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import re
import pandas as pd
import torch
from hqq.core.quantize import Quantizer as hQuant
from safetensors.torch import save_file as safe_save
from scipy.stats import kurtosis
from torch import uint8
from lm_quant_toolkit.eval.common import calc_bits
from lm_quant_toolkit.utils.safetensors import get_tensor, get_tensor_dual
def load_weight(matrix_name, base_dir):
m = f"{matrix_name}.weight"
return get_tensor(m, base_dir)
def load_weight_dual(prefix, base_dir, st_file):
o = f"{prefix}.weight"
q = f"{prefix}.qweight"
return get_tensor_dual(o, q, base_dir, st_file)
def dequantize(wq, meta):
# Zero/Scale packed together
if "zero_scale" in meta:
zero_scale = meta["zero_scale"]
if zero_scale.dtype == uint8:
meta["zero_q"], meta["scale_q"] = zero_scale[0], zero_scale[1]
else:
meta["zero"], meta["scale"] = zero_scale[0], zero_scale[1]
if meta["quant_zero"]:
meta["zero"] = hQuant.dequantize(meta["zero_q"], meta["meta_zero"])
if meta["quant_scale"]:
meta["scale"] = hQuant.dequantize(meta["scale_q"], meta["meta_scale"])
return hQuant.dequantize(wq, meta)
def restore_weight(matrix, state_dict):
key = matrix
if key in state_dict:
m_dikt = state_dict[key]
if "meta" in m_dikt:
meta_dict = m_dikt["meta"]
meta_scale_dict = meta_dict.get("meta_scale", None)
b1 = meta_dict["nbits"]
g1 = meta_dict["group_size"]
b2 = meta_scale_dict["nbits"] if meta_scale_dict else 8
g2 = meta_scale_dict["group_size"] if meta_scale_dict else 128
quant_config = {
"b1": b1,
"g1": g1,
"b2": b2,
"g2": g2,
}
wq = dequantize(m_dikt["W_q"], meta_dict)
return wq, quant_config
else:
return None, None
else:
return None, None
def save_compare_pair(
base_dir, quant_base_dir, quant_cfg, model_id, layers, output_dir
):
file_path = f"{quant_base_dir}/{model_id}-{quant_cfg}-hqq/qmodel.pt"
state_dict = torch.load(file_path, map_location="cpu")
tensors = {}
metadata = {}
# walk thru the linear layers
# for each layer
# for each linear module
# load the original weight
# load the quantized weight and dequantized
# save the two matrix into a combined safetensors
for layer in range(layers):
matricies = [
f"model.layers.{layer}.mlp.down_proj",
f"model.layers.{layer}.mlp.gate_proj",
f"model.layers.{layer}.mlp.up_proj",
f"model.layers.{layer}.self_attn.k_proj",
f"model.layers.{layer}.self_attn.o_proj",
f"model.layers.{layer}.self_attn.q_proj",
f"model.layers.{layer}.self_attn.v_proj",
]
for matrix in matricies:
wq, quant_cfg = restore_weight(matrix, state_dict)
if wq is None:
# skip unquantized matrix
continue
wo = load_weight(matrix, base_dir)
tensors[f"{matrix}.weight"] = wo
tensors[f"{matrix}.qweight"] = wq
metadata[f"{matrix}.quant_cfg.b1"] = str(quant_cfg["b1"])
metadata[f"{matrix}.quant_cfg.b2"] = str(quant_cfg["b2"])
metadata[f"{matrix}.quant_cfg.g1"] = str(quant_cfg["g1"])
metadata[f"{matrix}.quant_cfg.g2"] = str(quant_cfg["g2"])
output_fp = f"{output_dir}/{model_id}-cmp.safetensors"
safe_save(tensors, output_fp, metadata=metadata)
def compare_pair(model_id, layers, output_dir):
st_file = f"{output_dir}/{model_id}-cmp.safetensors"
for layer in range(layers):
matricies = [
f"model.layers.{layer}.mlp.down_proj",
f"model.layers.{layer}.mlp.gate_proj",
f"model.layers.{layer}.mlp.up_proj",
f"model.layers.{layer}.self_attn.k_proj",
f"model.layers.{layer}.self_attn.o_proj",
f"model.layers.{layer}.self_attn.q_proj",
f"model.layers.{layer}.self_attn.v_proj",
]
for matrix in matricies:
wo, wq = load_weight_dual(matrix, output_dir, st_file)
diff = torch.norm(wo - wq).item()
kurt_peason = kurtosis(
