chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,121 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# ruff: noqa: E741
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import te
+from tvm.contrib import hipblas
+from tvm.testing import env
+
+
+def verify_matmul_add(in_dtype, out_dtype, rtol=1e-5):
+    n = 1024
+    l = 128
+    m = 236
+    A = te.placeholder((n, l), name="A", dtype=in_dtype)
+    B = te.placeholder((l, m), name="B", dtype=in_dtype)
+    C = hipblas.matmul(A, B, dtype=out_dtype)
+
+    def verify(target="rocm"):
+        if not tvm.get_global_func("tvm.contrib.hipblas.matmul", True):
+            print("skip because extern function is not available")
+            return
+        f = tvm.compile(te.create_prim_func([A, B, C]), target=target)
+
+        def run_and_check():
+            dev = tvm.rocm(0)
+            a = tvm.runtime.tensor(np.random.uniform(0, 128, size=(n, l)).astype(A.dtype), dev)
+            b = tvm.runtime.tensor(np.random.uniform(0, 128, size=(l, m)).astype(B.dtype), dev)
+            c = tvm.runtime.tensor(np.zeros((n, m), dtype=C.dtype), dev)
+            f(a, b, c)
+            tvm.testing.assert_allclose(
+                c.numpy(),
+                np.dot(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)),
+                rtol=rtol,
+            )
+
+        tvm.testing.run_with_gpu_lock(run_and_check)
+
+    verify()
+
+
+def roundoff(v, d):
+    return int(np.floor((v + d - 1) / d) * d)
+
+
+def verify_batch_matmul(Ashape, Bshape, Cshape, in_dtype, out_dtype, rtol=1e-5):
+    A = te.placeholder(Ashape, name="A", dtype=in_dtype)
+    B = te.placeholder(Bshape, name="B", dtype=in_dtype)
+    C = hipblas.batch_matmul(A, B, dtype=out_dtype)
+
+    f = tvm.compile(te.create_prim_func([A, B, C]), target="rocm")
+
+    def run_and_check():
+        dev = tvm.rocm(0)
+        if "int" in in_dtype:
+            a = tvm.runtime.tensor(np.random.uniform(1, 10, size=Ashape).astype(in_dtype), dev)
+            b = tvm.runtime.tensor(np.random.uniform(1, 10, size=Bshape).astype(in_dtype), dev)
+        else:
+            a = tvm.runtime.tensor(np.random.uniform(size=Ashape).astype(A.dtype), dev)
+            b = tvm.runtime.tensor(np.random.uniform(size=Bshape).astype(B.dtype), dev)
+
+        c = tvm.runtime.tensor(np.zeros(Cshape, dtype=C.dtype), dev)
+        f(a, b, c)
+        tvm.testing.assert_allclose(
+            c.numpy(),
+            np.matmul(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)).astype(C.dtype),
+            rtol=rtol,
+        )
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
+def test_matmul_add():
+    verify_matmul_add("float", "float", rtol=1e-3)
+    verify_matmul_add("float16", "float")
+    verify_matmul_add("float16", "float16", rtol=1e-2)
+    verify_matmul_add("int8", "int32")
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
+def test_batch_matmul():
+    if not tvm.get_global_func("tvm.contrib.hipblas.batch_matmul", True):
+        print("skip because extern function is not available")
+        return
+
+    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float", "float")
+    verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float", "float")
+    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float")
+    verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float")
+    verify_batch_matmul(
+        (16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
+    )
+    verify_batch_matmul(
+        (16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
+    )
+
+    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "int8", "int32")
+
+
+if __name__ == "__main__":
+    tvm.testing.main()