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Address Scalings in Dolomite Conversion #8

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Address Scalings in Dolomite Conversion #8

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34 changes: 28 additions & 6 deletions src/instructlab/dolomite/hf_models/model_conversion/llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,6 +229,9 @@ def export_to_huggingface_llama(
config.num_key_value_heads,
config.n_embd // config.n_head,
AttentionHeadType(config.attention_head_type),
m_emb=config.m_emb,
m_residual=config.m_residual,
# m_width=config.m_width,
)

SafeTensorsWeightsManager.save_state_dict(state_dict, save_path)
Expand Down Expand Up @@ -285,11 +288,25 @@ def _export_state_dict_to_huggingface(
num_key_value_heads: int,
head_dim: int,
attention_head_type: AttentionHeadType,
m_residual: float = None,
m_emb: float = None,
m_width: float = None,
) -> None:
if m_residual is None:
m_residual = 1.
if m_emb is None:
m_emb = 1.

# NOTE: this will not work since the norms are tied
# has_m_width = False
# if m_width is None:
# has_m_width = True
# m_width = 1.

state_dict = {
"model.embed_tokens.weight": safetensors_weight_manager.get_tensor(
"transformer.wte.weight"
),
) * m_emb,
"model.norm.weight": safetensors_weight_manager.get_tensor(
"transformer.ln_f.weight"
),
Expand All @@ -298,7 +315,12 @@ def _export_state_dict_to_huggingface(
if safetensors_weight_manager.has_tensor("lm_head.weight"):
state_dict["lm_head.weight"] = safetensors_weight_manager.get_tensor(
"lm_head.weight"
)
) / m_width
# elif has_m_width:
# # int this we cannot tie
# state_dict["lm_head.weight"] = safetensors_weight_manager.get_tensor(
# "transformer.wte.weight"
# ) / m_width

for layer_idx in range(num_layers):
state_dict[f"model.layers.{layer_idx}.input_layernorm.weight"] = (
Expand Down Expand Up @@ -332,13 +354,13 @@ def _export_state_dict_to_huggingface(
state_dict[f"model.layers.{layer_idx}.mlp.down_proj.weight"] = (
safetensors_weight_manager.get_tensor(
f"transformer.h.{layer_idx}.mlp.c_proj.weight"
)
) * m_residual
)
if f"transformer.h.{layer_idx}.mlp.c_proj.bias" in safetensors_weight_manager:
state_dict[f"model.layers.{layer_idx}.mlp.down_proj.bias"] = (
safetensors_weight_manager.get_tensor(
f"transformer.h.{layer_idx}.mlp.c_proj.bias"
)
) * m_residual
)

query_weight, key_weight, value_weight = (
Expand Down Expand Up @@ -376,12 +398,12 @@ def _export_state_dict_to_huggingface(
safetensors_weight_manager.get_tensor(
f"transformer.h.{layer_idx}.attn.c_proj.weight"
)
)
) * m_residual
if f"transformer.h.{layer_idx}.attn.c_proj.bias" in safetensors_weight_manager:
state_dict[f"model.layers.{layer_idx}.self_attn.o_proj.bias"] = (
safetensors_weight_manager.get_tensor(
f"transformer.h.{layer_idx}.attn.c_proj.bias"
)
)
) * m_residual

return state_dict