark.parametrize(
     "func, arg, kwargs",
     [
@@ -1082,11 +1099,10 @@ def test_array_creation_from_dpctl(copy, device):
     assert isinstance(result, dpnp_array)
 
 
-@pytest.mark.parametrize("device", valid_dev, ids=dev_ids)
-@pytest.mark.parametrize("arr_dtype", get_all_dtypes(no_float16=True))
+@pytest.mark.parametrize("device, dt", get_all_dev_dtypes())
 @pytest.mark.parametrize("shape", [tuple(), (2,), (3, 0, 1), (2, 2, 2)])
-def test_from_dlpack(arr_dtype, shape, device):
-    X = dpnp.ones(shape=shape, dtype=arr_dtype, device=device)
+def test_from_dlpack(shape, device, dt):
+    X = dpnp.ones(shape=shape, dtype=dt, device=device)
     Y = dpnp.from_dlpack(X)
     assert_array_equal(X, Y)
     assert X.__dlpack_device__() == Y.__dlpack_device__()
@@ -1098,10 +1114,9 @@ def test_from_dlpack(arr_dtype, shape, device):
         assert V.strides == W.strides
 
 
-@pytest.mark.parametrize("device", valid_dev, ids=dev_ids)
-@pytest.mark.parametrize("arr_dtype", get_all_dtypes(no_float16=True))
-def test_from_dlpack_with_dpt(arr_dtype, device):
-    X = dpt.ones((64,), dtype=arr_dtype, device=device)
+@pytest.mark.parametrize("device, dt", get_all_dev_dtypes())
+def test_from_dlpack_with_dpt(device, dt):
+    X = dpt.ones((64,), dtype=dt, device=device)
     Y = dpnp.from_dlpack(X)
     assert_array_equal(X, Y)
     assert isinstance(Y, dpnp.dpnp_array.dpnp_array)