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

src/targets/gpu/abs.cpp

@@ -18,7 +18,8 @@ argument miopen_abs::compute(context& ctx,
                              const shape& output_shape,
                              const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/batchnorm.cpp

@@ -22,7 +22,8 @@ argument miopen_batch_norm_inference::compute(context& ctx,
     auto y_desc  = make_tensor(output_shape);
     auto bn_desc = make_tensor(args[3].get_shape());
 
-    float alpha = 1.0, beta = 0.0f;
+    float alpha = 1.0;
+    float beta  = 0.0f;
 
     miopenBatchNormalizationForwardInference(ctx.get_stream().get_miopen(),
                                              miopenBatchNormMode_t(op.bn_mode),

src/targets/gpu/convolution.cpp

@@ -21,7 +21,8 @@ argument miopen_convolution::compute(context& ctx,
     auto w_desc = make_tensor(args[1].get_shape());
     auto y_desc = make_tensor(output_shape);
 
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     miopenConvolutionForward(ctx.get_stream().get_miopen(),
                              &alpha,
                              x_desc.get(),
@@ -63,7 +64,7 @@ shape miopen_convolution::compile(context& ctx,
 
     int algo_count = 1;
     miopenConvAlgoPerf_t perf;
-    miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
+    auto status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
                                                         x_desc.get(),
                                                         x.implicit(),
                                                         w_desc.get(),
@@ -77,6 +78,8 @@ shape miopen_convolution::compile(context& ctx,
                                                         workspace.implicit(),
                                                         workspace_size,
                                                         false);
+    if(status != miopenStatusSuccess)
+        MIGRAPHX_THROW("Find convolution failed");
     algo = perf.fwd_algo;
     return shape{shape::int8_type, {perf.memory}};
 }

src/targets/gpu/elu.cpp

@@ -18,7 +18,8 @@ argument miopen_elu::compute(context& ctx,
                              const shape& output_shape,
                              const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/fuse_ops.cpp

@@ -265,7 +265,8 @@ struct miopen_conv_bias
     argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
     {
         auto fargs  = make_fused_args();
-        float alpha = 1, beta = 0;
+        float alpha = 1;
+        float beta  = 0;
         miopenSetOpArgsConvForward(fargs.get(), conv, &alpha, &beta, args[1].implicit());
         miopenSetOpArgsBiasForward(fargs.get(), bias, &alpha, &beta, args[3].implicit());
         return f.execute(ctx, fargs, args[0], args[4]);
@@ -308,7 +309,8 @@ struct miopen_conv_bias_relu
     argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
     {
         auto fargs  = make_fused_args();
-        float alpha = 1, beta = 0;
+        float alpha = 1;
+        float beta  = 0;
         miopenSetOpArgsConvForward(fargs.get(), conv, &alpha, &beta, args[1].implicit());
         miopenSetOpArgsBiasForward(fargs.get(), bias, &alpha, &beta, args[3].implicit());
         miopenSetOpArgsActivForward(fargs.get(), relu, &alpha, &beta, 0, 0, 0);

src/targets/gpu/hip.cpp

@@ -16,7 +16,8 @@ std::string hip_error(int error) { return hipGetErrorString(static_cast<hipError
 
 std::size_t get_available_gpu_memory()
 {
-    size_t free, total;
+    size_t free;
+    size_t total;
     auto status = hipMemGetInfo(&free, &total);
     if(status != hipSuccess)
         MIGRAPHX_THROW("Failed getting available memory: " + hip_error(status));
@@ -72,13 +73,13 @@ argument allocate_gpu(const shape& s, bool host)
     return {s, [p]() mutable { return reinterpret_cast<char*>(p.get()); }};
 }
 
-argument to_gpu(argument arg, bool host)
+argument to_gpu(const argument& arg, bool host)
 {
     auto p = share(write_to_gpu(arg.data(), arg.get_shape().bytes(), host));
     return {arg.get_shape(), [p]() mutable { return reinterpret_cast<char*>(p.get()); }};
 }
 
