more coding conventions fix

src/targets/gpu/device/include/migraph/gpu/device/nary.hpp

@@ -10,16 +10,25 @@ namespace migraph {
 namespace gpu {
 namespace device {
 
-template <class... Arguments>
-auto nary(argument result, Arguments... args)
+template <class T>
+using vec4 = T __attribute__((ext_vector_type(4)));
+
+template <class T>
+__device__ __host__ vec4<T>* as_vec4(T* x)
 {
-    return [=](auto f) {
-        if(all_of({args...}, [](const shape& s) { return s.standard(); }))
-            nary_standard(result, args...)(f);
-        else
-            nary_nonstandard(result, args...)(f);
+    return reinterpret_cast<vec4<T>*>(x);
+}
 
-    };
+template <class T>
+__device__ __host__ T* as_pointer(vec4<T>* x)
+{
+    return reinterpret_cast<T*>(x);
+}
+
+template <class... Ts>
+auto pack_vec4(Ts... xs)
+{
+    return [=](auto f, std::size_t n) { return f(as_vec4(xs)[n]...); };
 }
 
 template <class F, class... Arguments>
@@ -28,14 +37,12 @@ auto nary_nonstandard_impl(F f, argument result, Arguments... args)
     const auto& output_shape = result.get_shape();
     visit_all(result, args...)([&](auto output, auto... inputs) {
         visit_tensor_size(output_shape.lens().size(), [&](auto ndim) {
-            auto data = make_sequence(
-                std::make_pair(hip_tensor_descriptor<ndim>{inputs.get_shape().lens(),
-                                                           inputs.get_shape().strides()},
-                               inputs.data())...);
-            hip_tensor_descriptor<ndim> out_desc(output_shape.lens(), output_shape.strides());
+            auto data = pack(
+                std::make_pair(hip_tensor_descriptor<ndim>{inputs.get_shape()}, inputs.data())...);
+            hip_tensor_descriptor<ndim> out_desc(output_shape);
             auto* outp = output.data();
             gs_launch(output_shape.elements())([=](auto i) {
-                data([&](auto... ps) {
+                data([&](auto&&... ps) {
                     auto outidx = out_desc.multi(i);
                     outp[i]     = f(ps.second[ps.first.linear(outidx)]...);
                 });
@@ -44,24 +51,199 @@ auto nary_nonstandard_impl(F f, argument result, Arguments... args)
     });
 }
 
