[Metal] Add tests for simdgroup register GEMM and LowerSIMDGroupCopy

Codegen tests verify correct Metal shader output (simdgroup_store to
device, no C_local simdgroup_load from shared, make_filled init).
Correctness tests verify numerical results against torch.matmul
across square/non-square blocks and matrix sizes.

Author: oraluben

testing/python/metal/test_metal_simdgroup_store.py

@@ -0,0 +1,133 @@
+"""Test Metal simdgroup register GEMM with direct simdgroup_store to device memory.
+
+These tests verify the simdgroup register accumulation path where C is allocated
+in metal.simdgroup scope. This eliminates C_simd load/store round-trips through
+shared memory on each K iteration. The final T.copy(C_local, C[...]) is lowered
+to simdgroup_store directly to device memory via LowerSIMDGroupStore.
+"""
+
+import tilelang
+from tilelang import tvm as tvm
+import tilelang.testing
+import tilelang.language as T
+import torch
+
+
+def _make_simdgroup_gemm_func(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
+    @T.prim_func
+    def gemm_kernel(
+        A: T.Tensor((M, K), dtype),
+        B: T.Tensor((K, N), dtype),
+        C: T.Tensor((M, N), accum_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
+            A_shared = T.alloc_shared((block_M, block_K), dtype, scope="shared")
+            B_shared = T.alloc_shared((block_K, block_N), dtype, scope="shared")
+            C_local = T.alloc_simdgroup((block_M, block_N), accum_dtype)
+            T.clear(C_local)
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0):
+                T.copy(A[by * block_M, ko * block_K], A_shared)
+                T.copy(B[ko * block_K, bx * block_N], B_shared)
+                T.gemm(A_shared, B_shared, C_local)
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return gemm_kernel
+
+
+matmul_simdgroup = tilelang.jit(_make_simdgroup_gemm_func)
+
+
+def assert_simdgroup_store_correctness(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32, atol=1e-2):
+    kernel = matmul_simdgroup(M, N, K, block_M, block_N, block_K, dtype=dtype, accum_dtype=accum_dtype)
+
+    torch_dtype = dtype.as_torch()
+    torch_accum_dtype = accum_dtype.as_torch()
+    a = torch.randn(M, K, dtype=torch_dtype, device="mps")
+    b = torch.randn(K, N, dtype=torch_dtype, device="mps")
+    c = torch.zeros(M, N, dtype=torch_accum_dtype, device="mps")
+
+    kernel(a, b, c)
+
+    ref = a.to(torch_accum_dtype) @ b.to(torch_accum_dtype)
+    assert torch.allclose(ref, c, atol=atol), (
+        f"Result mismatch for M={M}, N={N}, K={K}, "
+        f"block=({block_M},{block_N},{block_K}), dtype={dtype}\n"
+        f"max diff: {(ref - c).abs().max().item()}"
+    )
+
+
+def assert_simdgroup_store_codegen(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
+    func = _make_simdgroup_gemm_func(M, N, K, block_M, block_N, block_K, dtype=dtype, accum_dtype=accum_dtype)
+    with tvm.transform.PassContext(), tvm.target.Target("metal"):
+        artifact = tilelang.lower(func, target="metal")
+
+    src = artifact.kernel_source
+    assert src is not None
+    assert "kernel void" in src
+    assert "simdgroup_multiply_accumulate" in src
+    assert "make_filled_simdgroup_matrix" in src
+
+    assert "simdgroup_float8x8" in src or "simdgroup_half8x8" in src, "Expected simdgroup_float8x8 or simdgroup_half8x8 for C accumulator"
+
+    store_to_device = src.count("simdgroup_store(C_local")
+    assert store_to_device > 0, "Expected simdgroup_store of C_local to device memory"
+
+    load_c_from_shared = [line for line in src.split("\n") if "simdgroup_load" in line and "C_local" in line]
+    assert len(load_c_from_shared) == 0, f"C_local should not be loaded from shared memory, but found: {load_c_from_shared}"
+
+
+# --- Codegen tests (cross-platform) ---
+
+
+def test_codegen_square_small():
+    assert_simdgroup_store_codegen(64, 64, 64, 16, 16, 16)
+
+
+def test_codegen_square_large():
+    assert_simdgroup_store_codegen(128, 128, 128, 32, 32, 32)
+
+
+def test_codegen_non_square():
+    assert_simdgroup_store_codegen(128, 128, 128, 32, 64, 16)
+
+
+def test_codegen_float32_accum():
+    assert_simdgroup_store_codegen(64, 64, 64, 16, 16, 16, dtype=T.float32, accum_dtype=T.float32)
+
+
+# --- Correctness tests (require Metal hardware) ---
+
+
+@tilelang.testing.requires_metal
+def test_correctness_16x16x16():
+    assert_simdgroup_store_correctness(128, 128, 128, 16, 16, 16)
+
+
+@tilelang.testing.requires_metal
+def test_correctness_32x32x32():
+    assert_simdgroup_store_correctness(128, 128, 128, 32, 32, 32)
+
+
+@tilelang.testing.requires_metal
+def test_correctness_non_square_block():
+    assert_simdgroup_store_correctness(128, 128, 128, 32, 64, 16)
+
+
+@tilelang.testing.requires_metal
+def test_correctness_64x64x32():
+    assert_simdgroup_store_correctness(128, 128, 128, 64, 64, 32)
+
+
+@tilelang.testing.requires_metal
+def test_correctness_large_matrix():
+    assert_simdgroup_store_correctness(1024, 1024, 1024, 32, 32, 32, atol=1.0)
+
+
+@tilelang.testing.requires_metal
+def test_correctness_non_square_matrix():
+    assert_simdgroup_store_correctness(256, 512, 128, 32, 32, 16)
+
+
+if __name__ == "__main__":
+    if torch.mps.is_available():
+        tilelang.testing.main()