[Spyre-Next] SpyreSiluAndMul Tests

vllm_spyre_next/tests/test_silu_and_mul.py

@@ -0,0 +1,40 @@
+"""
+Test SpyreSiluAndMul custom op correctness against a reference implementation.
+"""
+
+import pytest
+import torch
+
+@pytest.fixture
+def dummy_tensor():
+    return torch.randn(4, 256, dtype=torch.float32)  # 2*d=256, so d=128
+
+
+def mock_forward_native(x):
+    """Mock: return ones with output shape (halves last dim)."""
+    d = x.shape[-1] // 2
+    return torch.ones(x.shape[:-1] + (d,), dtype=x.dtype, device=x.device)
+
+
+@pytest.mark.spyre
+@pytest.mark.siluandmul
+def test_siluandmul_oot_dispatch(default_vllm_config, monkeypatch, dummy_tensor):
+    """Verify SiluAndMul OOT registration: class swap and forward_oot routing."""
+    from vllm.model_executor.layers.activation import SiluAndMul
+    from vllm_spyre_next.custom_ops.silu_and_mul import SpyreSiluAndMul
+
+    layer = SiluAndMul()
+
+    # OOT class swap: SiluAndMul.__new__ should produce SpyreSiluAndMul
+    assert isinstance(layer, SpyreSiluAndMul)
+
+    # dispatch_forward should have selected forward_oot
+    assert layer._forward_method == layer.forward_oot
+
+    # Mock forward_native (called by forward_oot) with a known transform
+    monkeypatch.setattr(layer, "forward_native", mock_forward_native)
+    out = layer.forward_oot(dummy_tensor)
+
+    # Expected: ones with shape [4, 128] (halved last dim)
+    expected = torch.ones(4, 128, dtype=dummy_tensor.dtype)
+    assert torch.allclose(out, expected)

vllm_spyre_next/vllm_spyre_next/testing/pytest_plugin.py

@@ -526,6 +526,28 @@ def sort_key(item: pytest.Item) -> tuple[int, int]:
     items.sort(key=sort_key)
 
 
+def _convert_yaml_value(value):
+    """Convert YAML string values to Python objects where appropriate.
+
+    Handles torch dtype strings like "torch.half" -> torch.half.
+    """
+    if isinstance(value, str):
+        import torch
+
+        dtype_map = {
+            "torch.half": torch.half,
+            "torch.float16": torch.float16,
+            "torch.bfloat16": torch.bfloat16,
+            "torch.float32": torch.float32,
+            "torch.float": torch.float,
+            "torch.float64": torch.float64,
+            "torch.double": torch.double,
+        }
+        if value in dtype_map:
+            return dtype_map[value]
+    return value
+
+
 @pytest.hookimpl(tryfirst=True)
 def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
     """Apply parameter overrides from YAML config."""
@@ -554,7 +576,7 @@ def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
         for i, marker in enumerate(metafunc.definition.own_markers):
             if marker.name == "parametrize" and marker.args[0] == po.param_name:
                 metafunc.definition.own_markers[i] = pytest.mark.parametrize(
-                    po.param_name, list(po.values)
+                    po.param_name, [_convert_yaml_value(v) for v in po.values]
                 ).mark
                 break
 

vllm_spyre_next/vllm_spyre_next/testing/upstream_tests.yaml

@@ -33,6 +33,18 @@ tests:
     #           hidden_size: [64]
     #   block_list:
     #     - test: "test_fused_rms_norm_quant"
+    - rel_path: tests/kernels/core/test_activation.py
+      allow_list:
+        - test: "test_act_and_mul"
+          mode: mandatory_pass
+          tags: [siluandmul, upstream]
+          params:
+            allow:
+              activation: [silu_and_mul]
+            override:
+              num_tokens: [7, 83, 1024]
+              d: [8, 64, 128, 1024]
+              device: [cpu]
     - rel_path: tests/models/language/generation/test_common.py
       allow_list:
         - test: "test_models"