+template <class F>
+void binary_broadcast_vec_impl(F f,
+                               const argument& result,
+                               const argument& arg1,
+                               const argument& arg2)
+{
+    const auto& output_shape = result.get_shape();
+    const auto& b_shape      = arg2.get_shape();
+    auto bdim =
+        std::distance(b_shape.strides().begin(),
+                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
+                          return x != 0;
+                      }));
+    auto bdim_len         = output_shape.lens()[bdim];
+    auto bdim_stride      = output_shape.strides()[bdim];
+    auto bdim_next_stride = bdim_stride * bdim_len;
+
+    visit_all(result, arg1, arg2)([&](auto output, auto input1, auto input2) {
+        using type = std::remove_cv_t<typename decltype(output)::value_type>;
+        auto* xp   = as_vec4(input1.data());
+        auto* yp   = as_vec4(input2.data());
+        auto* outp = as_vec4(output.data());
+
+        const std::size_t vec_size     = 4;
+        const std::size_t nlocal       = 1024;
+        const std::size_t nglobal      = 256 * nlocal;
+        const std::size_t n            = output.size() / vec_size;
+        const std::size_t bdim_vec_len = bdim_len / vec_size;
+
+        launch(nglobal, nlocal)([=](auto idx) __device__ {
+            MIGRAPH_DEVICE_SHARED vec4<type> buffer[2048 / vec_size];
+            // Load bias into LDS
+            for(size_t i = idx.local; i < bdim_vec_len; i += nlocal)
+            {
+                buffer[i] = yp[i];
+            }
+            __syncthreads();
+            auto* bp = as_pointer(buffer);
+            // Process the data
+            for(size_t i = idx.global; i < n; i += nglobal)
+            {
+                auto bidx      = ((i * vec_size) % bdim_next_stride) / bdim_stride;
+                auto b         = bp[bidx];
+                vec4<type> x   = xp[i];
+                vec4<type> out = outp[i];
+                for(std::size_t j = 0; j < vec_size; j++)
+                {
+                    out[j] = f(x[j], b);
+                }
+                outp[i] = out;
+            }
+        });
+    });
+}
+
+template <class F>
+void binary_broadcast_impl(F f, const argument& result, const argument& arg1, const argument& arg2)
+{
+    const auto& output_shape = result.get_shape();
+    const auto& b_shape      = arg2.get_shape();
+    auto bdim =
+        std::distance(b_shape.strides().begin(),
+                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
+                          return x != 0;
+                      }));
+    auto bdim_len         = output_shape.lens()[bdim];
+    auto bdim_stride      = output_shape.strides()[bdim];
+    auto bdim_next_stride = bdim_stride * bdim_len;
+
+    visit_all(result, arg1, arg2)([&](auto output, auto input1, auto input2) {
+        using type = std::remove_cv_t<typename decltype(output)::value_type>;
+        auto* xp   = input1.data();
+        auto* yp   = input2.data();
+        auto* outp = output.data();
+
+        const std::size_t nlocal  = 1024;
+        const std::size_t nglobal = 256 * nlocal;
+        const std::size_t n       = output.size();
+
+        launch(nglobal, nlocal)([=](auto idx) __device__ {
+            MIGRAPH_DEVICE_SHARED type buffer[2048];
+            // Load bias into LDS
+            for(size_t i = idx.local; i < bdim_len; i += nlocal)
+            {
+                buffer[i] = yp[i];
+            }
+            __syncthreads();
+            // Process the data
+            for(size_t i = idx.global; i < n; i += nglobal)
+            {
+                auto bidx = (i % bdim_next_stride) / bdim_stride;
+                auto b    = buffer[bidx];
+                type x    = xp[i];
+                outp[i]   = f(x, b);
+            }
+        });
+    });
+}
+
+template <class F, class... Arguments>
+void nary_standard_vec_impl(F f, argument result, Arguments... args)
+{
+    // assert(x.get_shape().elements() == y.get_shape().elements());
+    const auto& output_shape = result.get_shape();
+    visit_all(result, args...)([&](auto output, auto... inputs) {
+        using type                 = std::remove_cv_t<typename decltype(output)::value_type>;
+        const std::size_t vec_size = 4;
+        auto data                  = pack_vec4(inputs.data()...);
+        auto* outp                 = as_vec4(output.data());
+        gs_launch(output_shape.elements() / vec_size)([=](auto i) {
+            vec4<type> out = outp[i];
+            data(
+                [&](auto... xs) {
+                    for(std::size_t j = 0; j < vec_size; j++)
+                    {
+                        out[j] = f(xs[j]...);
+                    }
+                },
+                i);
+            outp[i] = out;
+        });
+    });
+}
+
+template <class F, class... Arguments>
+void nary_standard_impl(F f, argument result, Arguments... args)
+{
+    // assert(x.get_shape().elements() == y.get_shape().elements());
+    const auto& output_shape = result.get_shape();
+    visit_all(result, args...)([&](auto output, auto... inputs) {
+        auto data  = pack(inputs.data()...);
+        auto* outp = output.data();
+        gs_launch(output_shape.elements())(
+            [=](auto i) { data([&](auto... xps) { outp[i] = f(xps[i]...); }); });
+    });
+}
+
+template <class F, class... Arguments>
+void nary_impl(F f, argument result, Arguments... args)
+{
+    bool standard = all_of({args.get_shape()...}, [](const shape& s) { return s.standard(); });
+    bool packed   = all_of({args.get_shape()...}, [](const shape& s) { return s.packed(); });
+    bool same_shapes =
+        all_of({args.get_shape()...}, [&](const shape& s) { return s == result.get_shape(); });
+    if(standard or (packed and same_shapes))
+        nary_standard_impl(f, result, args...);
+    else
+        nary_nonstandard_impl(f, result, args...);
+}
+
 template <class... Arguments>
 auto nary_nonstandard(argument result, Arguments... args)
 {
-    return [=](auto f) { return nary_nonstandard_impl(f, result, args...); };
+    return [=](auto f) { nary_nonstandard_impl(f, result, args...); };
 }
 
