[Relay, TOPI] Support dynamic slicing on first few axes, keeping the …

…rest static (apache#8068)

* Supporting dynamic slice on first few axes

* fix index normalization

* update dynamic slice tests

* pylint fix

* fix loop index dtype

* fix more dtype issue

include/tvm/topi/transform.h

@@ -569,16 +569,25 @@ inline te::Tensor dynamic_strided_slice(const te::Tensor& x, const te::Tensor& b
                                         std::string tag = topi::kInjective) {
   int64_t src_tensor_dim = x->shape.size();
   Array<PrimExpr> out_shape;
-  for (int64_t i = 0; i < src_tensor_dim; ++i) {
+  const int64_t num_dynamic_axes = begin->shape[0].as<IntImmNode>()->value;
+  for (int64_t i = 0; i < num_dynamic_axes; ++i) {
     out_shape.push_back(tvm::tir::Var("dim"));
   }
+  for (int64_t i = num_dynamic_axes; i < src_tensor_dim; ++i) {
+    out_shape.push_back(x->shape[i]);
+  }
   return te::compute(
       out_shape,
       [&](const Array<tvm::tir::Var>& indices) {
         Array<PrimExpr> real_indices;
-        for (int32_t i = 0; i < src_tensor_dim; ++i) {
+        // dynamic slicing
+        for (int32_t i = 0; i < num_dynamic_axes; ++i) {
           real_indices.push_back(indices[i] * strides(i) + tvm::min(begin(i), x->shape[i] - 1));
         }
+        // keep input dim
+        for (int32_t i = num_dynamic_axes; i < src_tensor_dim; ++i) {
+          real_indices.push_back(indices[i]);
+        }
         return x(real_indices);
       },
       name, tag);

python/tvm/relay/frontend/onnx.py

@@ -2703,11 +2703,7 @@ def conditionally_squeeze_scalar(x):
             boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold
         )
 
-        three = _op.const(np.array([3]), dtype="int64")
-        begin = _op.const(np.array([0, 0]), dtype="int64")
-        end = _op.concatenate([nms_out[1], three], axis=0)
-        strides = _op.const(np.array([1, 1]), dtype="int64")
-        return _op.strided_slice(nms_out[0], begin, end, strides)
+        return _op.strided_slice(nms_out[0], _op.const([0], dtype="int64"), nms_out[1])
 
 
 class ATen(OnnxOpConverter):

python/tvm/relay/op/transform.py

@@ -950,12 +950,11 @@ def strided_slice(data, begin, end, strides=None, slice_mode="end"):
             end = const(list(end))
         if isinstance(strides, (tuple, list)):
             strides = const(list(strides))
-        begin = _make.where(
-            begin < cast_like(const(0), begin), begin + cast_like(shape_of(data), begin), begin
-        )
-        begin = _make.where(
-            begin >= cast_like(shape_of(data), begin), cast_like(shape_of(data), begin), begin
-        )
+
+        ishape = cast_like(shape_of(data), begin)
+        ishape_slice = slice_like(ishape, begin)
+        begin = _make.where(begin < cast_like(const(0), begin), begin + ishape_slice, begin)
+        begin = _make.where(begin >= ishape_slice, ishape_slice, begin)
         return _dyn_make.strided_slice(data, begin, end, strides, slice_mode)
     return _make.strided_slice(data, begin, end, strides, slice_mode)
 

src/relay/op/dyn/tensor/transform.cc

@@ -466,12 +466,20 @@ bool StridedSliceRel(const Array<Type>& types, int num_inputs, const Attrs& attr
   auto dshape = data->shape;
   int64_t num_axis = dshape.size();
 
+  const auto* begin = types[1].as<TensorTypeNode>();
+  ICHECK(begin);
+
   // calculate output shape
   std::vector<IndexExpr> oshape(num_axis);
-  for (int64_t i = 0; i < num_axis; ++i) {
+  int64_t num_dynamic_axes = begin->shape[0].as<IntImmNode>()->value;
+  for (int64_t i = 0; i < num_dynamic_axes; ++i) {
     oshape[i] = Any();
   }
 
