Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into rocblas_api_opt

.github/workflows/ci.yaml

@@ -142,10 +142,12 @@ jobs:
       with:
         python-version: 3.6
     - name: Install pyflakes
-      run: pip install pyflakes==2.3.1
+      run: pip install pyflakes==2.3.1 mypy==0.931
 
     - name: Run pyflakes
-      run: pyflakes examples/ tools/ src/ test/ doc/
+      run: |
+        pyflakes examples/ tools/ src/ test/ doc/
+        mypy tools/api.py
 
   linux:
 

src/include/migraphx/iota_iterator.hpp

@@ -89,6 +89,13 @@ inline std::ptrdiff_t operator-(basic_iota_iterator<F, Iterator> x,
     return x.index - y.index;
 }
 
+template <class F, class Iterator>
+inline basic_iota_iterator<F, Iterator> operator-(basic_iota_iterator<F, Iterator> x,
+                                                  std::ptrdiff_t y)
+{
+    return x -= y;
+}
+
 template <class F, class Iterator>
 inline bool operator==(basic_iota_iterator<F, Iterator> x, basic_iota_iterator<F, Iterator> y)
 {

src/onnx/parse_resize.cpp

@@ -163,9 +163,9 @@ static std::string get_nearest_mode(const onnx_parser::attribute_map& attr)
 
 struct parse_resize : op_parser<parse_resize>
 {
-    std::vector<op_desc> operators() const { return {{"Resize"}}; }
+    std::vector<op_desc> operators() const { return {{"Resize"}, {"Upsample"}}; }
 
-    instruction_ref parse(const op_desc& /*opd*/,
+    instruction_ref parse(const op_desc& opd,
                           const onnx_parser& /*parser*/,
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
@@ -183,7 +183,7 @@ struct parse_resize : op_parser<parse_resize>
         if(contains(info.attributes, "exclude_outside") and
            info.attributes.at("exclude_outside").i() == 1)
         {
-            MIGRAPHX_THROW("PARSE_RESIZE: exclude_outside 1 is not supported!");
+            MIGRAPHX_THROW("PARSE_" + opd.op_name + ": exclude_outside 1 is not supported!");
         }
 
         // input data shape info
@@ -215,12 +215,14 @@ struct parse_resize : op_parser<parse_resize>
             if(type == shape::int64_type)
             {
                 auto arg_out_s = arg->eval();
-                check_arg_empty(arg_out_s, "PARSE_RESIZE: dynamic output size is not supported!");
+                check_arg_empty(arg_out_s,
+                                "PARSE_" + opd.op_name + ": dynamic output size is not supported!");
                 arg_out_s.visit([&](auto ol) { out_lens.assign(ol.begin(), ol.end()); });
 
                 if(out_lens.size() != in_lens.size())
                 {
-                    MIGRAPHX_THROW("PARSE_RESIZE: specified output size does not match input size");
+                    MIGRAPHX_THROW("PARSE_" + opd.op_name +
+                                   ": specified output size does not match input size");
                 }
 
                 // compute the scale
@@ -239,12 +241,14 @@ struct parse_resize : op_parser<parse_resize>
                 {
                     auto arg_scale = arg->eval();
                     check_arg_empty(arg_scale,
-                                    "PARSE_RESIZE: dynamic input scale is not supported!");
+                                    "PARSE_" + opd.op_name +
+                                        ": dynamic input scale is not supported!");
 
                     arg_scale.visit([&](auto v) { vec_scale.assign(v.begin(), v.end()); });
                     if(in_lens.size() != vec_scale.size())
                     {
-                        MIGRAPHX_THROW("PARSE_RESIZE: ranks of input and scale are different!");
+                        MIGRAPHX_THROW("PARSE_" + opd.op_name +
+                                       ": ranks of input and scale are different!");
                     }
 
