code: def _mha_backward(
      sm_scale: float,
      causal: bool,
      block_q: int,
      block_k: int,
      backward_pass_impl: str,
      num_warps: int | None,
      num_stages: int,
      grid: Any,
      interpret: bool,
      debug: bool,
      res,
      do,
  ):
      del num_warps, num_stages, grid
      q, k, v, segment_ids, out, l, m = res
  
      if backward_pass_impl == "xla":
          return jax.vjp(
              functools.partial(
                  mha_reference, sm_scale=sm_scale, causal=causal
              ),
              q,
              k,
              v,
              segment_ids,
          )[1](do)
      elif backward_pass_impl == "triton":
          batch_size, seq_len, num_heads, head_dim = q.shape
          block_q = min(block_q, seq_len)
          block_k = min(block_k, seq_len)
          do_scaled, delta = _preprocess_backward(
              out, do, l, block_q, debug, interpret
          )
          # We accumulate into dq so we need to initialize it to zeros.
          dq = jnp.zeros(q.shape, jnp.float32)
          out_shapes = [
              jax.ShapeDtypeStruct(dq.shape, dq.dtype),
              jax.ShapeDtypeStruct(k.shape, k.dtype),
              jax.ShapeDtypeStruct(v.shape, v.dtype),
          ]
  
          in_specs = [
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda j, k: (j, k, 0), (None, None, seq_len)
              ),
              pl.BlockSpec(
                  lambda j, k: (j, k, 0), (None, None, seq_len)
              ),
              pl.BlockSpec(
                  lambda j, k: (j, k, 0), (None, None, seq_len)
              ),
              pl.BlockSpec(
                  lambda j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
          ]
          if segment_ids is None:
              in_specs.insert(3, None)  # type: ignore[arg-type]
              input_output_aliases = {8: 0}
          else:
              in_specs.insert(
                  3, pl.BlockSpec(lambda j, k: (j, 0), (None, seq_len))
              )
              input_output_aliases = {9: 0}
          grid = (batch_size, num_heads)
          # TODO(sharadmv): figure out why num_warps=8 doesn't work!
          num_warps = 8
          dq, dk, dv = pl.pallas_call(
              functools.partial(
                  mha_backward_kernel,
                  block_q=block_q,
                  block_d=head_dim,
                  block_k=block_k,
                  sm_scale=sm_scale,
                  causal=causal,
              ),
              grid=grid,
              out_shape=out_shapes,
              in_specs=in_specs,
              out_specs=[
                  pl.BlockSpec(
                      lambda j, k: (j, 0, k, 0),
                      (None, seq_len, None, head_dim),
                  ),
                  pl.BlockSpec(
                      lambda j, k: (j, 0, k, 0),
                      (None, seq_len, None, head_dim),
                  ),
                  pl.BlockSpec(
                      lambda j, k: (j, 0, k, 0),
                      (None, seq_len, None, head_dim),
                  ),
              ],
              name="mha_backward",
              debug=debug,
              interpret=interpret,
              num_warps=num_warps,
              num_stages=1,
              input_output_aliases=input_output_aliases,
          )(q, k, v, segment_ids, out, do_scaled, l, m, delta, dq)
      else:
          raise ValueError(
              f"Invalid backward pass implementation: {backward_pass_impl}"
          )
      return dq.astype(q.dtype), dk, dv, None
  
file_path: jax/jax/experimental/pallas/ops/attention.py
imports: from __future__ import annotations
  import functools
  from typing import Any, Optional
  import jax
  from jax import lax
  from jax.experimental import pallas
  import jax.numpy
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: _mha_backward(sm_scale, causal, block_q, block_k, backward_pass_impl, num_warps, num_stages, grid, interpret, debug, res, do) and returns: dq,  dk,  dv,  None, jax