Instructions to use ZibinDong/ActionCodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZibinDong/ActionCodec2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZibinDong/ActionCodec2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 14,788 Bytes
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#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <algorithm>
#include <cstdint>
#include <memory>
#include <stdexcept>
#include <vector>
#include "parallel.hpp"
#include "token_streams.hpp"
namespace py=pybind11;
namespace {
using State=std::vector<int64_t>;
using States=std::vector<State>;
class Grammar {
public:
int64_t horizon,vocabulary;
virtual ~Grammar()=default;
virtual State initial()const=0;
virtual State advance(const State&,int64_t)const=0;
virtual int64_t covered(const State&)const=0;
virtual int64_t next_dimension(const State&)const=0;
virtual bool allowed(const State&,int64_t)const=0;
virtual std::shared_ptr<Grammar> clone(int64_t)const=0;
py::array_t<bool> mask(const State& state)const {
py::array_t<bool> result(vocabulary);
py::gil_scoped_release release;
for(int64_t token=0;token<vocabulary;++token)result.mutable_data()[token]=allowed(state,token);
return result;
}
};
class SetGrammar:public Grammar {
uint64_t full_;
using Patterns=std::vector<std::vector<std::pair<int64_t,uint64_t>>>;
std::shared_ptr<const std::vector<int64_t>> roots_;
std::shared_ptr<const Patterns> patterns_;
std::shared_ptr<const std::vector<bool>> scope_;
public:
SetGrammar(int64_t steps,uint64_t full,std::vector<int64_t> roots,
std::vector<std::vector<std::pair<int64_t,uint64_t>>> patterns)
:full_(full),roots_(std::make_shared<const std::vector<int64_t>>(std::move(roots))),
patterns_(std::make_shared<const Patterns>(std::move(patterns))) {
if(steps<1 || !full || roots_->size()!=patterns_->size())throw std::invalid_argument("invalid grammar metadata");
horizon=steps;vocabulary=roots_->size();
std::vector<bool> scopes;
for(const auto& pattern:*patterns_) {
bool scope=true;
for(auto[offset,support]:pattern) {
if(offset<0)throw std::invalid_argument("negative grammar pattern offset");
scope=scope && !(support & ~full_);
}
scopes.push_back(scope);
}
scope_=std::make_shared<const std::vector<bool>>(std::move(scopes));
}
std::shared_ptr<Grammar> clone(int64_t steps)const override {
if(steps<1)throw std::invalid_argument("horizon must be positive");
auto result=std::make_shared<SetGrammar>(*this);result->horizon=steps;return result;
}
State initial()const override{return State(horizon,0);}
int64_t covered(const State& state)const override {
if(static_cast<int64_t>(state.size())!=horizon)throw std::invalid_argument("invalid grammar state");
for(int64_t i=0;i<horizon;++i)if(static_cast<uint64_t>(state[i])!=full_)return i;
return horizon;
}
int64_t next_dimension(const State& state)const override {
int64_t front=covered(state);if(front==horizon)return -1;
uint64_t missing=full_ & ~static_cast<uint64_t>(state[front]);
int64_t d=0;while(!(missing & 1)){missing>>=1;++d;}return d;
}
bool allowed(const State& state,int64_t token)const override {
if(token<0 || token>=vocabulary || !(*scope_)[token])return false;
const int64_t front=covered(state);
if(front==horizon || (*roots_)[token]!=next_dimension(state))return false;
for(auto[offset,support]:(*patterns_)[token]) {
if(front+offset>=horizon || (static_cast<uint64_t>(state[front+offset]) & support))return false;
}
return true;
}
State advance(const State& state,int64_t token)const override {
if(!allowed(state,token))return {};
State result=state;int64_t front=covered(state);
for(auto[offset,support]:(*patterns_)[token])result[front+offset]|=static_cast<int64_t>(support);
return result;
}
};
class PlainGrammar:public Grammar {
std::shared_ptr<const std::vector<int64_t>> lengths_,dims_;
std::shared_ptr<const std::vector<bool>> phase_;
public:
PlainGrammar(int64_t steps,std::vector<int64_t> lengths,std::vector<int64_t> dims,
py::array_t<bool,py::array::c_style|py::array::forcecast> phase)
:lengths_(std::make_shared<const std::vector<int64_t>>(std::move(lengths))),
dims_(std::make_shared<const std::vector<int64_t>>(std::move(dims))) {
horizon=steps;vocabulary=lengths_->size();
if(steps<1 || dims_->empty() || phase.ndim()!=2 || phase.shape(0)!=static_cast<int64_t>(dims_->size()) || phase.shape(1)!=vocabulary)
throw std::invalid_argument("invalid plain-BPE grammar metadata");
for(auto value:*lengths_)if(value<1)throw std::invalid_argument("invalid token expansion");
phase_=std::make_shared<const std::vector<bool>>(phase.data(),phase.data()+phase.size());
}
std::shared_ptr<Grammar> clone(int64_t steps)const override {
