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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "unpack_bits_function.h"
#include <vespa/eval/eval/operation.h>
#include <vespa/eval/eval/value.h>
#include <vespa/eval/eval/node_tools.h>
#include <vespa/eval/eval/basic_nodes.h>
#include <vespa/eval/eval/operator_nodes.h>
#include <vespa/eval/eval/call_nodes.h>
#include <vespa/eval/eval/tensor_nodes.h>
#include <vespa/eval/eval/wrap_param.h>
#include <cassert>
namespace vespalib::eval {
using namespace vespalib::eval::nodes;
using tensor_function::Lambda;
using tensor_function::MapSubspaces;
using tensor_function::wrap_param;
using tensor_function::unwrap_param;
using tensor_function::inject;
namespace {
//-----------------------------------------------------------------------------
template <typename OCT, bool big>
void my_unpack_bits_op(InterpretedFunction::State &state, uint64_t param) {
const ValueType &res_type = unwrap_param<ValueType>(param);
auto packed_cells = state.peek(0).cells().typify<Int8Float>();
auto unpacked_cells = state.stash.create_uninitialized_array<OCT>(packed_cells.size() * 8);
OCT *dst = unpacked_cells.begin();
for (Int8Float cell: packed_cells) {
if constexpr (big) {
for (int n = 7; n >= 0; --n) {
*dst++ = (OCT) bool(cell.get_bits() & (1 << n));
}
} else {
for (int n = 0; n <= 7; ++n) {
*dst++ = (OCT) bool(cell.get_bits() & (1 << n));
}
}
}
Value &result_ref = state.stash.create<ValueView>(res_type, state.peek(0).index(), TypedCells(unpacked_cells));
state.pop_push(result_ref);
}
//-----------------------------------------------------------------------------
struct MyGetFun {
template <typename OCT, typename BIG> static auto invoke() {
return my_unpack_bits_op<OCT, BIG::value>;
}
};
using MyTypify = TypifyValue<TypifyCellType,TypifyBool>;
//-----------------------------------------------------------------------------
bool compatible_types(const ValueType &packed, const ValueType &unpacked) {
const auto &pdims = packed.dimensions();
const auto &udims = unpacked.dimensions();
if ((pdims.size() > 0) &&
(packed.cell_type() == CellType::INT8) &&
(packed.is_dense() && unpacked.is_dense()) &&
(pdims.size() == udims.size()))
{
for (size_t i = 0; i < pdims.size() - 1; ++i) {
if (udims[i].size != pdims[i].size) {
return false;
}
}
return udims.back().size == (pdims.back().size * 8);
}
return false;
}
bool is_little_bit_expr(const Node &node, size_t wanted_param) {
// 'x%8'
if (auto mod = as<Mod>(node)) {
if (auto param = as<Symbol>(mod->lhs())) {
if (auto eight = as<Number>(mod->rhs())) {
return ((param->id() == wanted_param) && (eight->value() == 8.0));
}
}
}
return false;
}
bool is_big_bit_expr(const Node &node, size_t wanted_param) {
// '7-(x%8)'
if (auto sub = as<Sub>(node)) {
if (auto seven = as<Number>(sub->lhs())) {
return ((seven->value() == 7.0) && is_little_bit_expr(sub->rhs(), wanted_param));
}
}
return false;
}
bool is_ident_expr(const Node &node, size_t wanted_param) {
// 'x'
if (auto param = as<Symbol>(node)) {
return (param->id() == wanted_param);
}
return false;
}
bool is_byte_expr(const Node &node, size_t wanted_param) {
// 'x/8'
if (auto div = as<Div>(node)) {
if (auto param = as<Symbol>(div->lhs())) {
if (auto eight = as<Number>(div->rhs())) {
return ((param->id() == wanted_param) && (eight->value() == 8.0));
}
}
}
return false;
}
bool is_byte_peek(const TensorPeek &peek, size_t dim_cnt) {
if (auto param = as<Symbol>(peek.param())) {
if ((dim_cnt > 0) &&
(param->id() == dim_cnt) &&
(peek.dim_list().size() == dim_cnt) &&
(peek.num_children() == (dim_cnt + 1)))
{
for (size_t i = 0; i < dim_cnt - 1; ++i) {
if (!is_ident_expr(peek.get_child(i + 1), i)) {
return false;
}
}
return is_byte_expr(peek.get_child(dim_cnt), dim_cnt - 1);
}
}
return false;
}
//-----------------------------------------------------------------------------
struct Result {
const bool is_unpack_bits;
const bool is_big_bitorder;
const ValueType &src_type;
};
Result detect_unpack_bits(const ValueType &dst_type,
size_t num_bindings,
const Function &lambda,
const NodeTypes &types)
{
size_t dim_cnt = dst_type.count_indexed_dimensions();
if ((num_bindings == 1) && (lambda.num_params() == (dim_cnt + 1))) {
if (auto bit = as<Bit>(lambda.root())) {
if (auto peek = as<TensorPeek>(bit->get_child(0))) {
const ValueType &src_type = types.get_type(peek->param());
if (compatible_types(src_type, dst_type) && is_byte_peek(*peek, dim_cnt)) {
assert(dim_cnt > 0);
if (is_big_bit_expr(bit->get_child(1), dim_cnt - 1)) {
return {true, true, src_type};
} else if (is_little_bit_expr(bit->get_child(1), dim_cnt - 1)) {
return {true, false, src_type};
}
}
}
}
}
return {false, false, dst_type};
}
} // namespace <unnamed>
UnpackBitsFunction::UnpackBitsFunction(const ValueType &res_type_in,
const TensorFunction &packed,
bool big_bitorder)
: Op1(res_type_in, packed),
_big_bitorder(big_bitorder)
{
}
InterpretedFunction::Instruction
UnpackBitsFunction::compile_self(const CTFContext &) const
{
const ValueType &res_type = result_type();
auto op = typify_invoke<2,MyTypify,MyGetFun>(res_type.cell_type(), _big_bitorder);
return InterpretedFunction::Instruction(op, wrap_param<ValueType>(res_type));
}
const TensorFunction &
UnpackBitsFunction::optimize(const TensorFunction &expr, Stash &stash)
{
if (auto lambda = as<Lambda>(expr)) {
auto result = detect_unpack_bits(lambda->result_type(), lambda->bindings().size(), lambda->lambda(), lambda->types());
if (result.is_unpack_bits) {
assert(lambda->bindings().size() == 1);
const TensorFunction &input = inject(result.src_type, lambda->bindings()[0], stash);
return stash.create<UnpackBitsFunction>(lambda->result_type(), input, result.is_big_bitorder);
}
}
if (auto map_subspaces = as<MapSubspaces>(expr)) {
if (auto lambda = as<TensorLambda>(map_subspaces->lambda().root())) {
auto result = detect_unpack_bits(lambda->type(), lambda->bindings().size(), lambda->lambda(), map_subspaces->types());
if (result.is_unpack_bits) {
return stash.create<UnpackBitsFunction>(map_subspaces->result_type(), map_subspaces->child(), result.is_big_bitorder);
}
}
}
return expr;
}
} // namespace
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