Merge branch 'refactor_dynamic_compute' of...

Merge branch 'refactor_dynamic_compute' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

test/onnx/gen_onnx.py

@@ -3647,6 +3647,16 @@ def neg_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def neg_dynamic_test():
+    x = helper.make_tensor_value_info('0', TensorProto.INT64, [None, 3])
+    y = helper.make_tensor_value_info('1', TensorProto.INT64, [None, 3])
+
+    node = onnx.helper.make_node('Neg', inputs=['0'], outputs=['1'])
+
+    return ([node], [x], [y])
+
+
 @onnx_test
 def nms_test():
     b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
@@ -5280,6 +5290,20 @@ def sinh_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def sinh_dynamic_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+
+    node = onnx.helper.make_node(
+        'Sinh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
 @onnx_test
 def size_float_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])

test/onnx/onnx_test.cpp

@@ -857,7 +857,6 @@ TEST_CASE(conv_autopad_same_test)
     auto l1  = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
     migraphx::op::convolution op;
     op.padding = {1, 1, 1, 1};
-    op.padding_mode = migraphx::op::padding_mode_t::same;
     mm->add_instruction(op, l0, l1);
 
     auto prog = optimize_onnx("conv_autopad_same_test.onnx");
@@ -1034,13 +1033,9 @@ TEST_CASE(conv_dynamic_batch_same_upper)
     auto l0  = mm->add_parameter(
         "0", {migraphx::shape::float_type, {{1, 10, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}});
     auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
-    auto c0 =
-        mm->add_instruction(migraphx::make_op("convolution",
-                                              {{"padding", {1, 1, 1, 1}},
-                                               {"stride", {1, 1}},
-                                               {"dilation", {1, 1}},
-                                               {"padding_mode", migraphx::op::padding_mode_t::same},
-                                               {"use_dynamic_same_auto_pad", false}}),
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {1, 1, 1, 1}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
         l0,
         l1);
     mm->add_return({c0});
@@ -1064,8 +1059,7 @@ TEST_CASE(conv_dynamic_img_same_upper)
                           {{"padding", {0, 0}},
                            {"stride", {1, 1}},
                            {"dilation", {1, 1}},
-                           {"padding_mode", migraphx::op::padding_mode_t::same_upper},
-                           {"use_dynamic_same_auto_pad", true}}),
+                           {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
         l0,
         l1);
     mm->add_return({c0});
@@ -1089,8 +1083,7 @@ TEST_CASE(conv_dynamic_kernel_same_lower)
                           {{"padding", {0, 0}},
                            {"stride", {1, 1}},
                            {"dilation", {1, 1}},
-                           {"padding_mode", migraphx::op::padding_mode_t::same_lower},
-                           {"use_dynamic_same_auto_pad", true}}),
+                           {"padding_mode", migraphx::op::padding_mode_t::same_lower}}),
         l0,
         l1);
     mm->add_return({c0});
@@ -3483,6 +3476,21 @@ TEST_CASE(neg_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(neg_dynamic_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::int64_type, {{1, 10, 0}, {3, 3, 0}}};
+    auto input = mm->add_parameter("0", s);
+    auto ret   = mm->add_instruction(migraphx::make_op("neg"), input);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 10, 0};
+    auto prog                     = migraphx::parse_onnx("neg_dynamic_test.onnx", options);
+    EXPECT(p == prog);
+}
+
 TEST_CASE(nms_test)
 {
     migraphx::program p;
@@ -5206,6 +5214,29 @@ TEST_CASE(sinh_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(sinh_dynamic_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape::dynamic_dimension dd{1, 10, 0};
+    std::vector<migraphx::shape::dynamic_dimension> dyn_dims;
+    dyn_dims.push_back(dd);
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, dyn_dims});
+    mm->add_instruction(migraphx::make_op("sinh"), input);
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = dd;
+    auto prog                     = parse_onnx("sinh_dynamic_test.onnx", options);
+    auto* mm_onnx                 = prog.get_main_module();
+    auto last_ins                 = std::prev(mm_onnx->end());
+    if(last_ins->name() == "@return")
+    {
+        mm->remove_instruction(last_ins);
+    }
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(size_float_test)
 {
     migraphx::program p;

