Bugfix and more test for axis fusion, new workload (#50)

* upd

* upd

python/tvm/script/tir/special_stmt.py

@@ -935,7 +935,7 @@ def dense_variable(
                 )
 
             length, nnz = shape
-            indptr_len = parent_axis.length + 1
+            indptr_len = parent_axis.nnz + 1
             indptr_buf = tvm.tir.decl_buffer(
                 (indptr_len,), dtype=idtype, name=names[0] + "_indptr", span=span
             )

src/tir/transforms/lower_sparse_tir.cc

@@ -529,7 +529,7 @@ class IndexTransformer : public StmtExprMutator {
 
         const Optional<Var>& loop_var = axis2loop_var.Get(axis->GetParentAxis().value());
         CHECK(loop_var.defined()) << "ValueError: The parent axis of " << axis
-                                  << "does not appear in the sparse block";
+                                  << " does not appear in the sparse block";
 
         if (LoopVarAppears(loop_var.value())) {
           return true;
@@ -559,6 +559,10 @@ class IndexTransformer : public StmtExprMutator {
     for (const SpIterVar& sp_iter_var : sp_block->sp_iter_vars) {
       Var loop_var("v_" + sp_iter_var->var->name_hint);
       var_map.Set(sp_iter_var->var, loop_var);
+      if (auto fused_axis = sp_iter_var->axis.as<FusedAxisNode>()) {
+        // handle the special case of fused_axis
+        axis2loop_var.Set(fused_axis->group[fused_axis->index], loop_var);
+      }
       axis2loop_var.Set(sp_iter_var->axis, loop_var);
     }
 

tests/python/sparsetir/bench_rgcn_new.py

@@ -0,0 +1,54 @@
+from dgl.heterograph import DGLHeteroGraph
+import tvm
+import tvm.testing
+import tvm.tir as tir
+import scipy.sparse as sp
+import numpy as np
+import dgl
+import dgl.function as fn
+import torch as th
+from tvm.script import tir as T
+from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
+
+
+@T.prim_func
+def rgcn_hetero_forward(
+    offset_ntype: T.handle,
+    w: T.handle,
+    x: T.handle,
+    y: T.handle,
+    indptr_i: T.handle,
+    indptr_j: T.handle,
+    indices_j: T.handle,
+    n: T.int32,
+    r: T.int32,
+    feat_size: T.int32,
+    nnz_i: T.int32,
+    nnz_j: T.int32
+):
+    I_flatten = T.dense_fixed(n)
+    R = T.dense_fixed(r)
+    I = T.dense_variable(R, (n, nnz_i), indptr_i, "int32")
+    J = T.sparse_variable(I, (n, nnz_j), (indptr_j, indices_j), "int32")
+    F_in = T.dense_fixed(feat_size)
+    F_out = T.dense_fixed(feat_size)
+    offset = T.match_sparse_buffer(offset_ntype, (R,), "int32")
+    W = T.match_sparse_buffer(w, (R, F_out, F_in), "float32")
+    X = T.match_sparse_buffer(x, (I_flatten, F_in), "float32")
+    Y = T.match_sparse_buffer(y, (I_flatten, R, F_out), "float32")
+    with T.iter([T.fuse(R, I), F_out, J, F_in], "SSSRR", "rgcn-hetero-forward") as [
+        vr, vi, vout, vj, vin
+    ]:
+        with T.init():
+            Y[offset[vr] + vi, vr, vout] = 0.
+        Y[offset[vr] + vi, vr, vout] = Y[offset[vr] + vi, vr, vout] + W[vr, vout, vin] * X[vj, vin]
+
+
+def test_lower_rgcn_hetero():
+    mod = tvm.IRModule.from_expr(rgcn_hetero_forward)
+    mod = tvm.tir.transform.LowerSparseTIR()(mod)
+    print(mod["main"].script())
+
+
+if __name__ == "__main__":
+    test_lower_rgcn_hetero()