+  for (int64_t i = num_dynamic_axes; i < num_axis; ++i) {
+    oshape[i] = dshape[i];
+  }
+
   reporter->Assign(types[4], TensorType(oshape, data->dtype));
   return true;
 }
@@ -484,11 +492,12 @@ Array<te::Tensor> StridedSliceCompute(const Attrs& attrs, const Array<te::Tensor
   te::Tensor strides = inputs[3];
   // Dynamic computation
   int64_t data_rank = data->shape.size();
-  ICHECK(begin->shape[0].as<IntImmNode>()->value == data_rank &&
-         end->shape[0].as<IntImmNode>()->value == data_rank &&
-         strides->shape[0].as<IntImmNode>()->value == data_rank)
-      << "begin, end, and strides are required to have the same length"
-      << " if they are dynamic variables.";
+  int64_t num_dynamic_axes = begin->shape[0].as<IntImmNode>()->value;
+  ICHECK(end->shape[0].as<IntImmNode>()->value == num_dynamic_axes &&
+         strides->shape[0].as<IntImmNode>()->value == num_dynamic_axes)
+      << "begin, end, strides should have the same length if they are dynamic variables";
+  ICHECK(num_dynamic_axes <= data_rank)
+      << "the number of dynamic axes to slice should be less than or equal to the data rank";
   return Array<te::Tensor>{topi::dynamic_strided_slice(data, begin, end, strides)};
 }
 

tests/python/relay/dyn/test_dynamic_op_level4.py

@@ -25,16 +25,19 @@
 
 @tvm.testing.uses_gpu
 def test_dynamic_strided_slice():
-    def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True, dtype="int32"):
+    def verify(dshape, begin, end, strides, slice_mode="end", test_ref=True, dtype="int32"):
         x = relay.var("x", relay.TensorType(dshape, "float32"))
         ndim = len(dshape)
+        slice_dim = len(begin)
         begin = begin if begin else [0] * ndim
-        end = end if end else list(dshape)
+        end = end if end else list(dshape)[:slice_dim]
         if strides:
             if len(strides) == 1:
-                strides = strides * ndim
+                strides = strides * slice_dim
         else:
-            strides = [1] * ndim
+            strides = [1] * slice_dim
+
+        num_static_axes = len(dshape) - len(begin)
 
         # target numpy result
         x_data = np.random.uniform(size=dshape).astype("float32")
@@ -54,7 +57,10 @@ def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True,
         func = relay.Function(inputs, z)
 
         func = run_infer_type(func)
-        text = func.astext()
+
+        if num_static_axes > 0:
+            oshape = run_infer_type(z).checked_type.shape
+            assert tuple(oshape[-num_static_axes:]) == dshape[-num_static_axes:]
 
         if not test_ref:
             return
@@ -69,22 +75,24 @@ def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True,
         [0, 20, 20, 0],
         [1, 140, 140, 3],
         [1, 1, 1, 1],
-        (1, 120, 120, 3),
         dtype="int64",
     )
-    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], (1, 3, 3), dtype="int16")
-    verify((3, 4, 3), [0, 0, 0], [4, -5, 4], [1, -1, 2], (3, 1, 2))
-    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], None, (2, 3, 3))
-    verify((3, 4, 3), [1, 1, 0], [4, 1000, 3], None, (2, 3, 3))
-    verify((3, 4, 3), [1, 1, 0], [4, 4, 4], None, (2, 3, 3))
-    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], None, (2, 3, 3))
-    verify((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], (1, 4, 3))
-    verify((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1], (1, 2, 3))
-    verify((20, 10, 5), [20, 10, 4], [0, 0, 1], [-1, -3, -2], (19, 3, 2))
-    verify(
-        (3, 4, 3), [1, 0, 0], [3, -1, 3], [1, 1, 1], (2, 4, 3), slice_mode="size", test_ref=False
-    )
-    verify((3, 4, 3), [1, 0, 0], [-1, 2, 3], [1, 1, 1], (2, 2, 3), slice_mode="size", test_ref=True)
+    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], dtype="int16")
+    verify((3, 4, 3), [0, 0, 0], [4, -5, 4], [1, -1, 2])
+    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], None)
+    verify((3, 4, 3), [1, 1, 0], [4, 1000, 3], None)
+    verify((3, 4, 3), [1, 1, 0], [4, 4, 4], None)
+    verify((3, 4, 3), [1, 1, 0], [4, 4, 3], None)
+    verify((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1])
+    verify((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1])
+    verify((20, 10, 5), [20, 10, 4], [0, 0, 1], [-1, -3, -2])
+    verify((3, 4, 3), [1, 0, 0], [3, -1, 3], [1, 1, 1], slice_mode="size", test_ref=False)
+    verify((3, 4, 3), [1, 0, 0], [-1, 2, 3], [1, 1, 1], slice_mode="size", test_ref=True)
+
+    # Slicing along first few axes, where the rest of axes remain static
+    verify((3, 4, 3), [0], [2], None)
+    verify((3, 4, 3), [1], [4], [2])
+    verify((3, 4, 3), [1, 0], [4, 2], [2, 1])
 
 
 if __name__ == "__main__":