Creates tensor in target device to avoid high CPU occupation. (#695)

anyvoice

2023-07-02 cf36ce977c0b8bd54c906e63ce31931ac060178f

Creates tensor in target device to avoid high CPU occupation. (#695)

 funasr/modules/embedding.py

@@ -393,8 +393,9 @@
    def encode(self, positions: torch.Tensor = None, depth: int = None, dtype: torch.dtype = torch.float32):
        batch_size = positions.size(0)
        positions = positions.type(dtype)
        log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype)) / (depth / 2 - 1)
        inv_timescales = torch.exp(torch.arange(depth / 2).type(dtype) * (-log_timescale_increment))
        device = positions.device
        log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype, device=device)) / (depth / 2 - 1)
        inv_timescales = torch.exp(torch.arange(depth / 2, device=device).type(dtype) * (-log_timescale_increment))
        inv_timescales = torch.reshape(inv_timescales, [batch_size, -1])
        scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape(inv_timescales, [1, 1, -1])
        encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2)
@@ -402,7 +403,7 @@

    def forward(self, x):
        batch_size, timesteps, input_dim = x.size()
        positions = torch.arange(1, timesteps+1)[None, :]
        positions = torch.arange(1, timesteps+1, device=x.device)[None, :]
        position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)

        return x + position_encoding