Default implementation of Jamba

[erlebach]

Here is a section of code in JambaLM

class Jamba(nn.Module):
    def __init__(self, config: JambaLMConfig):
        super().__init__()

        self.config = config

        # init each model layer, decide if it's mamba/attention and has experts or not
        decoder_layers = []
        for i in range(config.n_layers):
            is_attn = (
                True
                if (i - self.config.attn_layer_offset) % self.config.attn_layer_period
                == 0
                else False
            )
            is_expert = (
                True
                if (i - self.config.expert_layer_offset)
                % self.config.expert_layer_period
                == 0
                else False
            )

You'll notice that the structure of is_attn and is_expert is identical. Furthermore, in the default configuration provided, is_attn=is_expert=False, and they are both true at the same time. As a result, all the layers in this default Jamba architecture are all the same. Of course I can change that, but this is surely not intended given that this code is didactic. Thanks.

[alxndrTL]

You'll notice that the structure of is_attn and is_expert is identical.

Why do you say that ?

is_attn = True if (i - self.config.attn_layer_offset) % self.config.attn_layer_period == 0 else False
is_expert = True if (i - self.config.expert_layer_offset) % self.config.expert_layer_period == 0 else False

for a given i, is_attn and is_expert is certainly not the same, given that the offsets and periods are differents (and indeed they are different in the default config)

As a result, all the layers in this default Jamba architecture are all the same.
I don't know how you can assume that

[erlebach]

UPDATE: I printed out the layers, and they alternate between attn and expert as it should. So it is on me to figure this out.
Thanks for replying to me.