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Adding mean and where ops optimized on HiFi
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| /* | ||
| * Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| * All rights reserved. | ||
| * | ||
| * This source code is licensed under the BSD-style license found in the | ||
| * LICENSE file in the root directory of this source tree. | ||
| */ | ||
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| #include <executorch/kernels/portable/cpu/util/kernel_ops_util.h> | ||
| #include <executorch/kernels/portable/cpu/util/reduce_util.h> | ||
| #include <executorch/runtime/kernel/kernel_includes.h> | ||
| #include <executorch/runtime/platform/assert.h> | ||
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| #include <executorch/backends/cadence/hifi/kernels/kernels.h> | ||
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| using exec_aten::ScalarType; | ||
| using exec_aten::Tensor; | ||
| using executorch::aten::RuntimeContext; | ||
| using executorch::runtime::ArrayRef; | ||
| using torch::executor::Error; | ||
| using torch::executor::optional; | ||
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| namespace cadence { | ||
| namespace impl { | ||
| namespace HiFi { | ||
| namespace native { | ||
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| int prepare_data( | ||
| const Tensor& in, | ||
| Tensor& out, | ||
| optional<ArrayRef<int64_t>> dim_list, | ||
| int* inp_shape, | ||
| int* out_shape, | ||
| int* p_axis, | ||
| int num_inp_dims, | ||
| int num_out_dims) { | ||
| for (int i = 0; i < num_inp_dims; i++) { | ||
| inp_shape[i] = in.size(i); | ||
| } | ||
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| for (int i = 0; i < num_out_dims; i++) { | ||
| out_shape[i] = out.size(i); | ||
| } | ||
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| int num_axis_dims = 0; | ||
| for (const auto& d : dim_list.value()) { | ||
| if (d < 0) { | ||
| p_axis[num_axis_dims] = num_inp_dims + d; | ||
| num_axis_dims++; | ||
| } else { | ||
| p_axis[num_axis_dims] = d; | ||
| num_axis_dims++; | ||
| } | ||
| } | ||
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| return num_axis_dims; | ||
| } | ||
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| Tensor& mean_dim_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& in, | ||
| optional<ArrayRef<int64_t>> dim_list, | ||
| bool keepdim, | ||
| optional<ScalarType> dtype, | ||
| Tensor& out) { | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| torch::executor::check_mean_dim_args(in, dim_list, keepdim, dtype, out), | ||
| InvalidArgument, | ||
| out); | ||
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| ET_KERNEL_CHECK( | ||
| ctx, | ||
| torch::executor::resize_reduction_out(in, dim_list, keepdim, out) == | ||
| Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
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| constexpr auto name = "mean.out"; | ||
| constexpr int kNnlibMaxDim = 4; | ||
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| bool optimized = 1; | ||
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| if (out.scalar_type() != ScalarType::Float) | ||
| optimized = 0; | ||
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| if (in.dim() > kNnlibMaxDim) | ||
| optimized = 0; | ||
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| if (optimized) { | ||
| float* __restrict__ p_out = out.mutable_data_ptr<float>(); | ||
| const float* __restrict__ p_inp = | ||
| (const float* __restrict__)in.const_data_ptr<float>(); | ||
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| int num_elm = in.numel(); | ||
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| int num_inp_dims = in.dim(); | ||
| int num_out_dims = out.dim(); | ||
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| int inp_shape[kNnlibMaxDim]; | ||
| int out_shape[kNnlibMaxDim]; | ||
| int p_axis[kNnlibMaxDim]; | ||
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| for (int i = 0; i < kNnlibMaxDim; i++) { | ||
| out_shape[i] = 1; | ||
| inp_shape[i] = 1; | ||
| p_axis[i] = 1; | ||
| } | ||
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| int num_axis_dims = prepare_data( | ||
| in, | ||
| out, | ||
| dim_list, | ||
| inp_shape, | ||
| out_shape, | ||
| p_axis, | ||
| num_inp_dims, | ||
| num_out_dims); | ||
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| if (num_axis_dims == num_inp_dims) { | ||
| num_out_dims = 1; | ||
| out_shape[0] = 1; | ||
| } | ||
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| int scratch_size = xa_nn_reduce_getsize_nhwc( | ||
| -3, inp_shape, num_inp_dims, p_axis, num_axis_dims, 1); | ||
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| void* __restrict__ p_scratch_in = (void* __restrict__)malloc(scratch_size); | ||
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| xa_nn_reduce_mean_4D_f32_f32( | ||
| p_out, | ||
| out_shape, | ||
| p_inp, | ||
| inp_shape, | ||
| p_axis, | ||
| num_out_dims, | ||
| num_inp_dims, | ||
| num_axis_dims, | ||
| p_scratch_in); | ||
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| return out; | ||
| } | ||
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| ET_SWITCH_REALHB_TYPES(in.scalar_type(), ctx, name, CTYPE_IN, [&] { | ||
| ET_SWITCH_FLOATH_TYPES(out.scalar_type(), ctx, name, CTYPE_OUT, [&] { | ||
| CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>(); | ||
| const size_t num = torch::executor::get_reduced_dim_product(in, dim_list); | ||
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| for (size_t out_ix = 0; out_ix < out.numel(); ++out_ix) { | ||
| CTYPE_OUT sum = 0; | ||
| if (in.numel() > 0) { | ||
| sum = torch::executor::map_reduce_over_dim_list<CTYPE_IN, CTYPE_OUT>( | ||
| [](CTYPE_IN v) { return static_cast<CTYPE_OUT>(v); }, | ||
| [](CTYPE_OUT outv, CTYPE_OUT acc) { return acc + outv; }, | ||
| in, | ||
| dim_list, | ||
| out_ix); | ||
| } | ||
| out_data[out_ix] = sum / static_cast<float>(num); | ||
| } | ||
| }); | ||
| }); | ||
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| return out; | ||
| } | ||
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| } // namespace native | ||
| } // namespace HiFi | ||
| } // namespace impl | ||
| } // namespace cadence |
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