code: def forward(
      ctx,
      use_time_mask,
      is_training,
      heads,
      scale,
      inputs,
      input_weights,
      output_weights,
      input_biases,
      output_biases,
      mask,
      is_additive_mask,
      dropout_prob,
  ):
      use_biases_t = torch.tensor([input_biases is not None])
      heads_t = torch.tensor([heads])
      scale_t = torch.tensor([scale])
      dropout_prob_t = torch.tensor([dropout_prob])
      null_tensor = torch.tensor([])
      head_dim = inputs.size(2) // heads
  
      # Input Linear GEMM
      # input1: (activations) [seql_q, seqs, embed_dim(1024)]
      # input2: (weights)     [embed_dim*3 (3072), embed_dim (1024)] (transpose [0,1])
      # output:               [seql_q, seqs, embed_dim*3]
      # GEMM: ( (seql_q*seqs) x embed_dim ) x ( embed_dim x embed_dim*3 ) = (seql_q*seqs x embed_dim*3)
      if use_biases_t[0]:
          input_lin_results = torch.addmm(
              input_biases,
              inputs.view(
                  inputs.size(0) * inputs.size(1), inputs.size(2)
              ),
              input_weights.transpose(0, 1),
              beta=1.0,
              alpha=1.0,
          )
      else:
          input_lin_results = torch.mm(
              inputs.view(
                  inputs.size(0) * inputs.size(1), inputs.size(2)
              ),
              input_weights.transpose(0, 1),
          )
      input_lin_results = input_lin_results.view(
          inputs.size(0), inputs.size(1), input_weights.size(0)
      )
  
      # Slice out q,k,v from one big Input Linear outuput (should only impact meta data, no copies!)
      # Sequences and heads are combined to make the batch of the Batched GEMM
      # input_lin_results: [seql_q, seqs, heads(16), 3, head_dim(64)]
      # input_lin_results: [seql_q, batches=seqs*heads, 3, head_dim]
      input_lin_results = input_lin_results.view(
          inputs.size(0), inputs.size(1) * heads, 3, head_dim
      )
      queries = input_lin_results[:, :, 0, :]
      keys = input_lin_results[:, :, 1, :]
      values = input_lin_results[:, :, 2, :]
  
      # Matmul1 Batched GEMMs
      # The output tensor is specified prior to the Batch GEMM because baddbmm requires its specification
      # baddbmm is used to apply the scale parameter via the Batched GEMM's alpha parameter instead of
      # a separate elementwise operation.
      # Input1: (Queries) [seql_q, seqs*heads, head_dim] tranpose(0,1)
      # Input2: (Keys)    [seql_k, seqs*heads, head_dim] transpose(0,1)
      # output:           [seqs*heads, seql_q, seql_k]
      # GEMM: Per batch: ( seql_q x head_dim ) x ( head_dim x seql_k ) = ( seql_q x seql_k )
      matmul1_results = torch.empty(
          (queries.size(1), queries.size(0), keys.size(0)),
          dtype=queries.dtype,
          device=torch.device("cuda"),
      )
      matmul1_results = torch.baddbmm(
          matmul1_results,
          queries.transpose(0, 1),
          keys.transpose(0, 1).transpose(1, 2),
          out=matmul1_results,
          beta=0.0,
          alpha=scale_t[0],
      )
  
      if mask is not None:
          # Self Attention Time Mask
          if use_time_mask:
              assert len(mask.size()) == 2, "Timing mask is not 2D!"
              assert mask.size(0) == mask.size(
                  1
              ), "Sequence length should match!"
              mask = mask.to(torch.bool)
              matmul1_results = matmul1_results.masked_fill_(
                  mask, float("-inf")
              )
          # Key Padding Mask
          else:
              batches, seql_q, seql_k = matmul1_results.size()
              seqs = int(batches / heads)
              matmul1_results = matmul1_results.view(
                  seqs, heads, seql_q, seql_k
              )
              if is_additive_mask:
                  matmul1_results = matmul1_results + mask.unsqueeze(
                      1
                  ).unsqueeze(2)
              else:
                  mask = mask.to(torch.bool)
                  matmul1_results = matmul1_results.masked_fill_(
                      mask.unsqueeze(1).unsqueeze(2), float("-inf")
                  )
              matmul1_results = matmul1_results.view(
                  seqs * heads, seql_q, seql_k
              )
  
      softmax_results = F.softmax(matmul1_results, dim=-1)
  
      # Dropout - is not executed for inference
      if is_training:
          dropout_results, dropout_mask = torch._fused_dropout(
              softmax_results, p=(1.0 - dropout_prob_t[0])
          )
      else:
          dropout_results = softmax_results
          dropout_mask = null_tensor
  
      # Matmul2 Batched GEMMs
      # The output tensor specification is needed here to specify the non-standard output.
      # Given that pytorch cannot currently perform autograd with an output tensor specified,
      # this requires a backward pass specified.
      # Input1: from_softmax [seqs*heads, seql_q, seql_k]
      # Input2: (values)     [seql_v, seqs*heads, head_dim] transpose(0,1)
      # Output:              [seql_q, seqs*heads, head_dim] transpose(0,1)
      # GEMM: Per batch: ( seql_q x seql_k ) x ( seql_k x head_dim ) = (seql_q x head_dim)
      matmul2_results = torch.empty(
          (
              dropout_results.size(1),
              dropout_results.size(0),
              values.size(2),
          ),
          dtype=dropout_results.dtype,
          device=torch.device("cuda"),
      ).transpose(1, 0)
      matmul2_results = torch.bmm(
          dropout_results, values.transpose(0, 1), out=matmul2_results
      )
      matmul2_results = (
          matmul2_results.transpose(0, 1)
          .contiguous()
          .view(inputs.size(0), inputs.size(1), inputs.size(2))
      )
  
      # Output Linear GEMM
      # Input1: (activations) [seql_q, seqs, embed_dim=heads*head_dim]
      # Input2: (weights)     [ embed_dim, embed_dim ] transpose(0,1)
      # Output:               [ seql_q, seqs, embed_dim ]
      # GEMM: ( seql_q*seqs x embed_dim ) x ( embed_dim x embed_dim ) = ( seql_q*seqs x embed_dim )
      if use_biases_t[0]:
          outputs = torch.addmm(
              output_biases,
              matmul2_results.view(
                  inputs.size(0) * inputs.size(1), inputs.size(2)
              ),
              output_weights.transpose(0, 1),
              beta=1.0,
              alpha=1.0,
          )
      else:
          outputs = torch.mm(
              matmul2_results.view(
                  inputs.size(0) * inputs.size(1), inputs.size(2)
              ),
              output_weights.transpose(0, 1),
          )
      outputs = outputs.view(
          inputs.size(0), inputs.size(1), output_weights.size(0)
      )
  
      ctx.save_for_backward(
          use_biases_t,
          heads_t,
          scale_t,
          matmul2_results,
          dropout_results,
          softmax_results,
          input_lin_results,
          inputs,
          input_weights,
          output_weights,
          dropout_mask,
          dropout_prob_t,
      )
  
      return outputs.detach()
  
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 1 for calling the SelfAttnFunc obj's underlying member method code with example usage: obj.forward(ctx, use_time_mask, is_training, heads, scale, inputs, input_weights, output_weights, input_biases, output_biases, mask, is_additive_mask, dropout_prob) and returns: outputs