def attention_pytorch(qkv, dropout_p=0.0, causal=True):
    """
    Arguments:
        qkv: (batch_size, seqlen, 3, nheads, head_dim)
        dropout_p: float
    Output:
        output: (batch_size, seqlen, nheads, head_dim)
    """
    batch_size, seqlen, _, nheads, d = qkv.shape
    q, k, v = qkv.unbind(dim=2)
    q = rearrange(q, 'b t h d -> (b h) t d')
    k = rearrange(k, 'b s h d -> (b h) d s')
    softmax_scale = 1.0 / math.sqrt(d)
    # Preallocate attn_weights for `baddbmm`
    scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device)
    scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale),
                       '(b h) t s -> b h t s', h=nheads)
    if causal:
        # "triu_tril_cuda_template" not implemented for 'BFloat16'
        # So we have to construct the mask in float
        causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
        # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
        scores = scores + causal_mask.to(dtype=scores.dtype)
    attention = torch.softmax(scores, dim=-1)
    attention_drop = F.dropout(attention, dropout_p)
    output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
    return output.to(dtype=qkv.dtype)

    Arguments:
        qkv: (batch_size, seqlen, 3, nheads, head_dim)
        dropout_p: float
    Output:
        output: (batch_size, seqlen, nheads, head_dim)
    
benchmarks/benchmark_causal.py