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fix(examples): te_llama compatibility with transformers >= 4.57 #2572

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fix(examples): te_llama compatibility with transformers >= 4.57 #2572

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fix(examples): te_llama compatibility with HuggingFace transformers >…
sbhavani Jan 7, 2026
3105972
docs(te_llama): add requirements.txt
sbhavani Jan 9, 2026
3568a8d
fix(docs): add missing notebook output names
sbhavani Jan 23, 2026
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5 changes: 5 additions & 0 deletions docs/examples/te_llama/requirements.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,5 @@
transformers==4.57.0
accelerate==1.10.0
peft==0.15.2
datasets==4.0.0
sentencepiece==0.2.1
15 changes: 10 additions & 5 deletions docs/examples/te_llama/te_llama.py
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Expand Up @@ -72,10 +72,15 @@ def forward(self, hidden_states, *args, attention_mask, **kwargs):
forward pass of the `TransformerLayer`. Also, make sure the output
format matches the output of the HF's `LlamaDecoderLayer`.
"""
return (
super().forward(
hidden_states, attention_mask=attention_mask, rotary_pos_emb=self.te_rope_emb
),
# Handle case where hidden_states might be a tuple (from previous layer output)
# This can happen with older versions of HuggingFace transformers
if isinstance(hidden_states, tuple):
hidden_states = hidden_states[0]
Comment on lines +75 to +78
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@greptile-apps greptile-apps bot Jan 9, 2026

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Comment is misleading about when tuple unpacking is needed.

With the new code that returns tensor directly, hidden_states should never be a tuple when called from HuggingFace's LlamaModel forward loop (in any version). The old code returned (tensor,) for transformers < 4.57, but HF's loop extracted it with layer_outputs[0] before passing to the next layer.

This check appears to be defensive programming rather than addressing a real backward compatibility scenario. Consider clarifying the comment to explain this is a safety check rather than expected behavior.

Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!

Comment on lines +75 to +78
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style: Comment is misleading - hidden_states was never actually a tuple in any HF version since the loop always extracted with layer_outputs[0] before passing to next layer. This is defensive programming rather than backward compatibility. Consider clarifying the comment.

Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!


# Return tensor directly for HuggingFace transformers >= 4.57
# (older versions wrapped output in tuple and extracted with layer_outputs[0])
return super().forward(
hidden_states, attention_mask=attention_mask, rotary_pos_emb=self.te_rope_emb
)


Expand Down Expand Up @@ -162,7 +167,7 @@ def replace_params(hf_state_dict, te_state_dict, config):
# collect all layer prefixes to update
all_layer_prefixes = set()
for param_key in hf_state_dict.keys():
layer_prefix_pat = "model.layers.\d+."
layer_prefix_pat = r"model.layers.\d+."
m = re.match(layer_prefix_pat, param_key)
if m is not None:
all_layer_prefixes.add(m.group())
Expand Down
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