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from dataclasses import dataclass |
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from typing import Union |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from utils import bias_gelu_impl |
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from mamba_config import MambaConfig |
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class MLP(nn.Module): |
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def __init__( |
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self, config: MambaConfig, is_expert: bool = False, layer_idx=None |
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): |
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super().__init__() |
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self.config: MambaConfig = config |
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self.layer = layer_idx |
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ffn_hidden_size_1 = self.config.ffn_hidden_size |
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ffn_hidden_size_2 = self.config.ffn_hidden_size |
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if self.config.gated_linear_unit: |
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ffn_hidden_size_1 *= 2 |
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self.linear_fc1 = nn.Linear(self.config.hidden_size, ffn_hidden_size_1, bias = self.config.add_bias_linear, device = self.config.device) |
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self.linear_fc1.is_expert = is_expert |
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if self.config.gated_linear_unit: |
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def glu(x): |
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x = torch.chunk(x, 2, dim=-1) |
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return self.config.activation_func(x[0]) * x[1] |
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self.activation_func = glu |
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else: |
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self.activation_func = self.config.activation_func |
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self.linear_fc2 = nn.Linear(ffn_hidden_size_2, self.config.hidden_size, bias = self.config.add_bias_linear, device = self.config.device) |
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def forward(self, hidden_states, inference_params=None): |
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intermediate = self.linear_fc1(hidden_states) |
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intermediate = self.activation_func(intermediate) |
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output = self.linear_fc2(intermediate) |
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return output |
