Merge branch 'develop' into blas_tuning

src/targets/gpu/int8_gemm_pack.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <migraphx/gpu/int8_gemm_pack.hpp>
-#include <migraphx/gpu/device/int8_gemm_pack.hpp>
-#include <migraphx/gpu/context.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape hip_int8_gemm_pack_a::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{{inputs.at(0)}, *this}.has(1).not_broadcasted().packed();
-    return inputs.at(0);
-}
-
-argument
-hip_int8_gemm_pack_a::compute(context& ctx, const shape&, const std::vector<argument>& args) const
-{
-    device::int8_gemm_pack_a(ctx.get_stream().get(), args[1], args[0]);
-    return args[1];
-}
-
-shape hip_int8_gemm_pack_b::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{{inputs.at(0)}, *this}.has(1).not_broadcasted().packed();
-    return inputs.at(0);
-}
-
-argument
-hip_int8_gemm_pack_b::compute(context& ctx, const shape&, const std::vector<argument>& args) const
-{
-    device::int8_gemm_pack_b(ctx.get_stream().get(), args[1], args[0]);
-    return args[1];
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/lowering.cpp

@@ -61,9 +61,8 @@ struct miopen_apply
     const lowering* pass     = nullptr;
     std::unordered_map<std::string, std::function<instruction_ref(instruction_ref)>> apply_map{};
     instruction_ref last{};
-    bool offload_copy   = false;
-    bool int8_x4_format = true;
-    bool compute_fp32   = false;
+    bool offload_copy = false;
+    bool compute_fp32 = false;
 
     context& get_context() const
     {
@@ -84,10 +83,8 @@ struct miopen_apply
         assert(mod != nullptr);
         assert(pass != nullptr);
 
-        auto& ctx      = get_context();
-        int8_x4_format = get_int8_x4_format(ctx);
-        compute_fp32   = get_compute_fp32_flag();
-        offload_copy   = (mod == mpm->get_root_module()) ? pass->offload_copy : false;
+        compute_fp32 = get_compute_fp32_flag();
+        offload_copy = (mod == mpm->get_root_module()) ? pass->offload_copy : false;
 
         add_generic_op("contiguous");
         add_extend_op("argmax");
@@ -231,18 +228,15 @@ struct miopen_apply
             assert(refs.size() == 2);
             auto output = insert_allocation(ins, ins->get_shape());
             refs.push_back(output);
-            return mod->replace_instruction(
-                ins, rocblas_gemm<Op>{Op{}, 1, 0, int8_x4_format, compute_fp32}, refs);
+            return mod->replace_instruction(ins, rocblas_gemm<Op>{Op{}, 1, 0, compute_fp32}, refs);
         });
     }
 
     void add_convolution_op(const std::string& name)
     {
         apply_map.emplace(name, [=](instruction_ref ins) {
-            operation conv = make_op(
-                "gpu::" + name,
-                {{"op", ins->get_operator().to_value()}, {"int8_x4_format", int8_x4_format}});
-            auto output = insert_allocation(ins, ins->get_shape());
+            operation conv = make_op("gpu::" + name, {{"op", ins->get_operator().to_value()}});
+            auto output    = insert_allocation(ins, ins->get_shape());
 
             return mod->replace_instruction(ins,
                                             make_op("gpu::miopen_op", {{"op", to_value(conv)}}),