test/op_shape_test.cpp

@@ -261,8 +261,7 @@ TEST_CASE(convolution_shape)
                  migraphx::make_op("convolution",
                                    {{"stride", {1, 1}},
                                     {"dilation", {1, 1}},
-                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper},
-                                    {"use_dynamic_same_auto_pad", true}}),
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
                  input_dyn_shape,
                  weights_shape);
 
@@ -275,8 +274,7 @@ TEST_CASE(convolution_shape)
                  migraphx::make_op("convolution",
                                    {{"stride", {1, 1}},
                                     {"dilation", {1, 1}},
-                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper},
-                                    {"use_dynamic_same_auto_pad", true}}),
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
                  input_dyn_shape,
                  weights_shape);
 
@@ -290,8 +288,7 @@ TEST_CASE(convolution_shape)
                  migraphx::make_op("convolution",
                                    {{"stride", {1, 1}},
                                     {"dilation", {1, 1}},
-                                    {"padding_mode", migraphx::op::padding_mode_t::same_lower},
-                                    {"use_dynamic_same_auto_pad", true}}),
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_lower}}),
                  input_dyn_shape,
                  weights_shape);
 }

test/tf/tf_test.cpp

@@ -327,7 +327,6 @@ migraphx::program create_conv()
         mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 32}}, weight_data);
 
     migraphx::op::convolution op;
-    op.padding_mode = migraphx::op::padding_mode_t::same;
     op.padding  = {1, 1, 1, 1};
     op.stride   = {1, 1};
     op.dilation = {1, 1};
@@ -406,7 +405,6 @@ TEST_CASE(depthwiseconv_test)
         mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 1}}, weight_data);
 
     migraphx::op::convolution op;
-    op.padding_mode = migraphx::op::padding_mode_t::same;
     op.padding  = {1, 1};
     op.stride   = {1, 1};
     op.dilation = {1, 1};

test/verify/quant_conv_default_mode.cpp

@@ -37,10 +37,7 @@ struct quant_conv_default_mode : verify_program<quant_conv_default_mode>
         auto pa = mm->add_parameter("a", a_shape);
         migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
         auto pc = mm->add_parameter("c", c_shape);
-        mm->add_instruction(
-            migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
-            pa,
-            pc);
+        mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
         return p;
     }
 };

test/verify/quant_conv_int8x4_default.cpp

@@ -37,10 +37,7 @@ struct quant_conv_int8x4_default : verify_program<quant_conv_int8x4_default>
         auto pa = mm->add_parameter("a", a_shape);
         migraphx::shape c_shape{migraphx::shape::int8_type, {16, 16, 3, 3}};
         auto pc = mm->add_parameter("c", c_shape);
-        mm->add_instruction(
-            migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
-            pa,
-            pc);
+        mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
         return p;
     }
 };

test/verify/quant_conv_valid_mode.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 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 "verify_program.hpp"
-#include <migraphx/program.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/op/quant_convolution.hpp>
-
-struct quant_conv_valid_mode : verify_program<quant_conv_valid_mode>
-{
-    migraphx::program create_program() const
-    {
-        migraphx::program p;
-        auto* mm = p.get_main_module();
-        migraphx::shape a_shape{migraphx::shape::int8_type, {2, 3, 4, 4}};
-        auto pa = mm->add_parameter("a", a_shape);
-        migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
-        auto pc = mm->add_parameter("c", c_shape);
-        mm->add_instruction(
-            migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::valid},
-            pa,
-            pc);
-        return p;
-    }
-};

