[hipDNN] ALMIOPEN-1943 Add pytest suite and CI workflow for Python bindings

.github/actions/ci-env/action.yml

@@ -4,7 +4,7 @@ description: "Single source of truth for shared CI constants (TheRock ref, Docke
 outputs:
   therock-ref:
     description: "TheRock commit hash to use"
-    value: "94675052fd427fd2cc6ec9180079b3a0dc7d26b2" # 2026-05-27 commit
+    value: "9c0ca0a4a6900c6dfb5ede35f2034f303b53433e"
   docker-image:
     description: "Docker container image for Linux builds"
     value: "ghcr.io/rocm/therock_build_manylinux_x86_64@sha256:48492540591673fdc8d51beb89bde41e7ba13cbb528f643c0a481ba42c4058f2"

.github/scripts/therock_matrix.py

@@ -109,6 +109,7 @@
         "projects_to_test": [
             "hipdnn",
             "hipdnn_install",
+            "hipdnn_python_bindings",
             "hipdnn-samples",
             "miopenprovider",
             "hipblasltprovider",

.github/workflows/therock-ci-linux.yml

@@ -51,8 +51,7 @@ jobs:
         with:
           repository: "ROCm/TheRock"
           path: TheRock
-          ref: ${{ inputs.therock_ref }}
-
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
       - name: Install python deps
         run: |
           pip install -r TheRock/requirements.txt

.github/workflows/therock-ci-nightly.yml

@@ -43,8 +43,7 @@ jobs:
         with:
           repository: "ROCm/TheRock"
           path: TheRock
-          ref: ${{ steps.ci-env.outputs.therock-ref }}
-
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
       - name: Set up Python
         uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
         with:

.github/workflows/therock-ci-windows.yml

@@ -52,7 +52,7 @@ jobs:
         with:
           repository: "ROCm/TheRock"
           path: "TheRock"
-          ref: ${{ inputs.therock_ref }}
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
 
       - name: Set up Python
         uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0

.github/workflows/therock-ci.yml

@@ -69,8 +69,7 @@ jobs:
         with:
           repository: "ROCm/TheRock"
           path: TheRock
-          ref: ${{ steps.ci-env.outputs.therock-ref }}
-
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
       - name: Set up Python
         uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
         with:

.github/workflows/therock-test-component.yml

@@ -73,7 +73,8 @@ jobs:
         uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
         with:
           repository: "ROCm/TheRock"
-          ref: ${{ inputs.therock_ref }}
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
+
 
       - name: Configure git for long paths on Windows
         if: ${{ runner.os == 'Windows' }}

.github/workflows/therock-test-packages.yml

@@ -46,7 +46,7 @@ jobs:
         uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
         with:
           repository: "ROCm/TheRock"
-          ref: ${{ inputs.therock_ref }}
+          ref: 9c0ca0a4a6900c6dfb5ede35f2034f303b53433e
 
       - name: Setting up Python
         uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0

projects/hipdnn/python/CMakeLists.txt

@@ -59,4 +59,7 @@ else()
     install(TARGETS hipdnn_frontend_python DESTINATION "${_staging}")
     install(FILES "${CMAKE_CURRENT_SOURCE_DIR}/hipdnn_frontend/__init__.py"
             DESTINATION "${_staging}")
+    install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/hipdnn_frontend/test/"
+            DESTINATION "share/hipdnn/tests/python"
+            FILES_MATCHING PATTERN "*.py")
 endif()

projects/hipdnn/python/hipdnn_frontend/test/__init__.py

@@ -0,0 +1,2 @@
+# Copyright © Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier:  MIT

projects/hipdnn/python/hipdnn_frontend/test/conftest.py

@@ -0,0 +1,24 @@
+# Copyright © Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier:  MIT
+
+"""Shared pytest fixtures for hipDNN Python binding tests."""
+
+import pytest
+
+import hipdnn_frontend as hipdnn
+
+
+@pytest.fixture()
+def handle():
+    """Create a hipDNN handle for GPU operations."""
+    return hipdnn.create_handle()
+
+
+@pytest.fixture()
+def graph():
+    """Create a hipDNN Graph configured with FLOAT data types."""
+    g = hipdnn.Graph()
+    g.set_io_data_type(hipdnn.DataType.FLOAT)
+    g.set_intermediate_data_type(hipdnn.DataType.FLOAT)
+    g.set_compute_data_type(hipdnn.DataType.FLOAT)
+    return g

