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# scripts for converting pretrained hf model weights to fla style
# calling the code to make conversions for mistralai/Mistral-7B-v0.1 would achieve the following results:
# | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
# |--------------|------:|------|-----:|----------|-----:|---|-----:|
# |arc_challenge | 1|none | 0|acc |0.5043|± |0.0146|
# | | |none | 0|acc_norm |0.5392|± |0.0146|
# |arc_easy | 1|none | 0|acc |0.8081|± |0.0081|
# | | |none | 0|acc_norm |0.7946|± |0.0083|
# |boolq | 2|none | 0|acc |0.8373|± |0.0065|
# |copa | 1|none | 0|acc |0.9300|± |0.0256|
# |hellaswag | 1|none | 0|acc |0.6127|± |0.0049|
# | | |none | 0|acc_norm |0.8100|± |0.0039|
# |lambada_openai| 1|none | 0|perplexity|3.1810|± |0.0583|
# | | |none | 0|acc |0.7563|± |0.0060|
# |openbookqa | 1|none | 0|acc |0.3260|± |0.0210|
# | | |none | 0|acc_norm |0.4380|± |0.0222|
# |piqa | 1|none | 0|acc |0.8069|± |0.0092|
# | | |none | 0|acc_norm |0.8215|± |0.0089|
# |sciq | 1|none | 0|acc |0.9580|± |0.0063|
# | | |none | 0|acc_norm |0.9390|± |0.0076|
# |winogrande | 1|none | 0|acc |0.7395|± |0.0123|
import argparse
import warnings
import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import fla # noqa
def sizeof_fmt(num, suffix='B'):
for unit in ('', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi'):
if abs(num) < 1024.0:
return f'{num:.2f}{unit}{suffix}'
num /= 1024.0
return f'{num:.2f}Yi{suffix}'
def convert(
llama: str,
config: str,
output: str,
precision: str = 'float32',
):
AutoTokenizer.from_pretrained(llama).save_pretrained(output)
llama = AutoModelForCausalLM.from_pretrained(llama, torch_dtype=precision)
print(f"Loading Llama ...\n{llama}")
config = AutoConfig.from_pretrained(config)
config.torch_dtype = precision
model = AutoModelForCausalLM.from_config(config)
if precision in ['float16', 'fp16']:
model = model.to(torch.float16)
elif precision in ['bfloat16', 'bf16']:
model = model.to(torch.bfloat16)
num_parameters = model.num_parameters()
print(f"Initializing the model from the config:\n{config}\n{model}")
print(f"Number of parameters in total: {num_parameters} ({sizeof_fmt(num_parameters)})")
print("Copying the weights from Llama to the model ...")
vocab_size = llama.model.embed_tokens.weight.shape[0]
if model.model.embeddings.weight.shape[0] != vocab_size:
warnings.warn(f"Llama and the model have different embedding sizes "
f"({vocab_size} vs {model.model.embeddings.weight.shape[0]}), "
f"the model embeddings will be extended with randomly initialized values or truncated")
vocab_size = min(model.model.embeddings.weight.shape[0], vocab_size)
print("llama.model.embed_tokens -> model.model.embeddings")
model.model.embeddings.weight.data[:vocab_size].copy_(llama.model.embed_tokens.weight[:vocab_size])
torch.testing.assert_close(model.model.embeddings.weight[:vocab_size], llama.model.embed_tokens.weight[:vocab_size])
for i in range(config.num_hidden_layers):
if hasattr(model.model.layers[i], 'attn_norm'):
if model.model.layers[i].attn_norm.weight is not None:
print(f"llama.model.layers{i}.input_layernorm.weight -> model.model.layers{i}.attn_norm.weight")
model.model.layers[i].attn_norm.weight.data.copy_(llama.model.layers[i].input_layernorm.weight)
torch.testing.assert_close(model.model.layers[i].attn_norm.weight,
llama.model.layers[i].input_layernorm.weight)
if model.model.layers[i].attn_norm.bias is not None:
print(f"llama.model.layers{i}.input_layernorm.bias -> model.model.layers{i}.attn_norm.bias")
model.model.layers[i].attn_norm.bias.data.copy_(llama.model.layers[i].input_layernorm.bias)
torch.testing.assert_close(model.model.layers[i].attn_norm.bias,
llama.model.layers[i].input_layernorm.bias)