 template <class... Arguments>
 auto nary_standard(argument result, Arguments... args)
+{
+    return [=](auto f) { nary_standard_impl(f, result, args...); };
+}
+
+template <class... Arguments>
+auto nary(argument result, Arguments... args)
+{
+    return [=](auto f) { nary_impl(f, result, args...); };
+}
+
+inline auto nary(const argument& result, const argument& arg1, const argument& arg2)
 {
     return [=](auto f) {
-        // assert(x.get_shape().elements() == y.get_shape().elements());
-        const auto& output_shape = result.get_shape();
-        visit_all(result, args...)([&](auto output, auto... inputs) {
-            auto data  = make_sequence(inputs.data()...);
-            auto* outp = output.data();
-            gs_launch(output_shape.elements())(
-                [=](auto i) { data([&](auto... xps) { outp[i] = f(xps[i]...); }); });
-        });
+        // TODO: Check result and arg1 shape is the same
+        if(arg1.get_shape().standard() and arg2.get_shape().broadcasted())
+        {
+            auto not_zero       = [](auto x) { return x != 0; };
+            const auto& strides = arg2.get_shape().strides();
+            auto b_it           = std::find_if(strides.begin(), strides.end(), not_zero);
+            auto b_idx          = std::distance(strides.begin(), b_it);
+            auto b_len          = result.get_shape().lens()[b_idx];
+            auto b_stride       = result.get_shape().strides()[b_idx];
+            assert(arg2.get_shape().lens()[b_idx] == b_len);
+            if(b_len <= 2048 and std::none_of(std::next(b_it), strides.end(), not_zero))
+            {
+                const bool divisible_by_4 = (b_len % 4 == 0) and (b_stride % 4 == 0) and
+                                            (arg1.get_shape().elements() % 4 == 0);
+                if(divisible_by_4)
+                    binary_broadcast_vec_impl(f, result, arg1, arg2);
+                else
+                    binary_broadcast_impl(f, result, arg1, arg2);
+                return;
+            }
+        }
+        nary_impl(f, result, arg1, arg2);
     };
 }
 

src/targets/gpu/device/include/migraph/gpu/device/tensor.hpp

@@ -2,6 +2,7 @@
 #define MIGRAPH_GUARD_RTGLIB_DEAVICE_TENSOR_HPP
 
 #include <hip/hip_runtime.h>
+#include <migraph/functional.hpp>
 
 namespace migraph {
 namespace gpu {
@@ -53,14 +54,13 @@ template <size_t NDim>
 struct hip_tensor_descriptor
 {
     __device__ __host__ hip_tensor_descriptor() = default;
-    template <typename T, typename V>
-    __device__ __host__ hip_tensor_descriptor(const T& lens_ext, const V& strides_ext)
+
+    hip_tensor_descriptor(const shape& s)
     {
-        for(size_t i = 0; i < NDim; i++)
-            lens[i] = lens_ext[i];
-        for(size_t i = 0; i < NDim; i++)
-            strides[i] = strides_ext[i];
+        std::copy(s.lens().begin(), s.lens().end(), lens);
+        std::copy(s.strides().begin(), s.strides().end(), strides);
     }
+
     __device__ __host__ hip_index<NDim> multi(size_t idx) const
     {
         hip_index<NDim> result{};

src/targets/gpu/eliminate_allocation.cpp

@@ -20,7 +20,7 @@ void eliminate_allocation::apply(program& p) const
             continue;
         allocs.emplace_back(ins, n);
         std::size_t size = ins->get_shape().bytes();
-        n += size + (size % 4);
+        n += size + (size % 32);
     }
     auto mem = p.add_parameter("memory", shape{shape::int8_type, {n}});
     for(auto&& pp : allocs)

