[TOPI] Rename output tensors for better readability (#3006)

diff --git a/topi/include/topi/broadcast.h b/topi/include/topi/broadcast.h
index c1675ca..1bbb6e1 100644 (file)
--- a/topi/include/topi/broadcast.h
+++ b/topi/include/topi/broadcast.h
@@ -46,7 +46,7 @@ namespace topi {
  */
 inline tvm::Tensor broadcast_to(const tvm::Tensor& t,
                                 const tvm::Array<tvm::Expr>& output_shape,
-                                std::string name = "tensor",
+                                std::string name = "T_broadcast_to",
                                 std::string tag = kBroadcast) {
   CHECK_GE(output_shape.size(), t->shape.size())
       << "Not a broadcast, output dimensionality smaller than input.\noutput: "
@@ -66,35 +66,35 @@ inline tvm::Tensor broadcast_to(const tvm::Tensor& t,
       tag);
 }
 
-#define TOPI_DEFINE_BCAST_OP(Name, ComputeRule)                   \
-  inline tvm::Expr Name(const tvm::Expr& a,                       \
-                        const tvm::Expr& b) {                     \
-    ComputeRule;                                                  \
-  }                                                               \
-  inline tvm::Tensor Name(const tvm::Tensor& A,                   \
-                          const tvm::Tensor& B,                   \
-                          std::string name = "tensor",            \
-                          std::string tag = kBroadcast) {         \
-    auto l = [](tvm::Expr a, tvm::Expr b) { ComputeRule; };       \
-    return detail::WithBroadcast(l, A, B, name, tag);             \
-  }                                                               \
-  inline tvm::Tensor Name(const tvm::Tensor& A,                   \
-                          const tvm::Expr& B,                     \
-                          std::string name = "tensor",            \
-                          std::string tag = kElementWise) {       \
+#define TOPI_DEFINE_BCAST_OP(Name, ComputeRule)                       \
+  inline tvm::Expr Name(const tvm::Expr& a,                           \
+                        const tvm::Expr& b) {                         \
+    ComputeRule;                                                      \
+  }                                                                   \
+  inline tvm::Tensor Name(const tvm::Tensor& A,                       \
+                          const tvm::Tensor& B,                       \
+                          std::string name = "T_" #Name,              \
+                          std::string tag = kBroadcast) {             \
+    auto l = [](tvm::Expr a, tvm::Expr b) { ComputeRule; };           \
+    return detail::WithBroadcast(l, A, B, name, tag);                 \
+  }                                                                   \
+  inline tvm::Tensor Name(const tvm::Tensor& A,                       \
+                          const tvm::Expr& B,                         \
+                          std::string name = "T_" #Name,              \
+                          std::string tag = kElementWise) {           \
     auto l = [](tvm::Expr a, tvm::Expr b) { ComputeRule; };           \
     return compute(A->shape, [&](const ::tvm::Array<::tvm::Var>& i) { \
-        return l(A(i), B);                                        \
-      }, name, tag);                                              \
-  }                                                               \
-  inline tvm::Tensor Name(const tvm::Expr& A,                     \
-                          const tvm::Tensor& B,                   \
-                          std::string name = "tensor",            \
-                          std::string tag = kElementWise) {       \
-    auto l = [&](tvm::Expr a, tvm::Expr b) { ComputeRule; };      \
+        return l(A(i), B);                                            \
+      }, name, tag);                                                  \
+  }                                                                   \
+  inline tvm::Tensor Name(const tvm::Expr& A,                         \
+                          const tvm::Tensor& B,                       \
+                          std::string name = "T_" #Name,              \
+                          std::string tag = kElementWise) {           \
+    auto l = [&](tvm::Expr a, tvm::Expr b) { ComputeRule; };          \
     return compute(B->shape, [&](const ::tvm::Array<::tvm::Var>& i) { \
-        return l(A, B(i));                                        \
-      }, name, tag);                                              \
+        return l(A, B(i));                                            \
+      }, name, tag);                                                  \
   }
 