                     std::transform(in_lens.begin(),

src/onnx/parse_upsample.cpp

-#include <migraphx/onnx/op_parser.hpp>
-#include <migraphx/onnx/checks.hpp>
-#include <migraphx/ranges.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/make_op.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace onnx {
-
-struct parse_upsample : op_parser<parse_upsample>
-{
-    std::vector<op_desc> operators() const { return {{"Upsample"}}; }
-
-    instruction_ref parse(const op_desc& /*opd*/,
-                          const onnx_parser& /*parser*/,
-                          onnx_parser::node_info info,
-                          std::vector<instruction_ref> args) const
-    {
-        if(contains(info.attributes, "mode"))
-        {
-            auto mode = info.attributes.at("mode").s();
-            if(mode != "nearest")
-            {
-                MIGRAPHX_THROW("PARSE_UPSAMPLE: only nearest mode is supported!");
-            }
-        }
-
-        auto arg_scale = args[1]->eval();
-        check_arg_empty(arg_scale, "PARSE_UPSAMPLE: only constant scale is supported!");
-        std::vector<float> vec_scale;
-        arg_scale.visit([&](auto v) { vec_scale.assign(v.begin(), v.end()); });
-
-        auto in_s    = args[0]->get_shape();
-        auto in_lens = in_s.lens();
-        if(in_lens.size() != vec_scale.size())
-        {
-            MIGRAPHX_THROW("PARSE_UPSAMPLE: ranks of input and scale are different!");
-        }
-
-        std::vector<std::size_t> out_lens(in_lens.size());
-        std::transform(in_lens.begin(),
-                       in_lens.end(),
-                       vec_scale.begin(),
-                       out_lens.begin(),
-                       [&](auto idx, auto scale) { return static_cast<std::size_t>(idx * scale); });
-
-        std::vector<float> idx_scale(in_lens.size());
-        std::transform(
-            out_lens.begin(),
-            out_lens.end(),
-            in_lens.begin(),
-            idx_scale.begin(),
-            [](auto od, auto id) { return (od == id) ? 1.0f : (id - 1.0f) / (od - 1.0f); });
-
-        shape out_s{in_s.type(), out_lens};
-        std::vector<int> ind(out_s.elements());
-
-        // map out_idx to in_idx
-        shape_for_each(out_s, [&](auto idx) {
-            auto in_idx = idx;
-            std::transform(idx.begin(),
-                           idx.end(),
-                           idx_scale.begin(),
-                           in_idx.begin(),
-                           // nearest mode
-                           [](auto index, auto scale) {
-                               return static_cast<std::size_t>(std::round(index * scale));
-                           });
-
-            ind[out_s.index(idx)] = static_cast<int64_t>(in_s.index(in_idx));
-        });
-
-        // reshape input to one-dimension
-        std::vector<int64_t> rsp_lens = {static_cast<int64_t>(in_s.elements())};
-        shape ind_s{shape::int32_type, out_lens};
-        auto rsp     = info.add_instruction(make_op("reshape", {{"dims", rsp_lens}}), args[0]);
-        auto ins_ind = info.add_literal(literal(ind_s, ind));
-        return info.add_instruction(make_op("gather", {{"axis", 0}}), rsp, ins_ind);
-    }
-};
-
-} // namespace onnx
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/program.cpp

@@ -180,6 +180,63 @@ void program::finalize()
     mm->finalize(this->impl->ctx);
 }
 
+template <class T>
+std::string classify(T x)
+{
+    switch(std::fpclassify(x))
+    {
+    case FP_INFINITE: return "inf";
+    case FP_NAN: return "nan";
+    case FP_NORMAL: return "normal";
+    case FP_SUBNORMAL: return "subnormal";
+    case FP_ZERO: return "zero";
+    default: return "unknown";
+    }
+}
+
+std::unordered_set<std::string> classify_argument(const argument& a)
+{
+    std::unordered_set<std::string> result;
+    a.visit(
+        [&](auto t) {
+            for(const auto& x : t)
+                result.insert(classify(x));
+        },
+        [&](const auto& xs) {
+            for(const auto& x : xs)
+            {
+                auto r = classify_argument(x);
+                result.insert(r.begin(), r.end());
+            }
+        });
+    return result;
+}
+
+void preview_argument(std::ostream& os, const argument& a)
+{
+    a.visit(
+        [&](auto t) {
+            if(t.size() <= 10)
+            {
+                os << t;
+            }
+            else
+            {
+                os << to_string_range(t.begin(), t.begin() + 5);
+                os << ", ..., ";
+                os << to_string_range(t.end() - 5, t.end());
+            }
+        },
+        [&](const auto& xs) {
+            for(const auto& x : xs)
+            {
+                os << '{';
+                preview_argument(os, x);
+                os << '}';
+            }
+        });
+}
+
 template <class F>
 std::vector<argument> generic_eval(const module* mod,
                                    context& ctx,
@@ -312,8 +369,21 @@ std::vector<argument> program::eval(parameter_map params) const
                                 if(trace_level > 1 and ins->name().front() != '@' and
                                    ins->name() != "load" and not result.empty())
                                 {
-                                    target tgt = make_target(this->impl->target_name);
-                                    std::cout << "Output: " << tgt.copy_from(result) << std::endl;
+                                    target tgt  = make_target(this->impl->target_name);
+                                    auto buffer = tgt.copy_from(result);
+                                    if(trace_level == 2)
+                                    {
+                                        std::cout << "Output has "
+                                                  << to_string_range(classify_argument(buffer))
+                                                  << std::endl;
+                                        std::cout << "Output: ";
+                                        preview_argument(std::cout, buffer);
+                                        std::cout << std::endl;
+                                    }
+                                    else
+                                    {
+                                        std::cout << "Output: " << buffer << std::endl;
+                                    }
                                 }
                                 return result;
                             }));