model.model.layers[i].attn_norm.eps = llama.model.layers[i].input_layernorm.variance_epsilon
if hasattr(model.model.layers[i].attn, 'norm'):
if model.model.layers[i].attn.norm.weight is not None:
print(f"llama.model.layers{i}.input_layernorm.weight -> model.model.layers{i}.attn.norm.weight")
model.model.layers[i].attn.norm.weight.data.copy_(llama.model.layers[i].input_layernorm.weight)
torch.testing.assert_close(model.model.layers[i].attn.norm.weight,
llama.model.layers[i].input_layernorm.weight)
if model.model.layers[i].attn.norm.bias is not None:
print(f"llama.model.layers{i}.input_layernorm.bias -> model.model.layers{i}.attn.norm.bias")
model.model.layers[i].attn.norm.bias.data.copy_(llama.model.layers[i].input_layernorm.bias)
torch.testing.assert_close(model.model.layers[i].attn.norm.bias,
llama.model.layers[i].input_layernorm.bias)
model.model.layers[i].attn.norm.eps = llama.model.layers[i].input_layernorm.variance_epsilon
print(f"llama.model.layers{i}.attn.q_proj.weight -> model.model.layers{i}.attn.q_proj.weight")
model.model.layers[i].attn.q_proj.weight.data.copy_(llama.model.layers[i].self_attn.q_proj.weight)
torch.testing.assert_close(model.model.layers[i].attn.q_proj.weight, llama.model.layers[i].self_attn.q_proj.weight)
if hasattr(llama.model.layers[i].self_attn.q_proj, 'bias') and hasattr(model.model.layers[i].attn.q_proj, 'bias'):
print(f"llama.model.layers{i}.attn.q_proj.bias -> model.model.layers{i}.attn.q_proj.bias")
model.model.layers[i].attn.q_proj.bias.data.copy_(llama.model.layers[i].self_attn.q_proj.bias)
torch.testing.assert_close(model.model.layers[i].attn.q_proj.bias, llama.model.layers[i].self_attn.q_proj.bias)
print(f"llama.model.layers.{i}.attn.k_proj.weight -> model.model.layers.{i}.attn.k_proj.weight")
model.model.layers[i].attn.k_proj.weight.data.copy_(llama.model.layers[i].self_attn.k_proj.weight)
torch.testing.assert_close(model.model.layers[i].attn.k_proj.weight, llama.model.layers[i].self_attn.k_proj.weight)
if hasattr(llama.model.layers[i].self_attn.k_proj, 'bias') and hasattr(model.model.layers[i].attn.k_proj, 'bias'):
print(f"llama.model.layers{i}.attn.k_proj.bias -> model.model.layers{i}.attn.k_proj.bias")
model.model.layers[i].attn.k_proj.bias.data.copy_(llama.model.layers[i].self_attn.k_proj.bias)
torch.testing.assert_close(model.model.layers[i].attn.k_proj.bias, llama.model.layers[i].self_attn.k_proj.bias)
print(f"llama.model.layers.{i}.attn.v_proj.weight -> model.model.layers.{i}.attn.v_proj.weight")
model.model.layers[i].attn.v_proj.weight.data.copy_(llama.model.layers[i].self_attn.v_proj.weight)
torch.testing.assert_close(model.model.layers[i].attn.v_proj.weight, llama.model.layers[i].self_attn.v_proj.weight)
if hasattr(llama.model.layers[i].self_attn.v_proj, 'bias') and hasattr(model.model.layers[i].attn.v_proj, 'bias'):
print(f"llama.model.layers{i}.attn.v_proj.bias -> model.model.layers{i}.attn.v_proj.bias")
model.model.layers[i].attn.v_proj.bias.data.copy_(llama.model.layers[i].self_attn.v_proj.bias)
torch.testing.assert_close(model.model.layers[i].attn.v_proj.bias, llama.model.layers[i].self_attn.v_proj.bias)
print(f"llama.model.layers.{i}.attn.o_proj.weight -> model.model.layers.{i}.attn.o_proj.weight")
model.model.layers[i].attn.o_proj.weight.data.copy_(llama.model.layers[i].self_attn.o_proj.weight)
torch.testing.assert_close(model.model.layers[i].attn.o_proj.weight, llama.model.layers[i].self_attn.o_proj.weight)
if hasattr(model.model.layers[i], 'mlp_norm'):
if model.model.layers[i].mlp_norm.weight is not None:
print(f"llama.model.layers{i}.post_attention_layernorm.weight -> model.model.layers{i}.mlp_norm.weight")
model.model.layers[i].mlp_norm.weight.data.copy_(llama.model.layers[i].post_attention_layernorm.weight)
torch.testing.assert_close(model.model.layers[i].mlp_norm.weight,