src/targets/gpu/pack_int8_args.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <iterator>
-#include <migraphx/gpu/pack_int8_args.hpp>
-#include <migraphx/gpu/int8_gemm_pack.hpp>
-#include <migraphx/gpu/int8_conv_pack.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/instruction_ref.hpp>
-#include <migraphx/program.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/make_op.hpp>
-#include <migraphx/permutation.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-static instruction_ref pad_ins(module& m, instruction_ref ins, int offset)
-{
-    auto s                         = ins->get_shape();
-    auto lens                      = s.lens();
-    auto k                         = lens[lens.size() + offset];
-    auto pad_k                     = (k + 3) / 4 * 4;
-    auto pad_lens                  = lens;
-    pad_lens[lens.size() + offset] = pad_k;
-    auto ret_ins                   = ins;
-    if(pad_k != k)
-    {
-        std::vector<int64_t> pad_dims(lens.size() * 2, 0);
-        pad_dims[lens.size() + offset] = pad_k - k;
-        shape ps{s.type(), pad_lens};
-        auto ins_out =
-            m.insert_instruction(ins, make_op("hip::allocate", {{"shape", to_value(ps)}}));
-        auto pad = make_op("pad", {{"pads", pad_dims}});
-        ret_ins =
-            m.insert_instruction(std::next(ins), make_op("gpu::pad", pad.to_value()), ins, ins_out);
-    }
-
-    return ret_ins;
-}
-
-static std::vector<instruction_ref> pad_inputs(module& m, instruction_ref ins)
-{
-    std::vector<instruction_ref> ret_inputs;
-    auto inputs = ins->inputs();
-    auto in0    = inputs.at(0);
-    auto sa     = in0->get_shape();
-    bool transa = sa.transposed();
-    if(transa)
-    {
-        auto perm = find_permutation(sa);
-        auto val  = in0->get_operator().to_value();
-        if(val.contains("dims"))
-        {
-            int offset = static_cast<int>(perm.back()) - static_cast<int>(perm.size());
-            auto t_in  = in0->inputs().front();
-            auto p_in  = pad_ins(m, t_in, offset);
-            auto dims  = val.at("dims").to_vector<int64_t>();
-            auto r_in =
-                m.insert_instruction(ins, make_op("transpose", {{"permutation", dims}}), p_in);
-            ret_inputs.push_back(r_in);
-        }
-        else
-        {
-            shape cs{in0->get_shape().type(), in0->get_shape().lens()};
-            auto con_out =
-                m.insert_instruction(ins, make_op("hip::allocate", {{"shape", to_value(cs)}}));
-            auto cin0 = m.insert_instruction(ins, make_op("gpu::contiguous"), in0, con_out);
-            ret_inputs.push_back(pad_ins(m, cin0, -1));
-        }
-    }
-    else
-    {
-        ret_inputs.push_back(pad_ins(m, in0, -1));
-    }
-
-    auto in1    = inputs.at(1);
-    auto sb     = in1->get_shape();
-    bool transb = sb.transposed();
-    if(transb)
-    {
-        auto perm = find_permutation(sb);
-        auto val  = in1->get_operator().to_value();
-        if(val.contains("dims"))
-        {
-            int offset = static_cast<int>(perm[perm.size() - 2]) - static_cast<int>(perm.size());
-            auto t_in  = in1->inputs().front();
-            auto p_in  = pad_ins(m, t_in, offset);
-            auto dims  = val.at("dims").to_vector<int64_t>();
-            auto r_in =
-                m.insert_instruction(ins, make_op("transpose", {{"permutation", dims}}), p_in);
-            ret_inputs.push_back(r_in);
-        }
-        else
-        {
-            shape cs{in1->get_shape().type(), in1->get_shape().lens()};
-            auto con_out =
-                m.insert_instruction(ins, make_op("hip::allocate", {{"shape", to_value(cs)}}));
