self-attention layer abstract class. Self-attention layer takes input with size [b, s, h] and returns output of the same size.
| 50 | |
| 51 | |
| 52 | class SelfAttention(torch.nn.Module): |
| 53 | """self-attention layer abstract class. |
| 54 | Self-attention layer takes input with size [b, s, h] |
| 55 | and returns output of the same size. |
| 56 | """ |
| 57 | |
| 58 | def __init__( |
| 59 | self, |
| 60 | hidden_size, |
| 61 | num_attention_heads, |
| 62 | layer_number, |
| 63 | fp16=True, |
| 64 | attention_softmax_in_fp32=True, |
| 65 | ): |
| 66 | super(SelfAttention, self).__init__() |
| 67 | self.hidden_size = hidden_size |
| 68 | self.num_attention_heads = num_attention_heads |
| 69 | self.fp16 = fp16 |
| 70 | self.attention_softmax_in_fp32 = attention_softmax_in_fp32 |
| 71 | self.layer_number = max(1, layer_number) |
| 72 | |
| 73 | assert self.hidden_size % self.num_attention_heads == 0 |
| 74 | self.hidden_size_per_attention_head = int(self.hidden_size // self.num_attention_heads) |
| 75 | |
| 76 | self.query = torch.nn.Linear(self.hidden_size, self.hidden_size) |
| 77 | self.key = torch.nn.Linear(self.hidden_size, self.hidden_size) |
| 78 | self.value = torch.nn.Linear(self.hidden_size, self.hidden_size) |
| 79 | |
| 80 | self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) |
| 81 | self.softmax = torch.nn.Softmax(dim=-1) |
| 82 | |
| 83 | self.dense = torch.nn.Linear(self.hidden_size, self.hidden_size) |
| 84 | |
| 85 | def forward( |
| 86 | self, |
| 87 | hidden_states, |
| 88 | attention_mask, |
| 89 | layer_past=None, |
| 90 | get_key_value=False, |
| 91 | prompt_length=None, |
| 92 | context_length=None, |
| 93 | layer_id=0, |
| 94 | ): |
| 95 | # hidden_states: [sq, b, h] |
| 96 | |
| 97 | # ===================== |
| 98 | # Query, Key, and Value |
| 99 | # ===================== |
| 100 | |
| 101 | if hasattr(torch._C, 'grouped_matmul_bias') and not isinstance(self.query, QuantizedLinear): |
| 102 | query_layer, key_layer, value_layer = torch._C.grouped_matmul_bias([hidden_states, hidden_states, hidden_states], |
| 103 | [self.query.weight, self.key.weight, self.value.weight], |
| 104 | [self.query.bias, self.key.bias, self.value.bias]) |
| 105 | else: |
| 106 | query_layer = self.query(hidden_states) |
| 107 | key_layer = self.key(hidden_states) |
| 108 | value_layer = self.value(hidden_states) |
| 109 |