 

diff --git a/topi/include/topi/elemwise.h b/topi/include/topi/elemwise.h
index 9a32e34..5b6a96d 100644 (file)
--- a/topi/include/topi/elemwise.h
+++ b/topi/include/topi/elemwise.h
@@ -38,7 +38,7 @@ using namespace tvm;
 // Unary intrinsic operators
 #define TOPI_DECLARE_UNARY_OP(OpName)                           \
   inline Tensor OpName(const Tensor& x,                         \
-                       std::string name = "tensor",             \
+                       std::string name = "T_" #OpName,         \
                        std::string tag = kElementWise) {        \
     return compute(x->shape, [&](const Array<Var>& i) {         \
         return ::tvm::OpName(x(i));                             \
@@ -66,7 +66,7 @@ TOPI_DECLARE_UNARY_OP(abs);
 * \return A Tensor whose op member is the identity operation
 */
 inline Tensor identity(const Tensor& x,
-                       std::string name = "tensor",
+                       std::string name = "T_identity",
                        std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     return x(i);
@@ -83,7 +83,7 @@ inline Tensor identity(const Tensor& x,
 * \return A Tensor whose op member is the negation operation
 */
 inline Tensor negative(const Tensor& x,
-                       std::string name = "tensor",
+                       std::string name = "T_negative",
                        std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     return -x(i);
@@ -100,7 +100,7 @@ inline Tensor negative(const Tensor& x,
 * \return A Tensor whose op member is the logical NOT operation
 */
 inline Tensor logical_not(const Tensor& x,
-                          std::string name = "tensor",
+                          std::string name = "T_logical_not",
                           std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     return !x(i);
@@ -117,7 +117,7 @@ inline Tensor logical_not(const Tensor& x,
 * \return A Tensor whose op member is the sign
 */
 inline Tensor sign(const Tensor& x,
-                   std::string name = "tensor",
+                   std::string name = "T_sign",
                    std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     Expr zero = make_zero(x->dtype);
@@ -144,7 +144,7 @@ inline Tensor sign(const Tensor& x,
 inline Tensor clip(const Tensor& x,
                    const Expr& a_min,
                    const Expr& a_max,
-                   std::string name = "tensor",
+                   std::string name = "T_clip",
                    std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     auto min_val = tvm::cast(x->dtype, a_min);
@@ -167,7 +167,7 @@ inline Tensor clip(const Tensor& x,
  */
 inline Tensor cast(const Tensor& x,
                    Type type,
-                   std::string name = "tensor",
+                   std::string name = "T_cast",
                    std::string tag = kElementWise) {
   return compute(x->shape, [&](const Array<Var>& i) {
     auto expr = x(i);
@@ -193,7 +193,7 @@ inline Tensor cast(const Tensor& x,
 * \return A Tensor whose op member is the sum operation
 */
 inline Tensor elemwise_sum(const Array<Tensor>& xs,
-                           std::string name = "tensor",
+                           std::string name = "T_elemwise_sum",
                            std::string tag = kElementWise) {
   CHECK_GT(xs.size(), 0) << "elemwise sum must have at least one input tensor.";
   return compute(xs[0]->shape, [&](const Array<Var>& i) {
@@ -219,7 +219,7 @@ inline Tensor elemwise_sum(const Array<Tensor>& xs,
 inline Tensor full(const Array<Expr>& shape,
                    Type dtype,
                    const Expr fill_value,
-                   std::string name = "tensor",
+                   std::string name = "T_full",
                    std::string tag = kElementWise) {
   Expr ev = cast(dtype, fill_value);
   if (!ev.defined()) {
@@ -243,7 +243,7 @@ inline Tensor full(const Array<Expr>& shape,
 */
 inline Tensor full_like(const Tensor& x,
                         const Expr fill_value,
-                        std::string name = "tensor",
+                        std::string name = "T_full_like",
                         std::string tag = kElementWise) {
   Expr ev = cast(x->dtype, fill_value);
   return compute(x->shape, [&](const Array<Var>& i) {

