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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "dense_lambda_peek_optimizer.h"
#include "dense_lambda_peek_function.h"
#include "dense_cell_range_function.h"
#include <vespa/eval/instruction/replace_type_function.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/llvm/compile_cache.h>
#include <vespa/vespalib/util/small_vector.h>
#include <optional>
using namespace vespalib::eval::nodes;
namespace vespalib::eval {
namespace {
// 'simple peek': deterministic peek into a single parameter with
// compilable dimension index expressions.
const TensorPeek *find_simple_peek(const tensor_function::Lambda &lambda) {
const Function &function = lambda.lambda();
const size_t num_dims = lambda.result_type().dimensions().size();
auto peek = as<TensorPeek>(function.root());
if (peek && (function.num_params() == (num_dims + 1))) {
auto param = as<Symbol>(peek->get_child(0));
if (param && (param->id() == num_dims)) {
for (size_t i = 1; i < peek->num_children(); ++i) {
const Node &dim_expr = peek->get_child(i);
if (NodeTools::min_num_params(dim_expr) > num_dims) {
return nullptr;
}
if (CompiledFunction::detect_issues(dim_expr)) {
return nullptr;
}
}
return peek;
}
}
return nullptr;
}
Node_UP make_dim_expr(const TensorPeek::Dim &src_dim) {
if (src_dim.second.is_expr()) {
return NodeTools::copy(*src_dim.second.expr);
} else {
return std::make_unique<Number>(as_number(src_dim.second.label));
}
}
template <typename OP>
Node_UP make_op(Node_UP a, Node_UP b) {
auto res = std::make_unique<OP>();
res->bind(std::move(a), std::move(b));
return res;
}
Node_UP make_floor(Node_UP a) {
auto res = std::make_unique<Floor>();
res->bind_next(std::move(a));
return res;
}
struct PeekAnalyzer {
SmallVector<size_t> dst_dim_sizes;
SmallVector<size_t> src_dim_sizes;
SmallVector<CompiledFunction::UP> src_dim_funs;
std::shared_ptr<Function const> src_idx_fun;
struct CellRange {
size_t offset;
size_t length;
bool is_full(size_t num_cells) const {
return ((offset == 0) && (length == num_cells));
}
};
struct Result {
bool valid;
std::optional<CellRange> cell_range;
static Result simple(CellRange range) { return Result{true, range}; }
static Result complex() { return Result{true, std::nullopt}; }
static Result invalid() { return Result{false, std::nullopt}; }
};
PeekAnalyzer(const ValueType &dst_type, const ValueType &src_type,
const TensorPeek::DimList &dim_list)
{
for (const auto& dim: dst_type.dimensions()) {
dst_dim_sizes.push_back(dim.size);
}
for (const auto& dim: src_type.dimensions()) {
src_dim_sizes.push_back(dim.size);
}
Node_UP idx_expr;
size_t num_params = dst_dim_sizes.size();
for (size_t i = 0; i < dim_list.size(); ++i) {
auto dim_expr = make_dim_expr(dim_list[i]);
src_dim_funs.push_back(std::make_unique<CompiledFunction>(*dim_expr, num_params, PassParams::ARRAY));
if (i == 0) {
idx_expr = std::move(dim_expr);
} else {
idx_expr = make_floor(std::move(idx_expr));
idx_expr = make_op<Mul>(std::move(idx_expr), std::make_unique<Number>(src_dim_sizes[i]));
idx_expr = make_op<Add>(std::move(idx_expr), std::move(dim_expr));
}
}
src_idx_fun = Function::create(std::move(idx_expr), dst_type.dimension_names());
}
bool step_params(SmallVector<double> ¶ms) {
for (size_t idx = params.size(); idx-- > 0; ) {
if (size_t(params[idx] += 1.0) < dst_dim_sizes[idx]) {
return true;
} else {
params[idx] = 0.0;
}
}
return false;
}
size_t calculate_index(const SmallVector<size_t> &src_address) {
size_t result = 0;
for (size_t i = 0; i < src_address.size(); ++i) {
result *= src_dim_sizes[i];
result += src_address[i];
}
return result;
}
Result analyze_indexes() {
CellRange range{0,0};
bool is_complex = false;
SmallVector<double> params(dst_dim_sizes.size(), 0.0);
SmallVector<size_t> src_address(src_dim_sizes.size(), 0);
do {
for (size_t i = 0; i < src_dim_funs.size(); ++i) {
auto dim_fun = src_dim_funs[i]->get_function();
size_t dim_idx = (int64_t) dim_fun(¶ms[0]);
if (dim_idx >= src_dim_sizes[i]) {
return Result::invalid();
}
src_address[i] = dim_idx;
}
size_t idx = calculate_index(src_address);
if (range.length == 0) {
range.offset = idx;
}
if (idx == (range.offset + range.length)) {
++range.length;
} else {
is_complex = true;
}
} while(step_params(params));
if (is_complex) {
return Result::complex();
}
return Result::simple(range);
}
};
} // namespace <unnamed>
const TensorFunction &
DenseLambdaPeekOptimizer::optimize(const TensorFunction &expr, Stash &stash)
{
if (auto lambda = as<tensor_function::Lambda>(expr)) {
if (auto peek = find_simple_peek(*lambda)) {
const ValueType &dst_type = lambda->result_type();
const ValueType &src_type = lambda->types().get_type(peek->param());
if (src_type.is_dense()) {
assert(lambda->bindings().size() == 1);
assert(src_type.dimensions().size() == peek->dim_list().size());
size_t param_idx = lambda->bindings()[0];
PeekAnalyzer analyzer(dst_type, src_type, peek->dim_list());
auto result = analyzer.analyze_indexes();
if (result.valid) {
const auto &get_param = tensor_function::inject(src_type, param_idx, stash);
if (result.cell_range && (dst_type.cell_type() == src_type.cell_type())) {
auto cell_range = result.cell_range.value();
if (cell_range.is_full(src_type.dense_subspace_size())) {
return ReplaceTypeFunction::create_compact(dst_type, get_param, stash);
} else {
return stash.create<DenseCellRangeFunction>(dst_type, get_param,
cell_range.offset, cell_range.length);
}
} else {
return stash.create<DenseLambdaPeekFunction>(dst_type, get_param,
std::move(analyzer.src_idx_fun));
}
}
}
}
}
return expr;
}
} // namespace
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