-            auto cin1 = m.insert_instruction(ins, make_op("gpu::contiguous"), in1, con_out);
-            ret_inputs.push_back(pad_ins(m, cin1, -2));
-        }
-    }
-    else
-    {
-        ret_inputs.push_back(pad_ins(m, in1, -2));
-    }
-    std::copy(inputs.begin() + 2, inputs.end(), std::back_inserter(ret_inputs));
-
-    return ret_inputs;
-}
-
-void pack_int8_args::apply(module& m) const
-{
-    for(auto ins : iterator_for(m))
-    {
-        if(ins->name() == "gpu::quant_gemm")
-        {
-            auto val = ins->get_operator().to_value();
-            assert(val.contains("int8_x4_format"));
-            if(not val.at("int8_x4_format").to<bool>())
-            {
-                continue;
-            }
-            auto inputs = ins->inputs();
-            auto lens   = inputs.at(0)->get_shape().lens();
-            // gemm need the k to be multiple of 4, so need packing that dimension
-            auto old_inputs = inputs;
-            if((lens.back() % 4) != 0)
-            {
-                inputs = pad_inputs(m, ins);
-            }
-
-            bool transa = inputs[0]->get_shape().transposed();
-            bool transb = inputs[1]->get_shape().transposed();
-            if(not transb)
-            {
-                auto packed_b = m.insert_instruction(
-                    ins, make_op("hip::allocate", {{"shape", to_value(inputs[1]->get_shape())}}));
-                auto output_b = m.insert_instruction(
-                    ins, make_op("gpu::int8_gemm_pack_a"), {inputs[1], packed_b});
-                inputs[1] = output_b;
-            }
-
-            if(transa)
-            {
-                auto packed_a = m.insert_instruction(
-                    ins, make_op("hip::allocate", {{"shape", to_value(inputs[0]->get_shape())}}));
-                auto output_a = m.insert_instruction(
-                    ins, make_op("gpu::int8_gemm_pack_b"), {inputs[0], packed_a});
-                inputs[0] = output_a;
-            }
-
-            if(inputs != old_inputs)
-            {
-                m.replace_instruction(ins, ins->get_operator(), inputs);
-            }
-        }
-        else if(ins->name() == "gpu::quant_convolution")
-        {
-            auto val = ins->get_operator().to_value();
-            if(not val.at("int8_x4_format").to<bool>())
-            {
-                continue;
-            }
-
-            auto inputs   = ins->inputs();
-            auto packed_x = m.insert_instruction(
-                ins,
-                make_op("hip::allocate",
-                        {{"shape", to_value(pack_int8_shape(inputs[0]->get_shape()))}}));
-            auto output_x =
-                m.insert_instruction(ins, make_op("gpu::int8_conv_pack"), {inputs[0], packed_x});
-            instruction::replace_argument(ins, inputs[0], output_x);
-
-            auto packed_w = m.insert_instruction(
-                ins,
-                make_op("hip::allocate",
-                        {{"shape", to_value(pack_int8_shape(inputs[1]->get_shape()))}}));
-            auto output_w =
-                m.insert_instruction(ins, make_op("gpu::int8_conv_pack"), {inputs[1], packed_w});
-            instruction::replace_argument(ins, inputs[1], output_w);
-        }
-    }
-}
-
-shape pack_int8_args::pack_int8_shape(const shape& s) const
-{
-    if(s.type() != shape::int8_type)
-    {
-        MIGRAPHX_THROW("PACK_INT8_ARGS: only process int8_type");
-    }
-
-    auto lens    = s.lens();
-    auto strides = s.strides();
-    lens[1]      = (lens[1] + 3) / 4 * 4;
-    strides[0]   = strides[1] * lens[1];
-
-    return {s.type(), lens, strides};
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/rocblas.cpp