src/targets/gpu/fuse_ops.cpp

@@ -12,7 +12,7 @@ struct hip_add_relu
     std::string name() const { return "hip::add_relu"; }
     shape compute_shape(const std::vector<shape>& inputs) const
     {
-        check_shapes{inputs}.has(3).standard();
+        check_shapes{inputs, *this}.has(3);
         return inputs.front();
     }
     argument compute(context&, const shape&, const std::vector<argument>& args) const

src/targets/gpu/include/migraph/gpu/device/add.hpp

+
+#ifndef MIGRAPH_GUARD_RTGLIB_DEVICE_ADD_HPP
+#define MIGRAPH_GUARD_RTGLIB_DEVICE_ADD_HPP
+
+#include <migraph/argument.hpp>
+
+namespace migraph {
+namespace gpu {
+namespace device {
+
+void add(const argument& result, const argument& arg1, const argument& arg2);
+
+} // namespace device
+} // namespace gpu
+} // namespace migraph
+
+#endif

src/targets/gpu/include/migraph/gpu/device/add_relu.hpp

@@ -8,7 +8,7 @@ namespace migraph {
 namespace gpu {
 namespace device {
 
-void add_relu(argument result, argument arg1, argument arg2);
+void add_relu(const argument& result, const argument& arg1, const argument& arg2);
 
 } // namespace device
 } // namespace gpu

src/targets/gpu/lowering.cpp

@@ -9,6 +9,7 @@
 #include <migraph/gpu/hip.hpp>
 #include <migraph/dfor.hpp>
 #include <migraph/gpu/device/contiguous.hpp>
+#include <migraph/gpu/device/add.hpp>
 #include <migraph/iterator_for.hpp>
 #include <migraph/gpu/rocblas.hpp>
 #include <migraph/gpu/context.hpp>
@@ -170,6 +171,23 @@ struct miopen_pooling
     }
 };
 
+struct hip_add
+{
+    std::string name() const { return "gpu::add"; }
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        // check_shapes{inputs, *this}.has(3).standard();
+        check_shapes{inputs, *this}.has(3);
+        return inputs.at(0);
+    }
+
+    argument compute(context&, const shape&, const std::vector<argument>& args) const
+    {
+        device::add(args[2], args[0], args[1]);
+        return args[2];
+    }
+};
+
 struct miopen_add
 {
     std::string name() const { return "gpu::add"; }
@@ -204,7 +222,7 @@ struct miopen_add
 struct miopen_gemm
 {
     gemm op;
-    std::string name() const { return "gpu::convolution"; }
+    std::string name() const { return "gpu::gemm"; }
     shape compute_shape(const std::vector<shape>& inputs) const
     {
         check_shapes{inputs, *this}.has(3);
@@ -339,7 +357,7 @@ struct miopen_apply
 
     instruction_ref insert_allocation(instruction_ref ins, const shape& s, std::string tag = "")
     {
-        if(ins == --prog->end())
+        if(ins == --prog->end() and not tag.empty())
         {
             return prog->add_parameter("output", s);
         }
@@ -392,7 +410,7 @@ struct miopen_apply
     {
         auto output = insert_allocation(ins, ins->result);
         return prog->replace_instruction(
-            ins, miopen_add{}, ins->arguments.at(0), ins->arguments.at(1), output);
+            ins, hip_add{}, ins->arguments.at(0), ins->arguments.at(1), output);
     }
 
     instruction_ref apply_gemm(instruction_ref ins)

src/targets/gpu/target.cpp

@@ -11,6 +11,7 @@
 #include <migraph/dead_code_elimination.hpp>
 #include <migraph/simplify_reshapes.hpp>
 #include <migraph/eliminate_contiguous.hpp>
+#include <migraph/fwd_conv_batchnorm_rewrite.hpp>
 
 namespace migraph {
 namespace gpu {
@@ -21,6 +22,8 @@ std::vector<pass> target::get_passes(migraph::context& gctx) const
     // clang-format off
     return
     {
+        dead_code_elimination{},
+        fwd_conv_batchnorm_rewrite{},
         dead_code_elimination{},
         auto_contiguous{},
         simplify_reshapes{},

test/gpu/miopen.cpp

@@ -8,7 +8,7 @@
 #include <migraph/gpu/hip.hpp>
 #include <migraph/manage_ptr.hpp>
 #include <migraph/type_name.hpp>
-#include <migraph/verify.hpp>
+#include <migraph/verify_args.hpp>
 
