Add more examples part 1 (sddmm) (apache#22)

* upd

* upd

* upd

src/target/source/literal/cuda_binary_search.h

@@ -26,15 +26,15 @@
 
 static constexpr const char* _cuda_binary_search_def = R"(
 template <typename DType>
-__forceinline__ __device__ int32_t __lower_bound(
+__forceinline__ __device__ int __lower_bound(
     const DType* __restrict__ arr,
     DType val,
-    int32_t l,
-    int32_t r) {
-  int32_t low = l - 1, high = r;
+    int l,
+    int r) {
+  int low = l - 1, high = r;
   /* loop invariant: low < mid < high, arr[low] < val, arr[high] >= val */
   while (low + 1 < high) {
-    int32_t mid = (low + high) >> 1;
+    int mid = (low + high) >> 1;
     if (arr[mid] < val) {
       low = mid;
     } else {
@@ -46,15 +46,15 @@ __forceinline__ __device__ int32_t __lower_bound(
 }
 
 template <typename DType>
-__forceinline__ __device__ int32_t __upper_bound(
+__forceinline__ __device__ int __upper_bound(
     const DType* __restrict__ arr,
     DType val,
-    int32_t l,
-    int32_t r) {
-  int32_t low = l - 1, high = r;
+    int l,
+    int r) {
+  int low = l - 1, high = r;
   /* loop invariant: low < mid < high, arr[low] < val, arr[high] > val */
   while (low + 1 < high) {
-    int32_t mid = (low + high) >> 1;
+    int mid = (low + high) >> 1;
     if (arr[mid] > val) {
       high = mid;
     } else {

tests/python/sparsetir/test_tir_sparse_correctness.py

@@ -92,6 +92,25 @@ def ellmm_tir(a: T.handle, b: T.handle, c: T.handle, indices: T.handle, M: T.int
                 B[A_indices[vi * NNZ_COLS + vj] * K + vk]
 
 
+@T.prim_func
+def sddmm_tir(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.handle, M: T.int32, N: T.int32, K: T.int32, NNZ: T.int32) -> None:
+    T.func_attr({"global_symbol": "main", "tir.noalis": True})
+    A = T.match_buffer(a, (M * K,), "float32")
+    B = T.match_buffer(b, (N * K,), "float32")
+    C_data = T.match_buffer(c, (NNZ,), "float32")
+    C_indptr = T.match_buffer(indptr, (M + 1,), "int32")
+    C_indices = T.match_buffer(indices, (NNZ,), "int32")
+    for ij, k in T.grid(NNZ, K):
+        with T.block("sddmm"):
+            vij, vk = T.axis.remap("SR", [ij, k])
+            T.reads([A[0: M * K], B[0: N * K], C_data[vij], C_indices[vij], C_indptr[0: M + 1]])
+            T.writes([C_data[vij]])
+            with T.init():
+                C_data[vij] = 0.
+            C_data[vij] = C_data[vij] + \
+                A[T.lower_bound(C_indptr.data, vij, 0, M + 1) * K + vk] * B[C_indices[vij] * K + vk]
+
+
 def test_csrmm():
     # generate random input
     m = 4096
@@ -219,6 +238,50 @@ def test_ellmm():
     assert np.allclose(y_ground_truth.reshape(-1), Y_nd.numpy())
 
 
+def test_sddmm():
+    # generate random input
+    m = 4096
+    n = 4096
+    k = 256
+    C = sp.random(m, n, dtype="float32", density=0.0125, format='csr')
+    indptr = C.indptr
+    indices = C.indices
+    C_coo = C.tocoo()
+    nnz = C.nnz
+    x = np.random.rand(m, k).astype("float32")
+    y = np.random.rand(n, k).astype("float32")
+    z_ground_truth = np.matmul(x, y.transpose())[C_coo.row, C_coo.col]
+    z = np.zeros((nnz,)).astype("float32")
+
+    # specialize function
+    _, _, _, _, _, M, N, K, NNZ = sddmm_tir.params
+    sch = tir.Schedule(
+        sddmm_tir.specialize(
+            {M: m, N: n, K: k, NNZ: nnz}
+        )
+    )
+    blk = sch.get_block("sddmm")
+    ij, k = sch.get_loops(blk)
+    #sch.decompose_reduction(blk, ij)
+    sch.bind(ij, "blockIdx.x")
+    ko, ki = sch.split(k, [None, 1])
+    sch.bind(ki, "threadIdx.x")
+
+    # convert numpy tensor to tvm ndarray
+    C_indices = tvm.nd.array(indices.astype("int32"), device=tvm.cuda(0))
+    C_indptr = tvm.nd.array(indptr.astype("int32"), device=tvm.cuda(0))
+    X_nd = tvm.nd.array(x.reshape(-1), device=tvm.cuda(0))
+    Y_nd = tvm.nd.array(y.reshape(-1), device=tvm.cuda(0))
+    C_data = tvm.nd.array(z, device=tvm.cuda(0))
+
+    # build function
+    f = tvm.build(sch.mod['main'], target="cuda")
+    f(X_nd, Y_nd, C_data, C_indptr, C_indices)
+
+    # assertion
+    np.allclose(z_ground_truth, C_data.numpy())
+
+
 def test_bmm():
     # TODO(zihao)
     pass
@@ -228,4 +291,5 @@ def test_bmm():
     test_csrmm()
     test_bsrmm()
     test_ellmm()
+    test_sddmm()
     test_bmm()

tests/python/unittest/test_tir_intrin.py

@@ -253,19 +253,21 @@ def test_fma():
     assert mod["test_tir_fma"].body.body.value.op.name == "tir.call_llvm_pure_intrin"
 
 
-@tvm.script.tir
-def binary_search(a: ty.handle, b: ty.handle, c: ty.handle, d: ty.handle) -> None:
-    n = tir.var('int32')
-    m = tir.var('int32')
-    A = tir.match_buffer(a, (n,), dtype='int32')
-    B = tir.match_buffer(b, (m,), dtype='int32')
-    C = tir.match_buffer(c, (m,), dtype='int32')
-    D = tir.match_buffer(d, (m,), dtype='int32')
-    with tir.block([m], 'search') as [vi]:
-        tir.reads([A[0:n], B[vi]])
-        tir.writes([C[vi], D[vi]])
-        C[vi] = tir.lower_bound(A.data, B[vi], 0, n)
-        D[vi] = tir.upper_bound(A.data, B[vi], 0, n)
+@T.prim_func
+def binary_search(a: T.handle, b: T.handle, c: T.handle, d: T.handle) -> None:
+    n = T.var('int32')
+    m = T.var('int32')
+    A = T.match_buffer(a, (n,), dtype='int32')
+    B = T.match_buffer(b, (m,), dtype='int32')
+    C = T.match_buffer(c, (m,), dtype='int32')
+    D = T.match_buffer(d, (m,), dtype='int32')
+    for i in T.serial(0, m):
+        with T.block('search'):
+            vi = T.axis.S(m, i)
+            T.reads([A[0:n], B[vi]])
+            T.writes([C[vi], D[vi]])
+            C[vi] = T.lower_bound(A.data, B[vi], 0, n)
+            D[vi] = T.upper_bound(A.data, B[vi], 0, n)
 
 
 def test_binary_search():