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fix: bindings unit tests for nanobind #6221

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Jul 22, 2025
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fix: bindings unit tests for nanobind #6221

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2 changes: 1 addition & 1 deletion cpp/tensorrt_llm/nanobind/batch_manager/bindings.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ void initBindings(nb::module_& m)
}
});

PybindUtils::bindSet<tb::ReqIdsSet>(m, "ReqIdsSet");
NanobindUtils::bindSet<tb::ReqIdsSet>(m, "ReqIdsSet");

nb::enum_<tb::LlmRequestType>(m, "LlmRequestType")
.value("LLMREQUEST_TYPE_CONTEXT_AND_GENERATION", tb::LLMREQUEST_TYPE_CONTEXT_AND_GENERATION)
Expand Down
13 changes: 12 additions & 1 deletion cpp/tensorrt_llm/nanobind/batch_manager/kvCacheManager.cpp
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Expand Up @@ -48,6 +48,9 @@ using SizeType32 = tensorrt_llm::runtime::SizeType32;
using TokenIdType = tensorrt_llm::runtime::TokenIdType;
using VecTokens = std::vector<TokenIdType>;
using CudaStreamPtr = std::shared_ptr<tensorrt_llm::runtime::CudaStream>;
using CacheBlockIds = std::vector<std::vector<SizeType32>>;

NB_MAKE_OPAQUE(CacheBlockIds);

namespace
{
Expand Down Expand Up @@ -424,7 +427,15 @@ void tb::kv_cache_manager::KVCacheManagerBindings::initBindings(nb::module_& m)
.def("get_newly_allocated_block_ids", &BaseKVCacheManager::getNewlyAllocatedBlockIds)
.def("flush_iteration_events", &BaseKVCacheManager::flushIterationEvents);

nb::bind_vector<std::vector<std::vector<SizeType32>>>(m, "CacheBlockIds");
nb::bind_vector<CacheBlockIds>(m, "CacheBlockIds")
.def("__getstate__", [](CacheBlockIds const& v) { return nb::make_tuple(v); })
.def("__setstate__",
[](CacheBlockIds& self, nb::tuple const& t)
{
if (t.size() != 1)
throw std::runtime_error("Invalid state!");
new (&self) CacheBlockIds(nb::cast<std::vector<std::vector<SizeType32>>>(t[0]));
});

nb::enum_<tbk::CacheType>(m, "CacheType")
.value("SELF", tbk::CacheType::kSELF)
Expand Down
9 changes: 6 additions & 3 deletions cpp/tensorrt_llm/nanobind/bindings.cpp
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Expand Up @@ -359,9 +359,12 @@ NB_MODULE(TRTLLM_NB_MODULE, m)
config.earlyStopping, config.noRepeatNgramSize, config.numReturnSequences, config.minP,
config.beamWidthArray);
};
auto SamplingConfigSetState = [](tr::SamplingConfig& self, nb::tuple t) -> tr::SamplingConfig
auto SamplingConfigSetState = [](tr::SamplingConfig& self, nb::tuple t)
{
assert(t.size() == 19);
if (t.size() != 19)
{
throw std::runtime_error("Invalid SamplingConfig state!");
}

tr::SamplingConfig config;
config.beamWidth = nb::cast<SizeType32>(t[0]);
Expand All @@ -384,7 +387,7 @@ NB_MODULE(TRTLLM_NB_MODULE, m)
config.minP = nb::cast<OptVec<float>>(t[17]);
config.beamWidthArray = nb::cast<OptVec<std::vector<SizeType32>>>(t[18]);

return config;
new (&self) tr::SamplingConfig(config);
};

nb::class_<tr::SamplingConfig>(m, "SamplingConfig")
Expand Down
39 changes: 3 additions & 36 deletions cpp/tensorrt_llm/nanobind/common/bindTypes.h
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Expand Up @@ -21,44 +21,11 @@
#include <nanobind/nanobind.h>
#include <nanobind/stl/string.h>

namespace PybindUtils
namespace NanobindUtils
{

namespace nb = nanobind;

template <typename T>
void bindList(nb::module_& m, std::string const& name)
{
nb::class_<T>(m, name.c_str())
.def(nb::init<>())
.def("push_back", [](T& lst, const typename T::value_type& value) { lst.push_back(value); })
.def("pop_back", [](T& lst) { lst.pop_back(); })
.def("push_front", [](T& lst, const typename T::value_type& value) { lst.push_front(value); })
.def("pop_front", [](T& lst) { lst.pop_front(); })
.def("__len__", [](T const& lst) { return lst.size(); })
.def(
"__iter__", [](T& lst) { return nb::make_iterator(nb::type<T>(), "iterator", lst.begin(), lst.end()); },
nb::keep_alive<0, 1>())
.def("__getitem__",
[](T const& lst, size_t index)
{
if (index >= lst.size())
throw nb::index_error();
auto it = lst.begin();
std::advance(it, index);
return *it;
})
.def("__setitem__",
[](T& lst, size_t index, const typename T::value_type& value)
{
if (index >= lst.size())
throw nb::index_error();
auto it = lst.begin();
std::advance(it, index);
*it = value;
});
}

template <typename T>
void bindSet(nb::module_& m, std::string const& name)
{
Expand Down Expand Up @@ -93,8 +60,8 @@ void bindSet(nb::module_& m, std::string const& name)
{
s.insert(item);
}
return s;
new (&v) T(s);
});
}

