[1]: lDo you want to use gradient clipping? [yes/No]: NoDo you want to enable 'deepspeed. zero. init' when using ZeR0 Stage 3 for constructing massive models? [yes/No]: NoDo you want to enable Mixture of-Experts training (MoE)? [ves/No]:How many cPu(s) should be used for dis...
关于zero-rl的碎碎念和想法 相比cold-start-sft-->rl的传统流程,笔者更偏爱base上的rl。base上的rl在理论和实践层面都对未来模型的优化方向有重要的指导意义。 理论层面: haotian:PPO as Bayesian-Inference,policy-gradient+kl-constraint可以推导出residual-energy-based-model的形式。有了该形式,问题转变为如何高效...
ZeRO-R则针对Residual States的三个方面分别进行优化: Partitioned Activation Checkpointing 在TP中对模型参数进行切分单独计算,但Activation在每个device中都需要一份,则在checkpointing的时候,可以对activation按照TP的方式进行切分,使得每个device保存一部分,需要的时候再进行all-gather,见deepspeed checkpointing代码。 对于...
跳跃的样子，写成代码就是：1class BasicBlock(nn.Module):2 """ 3 Basic residual block with 2 convolutions and a skip connection 4 before the last ReLU activation. 5 """ 6 7 def __init__(self, inplanes, planes, stride=1, downsample=None): 8 super(BasicBloc...
device('cuda') class Flatten(nn.Module): def __init__(self): super(Flatten, self).__init__() def forward(self, x): return x.view(x.size(0), -1) class ResidualBlock(nn.Module): def __init__(self, n_f): super(ResidualBlock, self).__init__() self.residual = nn....
如下图所示,策略-价值网络由 1 个 Convolutional block、19 或 39 个 Residual Block、1 个 Policy Head 和 1 个 Value Head 组成,其中 Policy Head 输出 p ,而 Value Head 输出 v。 Convolutional block 策略-价值网络的第一块是 Convolitional block,它由 1 个卷积层、1 个批归一化层和 1 个 ReLU ...
1classBasicBlock(nn.Module):2"""3 Basic residual block with 2 convolutions and a skip connection4 before the last ReLUactivation.5 """67def__init__(self,inplanes,planes,stride=1,downsample=None):8super(BasicBlock,self).__init__()910self.conv1=nn.Conv2d(inplanes,planes,kernel_size=3...
residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, ...
layer_norm_1(x)) # Residual connection x = x + self.feed_forward_layer(self.layer_norm_2(x)) # Residual connection return x class TransformerLanguageModel(nn.Module): def __init__(self, hyp, max_token_value): super().__init__() self.d_model = hyp["d_model"] self.context_...
defresidual_inner(inputs):conv_layer1=mg_batchn(mg_conv2d(inputs))initial_output=mg_activation(conv_layer1)conv_layer2=mg_batchn(mg_conv2d(initial_output))returnconv_layer2# 残差网络defmg_res_layer(inputs):residual=residual_inner(inputs)# 加一下output=mg_activation(inputs+residual)return...