Add support for Shrink ONNX operator

src/onnx/parse_shrink.cpp

@@ -0,0 +1,85 @@
+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_shrink : op_parser<parse_shrink>
+{
+    std::vector<op_desc> operators() const { return {{"Shrink"}}; }
+
+    instruction_ref parse(const op_desc&,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        float bias = 0.0;
+        if(contains(info.attributes, "bias"))
+        {
+            bias = parser.parse_value(info.attributes.at("bias")).at<float>();
+        }
+        float lambd = 0.5;
+        if(contains(info.attributes, "lambd"))
+        {
+            lambd = parser.parse_value(info.attributes.at("lambd")).at<float>();
+        }
+
+        auto x             = args[0];
+        auto x_shape       = x->get_shape();
+        auto x_type        = x_shape.type();
+        auto lit_bias      = info.add_literal(bias);
+        auto lit_neg_lambd = info.add_literal(-lambd);
+        auto lit_lambd     = info.add_literal(lambd);
+
+        auto x_plus_bias = info.add_common_op("add", x, lit_bias);
+        auto x_min_bias  = info.add_common_op("sub", x, lit_bias);
+
+        auto cond1   = info.add_common_op("less", x, lit_neg_lambd);
+        auto cond2_a = info.add_common_op("not", cond1);
+        auto cond2_b = info.add_common_op("greater", x, lit_lambd);
+        auto cond2   = info.add_common_op("logical_and", cond2_a, cond2_b);
+
+        auto mul1 = info.add_instruction(make_op("convert", {{"target_type", x_type}}), cond1);
+        auto mul2 = info.add_instruction(make_op("convert", {{"target_type", x_type}}), cond2);
+
+        auto first  = info.add_common_op("mul", mul1, x_plus_bias);
+        auto second = info.add_common_op("mul", mul2, x_min_bias);
+        auto ret    = info.add_common_op("add", first, second);
+        if(ret->get_shape().type() != x_type)
+        {
+            ret = info.add_instruction(make_op("convert", {{"target_type", x_type}}), ret);
+        }
+        return ret;
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

test/onnx/gen_onnx.py

@@ -6718,6 +6718,101 @@ def shape_gather_test():
     return ([node_const, node_shape, node_gather], [x], [z])
 
 
+@onnx_test()
+def shrink_hard_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=1.5,
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def shrink_soft_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=1.5,
+        bias=1.5,
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def shrink_verify_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [5])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=-5.0,
+        bias=1.0,
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def shrink_verify2_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [5])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=-6.0,
+        bias=5.0,
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def shrink_int8_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [3, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [3, 3])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=1.5,
+        bias=1.5,
+    )
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def shrink_uint8_test():
+    x = helper.make_tensor_value_info('x', TensorProto.UINT8, [3, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.UINT8, [3, 3])
+
+    node = onnx.helper.make_node(
+        "Shrink",
+        inputs=["x"],
+        outputs=["y"],
+        lambd=5.0,
+        bias=-4.5,
+    )
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def sign_test():
     x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5])

test/onnx/onnx_test.cpp

@@ -6610,6 +6610,73 @@ TEST_CASE(shape_gather_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(shrink_hard_test)
+{
+    migraphx::program p;
+    float bias  = 0.0;
+    float lambd = 1.5;
+    std::vector<size_t> lens{5};
+    auto* mm           = p.get_main_module();
+    auto x             = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, lens});
+    auto lit_bias      = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {bias}});
+    auto lit_neg_lambd = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {-lambd}});
+    auto lit_lambd     = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {lambd}});
+
+    auto x_plus_bias = add_common_op(*mm, migraphx::make_op("add"), {x, lit_bias});
+    auto x_min_bias  = add_common_op(*mm, migraphx::make_op("sub"), {x, lit_bias});
+
+    auto cond1   = add_common_op(*mm, migraphx::make_op("less"), {x, lit_neg_lambd});
+    auto cond2_a = add_common_op(*mm, migraphx::make_op("not"), {cond1});
+    auto cond2_b = add_common_op(*mm, migraphx::make_op("greater"), {x, lit_lambd});
+    auto cond2   = add_common_op(*mm, migraphx::make_op("logical_and"), {cond2_a, cond2_b});
+
+    auto mul1 = mm->add_instruction(
+        migraphx::make_op("convert", {{"target_type", migraphx::shape::float_type}}), cond1);
+    auto mul2 = mm->add_instruction(
+        migraphx::make_op("convert", {{"target_type", migraphx::shape::float_type}}), cond2);
+
+    auto first  = add_common_op(*mm, migraphx::make_op("mul"), {mul1, x_plus_bias});
+    auto second = add_common_op(*mm, migraphx::make_op("mul"), {mul2, x_min_bias});
+    add_common_op(*mm, migraphx::make_op("add"), {first, second});
+    auto prog = optimize_onnx("shrink_hard_test.onnx");
+    EXPECT(p == prog);
+}
+
+TEST_CASE(shrink_int8_test)
+{
+    migraphx::program p;
+    float bias  = 1.5;
+    float lambd = 1.5;
+    std::vector<size_t> lens{3, 3};
+    auto* mm           = p.get_main_module();
+    auto x             = mm->add_parameter("x", migraphx::shape{migraphx::shape::int8_type, lens});
+    auto lit_bias      = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {bias}});
+    auto lit_neg_lambd = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {-lambd}});
+    auto lit_lambd     = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {lambd}});
+
+    auto x_plus_bias = add_common_op(*mm, migraphx::make_op("add"), {x, lit_bias});
+    auto x_min_bias  = add_common_op(*mm, migraphx::make_op("sub"), {x, lit_bias});
+
+    auto cond1   = add_common_op(*mm, migraphx::make_op("less"), {x, lit_neg_lambd});
+    auto cond2_a = add_common_op(*mm, migraphx::make_op("not"), {cond1});
+    auto cond2_b = add_common_op(*mm, migraphx::make_op("greater"), {x, lit_lambd});
+    auto cond2   = add_common_op(*mm, migraphx::make_op("logical_and"), {cond2_a, cond2_b});
+
+    auto mul1 = mm->add_instruction(
+        migraphx::make_op("convert", {{"target_type", migraphx::shape::int8_type}}), cond1);
+    auto mul2 = mm->add_instruction(
+        migraphx::make_op("convert", {{"target_type", migraphx::shape::int8_type}}), cond2);
+
+    auto first  = add_common_op(*mm, migraphx::make_op("mul"), {mul1, x_plus_bias});
+    auto second = add_common_op(*mm, migraphx::make_op("mul"), {mul2, x_min_bias});
+    auto ret    = add_common_op(*mm, migraphx::make_op("add"), {first, second});
+    mm->add_instruction(migraphx::make_op("convert", {{"target_type", migraphx::shape::int8_type}}),
+                        ret);
+    auto prog = optimize_onnx("shrink_int8_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(sign_test)
 {
     migraphx::program p;