test/verify/test_elu.cpp

@@ -34,7 +34,7 @@ struct test_elu : verify_program<test_elu>
         migraphx::program p;
         auto* mm = p.get_main_module();
         auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
-        mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 1.0}}), x);
+        mm->add_instruction(migraphx::make_op("elu", {{"alpha", 0.8}}), x);
         return p;
     }
 };

test/verify/test_leaky_relu.cpp

@@ -34,7 +34,7 @@ struct test_leaky_relu : verify_program<test_leaky_relu>
         migraphx::program p;
         auto* mm = p.get_main_module();
         auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
-        mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 0.01}}), x);
+        mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 0.41}}), x);
         return p;
     }
 };

tools/include/operation.hpp

@@ -32,6 +32,7 @@
 #include <utility>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/argument.hpp>
@@ -94,6 +95,46 @@ bool has_finalize(const operation& x);
 
 #else
 
+struct dyn_output
+{
+    // original shape from the instruction
+    shape ins_shape;
+    // shape computed at eval time using input arguments
+    shape computed_shape;
+};
+
+/**
+ * Handle dynamic and static shape at evaluation time.
+ * If converted to shape type, returns original ins_shape.
+ * If converted to dyn_output type, will compute an output shape using the input arguments.
+ */
+template <class F>
+struct compute_output_shape
+{
+    F ins_inputs;
+
+    operator dyn_output() const
+    {
+        return ins_inputs([](const auto& x, shape ins_shape, const std::vector<argument>& inputs) {
+            if(ins_shape.dynamic())
+                return dyn_output{ins_shape, compute_shape(x, to_shapes(inputs))};
+            return dyn_output{ins_shape, ins_shape};
+        });
+    }
+
+    operator shape() const
+    {
+        return ins_inputs(
+            [](const auto&, shape ins_shape, const std::vector<argument>&) { return ins_shape; });
+    }
+};
+
+template <class F>
+compute_output_shape<F> make_compute_output_shape(F f)
+{
+    return {f};
+}
+
 namespace detail {
 
 namespace operation_operators {
@@ -199,9 +240,12 @@ auto compute_op(rank<1>,
                 context& ctx,
                 const shape& output_shape,
                 const std::vector<argument>& input)
-    -> decltype(x.compute(auto_any_cast(ctx), output_shape, input))
+    -> decltype(x.compute(auto_any_cast(ctx),
+                          make_compute_output_shape(pack(x, output_shape, input)),
+                          input))
 {
-    return x.compute(auto_any_cast(ctx), output_shape, input);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -220,9 +264,9 @@ compute_op(const T& x, context& ctx, const shape& output_shape, const std::vecto
 
 template <class T>
 auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input))
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input))
 {
-    return x.compute(output_shape, input);
+    return x.compute(make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -244,9 +288,11 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -278,9 +324,17 @@ auto compute_op(rank<4>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(auto_any_cast(ctx), output, inputs, module_args, f))
+                F f) -> decltype(x.compute(auto_any_cast(ctx),
+                                           make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs, module_args, f);
+    return x.compute(auto_any_cast(ctx),
+                     make_compute_output_shape(pack(x, output, inputs)),
+                     inputs,
+                     module_args,
+                     f);
 }
 
 template <class T, class F>
@@ -290,9 +344,11 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -302,9 +358,10 @@ auto compute_op(rank<2>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(output, inputs))
+                F)
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs))
 {
-    return x.compute(output, inputs);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -314,9 +371,12 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(auto_any_cast(ctx), output, inputs))
+                F) -> decltype(x.compute(auto_any_cast(ctx),
+                                         make_compute_output_shape(pack(x, output, inputs)),
+                                         inputs))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -348,7 +408,8 @@ auto is_context_free_op(rank<1>,
                         const T& x,
                         const shape& output_shape,
                         const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input), std::true_type{});
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input),
+                std::true_type{});
 
 template <class T>
 auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)