Merge branch 'develop' into concat2

test/gpu/gemm_tune.cpp

+/*
+ * 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 <iostream>
+#include <vector>
+#include <migraphx/gpu/gemm.hpp>
+#include <hip/hip_runtime_api.h>
+#include <migraphx/gpu/target.hpp>
+#include <migraphx/verify.hpp>
+#include <test.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/iterator_for.hpp>
+
+// includes needed for run_lowering
+#include <migraphx/gpu/lowering.hpp>
+#include <migraphx/auto_contiguous.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/pass_manager.hpp>
+
+// Abbreviated lowering; we don't need the usual cleanup passes for this test
+void run_lowering(migraphx::program& p, bool offload_copy = false)
+{
+    auto ctx = migraphx::gpu::context{};
+    migraphx::run_passes(
+        *p.get_main_module(),
+        {migraphx::auto_contiguous{}, migraphx::gpu::lowering{&ctx, offload_copy}});
+}
+
+/**
+ * Tests the automatic GEMM tuning feature.  In the finalize() method of the gemm op,
+ * rocBLAS API functions are called to quickly benchmark all the GEMM solutions
+ * available in the currently installed rocBLAS library and choose the index of the fastest.
+ */
+TEST_CASE(gemm_tune_with_rocblas)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {2, 3}};
+    auto a = mm->add_parameter("a", sa);
+    auto b = mm->add_parameter("b", sb);
+
+    migraphx::operation dot_op = migraphx::make_op("dot");
+    mm->add_instruction(dot_op, a, b);
+
+    // lowering adds gemm implementation for dot operator
+    run_lowering(p);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            // gemm_op.to_value().debug_print();
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+// GEMM tuning of a strided-batch matrix; invokes rocblas_gemm_strided_batched_ex
+TEST_CASE(gemm_tune_strided)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {4, 2, 2}};
+    migraphx::shape s_output{migraphx::shape::float_type, {4, 2, 2}};
+    auto a      = mm->add_parameter("a", sa);
+    auto b      = mm->add_parameter("b", sb);
+    auto output = mm->add_parameter("out", s_output);
+
+    auto gemm_oper = migraphx::make_op("gpu::gemm", {{"beta", 2}});
+    mm->add_instruction(gemm_oper, a, b, output);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+            auto gemmv   = gemm_op.to_value();
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+// GEMM tuning of a strided-batch matrix; created by lowering
+TEST_CASE(gemm_tune_strided_lowered)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    // At time of writing this test, gemm_impl considers a shape is strided if it has
+    // at least three dimensions and the 3rd-to-last is nonzero, invoking
+    // rocblas_gemm_strided_batched_ex. Also, DOT operator requires all dimensions except the last
+    // two to be equal.
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2, 5}};
+    migraphx::shape sb{migraphx::shape::float_type, {4, 5, 3}};
+    auto a = mm->add_parameter("a", sa);
+    auto b = mm->add_parameter("b", sb);
+
+    migraphx::operation dot_op = migraphx::make_op("dot");
+    mm->add_instruction(dot_op, a, b);
+
+    // lowering adds gemm implementation for dot operator
+    run_lowering(p);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+TEST_CASE(gemm_tune_invalid_sol_index)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {2, 3}};
+    migraphx::shape s_output{migraphx::shape::float_type, {4, 3}};
+    auto a      = mm->add_parameter("a", sa);
+    auto b      = mm->add_parameter("b", sb);
+    auto output = mm->add_parameter("out", s_output);
+
+    auto gemm_oper = migraphx::make_op("gpu::gemm", {{"solution_idx", 987654321}});
+    mm->add_instruction(gemm_oper, a, b, output);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+            auto gemmv   = gemm_op.to_value();
+
+            // given invalid starting index, should return default 0
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#endif
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/gpu/jit.cpp

@@ -139,7 +139,8 @@ const std::string math_template = R"__migraphx__(
 #include <migraphx/kernels/pointwise.hpp>
 #include <migraphx/kernels/math.hpp>
 #include <migraphx/kernels/types.hpp>
-using namespace migraphx;
+
+namespace migraphx {
 extern "C" {
 __global__ void kernel(${type}* p) 
 {
@@ -148,6 +149,7 @@ __global__ void kernel(${type}* p)
 
 }
 }
+}
 
 int main() {}
 
@@ -348,7 +350,10 @@ TEST_CASE(compile_math)
     auto vec_sizes = {2, 4, 6};
     for(auto&& t : migraphx::shape::types())
     {
-        if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
+        if(contains({migraphx::shape::bool_type,
+                     migraphx::shape::fp8e4m3fnuz_type,
+                     migraphx::shape::tuple_type},
+                    t))
             continue;
         auto name = migraphx::shape::cpp_type(t);
         if(t == migraphx::shape::half_type)
@@ -396,7 +401,10 @@ TEST_CASE(assert_type_min_max)
     migraphx::gpu::hip_compile_options options;
     for(auto&& t : migraphx::shape::types())
     {
-        if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
+        if(contains({migraphx::shape::bool_type,
+                     migraphx::shape::fp8e4m3fnuz_type,
+                     migraphx::shape::tuple_type},
+                    t))
             continue;
         auto name = migraphx::shape::cpp_type(t);
         if(t == migraphx::shape::half_type)

test/include/test.hpp

@@ -24,6 +24,7 @@
 
 #include <atomic>
 #include <algorithm>
+#include <array>
 #include <cassert>
 #include <cstdio>
 #include <cstdlib>

test/jit.cpp

@@ -47,7 +47,11 @@ compile_function(const std::string& src, const std::string& flags, const std::st
 {
     migraphx::src_compiler compiler;
     compiler.flags = flags + "-std=c++14 -fPIC -shared";
+#ifdef _WIN32
+    compiler.output = "simple.dll";
+#else
     compiler.output = "libsimple.so";
+#endif
     migraphx::src_file f{"main.cpp", src};
     auto image = compiler.compile({f});
     return migraphx::dynamic_loader{image}.get_function<F>(fname);

test/onnx/.onnxrt-commit

-2eeafc37bca21dc8bf337dda7020b486543162d7
+a5537f2f563d4975c7e6121a7eb260bbbfd9455a

test/onnx/averagepool_dilate_test.onnx

+averagepool_dilate_test:

