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import math
# Helper function to pretty-print message sizes
def convert_params(params):
if params == 0:
return "0"
size_name = ("", "K", "M", "B", "T", "P", "E", "Z", "Y")
i = int(math.floor(math.log(params, 1000)))
p = math.pow(1000, i)
s = round(params / p, 2)
return "%s %s" % (s, size_name[i])
# Parameter Calculation function
def calc_params(vocab_size, tied_embeddings, hidden_size, sequence_length, num_layers, moe, num_experts, expert_interval, topk, ffn_expansion_factor, num_mlp_linears, kv_size_ratio):
if tied_embeddings:
embedding_params = hidden_size * vocab_size
else:
embedding_params = 2 * hidden_size * vocab_size
position_embedding_params = hidden_size * sequence_length
attention_params = int(2 * (1 + kv_size_ratio) * num_layers * hidden_size * hidden_size)
layernorm_params = 13 * num_layers * hidden_size
if moe:
num_expert_layers = num_layers / expert_interval
ffn_expert_params = num_mlp_linears * ffn_expansion_factor * num_expert_layers * num_experts * hidden_size * hidden_size
ffn_dense_params = num_mlp_linears * ffn_expansion_factor * (num_layers - num_expert_layers) * hidden_size * hidden_size
ffn_params = ffn_expert_params + ffn_dense_params
gating_params = num_expert_layers * hidden_size * num_experts
else:
ffn_params = num_mlp_linears * ffn_expansion_factor * num_layers * hidden_size * hidden_size
total_params = embedding_params + attention_params + ffn_params + position_embedding_params + layernorm_params
if moe:
total_params += gating_params
return f"""
Embedding parameters: {convert_params(embedding_params)}
Attention parameters: {convert_params(attention_params)}
FFN parameters: {convert_params(ffn_params)}
{'Gating parameters: ' + convert_params(gating_params) if moe else ''}
Total Params in the Model: {convert_params(total_params)}
"""
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