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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import gradio as gr
def calculate_weight_diff(base_weight, chat_weight):
return torch.abs(base_weight - chat_weight).mean().item()
def calculate_layer_diffs(base_model, chat_model, load_one_at_a_time=False):
layer_diffs = []
layers = zip(base_model.model.layers, chat_model.model.layers)
if load_one_at_a_time:
for base_layer, chat_layer in tqdm(layers, total=len(base_model.model.layers)):
layer_diff = {
'input_layernorm': calculate_weight_diff(base_layer.input_layernorm.weight, chat_layer.input_layernorm.weight),
'mlp_down_proj': calculate_weight_diff(base_layer.mlp.down_proj.weight, chat_layer.mlp.down_proj.weight),
'mlp_gate_proj': calculate_weight_diff(base_layer.mlp.gate_proj.weight, chat_layer.mlp.gate_proj.weight),
'mlp_up_proj': calculate_weight_diff(base_layer.mlp.up_proj.weight, chat_layer.mlp.up_proj.weight),
'post_attention_layernorm': calculate_weight_diff(base_layer.post_attention_layernorm.weight, chat_layer.post_attention_layernorm.weight),
'self_attn_q_proj': calculate_weight_diff(base_layer.self_attn.q_proj.weight, chat_layer.self_attn.q_proj.weight),
'self_attn_k_proj': calculate_weight_diff(base_layer.self_attn.k_proj.weight, chat_layer.self_attn.k_proj.weight),
'self_attn_v_proj': calculate_weight_diff(base_layer.self_attn.v_proj.weight, chat_layer.self_attn.v_proj.weight),
'self_attn_o_proj': calculate_weight_diff(base_layer.self_attn.o_proj.weight, chat_layer.self_attn.o_proj.weight)
}
layer_diffs.append(layer_diff)
base_layer, chat_layer = None, None
del base_layer, chat_layer
else:
for base_layer, chat_layer in tqdm(layers, total=len(base_model.model.layers)):
layer_diff = {
'input_layernorm': calculate_weight_diff(base_layer.input_layernorm.weight, chat_layer.input_layernorm.weight),
'mlp_down_proj': calculate_weight_diff(base_layer.mlp.down_proj.weight, chat_layer.mlp.down_proj.weight),
'mlp_gate_proj': calculate_weight_diff(base_layer.mlp.gate_proj.weight, chat_layer.mlp.gate_proj.weight),
'mlp_up_proj': calculate_weight_diff(base_layer.mlp.up_proj.weight, chat_layer.mlp.up_proj.weight),
'post_attention_layernorm': calculate_weight_diff(base_layer.post_attention_layernorm.weight, chat_layer.post_attention_layernorm.weight),
'self_attn_q_proj': calculate_weight_diff(base_layer.self_attn.q_proj.weight, chat_layer.self_attn.q_proj.weight),
'self_attn_k_proj': calculate_weight_diff(base_layer.self_attn.k_proj.weight, chat_layer.self_attn.k_proj.weight),
'self_attn_v_proj': calculate_weight_diff(base_layer.self_attn.v_proj.weight, chat_layer.self_attn.v_proj.weight),
'self_attn_o_proj': calculate_weight_diff(base_layer.self_attn.o_proj.weight, chat_layer.self_attn.o_proj.weight)
}
layer_diffs.append(layer_diff)
return layer_diffs
def visualize_layer_diffs(layer_diffs):
num_layers = len(layer_diffs)
num_components = len(layer_diffs[0])
fig, axs = plt.subplots(1, num_components, figsize=(24, 8))
fig.suptitle(f"{base_model_name} <> {chat_model_name}", fontsize=16)
for i, component in tqdm(enumerate(layer_diffs[0].keys()), total=len(layer_diffs[0].keys())):
component_diffs = [[layer_diff[component]] for layer_diff in layer_diffs]
sns.heatmap(component_diffs, annot=True, fmt=".9f", cmap="YlGnBu", ax=axs[i], cbar=False)
axs[i].set_title(component)
axs[i].set_xlabel("Difference")
axs[i].set_ylabel("Layer")
axs[i].set_xticks([])
axs[i].set_yticks(range(num_layers))
axs[i].set_yticklabels(range(num_layers))
axs[i].invert_yaxis()
plt.tight_layout()
return fig
def gradio_interface(base_model_name, chat_model_name, load_one_at_a_time=False):
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.bfloat16)
chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name, torch_dtype=torch.bfloat16)
layer_diffs = calculate_layer_diffs(base_model, chat_model, load_one_at_a_time=load_one_at_a_time)
fig = visualize_layer_diffs(layer_diffs)
return fig
iface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Textbox(lines=2, placeholder="Enter base model name"),
gr.Textbox(lines=2, placeholder="Enter chat model name"),
gr.Checkbox(label="Load one layer at a time")
],
outputs="image",
title="Model Weight Difference Visualizer"
)
iface.launch()
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