e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
150 lines
4.6 KiB
Python
150 lines
4.6 KiB
Python
"""
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Implementation of Multi-layer Perception (MLP).
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Residual model is taken from https://github.com/ALRhub/d3il/blob/main/agents/models/common/mlp.py
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"""
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import torch
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from torch import nn
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from collections import OrderedDict
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import logging
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activation_dict = nn.ModuleDict(
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{
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"ReLU": nn.ReLU(),
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"ELU": nn.ELU(),
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"GELU": nn.GELU(),
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"Tanh": nn.Tanh(),
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"Mish": nn.Mish(),
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"Identity": nn.Identity(),
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"Softplus": nn.Softplus(),
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}
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)
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class MLP(nn.Module):
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def __init__(
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self,
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dim_list,
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append_dim=0,
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append_layers=None,
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activation_type="Tanh",
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out_activation_type="Identity",
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use_layernorm=False,
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use_layernorm_final=False,
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dropout=0,
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use_drop_final=False,
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verbose=False,
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):
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super(MLP, self).__init__()
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# Construct module list: if use `Python List`, the modules are not
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# added to computation graph. Instead, we should use `nn.ModuleList()`.
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self.moduleList = nn.ModuleList()
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self.append_layers = append_layers
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num_layer = len(dim_list) - 1
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for idx in range(num_layer):
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i_dim = dim_list[idx]
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o_dim = dim_list[idx + 1]
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if append_dim > 0 and idx in append_layers:
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i_dim += append_dim
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linear_layer = nn.Linear(i_dim, o_dim)
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# Add module components
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layers = [("linear_1", linear_layer)]
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if use_layernorm and (idx < num_layer - 1 or use_layernorm_final):
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layers.append(("norm_1", nn.LayerNorm(o_dim)))
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if dropout > 0 and (idx < num_layer - 1 or use_drop_final):
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layers.append(("dropout_1", nn.Dropout(dropout)))
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# add activation function
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act = (
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activation_dict[activation_type]
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if idx != num_layer - 1
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else activation_dict[out_activation_type]
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)
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layers.append(("act_1", act))
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# re-construct module
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module = nn.Sequential(OrderedDict(layers))
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self.moduleList.append(module)
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if verbose:
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logging.info(self.moduleList)
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def forward(self, x, append=None):
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for layer_ind, m in enumerate(self.moduleList):
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if append is not None and layer_ind in self.append_layers:
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x = torch.cat((x, append), dim=-1)
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x = m(x)
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return x
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class ResidualMLP(nn.Module):
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"""
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Simple multi layer perceptron network with residual connections for
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benchmarking the performance of different networks. The resiudal layers
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are based on the IBC paper implementation, which uses 2 residual lalyers
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with pre-actication with or without dropout and normalization.
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"""
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def __init__(
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self,
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dim_list,
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activation_type="Mish",
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out_activation_type="Identity",
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use_layernorm=False,
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use_layernorm_final=False,
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):
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super(ResidualMLP, self).__init__()
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hidden_dim = dim_list[1]
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num_hidden_layers = len(dim_list) - 3
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assert num_hidden_layers % 2 == 0
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self.layers = nn.ModuleList([nn.Linear(dim_list[0], hidden_dim)])
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self.layers.extend(
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[
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TwoLayerPreActivationResNetLinear(
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hidden_dim=hidden_dim,
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activation_type=activation_type,
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use_layernorm=use_layernorm,
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)
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for _ in range(1, num_hidden_layers, 2)
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]
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)
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self.layers.append(nn.Linear(hidden_dim, dim_list[-1]))
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if use_layernorm_final:
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self.layers.append(nn.LayerNorm(dim_list[-1]))
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self.layers.append(activation_dict[out_activation_type])
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def forward(self, x):
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for _, layer in enumerate(self.layers):
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x = layer(x)
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return x
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class TwoLayerPreActivationResNetLinear(nn.Module):
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def __init__(
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self,
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hidden_dim,
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activation_type="Mish",
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use_layernorm=False,
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):
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super().__init__()
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self.l1 = nn.Linear(hidden_dim, hidden_dim)
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self.l2 = nn.Linear(hidden_dim, hidden_dim)
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self.act = activation_dict[activation_type]
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if use_layernorm:
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self.norm1 = nn.LayerNorm(hidden_dim, eps=1e-06)
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self.norm2 = nn.LayerNorm(hidden_dim, eps=1e-06)
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def forward(self, x):
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x_input = x
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if hasattr(self, "norm1"):
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x = self.norm1(x)
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x = self.l1(self.act(x))
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if hasattr(self, "norm2"):
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x = self.norm2(x)
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x = self.l2(self.act(x))
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return x + x_input
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