| 82 | |
| 83 | # --------- Parameter Server -------------------- |
| 84 | class ParameterServer(nn.Module): |
| 85 | def __init__(self, num_gpus=0): |
| 86 | super().__init__() |
| 87 | model = Net(num_gpus=num_gpus) |
| 88 | self.model = model |
| 89 | if torch.accelerator.is_available() and num_gpus > 0: |
| 90 | acc = torch.accelerator.current_accelerator() |
| 91 | self.input_device = torch.device(f'{acc}:0') |
| 92 | else: |
| 93 | self.input_device = torch.device("cpu") |
| 94 | |
| 95 | def forward(self, inp): |
| 96 | inp = inp.to(self.input_device) |
| 97 | out = self.model(inp) |
| 98 | # This output is forwarded over RPC, which as of 1.5.0 only accepts CPU tensors. |
| 99 | # Tensors must be moved in and out of GPU memory due to this. |
| 100 | out = out.to("cpu") |
| 101 | return out |
| 102 | |
| 103 | # Use dist autograd to retrieve gradients accumulated for this model. |
| 104 | # Primarily used for verification. |
| 105 | def get_dist_gradients(self, cid): |
| 106 | grads = dist_autograd.get_gradients(cid) |
| 107 | # This output is forwarded over RPC, which as of 1.5.0 only accepts CPU tensors. |
| 108 | # Tensors must be moved in and out of GPU memory due to this. |
| 109 | cpu_grads = {} |
| 110 | for k, v in grads.items(): |
| 111 | k_cpu, v_cpu = k.to("cpu"), v.to("cpu") |
| 112 | cpu_grads[k_cpu] = v_cpu |
| 113 | return cpu_grads |
| 114 | |
| 115 | # Wrap local parameters in a RRef. Needed for building the |
| 116 | # DistributedOptimizer which optimizes parameters remotely. |
| 117 | def get_param_rrefs(self): |
| 118 | param_rrefs = [rpc.RRef(param) for param in self.model.parameters()] |
| 119 | return param_rrefs |
| 120 | |
| 121 | param_server = None |
| 122 | global_lock = Lock() |
no outgoing calls
no test coverage detected