+Y
+
x
y"AveragePool*
+	dilations@
*
+kernel_shape@
*
+pads@
@

*
+strides@

averagepool_dilate_testZ
+
x
+

+

+
+b
+
y
+

+

+
+B
\ No newline at end of file

test/onnx/gen_onnx.py

@@ -276,6 +276,22 @@ def averagepool_1d_test():
     return ([node], [x], [out])
 
 
+@onnx_test()
+def averagepool_dilate_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4, 2])
+
+    node = onnx.helper.make_node('AveragePool',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 kernel_shape=[2],
+                                 strides=[1],
+                                 pads=[1, 1],
+                                 dilations=[3])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def averagepool_3d_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5])
@@ -4484,6 +4500,177 @@ def lrn_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def lstm_bi_layout_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [2, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [2, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 2, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 2, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [2, 60])
+
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 2, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', '', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='bidirectional',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [cellout])
+
+
+@onnx_test()
+def lstm_bi_layout_last_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [2, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [2, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 2, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 2, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [2, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 2, 20])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 2, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs', 'output'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='bidirectional',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs, output])
+
+
+@onnx_test()
+def lstm_f_layout_hs_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 1, 20])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs', 'output'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='forward',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs, output])
+
+
+@onnx_test()
+def lstm_f_layout_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', '', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='forward',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [cellout])
+
+
+@onnx_test()
+def lstm_r_layout_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='reverse',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs])
+
+
+@onnx_test()
+def lstm_r_layout_hs_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 1, 20])
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', 'output', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='reverse',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [output, cellout])
+
+
 @onnx_test()
 def matmul_bmbm_test():
     m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
@@ -4711,6 +4898,22 @@ def maxpool_notset_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def maxpool_dilate_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4, 2])
+
+    node = onnx.helper.make_node('MaxPool',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 kernel_shape=[2],
+                                 strides=[1],
+                                 pads=[1, 1],
+                                 dilations=[3])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def maxpool_same_upper_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
@@ -5791,6 +5994,263 @@ def qlinearadd_bcast_test():
             [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
 
 
+@onnx_test()
+def qlinearaveragepool_1d_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 32])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 31])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [16])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2],
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 3, 3])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.015])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [16])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2, 2],
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_ceil_test():
+    x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 2, 2])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
+                                      [0])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[3, 3],
+        strides=[2, 2],
+        ceil_mode=True,
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_dilations_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 2, 2])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [84])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2, 2],
+        strides=[1, 1],
+        dilations=[2, 2],
+        ceil_mode=True,
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_pads_count_include_pad_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 6, 6])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.01])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [32])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[3, 3],
+        pads=[2, 2, 2, 2],
+        count_include_pad=1,
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_same_lower_test():
+    x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 3, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 3, 4, 4])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
+                                      [0])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2, 2],
+        auto_pad="SAME_LOWER",
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_same_upper_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [32])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 4, 4])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2, 2],
+        auto_pad="SAME_UPPER",
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_2d_strides_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 8, 8])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [8])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[5, 5],
+        strides=[2, 2],
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_3d_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 3, 3, 3])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2, 2])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.02])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [0])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[2, 2, 2],
+    )
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_notset_test():
+    x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 5, 5])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
+                                      [0])
+    y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 1, 1])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
+                                      [10])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[6, 6],
+        strides=[2, 2],
+        pads=[0, 0, 1, 1],
+        channels_last=0,
+        auto_pad='NOTSET')
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
+@onnx_test()
+def qlinearaveragepool_nt_cip_test():
+    x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 5, 5])
+    x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
+    x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
+                                      [0])
+    y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 1, 1])
+    y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
+    y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
+                                      [10])
+
+    node = onnx.helper.make_node(
+        'QLinearAveragePool',
+        inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
+        outputs=['y'],
+        kernel_shape=[6, 6],
+        strides=[2, 2],
+        pads=[0, 0, 1, 1],
+        channels_last=0,
+        auto_pad='NOTSET',
+        count_include_pad=1)
+
+    return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
+
+
 @onnx_test()
 def qlinearconv_test():
     # https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html
@@ -5923,6 +6383,26 @@ def qlinearglobalavgpool_test():
     return ([n], [x], [y], [sc_x, z_pt_x, sc_y, z_pt_y])
 
 
+@onnx_test()
+def qlinearleakyrelu_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT8, [64])
+    sc_x = helper.make_tensor('X_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_x = helper.make_tensor('X_zero_point', TensorProto.INT8, [], [0])
+
+    sc_y = helper.make_tensor('Y_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_y = helper.make_tensor('Y_zero_point', TensorProto.INT8, [], [10])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.INT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearLeakyRelu',
+        inputs=['X', 'X_scale', 'X_zero_point', 'Y_scale', 'Y_zero_point'],
+        outputs=['Y'],
+        alpha=1.1,
+    )
+    return ([node], [x], [y], [sc_x, zero_pt_x, sc_y, zero_pt_y])
+
+
 def qlinearmatmul_1D_test():
     a = helper.make_tensor_value_info('A', TensorProto.UINT8, [8])
     sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
@@ -6008,6 +6488,81 @@ def qlinearmatmul_3D_test():
             [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
 