-argument from_gpu(argument arg)
+argument from_gpu(const argument& arg)
 {
     argument result;
     arg.visit([&](auto x) {
@@ -98,7 +99,7 @@ void set_device(std::size_t id)
 
 void gpu_sync() { hipDeviceSynchronize(); }
 
-void copy_to_gpu(argument src, argument dst)
+void copy_to_gpu(const argument& src, const argument& dst)
 {
     std::size_t src_size = src.get_shape().bytes();
     std::size_t dst_size = dst.get_shape().bytes();

src/targets/gpu/include/migraphx/gpu/hip.hpp

@@ -9,17 +9,17 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-migraphx::argument allocate_gpu(const migraphx::shape& s, bool host = false);
+argument allocate_gpu(const shape& s, bool host = false);
 
-migraphx::argument to_gpu(migraphx::argument arg, bool host = false);
+argument to_gpu(const argument& arg, bool host = false);
 
-migraphx::argument from_gpu(migraphx::argument arg);
+argument from_gpu(const argument& arg);
 
 void set_device(std::size_t id);
 
 void gpu_sync();
 
-void copy_to_gpu(argument src, argument dst);
+void copy_to_gpu(const argument& src, const argument& dst);
 
 struct hip_allocate
 {

src/targets/gpu/leaky_relu.cpp

@@ -18,7 +18,8 @@ argument miopen_leaky_relu::compute(context& ctx,
                                     const shape& output_shape,
                                     const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/pooling.cpp

@@ -20,7 +20,8 @@ argument miopen_pooling::compute(context& ctx,
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
 
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
 
     miopenPoolingForward(ctx.get_stream().get_miopen(),
                          pd.get(),

src/targets/gpu/relu.cpp

@@ -18,7 +18,8 @@ argument miopen_relu::compute(context& ctx,
                               const shape& output_shape,
                               const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/sigmoid.cpp

@@ -18,7 +18,8 @@ argument miopen_sigmoid::compute(context& ctx,
                                  const shape& output_shape,
                                  const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/softmax.cpp

@@ -18,7 +18,8 @@ argument miopen_softmax::compute(context& ctx,
                                  const shape& output_shape,
                                  const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenSoftmaxForward(ctx.get_stream().get_miopen(),

src/targets/gpu/tanh.cpp

@@ -18,7 +18,8 @@ argument miopen_tanh::compute(context& ctx,
                               const shape& output_shape,
                               const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

test/cpu_ops_test.cpp

@@ -333,9 +333,15 @@ TEST_CASE(im2col_3x3_with_padding_test)
 TEST_CASE(batch_norm_inference_test)
 {
     migraphx::program p;
-    const size_t width = 2, height = 2, channels = 4, batches = 2;
-    const float x_val = 8.0f, mean_val = 2.0f, variance_val = 4.0f, scale_val = 2.0f,
-                bias_val   = 1.0f;
+    const size_t width       = 2;
+    const size_t height      = 2;
+    const size_t channels    = 4;
+    const size_t batches     = 2;
+    const float x_val        = 8.0;
+    const float mean_val     = 2.0;
+    const float variance_val = 4.0;
+    const float scale_val    = 2.0f;
+    const float bias_val     = 1.0f;
     const float output_val = scale_val * (x_val - mean_val) / (std::sqrt(variance_val)) + bias_val;
 
