Sync from v0.13

tests/kernels/quantization/test_hadacore.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+
+import pytest
+import torch
+from compressed_tensors.transform import deterministic_hadamard_matrix
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+
+if current_platform.is_rocm():
+    pytest.skip(
+        "These tests require hadacore_transform, not supported on ROCm.",
+        allow_module_level=True,
+    )
+
+
+@pytest.mark.parametrize("batch_size", [1, 32])
+@pytest.mark.parametrize("hidden_dim", [2**n for n in range(10)])
+def test_hadacore(batch_size, hidden_dim, dtype=torch.bfloat16, device="cuda"):
+    x = torch.eye(hidden_dim, dtype=dtype, device=device)
+    hadamard = deterministic_hadamard_matrix(
+        hidden_dim, dtype=torch.float64, device="cuda"
+    ) / math.sqrt(hidden_dim)
+
+    y = ops.hadacore_transform(x.clone())
+    y_true = (x.to(hadamard.dtype) @ hadamard.T).to(y.dtype)
+    assert torch.allclose(y, y_true)
+
+    y = ops.hadacore_transform(y)
+    assert torch.allclose(y, x)