 
+@onnx_test()
+def qlinearmul_test():
+    a = helper.make_tensor_value_info('A', TensorProto.UINT8, [64])
+    sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_a = helper.make_tensor('A_zero_point', TensorProto.UINT8, [], [0])
+
+    b = helper.make_tensor_value_info('B', TensorProto.UINT8, [64])
+    sc_b = helper.make_tensor('B_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_b = helper.make_tensor('B_zero_point', TensorProto.UINT8, [], [16])
+
+    sc_c = helper.make_tensor('C_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_c = helper.make_tensor('C_zero_point', TensorProto.UINT8, [],
+                                   [100])
+
+    c = helper.make_tensor_value_info('C', TensorProto.UINT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearMul',
+        inputs=[
+            'A', 'A_scale', 'A_zero_point', 'B', 'B_scale', 'B_zero_point',
+            'C_scale', 'C_zero_point'
+        ],
+        outputs=['C'],
+    )
+    return ([node], [a, b], [c],
+            [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
+
+
+@onnx_test()
+def qlinearmul_bcast_test():
+    a = helper.make_tensor_value_info('A', TensorProto.INT8, [64])
+    sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_a = helper.make_tensor('A_zero_point', TensorProto.INT8, [], [0])
+
+    b = helper.make_tensor_value_info('B', TensorProto.INT8, [1, 1, 64])
+    sc_b = helper.make_tensor('B_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_b = helper.make_tensor('B_zero_point', TensorProto.INT8, [], [128])
+
+    sc_c = helper.make_tensor('C_scale', TensorProto.FLOAT, [], [0.15])
+    zero_pt_c = helper.make_tensor('C_zero_point', TensorProto.INT8, [], [32])
+
+    c = helper.make_tensor_value_info('C', TensorProto.INT8, [1, 1, 64])
+
+    node = onnx.helper.make_node(
+        'QLinearMul',
+        inputs=[
+            'A', 'A_scale', 'A_zero_point', 'B', 'B_scale', 'B_zero_point',
+            'C_scale', 'C_zero_point'
+        ],
+        outputs=['C'],
+    )
+    return ([node], [a, b], [c],
+            [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
+
+
+@onnx_test()
+def qlinearsigmoid_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT8, [64])
+    sc_x = helper.make_tensor('X_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_x = helper.make_tensor('X_zero_point', TensorProto.INT8, [], [0])
+
+    sc_y = helper.make_tensor('Y_scale', TensorProto.FLOAT, [], [0.0035])
+    zero_pt_y = helper.make_tensor('Y_zero_point', TensorProto.INT8, [],
+                                   [-128])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.INT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearSigmoid',
+        inputs=['X', 'X_scale', 'X_zero_point', 'Y_scale', 'Y_zero_point'],
+        outputs=['Y'],
+    )
+    return ([node], [x], [y], [sc_x, zero_pt_x, sc_y, zero_pt_y])
+
+
 @onnx_test()
 def quantizelinear_test():
     arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5])
@@ -7087,6 +7642,16 @@ def roialign_test():
     return ([node], [x, roi, bi], [y])
 
 
+@onnx_test()
+def round_half_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [4, 4])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [4, 4])
+
+    node = onnx.helper.make_node('Round', inputs=['x'], outputs=['y'])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def scatter_add_test():
     x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
@@ -7147,8 +7712,7 @@ def scatter_none_test():
     return ([node], [x, i, u], [y])
 
 
-@onnx_test()
-def scatternd_add_test():
+def make_scatternd_test(reduction="none"):
     data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
     indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
                                             [2, 1, 2])
@@ -7160,44 +7724,39 @@ def scatternd_add_test():
     node = onnx.helper.make_node('ScatterND',
                                  inputs=['data', 'indices', 'updates'],
                                  outputs=['output'],
-                                 reduction="add")
+                                 reduction=reduction)
 
     return ([node], [data, indices, updates], [output])
 
 
+@onnx_test()
+def scatternd_add_test():
+    return make_scatternd_test("add")
+
+
 @onnx_test()
 def scatternd_mul_test():
-    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
-    indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
-                                            [2, 1, 2])
-    updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT,
-                                            [2, 1, 2])
-    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
-                                           [2, 2, 2])
+    return make_scatternd_test("mul")
 
-    node = onnx.helper.make_node('ScatterND',
-                                 inputs=['data', 'indices', 'updates'],
-                                 outputs=['output'],
-                                 reduction="mul")
 
-    return ([node], [data, indices, updates], [output])
+@onnx_test()
+def scatternd_max_test():
+    return make_scatternd_test("max")
+
+
+@onnx_test()
+def scatternd_min_test():
+    return make_scatternd_test("min")
 
 
 @onnx_test()
 def scatternd_test():
-    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
-    indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
-                                            [2, 1, 2])
-    updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT,
-                                            [2, 1, 2])
-    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
-                                           [2, 2, 2])
+    return make_scatternd_test()
 
-    node = onnx.helper.make_node('ScatterND',
-                                 inputs=['data', 'indices', 'updates'],
-                                 outputs=['output'])
 
-    return ([node], [data, indices, updates], [output])
+@onnx_test()
+def scatternd_invalid_reduction_test():
+    return make_scatternd_test("invalid")
 
 
 @onnx_test()
@@ -8006,6 +8565,32 @@ def slice_var_input_dyn1():
     return ([node], [data, starts, ends, axes], [output])
 
 
+@onnx_test()
+def slice_var_input_default_steps():
+    step = np.array([1, 1])
+    step_tensor = helper.make_tensor(name="step",
+                                     data_type=TensorProto.INT64,
+                                     dims=step.shape,
+                                     vals=step.astype(int))
+    arg_step = helper.make_node("Constant",
+                                inputs=[],
+                                outputs=['arg_step'],
+                                value=step_tensor)
+
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [None, 2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT64, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT64, [2])
+    axes = helper.make_tensor_value_info('axes', TensorProto.INT64, [2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node(
+        'Slice',
+        inputs=['data', 'starts', 'ends', 'axes', 'arg_step'],
+        outputs=['output'])
+
+    return ([arg_step, node], [data, starts, ends, axes], [output])
+
+
 @onnx_test()
 def slice_var_input_steps_error():
     step = np.array([2, 1])
@@ -8019,9 +8604,9 @@ def slice_var_input_steps_error():
                                 value=step_tensor)
 
     data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 2])
-    starts = helper.make_tensor_value_info('starts', TensorProto.FLOAT, [2])
-    ends = helper.make_tensor_value_info('ends', TensorProto.FLOAT, [2])
-    axes = helper.make_tensor_value_info('axes', TensorProto.FLOAT, [2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT64, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT64, [2])
+    axes = helper.make_tensor_value_info('axes', TensorProto.INT64, [2])
     output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
 
     node = onnx.helper.make_node(
@@ -8958,6 +9543,97 @@ def undefined_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def unique_dynamic_sorted_test():
+    x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=1)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_dynamic_sorted_3D_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT64, [4, 4, 4])
+    y = helper.make_tensor_value_info('Y', TensorProto.INT64, [16])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [16])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [64])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [16])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        sorted=1)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_dynamic_unsorted_test():
+    x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=0)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_sorted_test():
+    x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=1)
+    return ([node], [], [y, y_ind, x_ind, count], [x])
+
+
+@onnx_test()
+def unique_unsorted_test():
+    x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=0)
+    return ([node], [], [y, y_ind, x_ind, count], [x])
+
+
 @onnx_test()
 def unknown_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
@@ -9031,6 +9707,20 @@ def upsample_test():
     return ([node], [X], [Y], [scale_tensor])
 