projects/hipdnn/python/hipdnn_frontend/test/helpers.py

@@ -0,0 +1,88 @@
+# Copyright © Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier:  MIT
+
+"""Shared helper functions for hipDNN Python binding tests."""
+
+import numpy as np
+
+import hipdnn_frontend as hipdnn
+
+
+def build_conv_fprop_graph(
+    graph,
+    n=16,
+    c=16,
+    h=16,
+    w=16,
+    k=16,
+    r=3,
+    s=3,
+    stride=1,
+    pad=1,
+    dilation=1,
+):
+    """Build a complete convolution forward propagation graph.
+
+    Returns:
+        Tuple of (graph, x_tensor, weight_tensor, y_tensor, out_h, out_w).
+    """
+    out_h = (h + 2 * pad - dilation * (r - 1) - 1) // stride + 1
+    out_w = (w + 2 * pad - dilation * (s - 1) - 1) // stride + 1
+
+    graph.set_name("conv_fprop_test")
+
+    x = hipdnn.Tensor.create([n, c, h, w], hipdnn.DataType.FLOAT)
+    x.set_name("input_x")
+
+    weight = hipdnn.Tensor.create([k, c, r, s], hipdnn.DataType.FLOAT)
+    weight.set_name("weight")
+
+    conv_attrs = hipdnn.ConvFpropAttributes()
+    conv_attrs.set_name("conv_fprop_node")
+    conv_attrs.set_padding([pad, pad])
+    conv_attrs.set_stride([stride, stride])
+    conv_attrs.set_dilation([dilation, dilation])
+
+    y = graph.conv_fprop(x, weight, conv_attrs)
+    y.set_name("output_y")
+    y.set_output(True)
+
+    return graph, x, weight, y, out_h, out_w
+
+
+def execute_graph(graph, handle, tensor_uid_to_data):
+    """Execute a graph with the given tensor data.
+
+    Args:
+        graph: A fully-built hipDNN graph (validated, built, plans created).
+        handle: A hipDNN handle.
+        tensor_uid_to_data: Dict mapping tensor UIDs to numpy arrays.
+            Output tensors should have zero-initialized arrays.
+
+    Returns:
+        Dict mapping tensor UIDs to result numpy arrays (copied from device).
+    """
+    buffers = {}
+    variant_pack = {}
+    for uid, data in tensor_uid_to_data.items():
+        buf = hipdnn.DeviceBuffer(data.nbytes)
+        buf.copy_from_host(data.tobytes())
+        buffers[uid] = (buf, data.shape, data.dtype)
+        variant_pack[uid] = buf.ptr()
+
+    workspace_size = graph.get_workspace_size()
+    workspace_buffer = None
+    workspace_ptr = 0
+    if workspace_size > 0:
+        workspace_buffer = hipdnn.DeviceBuffer(workspace_size)
+        workspace_ptr = workspace_buffer.ptr()
+
+    exec_result = graph.execute(handle, variant_pack, workspace_ptr)
+    assert exec_result.is_good(), f"Graph execution failed: {exec_result.get_message()}"
+
+    results = {}
+    for uid, (buf, shape, dtype) in buffers.items():
+        host_bytes = buf.copy_to_host()
+        results[uid] = np.frombuffer(host_bytes, dtype=dtype).reshape(shape)
+
+    return results

