Improve parsing slice (#467)

* refine slice implementation

* clang format

* fix cppcheck error

* clang format

* fix a bug in parsing slice

* add unit tests for slice operator

* clang format

* fix cppcheck error

* add the numpy package in the Dockerfile

* add missing onnx file

* fix cppcheck error

* fix cppcheck error

* add one more unit test related to slice and split

* clang format
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

Dockerfile

@@ -52,7 +52,7 @@ ENV LC_ALL=C.UTF-8
 ENV LANG=C.UTF-8
 
 # Install cget
-RUN pip3 install cget
+RUN pip3 install cget && pip3 install numpy
 
 # Install rclone
 RUN pip install https://github.com/pfultz2/rclone/archive/master.tar.gz

src/include/migraphx/op/slice.hpp

 #ifndef MIGRAPHX_GUARD_OPERATORS_SLICE_HPP
 #define MIGRAPHX_GUARD_OPERATORS_SLICE_HPP
 
-#include <array>
 #include <migraphx/operation.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/streamutils.hpp>
-#include <migraphx/literal.hpp>
-#include <migraphx/shape_for_each.hpp>
 #include <migraphx/config.hpp>
 #include <cmath>
 #include <utility>
+#include <vector>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -30,6 +28,54 @@ struct slice
 
     std::string name() const { return "slice"; }
 
+    void tune_attributes(std::vector<int64_t>& tuned_axes,
+                         std::vector<int64_t>& tuned_starts,
+                         std::vector<int64_t>& tuned_ends,
+                         const std::vector<std::size_t>& lens) const
+    {
+        // tune axes
+        int64_t n_rank = static_cast<int64_t>(lens.size());
+        if(!std::all_of(tuned_axes.begin(), tuned_axes.end(), [=](auto i) {
+               return (i < n_rank and i >= -n_rank);
+           }))
+        {
+            MIGRAPHX_THROW("SLICE: input axis " + to_string_range(tuned_axes) + " out of range");
+        }
+        std::transform(tuned_axes.begin(), tuned_axes.end(), tuned_axes.begin(), [=](auto i) {
+            return (i < 0) ? (i + n_rank) : i;
+        });
+
+        std::vector<int64_t> axis_lens(tuned_axes.size());
+        std::transform(tuned_axes.begin(), tuned_axes.end(), axis_lens.begin(), [&](auto axis) {
+            return lens[axis];
+        });
+
+        // tune starts
+        std::transform(tuned_starts.begin(),
+                       tuned_starts.end(),
+                       axis_lens.begin(),
+                       tuned_starts.begin(),
+                       [=](auto i, auto dim) {
+                           i = (i < -dim) ? -dim : ((i > dim) ? dim : i);
+                           return (i < 0) ? (i + dim) : i;
+                       });
+
+        // tune ends
+        std::transform(tuned_ends.begin(),
+                       tuned_ends.end(),
+                       axis_lens.begin(),
+                       tuned_ends.begin(),
+                       [=](auto i, auto dim) {
+                           i = (i < -dim) ? -dim : ((i > dim) ? dim : i);
+                           return (i < 0) ? (i + dim) : i;
+                       });
+
+        if(!(tuned_ends >= tuned_starts))
+        {
+            MIGRAPHX_THROW("SLICE: starts and ends does not match");
+        }
+    }
+
     auto fix_index(const std::vector<std::size_t>& lens, std::size_t axis, int64_t index) const
     {
         int64_t r = std::min(index, static_cast<int64_t>(lens[axis]));
@@ -40,22 +86,27 @@ struct slice
 
