Description
Looking forward to getting to grips with nnx
, I went about implementing the apparently missing gemma2_2b
model.
After coming up with a suitable TransformerConfig
, I was surprised that I couldn't get a forward pass to run : So I added 'Grouped-Query Attention' (mirroring what can be seen in the GDM gemma
library).
Still no dice, since (although I checked all the params were getting set correctly), garbage logits were being returned. So I labouriously went through each layer, checking for numerical differences, only to find that in modules.Block
, the ordering of the sub-modules is clearly in the wrong order (look at the pre_ffw_norm
and the post_attn_norm
, and the residual paths, for instance):
class Block(nnx.Module):
#...
def __call__(self, x, ...):
inputs_normalized = self.pre_attention_norm(x)
cache, attn_output = self.attn(
inputs_normalized,
segment_pos,
cache,
attn_mask,
)
attn_output += x
residual = attn_output
attn_output = self.pre_ffw_norm(attn_output)
if self.use_post_attn_norm:
attn_output = self.post_attn_norm(attn_output)
self.sow_config.maybe_sow_rs_after_attention(attn_output, self)
outputs = self.mlp(attn_output)
if self.use_post_ffw_norm:
outputs = self.post_ffw_norm(outputs)
outputs = residual + outputs
self.sow_config.maybe_sow_rs_after_ffw(outputs, self)
return cache, outputs
After fixing this up, the model's final logits
come out pretty close to the GDM gemma
ones (small errors seem to creep in along the way due to bfloat16/float16). This is good news - though it is slightly worrying that this example has been unusable over the whole period, since its actual functionality as an implementation of gemma models does not appear to have been tested...
Please let me know if you'd like a PR to cover (at minimum) adding a working gemma2_2b