/* Copyright 2025 The JAX Authors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "jaxlib/py_client_cpu.h"

#include <Python.h>

#include <cstddef>
#include <cstdint>
#include <cstring>
#include <memory>
#include <optional>
#include <string>
#include <string_view>
#include <utility>

#include "absl/algorithm/container.h"
#include "absl/base/thread_annotations.h"
#include "absl/container/inlined_vector.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_format.h"
#include "absl/types/span.h"
#include "include/dlpack/dlpack.h"
#include "nanobind/nanobind.h"
#include "jaxlib/ffi.h"
#include "jaxlib/free_threading.h"
#include "xla/ffi/api/ffi.h"
#include "xla/pjrt/host_callback.h"
#include "xla/pjrt/transpose.h"
#include "xla/primitive_util.h"
#include "xla/python/nb_numpy.h"
#include "xla/python/types.h"
#include "xla/shape_util.h"
#include "xla/util.h"
#include "xla/xla_data.pb.h"

namespace jax {

struct CpuTransposePlanCache {
  static ffi::TypeId id;
  static ffi::TypeInfo info;

  explicit CpuTransposePlanCache(int capacity) : cache(capacity) {}
  ft_mutex mu;
  xla::TransposePlanCache cache ABSL_GUARDED_BY(mu);
};

ffi::TypeId CpuTransposePlanCache::id =
    ffi::TypeId{reinterpret_cast<intptr_t>(&CpuTransposePlanCache::id)};
ffi::TypeInfo CpuTransposePlanCache::info =
    ffi::MakeTypeInfo<CpuTransposePlanCache>();

XLA_FFI_REGISTER_TYPE(ffi::GetXlaFfiApi(), "CpuTransposePlanCache",
                      &CpuTransposePlanCache::id, &CpuTransposePlanCache::info);

static ffi::ErrorOr<std::unique_ptr<CpuTransposePlanCache>>
CpuTransposePlanCacheInstantiate(uint64_t index) {
  return std::make_unique<CpuTransposePlanCache>(/*capacity=*/16);
}

XLA_FFI_DEFINE_HANDLER_SYMBOL(
    kCpuTransposePlanCacheInstantiate, CpuTransposePlanCacheInstantiate,
    ffi::Ffi::BindInstantiate().Attr<uint64_t>("index"));

ffi::Error XlaFfiPythonCpuCallback(xla::FfiLoadedHostCallbacks* callbacks,
                                   CpuTransposePlanCache* transpose_cache,
                                   uint64_t index, ffi::RemainingArgs args,
                                   ffi::RemainingRets rets) {
  nb::gil_scoped_acquire gil;
  auto callback = nb::borrow<nb::callable>(
      static_cast<PyObject*>(callbacks->callbacks[index]));
  nb::tuple_builder nb_args(args.size());
  for (size_t i = 0; i < args.size(); ++i) {
    auto arg = args.get<ffi::AnyBuffer>(i);
    auto ptype = static_cast<xla::PrimitiveType>(arg->element_type());
    // TODO(b/395428868): Remove this check once we support subbyte types.
    if (ptype == xla::S1 || ptype == xla::U1) {
      return ffi::Error(ffi::ErrorCode::kUnimplemented,
                        absl::StrFormat("Unsupported primitive type: %s",
                                        xla::PrimitiveType_Name(ptype)));
    }
    if (ptype == xla::TOKEN) {
      nb_args.put(nb::none());
      continue;
    }
    auto maybe_dtype = xla::PrimitiveTypeToNbDtype(ptype);
    if (!maybe_dtype.ok()) {
      return ffi::Error::Internal(maybe_dtype.status().ToString());
    }
    auto dtype = maybe_dtype.value();
    auto dims = absl::Span<const int64_t>(arg->dimensions().begin(),
                                          arg->dimensions().size());
    // TODO(b/402422886): Remove this once we form Jax arrays directly instead
    std::unique_ptr<char[]> buffer;
    const void* data = arg->untyped_data();
    size_t bits_per_element = xla::primitive_util::BitWidth(ptype);
    if (bits_per_element == 2 || bits_per_element == 4) {
      // NOTE(dsuo): FFI arguments and return buffers are sized assuming
      // minimum 1-byte element sizes, even if the data itself is packed. We
      // assume that 2-bit and 4-bit types are packed.
      size_t size_bytes =
          xla::CeilOfRatio<size_t>(arg->element_count() * bits_per_element, 8);
      auto* new_buffer = new char[arg->element_count()];
      xla::UnpackIntN(
          bits_per_element,
          absl::MakeConstSpan(static_cast<const char*>(data), size_bytes),
          absl::MakeSpan(new_buffer, arg->element_count()));
      buffer.reset(new_buffer);
      data = buffer.get();
    }
    // We pass in data using default numpy layout i.e., std::nullopt.
    auto array = xla::nb_numpy_ndarray(dtype, dims, std::nullopt, data);
    array.attr("flags").attr("writeable") = nb::bool_(false);
    nb_args.put(std::move(array));
  }

