Instructions to use ZibinDong/ActionCodec2-1st-order with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZibinDong/ActionCodec2-1st-order with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZibinDong/ActionCodec2-1st-order", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 4,914 Bytes
d20d01e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | // pybind11 entry point for the ActionCodec2 physical-stage kernels.
//
// These are the encode/decode kernels the runtime bundled with an ActionCodec2
// Hugging Face artifact calls when a native module is available. Each one has
// a bit-identical NumPy implementation in that runtime.
#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <cstdint>
#include <stdexcept>
#include "parallel.hpp"
namespace py = pybind11;
py::array_t<int32_t> so3_second_order_encode_batch(
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
const py::array_t<int32_t, py::array::c_style | py::array::forcecast>&,
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
int32_t, bool, double, double, bool, int);
py::array_t<int32_t> additive_second_order_encode_batch(
const py::array_t<float, py::array::c_style | py::array::forcecast>&,
const py::array_t<int32_t, py::array::c_style | py::array::forcecast>&,
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&, bool, int);
py::array_t<float> physical_decode_batch(
const py::array_t<float>&, const py::array_t<int32_t>&, const py::array_t<uint8_t>&,
const py::array_t<float>&, const py::array_t<float>&,
const py::array_t<int32_t, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&, int32_t, int);
py::array_t<int32_t> physical_encode_batch(
const py::array_t<float>&, const py::array_t<int32_t>&, const py::array_t<uint8_t>&,
const py::array_t<float>&, const py::array_t<float>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&, int32_t, int,
const py::array_t<float>&, bool);
py::array_t<int32_t> so3_encode_batch(
const py::array_t<double, py::array::c_style | py::array::forcecast>&,
const py::array_t<int32_t, py::array::c_style | py::array::forcecast>&,
const py::array_t<float, py::array::c_style | py::array::forcecast>&, int32_t, bool, bool,
double, bool, int);
void register_setbpe(py::module_& module);
void register_resample(py::module_& module);
PYBIND11_MODULE(_actioncodec2_native, module) {
module.doc() = "ActionCodec2 encode/decode kernels: physical stage, resampling, Set-BPE";
register_setbpe(module);
register_resample(module);
module.def(
"set_thread_budget",
[](int threads) {
if (threads < 0) throw std::invalid_argument("thread budget must be non-negative");
ac2::thread_budget().store(threads);
},
py::arg("threads"),
"Cap threads per call for this process; 0 uses every CPU it may run on.");
module.def("thread_budget", [] { return ac2::thread_budget().load(); });
module.def("plan_threads", &ac2::plan_threads, py::arg("count"), py::arg("requested"),
py::arg("seconds_each"), "Threads a call would use for ``count`` items.");
module.def("so3_second_order_encode_batch", &so3_second_order_encode_batch,
py::arg("table"), py::arg("bins"), py::arg("quaternions"),
py::arg("previous_first_order"), py::arg("input_order"), py::arg("left"),
py::arg("limit"), py::arg("radius"), py::arg("smooth"), py::arg("threads") = 0);
module.def("additive_second_order_encode_batch", &additive_second_order_encode_batch,
py::arg("table"), py::arg("bins"), py::arg("positions"),
py::arg("previous_first_order"), py::arg("epsilon"), py::arg("smooth"),
py::arg("threads") = 0);
module.def("physical_decode_batch", &physical_decode_batch, py::arg("table"),
py::arg("bins"), py::arg("binary"), py::arg("lower"), py::arg("upper"),
py::arg("cells"), py::arg("initial"), py::arg("initial_velocity"),
py::arg("order"), py::arg("threads") = 0);
module.def("physical_encode_batch", &physical_encode_batch, py::arg("table"), py::arg("bins"),
py::arg("binary"), py::arg("lower"), py::arg("upper"), py::arg("positions"),
py::arg("initial"), py::arg("initial_velocity"), py::arg("order"),
py::arg("threads") = 0, py::arg("epsilon") = py::array_t<float>(),
py::arg("smooth") = false);
module.def("so3_encode_batch", &so3_encode_batch, py::arg("table"), py::arg("bins"),
py::arg("actions"), py::arg("input_order"), py::arg("rotation_vector"),
py::arg("left"), py::arg("limit"), py::arg("smooth"), py::arg("threads") = 0);
}
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