     auto compute_offset(const shape& s) const
     {
+        std::vector<int64_t> tuned_axes      = axes;
+        std::vector<int64_t> tuned_starts    = starts;
+        std::vector<int64_t> tuned_ends      = ends;
         const std::vector<std::size_t>& lens = s.lens();
+        tune_attributes(tuned_axes, tuned_starts, tuned_ends, lens);
+
         const std::vector<std::size_t>& strides = s.strides();
         auto offset                             = 0;
-        if(!axes.empty())
+        if(!tuned_axes.empty())
         {
-            for(std::size_t i = 0; i < axes.size(); i++)
+            for(std::size_t i = 0; i < tuned_axes.size(); i++)
             {
-                auto axis = axes[i];
-                offset += fix_index(lens, axis, starts[i]) * strides[axis];
+                auto axis = tuned_axes[i];
+                offset += fix_index(lens, axis, tuned_starts[i]) * strides[axis];
             }
         }
         else
         {
             for(std::size_t axis = 0; axis < lens.size(); axis++)
             {
-                offset += fix_index(lens, axis, starts[axis]) * strides[axis];
+                offset += fix_index(lens, axis, tuned_starts[axis]) * strides[axis];
             }
         }
         return offset;
@@ -69,14 +120,19 @@ struct slice
         const auto& old_strides = input_shape.strides();
         if(starts.size() != axes.size() || axes.size() != ends.size())
         {
-            MIGRAPHX_THROW("inconsistent sizes");
+            MIGRAPHX_THROW("SLICE: inconsistent sizes");
         }
+
+        std::vector<int64_t> tuned_axes   = axes;
+        std::vector<int64_t> tuned_starts = starts;
+        std::vector<int64_t> tuned_ends   = ends;
+        tune_attributes(tuned_axes, tuned_starts, tuned_ends, old_lens);
         std::vector<std::size_t> new_lens = old_lens;
-        for(std::size_t i = 0; i < axes.size(); i++)
+        for(std::size_t i = 0; i < tuned_axes.size(); i++)
         {
-            auto axis = axes[i];
-            new_lens[axis] =
-                fix_index(old_lens, axis, ends[i]) - fix_index(old_lens, axis, starts[i]);
+            auto axis      = tuned_axes[i];
+            new_lens[axis] = fix_index(old_lens, axis, tuned_ends[i]) -
+                             fix_index(old_lens, axis, tuned_starts[i]);
         }
         return shape{t, new_lens, old_strides};
     }

src/onnx/onnx.cpp

@@ -776,28 +776,56 @@ struct onnx_parser
     parse_slice(const std::string&, node_info info, std::vector<instruction_ref> args)
     {
         op::slice op;
-        std::vector<size_t> dims = args[0]->get_shape().lens();
-        size_t num_dims          = dims.size();
-        if(contains(info.attributes, "axes"))
+
+        // slice can have up to 5 inputs, we first check the 5th one
+        // to decide whether MIGRAPHX can handle this slice
+        if(args.size() == 5)
+        {
+            migraphx::argument step_arg = args.back()->eval();
+            check_arg_empty(step_arg, "PARSE_SLICE: cannot handle variable steps for slice");
+            std::vector<int> steps;
+            step_arg.visit([&](auto s) { steps.assign(s.begin(), s.end()); });
+            if(!std::all_of(steps.begin(), steps.end(), [](auto s) { return s == 1; }))
+            {
+                MIGRAPHX_THROW("PARSE_SLICE: cannot handle step other than 1");
+            }
+        }
+
+        if(args.size() >= 4)
+        {
+            migraphx::argument axes_arg = args.at(3)->eval();
+            check_arg_empty(axes_arg, "PARSE_SLICE: cannot handle variable axes for slice");
+            axes_arg.visit([&](auto s) { op.axes.assign(s.begin(), s.end()); });
+        }
+        else if(contains(info.attributes, "axes"))
         {
             literal s = parse_value(info.attributes.at("axes"));
             s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
         }
-        else
+
+        if(args.size() >= 3)
         {
-            op.axes = std::vector<int64_t>(num_dims);
-            std::iota(op.axes.begin(), op.axes.end(), 0);
+            migraphx::argument end_arg = args.at(2)->eval();
+            check_arg_empty(end_arg, "PARSE_SLICE: cannot handle variable ends for slice");
+            end_arg.visit([&](auto s) { op.ends.assign(s.begin(), s.end()); });
         }
-
-        if(contains(info.attributes, "ends"))
+        else if(contains(info.attributes, "ends"))
         {
             op.ends = get_indices(info.attributes.at("ends"));
         }
-        if(contains(info.attributes, "starts"))
+
+        if(args.size() >= 2)
+        {
+            migraphx::argument start_arg = args.at(1)->eval();
+            check_arg_empty(start_arg, "PARSE_SLICE: cannot handle variable starts for slice");
+            start_arg.visit([&](auto s) { op.starts.assign(s.begin(), s.end()); });
+        }
+        else if(contains(info.attributes, "starts"))
         {
             literal s = parse_value(info.attributes.at("starts"));
             s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
         }
+
         return prog.add_instruction(op, args[0]);
     }
 

