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Showing content from https://github.com/EleutherAI/pythia/issues/135 below:

Inconsistent init methods of pythia-6.9b model · Issue #135 · EleutherAI/pythia · GitHub

• Pricing

Hi, I found that the init method of parameters in pythia-6.9B model is inconsistent with the standard deviation of the step0 checkpoint. Table 6 in the paper shows that init-method is small-init and output-layer-init-method is wang-init. But I got different std values from step0 models.

Inconsistent std values:

input_layer_std: 0.009882117688026186(small_init), 0.02(std calculated from step0 model paramters)
output_layer_std: 0.0009765625(wang_init), 0.0025(std calculated from step0 model paramters)

Could you provide the real init method? Thanks!

Config Table 6:

Here are the reproducible script and results.

import math
from transformers import GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained(
  "EleutherAI/pythia-6.9b",
  revision="step0",
)

model_dim = 4096 # Pythia-6.9b

# compute right std values of the two init methods 
# reference https://github.com/EleutherAI/gpt-neox/blob/v1.0/megatron/model/init_functions.py#L101-L118 

small_init_std = (2/(5* model_dim)) ** 0.5
wang_init_std = 2 / (32 * math.sqrt(model_dim))
print('small_init_std:', small_init_std)
print('wang_init_std:', wang_init_std)

for n, p in model.named_parameters():
    print(n, p.shape, p.std().item())

Results:

small_init_std: 0.009882117688026186
wang_init_std: 0.0009765625

gpt_neox.embed_in.weight torch.Size([50432, 4096]) 0.019999271258711815
gpt_neox.layers.0.input_layernorm.weight torch.Size([4096]) 0.0
gpt_neox.layers.0.input_layernorm.bias torch.Size([4096]) 0.0
gpt_neox.layers.0.post_attention_layernorm.weight torch.Size([4096]) 0.0
gpt_neox.layers.0.post_attention_layernorm.bias torch.Size([4096]) 0.0
gpt_neox.layers.0.attention.query_key_value.weight torch.Size([12288, 4096]) 0.019999688491225243
gpt_neox.layers.0.attention.query_key_value.bias torch.Size([12288]) 0.0
gpt_neox.layers.0.attention.dense.weight torch.Size([4096, 4096]) 0.002499272581189871
gpt_neox.layers.0.attention.dense.bias torch.Size([4096]) 0.0
gpt_neox.layers.0.mlp.dense_h_to_4h.weight torch.Size([16384, 4096]) 0.019998779520392418
gpt_neox.layers.0.mlp.dense_h_to_4h.bias torch.Size([16384]) 0.0
gpt_neox.layers.0.mlp.dense_4h_to_h.weight torch.Size([4096, 16384]) 0.0024998513981699944
gpt_neox.layers.0.mlp.dense_4h_to_h.bias torch.Size([4096]) 0.0
gpt_neox.layers.1.input_layernorm.weight torch.Size([4096]) 0.0
gpt_neox.layers.1.input_layernorm.bias torch.Size([4096]) 0.0
gpt_neox.layers.1.post_attention_layernorm.weight torch.Size([4096]) 0.0
gpt_neox.layers.1.post_attention_layernorm.bias torch.Size([4096]) 0.0
gpt_neox.layers.1.attention.query_key_value.weight torch.Size([12288, 4096]) 0.01999974064528942
gpt_neox.layers.1.attention.query_key_value.bias torch.Size([12288]) 0.0
gpt_neox.layers.1.attention.dense.weight torch.Size([4096, 4096]) 0.0025000576861202717
gpt_neox.layers.1.attention.dense.bias torch.Size([4096]) 0.0
gpt_neox.layers.1.mlp.dense_h_to_4h.weight torch.Size([16384, 4096]) 0.02000279724597931
gpt_neox.layers.1.mlp.dense_h_to_4h.bias torch.Size([16384]) 0.0
gpt_neox.layers.1.mlp.dense_4h_to_h.weight torch.Size([4096, 16384]) 0.002499587135389447
gpt_neox.layers.1.mlp.dense_4h_to_h.bias torch.Size([4096]) 0.0
...

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