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

src/onnx/onnx.cpp

@@ -68,11 +68,13 @@ struct onnx_parser
 
         add_mem_op("ArgMax", &onnx_parser::parse_argmax);
         add_mem_op("ArgMin", &onnx_parser::parse_argmin);
+        add_mem_op("Cast", &onnx_parser::parse_cast);
         add_mem_op("Clip", &onnx_parser::parse_clip);
         add_mem_op("LRN", &onnx_parser::parse_lrn);
         add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
         add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
         add_mem_op("Elu", &onnx_parser::parse_elu);
+        add_mem_op("Expand", &onnx_parser::parse_expand);
         add_mem_op("Constant", &onnx_parser::parse_constant);
         add_mem_op("Conv", &onnx_parser::parse_conv);
         add_mem_op("MaxPool", &onnx_parser::parse_pooling);
@@ -93,6 +95,7 @@ struct onnx_parser
         add_mem_op("Gather", &onnx_parser::parse_gather);
         add_mem_op("Shape", &onnx_parser::parse_shape);
         add_mem_op("ConstantFill", &onnx_parser::parse_constant_fill);
+        add_mem_op("ConstantOfShape", &onnx_parser::parse_constant_of_shape);
         add_mem_op("Transpose", &onnx_parser::parse_transpose);
         add_mem_op("RNN", &onnx_parser::parse_rnn);
         add_mem_op("GRU", &onnx_parser::parse_gru);
@@ -464,8 +467,7 @@ struct onnx_parser
         if(args.size() == 2)
         {
             auto s = args[1]->eval();
-            if(s.empty())
-                MIGRAPHX_THROW("Dynamic shape is not supported.");
+            check_arg_empty(s, "Reshape: dynamic shape is not supported");
             s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
         }
         return prog.add_instruction(op, args[0]);
@@ -544,7 +546,13 @@ struct onnx_parser
                                    attribute_map attributes,
                                    const std::vector<instruction_ref>&)
     {
-        literal v     = parse_value(attributes.at("value"));
+        literal v = parse_value(attributes.at("value"));
+        // return empty literal
+        if(v.get_shape().elements() == 0)
+        {
+            return prog.add_literal(literal{});
+        }
+
         auto dim_size = attributes.at("value").t().dims_size();
         // if dim_size is 0, it is a scalar
         if(dim_size == 0)
@@ -872,10 +880,7 @@ struct onnx_parser
             }
 
             migraphx::argument in = args[0]->eval();
-            if(in.empty())
-            {
-                MIGRAPHX_THROW("ConstantFill: cannot handle dynamic shape as input");
-            }
+            check_arg_empty(in, "ConstantFill: dynamic shape is not supported");
 
             std::vector<std::size_t> dims;
             in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
@@ -903,6 +908,74 @@ struct onnx_parser
         }
     }
 
+    instruction_ref parse_constant_of_shape(const std::string&,
+                                            attribute_map attributes,
+                                            std::vector<instruction_ref> args)
+    {
+        literal l_val{};
+        if(contains(attributes, "value"))
+        {
+            l_val = parse_value(attributes.at("value"));
+            if(l_val.get_shape().elements() != 1)
+            {
+                MIGRAPHX_THROW("ConstantOfShape: attribute value can contain only 1 elements!");
+            }
+        }
+        else
+        {
+            l_val = literal({shape::float_type, {1}, {0}}, {0.0f});
+        }
+
+        // input is empty, output is a scalar
+        auto type = l_val.get_shape().type();
+
+        if(args.empty())
+        {
+            MIGRAPHX_THROW("ConstantOfShape : must have 1 input!");
+        }
+        else
+        {
+            migraphx::shape s;
+            // empty input tensor, output is a scalar
+            if(args[0]->get_shape().elements() == 0)
+            {
+                s = migraphx::shape{type, {1}, {0}};
+            }
+            else
+            {
+                migraphx::argument in = args[0]->eval();
+                check_arg_empty(in, "ConstantOfShape: dynamic shape is not supported");
+
+                std::vector<std::size_t> dims;
+                in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
+                s = migraphx::shape{type, dims};
+            }
+
+            literal l_out{};
+            l_val.visit([&](auto val) {
+                using val_type = std::remove_cv_t<typename decltype(val)::value_type>;
+                // l_val contains only one element
+                std::vector<val_type> out_vec(s.elements(), *val.begin());
+                l_out = literal(s, out_vec);
+            });
+
+            return prog.add_literal(l_out);
+        }
+    }
+
+    instruction_ref
+    parse_expand(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
+    {
+        auto in_lens             = args[0]->get_shape().lens();
+        migraphx::argument arg_s = args[1]->eval();
+        check_arg_empty(arg_s, "Expand: dynamic shape is not supported");
+        std::vector<std::size_t> dims;
+        arg_s.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
+        auto out_lens = compute_broadcasted_lens(in_lens, dims);
+
+        return prog.add_instruction(op::multibroadcast{out_lens}, args[0]);
+    }
+
     std::vector<instruction_ref>
     parse_rnn(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
@@ -1325,6 +1398,19 @@ struct onnx_parser
         }
     }
 
