HiFi optimizations for mean, where, min, max, pow, rem and quantized_linear operators. #6867
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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/backends/cadence/hifi/kernels/kernels.h> | ||
| #include <executorch/kernels/portable/cpu/scalar_utils.h> | ||
| #include <executorch/kernels/portable/cpu/util/broadcast_util.h> | ||
| #include <executorch/kernels/portable/cpu/util/math_util.h> | ||
| #include <executorch/runtime/kernel/kernel_includes.h> | ||
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| using exec_aten::ScalarType; | ||
| using exec_aten::Tensor; | ||
| using executorch::aten::RuntimeContext; | ||
| using executorch::runtime::can_cast; | ||
| using executorch::runtime::canCast; | ||
| using executorch::runtime::CppTypeToScalarType; | ||
| using executorch::runtime::promoteTypes; | ||
| using torch::executor::apply_binary_elementwise_fn; | ||
| using torch::executor::Error; | ||
| using torch::executor::resize_to_broadcast_target_size; | ||
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| namespace impl { | ||
| namespace HiFi { | ||
|
There was a problem hiding this comment. Can the name spaces of all the ops build as To align with other ops? HiFi example: https://github.com/pytorch/executorch/blob/main/backends/cadence/hifi/operators/op_add.cpp#L25 |
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| namespace native { | ||
| namespace { | ||
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| template < | ||
| bool can_cast, | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct MaximumInner; | ||
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| template < | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct MaximumInner<true, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> { | ||
| static void run(const Tensor& a, const Tensor& b, Tensor& out) { | ||
| apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>( | ||
| // NOLINTNEXTLINE(facebook-hte-ConstantArgumentPassByValue) | ||
| [](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); | ||
| CTYPE_IN value = | ||
| torch::executor::native::utils::max_override(a_casted, b_casted); | ||
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| return static_cast<CTYPE_OUT>(value); | ||
| }, | ||
| a, | ||
| b, | ||
| out); | ||
| } | ||
| }; | ||
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| struct ReportCanCastBug { | ||
| static void run(const Tensor&, const Tensor&, Tensor&) { | ||
| ET_DCHECK_MSG(false, "BUG: canCast should have been checked above"); | ||
| } | ||
| }; | ||
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| template < | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct MaximumInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> | ||
| : public ReportCanCastBug {}; | ||
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| } // namespace | ||
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| Tensor& maximum_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Tensor& b, | ||
| Tensor& out) { | ||
| (void)ctx; | ||
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| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_to_broadcast_target_size(a, b, out) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
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| constexpr int kNnlibMaxDim = 4; /*fallback if broadcast and dim > 4 */ | ||
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| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = b.scalar_type(); | ||
| ScalarType common_type = promoteTypes(a_type, b_type, /*half_to_float*/ true); | ||
| ScalarType out_type = out.scalar_type(); | ||
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| ET_KERNEL_CHECK(ctx, canCast(common_type, out_type), InvalidArgument, out); | ||
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| bool optimized = true; | ||
| /*find broadcast*/ | ||
| bool a_is_broadcasted = !out.sizes().equals(a.sizes()); | ||
| bool b_is_broadcasted = !out.sizes().equals(b.sizes()); | ||
| bool broadcast = (a_is_broadcasted || b_is_broadcasted); | ||
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| 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 ((a_type != ScalarType::Float) || (b_type != ScalarType::Float)) | ||
| optimized = false; | ||
| if ((broadcast == true) && (max_dim > kNnlibMaxDim)) | ||
| optimized = false; | ||
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| if (optimized) { | ||
| float* a_data = a.mutable_data_ptr<float>(); | ||
| float* b_data = b.mutable_data_ptr<float>(); | ||
| float* out_data = out.mutable_data_ptr<float>(); | ||
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| if (broadcast == true) { | ||
| int out_shape[kNnlibMaxDim]; | ||
| int inp1_shape[kNnlibMaxDim]; | ||
| int inp2_shape[kNnlibMaxDim]; | ||
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| for (int i = 0; i < kNnlibMaxDim; i++) { | ||
| out_shape[i] = 1; | ||
| inp1_shape[i] = 1; | ||
| inp2_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); | ||
| } | ||
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| for (int i = 0; i < a.dim(); i++) | ||
| inp1_shape[i + off_a] = a.size(i); | ||
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| for (int i = 0; i < b.dim(); i++) | ||
| inp2_shape[i + off_b] = b.size(i); | ||
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| xa_nn_elm_maximum_broadcast_4D_f32xf32_f32( | ||
| out_data, out_shape, a_data, inp1_shape, b_data, inp2_shape); | ||
| } else { | ||
| xa_nn_elm_maximum_f32xf32_f32(out_data, a_data, b_data, out.numel()); | ||
| } | ||
| return out; | ||
| } | ||
| ET_SWITCH_REALHB_TYPES(a_type, ctx, "maximum.out", CTYPE_A, [&]() { | ||
| ET_SWITCH_REALHB_TYPES(b_type, ctx, "maximum.out", CTYPE_B, [&]() { | ||
| using CTYPE_IN = typename torch::executor:: | ||
| promote_types<CTYPE_A, CTYPE_B, /*half_to_float*/ true>::type; | ||
| ET_DCHECK(CppTypeToScalarType<CTYPE_IN>::value == common_type); | ||
| ET_SWITCH_REALHB_TYPES(out_type, ctx, "maximum.out", CTYPE_OUT, [&]() { | ||
| MaximumInner< | ||
| can_cast<CTYPE_IN, CTYPE_OUT>::value, | ||
| CTYPE_A, | ||
| CTYPE_B, | ||
| CTYPE_IN, | ||
| CTYPE_OUT>::run(a, b, out); | ||
| }); | ||
| }); | ||
| }); | ||
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| return out; | ||
| } | ||
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| } // namespace native | ||
| } // namespace HiFi | ||
| } // namespace impl | ||
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Please change all kernel_names here to match cadence::impl::HiFi::native::OP_NAME. For example: cadence::impl::HiFi::native::add_out