 
+@onnx_test()
+def upsample_ver7_test():
+    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2])
+    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6])
+
+    node = onnx.helper.make_node('Upsample',
+                                 inputs=['X'],
+                                 outputs=['Y'],
+                                 mode='nearest',
+                                 scales=[1.0, 1.0, 2.0, 3.0])
+
+    return ([node], [X], [Y])
+
+
 @onnx_test()
 def variable_batch_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT,

test/onnx/maxpool_dilate_test.onnx

+maxpool_dilate_test:

+U
+
x
y"MaxPool*
+	dilations@
*
+kernel_shape@
*
+pads@
@

*
+strides@

maxpool_dilate_testZ
+
x
+

+

+
+b
+
y
+

+

+
+B
\ No newline at end of file

test/onnx/onnx_rnn_test.cpp

@@ -1092,6 +1092,115 @@ TEST_CASE(lstm_forward)
     }
 }
 
+TEST_CASE(lstm_forward_layout)
+{
+    std::size_t sl   = 5;  // sequence len
+    std::size_t bs   = 3;  // batch size
+    std::size_t hs   = 20; // hidden size
+    std::size_t is   = 10; // input size
+    std::size_t nd   = 1;  // num directions
+    float clip       = 0.0f;
+    int input_forget = 1;
+    migraphx::shape seq_shape{migraphx::shape::float_type, {bs, sl, is}};
+    migraphx::shape w_shape{migraphx::shape::float_type, {nd, 4 * hs, is}};
+    migraphx::shape r_shape{migraphx::shape::float_type, {nd, 4 * hs, hs}};
+    migraphx::shape bias_shape{migraphx::shape::float_type, {nd, 8 * hs}};
+    migraphx::shape sl_shape{migraphx::shape::int32_type, {bs}};
+    migraphx::shape ih_shape{migraphx::shape::float_type, {bs, nd, hs}};
+    migraphx::shape pph_shape{migraphx::shape::float_type, {nd, 3 * hs}};
+
+    // 8 args, hs and last output
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::forward)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        auto last_output = mm->add_instruction(migraphx::make_op("rnn_last_hs_output"), out_hs);
+        std::vector<int64_t> perm_hid{2, 0, 1, 3};
+        out_hs = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm_hid}}),
+                                     out_hs);
+        mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), last_output);
+
+        auto prog = optimize_onnx("lstm_f_layout_hs_test.onnx");
+
+        EXPECT(p == prog);
+    }
+    // 8 args, cell output
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::forward)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        auto last_cell = mm->add_instruction(migraphx::make_op("rnn_last_cell_output"), out_hs);
+        mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), last_cell);
+        auto prog = optimize_onnx("lstm_f_layout_cell_test.onnx");
+
+        EXPECT(p == prog);
+    }
+}
+
 // activation functions
 TEST_CASE(lstm_forward_actv_func)
 {
@@ -1342,6 +1451,117 @@ TEST_CASE(lstm_reverse)
     }
 }
 
+TEST_CASE(lstm_reverse_layout)
+{
+    std::size_t sl   = 5;  // sequence len
+    std::size_t bs   = 3;  // batch size
+    std::size_t hs   = 20; // hidden size
+    std::size_t is   = 10; // input size
+    std::size_t nd   = 1;  // num directions
+    float clip       = 0.0f;
+    int input_forget = 1;
+    migraphx::shape seq_shape{migraphx::shape::float_type, {bs, sl, is}};
+    migraphx::shape w_shape{migraphx::shape::float_type, {nd, 4 * hs, is}};
+    migraphx::shape r_shape{migraphx::shape::float_type, {nd, 4 * hs, hs}};
+    migraphx::shape bias_shape{migraphx::shape::float_type, {nd, 8 * hs}};
+    migraphx::shape sl_shape{migraphx::shape::int32_type, {bs}};
+    migraphx::shape ih_shape{migraphx::shape::float_type, {bs, nd, hs}};
+    migraphx::shape pph_shape{migraphx::shape::float_type, {nd, 3 * hs}};
+
+    // 8 args, hs output
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::reverse)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        std::vector<int64_t> perm_hid{2, 0, 1, 3};
+        out_hs    = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm_hid}}),
+                                     out_hs);
+        auto prog = optimize_onnx("lstm_r_layout_test.onnx");
+
+        EXPECT(p == prog);
+    }
+
+    // 8 args, last and cell output
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::reverse)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        auto last_output = mm->add_instruction(migraphx::make_op("rnn_last_hs_output"), out_hs);
+        auto last_cell   = mm->add_instruction(migraphx::make_op("rnn_last_cell_output"), out_hs);
+        last_output = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}),
+                                          last_output);
+        mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), last_cell);
+
+        auto prog = optimize_onnx("lstm_r_layout_hs_cell_test.onnx");
+
+        EXPECT(p == prog);
+    }
+}
+
 TEST_CASE(lstm_bidirectional)
 {
     std::size_t sl   = 5;  // sequence len
@@ -1594,6 +1814,118 @@ TEST_CASE(lstm_bidirectional)
     }
 }
 