} // namespace PybindUtils
} // namespace NanobindUtils
123 changes: 35 additions & 88 deletions cpp/tensorrt_llm/nanobind/common/customCasters.h
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@
#include <torch/csrc/autograd/variable.h>
#include <torch/extension.h>
#include <torch/torch.h>
#include <vector>

// Pybind requires to have a central include in order for type casters to work.
// Opaque bindings add a type caster, so they have the same requirement.
Expand All @@ -48,7 +49,6 @@ NB_MAKE_OPAQUE(tensorrt_llm::batch_manager::ReqIdsSet)
NB_MAKE_OPAQUE(std::vector<tensorrt_llm::batch_manager::SlotDecoderBuffers>)
NB_MAKE_OPAQUE(std::vector<tensorrt_llm::runtime::decoder_batch::Request>)
NB_MAKE_OPAQUE(std::vector<tensorrt_llm::runtime::SamplingConfig>)
NB_MAKE_OPAQUE(std::vector<std::vector<tensorrt_llm::runtime::SizeType32>>)

namespace nb = nanobind;

Expand Down Expand Up @@ -128,70 +128,6 @@ struct type_caster<tensorrt_llm::common::OptionalRef<T>>
}
};

template <typename T>
struct PathCaster
{

private:
static PyObject* unicode_from_fs_native(std::string const& w)
{
return PyUnicode_DecodeFSDefaultAndSize(w.c_str(), ssize_t(w.size()));
}

static PyObject* unicode_from_fs_native(std::wstring const& w)
{
return PyUnicode_FromWideChar(w.c_str(), ssize_t(w.size()));
}

public:
static handle from_cpp(T const& path, rv_policy, cleanup_list* cleanup)
{
if (auto py_str = unicode_from_fs_native(path.native()))
{
return module_::import_("pathlib").attr("Path")(steal<object>(py_str), cleanup).release();
}
return nullptr;
}

bool from_python(handle src, uint8_t flags, cleanup_list* cleanup)
{
PyObject* native = nullptr;
if constexpr (std::is_same_v<typename T::value_type, char>)
{
if (PyUnicode_FSConverter(src.ptr(), &native) != 0)
{
if (auto* c_str = PyBytes_AsString(native))
{
// AsString returns a pointer to the internal buffer, which
// must not be free'd.
value = c_str;
}
}
}
else if constexpr (std::is_same_v<typename T::value_type, wchar_t>)
{
if (PyUnicode_FSDecoder(src.ptr(), &native) != 0)
{
if (auto* c_str = PyUnicode_AsWideCharString(native, nullptr))
{
// AsWideCharString returns a new string that must be free'd.
value = c_str; // Copies the string.
PyMem_Free(c_str);
}
}
}
Py_XDECREF(native);
if (PyErr_Occurred())
{
PyErr_Clear();
return false;
}
return true;
}

NB_TYPE_CASTER(T, const_name("os.PathLike"));
};

template <>
class type_caster<tensorrt_llm::executor::StreamPtr>
{
Expand Down Expand Up @@ -311,34 +247,45 @@ struct type_caster<at::Tensor>

bool from_python(nb::handle src, uint8_t, cleanup_list*) noexcept
{
nb::object capsule = nb::getattr(src, "__dlpack__")();
DLManagedTensor* dl_managed = static_cast<DLManagedTensor*>(PyCapsule_GetPointer(capsule.ptr(), "dltensor"));
PyCapsule_SetDestructor(capsule.ptr(), nullptr);
value = at::fromDLPack(dl_managed).alias();
return true;
PyObject* obj = src.ptr();
if (THPVariable_Check(obj))
{
value = THPVariable_Unpack(obj);
return true;
}
return false;
}

static handle from_cpp(at::Tensor tensor, rv_policy, cleanup_list*) noexcept
static handle from_cpp(at::Tensor src, rv_policy, cleanup_list*) noexcept
{
DLManagedTensor* dl_managed = at::toDLPack(tensor);
if (!dl_managed)
return nullptr;

nanobind::object capsule = nb::steal(PyCapsule_New(dl_managed, "dltensor",
[](PyObject* obj)
{
DLManagedTensor* dl = static_cast<DLManagedTensor*>(PyCapsule_GetPointer(obj, "dltensor"));
dl->deleter(dl);
}));
if (!capsule.is_valid())
return THPVariable_Wrap(src);
}
};

template <typename T>
struct type_caster<std::vector<std::reference_wrapper<T const>>>
{
using VectorType = std::vector<std::reference_wrapper<T const>>;

