code: def forward(
      ctx,
      input,
      context,
      input_mask,
      config,
      attn_qkvw,
      attn_qw,
      attn_kw,
      attn_vw,
      attn_qkvb,
      num_attention_heads_per_partition,
      norm_factor,
      hidden_size_per_partition,
      attn_ow,
      attn_ob,
      do_out_bias,
      score_context_func,
      linear_func,
      triton_flash_attn_kernel,
      rope_theta,
  ):
      def _transpose_for_context(x):
          x = x.permute(0, 2, 1, 3)
          new_x_layer_shape = x.size()[:-2] + (
              hidden_size_per_partition,
          )
          return x.reshape(*new_x_layer_shape)
  
      def _transpose_for_scores(x):
          attention_head_size = (
              x.shape[-1] // num_attention_heads_per_partition
          )
          new_x_shape = x.size()[:-1] + (
              num_attention_heads_per_partition,
              attention_head_size,
          )
          x = x.reshape(*new_x_shape)
          x = x.permute(0, 2, 1, 3)
          return x.contiguous()
  
      def selfAttention_fp(input, context, input_mask):
          if (
              config.dtype in [torch.half, torch.float16]
              and input.dtype == torch.float32
          ):
              input = input.half()
          head_size = input.shape[-1] // config.heads
          do_flash_attn = head_size <= 128
          scale = (1 / norm_factor) * (1 / norm_factor)
          if do_flash_attn and context is None:
              qkv_out = linear_func(
                  input,
                  attn_qkvw,
                  attn_qkvb if attn_qkvb is not None else attn_qkvw,
                  attn_qkvb is not None,
                  do_flash_attn,
                  config.heads,
                  False,
                  rope_theta,
              )
  
              context_layer = triton_flash_attn_kernel(
                  qkv_out[0],
                  qkv_out[1],
                  qkv_out[2],
                  scale,
                  input.shape[-2] % 128 == 0,
              )
              context_layer = _transpose_for_context(
                  context_layer[:, :, :, :head_size]
              )
  
          else:
              do_flash_attn = False
              if context is not None:
                  query = torch.matmul(input, attn_qw)
                  key = torch.matmul(context, attn_kw)
                  value = torch.matmul(context, attn_vw)
              else:
                  qkv = torch.matmul(input, attn_qkvw)
                  query, key, value = qkv.chunk(3, dim=-1)
                  query = query.contiguous()
                  key = key.contiguous()
                  value = value.contiguous()
              query, key, value = inference_module.pad_transform_fp16(
                  query, key, value, config.heads, do_flash_attn
              )
              attention_scores = (
                  torch.matmul(query, key.transpose(-1, -2)) * scale
              ).softmax(dim=-1)
              context_layer = _transpose_for_context(
                  torch.matmul(attention_scores, value)
              )
  
          output = linear_func(
              context_layer,
              attn_ow,
              attn_ob,
              do_out_bias,
              False,
              config.heads,
              False,
              rope_theta,
          )
          return output
  
      output = selfAttention_fp(input, context, input_mask)
  
      return output
  
file_path: DeepSpeed/deepspeed/ops/transformer/inference/diffusers_attention.py
imports: import math
  import torch
  from torch.autograd import Function
  import torch.nn
  from packaging import version
  from deepspeed.utils.logging import log_dist
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import InferenceBuilder
label_desc: Use image node_id 1 for calling the DeepSpeedDiffusersAttentionFunction obj's underlying member method code with example usage: obj.forward(ctx, input, context, input_mask, config, attn_qkvw, attn_qw, attn_kw, attn_vw, attn_qkvb, num_attention_heads_per_partition, norm_factor, hidden_size_per_partition, attn_ow, attn_ob, do_out_bias, score_context_func, linear_func, triton_flash_attn_kernel, rope_theta) and returns: output, x, x, output