+TEST_CASE(lstm_bidirectional_layout)
+{
+    std::size_t sl   = 5;  // sequence len
+    std::size_t bs   = 3;  // batch size
+    std::size_t hs   = 20; // hidden size
+    std::size_t is   = 10; // input size
+    std::size_t nd   = 2;  // num directions
+    float clip       = 0.0f;
+    int input_forget = 1;
+    migraphx::shape seq_shape{migraphx::shape::float_type, {bs, sl, is}};
+    migraphx::shape w_shape{migraphx::shape::float_type, {nd, 4 * hs, is}};
+    migraphx::shape r_shape{migraphx::shape::float_type, {nd, 4 * hs, hs}};
+    migraphx::shape bias_shape{migraphx::shape::float_type, {nd, 8 * hs}};
+    migraphx::shape sl_shape{migraphx::shape::int32_type, {bs}};
+    migraphx::shape ih_shape{migraphx::shape::float_type, {bs, nd, hs}};
+    migraphx::shape pph_shape{migraphx::shape::float_type, {nd, 3 * hs}};
+    // 0 activation function
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::bidirectional)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        auto last_output = mm->add_instruction(migraphx::make_op("rnn_last_hs_output"), out_hs);
+        std::vector<int64_t> perm_hid{2, 0, 1, 3};
+        out_hs = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm_hid}}),
+                                     out_hs);
+        mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), last_output);
+        auto prog = optimize_onnx("lstm_bi_layout_last_test.onnx");
+
+        EXPECT(p == prog);
+    }
+    {
+        migraphx::program p;
+        auto* mm     = p.get_main_module();
+        auto seq     = mm->add_parameter("seq", seq_shape);
+        auto w       = mm->add_parameter("w", w_shape);
+        auto r       = mm->add_parameter("r", r_shape);
+        auto bias    = mm->add_parameter("bias", bias_shape);
+        auto seq_len = mm->add_parameter("seq_len", sl_shape);
+        auto ih      = mm->add_parameter("h0", ih_shape);
+        auto ic      = mm->add_parameter("c0", ih_shape);
+        auto pph     = mm->add_parameter("pph", pph_shape);
+
+        std::vector<int64_t> perm{1, 0, 2};
+        seq = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), seq);
+        ih  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ih);
+        ic  = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), ic);
+
+        auto out_hs = mm->add_instruction(
+            migraphx::make_op(
+                "lstm",
+                {{"hidden_size", hs},
+                 {"actv_func",
+                  migraphx::to_value(std::vector<migraphx::operation>{migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("sigmoid"),
+                                                                      migraphx::make_op("tanh"),
+                                                                      migraphx::make_op("tanh")})},
+                 {"direction", migraphx::to_value(migraphx::op::rnn_direction::bidirectional)},
+                 {"clip", clip},
+                 {"input_forget", input_forget}}),
+            seq,
+            w,
+            r,
+            bias,
+            seq_len,
+            ih,
+            ic,
+            pph);
+        auto last_cell = mm->add_instruction(migraphx::make_op("rnn_last_cell_output"), out_hs);
+        mm->add_instruction(migraphx::make_op("transpose", {{"permutation", perm}}), last_cell);
+        auto prog = optimize_onnx("lstm_bi_layout_cell_test.onnx");
+
+        EXPECT(p == prog);
+    }
+}
+
 TEST_CASE(lstm_bi_actv_funcs)
 {
     std::size_t sl   = 5;  // sequence len

test/onnx/onnx_test.cpp

@@ -296,13 +296,32 @@ TEST_CASE(averagepool_1d_test)
                                           {{"mode", migraphx::op::pooling_mode::average},
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
-                                           {"lengths", {3}}}),
+                                           {"lengths", {3}},
+                                           {"dilations", {1}}}),
                         l0);
 
     auto prog = optimize_onnx("averagepool_1d_test.onnx");
     EXPECT(p == prog);
 }
 
+TEST_CASE(averagepool_dilate_test)
+{
+    migraphx::program p;
+    auto* mm   = p.get_main_module();
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1, 4, 3}});
+    mm->add_instruction(migraphx::make_op("pooling",
+                                          {{"mode", migraphx::op::pooling_mode::average},
+                                           {"padding", {1, 1}},
+                                           {"stride", {1}},
+                                           {"lengths", {2}},
+                                           {"dilations", {3}}}),
+                        input);
+
+    auto prog = optimize_onnx("averagepool_dilate_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(averagepool_3d_test)
 {
     migraphx::program p;
@@ -312,7 +331,8 @@ TEST_CASE(averagepool_3d_test)
                                           {{"mode", migraphx::op::pooling_mode::average},
                                            {"padding", {0, 0, 0, 0, 0, 0}},
                                            {"stride", {1, 1, 1}},
-                                           {"lengths", {3, 3, 3}}}),
+                                           {"lengths", {3, 3, 3}},
+                                           {"dilations", {1, 1, 1}}}),
                         l0);
 
     auto prog = optimize_onnx("averagepool_3d_test.onnx");
@@ -332,6 +352,7 @@ TEST_CASE(averagepool_dyn_test)
                                                   {"mode", migraphx::op::pooling_mode::average},
                                                   {"stride", {2, 2, 2}},
                                                   {"lengths", {3, 3, 3}},
+                                                  {"dilations", {1, 1, 1}},
                                                   {"padding", {1, 1, 1, 1, 1, 1}},
                                                   {"padding_mode", 0},
                                               }),
@@ -357,6 +378,7 @@ TEST_CASE(averagepool_dyn_autopad_test)
                               {"mode", migraphx::op::pooling_mode::average},
                               {"stride", {2, 2, 2}},
                               {"lengths", {3, 3, 3}},
+                              {"dilations", {1, 1, 1}},
                               {"padding", {0, 0, 0, 0, 0, 0}},
                               {"padding_mode", migraphx::op::padding_mode_t::same_upper},
                           }),
@@ -394,7 +416,8 @@ TEST_CASE(averagepool_notset_test)
                                                        {{"mode", migraphx::op::pooling_mode::average},
                                                         {"padding", {2, 2, 2, 2}},
                                                         {"stride", {2, 2}},
-                                                        {"lengths", {6, 6}}}),
+                                                        {"lengths", {6, 6}},
+                                                        {"dilations", {1, 1}}}),
                                    input);
     auto ret   = mm->add_instruction(
         migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {2, 2}}}), ins);
@@ -415,7 +438,8 @@ TEST_CASE(averagepool_nt_cip_test)
                                                                       {{"mode", migraphx::op::pooling_mode::average},
                                                                        {"padding", {0, 0, 0, 0}},
                                                                        {"stride", {2, 2}},
-                                                                       {"lengths", {6, 6}}}),
+                                                                       {"lengths", {6, 6}},
+                                                                       {"dilations", {1, 1}}}),
                                    ins_pad);
     mm->add_return({ret});
 
