# Copyright 2024 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
#
#     https://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.

"""Module registering a lowering rule for pallas_call on GPU."""

from __future__ import annotations

import io
import json
from typing import Final

from jax._src import core as jax_core
from jax._src import deprecations
from jax._src import frozen_dict
from jax._src.interpreters import mlir
from jax._src.lib.mlir import ir
from jax._src.pallas import core as pallas_core
from jax._src.pallas.triton import core as triton_core
from jax._src.pallas.triton import gpu_info as gpu_info_lib
from jax._src.pallas.triton import lowering


CUSTOM_CALL_TARGET_NAME: Final = "__gpu$xla.gpu.triton"


def normalize_grid(grid: pallas_core.StaticGrid) -> tuple[int, int, int]:
  if isinstance(grid, int):
    grid = (grid,)
  elif len(grid) > 3:
    raise ValueError("`grid` should have three or fewer dimensions.")
  return tuple(grid) + (1,) * (3 - len(grid))  # pyrefly: ignore[bad-return]


def avals_to_layouts(avals):
  return [list(reversed(range(aval.ndim))) for aval in avals]


def pallas_call_lowering(
    ctx: mlir.LoweringRuleContext,
    *in_nodes,
    jaxpr: jax_core.Jaxpr,
    interpret: bool,
    debug: bool,
    input_output_aliases: tuple[tuple[int, int], ...],
    grid_mapping: pallas_core.GridMapping,
    mesh: pallas_core.Mesh | None,
    compiler_params: pallas_core.CompilerParams | None,
    cost_estimate: pallas_core.CostEstimate | None,
    out_avals: tuple[jax_core.AbstractValue, ...],
    metadata: frozen_dict.FrozenDict[str, str] | None,
    name: str | None,
):
  del interpret, out_avals, cost_estimate, name

  deprecations.warn(
      "jax-pallas-triton",
      "The Pallas Triton backend is deprecated and will be removed in"
      " a future JAX version. To keep using Pallas on GPU, please migrate"
      " to the Mosaic GPU backend. To keep using Triton, switch to"
      " the official Triton bindings and jax_triton.",
      stacklevel=2,
  )

  debug_info = jaxpr.debug_info
  if grid_mapping.num_dynamic_grid_bounds:
    raise NotImplementedError(
        "dynamic grid bounds not supported in the Triton backend"
    )
  if grid_mapping.num_index_operands:
    raise NotImplementedError(
        "scalar prefetch not implemented in the Triton backend"
    )
  if mesh is not None:
    raise NotImplementedError("mesh is not supported in the Triton backend")

  [lowering_platform] = ctx.platforms or ctx.module_context.platforms

  if compiler_params is None:
    triton_params = triton_core.CompilerParams()
  else:
    assert isinstance(compiler_params, triton_core.CompilerParams)
    triton_params = compiler_params

  num_warps = 4 if triton_params.num_warps is None else triton_params.num_warps
  num_stages = triton_params.num_stages
  if num_stages is None:
    num_stages = 1 if lowering_platform == "rocm" else 3

  if debug:
    print(f"\nThe kernel jaxpr for pallas_call {debug_info.func_src_info}:")
    print(jaxpr)
    print(f"The grid mapping for pallas_call {debug_info.func_src_info}:")
    print(grid_mapping)

  try:
    gpu_info = gpu_info_lib.get_gpu_info()
  except ValueError:
    raise RuntimeError(
        "No supported GPU devices found, please specify an abstract GPU "
        "device using AbstractDevice. See jax.sharding.use_abstract_mesh "
        "method for example."
    ) from None
  else:
    compute_capability = gpu_info.compute_capability

  # Sanitize the name to conform to NVPTX requirements. We do this here
  # to avoid the need to fetch the new name from PTX post compilation.
  name = mlir.sanitize_name(debug_info.func_name)
  lowering_result = lowering.lower_jaxpr_to_triton_module(
      jaxpr, grid_mapping, lowering_platform, compute_capability or None,
      mlir_ctx=ctx.module_context
  )
  module_op = lowering_result.module.operation
  if debug:
    print(f"\nThe Triton module for pallas_call {debug_info.func_src_info}:")
    print(module_op.get_asm(enable_debug_info=True, pretty_debug_info=True))

  grid_x, grid_y, grid_z = normalize_grid(lowering_result.grid)
  buf = io.BytesIO()
  module_op.write_bytecode(buf)

  serialized_metadata = None
  if metadata is not None:
    serialized_metadata = json.dumps(dict(metadata))

  out_types = [
      ir.RankedTensorType.get(
          bm.array_aval.shape, mlir.dtype_to_ir_type(bm.array_aval.dtype)
      )
      for bm in grid_mapping.block_mappings_output
  ]
  backend_config = dict(
      name=ir.StringAttr.get(name),
      ir=ir.StringAttr.get(buf.getvalue()),
      num_stages=mlir.i32_attr(num_stages),
      num_warps=mlir.i32_attr(num_warps),
      grid_x=mlir.i32_attr(grid_x),
      grid_y=mlir.i32_attr(grid_y),
      grid_z=mlir.i32_attr(grid_z),
      debug=ir.BoolAttr.get(debug),
  )
  if serialized_metadata is not None:
    # This field is unstable and may be removed in the future.
    backend_config["serialized_metadata"] = ir.StringAttr.get(
        serialized_metadata
    )
  return mlir.custom_call(
      call_target_name="__gpu$xla.gpu.triton",
      result_types=out_types,
      operands=in_nodes,
      backend_config=backend_config,
      api_version=4,
      operand_layouts=avals_to_layouts(ctx.avals_in),
      result_layouts=avals_to_layouts(ctx.avals_out),
      operand_output_aliases=dict(input_output_aliases),
  ).results


pallas_core.register_lowering_rule(triton_core.CompilerParams, pallas_call_lowering, "gpu")