tests/python/sparsetir/lowered_tir.py

@@ -394,3 +394,71 @@ def lowered_rgcn_forward(etype: T.handle, w: T.handle, x: T.handle, y: T.handle,
                             Y_data[vi * feat_size + vout] = T.float32(0)
                         Y_data[vi * feat_size + vout] = Y_data[vi * feat_size + vout] + W_data[(
                             E_data[J_indptr[vi] + vj] * feat_size + vout) * feat_size + vin] * X_data[J_indices[J_indptr[vi] + vj] * feat_size + vin]
+
+
+@T.prim_func
+def lowered_fused_reduction_4d_2d(x: T.handle, y: T.handle, indptr_j: T.handle, indptr_k: T.handle, indptr_l: T.handle, n: T.int32, nnz_j: T.int32, nnz_k: T.int32, nnz_l: T.int32) -> None:
+    # function attr dict
+    T.func_attr({"global_symbol": "main", "tir.noalias": True})
+    X_data = T.match_buffer(x, [nnz_l], dtype="float32")
+    Y_data = T.match_buffer(y, [nnz_j], dtype="float32")
+    J_indptr = T.match_buffer(indptr_j, [n + 1], dtype="int32")
+    K_indptr = T.match_buffer(indptr_k, [nnz_j + 1], dtype="int32")
+    L_indptr = T.match_buffer(indptr_l, [nnz_k + 1], dtype="int32")
+    # body
+    # with T.block("root")
+    for v_vi, v_vj in T.grid(1, nnz_j):
+        with T.block("reduction_4d_2d0"):
+            vi, vj = T.axis.remap("SS", [v_vi, v_vj])
+            T.reads(J_indptr[0: n + 1], K_indptr[0: nnz_j + 1],
+                    L_indptr[0: nnz_k + 1], X_data[0: nnz_l], Y_data[0: nnz_j])
+            T.writes(Y_data[0: nnz_j])
+            T.block_attr({"sparse": True})
+            for v_vk in T.serial(K_indptr[vj + 1] - K_indptr[vj]):
+                with T.block("reduction_4d_2d1"):
+                    vk = T.axis.reduce(K_indptr[vj + 1] - K_indptr[vj], v_vk)
+                    T.reads(J_indptr[0: n + 1], K_indptr[0: nnz_j + 1],
+                            L_indptr[0: nnz_k + 1], X_data[0: nnz_l], Y_data[0: nnz_j])
+                    T.writes(Y_data[0: nnz_j])
+                    T.block_attr({"sparse": True})
+                    with T.init():
+                        Y_data[vj] = T.float32(0)
+                    for v_vl in T.serial(L_indptr[K_indptr[vj] + vk + 1] - L_indptr[K_indptr[vj] + vk]):
+                        with T.block("reduction_4d_2d2"):
+                            vl = T.axis.reduce(
+                                L_indptr[K_indptr[vj] + vk + 1] - L_indptr[K_indptr[vj] + vk], v_vl)
+                            T.reads(J_indptr[0: n + 1], K_indptr[0: nnz_j + 1],
+                                    L_indptr[0: nnz_k + 1], X_data[0: nnz_l], Y_data[0: nnz_j])
+                            T.writes(Y_data[0: nnz_j])
+                            T.block_attr({"sparse": True})
+                            Y_data[vj] = Y_data[vj] + X_data[L_indptr[K_indptr[vj] + vk] + vl]
+
+
+@T.prim_func
+def lowered_fused_reduction_4d_3d(x: T.handle, y: T.handle, indptr_j: T.handle, indptr_k: T.handle, indptr_l: T.handle, n: T.int32, nnz_j: T.int32, nnz_k: T.int32, nnz_l: T.int32) -> None:
+    # function attr dict
+    T.func_attr({"global_symbol": "main", "tir.noalias": True})
+    X_data = T.match_buffer(x, [nnz_l], dtype="float32")
+    Y_data = T.match_buffer(y, [nnz_k], dtype="float32")
+    J_indptr = T.match_buffer(indptr_j, [n + 1], dtype="int32")
+    K_indptr = T.match_buffer(indptr_k, [nnz_j + 1], dtype="int32")
+    L_indptr = T.match_buffer(indptr_l, [nnz_k + 1], dtype="int32")
+    # body
+    # with T.block("root")
+    for v_vi, v_vj, v_vk in T.grid(1, 1, nnz_k):
+        with T.block("reduction_4d_3d0"):
+            vi, vj, vk = T.axis.remap("SSS", [v_vi, v_vj, v_vk])
+            T.reads(J_indptr[0: n + 1], K_indptr[0: nnz_j + 1],
+                    L_indptr[0: nnz_k + 1], X_data[0: nnz_l], Y_data[0: nnz_k])
+            T.writes(Y_data[0: nnz_k])
+            T.block_attr({"sparse": True})
+            for v_vl in T.serial(L_indptr[vk + 1] - L_indptr[vk]):
+                with T.block("reduction_4d_3d1"):
+                    vl = T.axis.reduce(L_indptr[vk + 1] - L_indptr[vk], v_vl)
+                    T.reads(J_indptr[0: n + 1], K_indptr[0: nnz_j + 1],
+                            L_indptr[0: nnz_k + 1], X_data[0: nnz_l], Y_data[0: nnz_k])
+                    T.writes(Y_data[0: nnz_k])
+                    T.block_attr({"sparse": True})
+                    with T.init():
+                        Y_data[vk] = T.float32(0)
+                    Y_data[vk] = Y_data[vk] + X_data[L_indptr[vk] + vl]

tests/python/sparsetir/sparse_tir_scripts.py

@@ -282,6 +282,54 @@ def square_sum_two_K(a: T.handle, b: T.handle, indptr_j: T.handle, indices_j: T.
         B[vi] = B[vi] + A[vi, vj, vk]
 