@@ -53,19 +53,6 @@ bool get_compute_fp32_flag()
     return (starts_with(device_name, "gfx9") and device_name >= "gfx908");
 }
 
-bool get_int8_x4_format(context& ctx)
-{
-#if ROCBLAS_VERSION_MAJOR >= 3
-    (void)(ctx);
-    return false;
-#else
-    // int8x4 packed format is only available starting from rocblas-v2.38 and it is deprecated in
-    // v3.0 and will be removed in v4.0
-    rocblas_gemm_flags flag;
-    rocblas_query_int8_layout_flag(ctx.get_stream().get_rocblas(), &flag);
-    return flag == rocblas_gemm_flags_pack_int8x4;
-#endif
-}
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/target.cpp

@@ -63,7 +63,6 @@
 #include <migraphx/gpu/fuse_ops.hpp>
 #include <migraphx/gpu/prefuse_ops.hpp>
 #include <migraphx/gpu/lowering.hpp>
-#include <migraphx/gpu/pack_int8_args.hpp>
 #include <migraphx/gpu/schedule_model.hpp>
 #include <migraphx/gpu/sync_device.hpp>
 #include <migraphx/gpu/target.hpp>
@@ -154,7 +153,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
         dead_code_elimination{},
         compile_miopen{&gctx},
         dead_code_elimination{},
-        pack_int8_args{},
         dead_code_elimination{},
         fuse_ops{&ctx, options.fast_math},
         dead_code_elimination{},

test/CMakeLists.txt

@@ -25,7 +25,7 @@
 cmake_policy(SET CMP0057 NEW)
 
 find_package(Threads REQUIRED)
-rocm_test_link_libraries(Threads::Threads migraphx migraphx_ref migraphx_onnx migraphx_tf)
+rocm_test_link_libraries(Threads::Threads migraphx migraphx_onnx migraphx_tf)
 rocm_test_include_directories(include)
 
 set(MIGRAPHX_DISABLE_LARGE_BUFFER_TESTS Off CACHE BOOL "")
@@ -146,7 +146,10 @@ endfunction()
 
 function(test_headers PREFIX)
     file(GLOB HEADERS CONFIGURE_DEPENDS ${ARGN})
-
+    if(NOT MIGRAPHX_USE_COMPOSABLEKERNEL)
+        list(REMOVE_ITEM HEADERS
+              ${CMAKE_SOURCE_DIR}/src/targets/gpu/include/migraphx/gpu/ck.hpp)
+    endif()
     foreach(HEADER ${HEADERS})
         file(RELATIVE_PATH HEADER_REL ${CMAKE_SOURCE_DIR} ${HEADER})
         string(MAKE_C_IDENTIFIER ${HEADER_REL} TEST_NAME)

test/gpu/fuse_mlir.cpp

@@ -152,6 +152,9 @@ TEST_CASE(int_quant_dot_tanh_fails)
 
 int main(int argc, const char* argv[])
 {
-    test::run(argc, argv);
+    if(migraphx::gpu::mlir_enabled())
+    {
+        test::run(argc, argv);
+    }
     return 0;
 }

test/onnx/.onnxrt-commit

-635d3faa3b3908d2806d009dc6872152cfcfcdda
+2eeafc37bca21dc8bf337dda7020b486543162d7

test/onnx/gen_onnx.py

@@ -149,6 +149,21 @@ def argmax_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def argmax_select_last_index_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
+
+    node = onnx.helper.make_node('ArgMax',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axis=2,
+                                 keepdims=0,
+                                 select_last_index=1)
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def argmax_dyn_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
@@ -177,6 +192,21 @@ def argmin_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def argmin_select_last_index_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
+
+    node = onnx.helper.make_node('ArgMin',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axis=3,
+                                 keepdims=0,
+                                 select_last_index=1)
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def asin_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
@@ -4681,6 +4711,77 @@ def mean_integral_test():
     return ([node], data, [mean])
 
 
+def mvn_default_axes_test_base(dims, type=TensorProto.FLOAT):
+    data = helper.make_tensor_value_info("data", type, dims)
+    out = helper.make_tensor_value_info("out", type, dims)
+    node = helper.make_node("MeanVarianceNormalization",
+                            inputs=["data"],
+                            outputs=["out"])
+
+    return ([node], [data], [out])
+
+
+@onnx_test()
+def mvn_default_axes_test():
+    return mvn_default_axes_test_base([2, 2, 2, 2])
+
+
+@onnx_test()
+def mvn_default_axes_fp16_test():
+    return mvn_default_axes_test_base([2, 2, 2, 2], TensorProto.FLOAT16)
+
+
+@onnx_test()
+def mvn_default_axes_rank_too_small_test():
+    return mvn_default_axes_test_base([2, 2, 2])
+
+
+@onnx_test()
+def mvn_default_axes_rank_too_big_test():
+    return mvn_default_axes_test_base([2, 2, 2, 2, 2])
+
+
+def mvn_n_rank_test_base(axes, dims, type=TensorProto.FLOAT):
+    data = helper.make_tensor_value_info("data", type, dims)
+    out = helper.make_tensor_value_info("out", type, dims)
+    node = helper.make_node("MeanVarianceNormalization",
+                            inputs=["data"],
+                            outputs=["out"],
+                            axes=axes)
+
+    return ([node], [data], [out])
+
+
+@onnx_test()
+def mvn_rank_2_test():
+    return mvn_n_rank_test_base([1], [2, 2])
+
+
+@onnx_test()
+def mvn_rank_2_fp16_test():
+    return mvn_n_rank_test_base([1], [2, 2], TensorProto.FLOAT16)
+
+
+@onnx_test()
+def mvn_rank_3_test():
+    return mvn_n_rank_test_base([0, 1], [2, 2, 2])
+
+
+@onnx_test()
+def mvn_rank_3_fp16_test():
+    return mvn_n_rank_test_base([0, 1], [2, 2, 2], TensorProto.FLOAT16)
+
+
+@onnx_test()
+def mvn_axes_rank_too_small_test():
+    return mvn_n_rank_test_base([0, 1, 2], [2, 2, 2])
+
+
+@onnx_test()
+def mvn_axes_rank_too_big_test():
+    return mvn_n_rank_test_base([0], [2, 2, 2])
+
+
 @onnx_test()
 def min_test():
     a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
@@ -8502,7 +8603,7 @@ def transpose_gather_test():
 