diff --git a/topi/include/topi/nn.h b/topi/include/topi/nn.h
index 10f5d49..dbeed4a 100644 (file)
--- a/topi/include/topi/nn.h
+++ b/topi/include/topi/nn.h
@@ -63,7 +63,7 @@ tvm::Expr Map(const tvm::Array<tvm::Expr>& exprs, T op) {
 template <typename T>
 inline tvm::Tensor relu(const tvm::Tensor& t,
                         T threshold = static_cast<T>(0),
-                        std::string name = "tensor",
+                        std::string name = "T_relu",
                         std::string tag = kElementWise) {
   return tvm::compute(
       t->shape,
@@ -87,7 +87,7 @@ inline tvm::Tensor relu(const tvm::Tensor& t,
 */
 inline tvm::Tensor leaky_relu(const tvm::Tensor& t,
                               double alpha = 0.1,
-                              std::string name = "tensor",
+                              std::string name = "T_leaky_relu",
                               std::string tag = kElementWise) {
   return tvm::compute(
     t->shape,
@@ -114,7 +114,7 @@ inline tvm::Tensor leaky_relu(const tvm::Tensor& t,
 inline tvm::Tensor prelu(const tvm::Tensor &x,
                          const tvm::Tensor &slope,
                          const int axis = 1,
-                         std::string name = "tensor",
+                         std::string name = "T_prelu",
                          std::string tag = kBroadcast) {
   CHECK((size_t)axis < x->shape.size()) <<
         "Wrong axis ("  << axis << ")value. ";
@@ -171,7 +171,7 @@ inline tvm::Tensor pad(const tvm::Tensor& t,
                        const tvm::Array<tvm::Expr>& pad_before,
                        tvm::Array<tvm::Expr> pad_after = tvm::Array<tvm::Expr>(),
                        Expr pad_value = Expr(),
-                       std::string name = "tensor",
+                       std::string name = "T_pad",
                        std::string tag = kElementWise) {
   if (pad_after.size() < pad_before.size()) {
     for (size_t i = pad_after.size(); i < pad_before.size(); ++i) {
@@ -247,7 +247,7 @@ inline tvm::Tensor conv2d_nchw(const tvm::Tensor& I,
                                int pad_w = 0,
                                int stride_h = 1,
                                int stride_w = 1,
-                               std::string name = "tensor",
+                               std::string name = "T_conv2d_nchw",
                                std::string tag = kConv2dNCHW) {
   CHECK_EQ(4, I->shape.size());
   CHECK_EQ(4, W->shape.size());
@@ -298,7 +298,7 @@ inline tvm::Tensor conv2d_hwcn(const tvm::Tensor& I,
                                int pad_w = 0,
                                int stride_h = 1,
                                int stride_w = 1,
-                               std::string name = "tensor",
+                               std::string name = "T_conv2d_hwcn",
                                std::string tag = kConv2dHWCN) {
   CHECK_EQ(4, I->shape.size());
   CHECK_EQ(4, W->shape.size());
@@ -349,7 +349,7 @@ inline tvm::Tensor depthwise_conv2d_nchw(const tvm::Tensor& I,
                                          int pad_w = 0,
                                          int stride_h = 1,
                                          int stride_w = 1,
-                                         std::string name = "tensor",
+                                         std::string name = "T_depthwise_conv2d_nchw",
                                          std::string tag = kDepthwiseConv2dNCHW) {
   CHECK_EQ(4, I->shape.size());
   CHECK_EQ(4, W->shape.size());
@@ -382,7 +382,7 @@ inline tvm::Tensor depthwise_conv2d_nhwc(const tvm::Tensor& I,
                                          int pad_w = 0,
                                          int stride_h = 1,
                                          int stride_w = 1,
-                                         std::string name = "tensor",
+                                         std::string name = "T_depthwise_conv2d_nhwc",
                                          std::string tag = kDepthwiseConv2dNHWC) {
   CHECK_EQ(4, I->shape.size());
   CHECK_EQ(4, W->shape.size());
@@ -435,7 +435,7 @@ inline tvm::Tensor group_conv2d_ngchw(const tvm::Tensor& I,
                                       int pad_w = 0,
                                       int stride_h = 1,
                                       int stride_w = 1,
-                                      std::string name = "tensor",
+                                      std::string name = "T_group_conv2d_ngchw",
                                       std::string tag = kGroupConv2d) {
   CHECK_EQ(5, I->shape.size());
   CHECK_EQ(5, W->shape.size());