projects/hipdnn/python/hipdnn_frontend/test/test_conv_dgrad.py

@@ -0,0 +1,104 @@
+# Copyright © Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier:  MIT
+
+"""Integration tests for convolution backward data gradient."""
+
+import numpy as np
+import pytest
+
+import hipdnn_frontend as hipdnn
+
+from .helpers import execute_graph
+
+# Dimensions used across tests
+N, C, H, W = 16, 16, 16, 16
+K, R, S = 16, 3, 3
+STRIDE, PAD, DIL = 1, 1, 1
+OUT_H = (H + 2 * PAD - DIL * (R - 1) - 1) // STRIDE + 1
+OUT_W = (W + 2 * PAD - DIL * (S - 1) - 1) // STRIDE + 1
+
+
+def _build_conv_dgrad_graph(graph):
+    """Build a conv_dgrad graph returning (graph, dy, weight, dx)."""
+    graph.set_name("conv_dgrad_test")
+
+    dy = hipdnn.Tensor.create([N, K, OUT_H, OUT_W], hipdnn.DataType.FLOAT)
+    dy.set_name("output_gradient_dy")
+
+    weight = hipdnn.Tensor.create([K, C, R, S], hipdnn.DataType.FLOAT)
+    weight.set_name("weight")
+
+    conv_attrs = hipdnn.ConvDgradAttributes()
+    conv_attrs.set_name("conv_dgrad_node")
+    conv_attrs.set_pre_padding([PAD, PAD])
+    conv_attrs.set_post_padding([PAD, PAD])
+    conv_attrs.set_stride([STRIDE, STRIDE])
+    conv_attrs.set_dilation([DIL, DIL])
+
+    dx = graph.conv_dgrad(dy, weight, conv_attrs)
+    dx.set_name("input_gradient_dx")
+    dx.set_output(True)
+
+    return graph, dy, weight, dx
+
+
+@pytest.mark.gpu
+@pytest.mark.integration
+class TestConvDgrad:
+    """Tests for convolution backward data gradient end-to-end pipeline."""
+
+    def test_graph_validates_successfully(self, graph):
+        """Build a conv_dgrad graph and verify validation passes."""
+        graph, dy, weight, dx = _build_conv_dgrad_graph(graph)
+
+        result = graph.validate()
+        assert result.is_good(), f"Validation failed: {result.get_message()}"
+
+    def test_operation_graph_builds(self, graph, handle):
+        """Build a conv_dgrad operation graph with backend handle."""
+        graph, dy, weight, dx = _build_conv_dgrad_graph(graph)
+
+        result = graph.validate()
+        assert result.is_good(), f"Validation failed: {result.get_message()}"
+
+        result = graph.build_operation_graph(handle)
+        assert result.is_good(), f"Build operation graph failed: {result.get_message()}"
+
+    def test_execution_plans_created(self, graph, handle):
+        """Build execution plans for conv_dgrad."""
+        graph, dy, weight, dx = _build_conv_dgrad_graph(graph)
+
+        assert graph.validate().is_good()
+        assert graph.build_operation_graph(handle).is_good()
+        assert graph.create_execution_plans().is_good()
+        assert graph.check_support().is_good()
+        assert graph.build_plans().is_good()
+
+    def test_execution_produces_nonzero_output(self, graph, handle):
+        """Full end-to-end conv_dgrad: execute and verify non-zero output."""
+        graph, dy, weight, dx = _build_conv_dgrad_graph(graph)
+
+        assert graph.validate().is_good()
+        assert graph.build_operation_graph(handle).is_good()
+        assert graph.create_execution_plans().is_good()
+        assert graph.check_support().is_good()
+        assert graph.build_plans().is_good()
+
+        dy_data = np.random.uniform(
+            0.0,
+            1.0,
+            [N, K, OUT_H, OUT_W],
+        ).astype(np.float32)
+        w_data = np.random.uniform(0.0, 1.0, [K, C, R, S]).astype(np.float32)
+        dx_data = np.zeros([N, C, H, W], dtype=np.float32)
+
+        tensor_data = {
+            dy.get_uid(): dy_data,
+            weight.get_uid(): w_data,
+            dx.get_uid(): dx_data,
+        }
+
+        results = execute_graph(graph, handle, tensor_data)
+        dx_result = results[dx.get_uid()]
+
+        assert not np.all(dx_result == 0), "Conv dgrad output is all zeros"