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import torch
from torch import nn
from mamba_ssm import Mamba2 as Mamba
import math
class MambaModel(nn.Module):
config = {}
def __init__(self, positional_embedding):
super().__init__()
mamba_config = {
"d_model": self.config["d_model"],
"d_state": self.config["d_state"],
"d_conv": self.config["d_conv"],
"expand": self.config["expand"],
}
self.mamba_forward = nn.Sequential(*[Mamba(**mamba_config) for _ in range(self.config["num_layers"])])
pe = positional_embedding[None, :, :]
if self.config.get("trainable_pe"):
self.pe = nn.Parameter(pe)
else: # fixed positional embedding
self.register_buffer("pe", pe)
def forward(self, output_shape, condition=None):
assert len(condition.shape) == 3
x = self.mamba_forward(self.pe.repeat(output_shape[0], 1, 1) + condition)
return x
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