Added equality check and upgraded concatenate op (#1172)

topi/include/topi/broadcast.h

@@ -9,6 +9,7 @@
 #include <string>
 
 #include "topi/detail/broadcast.h"
+#include "topi/detail/constant_utils.h"
 #include "topi/tags.h"
 
 namespace topi {
@@ -34,7 +35,7 @@ inline tvm::Tensor broadcast_to(const tvm::Tensor& t,
   auto bh = detail::BroadcastShape(output_shape, t->shape);
   CHECK_EQ(output_shape.size(), bh.common_shape.size());
   for (size_t i = 0; i < output_shape.size(); ++i) {
-    CHECK(tvm::ir::Equal(output_shape[i], bh.common_shape[i]));
+    CHECK(topi::detail::EqualCheck(output_shape[i], bh.common_shape[i]));
   }
   auto l = [&](tvm::Array<tvm::Var> ovars) {
     return t(detail::InputIndexFromBroadcast(ovars, t, bh.vars2, bh.all_vars));

topi/include/topi/detail/broadcast.h

@@ -12,6 +12,7 @@
 
 #include "tvm/ir_pass.h"
 #include "tvm/tvm.h"
+#include "topi/detail/constant_utils.h"
 
 namespace topi {
 namespace detail {
@@ -32,15 +33,15 @@ inline BroadcastHelper BroadcastShape(const tvm::Array<tvm::Expr>& shape1,
   int i;
   for (i = 1; i <= std::min(s1_size, s2_size); ++i) {
     bh.all_vars.push_front(tvm::Var());
-    if (tvm::ir::Equal(shape1[s1_size - i], shape2[s2_size - i])) {
+    if (topi::detail::EqualCheck(shape1[s1_size - i], shape2[s2_size - i])) {
       bh.common_shape.push_front(shape1[s1_size - i]);
       bh.vars1.push_front(bh.all_vars[0]);
       bh.vars2.push_front(bh.all_vars[0]);
-    } else if (tvm::ir::Equal(one, shape1[s1_size - i])) {
-      CHECK(!tvm::ir::Equal(one, shape2[s2_size - i]));
+    } else if (topi::detail::EqualCheck(one, shape1[s1_size - i])) {
+      CHECK(!topi::detail::EqualCheck(one, shape2[s2_size - i]));
       bh.common_shape.push_front(shape2[s2_size - i]);
       bh.vars2.push_front(bh.all_vars[0]);
-    } else if (tvm::ir::Equal(one, shape2[s2_size - i])) {
+    } else if (topi::detail::EqualCheck(one, shape2[s2_size - i])) {
       bh.common_shape.push_front(shape1[s1_size - i]);
       bh.vars1.push_front(bh.all_vars[0]);
     } else {

topi/include/topi/detail/constant_utils.h

@@ -65,6 +65,24 @@ inline std::vector<int> GetConstIntValues(Array<Expr> exprs, const std::string&
   return result;
 }
 
+/*!
+ * \brief Check weather the two expressions are equal or not, if not simplify the expressions and check again
+ * \note This is stronger equality check than tvm::ir::Equal
+ *
+ * \param lhs First expreesion
+ * \param rhs Second expreesion
+ *
+ * \return result True if both expressions are equal, else false
+ */
+inline bool EqualCheck(Expr lhs, Expr rhs) {
+  bool result = tvm::ir::Equal(lhs, rhs);
+  if (!result) {
+    Expr zero(0);
+    result = tvm::ir::Equal(tvm::ir::CanonicalSimplify(lhs-rhs), zero);
+  }
+  return result;
+}
+
 }  // namespace detail
 }  // namespace topi
 #endif  // TOPI_DETAIL_CONSTANT_UTILS_H_

topi/include/topi/nn.h

@@ -186,13 +186,13 @@ inline tvm::Tensor pad(const tvm::Tensor& t,
         indices.push_back(ovars[i]);
         continue;
       }
-      if (!tvm::ir::Equal(pad_before[i], 0)) {
+      if (!topi::detail::EqualCheck(pad_before[i], 0)) {
         sel.push_back(ovars[i] >= pad_before[i]);
         indices.push_back(ovars[i] - pad_before[i]);
       } else {
         indices.push_back(ovars[i]);
       }
-      if (!tvm::ir::Equal(pad_after[i], 0)) {
+      if (!topi::detail::EqualCheck(pad_after[i], 0)) {
         sel.push_back(tvm::ir::Simplify(ovars[i] < pad_before[i] + t->shape[i]));
       }
     }

topi/include/topi/transform.h

@@ -15,6 +15,7 @@
 #include "topi/detail/ravel_unravel.h"
 #include "topi/detail/constant_utils.h"
 #include "tvm/tvm.h"
+#include "tvm/ir_pass.h"
 
 namespace topi {
 using namespace tvm;
@@ -260,6 +261,7 @@ inline Tensor concatenate(const Array<Tensor>& inputs,
   for (size_t i = 1; i < axis_sizes.size(); ++i) {
     join_size += axis_sizes[i];
   }
+  join_size = tvm::ir::Simplify(join_size);
   Array<Expr> out_shape;
   for (size_t i = 0; i < inputs[0]->shape.size(); ++i) {
     out_shape.push_back(i == static_cast<size_t>(axis) ? join_size : inputs[0]->shape[i]);

