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import numpy as np
import pandas as pd
from scipy.stats import kurtosis
from lm_quant_toolkit.utils.hub import (
LLAMA_MODELS,
VIT_OPENCLIP_MODELS,
get_hf_model_storge_base_dir,
)
from lm_quant_toolkit.utils.pickle import load_state_dict
from lm_quant_toolkit.utils.safetensors import get_tensor
def calculate_kurtosis_llm(model_id, base_dir, layers, output_dir):
modules = [
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
"self_attn.o_proj",
"mlp.gate_proj",
"mlp.down_proj",
"mlp.up_proj",
]
dikts = []
for layer in range(layers):
for module in modules:
full_name = f"model.layers.{layer}.{module}.weight"
w = get_tensor(full_name, base_dir)
print(full_name, w.shape)
param_count = w.numel()
w = w.flatten().float().numpy()
kurt_pearson = kurtosis(
w, axis=None, fisher=False, bias=True, nan_policy="omit"
)
dikt = {
"module": module,
"layer": layer,
"param_count": param_count,
"kurtosis": kurt_pearson,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
short_id = model_id.split("/")[1]
csv_fp = f"{output_dir}/kurtosis-{short_id}.csv"
df.to_csv(csv_fp, index=False)
def calculate_kurtosis_vit(model_id, model_cfg, output_dir):
modules = [
"mlp.c_fc",
"mlp.c_proj",
]
dikts = []
state_dict = load_state_dict(model_id)
for layer_type, layers in model_cfg.items():
if layer_type == "vlayers":
model_type = "vision"
prefix = "visual.transformer"
else:
model_type = "text"
prefix = "transformer"
for i, module in enumerate(modules):
for layer in range(layers):
full_name = f"{prefix}.resblocks.{layer}.{module}.weight"
w = state_dict[full_name]
w = w.flatten().float().numpy()
kurt_pearson = kurtosis(
w, axis=None, fisher=False, bias=True, nan_policy="omit"
)
dikt = {
"module": f"{model_type}.{module}",
"layer": layer,
"kurtosis": kurt_pearson,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
short_id = model_id.split("/")[1]
csv_fp = f"{output_dir}/kurtosis-{short_id}.csv"
df.to_csv(csv_fp, index=False)
def summarize_vit_percentiles(
model_id,
model_cfg,
csv_fp,
wt_file="open_clip_pytorch_model.bin",
):
modules = [
"attn.in_proj_bias",
"attn.in_proj_weight",
"attn.out_proj.bias",
"attn.out_proj.weight",
"ln_1.bias",
"ln_1.weight",
"ln_2.bias",
"ln_2.weight",
"mlp.c_fc.bias",
"mlp.c_fc.weight",
"mlp.c_proj.bias",
"mlp.c_proj.weight",
]
dikts = []
state_dict = load_state_dict(model_id)
for layer_type, layers in model_cfg.items():
if layer_type == "vlayers":
model_type = "vision"
prefix = "visual.transformer"
else:
model_type = "text"
prefix = "transformer"
for i, module in enumerate(modules):
for layer in range(layers):
full_name = f"{prefix}.resblocks.{layer}.{module}"
w = state_dict[full_name]
param_count = w.numel()
w = w.flatten().float().numpy()
percentiles = np.percentile(np.abs(w), [0, 99, 99.9, 99.99, 100])
kurt_pearson = kurtosis(
w, axis=None, fisher=False, bias=True, nan_policy="omit"
)
dikt = {
"type": model_type,
"module": module,
"param_count": param_count,
"layer": layer,
"percentile_0": percentiles[0],
"percentile_99": percentiles[1],
"percentile_999": percentiles[2],
"percentile_9999": percentiles[3],
"percentile_100": percentiles[4],
"kurtosis": kurt_pearson,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
df.to_csv(csv_fp, index=False)
def summarize_llama_quantable_params(base_dir, layers, fp, llama2=True):
modules = {
"norm": {"layerwise": False, "quant": False},
"lm_head": {"layerwise": False, "quant": False, "prefix": ""},
"embed_tokens": {"layerwise": False, "quant": False},
"input_layernorm": {"layerwise": True, "quant": False},
"post_attention_layernorm": {"layerwise": True, "quant": False},
"mlp.down_proj": {"layerwise": True, "quant": True},
"mlp.gate_proj": {"layerwise": True, "quant": True},
