(self, args)
| 77 | |
| 78 | class FalconAttentionFused(nn.Module): |
| 79 | def __init__(self, args): |
| 80 | super().__init__() |
| 81 | self.args = args |
| 82 | self.n_local_heads = args.n_head |
| 83 | self.head_dim = args.hidden_size // args.n_head |
| 84 | |
| 85 | self.query_key_value = nn.Linear( |
| 86 | args.hidden_size, |
| 87 | args.n_head * self.head_dim + 2 * self.head_dim, |
| 88 | bias=False, |
| 89 | ) |
| 90 | |
| 91 | self.dense = nn.Linear( |
| 92 | args.n_head * self.head_dim, |
| 93 | args.hidden_size, |
| 94 | bias=False, |
| 95 | ) |
| 96 | |
| 97 | # following fastertransformer definition |
| 98 | |
| 99 | self.cache_v = ( |
| 100 | torch.zeros( |
| 101 | ( |
| 102 | max_batch_size, |
| 103 | 1, |
| 104 | max_seq_len, |
| 105 | self.head_dim, |
| 106 | ) |
| 107 | ) |
| 108 | .cuda() |
| 109 | .half() |
| 110 | ) # added to half |
| 111 | # 8: pack 8 fp16 in FT, if fp32 then use 4 |
| 112 | self.cache_k = ( |
| 113 | torch.zeros( |
| 114 | ( |
| 115 | max_batch_size, |
| 116 | 1, |
| 117 | self.head_dim // 8, |
| 118 | max_seq_len, |
| 119 | 8, |
| 120 | ) |
| 121 | ) |
| 122 | .cuda() |
| 123 | .half() |
| 124 | ) # added to half |
| 125 | |
| 126 | self.rotary_emb = RotaryEmbedding(self.head_dim) |
| 127 | |
| 128 | def forward( |
| 129 | self, |
nothing calls this directly
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