test/onnx/gen_onnx.py

@@ -1783,6 +1783,59 @@ def sinh_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def slice_5arg_test():
+    step = np.array([1, 1])
+    step_tensor = helper.make_tensor(name="step",
+                                     data_type=TensorProto.INT32,
+                                     dims=step.shape,
+                                     vals=step.astype(int))
+    arg_step = helper.make_node("Constant",
+                                inputs=[],
+                                outputs=['arg_step'],
+                                value=step_tensor)
+
+    axis = np.array([-1, -2])
+    axis_tensor = helper.make_tensor(name="axis",
+                                     data_type=TensorProto.INT32,
+                                     dims=axis.shape,
+                                     vals=axis.astype(int))
+    arg_axis = helper.make_node("Constant",
+                                inputs=[],
+                                outputs=['arg_axis'],
+                                value=axis_tensor)
+
+    end = np.array([-1, -1])
+    end_tensor = helper.make_tensor(name="end",
+                                    data_type=TensorProto.INT32,
+                                    dims=end.shape,
+                                    vals=end.astype(int))
+    arg_end = helper.make_node("Constant",
+                               inputs=[],
+                               outputs=['arg_end'],
+                               value=end_tensor)
+
+    start = np.array([-5, -3])
+    start_tensor = helper.make_tensor(name="start",
+                                      data_type=TensorProto.INT32,
+                                      dims=start.shape,
+                                      vals=start.astype(int))
+    arg_start = helper.make_node("Constant",
+                                 inputs=[],
+                                 outputs=['arg_start'],
+                                 value=start_tensor)
+
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 2])
+
+    node = onnx.helper.make_node(
+        'Slice',
+        inputs=['0', 'arg_start', 'arg_end', 'arg_axis', 'arg_step'],
+        outputs=['1'])
+
+    return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y])
+
+
 @onnx_test
 def slice_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
@@ -1808,6 +1861,23 @@ def softmax_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def split_minus_axis_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 5])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [10, 5])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [10, 5])
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x'],
+        outputs=['y1', 'y2', 'y3'],
+        axis=-1,
+    )
+
+    return ([node], [x], [y1, y2, y3])
+
+
 @onnx_test
 def split_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])

test/onnx/onnx_test.cpp

@@ -1376,6 +1376,22 @@ TEST_CASE(sinh_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(slice_5arg_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {5, 5}});
+    p.add_literal({{migraphx::shape::int32_type, {2}}, {-5, -3}});
+    p.add_literal({{migraphx::shape::int32_type, {2}}, {-1, -1}});
+    p.add_literal({{migraphx::shape::int32_type, {2}}, {-1, -2}});
+    p.add_literal({{migraphx::shape::int32_type, {2}}, {1, 1}});
+    auto ret = p.add_instruction(migraphx::op::slice{{-1, -2}, {-5, -3}, {-1, -1}}, l0);
+    p.add_return({ret});
+
+    auto prog = migraphx::parse_onnx("slice_5arg_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(slice_test)
 {
     migraphx::program p;
@@ -1396,6 +1412,20 @@ TEST_CASE(softmax_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(split_minus_axis_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10, 15}});
+    auto r1    = p.add_instruction(migraphx::op::slice{{-1}, {0}, {5}}, input);
+    auto r2    = p.add_instruction(migraphx::op::slice{{-1}, {5}, {10}}, input);
+    auto r3    = p.add_instruction(migraphx::op::slice{{-1}, {10}, {15}}, input);
+    p.add_return({r1, r2, r3});
+
+    auto prog = migraphx::parse_onnx("split_minus_axis_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(split_test)
 {
     migraphx::program p;

test/onnx/slice_5arg_test.onnx

+slice_5arg_test:
+0arg_step"Constant*
+value**

Bstep

+Barg_axis"Constant*,
+value* *

Baxis

+@arg_end"Constant*+
+value**

Bend

+D	arg_start"Constant*-
+value*!*

Bstart

+5
+
0
+	arg_start
+arg_end
+arg_axis
+arg_step
1"Sliceslice_5arg_testZ
+
0
+

+
+b
+
1
+

+
+B
\ No newline at end of file

test/onnx/split_minus_axis_test.onnx

+split_minus_axis_test:

+,
+
xy1y2y3"Split*
+axis

split_minus_axis_testZ
+
x
+

+
+
+b
+y1
+

+
+
+b
+y2
+

+
+
+b
+y3
+

+
+
+B
\ No newline at end of file