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Adding permute_copy operator kernel optimization
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| Original file line number | Diff line number | Diff line change |
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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/copy_ops_util.h> | ||
| #include <executorch/runtime/kernel/kernel_includes.h> | ||
| #include <executorch/backends/cadence/hifi/kernels/kernels.h> | ||
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| using exec_aten::SizesType; | ||
| using exec_aten::ScalarType; | ||
| using exec_aten::Tensor; | ||
| using executorch::runtime::IntArrayRef; | ||
| using torch::executor::Error; | ||
| using executorch::runtime::KernelRuntimeContext; | ||
| using executorch::runtime::kTensorDimensionLimit; | ||
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| namespace impl { | ||
| namespace HiFi { | ||
| namespace native { | ||
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| namespace { | ||
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| void increment_coordinate_permuted( | ||
| const Tensor& tensor, | ||
| size_t* const coordinate, | ||
| IntArrayRef dims) { | ||
| for (int i = dims.size() - 1; i >= 0; i--) { | ||
| size_t d = dims[i] >= 0 ? dims[i] : dims[i] + tensor.dim(); | ||
| coordinate[d]++; | ||
| if (coordinate[d] == tensor.size(d)) { | ||
| coordinate[d] = 0; | ||
| } else { | ||
| return; | ||
| } | ||
| } | ||
| } | ||
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| } // namespace | ||
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| Tensor& permute_copy_out( | ||
| KernelRuntimeContext& ctx, | ||
| const Tensor& in, | ||
| IntArrayRef dims, | ||
| Tensor& out) { | ||
| (void)ctx; | ||
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| ET_KERNEL_CHECK( | ||
| ctx, check_permute_copy_args(in, dims, out), InvalidArgument, out); | ||
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| ET_KERNEL_CHECK( | ||
| ctx, tensors_have_same_dim_order(in, out), InvalidArgument, out); | ||
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| Tensor::SizesType expected_out_size[kTensorDimensionLimit]; | ||
| size_t expected_out_dim = 0; | ||
| get_permute_copy_out_target_size( | ||
| in, dims, expected_out_size, &expected_out_dim); | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_tensor(out, {expected_out_size, expected_out_dim}) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
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| const auto in_type = out.scalar_type(); | ||
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| constexpr auto name = "permute_copy.out"; | ||
| constexpr int kNnlibMaxDim = 16; | ||
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| bool optimized = 0; | ||
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| if(out.scalar_type() == ScalarType::Float) | ||
| optimized = 1; | ||
| else if(out.scalar_type() == ScalarType::Char) | ||
| optimized = 1; | ||
| else if(out.scalar_type() == ScalarType::Byte) | ||
| optimized = 1; | ||
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| if(in.dim() > kNnlibMaxDim) | ||
| optimized = 0; | ||
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| if(optimized){ | ||
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| if(in_type == ScalarType::Float) | ||
| { | ||
| WORD32 * p_inp = (WORD32 *)in.const_data_ptr<float>(); | ||
| WORD32 * p_out = (WORD32 *)out.mutable_data_ptr<float>(); | ||
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| WORD32 num_inp_dims = in.dim(); | ||
| WORD32 num_out_dims = num_inp_dims; | ||
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| WORD32 p_inp_shape[kNnlibMaxDim]; | ||
| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_permute_vec[kNnlibMaxDim]; | ||
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| for(int i = 0; i < num_inp_dims; i++) | ||
| { | ||
| p_inp_shape[i] = in.size(i); | ||
| p_out_shape[i] = in.size(dims[i]); | ||
| p_permute_vec[i] = dims[i]; | ||
| } | ||
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| xa_nn_transpose_32_32(p_out, | ||
| p_out_shape, | ||
| p_inp, | ||
| p_inp_shape, | ||
| p_permute_vec, | ||
| num_out_dims, | ||
| num_inp_dims); | ||
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| return out; | ||
| } | ||
| else if(in_type == ScalarType::Char) | ||
| { | ||
| WORD8 * p_inp = (WORD8 *)in.const_data_ptr<char>(); | ||
| WORD8 * p_out = (WORD8 *)out.mutable_data_ptr<char>(); | ||
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| WORD32 num_inp_dims = in.dim(); | ||
| WORD32 num_out_dims = num_inp_dims; | ||
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| WORD32 p_inp_shape[kNnlibMaxDim]; | ||
| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_permute_vec[kNnlibMaxDim]; | ||
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| for(int i = 0; i < num_inp_dims; i++) | ||
| { | ||
| p_inp_shape[i] = in.size(i); | ||
| p_out_shape[i] = in.size(dims[i]); | ||
| p_permute_vec[i] = dims[i]; | ||
| } | ||
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| xa_nn_transpose_8_8(p_out, | ||
| p_out_shape, | ||
| p_inp, | ||
| p_inp_shape, | ||
| p_permute_vec, | ||
| num_out_dims, | ||
| num_inp_dims); | ||
|
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| return out; | ||
| } | ||
| else if(in_type == ScalarType::Byte) | ||
| { | ||
| WORD8 * p_inp = (WORD8 *)in.const_data_ptr<uint8_t>(); | ||
| WORD8 * p_out = (WORD8 *)out.mutable_data_ptr<uint8_t>(); | ||
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| WORD32 num_inp_dims = in.dim(); | ||
| WORD32 num_out_dims = num_inp_dims; | ||
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| WORD32 p_inp_shape[kNnlibMaxDim]; | ||
| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_permute_vec[kNnlibMaxDim]; | ||
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| for(int i = 0; i < num_inp_dims; i++) | ||
| { | ||
| p_inp_shape[i] = in.size(i); | ||
| p_out_shape[i] = in.size(dims[i]); | ||
| p_permute_vec[i] = dims[i]; | ||
| } | ||
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| xa_nn_transpose_8_8(p_out, | ||
| p_out_shape, | ||
| p_inp, | ||
| p_inp_shape, | ||
| p_permute_vec, | ||
| num_out_dims, | ||
| num_inp_dims); | ||
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| return out; | ||
| } | ||
| } | ||
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| size_t in_coord[kTensorDimensionLimit] = {0}; | ||
| size_t trailing_dims_memo[kTensorDimensionLimit]; | ||
| executorch::runtime::memoizeTrailingDims(in, trailing_dims_memo); | ||
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| // in and out must be the same dtype | ||
| ET_SWITCH_ALL_TYPES(in_type, ctx, name, CTYPE, [&] { | ||
| const CTYPE* const in_data = in.const_data_ptr<CTYPE>(); | ||
| CTYPE* const out_data = out.mutable_data_ptr<CTYPE>(); | ||
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| for (size_t i = 0; i < out.numel(); ++i) { | ||
| out_data[i] = | ||
| in_data[executorch::runtime::coordinateToIndexWithTrailingDimsMemo( | ||
| in, in_coord, trailing_dims_memo)]; | ||
| increment_coordinate_permuted(in, in_coord, dims); | ||
| } | ||
| }); | ||
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| return out; | ||
| } | ||
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| } // namespace native | ||
| } // namespace HiFi | ||
| } // namespace impl |
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