 
 @onnx_test()
-def trilu_test():
+def triu_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
 
@@ -8515,7 +8616,7 @@ def trilu_test():
 
 
 @onnx_test()
-def trilu_batch_diff_k_test():
+def triu_batch_diff_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
     k = np.array([2])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
@@ -8533,7 +8634,24 @@ def trilu_batch_diff_k_test():
 
 
 @onnx_test()
-def trilu_lower_test():
+def tril_batch_diff_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
+    k = np.array([2])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def tril_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
 
@@ -8542,7 +8660,7 @@ def trilu_lower_test():
 
 
 @onnx_test()
-def trilu_neg_k_test():
+def triu_neg_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     k = np.array([-1])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
@@ -8556,7 +8674,23 @@ def trilu_neg_k_test():
 
 
 @onnx_test()
-def trilu_out_k_test():
+def tril_neg_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
+    k = np.array([-1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def triu_out_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     k = np.array([5])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
@@ -8570,7 +8704,23 @@ def trilu_out_k_test():
 
 
 @onnx_test()
-def trilu_row_one_test():
+def tril_out_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
+    k = np.array([5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def triu_row_one_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
     k = np.array([1])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
@@ -8587,6 +8737,23 @@ def trilu_row_one_test():
     return ([node], [x], [y], [k_tensor])
 
 
+@onnx_test()
+def tril_row_one_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
+    k = np.array([1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
 @onnx_test()
 def undefined_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])

test/onnx/mvn_default_axes_fp16_test.onnx

+	mvn_default_axes_fp16_test:

+&
+dataout"MeanVarianceNormalizationmvn_default_axes_fp16_testZ
+data
+
+
+
+
+
+b
+out
+
+
+
+
+
+B
\ No newline at end of file

test/onnx/mvn_default_axes_rank_too_small_test.onnx

+	$mvn_default_axes_rank_too_small_test:

+&
+dataout"MeanVarianceNormalization$mvn_default_axes_rank_too_small_testZ
+data
+

+
+
+b
+out
+

+
+
+B
\ No newline at end of file

test/onnx/mvn_default_axes_test.onnx

+	mvn_default_axes_test:~
+&
+dataout"MeanVarianceNormalizationmvn_default_axes_testZ
+data
+

+
+
+
+b
+out
+

+
+
+
+B
\ No newline at end of file

test/onnx/mvn_rank_2_fp16_test.onnx

+	mvn_rank_2_fp16_test:z
+3
+dataout"MeanVarianceNormalization*
+axes@

mvn_rank_2_fp16_testZ
+data
+
+
+
+b
+out
+
+
+
+B
\ No newline at end of file

test/onnx/mvn_rank_2_test.onnx

+	mvn_rank_2_test:u
+3
+dataout"MeanVarianceNormalization*
+axes@

mvn_rank_2_testZ
+data
+

+
+b
+out
+

+
+B
\ No newline at end of file