 
+@T.prim_func
+def fused_reduction_4d_2d(
+    x: T.handle,
+    y: T.handle,
+    indptr_j: T.handle,
+    indptr_k: T.handle,
+    indptr_l: T.handle,
+    n: T.int32,
+    nnz_j: T.int32,
+    nnz_k: T.int32,
+    nnz_l: T.int32) -> None:
+    T.func_attr({"global_symbol": "main", "tir.noalias": True})
+    I = T.dense_fixed(n)
+    J = T.dense_variable(I, (32768, nnz_j), indptr_j, "int32")
+    K = T.dense_variable(J, (32768, nnz_k), indptr_k, "int32")
+    L = T.dense_variable(K, (32768, nnz_l), indptr_l, "int32")
+    X = T.match_sparse_buffer(x, (I, J, K, L), "float32")
+    Y = T.match_sparse_buffer(y, (I, J), "float32")
+    with T.iter([T.fuse(I, J), K, L], "SSRR", "reduction_4d_2d") as [vi, vj, vk, vl]:
+        with T.init():
+            Y[vi, vj] = 0.0
+        Y[vi, vj] = Y[vi, vj] + X[vi, vj, vk, vl]
+
+
+@T.prim_func
+def fused_reduction_4d_3d(
+    x: T.handle,
+    y: T.handle,
+    indptr_j: T.handle,
+    indptr_k: T.handle,
+    indptr_l: T.handle,
+    n: T.int32,
+    nnz_j: T.int32,
+    nnz_k: T.int32,
+    nnz_l: T.int32) -> None:
+    T.func_attr({"global_symbol": "main", "tir.noalias": True})
+    I = T.dense_fixed(n)
+    J = T.dense_variable(I, (32768, nnz_j), indptr_j, "int32")
+    K = T.dense_variable(J, (32768, nnz_k), indptr_k, "int32")
+    L = T.dense_variable(K, (32768, nnz_l), indptr_l, "int32")
+    X = T.match_sparse_buffer(x, (I, J, K, L), "float32")
+    Y = T.match_sparse_buffer(y, (I, J, K), "float32")
+    with T.iter([T.fuse(I, J, K), L], "SSSR", "reduction_4d_3d") as [vi, vj, vk, vl]:
+        with T.init():
+            Y[vi, vj, vk] = 0.0
+        Y[vi, vj, vk] = Y[vi, vj, vk] + X[vi, vj, vk, vl]
+
+
 @T.prim_func
 def rgcn_forward(
     etype: T.handle,

tests/python/sparsetir/test_tir_sparse_lower.py

@@ -63,21 +63,18 @@ def test_csr_element_wise():
     tvm.ir.assert_structural_equal(mod["main"], lowered_csr_element_wise, True)
 
 
-@pytest.mark.skip(reason="Under implementation")
 def test_bmm():
     mod = tvm.IRModule.from_expr(bmm)
     mod = tvm.tir.transform.LowerSparseTIR()(mod)
     tvm.ir.assert_structural_equal(mod["main"], lowered_bmm)
 
 
-@pytest.mark.skip(reason="Under implementation")
 def test_sddmm():
     mod = tvm.IRModule.from_expr(sddmm)
     mod = tvm.tir.transform.LowerSparseTIR()(mod)
     tvm.ir.assert_structural_equal(mod["main"], lowered_sddmm)
 
 
-@pytest.mark.skip(reason="Under implementation")
 def test_fused_sddmm():
     mod = tvm.IRModule.from_expr(fused_sddmm)
     mod = tvm.tir.transform.LowerSparseTIR()(mod)
@@ -96,6 +93,16 @@ def test_square_sum_two_K():
     tvm.ir.assert_structural_equal(mod["main"], lowered_square_sum_two_K, True)
 
 
+def test_fused_reduction():
+    mod = tvm.IRModule.from_expr(fused_reduction_4d_2d)
+    mod = tvm.tir.transform.LowerSparseTIR()(mod)
+    tvm.ir.assert_structural_equal(mod["main"], lowered_fused_reduction_4d_2d, True)
+
+    mod = tvm.IRModule.from_expr(fused_reduction_4d_3d)
+    mod = tvm.tir.transform.LowerSparseTIR()(mod)
+    tvm.ir.assert_structural_equal(mod["main"], lowered_fused_reduction_4d_3d, True)
+
+
 if __name__ == "__main__":
     test_csrmm()
     test_csrmm_dense_iter()
@@ -109,3 +116,4 @@ def test_square_sum_two_K():
     test_bmm()
     test_square_sum()
     test_square_sum_two_K()
+    test_fused_reduction()

tests/python/sparsetir/test_tir_sparse_tensorize.py

@@ -0,0 +1 @@
+# TODO