     migraphx::shape s{migraphx::shape::float_type, {batches, channels, height, width}};
@@ -757,7 +763,7 @@ void gemm_test()
                         1.33628943,  -0.11413,   -0.31270559, 1.59336732,  -0.19361027,
                         -0.91620867, 0.40108416, -0.06969921, 0.68483471,  -0.39906632,
                         -1.66423624, 0.69040076, -1.31490171, -0.11282616, -0.79391814};
-    std::vector<T> b = {6.09568541e-01,
+    std::vector<float> b = {6.09568541e-01,
                             -6.10527007e-01,
                             3.66646462e-01,
                             1.18951101e-01,
@@ -772,7 +778,7 @@ void gemm_test()
                             1.53027987e+00,
                             -3.81407415e-04,
                             -3.29650255e-01};
-    std::vector<T> c = {-1.56327541e+00,
+    std::vector<float> c = {-1.56327541e+00,
                             -7.09570140e-01,
                             -5.37424982e-01,
                             -2.22994831e-01,
@@ -793,11 +799,7 @@ void gemm_test()
     auto result = p.eval({});
     std::vector<T> results_vector(12);
     result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
-    float tol = 1e-6;
-    for(int i = 0; i < results_vector.size(); i++)
-    {
-        EXPECT(std::abs(results_vector[i] - c[i]) < tol);
-    }
+    EXPECT(migraphx::verify_range(c, results_vector));
 }
 TEST_CASE_REGISTER(gemm_test<float>)
 TEST_CASE_REGISTER(gemm_test<double>)
@@ -851,12 +853,7 @@ TEST_CASE(maxpool_test)
     // std::cout << result.get_shape() << std::endl;
     std::vector<float> results_vector(36);
     result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
-    float tol = 1e-6;
-    for(int i = 0; i < results_vector.size(); i++)
-    {
-        // std::cout << results_vector[i] << "          " << c[i] << std::endl;
-        EXPECT(std::abs(results_vector[i] - c[i]) < tol);
-    }
+    EXPECT(migraphx::verify_range(results_vector, c));
 }
 
 TEST_CASE(softmax_test)

test/include/rob.hpp

@@ -30,6 +30,7 @@ struct mem_data_ptr
     using type = T C::*;
 };
 
+// NOLINTNEXTLINE
 #define MIGRAPHX_ROB(name, Type, C, mem)               \
     struct name##_tag : mem_data_ptr<C, Type>          \
     {                                                  \

test/include/test.hpp

@@ -189,7 +189,7 @@ inline auto& get_test_cases()
 
 inline void add_test_case(std::string name, std::function<void()> f)
 {
-    get_test_cases().emplace_back(name, f);
+    get_test_cases().emplace_back(std::move(name), std::move(f));
 }
 
 struct auto_register
@@ -248,6 +248,7 @@ inline void run(int argc, const char* argv[])
 
 // NOLINTNEXTLINE
 #define TEST_CAT(x, ...) TEST_PRIMITIVE_CAT(x, __VA_ARGS__)
+// NOLINTNEXTLINE
 #define TEST_PRIMITIVE_CAT(x, ...) x##__VA_ARGS__
 
 // NOLINTNEXTLINE

test/matcher.cpp

@@ -385,7 +385,10 @@ struct match_find_sum
     migraphx::instruction_ref ins;
     auto matcher() const { return match::name("sum"); }
 
-    void apply(migraphx::program&, match::matcher_result r) const { EXPECT(bool{r.result == ins}); }
+    void apply(migraphx::program&, const match::matcher_result& r) const
+    {
+        EXPECT(bool{r.result == ins});
+    }
 };
 
 struct match_find_literal
@@ -393,7 +396,7 @@ struct match_find_literal
     migraphx::instruction_ref ins;
     auto matcher() const { return match::name("@literal"); }
 
-    void apply(migraphx::program&, match::matcher_result r) const
+    void apply(migraphx::program&, const match::matcher_result& r) const
     {
         EXPECT(bool{r.result != ins});
         EXPECT(r.result->name() == "@literal");

test/onnx/add_bcast_test.onnx

+add_bcast-example:

+-
+
0
+
1
2"Add*
+axis

*
+	broadcast

test-add_bcastZ
+
0
+

+
+
+
+Z
+
1
+

+
+b
+
2
+

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