@@ -437,6 +461,7 @@ TEST_CASE(averagepool_same_lower_test)
                               {"padding", {1, 1, 1, 1}},
                               {"stride", {1, 1}},
                               {"lengths", {2, 2}},
+                              {"dilations", {1, 1}},
                               {"padding_mode", migraphx::op::padding_mode_t::default_},
                           }),
         input);
@@ -459,7 +484,8 @@ TEST_CASE(averagepool_sl_cip_test)
                                                                       {{"mode", migraphx::op::pooling_mode::average},
                                                                        {"padding", {0, 0, 0, 0}},
                                                                        {"stride", {1, 1}},
-                                                                       {"lengths", {2, 2}}}),
+                                                                       {"lengths", {2, 2}},
+                                                                       {"dilations", {1, 1}}}),
                                    ins_pad);
     mm->add_return({ret});
     auto prog = migraphx::parse_onnx("averagepool_sl_cip_test.onnx");
@@ -476,7 +502,8 @@ TEST_CASE(averagepool_same_upper_test)
                                                        {{"mode", migraphx::op::pooling_mode::average},
                                                         {"padding", {1, 1, 1, 1}},
                                                         {"stride", {1, 1}},
-                                                        {"lengths", {2, 2}}}),
+                                                        {"lengths", {2, 2}},
+                                                        {"dilations", {1, 1}}}),
                                    input);
     auto ret   = mm->add_instruction(
         migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {6, 6}}}), ins);
@@ -1307,7 +1334,8 @@ TEST_CASE(conv_bn_relu_maxpool_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l7);
 
     auto prog = optimize_onnx("conv_bn_relu_maxpool_test.onnx");
@@ -1505,7 +1533,8 @@ TEST_CASE(conv_relu_maxpool_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l6);
 
     auto prog = optimize_onnx("conv_relu_maxpool_test.onnx");
@@ -1530,7 +1559,8 @@ TEST_CASE(conv_relu_maxpool_x2_test)
                                                     {{"mode", migraphx::op::pooling_mode::max},
                                                      {"padding", {0, 0, 0, 0}},
                                                      {"stride", {2, 2}},
-                                                     {"lengths", {2, 2}}}),
+                                                     {"lengths", {2, 2}},
+                                                     {"dilations", {1, 1}}}),
                                   l6);
 
     auto l8 = mm->add_parameter("3", {migraphx::shape::float_type, {1, 5, 5, 5}});
@@ -1546,7 +1576,8 @@ TEST_CASE(conv_relu_maxpool_x2_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l13);
 
     auto prog = optimize_onnx("conv_relu_maxpool_x2_test.onnx");
@@ -4245,6 +4276,7 @@ TEST_CASE(lppool_l1_test)
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
                                            {"lengths", {3}},
+                                           {"dilations", {1}},
                                            {"lp_order", 1}}),
                         l0);
     auto prog = optimize_onnx("lppool_l1_test.onnx");
@@ -4261,6 +4293,7 @@ TEST_CASE(lppool_l2_test)
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
                                            {"lengths", {3}},
+                                           {"dilations", {1}},
                                            {"lp_order", 2}}),
                         l0);
     auto prog = optimize_onnx("lppool_l2_test.onnx");
@@ -4513,7 +4546,8 @@ TEST_CASE(maxpool_notset_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 1, 1}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {6, 6}}}),
+                                           {"lengths", {6, 6}},
+                                           {"dilations", {1, 1}}}),
                         input);
 
     auto prog = optimize_onnx("maxpool_notset_test.onnx");
@@ -4521,6 +4555,24 @@ TEST_CASE(maxpool_notset_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(maxpool_dilate_test)
+{
+    migraphx::program p;
+    auto* mm   = p.get_main_module();
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1, 4, 3}});
+    mm->add_instruction(migraphx::make_op("pooling",
+                                          {{"mode", migraphx::op::pooling_mode::max},
+                                           {"padding", {1, 1}},
+                                           {"stride", {1}},
+                                           {"lengths", {2}},
+                                           {"dilations", {3}}}),
+                        input);
+
+    auto prog = optimize_onnx("maxpool_dilate_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(maxpool_same_upper_test)
 {
     migraphx::program p;
@@ -4530,7 +4582,8 @@ TEST_CASE(maxpool_same_upper_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 1, 1}},
                                            {"stride", {1, 1}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         input);
 
     auto prog = optimize_onnx("maxpool_same_upper_test.onnx");
@@ -4773,8 +4826,9 @@ TEST_CASE(multinomial_test)
     migraphx::shape s{migraphx::shape::float_type, {1}};
     std::vector<float> seed_data = {seed};
     auto seed_input              = mm->add_literal(migraphx::literal(s, seed_data));
-    auto rand_dummy =
-        mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
+    auto rand_dummy              = mm->add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
+                          std::vector<float>(batch_size * sample_size)});
 
     auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
     mm->add_instruction(migraphx::make_op("multinomial"), cdf, randoms);
@@ -4925,8 +4979,9 @@ TEST_CASE(multinomial_int64_test)
     auto seed_input         = mm->add_literal(migraphx::literal(s, data));
 
     // static size
-    auto rand_dummy =
-        mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
+    auto rand_dummy = mm->add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
+                          std::vector<float>(batch_size * sample_size)});
     auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
     mm->add_instruction(migraphx::make_op("multinomial", {{"dtype", dtype}}), cdf, randoms);
     auto prog = optimize_onnx("multinomial_int64_test.onnx");
@@ -5542,6 +5597,54 @@ TEST_CASE(qlinearadd_test)
     EXPECT(p.sort() == prog.sort());
 }
 
+TEST_CASE(qlinearaveragepool_notset_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto sc_x   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
+    auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
+
+    auto sc_y   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
+    auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {10}});
+
+    auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::int8_type, {1, 1, 5, 5}});
+
+    auto scale_x_bcast = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), sc_x);
+
+    auto z_pt_x_bcast = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), z_pt_x);
+
+    auto fp_x =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
+
+    auto fp_y =
+        mm->add_instruction(migraphx::make_op("pooling",
+                                              {{"mode", migraphx::op::pooling_mode::average},
+                                               {"padding", {2, 2, 2, 2}},
+                                               {"stride", {2, 2}},
+                                               {"lengths", {6, 6}}}),
+                            fp_x);
+
+    fp_y = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {2, 2}}}), fp_y);
+
+    auto scale_y_bcast = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), sc_y);
+
+    auto z_pt_y_bcast = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), z_pt_y);
+
+    auto y =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
+
+    mm->add_return({y});
+    auto prog = migraphx::parse_onnx("qlinearaveragepool_notset_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(qlinearconv_test)
 {
     migraphx::program p;
@@ -5642,6 +5745,46 @@ TEST_CASE(qlinearglobalavgpool_test)
     EXPECT(p.sort() == prog.sort());
 }
 