"mlp.up_proj": {"layerwise": True, "quant": True},
"self_attn.k_proj": {"layerwise": True, "quant": True},
"self_attn.o_proj": {"layerwise": True, "quant": True},
"self_attn.q_proj": {"layerwise": True, "quant": True},
"self_attn.v_proj": {"layerwise": True, "quant": True},
}
if llama2:
modules["self_attn.rotary_emb"] = {
"layerwise": True,
"quant": False,
"suffix": "inv_freq",
}
dikts = []
for module, data in modules.items():
suffix = data.get("suffix", "weight")
if data["layerwise"]:
for layer in range(layers):
full_name = f"model.layers.{layer}.{module}.{suffix}"
w = get_tensor(full_name, base_dir)
param_count = w.numel()
dikt = {
"module": module,
"layer": layer,
"param_count": param_count,
"quant_count": param_count if data["quant"] else 0,
}
dikts.append(dikt)
else:
prefix = data.get("prefix", "model")
if prefix == "":
full_name = f"{module}.{suffix}"
else:
full_name = f"{prefix}.{module}.{suffix}"
w = get_tensor(full_name, base_dir)
param_count = w.numel()
dikt = {
"module": module,
"layer": 0,
"param_count": param_count,
"quant_count": param_count if data["quant"] else 0,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
df.to_csv(fp, index=False)
def summarize_vit_quantable_params(
model_id,
csv_fp,
wt_file="open_clip_pytorch_model.bin",
):
quantables = [
"mlp.c_fc.weight",
"mlp.c_proj.weight",
]
dikts = []
state_dict = load_state_dict(model_id)
pat = r"(.*)\.resblocks\.(\d+)\.(.*)"
for key in state_dict:
m = re.match(pat, key)
if m is not None:
layer = int(m.group(2))
if "visual" in m.group(1):
type = "vision"
elif "transformer" in m.group(1):
type = "text"
else:
type = ""
module = m.group(3)
else:
layer = 0
module = key
if "visual" in key:
type = "vision"
elif "transformer" in key:
type = "text"
else:
type = ""
quantable = any([qnt in key for qnt in quantables])
w = state_dict[key]
param_count = w.numel()
dikt = {
"type": type,
"module": module,
"layer": layer,
"param_count": param_count,
"quant_count": param_count if quantable else 0,
}
dikts.append(dikt)
df = pd.DataFrame(dikts)
df.to_csv(csv_fp, index=False)
def aggregate_quantable_parameters(models, base_dir="data", unit=1_000_000_000):
ret = {}
for model in models:
csv_file = f"{base_dir}/quantable-{model}.csv"
df = pd.read_csv(csv_file)
total = df["param_count"].sum()
quant = df["quant_count"].sum()
pct = quant / total
ret[model] = (total / unit, quant / unit, pct * 100)
return ret
def summarize_known_llama_quantable_params(base_dir="data", skip_models=None):
for model_id, cfg in LLAMA_MODELS.items():
if skip_models is not None and len(skip_models) > 0:
if any([skip_model in model_id for skip_model in skip_models]):
continue
model_base_dir = get_hf_model_storge_base_dir(
model_id, cfg.get("base_dir", None)
)
layers = cfg["layers"]
llama2 = cfg.get("llama2", True)
csv_file = f"{base_dir}/quantable-{model_id.split('/')[1]}.csv"
summarize_llama_quantable_params(
model_base_dir, layers, csv_file, llama2=llama2
)
def summarize_known_vit_quantable_params(base_dir="data", skip_models=None):
for model_id, cfg in VIT_OPENCLIP_MODELS.items():
if skip_models is not None and len(skip_models) > 0:
if any([skip_model in model_id for skip_model in skip_models]):
continue
csv_file = f"{base_dir}/quantable-{model_id.split('/')[1]}.csv"
summarize_vit_quantable_params(model_id, csv_file)
def summarize_vit_wdist(base_dir="data"):
for model_id, cfg in VIT_OPENCLIP_MODELS.items():
csv_file = f"data/wdist-{model_id.split('/')[1]}.csv"
summarize_vit_percentiles(model_id, cfg, csv_file)
def calculate_vit_kurtosis():
for model_id, cfg in VIT_OPENCLIP_MODELS.items():
calculate_kurtosis_vit(model_id, cfg, "data")
if __name__ == "__main__":
calculate_vit_kurtosis()
# summarize_known_vit_quantable_params()
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