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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "generic_lambda.h"
#include <vespa/eval/eval/llvm/compiled_function.h>
#include <vespa/eval/eval/llvm/compile_cache.h>
#include <vespa/vespalib/util/small_vector.h>
using namespace vespalib::eval::tensor_function;
namespace vespalib::eval::instruction {
using Instruction = InterpretedFunction::Instruction;
using State = InterpretedFunction::State;
namespace {
//-----------------------------------------------------------------------------
bool step_labels(double *labels, const ValueType &type) {
for (size_t idx = type.dimensions().size(); idx-- > 0; ) {
if ((labels[idx] += 1.0) < type.dimensions()[idx].size) {
return true;
} else {
labels[idx] = 0.0;
}
}
return false;
}
struct ParamProxy : public LazyParams {
const SmallVector<double> &labels;
const LazyParams ¶ms;
const std::vector<size_t> &bindings;
ParamProxy(const SmallVector<double> &labels_in, const LazyParams ¶ms_in, const std::vector<size_t> &bindings_in)
: labels(labels_in), params(params_in), bindings(bindings_in) {}
const Value &resolve(size_t idx, Stash &stash) const override {
if (idx < labels.size()) {
return stash.create<DoubleValue>(labels[idx]);
}
return params.resolve(bindings[idx - labels.size()], stash);
}
};
//-----------------------------------------------------------------------------
struct CompiledParams {
const ValueType &result_type;
const std::vector<size_t> &bindings;
size_t num_cells;
CompileCache::Token::UP token;
CompiledParams(const Lambda &lambda)
: result_type(lambda.result_type()),
bindings(lambda.bindings()),
num_cells(result_type.dense_subspace_size()),
token(CompileCache::compile(lambda.lambda(), PassParams::ARRAY))
{
assert(lambda.lambda().num_params() == (result_type.dimensions().size() + bindings.size()));
}
};
template <typename CT>
void my_compiled_lambda_op(InterpretedFunction::State &state, uint64_t param) {
const CompiledParams ¶ms = unwrap_param<CompiledParams>(param);
SmallVector<double> args(params.result_type.dimensions().size() + params.bindings.size(), 0.0);
double *bind_next = &args[params.result_type.dimensions().size()];
for (size_t binding: params.bindings) {
*bind_next++ = state.params->resolve(binding, state.stash).as_double();
}
auto fun = params.token->get().get_function();
ArrayRef<CT> dst_cells = state.stash.create_uninitialized_array<CT>(params.num_cells);
CT *dst = &dst_cells[0];
do {
*dst++ = fun(&args[0]);
} while (step_labels(&args[0], params.result_type));
state.stack.push_back(state.stash.create<DenseValueView>(params.result_type, TypedCells(dst_cells)));
}
struct MyCompiledLambdaOp {
template <typename CT>
static auto invoke() { return my_compiled_lambda_op<CT>; }
};
//-----------------------------------------------------------------------------
struct InterpretedParams {
const ValueType &result_type;
const std::vector<size_t> &bindings;
size_t num_cells;
InterpretedFunction fun;
InterpretedParams(const Lambda &lambda, const ValueBuilderFactory &factory, CTFMetaData *meta)
: result_type(lambda.result_type()),
bindings(lambda.bindings()),
num_cells(result_type.dense_subspace_size()),
fun(InterpretedFunction::opts(factory).meta(meta), lambda.lambda().root(), lambda.types())
{
assert(lambda.lambda().num_params() == (result_type.dimensions().size() + bindings.size()));
}
};
template <typename CT>
void my_interpreted_lambda_op(InterpretedFunction::State &state, uint64_t param) {
const InterpretedParams ¶ms = unwrap_param<InterpretedParams>(param);
SmallVector<double> labels(params.result_type.dimensions().size(), 0.0);
ParamProxy param_proxy(labels, *state.params, params.bindings);
InterpretedFunction::Context ctx(params.fun);
ArrayRef<CT> dst_cells = state.stash.create_uninitialized_array<CT>(params.num_cells);
CT *dst = &dst_cells[0];
do {
*dst++ = params.fun.eval(ctx, param_proxy).as_double();
} while (step_labels(&labels[0], params.result_type));
state.stack.push_back(state.stash.create<DenseValueView>(params.result_type, TypedCells(dst_cells)));
}
struct MyInterpretedLambdaOp {
template <typename CT>
static auto invoke() { return my_interpreted_lambda_op<CT>; }
};
//-----------------------------------------------------------------------------
} // namespace <unnamed>
Instruction
GenericLambda::make_instruction(const tensor_function::Lambda &lambda_in,
const ValueBuilderFactory &factory, Stash &stash,
CTFMetaData *meta)
{
const ValueType & result_type = lambda_in.result_type();
assert(result_type.count_mapped_dimensions() == 0);
if (!CompiledFunction::detect_issues(lambda_in.lambda()) &&
lambda_in.types().all_types_are_double())
{
// can do compiled version
CompiledParams ¶ms = stash.create<CompiledParams>(lambda_in);
auto op = typify_invoke<1,TypifyCellType,MyCompiledLambdaOp>(result_type.cell_type());
return Instruction(op, wrap_param<CompiledParams>(params));
} else {
InterpretedParams ¶ms = stash.create<InterpretedParams>(lambda_in, factory, meta);
auto op = typify_invoke<1,TypifyCellType,MyInterpretedLambdaOp>(result_type.cell_type());
return Instruction(op, wrap_param<InterpretedParams>(params));
}
}
} // namespace
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