  // TODO(dsuo): Change this to use the Python vectorcall protocol, which allows
  // you to avoid constructing a tuple for the arguments.
  nb::tuple result_tuple;
  {
    xla::HostCallbackScope scope;
    try {
      auto result_object = callback(*nb::borrow<nb::args>(nb_args.commit()));
      result_tuple = nb::cast<nb::tuple>(result_object);
    } catch (nb::python_error& e) {
      return ffi::Error::Internal(
          absl::StrFormat("CpuCallback error calling callback: %s", e.what()));
    }
  }

  for (size_t i = 0; i < rets.size(); ++i) {
    auto ret = rets.get<ffi::AnyBuffer>(i).value();
    auto ptype = static_cast<xla::PrimitiveType>(ret->element_type());
    // TODO(b/402422886): Remove this once we form Jax arrays directly instead
    if (ptype == xla::S1 || ptype == xla::U1) {
      return ffi::Error(ffi::ErrorCode::kUnimplemented,
                        absl::StrFormat("Unsupported primitive type: %s",
                                        xla::PrimitiveType_Name(ptype)));
    }
    if (ptype == xla::TOKEN) continue;
    nb::object output =
        nb::borrow<nb::object>(PyTuple_GetItem(result_tuple.ptr(), i));
    xla::nb_numpy_ndarray array =
        xla::nb_numpy_ndarray::ensure(std::move(output));
    absl::Span<int64_t const> strides(
        reinterpret_cast<const int64_t*>(array.strides()), array.ndim());
    // We expect the output to be in default numpy layout.
    auto dims = absl::Span<const int64_t>(ret->dimensions().begin(),
                                          ret->dimensions().size());
    auto maybe_expected_shape = xla::ShapeUtil::MakeValidatedShape(ptype, dims);
    if (!maybe_expected_shape.ok()) {
      return ffi::Error::Internal(maybe_expected_shape.status().ToString());
    }
    auto expected_shape = maybe_expected_shape.value();
    auto expected_strides = ByteStridesForShape(expected_shape);

    const void* data = array.data();
    std::unique_ptr<char[]> buffer;
    size_t bits_per_element = xla::primitive_util::BitWidth(ptype);
    size_t size_bytes = array.size() * array.itemsize();
    if (strides != expected_strides) {
      xla::TransposePlan::Options options;
      options.elem_size_in_bytes = xla::primitive_util::ByteWidth(ptype);
      options.dims = absl::Span<const int64_t>(
          reinterpret_cast<const int64_t*>(array.shape()), array.ndim());
      absl::InlinedVector<int64_t, 4> reversed_layout;
      reversed_layout.resize(expected_shape.dimensions().size());
      absl::c_reverse_copy(expected_shape.layout().minor_to_major(),
                           reversed_layout.begin());
      options.permutation = reversed_layout;
      options.input_striding = xla::TransposePlan::Striding{strides};
      absl::StatusOr<std::shared_ptr<xla::TransposePlan>> maybe_plan;
      {
        ft_lock_guard lock(transpose_cache->mu);
        maybe_plan = transpose_cache->cache.GetOrCreate(options);
      }
      if (!maybe_plan.ok()) {
        return ffi::Error::Internal(maybe_plan.status().ToString());
      }
      auto plan = maybe_plan.value();
      if (bits_per_element == 2 || bits_per_element == 4) {
        // NOTE(dsuo): If the data needs to be unpacked, don't use return buffer
        // supplied by FFI directly.
        buffer = std::make_unique<char[]>(size_bytes);
        plan->Execute(data, buffer.get());
        data = buffer.get();
      } else {
        plan->Execute(data, ret->untyped_data());
        data = ret->untyped_data();
      }
    }