NB_TYPE_CASTER(VectorType, const_name("List[") + make_caster<T>::Name + const_name("]"));

bool from_python(handle src, uint8_t flags, cleanup_list* cleanup) noexcept
{
// Not needed for our use case since we only convert C++ to Python
return false;
}

static handle from_cpp(VectorType const& src, rv_policy policy, cleanup_list* cleanup) noexcept
{

std::vector<T> result;
result.reserve(src.size());
for (auto const& ref : src)
{
dl_managed->deleter(dl_managed);
return nullptr;
result.push_back(ref.get());
}
nanobind::module_ torch = nanobind::module_::import_("torch");
nanobind::object result = torch.attr("from_dlpack")(capsule);
capsule.release();
return result.release();

return make_caster<std::vector<T>>::from_cpp(result, policy, cleanup);
}
};
} // namespace detail
Expand Down
64 changes: 24 additions & 40 deletions cpp/tensorrt_llm/nanobind/executor/executor.cpp
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Expand Up @@ -52,58 +52,37 @@ struct dtype_traits<half>

namespace
{
// todo: Properly support FP8 and BF16 and verify functionality
tle::Tensor numpyToTensor(nb::ndarray<nb::numpy> const& array)
tle::Tensor numpyToTensor(nb::object const& object)
{
auto npDtype = array.dtype();
char kind = '\0';
switch (npDtype.code)
{
case static_cast<uint8_t>(nb::dlpack::dtype_code::Int):
kind = 'i'; // signed integer
break;
case static_cast<uint8_t>(nb::dlpack::dtype_code::UInt):
kind = 'u'; // unsigned integer
break;
case static_cast<uint8_t>(nb::dlpack::dtype_code::Float):
kind = 'f'; // floating point
break;
case static_cast<uint8_t>(nb::dlpack::dtype_code::Bfloat):
kind = 'f'; // brain floating point (treat as float kind)
break;
case static_cast<uint8_t>(nb::dlpack::dtype_code::Complex):
kind = 'c'; // complex
break;
default:
kind = 'V'; // void/other
break;
}
std::string dtype_name = nb::cast<std::string>(object.attr("dtype").attr("name"));
nb::object metadata = object.attr("dtype").attr("metadata");

tle::DataType dtype;
if (npDtype == nb::dtype<half>())
if (dtype_name == "float16")
{
dtype = tle::DataType::kFP16;
}
else if (npDtype == nb::dtype<float>())
else if (dtype_name == "float32")
{
dtype = tle::DataType::kFP32;
}
else if (npDtype == nb::dtype<int8_t>())
else if (dtype_name == "int8")
{
dtype = tle::DataType::kINT8;
}
else if (npDtype == nb::dtype<int32_t>())
else if (dtype_name == "int32")
{
dtype = tle::DataType::kINT32;
}
else if (npDtype == nb::dtype<int64_t>())
else if (dtype_name == "int64")
{
dtype = tle::DataType::kINT64;
}
else if (kind == 'V' && array.itemsize() == 1)
else if (dtype_name == "void8" && !metadata.is_none() && nb::cast<std::string>(metadata["dtype"]) == "float8")
{
dtype = tle::DataType::kFP8;
}
else if (kind == 'V' && array.itemsize() == 2)
else if (dtype_name == "void16" && !metadata.is_none() && nb::cast<std::string>(metadata["dtype"]) == "bfloat16")
{
dtype = tle::DataType::kBF16;
}
Expand All @@ -112,16 +91,21 @@ tle::Tensor numpyToTensor(nb::ndarray<nb::numpy> const& array)
TLLM_THROW("Unsupported numpy dtype.");
}

// todo: improve the following code
nb::object array_interface = object.attr("__array_interface__");
nb::object shape_obj = array_interface["shape"];
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i)
dims.reserve(nb::len(shape_obj));

for (size_t i = 0; i < nb::len(shape_obj); ++i)
{
dims.push_back(static_cast<int64_t>(array.shape(i)));
dims.push_back(nb::cast<int64_t>(shape_obj[i]));
}
tle::Shape shape(dims.data(), dims.size());

return tle::Tensor::of(dtype, const_cast<void*>(array.data()), shape);
nb::object data_obj = array_interface["data"];
uintptr_t addr = nb::cast<uintptr_t>(data_obj[0]);
void* data_ptr = reinterpret_cast<void*>(addr);
tle::Shape shape(dims.data(), dims.size());
return tle::Tensor::of(dtype, data_ptr, shape);
}

} // namespace
Expand Down Expand Up @@ -153,8 +137,8 @@ Executor::Executor(nb::bytes const& engineBuffer, std::string const& jsonConfigS
for (auto const& [rawName, rawArray] : managedWeights.value())
{
std::string name = nb::cast<std::string>(rawName);
nb::ndarray<nb::numpy> array = nb::cast<nb::ndarray<nb::numpy>>(rawArray);
managedWeightsMap->emplace(name, numpyToTensor(array));
nb::object array_obj = nb::cast<nb::object>(rawArray);
managedWeightsMap->emplace(name, numpyToTensor(array_obj));
}
}
mExecutor = std::make_unique<tle::Executor>(
Expand Down
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