+TEST_CASE(qlinearleakyrelu_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto x = mm->add_parameter("X", {migraphx::shape::int8_type, {64}});
+
+    auto sc_x   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
+
+    auto sc_y   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {10}});
+
+    auto scale_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_x);
+
+    auto z_pt_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_x);
+
+    auto fp_x =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
+
+    auto fp_y = mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 1.1}}), fp_x);
+
+    auto scale_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_y);
+
+    auto z_pt_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_y);
+
+    auto y =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
+
+    mm->add_return({y});
+
+    auto prog = migraphx::parse_onnx("qlinearleakyrelu_test.onnx");
+
+    EXPECT(p.sort() == prog.sort());
+}
+
 TEST_CASE(qlinearmatmul_1D_test)
 {
     migraphx::program p;
@@ -5754,6 +5897,99 @@ TEST_CASE(qlinearmatmul_2D_test)
     EXPECT(p.sort() == prog.sort());
 }
 
+TEST_CASE(qlinearmul_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto a = mm->add_parameter("A", {migraphx::shape::uint8_type, {64}});
+    auto b = mm->add_parameter("B", {migraphx::shape::uint8_type, {64}});
+
+    auto sc_a   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_a = mm->add_literal(migraphx::literal{migraphx::shape::uint8_type, {0}});
+
+    auto sc_b   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_b = mm->add_literal(migraphx::literal{migraphx::shape::uint8_type, {16}});
+
+    auto sc_c   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_c = mm->add_literal(migraphx::literal{migraphx::shape::uint8_type, {100}});
+
+    auto scale_a_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_a);
+
+    auto z_pt_a_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_a);
+
+    auto fp_a =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), a, scale_a_bcast, z_pt_a_bcast);
+
+    auto scale_b_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_b);
+
+    auto z_pt_b_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_b);
+
+    auto fp_b =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), b, scale_b_bcast, z_pt_b_bcast);
+
+    auto fp_c = mm->add_instruction(migraphx::make_op("mul"), fp_a, fp_b);
+
+    auto scale_c_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_c);
+
+    auto z_pt_c_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_c);
+
+    auto c =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_c, scale_c_bcast, z_pt_c_bcast);
+
+    mm->add_return({c});
+
+    auto prog = migraphx::parse_onnx("qlinearmul_test.onnx");
+
+    EXPECT(p.sort() == prog.sort());
+}
+
+TEST_CASE(qlinearsigmoid_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto x = mm->add_parameter("X", {migraphx::shape::int8_type, {64}});
+
+    auto sc_x   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
+
+    auto sc_y   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.0035}});
+    auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {-128}});
+
+    auto scale_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_x);
+
+    auto z_pt_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_x);
+
+    auto fp_x =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
+
+    auto fp_y = mm->add_instruction(migraphx::make_op("sigmoid"), fp_x);
+
+    auto scale_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_y);
+
+    auto z_pt_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_y);
+
+    auto y =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
+
+    mm->add_return({y});
+
+    auto prog = migraphx::parse_onnx("qlinearsigmoid_test.onnx");
+
+    EXPECT(p.sort() == prog.sort());
+}
+
 migraphx::instruction_ref insert_quantizelinear_clip(migraphx::module& m,
                                                      const migraphx::instruction_ref ins,
                                                      const migraphx::instruction_ref round,
@@ -5788,9 +6024,9 @@ TEST_CASE(quantizelinear_test)
     auto l1_mbcast =
         mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {5}}}), l1);
     auto div   = mm->add_instruction(migraphx::make_op("div"), l0, l1_mbcast);
-    auto round = mm->add_instruction(migraphx::make_op("round"), div);
-    auto s     = round->get_shape();
-    auto clip  = insert_quantizelinear_clip(*mm, div, round, s, 0, 255);
+    auto nearbyint = mm->add_instruction(migraphx::make_op("nearbyint"), div);
+    auto s         = nearbyint->get_shape();
+    auto clip      = insert_quantizelinear_clip(*mm, div, nearbyint, s, 0, 255);
     mm->add_instruction(
         migraphx::make_op("convert",
                           {{"target_type", migraphx::to_value(migraphx::shape::uint8_type)}}),
@@ -5813,9 +6049,9 @@ TEST_CASE(quantizelinear_int32_test)
                           {{"target_type", migraphx::to_value(migraphx::shape::float_type)}}),
         l0);
     auto div   = mm->add_instruction(migraphx::make_op("div"), l0, l1_mbcast);
-    auto round = mm->add_instruction(migraphx::make_op("round"), div);
-    auto s     = round->get_shape();
-    auto clip  = insert_quantizelinear_clip(*mm, div, round, s, 0, 255);
+    auto nearbyint = mm->add_instruction(migraphx::make_op("nearbyint"), div);
+    auto s         = nearbyint->get_shape();
+    auto clip      = insert_quantizelinear_clip(*mm, div, nearbyint, s, 0, 255);
     mm->add_instruction(
         migraphx::make_op("convert",
                           {{"target_type", migraphx::to_value(migraphx::shape::uint8_type)}}),
@@ -5835,7 +6071,7 @@ TEST_CASE(quantizelinear_zero_point_test)
     auto l1_mbcast =
         mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {5}}}), l1);
     auto div   = mm->add_instruction(migraphx::make_op("div"), l0, l1_mbcast);
-    auto round = mm->add_instruction(migraphx::make_op("round"), div);
+    auto round = mm->add_instruction(migraphx::make_op("nearbyint"), div);
     auto l2_mbcast =
         mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {5}}}), l2);
     l2_mbcast = mm->add_instruction(
@@ -5868,7 +6104,7 @@ migraphx::program make_quantizelinear_axis_prog()
         migraphx::make_op("broadcast", {{"axis", axis}, {"out_lens", input_lens}}), l1);
 
     auto div      = mm->add_instruction(migraphx::make_op("div"), l0, l1_bcast);
-    auto round    = mm->add_instruction(migraphx::make_op("round"), div);
+    auto round    = mm->add_instruction(migraphx::make_op("nearbyint"), div);
     auto l2_bcast = mm->add_instruction(
         migraphx::make_op("broadcast", {{"axis", axis}, {"out_lens", input_lens}}), l2);
     l2_bcast = mm->add_instruction(
@@ -6557,9 +6793,8 @@ TEST_CASE(resize_nonstd_input_test)
     auto tx =
         mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), inx);
     mm->add_instruction(migraphx::make_op("undefined"));
-    auto tx_cont = mm->add_instruction(migraphx::make_op("contiguous"), tx);
 
-    auto lrsp = mm->add_instruction(migraphx::make_op("reshape", {{"dims", {8}}}), tx_cont);
+    auto lrsp = mm->add_instruction(migraphx::make_op("reshape", {{"dims", {8}}}), tx);
     auto r    = mm->add_instruction(migraphx::make_op("gather", {{"axis", 0}}), lrsp, li);
     mm->add_return({r});
 