if(steps<1)throw std::invalid_argument("horizon must be positive");
auto result=std::make_shared<PlainGrammar>(*this);result->horizon=steps;return result;
}
State initial()const override{return {0};}
int64_t covered(const State& state)const override {
if(state.size()!=1 || state[0]<0)throw std::invalid_argument("invalid grammar state");
return state[0]/dims_->size();
}
int64_t next_dimension(const State& state)const override{return (*dims_)[state[0]%dims_->size()];}
bool allowed(const State& state,int64_t token)const override {
return token>=0 && token<vocabulary && state[0]+(*lengths_)[token]<=horizon*static_cast<int64_t>(dims_->size()) && (*phase_)[(state[0]%dims_->size())*vocabulary+token];
}
State advance(const State& state,int64_t token)const override {
if(!allowed(state,token))return {};
return {state[0]+(*lengths_)[token]};
}
};
using Grammars=std::vector<std::shared_ptr<Grammar>>;
int64_t next_component(const Grammars& grammars,const States& states,bool time_major) {
int64_t selected=grammars.size(),minimum=INT64_MAX;
for(size_t i=0;i<grammars.size();++i) {
const int64_t front=grammars[i]->covered(states[i]);
if(front>=grammars[i]->horizon)continue;
if(!time_major)return i;
if(front<minimum){minimum=front;selected=i;}
}
return selected;
}
States initial(const Grammars& grammars) {
States states;for(const auto& grammar:grammars)states.push_back(grammar->initial());return states;
}
bool advance(const Grammars& grammars,const std::vector<int64_t>& offsets,States& states,int64_t index,int64_t token) {
if(index<0 || index>=static_cast<int64_t>(grammars.size()))return false;
State next=grammars[index]->advance(states[index],token-offsets[index]);
if(next.empty())return false;
states[index]=std::move(next);
return true;
}
py::tuple state_tuple(const State& state) {
py::tuple result(state.size());for(size_t i=0;i<state.size();++i)result[i]=state[i];return result;
}
py::object route_advance(const Grammars& grammars,const std::vector<int64_t>& offsets,
bool time_major,int64_t index,States states,int64_t token) {
if(states.size()!=grammars.size() || offsets.size()!=grammars.size())throw std::invalid_argument("invalid routed metadata");
if(!advance(grammars,offsets,states,index,token))return py::none();
py::tuple local(states.size());for(size_t i=0;i<states.size();++i)local[i]=state_tuple(states[i]);
return py::make_tuple(next_component(grammars,states,time_major),local);
}
std::vector<int64_t> interleave(const Grammars& grammars,const std::vector<int64_t>& offsets,
bool time_major,const std::vector<std::vector<int64_t>>& streams) {
if(streams.size()!=grammars.size() || offsets.size()!=grammars.size())throw std::invalid_argument("one stream per component is required");
States states=initial(grammars);std::vector<size_t> cursor(streams.size());std::vector<int64_t> output;
int64_t index;
while((index=next_component(grammars,states,time_major))<static_cast<int64_t>(grammars.size())) {
if(cursor[index]==streams[index].size())throw std::invalid_argument("component stream ends before complete coverage");
const int64_t token=streams[index][cursor[index]++]+offsets[index];
if(!advance(grammars,offsets,states,index,token))throw std::invalid_argument("invalid routed component stream");
output.push_back(token);
}
for(size_t i=0;i<cursor.size();++i)if(cursor[i]!=streams[i].size())throw std::invalid_argument("extra component tokens");
return output;
}
std::vector<std::vector<int64_t>> split(const Grammars& grammars,const std::vector<int64_t>& offsets,
bool time_major,const std::vector<int64_t>& tokens) {
if(offsets.size()!=grammars.size())throw std::invalid_argument("invalid routed offsets");
States states=initial(grammars);std::vector<std::vector<int64_t>> output(grammars.size());
for(int64_t token:tokens) {
int64_t index=next_component(grammars,states,time_major);
if(!advance(grammars,offsets,states,index,token))throw std::invalid_argument("invalid routed token coverage");
output[index].push_back(token-offsets[index]);
}
if(next_component(grammars,states,time_major)!=static_cast<int64_t>(grammars.size()))
throw std::invalid_argument("Incomplete action token stream");
return output;
}
using Streams=std::vector<std::vector<int64_t>>;
using ComponentStreams=std::vector<Streams>;
Streams interleave_batch(const Grammars& grammars,const std::vector<int64_t>& offsets,
bool time_major,const ComponentStreams& streams) {
if(streams.size()!=grammars.size() || streams.empty() || streams.front().empty())
throw std::invalid_argument("one non-empty batch per component is required");
const int64_t batch=streams.front().size();
for(const auto& rows:streams)if(static_cast<int64_t>(rows.size())!=batch)
