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from openvino.runtime import Core
from tqdm import tqdm
import torch
from collections import OrderedDict
from pathlib import Path
import numpy as np
from collections import Counter
import os
QDTYPE_SPECIAL_VALUES={
'u4': [0, 1, 2, 4, 8],
'u8': [0, 1, 2, 4, 8, 16, 32, 64, 128],
'int8': [-1, -2, -4, -8, -16, -32, -64, -128, 0, 1, 2, 4, 8, 16, 32, 64]
}
zero_point_map = {
'u4': 8,
'u8': 128,
'int8': 0,
}
def get_uniq_value_stats(tensor, q_dtype):
if q_dtype not in QDTYPE_SPECIAL_VALUES.keys():
raise NotImplementedError(f"Unsupported q_dtype {q_dtype}")
value_counts = Counter(tensor.flatten())
total_elements = sum(value_counts.values())
top1_val, top1_count = value_counts.most_common(1)[0]
top1_tuple = (top1_val, top1_count/total_elements)
# Calculate ratio for each value
count_ratio_dict = {value: {'count': count, 'ratio': count / total_elements}
for value, count in value_counts.items()}
# # Find unique elements and their counts
# unique_values, counts = np.unique(tensor, return_counts=True)
# # Calculate the total number of elements in the tensor
# total_elements = tensor.size
# # Calculate the relative ratio for each unique value
# ratios = counts / total_elements
special_value_count = 0
special_value_ratio = 0
sparsity = 0
zero_count = 0
# for value, count, ratio in zip(unique_values, counts, ratios):
for value, vdict in count_ratio_dict.items():
count = vdict['count']
ratio = vdict['ratio']
if value == zero_point_map[q_dtype]:
sparsity = ratio
zero_count = count
# zero will enter both above and below
if value in QDTYPE_SPECIAL_VALUES[q_dtype]:
special_value_count += count
special_value_ratio += ratio
return dict(
numel=total_elements,
sparsity=sparsity,
special_value_ratio=special_value_ratio,
top1=top1_tuple,
raw=count_ratio_dict
)
def get_ir_pair(model_dir):
p = Path(model_dir)
return p/"openvino_model.xml", p/"openvino_model.bin"
# fc_numel = {
# 'llama-2-chat-7b ': {'min': 16777216, 'max': 45088768},
# 'mistral-7b ': {'min': 4194304, 'max': 58720256},
# 'gemma-2b-it': {'min': 524288, 'max': 33554432},
# }
fc_numel = {
'llama-2-chat-7b': [16777216, 45088768],
'mistral-7b': [4194304, 16777216, 58720256],
'gemma-2b-it': [524288, 4194304, 33554432],
}
ovir_folder = "stable-diffusion-pokemons-1-5-quantized/unet"
# model_key = compressed_weight_folder.split("/")[2]
ir_xml, ir_bin = get_ir_pair(ovir_folder)
ie = Core()
ir_model = ie.read_model(ir_xml)
model_params = OrderedDict()
csv_path = os.path.join(ovir_folder, "weight_dist.csv")
with open(csv_path, "w") as outfile:
outfile.write("layer,dtype,w_ndim,shape,numel,sparsity,special_val_ratio,top1_val_ratio,top1_val\n")
# for op in tqdm(ir_model.get_ordered_ops()):
for op in ir_model.get_ordered_ops():
if 'constant' in str(op.get_type_info()).lower():
shape = tuple(op.get_output_shape(0))
numel = np.prod(shape)
if op.data.dtype.name == "int8":
# print(f"{numel:15} | {str(shape):20} | {op.get_name():20} | {op.data.dtype.name}")
layer = op.get_name()
q_dtype = op.data.dtype.name
# model_params[layer] = {}
statdict = get_uniq_value_stats(op.data, op.data.dtype.name)
# print("joto")
# q_mode = "sym" if attr['q_zero_point'][0] == zero_point_map[attr['q_dtype']] else "asym"
# is_top1_zero_point = "zero_point" if statdict['top1'][0] == zero_point_map[attr['q_dtype']] else statdict['top1'][0] # zero point is per channel per group
# print(f"{layer:30} | {attr['q_dtype']} ({q_mode:>5}) | orig. shape: {str(attr['original_shape']):15} | numel: {statdict['numel']:>15,} | sparsity: {statdict['sparsity']:.2f} | special ratio: {statdict['special_value_ratio']:.2f} | top1 ratio: {statdict['top1'][1]:.2f} ({is_top1_zero_point:>10}) |")
print(f"{layer:30} | {q_dtype} | orig. shape: {str(shape):20} | numel: {statdict['numel']:>15,} | sparsity: {statdict['sparsity']:.2f} | special ratio: {statdict['special_value_ratio']:.2f} | top1 ratio: {statdict['top1'][1]:.2f} (val: {statdict['top1'][0]})")
shape_str = str(shape).replace(", "," x ")
outfile.write(f"{layer:>25},{q_dtype},{len(shape)},{shape_str:20},{statdict['numel']:>15},{statdict['sparsity']:.4f},{statdict['special_value_ratio']:.4f},{statdict['top1'][1]:.4f},{statdict['top1'][0]}\n")
print('Done!') |