 #include <miopen/miopen.h>
 
@@ -77,6 +77,12 @@ struct auto_print
 };
 std::array<std::function<void()>, 2> auto_print::handlers = {};
 
+template <class T>
+auto get_hash(const T& x)
+{
+    return std::hash<T>{}(x);
+}
+
 void compile_check(migraph::program& p, const migraph::target& t)
 {
     auto name = t.name();
@@ -100,7 +106,7 @@ migraph::argument run_cpu()
     migraph::program::parameter_map m;
     for(auto&& x : p.get_parameter_shapes())
     {
-        m[x.first] = migraph::generate_argument(x.second);
+        m[x.first] = migraph::generate_argument(x.second, get_hash(x.first));
     }
     return p.eval(m);
 }
@@ -112,52 +118,15 @@ migraph::argument run_gpu()
     auto p = v.create_program();
     auto_print pp{p, 1};
     compile_check(p, migraph::gpu::target{});
-
     migraph::program::parameter_map m;
     for(auto&& x : p.get_parameter_shapes())
     {
-        m[x.first] = migraph::gpu::to_gpu(migraph::generate_argument(x.second));
+        m[x.first] = migraph::gpu::to_gpu(migraph::generate_argument(x.second, get_hash(x.first)));
     }
 
     return migraph::gpu::from_gpu(p.eval(m));
 }
 
-void verify_args(const std::string& name,
-                 const migraph::argument& cpu_arg,
-                 const migraph::argument& gpu_arg)
-{
-    visit_all(cpu_arg, gpu_arg)([&](auto cpu, auto gpu) {
-        if(not migraph::verify_range(cpu, gpu))
-        {
-            // TODO: Check for nans
-            std::cout << "FAILED: " << name << std::endl;
-            // std::cout << cpu << std::endl;
-            // std::cout << gpu << std::endl;
-            if(migraph::range_zero(cpu))
-                std::cout << "Cpu data is all zeros" << std::endl;
-            if(migraph::range_zero(gpu))
-                std::cout << "Gpu data is all zeros" << std::endl;
-
-            auto idx = migraph::mismatch_idx(cpu, gpu, migraph::float_equal);
-            if(idx < migraph::range_distance(cpu))
-            {
-                std::cout << "Mismatch at " << idx << ": " << cpu[idx] << " != " << gpu[idx]
-                          << std::endl;
-            }
-
-            auto cpu_nan_idx = find_idx(cpu, migraph::not_finite);
-            if(cpu_nan_idx >= 0)
-                std::cout << "Non finite number found in cpu at " << cpu_nan_idx << ": "
-                          << cpu[cpu_nan_idx] << std::endl;
-
-            auto gpu_nan_idx = find_idx(gpu, migraph::not_finite);
-            if(gpu_nan_idx >= 0)
-                std::cout << "Non finite number found in gpu at " << gpu_nan_idx << ": "
-                          << gpu[gpu_nan_idx] << std::endl;
-        }
-    });
-}
-
 template <class V>
 void verify_program()
 {
@@ -210,6 +179,62 @@ struct test_add_broadcast
     }
 };
 
