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Yang Jun
2025-08-06 15:49:14 +08:00
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'''
Convert huggingface Bloom model. Use https://huggingface.co/bigscience/bloom as demo.
'''
import argparse
import configparser
import dataclasses
import os
from pathlib import Path
import torch
import torch.multiprocessing as multiprocessing
from convert import split_and_save_weight
from smoothquant import capture_activation_range, smooth_gemm
from tqdm import tqdm
from transformers import BloomForCausalLM, BloomTokenizerFast
from transformers.models.bloom.modeling_bloom import BloomBlock
from xtrt_llm._utils import str_dtype_to_torch, torch_to_numpy
@dataclasses.dataclass(frozen=True)
class ProgArgs:
out_dir: str
in_file: str
tensor_parallelism: int = 1
processes: int = 4
calibrate_kv_cache: bool = False
smoothquant: float = None
model: str = "bloom"
storage_type: str = "fp32"
dataset_cache_dir: str = None
load_model_on_cpu: bool = False
convert_model_on_cpu: bool = False
@staticmethod
def parse(args=None) -> 'ProgArgs':
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--out-dir',
'-o',
type=str,
help='file name of output directory',
required=True)
parser.add_argument('--in-file',
'-i',
type=str,
help='file name of input checkpoint file',
required=True)
parser.add_argument('--tensor-parallelism',
'-tp',
type=int,
help='Requested tensor parallelism for inference',
default=1)
parser.add_argument(
"--processes",
"-p",
type=int,
help=
"How many processes to spawn for conversion (default: 4). Set it to a lower value to reduce RAM usage.",
default=4)
parser.add_argument(
"--calibrate-kv-cache",
"-kv",
action="store_true",
help=
"Generate scaling factors for KV cache. Used for storing KV cache in int8."
)
parser.add_argument(
"--smoothquant",
"-sq",
type=float,
default=None,
help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
" to Smoothquant the model, and output int8 weights."
" A good first try is 0.5. Must be in [0, 1]")
parser.add_argument(
"--model",
default="bloom",
type=str,
help="Specify Bloom variants to convert checkpoints correctly",
choices=["bloom"])
parser.add_argument("--storage-type",
"-t",
type=str,
default="float32",
choices=["float32", "float16", "bfloat16"])
parser.add_argument("--dataset-cache-dir",
type=str,
default=None,
help="cache dir to load the hugging face dataset")
parser.add_argument("--load-model-on-cpu", action="store_true")
parser.add_argument("--convert-model-on-cpu", action="store_true")
return ProgArgs(**vars(parser.parse_args(args)))
def reorder_torch_qkv_weight_or_bias(v, model, is_bias=False):
""" Reorder the qkv weight.
Note that the shape of the fused QKV weights in HF is different from the
shape that XTRT-LLM requires.
HF: (num_heads x 3 x head_dim, hidden_size)
XTRT-LLM: (3 x num_heads x head_dim, hidden_size)
This is unlike to the other models in HF e.g. GPT where they have the
same shape with XTRT-LLM, i.e., (3 x num_heads x head_dim, hidden_size). We reshape the qkv
weight: (3 x num_heads x head_dim, hidden).
bias : (3 x num_heads x head_dim).
"""
n_head = model.transformer.num_heads
hidden_size = model.transformer.embed_dim
head_dim = hidden_size // n_head
# (3 x hidden, ...) view as (num_heads, 3, head_dim, ...)
v = v.reshape(n_head, 3, head_dim, -1)