throw std::invalid_argument("component batches must have the same size");
Streams output(batch);
py::gil_scoped_release release;
ac2::parallel_for(batch,0,[&](int64_t b) {
Streams local;for(const auto& rows:streams)local.push_back(rows[b]);
output[b]=interleave(grammars,offsets,time_major,local);
});
return output;
}
py::tuple decode_batch(const Grammars& prototypes,const std::vector<int64_t>& offsets,
bool time_major,const py::iterable& streams,
py::array_t<int64_t,py::array::c_style|py::array::forcecast> counts,
int64_t cells_per_step) {
if(prototypes.empty() || counts.ndim()!=1 || cells_per_step<1)
throw std::invalid_argument("invalid routed batch metadata");
const auto rows=ac2::checked_token_streams(streams,counts.size(),true);
int64_t steps=-1;
ComponentStreams output(prototypes.size(),Streams(rows.size()));
{
py::gil_scoped_release release;
for(const auto& row:rows) {
int64_t cells=0;
for(int64_t token:row) {
if(counts.data()[token]<1)throw std::invalid_argument("token is outside the routed profile vocabularies");
cells+=counts.data()[token];
}
if(cells%cells_per_step || cells/cells_per_step<1)
throw std::invalid_argument("Incomplete action token stream: " +
std::to_string(cells_per_step-cells%cells_per_step) +
" more coordinate values are needed to finish a step. "
"Constrain generation with codec.grammar(H, fps=...).next_token_mask(prefix) "
"and check is_complete(tokens) for the intended H before decoding. "
"Use codec.batch_decode(..., errors='return') to isolate invalid rows; "
"do not pad truncated tokens with zeros.");
if(steps>=0 && steps!=cells/cells_per_step)
throw std::invalid_argument("all token streams must decode to the same length; use codec.batch_decode(...) for independent variable-length outputs");
steps=cells/cells_per_step;
}
Grammars grammars;for(const auto& prototype:prototypes)grammars.push_back(prototype->clone(steps));
ac2::parallel_for(rows.size(),0,[&](int64_t b) {
auto local=split(grammars,offsets,time_major,rows[b]);
for(size_t i=0;i<local.size();++i)output[i][b]=std::move(local[i]);
});
}
return py::make_tuple(steps,output);
}
std::vector<int64_t> complete(const Grammars& grammars,const std::vector<int64_t>& offsets,
bool time_major,std::vector<int64_t> tokens,int64_t steps,
const std::vector<std::vector<int64_t>>& zero_atoms) {
if(grammars.empty() || offsets.size()!=grammars.size() || zero_atoms.size()!=grammars.size() ||
steps<1 || steps>grammars.front()->horizon)throw std::invalid_argument("invalid prefix completion metadata");
py::gil_scoped_release release;
States states=initial(grammars);
for(int64_t token:tokens) {
int64_t index=next_component(grammars,states,time_major);
if(!advance(grammars,offsets,states,index,token))throw std::invalid_argument("invalid routed token prefix");
}
for(size_t i=0;i<grammars.size();++i)if(grammars[i]->covered(states[i])<steps)
throw std::invalid_argument("action prefix does not cover the requested frames");
int64_t index;
while((index=next_component(grammars,states,time_major))<static_cast<int64_t>(grammars.size())) {
int64_t dimension=grammars[index]->next_dimension(states[index]);
if(dimension<0 || dimension>=static_cast<int64_t>(zero_atoms[index].size()))
throw std::invalid_argument("missing zero-motion primitive atom");
const int64_t token=offsets[index]+zero_atoms[index][dimension];
if(!advance(grammars,offsets,states,index,token))throw std::invalid_argument("invalid zero-motion suffix");
tokens.push_back(token);
}
return tokens;
}
} // namespace
void register_grammar(py::module_& module) {
py::class_<Grammar,std::shared_ptr<Grammar>>(module,"CoverageGrammar")
.def("advance",[](const Grammar& g,const State& s,int64_t token)->py::object {
State next=g.advance(s,token);return next.empty()?py::object(py::none()):py::object(state_tuple(next));
})
.def("mask",&Grammar::mask).def("covered",&Grammar::covered).def("clone",&Grammar::clone);
py::class_<SetGrammar,Grammar,std::shared_ptr<SetGrammar>>(module,"SetGrammar")
.def(py::init<int64_t,uint64_t,std::vector<int64_t>,std::vector<std::vector<std::pair<int64_t,uint64_t>>>>());
py::class_<PlainGrammar,Grammar,std::shared_ptr<PlainGrammar>>(module,"PlainGrammar")
.def(py::init<int64_t,std::vector<int64_t>,std::vector<int64_t>,py::array_t<bool,py::array::c_style|py::array::forcecast>>());
module.def("route_advance",&route_advance);
module.def("route_interleave",&interleave,py::call_guard<py::gil_scoped_release>());
module.def("route_split",&split,py::call_guard<py::gil_scoped_release>());
module.def("route_interleave_batch",&interleave_batch);
module.def("route_decode_batch",&decode_batch);
module.def("route_complete",&complete);
}
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