+    instruction_ref
+    parse_cast(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        if(!contains(attributes, "to"))
+        {
+            MIGRAPHX_THROW("PARSE_CAST: missing to type attribute!");
+        }
+
+        int to_type        = parse_value(attributes.at("to")).at<int>();
+        shape::type_t type = get_type(to_type);
+        return prog.add_instruction(op::convert{type}, std::move(args));
+    }
+
     void parse_from(std::istream& is)
     {
         onnx::ModelProto model;
@@ -1637,6 +1723,14 @@ struct onnx_parser
         }
         }
     }
+
+    void check_arg_empty(const argument& arg, const std::string& msg)
+    {
+        if(arg.empty())
+        {
+            MIGRAPHX_THROW(msg);
+        }
+    }
 };
 
 program parse_onnx(const std::string& name)

src/targets/gpu/device/reduce_sum.cpp

@@ -8,6 +8,7 @@ namespace device {
 
 void reduce_sum(hipStream_t stream, const argument& result, const argument& arg)
 {
+
     reduce(stream, result, arg, sum{}, 0, id{}, id{});
 }
 

src/tf/tf.cpp

@@ -164,6 +164,7 @@ struct tf_parser
         add_binary_op("Sub", op::sub{});
 
         add_mem_op("AvgPool", &tf_parser::parse_pooling);
+        add_mem_op("BatchMatMul", &tf_parser::parse_matmul, false);
         add_mem_op("BiasAdd", &tf_parser::parse_biasadd);
         add_mem_op("Cast", &tf_parser::parse_cast, false);
         add_mem_op("ConcatV2", &tf_parser::parse_concat, false);
@@ -538,6 +539,15 @@ struct tf_parser
             transb = attributes.at("transpose_a").b();
         }
 
+        if(contains(attributes, "adj_x"))
+        {
+            transa = attributes.at("adj_x").b();
+        }
+        if(contains(attributes, "adj_y"))
+        {
+            transb = attributes.at("adj_y").b();
+        }
+
         std::vector<int64_t> perm(args[0]->get_shape().lens().size());
         std::iota(perm.begin(), perm.end(), int64_t{0});
         // swap the last two elements

test/onnx/cast_test.onnx

+cast-example:F
+
+
x
y"Cast*	
+to

	test_castZ
+
x
+
+
+
+
+b
+
y
+
+

+
+B

test/onnx/const_of_shape2.onnx

+constant-of-shape:

+6shape"Constant*#
+value**Bshape_tensor 

+7
+shape
y"ConstantOfShape*
+value*
:

+Bvalue 
constant_of_shapeb
+
y
+

+
+
+B

test/onnx/const_of_shape3.onnx

+constant-of-shape:

+6shape"Constant*#
+value**Bshape_tensor 

+
+shape
y"ConstantOfShapeconstant_of_shapeb
+
y
+

+
+
+B

test/onnx/expand_test.onnx

+expand:

+7shape"Constant*$
+value**Bshape_tensor

+
+
x
+shape
y"ExpandexpandZ
+
x
+

+
+

+
b
+
y
+

+
+
+
+B

test/onnx/onnx_test.cpp

@@ -925,4 +925,78 @@ TEST_CASE(pow_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(cast_test)
+{
+    migraphx::program p;
+    auto l = p.add_parameter("x", migraphx::shape{migraphx::shape::half_type, {10}});
+    p.add_instruction(migraphx::op::convert{migraphx::shape::float_type}, l);
+
+    auto prog = migraphx::parse_onnx("cast_test.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(const_of_shape_float)
+{
+    migraphx::program p;
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::float_type, {2, 3, 4});
+    std::vector<float> vec(s.elements(), 10.0f);
+    p.add_literal(migraphx::literal(s, vec));
+
+    auto prog = migraphx::parse_onnx("const_of_shape1.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(const_of_shape_int64)
+{
+    migraphx::program p;
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::int64_type, {2, 3, 4});
+    std::vector<int64_t> vec(s.elements(), 10);
+    p.add_literal(migraphx::literal(s, vec));
+
+    auto prog = migraphx::parse_onnx("const_of_shape2.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(const_of_shape_no_value_attr)
+{
+    migraphx::program p;
+    migraphx::shape ss(migraphx::shape::int32_type, {3});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4}));
+    migraphx::shape s(migraphx::shape::float_type, {2, 3, 4});
+    std::vector<float> vec(s.elements(), 0.0f);
+    p.add_literal(migraphx::literal(s, vec));
+
+    auto prog = migraphx::parse_onnx("const_of_shape3.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(const_of_shape_empty_input)
+{
+    migraphx::program p;
+    p.add_literal(migraphx::literal());
+    migraphx::shape s(migraphx::shape::int64_type, {1}, {0});
+    std::vector<int64_t> vec(s.elements(), 10);
+    p.add_literal(migraphx::literal(s, vec));
+
+    auto prog = migraphx::parse_onnx("const_of_shape4.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(expand_test)
+{
+    migraphx::program p;
+    migraphx::shape s(migraphx::shape::float_type, {3, 1, 1});
+    auto param = p.add_parameter("x", s);
+    migraphx::shape ss(migraphx::shape::int32_type, {4});
+    p.add_literal(migraphx::literal(ss, {2, 3, 4, 5}));
+    p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, param);
+
+    auto prog = migraphx::parse_onnx("expand_test.onnx");
+    EXPECT(p == prog);
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/tf/batchmatmul_test.pb

+
+:
+
0Placeholder*
+shape:
*
+dtype0

+:
+
1Placeholder*
+dtype0
*
+shape:

+D
+batchmatmul1BatchMatMul
0
1*
+adj_x(
*
+adj_y(
*
+
T0
"
\ No newline at end of file

test/tf/tf_test.cpp

@@ -48,6 +48,21 @@ TEST_CASE(add_bcast_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(batchmatmul_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 8, 4}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 2, 4, 8}});
+
+    auto trans_l0 = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l0);
+    auto trans_l1 = p.add_instruction(migraphx::op::transpose{{0, 1, 3, 2}}, l1);
+
+    p.add_instruction(migraphx::op::dot{}, trans_l0, trans_l1);
+    auto prog = optimize_tf("batchmatmul_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(batchnorm_test)
 {
     float epsilon  = 1.001e-5f;