    // TODO(b/402422886): Remove this once we form Jax arrays directly instead
    // of packing/unpacking to/from numpy arrays.
    if (bits_per_element == 2 || bits_per_element == 4) {
      // NOTE(dsuo): FFI arguments and return buffers are sized assuming
      // minimum 1-byte element sizes, even if the data itself is packed. We
      // assume that 2-bit and 4-bit types are packed.
      const int64_t packed_size =
          xla::CeilOfRatio<size_t>(size_bytes * bits_per_element, 8);
      auto* new_buffer = new char[packed_size];
      xla::PackIntN(
          bits_per_element,
          absl::MakeConstSpan(static_cast<const char*>(data), size_bytes),
          absl::MakeSpan(new_buffer, packed_size));
      buffer.reset(new_buffer);
      data = buffer.get();
      size_bytes = packed_size;
    }

    // Copy data to output buffer if haven't already or modified the data to
    // write back.
    if (data != ret->untyped_data()) {
      std::memcpy(ret->untyped_data(), data, size_bytes);
    }
  }

  return ffi::Error::Success();
}

XLA_FFI_DEFINE_HANDLER_SYMBOL(
    kXlaFfiPythonCpuCallback, XlaFfiPythonCpuCallback,
    ffi::Ffi::Bind()
        .Ctx<ffi::UserData<xla::FfiLoadedHostCallbacks>>()
        .Ctx<ffi::State<CpuTransposePlanCache>>()
        .Attr<uint64_t>("index")
        .RemainingArgs()
        .RemainingRets());

XLA_FFI_REGISTER_HANDLER(ffi::GetXlaFfiApi(), "xla_ffi_python_cpu_callback",
                         "HOST",
                         {kCpuTransposePlanCacheInstantiate, nullptr, nullptr,
                          kXlaFfiPythonCpuCallback});
XLA_FFI_REGISTER_HANDLER(ffi::GetXlaFfiApi(),
                         "xla_ffi_partitioned_python_cpu_callback", "HOST",
                         {kCpuTransposePlanCacheInstantiate, nullptr, nullptr,
                          kXlaFfiPythonCpuCallback});

XLA_FFI_DEFINE_HANDLER_SYMBOL(
    kXlaBufferPythonCpuCallback, (XlaBufferCallback<kDLCPU>),
    ffi::Ffi::Bind()
        .Ctx<ffi::Context>()
        .Ctx<ffi::DeviceOrdinal>()
        .Ctx<ffi::UserData<xla::FfiLoadedHostCallbacks>>()
        .Attr<uint64_t>("index")
        .RemainingArgs()
        .RemainingRets());

XLA_FFI_REGISTER_HANDLER(ffi::GetXlaFfiApi(), "xla_buffer_python_cpu_callback",
                         "HOST", kXlaBufferPythonCpuCallback);

namespace {
ffi::Error DceSinkImpl(ffi::AnyBuffer x) { return ffi::Error::Success(); }

XLA_FFI_DEFINE_HANDLER_SYMBOL(dce_sink_cpu_ffi, DceSinkImpl,
                              ffi::Ffi::Bind().Arg<ffi::AnyBuffer>());
}  // namespace

XLA_FFI_REGISTER_HANDLER(ffi::GetXlaFfiApi(), "dce_sink", "HOST",
                         dce_sink_cpu_ffi);

}  // namespace jax