@@ -6998,7 +7233,7 @@ TEST_CASE(round_test)
     migraphx::program p;
     auto* mm   = p.get_main_module();
     auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::double_type, {10, 5}});
-    mm->add_instruction(migraphx::make_op("round"), input);
+    mm->add_instruction(migraphx::make_op("nearbyint"), input);
 
     auto prog = optimize_onnx("round_test.onnx");
     EXPECT(p == prog);
@@ -7042,20 +7277,35 @@ TEST_CASE(scatter_none_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(scatternd_test)
+void scatternd_test_base(const std::string& reduction, const std::string& onnx_file)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
     auto l0  = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
     auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
     auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
-    auto r  = mm->add_instruction(migraphx::make_op("scatternd_none"), l0, l1, l2);
+    auto r   = mm->add_instruction(migraphx::make_op("scatternd_" + reduction), l0, l1, l2);
     mm->add_return({r});
-    auto prog = migraphx::parse_onnx("scatternd_test.onnx");
+    auto prog = migraphx::parse_onnx(onnx_file);
 
     EXPECT(p == prog);
 }
 
+TEST_CASE(scatternd_test) { scatternd_test_base("none", "scatternd_test.onnx"); }
+
+TEST_CASE(scatternd_add_test) { scatternd_test_base("add", "scatternd_add_test.onnx"); }
+
+TEST_CASE(scatternd_mul_test) { scatternd_test_base("mul", "scatternd_mul_test.onnx"); }
+
+TEST_CASE(scatternd_max_test) { scatternd_test_base("max", "scatternd_max_test.onnx"); }
+
+TEST_CASE(scatternd_min_test) { scatternd_test_base("min", "scatternd_min_test.onnx"); }
+
+TEST_CASE(scatternd_invalid_reduction_test)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("scatternd_invalid_reduction_test.onnx"); }));
+}
+
 TEST_CASE(scatternd_dyn_test)
 {
     // dynamic input.
@@ -7079,34 +7329,6 @@ TEST_CASE(scatternd_dyn_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(scatternd_add_test)
-{
-    migraphx::program p;
-    auto* mm = p.get_main_module();
-    auto l0  = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
-    auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
-    auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
-    auto r  = mm->add_instruction(migraphx::make_op("scatternd_add"), l0, l1, l2);
-    mm->add_return({r});
-    auto prog = migraphx::parse_onnx("scatternd_add_test.onnx");
-
-    EXPECT(p == prog);
-}
-
-TEST_CASE(scatternd_mul_test)
-{
-    migraphx::program p;
-    auto* mm = p.get_main_module();
-    auto l0  = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
-    auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
-    auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
-    auto r  = mm->add_instruction(migraphx::make_op("scatternd_mul"), l0, l1, l2);
-    mm->add_return({r});
-    auto prog = migraphx::parse_onnx("scatternd_mul_test.onnx");
-
-    EXPECT(p == prog);
-}
-
 TEST_CASE(selu_test)
 {
     migraphx::program p;
@@ -7654,6 +7876,25 @@ TEST_CASE(slice_var_input_dyn1)
     EXPECT(p == prog);
 }
 
+TEST_CASE(slice_var_input_default_steps)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto data =
+        mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {{3, 8}, {2, 2}}});
+    auto starts = mm->add_parameter("starts", migraphx::shape{migraphx::shape::int64_type, {2}});
+    auto ends   = mm->add_parameter("ends", migraphx::shape{migraphx::shape::int64_type, {2}});
+    auto axes   = mm->add_parameter("axes", migraphx::shape{migraphx::shape::int64_type, {2}});
+    mm->add_literal({{migraphx::shape::int64_type, {2}}, {1, 1}});
+    auto ret = mm->add_instruction(migraphx::make_op("slice"), data, starts, ends, axes);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {3, 8};
+    auto prog                     = parse_onnx("slice_var_input_default_steps.onnx", options);
+    EXPECT(p == prog);
+}
+
 TEST_CASE(slice_var_input_steps_error)
 {
     EXPECT(test::throws([&] { migraphx::parse_onnx("slice_var_input_steps_error.onnx"); }));
@@ -8365,6 +8606,86 @@ TEST_CASE(undefined_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(unique_dynamic_sorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s{migraphx::shape::float_type, {6}};
+    auto x = mm->add_parameter("X", s);
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+
+    mm->add_return({y, y_ind, x_ind, count});
+    auto prog = migraphx::parse_onnx("unique_dynamic_sorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_dynamic_sorted_3D_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s{migraphx::shape::int64_type, {4, 4, 4}};
+    auto x = mm->add_parameter("X", s);
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+
+    mm->add_return({y, y_ind, x_ind, count});
+    auto prog = migraphx::parse_onnx("unique_dynamic_sorted_3D_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_sorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s_x{migraphx::shape::float_type, {6}};
+    std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
+    auto x                    = mm->add_literal(migraphx::literal(s_x, x_data));
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+    mm->add_return({y, y_idx, x_idx, count});
+    auto prog = migraphx::parse_onnx("unique_sorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_unsorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s_x{migraphx::shape::float_type, {6}};
+    std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
+    auto x                    = mm->add_literal(migraphx::literal(s_x, x_data));
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 0}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+    mm->add_return({y, y_idx, x_idx, count});
+    auto prog = migraphx::parse_onnx("unique_unsorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(unknown_test)
 {
     migraphx::program p;
@@ -8418,6 +8739,27 @@ TEST_CASE(upsample_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(upsample_ver7_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sx{migraphx::shape::float_type, {1, 1, 2, 2}};
+    auto ix = mm->add_parameter("X", sx);
+
+    migraphx::shape si{migraphx::shape::int32_type, {1, 1, 4, 6}};
+    std::vector<int> ind = {0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3, 3, 3};
+
+    auto li  = mm->add_literal(migraphx::literal(si, ind));
+    auto rsp = mm->add_instruction(migraphx::make_op("reshape", {{"dims", {4}}}), ix);
+    auto r   = mm->add_instruction(migraphx::make_op("gather", {{"axis", 0}}), rsp, li);
+    mm->add_return({r});
+
+    auto prog = migraphx::parse_onnx("upsample_ver7_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(unknown_test_throw_print_error)
 {
     migraphx::onnx_options options;