diff --git a/topi/include/topi/nn/pooling.h b/topi/include/topi/nn/pooling.h
index 86d5aff..8648c01 100644 (file)
--- a/topi/include/topi/nn/pooling.h
+++ b/topi/include/topi/nn/pooling.h
@@ -272,8 +272,8 @@ inline Tensor global_pool(const Tensor& x,
   auto height = x->shape[height_axis];
   auto width = x->shape[width_axis];
 
-  auto dheight = tvm::reduce_axis(Range(0, height));
-  auto dwidth = tvm::reduce_axis(Range(0, width));
+  auto dheight = tvm::reduce_axis(Range(0, height), "rv1");
+  auto dwidth = tvm::reduce_axis(Range(0, width), "rv2");
 
   if (pool_type == kMaxPool) {
     return tvm::compute(out_shape,

diff --git a/topi/include/topi/transform.h b/topi/include/topi/transform.h
index abb8455..a658ba3 100644 (file)
--- a/topi/include/topi/transform.h
+++ b/topi/include/topi/transform.h
@@ -57,7 +57,7 @@ using namespace topi::detail;
 inline Tensor expand_dims(const Tensor& x,
                           int axis,
                           int num_newaxis = 1,
-                          std::string name = "tensor",
+                          std::string name = "T_expand_dims",
                           std::string tag = kBroadcast) {
   int ndim = static_cast<int>(x->shape.size());
   CHECK(-ndim - 1 <= axis && axis <= ndim)
@@ -108,7 +108,7 @@ inline Tensor expand_dims(const Tensor& x,
 */
 inline Tensor transpose(const Tensor& x,
                         Array<Integer> axes,
-                        std::string name = "tensor",
+                        std::string name = "T_transpose",
                         std::string tag = kInjective) {
   if (!axes.defined() || axes.size() == 0) {
     axes = Array<Integer>();
@@ -164,7 +164,7 @@ inline Tensor transpose(const Tensor& x,
 */
 inline Tensor flip(const Tensor& x,
                    int axis = 0,
-                   std::string name = "tensor",
+                   std::string name = "T_flip",
                    std::string tag = kInjective) {
   size_t src_tensor_dim = x->shape.size();
   int axis_inp = axis;
@@ -204,7 +204,7 @@ inline Tensor flip(const Tensor& x,
 */
 inline Tensor reshape(const Tensor& x,
                       Array<Expr> newshape,
-                      std::string name = "tensor",
+                      std::string name = "T_reshape",
                       std::string tag = kInjective) {
   auto x_shape = x->shape;
   return compute(
@@ -229,7 +229,7 @@ inline Tensor reshape(const Tensor& x,
 inline Tensor squeeze(const Tensor& x,
                       Array<Integer> axis,
                       bool atleast1d = false,
-                      std::string name = "tensor",
+                      std::string name = "T_squeeze",
                       std::string tag = kInjective) {
   auto ndim = x->shape.size();
   std::vector<int> axis_val;
@@ -291,7 +291,7 @@ inline Tensor squeeze(const Tensor& x,
 */
 inline Tensor concatenate(const Array<Tensor>& inputs,
                           int axis = 0,
-                          std::string name = "tensor",
+                          std::string name = "T_concat",
                           std::string tag = kInjective) {
   int ndim = static_cast<int>(inputs[0]->shape.size());
   CHECK(-ndim <= axis && axis < ndim)
@@ -355,7 +355,7 @@ inline Tensor concatenate(const Array<Tensor>& inputs,
 */
 inline Tensor stack(const Array<Tensor>& inputs,
                     int axis = 0,
-                    std::string name = "tensor",
+                    std::string name = "T_stack",
                     std::string tag = kInjective) {
   int ndim = static_cast<int>(inputs[0]->shape.size());
   CHECK(-ndim - 1 <= axis && axis <= ndim)
@@ -408,7 +408,7 @@ inline Tensor stack(const Array<Tensor>& inputs,
 inline Array<Tensor> split(const Tensor& x,
                            Array<Integer> split_indices,
                            int axis,
-                           std::string name = "tensor",
+                           std::string name = "T_split",
                            std::string tag = kInjective) {
   if (axis < 0) {
     axis += static_cast<int>(x->shape.size());
@@ -486,7 +486,7 @@ inline Tensor strided_slice(const Tensor& x,
                             const Array<Integer>& begin,
                             const Array<Integer>& end,
                             const Array<Integer>& strides,
-                            std::string name = "tensor",
+                            std::string name = "T_strided_slice",
