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[AUTOGENERATED] [leo/test-ci] [ROCm] Specialized binary elementwise broadcast kernel for mixed dtypes with float/bfloat16/half #2849

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[AUTOGENERATED] [leo/test-ci] [ROCm] Specialized binary elementwise broadcast kernel for mixed dtypes with float/bfloat16/half #2849

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120 changes: 120 additions & 0 deletions aten/src/ATen/native/cuda/CUDALoops.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -893,6 +893,69 @@ struct type_specialized_kernel_launcher {
}
};

template <int arg_index>
struct type_specialized_broadcast_kernel_launcher {
template <
typename func_t,
typename array_t,
typename dtypes_t,
typename calc_t>
static void apply(
int64_t numel,
func_t f,
array_t data,
dtypes_t dtypes,
calc_t offset_calc) {
using traits = function_traits<func_t>;
using ret_t = typename traits::result_type;
using arg0_t = typename traits::template arg<0>::type;
using arg1_t = typename traits::template arg<1>::type;
if (dtypes[0] == rt_binary_specializations[arg_index][0] &&
dtypes[1] == rt_binary_specializations[arg_index][1] &&
dtypes[2] == rt_binary_specializations[arg_index][2]) {
using ret_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][0]>;
using arg0_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][1]>;
using arg1_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][2]>;
constexpr int grp_sz = 128;
launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
if (unrl) {
auto offsets0 = offset_calc.get(idx);
auto offsets1 = offset_calc.get(idx + grp_sz);
auto offsets2 = offset_calc.get(idx + grp_sz * 2);
auto offsets3 = offset_calc.get(idx + grp_sz * 3);
void* out0 = data[0] + offsets0[0];
void* out1 = data[0] + offsets1[0];
void* out2 = data[0] + offsets2[0];
void* out3 = data[0] + offsets3[0];
auto u = c10::load<arg0_cpp_t>(data[1] + offsets0[1]);
auto v = c10::load<arg1_cpp_t>(data[2] + offsets0[2]);
ret_t result0 = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
auto u1 = c10::load<arg0_cpp_t>(data[1] + offsets1[1]);
auto v1 = c10::load<arg1_cpp_t>(data[2]+ offsets1[2]);
ret_t result1 = f(c10::convert<arg0_t>(u1), c10::convert<arg1_t>(v1));
auto u2 = c10::load<arg0_cpp_t>(data[1] + offsets2[1]);
auto v2 = c10::load<arg1_cpp_t>(data[2] + offsets2[2]);
ret_t result2 = f(c10::convert<arg0_t>(u2), c10::convert<arg1_t>(v2));
auto u3 = c10::load<arg0_cpp_t>(data[1] + offsets3[1]);
auto v3 = c10::load<arg1_cpp_t>(data[2] + offsets3[2]);
ret_t result3 = f(c10::convert<arg0_t>(u3), c10::convert<arg1_t>(v3));
*(ret_cpp_t*)out0 = c10::convert<ret_cpp_t>(result0);
*(ret_cpp_t*)out1 = c10::convert<ret_cpp_t>(result1);
*(ret_cpp_t*)out2 = c10::convert<ret_cpp_t>(result2);
*(ret_cpp_t*)out3 = c10::convert<ret_cpp_t>(result3);
} else {
auto offsets = offset_calc.get(idx);
void* out = data[0] + offsets[0];
auto u = c10::load<arg0_cpp_t>(data[1] + offsets[1]);
auto v = c10::load<arg1_cpp_t>(data[2] + offsets[2]);
ret_t result = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
*(ret_cpp_t*)out = c10::convert<ret_cpp_t>(result);
}
});
}
}
};

} // namespace
#endif

Expand Down Expand Up @@ -994,6 +1057,63 @@ void gpu_kernel_impl(TensorIteratorBase& iter, const func_t& f) {
dtypes[i] = iter.dtype(i);
}
auto offset_calc = ::make_offset_calculator<traits::arity + 1>(iter);
<<<<<<< HEAD
=======
#ifdef USE_ROCM
if (check_binary_rt_types_for_specialization(iter)) {
// constexpr to reduce the amount of kernels generated for
// broadcast elementwise with mexed dtypes and limit which functors are actually
// applied to the load and store at compile time.
using func_tuple = typename traits::ArgsTuple;
if constexpr (
std::is_same_v<float, arg0_t> && traits::arity == 2 &&
check_binary_functor_types_for_specialization<
func_tuple,
float,
float,
traits::arity,
/*arg_num=*/0>::check()) {
memory::detail::static_unroll<
type_specialized_broadcast_kernel_launcher,
rt_binary_specializations.size()>::with_args(
numel,
f,
data,
dtypes,
offset_calc
);
return;
}
}

constexpr int grp_sz = 128;
launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
if (unrl) {
auto offsets0 = offset_calc.get(idx);
auto offsets1 = offset_calc.get(idx + grp_sz);
auto offsets2 = offset_calc.get(idx + grp_sz * 2);
auto offsets3 = offset_calc.get(idx + grp_sz * 3);
void* out0 = data[0] + offsets0[0];
void* out1 = data[0] + offsets1[0];
void* out2 = data[0] + offsets2[0];
void* out3 = data[0] + offsets3[0];
arg0_t result0 = invoke(f, &data[1], &offsets0[1], &dtypes[1], 1);
arg0_t result1 = invoke(f, &data[1], &offsets1[1], &dtypes[1], 1);
arg0_t result2 = invoke(f, &data[1], &offsets2[1], &dtypes[1], 1);
arg0_t result3 = invoke(f, &data[1], &offsets3[1], &dtypes[1], 1);
c10::cast_and_store<arg0_t>(dtypes[0], out0, result0);
c10::cast_and_store<arg0_t>(dtypes[0], out1, result1);
c10::cast_and_store<arg0_t>(dtypes[0], out2, result2);
c10::cast_and_store<arg0_t>(dtypes[0], out3, result3);
} else {
auto offsets = offset_calc.get(idx);
void* out = data[0] + offsets[0];
arg0_t result = invoke(f, &data[1], &offsets[1], &dtypes[1], 1);
c10::cast_and_store<arg0_t>(dtypes[0], out, result);
}
});
#else
>>>>>>> 5729657180 ([ROCm] Specialized binary elementwise broadcast kernel for mixed dtypes with float/bfloat16/half (#2791))
launch_legacy_kernel<128, 4>(numel, [=] GPU_LAMBDA(int idx) {
auto offsets = offset_calc.get(idx);
void* out = data[0] + offsets[0];
Expand Down