llama.model.layers[i].post_attention_layernorm.weight)
if model.model.layers[i].mlp_norm.bias is not None:
print(f"llama.model.layers{i}.post_attention_layernorm.bias -> model.model.layers{i}.mlp_norm.bias")
model.model.layers[i].mlp_norm.bias.data.copy_(llama.model.layers[i].post_attention_layernorm.bias)
torch.testing.assert_close(model.model.layers[i].mlp_norm.bias,
llama.model.layers[i].post_attention_layernorm.bias)
model.model.layers[i].mlp_norm.eps = llama.model.layers[i].post_attention_layernorm.variance_epsilon
if hasattr(model.model.layers[i].mlp, 'norm'):
if model.model.layers[i].mlp.norm.weight is not None:
print(f"llama.model.layers{i}.post_attention_layernorm.weight -> model.model.layers{i}.mlp.norm.weight")
model.model.layers[i].mlp.norm.weight.data.copy_(llama.model.layers[i].post_attention_layernorm.weight)
torch.testing.assert_close(model.model.layers[i].mlp.norm.weight,
llama.model.layers[i].post_attention_layernorm.weight)
if model.model.layers[i].mlp.norm.bias is not None:
print(f"llama.model.layers{i}.post_attention_layernorm.bias -> model.model.layers{i}.mlp.norm.bias")
model.model.layers[i].mlp.norm.bias.data.copy_(llama.model.layers[i].post_attention_layernorm.bias)
torch.testing.assert_close(model.model.layers[i].mlp.norm.bias,
llama.model.layers[i].post_attention_layernorm.bias)
model.model.layers[i].mlp.norm.eps = llama.model.layers[i].post_attention_layernorm.variance_epsilon
print(f"llama.model.layers.{i}.mlp.gate_proj.weight -> model.model.layers.{i}.mlp.gate_proj.weight")
model.model.layers[i].mlp.gate_proj.weight.data.copy_(llama.model.layers[i].mlp.gate_proj.weight)
torch.testing.assert_close(model.model.layers[i].mlp.gate_proj.weight, llama.model.layers[i].mlp.gate_proj.weight)
print(f"llama.model.layers.{i}.mlp.up_proj.weight -> model.model.layers.{i}.mlp.up_proj.weight")
model.model.layers[i].mlp.up_proj.weight.data.copy_(llama.model.layers[i].mlp.up_proj.weight)
torch.testing.assert_close(model.model.layers[i].mlp.up_proj.weight, llama.model.layers[i].mlp.up_proj.weight)
print(f"llama.model.layers.{i}.mlp.down_proj.weight -> model.model.layers.{i}.mlp.down_proj.weight")
model.model.layers[i].mlp.down_proj.weight.data.copy_(llama.model.layers[i].mlp.down_proj.weight)
torch.testing.assert_close(model.model.layers[i].mlp.down_proj.weight,
llama.model.layers[i].mlp.down_proj.weight)
if model.model.norm.weight is not None:
print("llama.model.norm.weight -> model.model.norm.weight")
model.model.norm.weight.data.copy_(llama.model.norm.weight)
torch.testing.assert_close(model.model.norm.weight, llama.model.norm.weight)
if model.model.norm.bias is not None:
print("llama.model.norm.bias -> model.model.norm.bias")
model.model.norm.bias.data.copy_(llama.model.norm.bias)
torch.testing.assert_close(model.model.norm.bias, llama.model.norm.bias)
model.model.norm.eps = llama.model.norm.variance_epsilon
if not model.config.tie_word_embeddings:
print("llama.model.lm_head.weight -> model.lm_head.weight")
model.lm_head.weight.data[:vocab_size].copy_(llama.lm_head.weight[:vocab_size])
torch.testing.assert_close(model.lm_head.weight[:vocab_size], llama.lm_head.weight[:vocab_size])
model.config.rope_theta = llama.config.rope_theta
print(f"Saving converted model to {output} ...\n{model}")
model.save_pretrained(output)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", default='mistralai/Mistral-7B-v0.1')
parser.add_argument("--config", default='configs/transformer_7B.json')
parser.add_argument("--output", default='converted/transformer-7B')
parser.add_argument('--precision', type=str, default='float32')
args = parser.parse_args()
convert(args.model, args.config, args.output, precision=args.precision)
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