                             std::string tag = kInjective) {
   size_t src_tensor_dim = static_cast<size_t>(x->shape.size());
   // Setup the ranges.
@@ -585,7 +585,7 @@ inline Tensor strided_slice(const Tensor& x,
 inline Array<Tensor> split_sections(const Tensor& x,
                                     int num_sections,
                                     int axis,
-                                    std::string name = "tensor",
+                                    std::string name = "T_split_sections",
                                     std::string tag = kInjective) {
   if (axis < 0) {
     axis += static_cast<int>(x->shape.size());
@@ -624,7 +624,7 @@ inline Array<Tensor> split_sections(const Tensor& x,
 inline Tensor take(const Tensor& a,
                    const Tensor& indices,
                    std::string mode = "clip",
-                   std::string name = "tensor",
+                   std::string name = "T_take",
                    std::string tag = kInjective) {
   Array<Expr> a_shape = a->shape;
   Array<Expr> out_shape = indices->shape;
@@ -664,7 +664,7 @@ inline Tensor take(const Tensor& a,
                    const Tensor& indices,
                    int axis,
                    std::string mode = "clip",
-                   std::string name = "tensor",
+                   std::string name = "T_take",
                    std::string tag = kInjective) {
   if (axis < 0) {
     axis += static_cast<int>(a->shape.size());
@@ -738,7 +738,7 @@ inline Tensor take(const Tensor& a,
 inline Tensor where(const Tensor& condition,
                     const Tensor& x,
                     const Tensor& y,
-                    std::string name = "tensor",
+                    std::string name = "T_where",
                     std::string tag = kInjective) {
   CHECK_EQ(x->shape.size(), y->shape.size())
     << "x and y must have the same shape.Got different number of dimension: "
@@ -786,7 +786,7 @@ inline Tensor where(const Tensor& condition,
 inline Tensor repeat(const Tensor& x,
                      int repeats,
                      int axis,
-                     std::string name = "tensor",
+                     std::string name = "T_repeat",
                      std::string tag = kBroadcast) {
   int ndim = static_cast<int>(x->shape.size());
   CHECK(-ndim - 1 <= axis && axis <= ndim)
@@ -835,7 +835,7 @@ inline Tensor repeat(const Tensor& x,
 */
 inline Tensor tile(const Tensor& x,
                    Array<Integer> reps,
-                   std::string name = "tensor",
+                   std::string name = "T_tile",
                    std::string tag = kBroadcast) {
   size_t ndim = x->shape.size();
   size_t rdim = reps.size();
@@ -892,7 +892,7 @@ inline Tensor tile(const Tensor& x,
 */
 inline Tensor gather_nd(const Tensor& data,
                         const Tensor& indices,
-                        std::string name = "tensor",
+                        std::string name = "T_gather_nd",
                         std::string tag = kInjective) {
   size_t ndim_d = data->shape.size();
   size_t ndim_i = indices->shape.size();
@@ -953,7 +953,7 @@ inline tvm::Tensor matmul(const tvm::Tensor& A,
                            const tvm::Tensor& B,
                            bool trans_a = false,
                            bool trans_b = false,
-                           std::string name = "tensor",
+                           std::string name = "T_matmul",
                            std::string tag = kMatMul) {
   tvm::Array<tvm::Expr> output_shape{A->shape[trans_a ? 1 : 0],
                                      B->shape[trans_b ? 0 : 1]};
@@ -979,7 +979,7 @@ inline tvm::Tensor matmul(const tvm::Tensor& A,
 inline Tensor tensordot(const Tensor& A,
                         const tvm::Tensor& B,
                         int axes = 2,
-                        std::string name = "tensor",
+                        std::string name = "T_tensordot",
                         std::string tag = kMatMul) {
   CHECK_GE(A->shape.size(), axes);
   CHECK_GE(B->shape.size(), axes);
@@ -1035,7 +1035,7 @@ inline Tensor tensordot(const Tensor& A,
                         const tvm::Tensor& B,
                         Array<Expr> A_axes,
                         Array<Expr> B_axes,
-                        std::string name = "tensor",
+                        std::string name = "T_tensordot",
                         std::string tag = kMatMul) {
   CHECK_EQ(A_axes.size(), B_axes.size());
 