topi/python/topi/transform.py

@@ -226,6 +226,7 @@ def concatenate(a_tuple, axis=0):
     axis_sizes = [a_tuple[i].shape[axis] for i in range(len(a_tuple))]
     out_shape = [a_tuple[0].shape[i] for i in range(0, axis)] + [sum(axis_sizes)]\
                 + [a_tuple[0].shape[i] for i in range(axis + 1, len(a_tuple[0].shape))]
+    out_shape[axis] = tvm.ir_pass.Simplify(out_shape[axis])
 
     def _compute(*indices):
         ret = a_tuple[0](*indices)

topi/tests/python_cpp/test_topi_transform.py

@@ -206,6 +206,70 @@ def verify_take(src_shape, indices_src, axis=None):
     for device in ["llvm", "opencl"]:
         check_device(device)
 
+def verify_concatenate_split(shapes, axis, indices_or_sections):
+    tensor_l_concatenate = []
+    for i, shape in enumerate(shapes):
+        tensor_l_concatenate.append(tvm.placeholder(shape, name="A" + str(i)))
+    out_tensor = topi.cpp.concatenate(tensor_l_concatenate, axis)
+    tensor_l = topi.cpp.split(out_tensor, indices_or_sections, axis)
+    tensor_l = list(tensor_l)
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+        target = topi.cpp.TEST_create_target(device)
+        if device == "llvm":
+            s = topi.cpp.generic.schedule_injective(target, tensor_l)
+        else:
+            s = topi.cpp.cuda.schedule_injective(target, tensor_l)
+        ctx = tvm.context(device, 0)
+        foo = tvm.build(s, tensor_l_concatenate + tensor_l, device, name="concatenate_split")
+        data_npys = [np.random.normal(size=shape).astype(tensor_l_concatenate[0].dtype) for shape in shapes]
+        out_npy_conc = np.concatenate(data_npys, axis=axis)
+        out_npys_split = np.split(out_npy_conc, indices_or_sections, axis=axis)
+        data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys]
+        out_nds = [tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=tensor_l[0].dtype) for out_npy in out_npys_split]
+        foo(*(data_nds + out_nds))
+        for out_nd, out_npy in zip(out_nds, out_npys_split):
+            np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
+
+    for device in ["llvm", "cuda", "opencl", "metal", "rocm"]:
+        check_device(device)
+
+def verify_concatenate_broadcast(shapes, axis, rhs_shape):
+    B = tvm.placeholder(shape=rhs_shape, name="B")
+    tensor_l = []
+    for i, shape in enumerate(shapes):
+        tensor_l.append(tvm.placeholder(shape, name="A" + str(i)))
+    out_tensor = topi.cpp.concatenate(tensor_l, axis)
+    C = topi.cpp.broadcast_add(out_tensor, B)
+    def check_device(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+        target = topi.cpp.TEST_create_target(device)
+        if device == "llvm":
+            s = topi.cpp.generic.schedule_injective(target, [C])
+        else:
+            s = topi.cpp.cuda.schedule_injective(target, [C])
+        ctx = tvm.context(device, 0)
+        foo = tvm.build(s, tensor_l + [B, C], device, name="broadcast_binary_add")
+        data_npys = [np.random.normal(size=shape).astype(tensor_l[0].dtype) for shape in shapes]
+        lhs_npy = np.concatenate(data_npys, axis=axis)
+        rhs_npy = np.random.uniform(size=rhs_shape).astype(B.dtype)
+        out_npy = lhs_npy + rhs_npy
+        data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys]
+        rhs_nd = tvm.nd.array(rhs_npy, ctx)
+        out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(B.dtype), ctx)
+        for _ in range(1):
+            foo(*(data_nds + [rhs_nd] + [out_nd]))
+        np.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4)
+
+    for device in ["llvm", "cuda", "opencl", "metal", "rocm"]:
+        check_device(device)
 
 def test_expand_dims():
     verify_expand_dims((3, 10), (3, 10, 1, 1), 2, 2)
@@ -258,6 +322,14 @@ def test_take():
     verify_take((2,2), [[[1,0],[0,1]]], 1)
     verify_take((4,3,5,6), [[2,1,0,0]], -2)
 
+def test_regression_1():
+    verify_concatenate_split([(2, 3, 4), (2, 2, 4), (2, 5, 4)], 1, [3, 7])
+    verify_concatenate_split([(3, 4), (2, 4), (3, 4)], 0, [1, 2, 3, 4])
+
+def test_regression_2():
+    verify_concatenate_broadcast([(5, 1, 3), (5, 1, 3)], 1, [2, 1])
+    verify_concatenate_broadcast([(5, 1, 2), (5, 1, 3)], 2, [1, 5])
+
 if __name__ == "__main__":
     test_concatenate()
     test_tranpose()
@@ -265,4 +337,6 @@ if __name__ == "__main__":
     test_reshape()
     test_squeeze()
     test_split()
-    test_take()
\ No newline at end of file
+    test_take()
+    test_regression_1()
+    test_regression_2()