test/onnx/verify_onnx.cpp

@@ -1708,6 +1708,112 @@ TEST_CASE(selu_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
+TEST_CASE(shrink_hard_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_hard_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::float_type, {5}};
+    std::vector<float> data{-2, -1, 0, 1, 2};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold = {-2, 0, 0, 0, 2};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(shrink_soft_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_soft_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::float_type, {5}};
+    std::vector<float> data{-2, -1, 0, 1, 2};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold = {-0.5, 0, 0, 0, 0.5};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(shrink_verify_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_verify_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::half_type, {5}};
+    std::vector<float> tmp = {-10.0, -5.0, 0.0, 5.0, 10.0};
+    std::vector<migraphx::half> data{tmp.cbegin(), tmp.cend()};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<migraphx::half> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    tmp = {-9.0, -4.0, 1.0, 4.0, 9.0};
+    std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(shrink_verify2_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_verify2_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::half_type, {5}};
+    std::vector<float> tmp = {-10.0, -5.0, 0.0, 5.0, 10.0};
+    std::vector<migraphx::half> data{tmp.cbegin(), tmp.cend()};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<migraphx::half> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    tmp = {-5.0, 0.0, 5.0, 10.0, 5.0};
+    std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(shrink_int8_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_int8_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::int8_type, {3, 3}};
+    std::vector<int8_t> data{-4, -3, -2, -1, 0, 1, 2, 3, 4};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<int8_t> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    std::vector<int8_t> gold = {-2, -1, 0, 0, 0, 0, 0, 1, 2};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(shrink_uint8_test)
+{
+    migraphx::program p = migraphx::parse_onnx("shrink_uint8_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::shape s{migraphx::shape::uint8_type, {3, 3}};
+    std::vector<uint8_t> data{1, 2, 3, 4, 5, 6, 7, 8, 9};
+    migraphx::parameter_map pp;
+    pp["x"] = migraphx::argument(s, data.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<uint8_t> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    std::vector<uint8_t> gold = {0, 0, 0, 0, 0, 10, 11, 12, 13};
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
 TEST_CASE(size_verify_test)
 {
     migraphx::program p = migraphx::parse_onnx("size_verify_test.onnx");

test/py/onnx_backend_test.py

@@ -249,8 +249,6 @@ def disabled_tests_onnx_1_7_0(backend_test):
     backend_test.exclude(r'test_reversesequence_time_cpu')
     backend_test.exclude(r'test_scan9_sum_cpu')
     backend_test.exclude(r'test_scan_sum_cpu')
-    backend_test.exclude(r'test_shrink_hard_cpu')
-    backend_test.exclude(r'test_shrink_soft_cpu')
     backend_test.exclude(r'test_slice_cpu')
     backend_test.exclude(r'test_slice_default_axes_cpu')
     backend_test.exclude(r'test_slice_default_steps_cpu')
@@ -463,7 +461,6 @@ def disabled_tests_onnx_1_7_0(backend_test):
     backend_test.exclude(r'test_sequence_model6_cpu')
     backend_test.exclude(r'test_sequence_model7_cpu')
     backend_test.exclude(r'test_sequence_model8_cpu')
-    backend_test.exclude(r'test_shrink_cpu')
     backend_test.exclude(r'test_strnorm_model_monday_casesensintive_lower_cpu')
     backend_test.exclude(
         r'test_strnorm_model_monday_casesensintive_nochangecase_cpu')