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import gradio as gr
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])
# calculates the params of a model given their hparams
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):
# Calculate embedding and unembedding params. If tied, re-use the same params
if tied_embeddings:
embedding_params = hidden_size * vocab_size
else:
embedding_params = 2 * hidden_size * vocab_size
position_embedding_params = hidden_size * sequence_length
# Each QKVO matrix is (hxh)
# Unless using GQA/MQA which makes K/V smaller
attention_params = int(2 * (1 + kv_size_ratio) * num_layers * hidden_size * hidden_size)
# (4*2)lh from the layernorm weights and biases for each of the QKV and mlp_in layernorms, 1h for the final layernorm.
# the extra 4lh is a mystery but we include it here
layernorm_params = 13 * num_layers * hidden_size
#ffn_params = 12 * num_layers * hidden_size * hidden_size
if moe:
# the number of layers that are MoE. (e.g. interval is 2 for GShard)
num_expert_layers = num_layers / expert_interval
# the number of FFN params for each MoE layer
ffn_expert_params = num_mlp_linears * ffn_expansion_factor * num_expert_layers * num_experts * hidden_size * hidden_size
# the number of FFN params for every dense layer
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
# the number of gating layer params assuming it's implemented as a simple linear layer
gating_params = num_expert_layers * hidden_size * num_experts
else:
# num_mlp_layers * (h x [ffn_expansion_factor * h]) FFN matrices
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
result = 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)}
"""
return result
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Transformer Model Parameter Calculator")
vocab_size = gr.Number(label="Vocab Size", value=51200)
tied_embeddings = gr.Checkbox(label="Tied Embeddings", value=False)
hidden_size = gr.Number(label="Hidden Size", value=6144)
sequence_length = gr.Number(label="Sequence Length", value=2048)
num_layers = gr.Number(label="Number of Layers", value=44)
ffn_expansion_factor = gr.Number(label="FFN Expansion Factor", value=4)
num_mlp_linears = gr.Number(label="Number of Linear Layers per MLP Block", value=2)
kv_size_ratio = gr.Number(label="KV Size Ratio", value=1.0)
# MoE Parameters inside an accordion
with gr.Accordion("MoE Parameters", open=False):
moe = gr.Checkbox(label="MoE", value=False)
num_experts = gr.Number(label="Number of Experts", value=8)
expert_interval = gr.Number(label="Expert Interval", value=1)
topk = gr.Number(label="Top k Routing", value=1)
result = gr.Textbox(label="Output", interactive=False)
def run_calculation(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):
return 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)
calculate_button = gr.Button("Calculate")
calculate_button.click(run_calculation,
inputs=[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],
outputs=[result])
demo.launch()
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