# permute to (3, num_heads, head_dim, ...)
v = v.permute((1, 0, 2, 3))
# final shape: weight=(3 x hidden, hidden) or bias=(3 x hidden)
if is_bias:
return v.reshape(3 * hidden_size)
return v.reshape(3 * hidden_size, hidden_size)
@torch.no_grad()
def smooth_bloom_model(model, scales, alpha, bloom_qkv_param, bloom_smoother):
# Smooth the activation and weights with smoother = $\diag{s}$
for name, module in model.named_modules():
if not isinstance(module, BloomBlock):
continue
# reorder qkv weight/bias and scales
param = module.self_attention.query_key_value.weight
param = reorder_torch_qkv_weight_or_bias(param, model, is_bias=False)
layer_name = name + ".self_attention.query_key_value"
act_range_qkv = scales.get(layer_name)
# (n_head x 3 x head_dim) -> (3 x n_head x head_dim)
act_range_qkv['w'] = reorder_torch_qkv_weight_or_bias(
act_range_qkv['w'], model, is_bias=True)
act_range_qkv['y'] = reorder_torch_qkv_weight_or_bias(
act_range_qkv['y'], model, is_bias=True)
scales[layer_name] = act_range_qkv
# qkv_proj
smoother = smooth_gemm(param, scales[layer_name]["x"],
module.input_layernorm.weight,
module.input_layernorm.bias, alpha)
scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
scales[layer_name]["w"] = param.abs().max(dim=1)[0]
bloom_qkv_param[layer_name] = param
# dense
# enabled for better accuracy with perf overhead of quantiztion
layer_name = name + ".self_attention.dense"
smoother = smooth_gemm(module.self_attention.dense.weight,
scales[layer_name]["x"], None, None, alpha)
bloom_smoother[layer_name] = smoother
scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
scales[layer_name]["w"] = module.self_attention.dense.weight.abs().max(
dim=1)[0]
# fc1
layer_name = name + ".mlp.dense_h_to_4h"
smoother = smooth_gemm(module.mlp.dense_h_to_4h.weight,
scales[layer_name]["x"],
module.post_attention_layernorm.weight,
module.post_attention_layernorm.bias, alpha)
scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
scales[layer_name]["w"] = module.mlp.dense_h_to_4h.weight.abs().max(
dim=1)[0]
# fc2
# enabled for better accuracy with perf overhead of quantiztion
layer_name = name + ".mlp.dense_4h_to_h"
smoother = smooth_gemm(module.mlp.dense_4h_to_h.weight,
scales[layer_name]["x"], None, None, alpha)
bloom_smoother[layer_name] = smoother
scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
scales[layer_name]["w"] = module.mlp.dense_4h_to_h.weight.abs().max(
dim=1)[0]
# Bloom uses nn.Linear for these following ops whose weight matrix is transposed compared to transformer.Conv1D
def transpose_weights(hf_name, param):
weight_to_transpose = [
"self_attention.query_key_value", "self_attention.dense",
"mlp.dense_h_to_4h", "mlp.dense_4h_to_h"
]
if any([k in hf_name for k in weight_to_transpose]):
if len(param.shape) == 2:
param = param.transpose(0, 1)
return param
def bloom_to_trt_llm_name(orig_name):
global_weights = {
"transformer.word_embeddings.weight": "model.wpe",
"transformer.word_embeddings_layernorm.bias":
"model.word_embeddings_layernorm.bias",
"transformer.word_embeddings_layernorm.weight":
"model.word_embeddings_layernorm.weight",
"transformer.ln_f.bias": "model.final_layernorm.bias",
"transformer.ln_f.weight": "model.final_layernorm.weight",
"lm_head.weight": "model.lm_head.weight"
}
if orig_name in global_weights:
return global_weights[orig_name]
_, _, layer_id, *weight_name = orig_name.split(".")
layer_id = int(layer_id)
weight_name = "transformer." + ".".join(weight_name)
per_layer_weights = {
"transformer.input_layernorm.bias": "input_layernorm.bias",
"transformer.input_layernorm.weight": "input_layernorm.weight",
"transformer.self_attention.query_key_value.bias":
"attention.query_key_value.bias",
"transformer.self_attention.query_key_value.weight":
"attention.query_key_value.weight",
"transformer.self_attention.dense.bias": "attention.dense.bias",
"transformer.self_attention.dense.weight": "attention.dense.weight",
"transformer.post_attention_layernorm.bias":
"post_attention_layernorm.bias",
"transformer.post_attention_layernorm.weight":
"post_attention_layernorm.weight",
"transformer.mlp.dense_h_to_4h.bias": "mlp.dense_h_to_4h.bias",
"transformer.mlp.dense_h_to_4h.weight": "mlp.dense_h_to_4h.weight",
"transformer.mlp.dense_4h_to_h.bias": "mlp.dense_4h_to_h.bias",