+struct test_add_broadcast2
+{
+    migraph::program create_program() const
+    {
+        migraph::program p;
+        migraph::shape s{migraph::shape::float_type, {3}};
+        auto x  = p.add_parameter("x", {migraph::shape::float_type, {2, 3, 4}});
+        auto y  = p.add_parameter("y", {migraph::shape::float_type, {3}});
+        auto by = p.add_instruction(migraph::broadcast{1}, x, y);
+        p.add_instruction(migraph::add{}, x, by);
+        return p;
+    }
+};
+
+struct test_add_broadcast3
+{
+    migraph::program create_program() const
+    {
+        migraph::program p;
+        migraph::shape s{migraph::shape::float_type, {3}};
+        auto x  = p.add_parameter("x", {migraph::shape::float_type, {2, 4, 5}});
+        auto y  = p.add_parameter("y", {migraph::shape::float_type, {4}});
+        auto by = p.add_instruction(migraph::broadcast{1}, x, y);
+        p.add_instruction(migraph::add{}, x, by);
+        return p;
+    }
+};
+
+struct test_add_broadcast4
+{
+    migraph::program create_program() const
+    {
+        migraph::program p;
+        migraph::shape s{migraph::shape::float_type, {3}};
+        auto x  = p.add_parameter("x", {migraph::shape::float_type, {2, 3, 5}});
+        auto y  = p.add_parameter("y", {migraph::shape::float_type, {3}});
+        auto by = p.add_instruction(migraph::broadcast{1}, x, y);
+        p.add_instruction(migraph::add{}, x, by);
+        return p;
+    }
+};
+
+struct test_add_broadcast5
+{
+    migraph::program create_program() const
+    {
+        migraph::program p;
+        migraph::shape s{migraph::shape::float_type, {3}};
+        auto x  = p.add_parameter("x", {migraph::shape::float_type, {2, 4, 8}});
+        auto y  = p.add_parameter("y", {migraph::shape::float_type, {4}});
+        auto by = p.add_instruction(migraph::broadcast{1}, x, y);
+        p.add_instruction(migraph::add{}, x, by);
+        return p;
+    }
+};
+
 struct test_conv_relu
 {
     migraph::program create_program() const
@@ -414,10 +439,49 @@ struct test_conv_bn_relu_pooling
     }
 };
 
+struct test_conv_bn_relu_pooling2
+{
+    static migraph::instruction_ref
+    add_bn(migraph::program& p, migraph::instruction_ref x, std::size_t channels)
+    {
+        migraph::shape vars{migraph::shape::float_type, {channels}};
+        auto scale    = p.add_literal(migraph::abs(migraph::generate_literal(vars, 1 + channels)));
+        auto bias     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 2 + channels)));
+        auto mean     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 3 + channels)));
+        auto variance = p.add_literal(migraph::abs(migraph::generate_literal(vars, 4 + channels)));
+        return p.add_instruction(migraph::batch_norm_inference{}, x, scale, bias, mean, variance);
+    }
+    migraph::program create_program() const
+    {
+        migraph::program p;
+
+        migraph::shape xs1{migraph::shape::float_type, {1, 512, 7, 7}};
+        migraph::shape xs2{migraph::shape::float_type, {1, 1024, 14, 14}};
+        migraph::shape ws1{migraph::shape::float_type, {2048, 512, 1, 1}};
+        migraph::shape ws2{migraph::shape::float_type, {2048, 1024, 1, 1}};
+        auto x1    = p.add_parameter("x1", xs1);
+        auto w1    = p.add_parameter("w1", ws1);
+        auto conv1 = p.add_instruction(migraph::convolution{{0, 0}, {1, 1}, {1, 1}}, x1, w1);
+        auto bn1   = add_bn(p, conv1, 2048);
+        auto x2    = p.add_parameter("x2", xs2);
+        auto w2    = p.add_parameter("w2", ws2);
+        auto conv2 = p.add_instruction(migraph::convolution{{0, 0}, {2, 2}, {1, 1}}, x2, w2);
+        auto bn2   = add_bn(p, conv2, 2048);
+        auto add   = p.add_instruction(migraph::add{}, bn1, bn2);
+        auto relu  = p.add_instruction(migraph::activation{"relu"}, add);
+        p.add_instruction(migraph::pooling{"average", {1, 1}, {2, 2}, {3, 3}}, relu);
+        return p;
+    }
+};
+
 int main()
 {
     verify_program<test_add>();
     verify_program<test_add_broadcast>();
+    verify_program<test_add_broadcast2>();
+    verify_program<test_add_broadcast3>();
+    verify_program<test_add_broadcast4>();
+    verify_program<test_add_broadcast5>();
     verify_program<test_conv_relu>();
     verify_program<test_add_relu>();
     verify_program<test_conv_pooling>();
@@ -431,4 +495,5 @@ int main()
     verify_program<test_batchnorm_inference>();
     verify_program<test_batchnorm_inference_2>();
     verify_program<test_conv_bn_relu_pooling>();
+    verify_program<test_conv_bn_relu_pooling2>();
 }