test/onnx/gen_onnx.py

@@ -5074,6 +5074,25 @@ def unknown_aten_test():
     return ([node], [x, y], [a])
 
 
+@onnx_test
+def upsample_linear_test():
+    scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+    scales_tensor = helper.make_tensor(name='scales',
+                                       data_type=TensorProto.FLOAT,
+                                       dims=scales.shape,
+                                       vals=scales.flatten().astype(
+                                           np.float32))
+    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2])
+    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [])
+
+    node = onnx.helper.make_node('Upsample',
+                                 inputs=['X', '', 'scales'],
+                                 outputs=['Y'],
+                                 mode='linear')
+
+    return ([node], [X], [Y], [scales_tensor])
+
+
 @onnx_test
 def upsample_test():
     scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)

test/onnx/onnx_test.cpp

@@ -3643,7 +3643,7 @@ TEST_CASE(resize_nonstd_input_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(resize_upsample_linear_ac_test)
+static auto create_upsample_linear_prog()
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
@@ -3734,6 +3734,12 @@ TEST_CASE(resize_upsample_linear_ac_test)
     auto add1  = mm->add_instruction(migraphx::make_op("add"), mul1, slc10);
     mm->add_return({add1});
 
+    return p;
+}
+
+TEST_CASE(resize_upsample_linear_ac_test)
+{
+    auto p    = create_upsample_linear_prog();
     auto prog = migraphx::parse_onnx("resize_upsample_linear_ac_test.onnx");
     EXPECT(p == prog);
 }
@@ -4753,6 +4759,13 @@ TEST_CASE(unknown_test_throw)
     EXPECT(test::throws([&] { migraphx::parse_onnx("unknown_test.onnx"); }));
 }
 
+TEST_CASE(upsample_linear_test)
+{
+    auto p    = create_upsample_linear_prog();
+    auto prog = migraphx::parse_onnx("upsample_linear_test.onnx");
+    EXPECT(p == prog);
+}
+
 TEST_CASE(upsample_test)
 {
     migraphx::program p;

tools/generate.sh

 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 SRC_DIR=$DIR/../src
-ls -1 $DIR/include/ | xargs -n 1 -P $(nproc) -I{} -t bash -c "python3.6 $DIR/te.py $DIR/include/{} | clang-format-5.0 -style=file > $SRC_DIR/include/migraphx/{}"
+PYTHON=python3
+if type -p python3.6 > /dev/null ; then
+    PYTHON=python3.6
+fi
+if type -p python3.8 > /dev/null ; then
+    PYTHON=python3.8
+fi
+ls -1 $DIR/include/ | xargs -n 1 -P $(nproc) -I{} -t bash -c "$PYTHON $DIR/te.py $DIR/include/{} | clang-format-5.0 -style=file > $SRC_DIR/include/migraphx/{}"
 
 function api {
-    python3.6 $DIR/api.py $SRC_DIR/api/migraphx.py $1 | clang-format-5.0 -style=file > $2
+    $PYTHON $DIR/api.py $SRC_DIR/api/migraphx.py $1 | clang-format-5.0 -style=file > $2
 }
 
 api $DIR/api/migraphx.h $SRC_DIR/api/include/migraphx/migraphx.h