wo.numpy(), axis=None, fisher=False, bias=True, nan_policy="omit"
)
kurt_fisher = kurtosis(
wo.numpy(), axis=None, fisher=True, bias=True, nan_policy="omit"
)
# print(f"{matrix} FNorm Diff: {diff:.5f} Kurtosis: {kurt:.2f}")
print(f"{matrix},{diff:.5f},{kurt_fisher:.3f},{kurt_peason:.3f}")
def is_linear_module(key):
self_attns = ["q_proj", "v_proj", "k_proj", "o_proj"]
mlps = ["gate_proj", "up_proj", "down_proj"]
modules = self_attns + mlps
for module in modules:
if module in key:
return True
return False
def extract_quant_config(base_dir, model_id, config, algo="hqq"):
file_path = f"{base_dir}/{model_id}-{config}-{algo}/qmodel.pt"
dikt = torch.load(file_path, map_location="cpu")
quant_configs = {}
mem_fp16_all_total = 0
mem_all_total = 0
mem_quant_total = 0
param_quant_total = 0
param_all_total = 0
# search quantized linear module with meta
for key in dikt.keys():
m_dikt = dikt[key]
if is_linear_module(key):
if "meta" in m_dikt:
meta_dict = m_dikt["meta"]
meta_scale_dict = meta_dict.get("meta_scale", None)
shape = meta_dict["shape"]
b1 = meta_dict["nbits"]
g1 = meta_dict["group_size"]
b2 = meta_scale_dict["nbits"] if meta_scale_dict else 8
g2 = meta_scale_dict["group_size"] if meta_scale_dict else 128
param_count = shape[0] * shape[1]
param_quant_total += param_count
param_all_total += param_count
memmb = (
(b1 + 2 * b2 / g1 + 32 / (g1 * g2)) * param_count / 8 / 1024 / 1024
)
mem_fp16_all_total += param_count * 2 / 1024 / 1024
mem_quant_total += memmb
mem_all_total += memmb
quant_configs[key] = {
"b1": b1,
"g1": g1,
"b2": b2,
"g2": g2,
"memmb": memmb,
"param_cnt": param_count,
}
else:
w = m_dikt["weight"]
mem_all_total += w.numel() * 2 / 1024 / 1024
mem_fp16_all_total += w.numel() * 2 / 1024 / 1024
param_all_total += w.numel()
return (
quant_configs,
mem_quant_total,
mem_all_total,
mem_fp16_all_total,
param_quant_total,
param_all_total,
)
def get_mem_usage_df(model_ids, confs, base_dir):
dikts = []
for model_id in model_ids:
for conf in confs:
configs, mem_quant_total, mem_all_total, mem_fp16_all_total, _, _ = (
extract_quant_config(base_dir, model_id, conf)
)
dikt = {
"model": model_id.split("/")[1],
"config": conf,
"mem_quant_total": mem_quant_total,
"mem_all_total": mem_all_total,
"mem_fp16_all_total": mem_fp16_all_total,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
return df
def dump_quant_allocation(
quant_dir, model_ids, confs, csv_fp="mxq-allot.csv", attempts=None, algo="mxq"
):
dikt = []
pat = re.compile(r"model\.layers\.(\d+)\.(.+)")
pat2 = re.compile(r"b(\d+)g(\d+)")
loops = ["mxq1"] if attempts is None else attempts
for attempt in loops:
snapshot_dir = f"{quant_dir}/{attempt}"
for model_id in model_ids:
for conf in confs:
m2 = re.match(pat2, conf)
if m2:
b1 = int(m2.group(1))
g1 = int(m2.group(2))
bit_budget = round(calc_bits(b1, g1), 2)
else:
bit_budget = conf.replace("_", ".")
(
configs,
mem_quant_total,
mem_all_total,
mem_fp16_all_total,
param_quant_total,
param_all_total,
) = extract_quant_config(snapshot_dir, model_id, conf, algo=algo)
for key, val in configs.items():
matcher = re.match(pat, key)
if matcher:
layer = matcher.group(1)
module = matcher.group(2)
val["model"] = model_id.split("/")[1]
val["layer"] = layer
val["module"] = module
val["bit_budget"] = bit_budget
val["params_quant_tot"] = param_quant_total
val["params_all_tot"] = param_all_total
dikt.append(val)
columns = [
"model",
"module",
"layer",
"memmb",
"param_cnt",
"bit_budget",
"b1",
"g1",
"b2",
"g2",
"params_quant_tot",
"params_all_tot",
]
df = pd.DataFrame(dikt)
df.to_csv(csv_fp, index=False, columns=columns)
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