code: def test_xformers_single_forward_parity(
      device,
      attn_dtype,
      key_padding_dtype,
      add_bias_kv,
      add_zero_attn,
      static_kv,
      batch_size,
      embedding,
      seq_len,
      num_heads,
  ):
      xformers_att_config = '{"name": "scaled_dot_product"}'
  
      attn_mask = (
          None
          if attn_dtype is None
          else _get_mask(
              to_dtype=attn_dtype, dim0=seq_len, dim1=seq_len
          ).to(device)
      )
      key_padding_mask = (
          None
          if key_padding_dtype is None
          else _get_mask(
              to_dtype=key_padding_dtype, dim0=batch_size, dim1=seq_len
          ).to(device)
      )
  
      q = torch.rand(seq_len, batch_size, embedding).to(device)
      q.requires_grad = True
      k = torch.rand(seq_len, batch_size, embedding).to(device)
      k.requires_grad = True
      v = torch.rand(seq_len, batch_size, embedding).to(device)
      v.requires_grad = True
  
      q_ = q.detach().clone()
      q_.requires_grad = True
      k_ = k.detach().clone()
      k_.requires_grad = True
      v_ = v.detach().clone()
      v_.requires_grad = True
  
      # TODO: dropouts in the two implementations lead to different entries dropped.
      _reset_seeds()
      xformers_mha = MultiheadAttention(
          embedding,
          num_heads,
          dropout=0.0,
          xformers_att_config=xformers_att_config,
          add_bias_kv=add_bias_kv,
          add_zero_attn=add_zero_attn,
      ).to(device)
      xformers_output, _ = xformers_mha(
          q,
          k,
          v,
          key_padding_mask=key_padding_mask,
          attn_mask=attn_mask,
          static_kv=static_kv,
      )
  
      _reset_seeds()
      original_mha = MultiheadAttention(
          embedding,
          num_heads,
          dropout=0.0,
          xformers_att_config=None,
          add_bias_kv=add_bias_kv,
          add_zero_attn=add_zero_attn,
      ).to(device)
      original_output, _ = original_mha(
          q_,
          k_,
          v_,
          key_padding_mask=key_padding_mask,
          attn_mask=attn_mask,
          static_kv=static_kv,
      )
  
      # account for when nan != nan
      if xformers_output.isnan().any() or original_output.isnan().any():
          rand = random.uniform(0, 1)
          xformers_output = xformers_output.masked_fill(
              xformers_output.isnan(), rand
          )
          original_output = original_output.masked_fill(
              original_output.isnan(), rand
          )
  
      # torch.equal works for cpu, on cuda allclose is needed.
      assert torch.allclose(
          xformers_output, original_output, atol=1e-06
      ), f"max diff is {torch.max(torch.abs(xformers_output - original_output))}"
  
      loss_xformers = torch.norm(xformers_output)
      loss_original = torch.norm(original_output)
      loss_xformers.backward()
      loss_original.backward()
  
      # torch.equal works for cpu, on cuda allclose is needed.
      assert torch.allclose(
          q.grad, q_.grad
      ), f"max diff is {torch.max(torch.abs(q.grad - q_.grad))}"
      assert torch.allclose(
          k.grad, k_.grad
      ), f"max diff is {torch.max(torch.abs(k.grad - k_.grad))}"
      assert torch.allclose(
          v.grad, v_.grad
      ), f"max diff is {torch.max(torch.abs(v.grad - v_.grad))}"
  
file_path: fairseq/tests/test_multihead_attention.py
imports: import random
  import unittest
  import pytest
  import torch
  from fairseq.modules.multihead_attention import MultiheadAttention, _mask_for_xformers
label_desc: Use image node_id 6 for calling a global function with example usage: test_xformers_single_forward_parity(device, attn_dtype, key_padding_dtype, add_bias_kv, add_zero_attn, static_kv, batch_size, embedding, seq_len, num_heads) without return types