| 108 | """ |
| 109 | |
| 110 | def __init__(self, init_method, |
| 111 | output_layer_init_method, layer_number): |
| 112 | super(ParallelSelfAttention, self).__init__() |
| 113 | args = get_args() |
| 114 | self.fp16 = args.fp16 |
| 115 | self.attention_softmax_in_fp32 = args.attention_softmax_in_fp32 |
| 116 | self.layer_number = max(1, layer_number) |
| 117 | |
| 118 | # Per attention head and per partition values. |
| 119 | world_size = mpu.get_model_parallel_world_size() |
| 120 | self.hidden_size_per_partition = mpu.divide( |
| 121 | args.hidden_size // 2 if args.compress else args.hidden_size, |
| 122 | world_size) |
| 123 | self.hidden_size_per_attention_head = mpu.divide( |
| 124 | args.hidden_size // 2 if args.compress else args.hidden_size, args.num_attention_heads) |
| 125 | self.num_attention_heads_per_partition = mpu.divide( |
| 126 | args.num_attention_heads, world_size) |
| 127 | if hasattr(args, 'attention_upweight'): |
| 128 | self.attention_upweight = args.attention_upweight |
| 129 | else: |
| 130 | self.attention_upweight = None |
| 131 | # Strided linear layer. |
| 132 | self.query = mpu.ColumnParallelLinear( |
| 133 | args.hidden_size, |
| 134 | args.hidden_size // 2 if args.compress else args.hidden_size, |
| 135 | gather_output=False, |
| 136 | init_method=init_method) |
| 137 | self.key = mpu.ColumnParallelLinear( |
| 138 | args.hidden_size, |
| 139 | args.hidden_size // 2 if args.compress else args.hidden_size, |
| 140 | gather_output=False, |
| 141 | init_method=init_method) |
| 142 | self.value = mpu.ColumnParallelLinear( |
| 143 | args.hidden_size, |
| 144 | args.hidden_size // 2 if args.compress else args.hidden_size, |
| 145 | gather_output=False, |
| 146 | init_method=init_method) |
| 147 | |
| 148 | self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) |
| 149 | self.softmax = torch.nn.Softmax(dim=-1) |
| 150 | |
| 151 | # Dropout. Note that for a single iteration, this layer will generate |
| 152 | # different outputs on different number of parallel partitions but |
| 153 | # on average it should not be partition dependent. |
| 154 | self.attention_dropout = torch.nn.Dropout(args.attention_dropout) |
| 155 | |
| 156 | # Output. |
| 157 | self.dense = mpu.RowParallelLinear( |
| 158 | args.hidden_size // 2 if args.compress else args.hidden_size, |
| 159 | args.hidden_size, |
| 160 | input_is_parallel=True if args.tensor_model_parallel_size > 1 else False, |
| 161 | init_method=output_layer_init_method, |
| 162 | skip_bias_add=True) |
| 163 | |
| 164 | def forward( |
| 165 | self, |