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* Adding mean operator kernel optimization * Merge from main * Adding compare operators kernel optimization * Code cleanup --------- Co-authored-by: dijopaul <[email protected]>
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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/scalar_utils.h> | ||
| #include <executorch/kernels/portable/cpu/util/broadcast_util.h> | ||
| #include <executorch/kernels/portable/cpu/util/functional_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::Scalar; | ||
| using exec_aten::ScalarType; | ||
| using exec_aten::Tensor; | ||
| using executorch::aten::RuntimeContext; | ||
| using executorch::runtime::CppTypeToScalarType; | ||
| using torch::executor::Error; | ||
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| namespace impl { | ||
| namespace HiFi { | ||
| namespace native { | ||
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| Tensor& eq_tensor_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Tensor& b, | ||
| Tensor& out) { | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_to_broadcast_target_size(a, b, out) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
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| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = b.scalar_type(); | ||
| ScalarType out_type = out.scalar_type(); | ||
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| constexpr auto name = "eq.Tensor_out"; | ||
| constexpr int kNnlibMaxDim = 4; /*fallback if broadcast and dim > 4 */ | ||
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| int a_dim = a.dim(), b_dim = b.dim(), out_dim = out.dim(); | ||
| bool optimized = 1; | ||
| /*find broadcast*/ | ||
| const bool a_is_broadcasted = !out.sizes().equals(a.sizes()); | ||
| const bool b_is_broadcasted = !out.sizes().equals(b.sizes()); | ||
| const bool broadcast = (a_is_broadcasted || b_is_broadcasted); | ||
| int max_dim = a.dim() > b.dim() ? a.dim() : b.dim(); | ||
| max_dim = out.dim() > max_dim ? out.dim() : max_dim; | ||
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| if (out_type != ScalarType::Float) | ||
| optimized = 0; | ||
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| if ((a_dim == 0) || (b_dim == 0)) | ||
| optimized = 0; | ||
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| if ((broadcast == 1) && (max_dim > kNnlibMaxDim)) | ||
| optimized = 0; | ||
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| if (optimized) { | ||
| int8_t* __restrict__ p_out = | ||
| (int8_t* __restrict__)out.mutable_data_ptr<int8_t>(); | ||
| const float* __restrict__ p_inp1 = | ||
| (const float* __restrict__)a.const_data_ptr<float>(); | ||
| const float* __restrict__ p_inp2 = | ||
| (const float* __restrict__)b.const_data_ptr<float>(); | ||
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| if (broadcast) { | ||
| int out_shape[kNnlibMaxDim]; | ||
| int inp1_shape[kNnlibMaxDim]; | ||
| int inp2_shape[kNnlibMaxDim]; | ||
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| for (int i = 0; i < kNnlibMaxDim; i++) { | ||
| inp1_shape[i] = 1; | ||
| inp2_shape[i] = 1; | ||
| out_shape[i] = 1; | ||
| } | ||
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| int off_o = kNnlibMaxDim - out.dim(); | ||
| int off_a = kNnlibMaxDim - a.dim(); | ||
| int off_b = kNnlibMaxDim - b.dim(); | ||
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| for (int i = 0; i < out.dim(); i++) | ||
| out_shape[i + off_o] = out.size(i); | ||
| for (int i = 0; i < a.dim(); i++) | ||
| inp1_shape[i + off_a] = a.size(i); | ||
| for (int i = 0; i < b.dim(); i++) | ||
| inp2_shape[i + off_b] = b.size(i); | ||
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| xa_nn_elm_greater_lesser_equal_broadcast_4D_f32xf32_f32( | ||
| p_out, out_shape, p_inp1, inp1_shape, p_inp2, inp2_shape, 4); | ||
| } else { | ||
| int num_elm = out.numel(); | ||
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| xa_nn_elm_greater_lesser_equal_f32xf32_f32( | ||
| p_out, p_inp1, p_inp2, num_elm, 4); | ||
| } | ||
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| return out; | ||
| } | ||
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| ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_REAL_TYPES_AND(Bool, b_type, ctx, name, CTYPE_B, [&]() { | ||
| using CTYPE_IN = | ||
| typename torch::executor::promote_types<CTYPE_A, CTYPE_B>::type; | ||
| ET_DCHECK( | ||
| CppTypeToScalarType<CTYPE_IN>::value == promoteTypes(a_type, b_type)); | ||
| ET_SWITCH_REAL_TYPES_AND(Bool, out_type, ctx, name, CTYPE_OUT, [&]() { | ||
| torch::executor:: | ||
| apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>( | ||
| [](const CTYPE_A val_a, const CTYPE_B val_b) { | ||
| CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a); | ||
| CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b); | ||
| bool value = a_casted == b_casted; | ||
| return static_cast<CTYPE_OUT>(value); | ||
| }, | ||
| a, | ||
| b, | ||
| out); | ||
| }); | ||
| }); | ||
| }); | ||
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| return out; | ||
| } | ||
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| Tensor& eq_scalar_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Scalar& b, | ||
| Tensor& out) { | ||
| (void)ctx; | ||
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| // Resize for dynamic shape | ||
| ET_KERNEL_CHECK_MSG( | ||
| ctx, | ||
| resize_tensor(out, a.sizes()) == Error::Ok, | ||
| InvalidArgument, | ||
| out, | ||
| "Failed to resize output tensor."); | ||
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| constexpr auto name = "eq.Scalar_out"; | ||
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| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = torch::executor::native::utils::get_scalar_dtype(b); | ||
| ScalarType out_type = out.scalar_type(); | ||
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| ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_SCALAR_OBJ_TYPES(b_type, ctx, name, CTYPE_B, [&]() { | ||
| using CTYPE_IN = | ||
| typename torch::executor::promote_types<CTYPE_A, CTYPE_B>::type; | ||
| ET_DCHECK( | ||
| CppTypeToScalarType<CTYPE_IN>::value == promoteTypes(a_type, b_type)); | ||
| ET_SWITCH_REAL_TYPES_AND(Bool, out_type, ctx, name, CTYPE_OUT, [&]() { | ||
| CTYPE_B val_b = 0; | ||
| torch::executor::native::utils::extract_scalar(b, &val_b); | ||
| torch::executor::apply_unary_map_fn( | ||
| [val_b](const CTYPE_A val_a) { | ||
| CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a); | ||
| CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b); | ||
| bool value = a_casted == b_casted; | ||
| return static_cast<CTYPE_OUT>(value); | ||
| }, | ||
| a.const_data_ptr<CTYPE_A>(), | ||
| out.mutable_data_ptr<CTYPE_OUT>(), | ||
| out.numel()); | ||
| }); | ||
| }); | ||
| }); | ||
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| return out; | ||
| } | ||
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| } // namespace native | ||
| } // namespace HiFi | ||
| } // namespace impl |
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