"transformer.mlp.dense_4h_to_h.weight": "mlp.dense_4h_to_h.weight",
}
return f"layers.{layer_id}.{per_layer_weights[weight_name]}"
@torch.no_grad()
def hf_bloom_converter(args: ProgArgs):
infer_tp = args.tensor_parallelism
multi_query_mode = True if args.model in ["santacoder", "starcoder"
] else False
saved_dir = Path(args.out_dir) / f"{infer_tp}-XPU"
saved_dir.mkdir(parents=True, exist_ok=True)
# load position_embedding from rank 0
model = BloomForCausalLM.from_pretrained(args.in_file,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True)
if args.load_model_on_cpu:
model = model.float()
model = model.cpu()
torch.cuda.empty_cache()
act_range = {}
bloom_qkv_param = {}
# smoother for inputs of self_attention.dense and mlp.dense_4h_to_h
bloom_smoother = {}
if args.smoothquant is not None or args.calibrate_kv_cache:
os.environ["TOKENIZERS_PARALLELISM"] = os.environ.get(
"TOKENIZERS_PARALLELISM", "false")
from datasets import load_dataset
dataset = load_dataset("lambada",
split="validation",
cache_dir=args.dataset_cache_dir)
act_range = capture_activation_range(
model, BloomTokenizerFast.from_pretrained(args.in_file), dataset)
if args.smoothquant is not None:
smooth_bloom_model(model, act_range, args.smoothquant,
bloom_qkv_param, bloom_smoother)
config = configparser.ConfigParser()
config["bloom"] = {}
for key in vars(args):
config["bloom"][key] = f"{vars(args)[key]}"
for k, v in vars(model.config).items():
config["bloom"][k] = f"{v}"
config["bloom"]["storage_dtype"] = args.storage_type
config["bloom"]["multi_query_mode"] = str(multi_query_mode)
with open(saved_dir / "config.ini", 'w') as configfile:
config.write(configfile)
storage_type = str_dtype_to_torch(args.storage_type)
global_trt_llm_weights = [
"model.wpe", "model.word_embeddings_layernorm.bias",
"model.word_embeddings_layernorm.weight", "model.final_layernorm.bias",
"model.final_layernorm.weight", "model.lm_head.weight"
]
int8_outputs = None
if args.calibrate_kv_cache:
int8_outputs = "kv_cache_only"
if args.smoothquant is not None:
int8_outputs = "all"
starmap_args = []
for name, param in model.named_parameters():
if "weight" not in name and "bias" not in name:
continue
trt_llm_name = bloom_to_trt_llm_name(name)
if args.convert_model_on_cpu:
param = param.cpu()
if name.replace(".weight", "") in bloom_smoother.keys():
smoother = bloom_smoother[name.replace(".weight", "")]
starmap_args.append(
(0, saved_dir, infer_tp,
f"{trt_llm_name}.smoother".replace(".weight", ""),
smoother.to(torch.float32), torch.float32, None, {
"int8_outputs": int8_outputs,
"multi_query_mode": multi_query_mode,
"local_dim": None,
}))
# reorder qkv weight and bias
if "attention.query_key_value.weight" in trt_llm_name:
if args.smoothquant is not None:
param = bloom_qkv_param.get(name.replace(".weight", ""))
else:
param = reorder_torch_qkv_weight_or_bias(param,
model,
is_bias=False)
if "attention.query_key_value.bias" in trt_llm_name:
param = reorder_torch_qkv_weight_or_bias(param, model, is_bias=True)
param = transpose_weights(name, param)
if trt_llm_name in global_trt_llm_weights:
torch_to_numpy(param.to(storage_type).cpu()).tofile(
saved_dir / f"{trt_llm_name}.bin")
else:
# Needed by QKV projection weight split. With multi_query_mode one does not simply take
# out_dim and divide it by 3 to get local_dim becuase out_dim = local_dim + 2 * head_size
local_dim = model.transformer.h[
0].attn.embed_dim if multi_query_mode else None
starmap_args.append(
(0, saved_dir, infer_tp, trt_llm_name, param.to(storage_type),
storage_type, act_range.get(name.replace(".weight", "")), {
"int8_outputs": int8_outputs,
"multi_query_mode": multi_query_mode,
"local_dim": local_dim
}))
starmap_args = tqdm(starmap_args, desc="saving weights")
if args.processes > 1:
with multiprocessing.Pool(args.processes) as pool:
pool.starmap(split_and_save_weight, starmap_args)
else:
# simpler for debug situations
for starmap_arg in starmap_args:
split_and_save_weight(*starmap_arg)
def run_conversion(args: ProgArgs):
print("\n=============== Arguments ===============")
for key, value in vars(args).items():
print(f"{key}: {value}")
print("========================================")
hf_bloom_converter(args)
if __name__ == "__main__":
torch.multiprocessing.set_start_method("spawn")
run_conversion(ProgArgs.parse())