code: def test_consistency(
      adjacents: bool,
      dim: int,
      padding: int,
      groups: int,
      internal_dtype: str,
      dtype_str: str,
  ):
      torch.manual_seed(1)
      heads, kvheads = 10, 2
      nqueries = [2, 1, 1]
      cache_lens = [27, padding - 5, padding // 2]
      device = torch.device("cuda")
      dtype = DTYPES[dtype_str]
  
      # Can we make the internals of attn_bias be on the gpu.
      attn_bias = (
          BlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
              q_seqlen=nqueries,
              kv_padding=padding,
              kv_seqlen=cache_lens,
          )
      )
  
      total_cache_length = len(cache_lens) * padding
      total_nqueries = sum(nqueries)
      if groups == 1:
          cache_k = torch.rand(
              1,
              total_cache_length,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          cache_v = torch.rand(
              1,
              total_cache_length,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          xq = torch.rand(
              1, total_nqueries, heads, dim, device=device, dtype=dtype
          )
          xk = torch.rand(
              1,
              total_nqueries,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          xv = torch.rand(
              1,
              total_nqueries,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
      else:
          cache_k = torch.rand(
              1,
              total_cache_length,
              groups,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          cache_v = torch.rand(
              1,
              total_cache_length,
              groups,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          xq = torch.rand(
              1,
              total_nqueries,
              groups,
              heads,
              dim,
              device=device,
              dtype=dtype,
          )
          xk = torch.rand(
              1,
              total_nqueries,
              groups,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
          xv = torch.rand(
              1,
              total_nqueries,
              groups,
              kvheads,
              dim,
              device=device,
              dtype=dtype,
          )
  
      cache_k_orig = cache_k.clone()
      cache_v_orig = cache_v.clone()
      out = rope_padded(
          xq,
          xk,
          xv,
          cache_k,
          cache_v,
          attn_bias,
          adjacents=adjacents,
          internal_dtype=internal_dtype,
      )
  
      seqpos = torch.tensor(
          [
              cache_lens[0] - 2,
              cache_lens[0] - 1,
              cache_lens[1] - 1,
              cache_lens[2] - 1,
          ],
          device=device,
      )
      cache_locs = [
          seqpos[0],
          seqpos[1],
          padding + seqpos[2],
          2 * padding + seqpos[3],
      ]
      baseline = _slow_rope if dtype_str == "f32" else _slow_rope2
      expected_out = baseline(xq, seqpos=seqpos, adjacents=adjacents)
      atol = 5e-3 if dim > 4000 and dtype_str != "f32" else 3e-3
      rtol = 8e-3 if dtype_str == "bf16" else 1e-5
      assert_allclose(out, expected_out, atol=atol, rtol=rtol)
  
      assert_allclose(cache_v[:, cache_locs], xv, atol=atol, rtol=rtol)
      cache_v[:, cache_locs] = cache_v_orig[:, cache_locs]
      assert torch.allclose(cache_v, cache_v_orig)
  
      slow_roped_xk = _slow_rope(xk, seqpos=seqpos, adjacents=adjacents)
      assert_allclose(
          cache_k[:, cache_locs],
          slow_roped_xk,
          atol=atol,
          rtol=rtol,
      )
      cache_k[:, cache_locs] = cache_k_orig[:, cache_locs]
      assert torch.allclose(cache_k, cache_k_orig)
  
file_path: xformers/tests/test_rope_padded.py
imports: from typing import Optional
  import pytest
  import torch
  from xformers.ops import rope_padded
  from xformers.ops.fmha.attn_bias import BlockDiagonalCausalWithOffsetPaddedKeysMask
  from .utils import assert_allclose
label_desc: Use image node_id 3 for calling a global function with example usage: test_consistency(adjacents, dim, padding, groups, internal_dtype, dtype_str) without return types