@@ -1084,7 +1084,7 @@ inline Tensor arange(const Expr start,
                      const Expr stop,
                      const Expr step,
                      Type dtype,
-                     std::string name = "tensor",
+                     std::string name = "T_arange",
                      std::string tag = kInjective) {
   Expr num_elem = tvm::cast(tvm::Int(32), tvm::ceil(
       tvm::cast(tvm::Float(32), stop - start) / step));
@@ -1106,7 +1106,7 @@ inline Tensor arange(const Expr start,
 inline Tensor layout_transform(const Tensor& src,
                                const std::string& src_layout,
                                const std::string& dst_layout,
-                               const std::string name = "layout_transform",
+                               const std::string name = "T_layout_trans",
                                const std::string tag = kInjective) {
   Layout src_layout_struct = LayoutNode::make(src_layout);
   Layout dst_layout_struct = LayoutNode::make(dst_layout);
@@ -1142,7 +1142,7 @@ inline Tensor layout_transform(const Tensor& src,
  */
 inline Tensor shape(const Tensor& src,
                     Type dtype,
-                    const std::string name = "shape",
+                    const std::string name = "T_shape",
                     const std::string tag = kInjective) {
   int ndim = static_cast<int>(src->shape.size());
   Array<Expr> out_shape{ndim};

diff --git a/topi/python/topi/nn/dense.py b/topi/python/topi/nn/dense.py
index 6c4aba1..f116e7c 100644 (file)
--- a/topi/python/topi/nn/dense.py
+++ b/topi/python/topi/nn/dense.py
@@ -47,7 +47,7 @@ def dense_default(data, weight, bias=None):
     k = tvm.reduce_axis((0, in_dim), name='k')
     matmul = tvm.compute((batch, out_dim), \
                          lambda i, j: tvm.sum(data[i, k] * weight[j, k], axis=k), \
-                         tag='dense')
+                         name='T_dense', tag='dense')
     if bias is not None:
         matmul = tvm.compute((batch, out_dim), \
                              lambda i, j: matmul[i, j] + bias[j], \

diff --git a/topi/python/topi/nn/softmax.py b/topi/python/topi/nn/softmax.py
index c02fd67..00bbe55 100644 (file)
--- a/topi/python/topi/nn/softmax.py
+++ b/topi/python/topi/nn/softmax.py
@@ -61,9 +61,11 @@ def softmax(x, axis=-1):
         return tvm.exp(x[indices] - max_elem[non_reduce_indices]) / expsum[non_reduce_indices]
 
     reduced_shape = tuple([dim for (i, dim) in enumerate(shape) if i != axis])
-    max_elem = tvm.compute(reduced_shape, _compute_max)
-    expsum = tvm.compute(reduced_shape, lambda *indices: _compute_expsum(max_elem, *indices))
-    return tvm.compute(shape, lambda *indices: _normalize(max_elem, expsum, *indices))
+    max_elem = tvm.compute(reduced_shape, _compute_max, name='T_softmax_maxelem')
+    expsum = tvm.compute(reduced_shape, lambda *indices: _compute_expsum(max_elem, *indices),
+                         name='T_softmax_expsum')
+    return tvm.compute(shape, lambda *indices: _normalize(max_elem, expsum, *indices),
+                       name='T_softmax_norm')
 
 
 @tvm.tag_scope(tag='log_softmax_output')