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support gguf_q4k_m / gguf_q4k_s (#10887)

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* initial commit

* UPDATE

* fix style

* fix style

* add gguf_q4k_s

* update comment

* fix
This commit is contained in:
Ruonan Wang 2024-05-17 06:30:09 +00:00 committed by GitHub
parent 981d668be6
commit f1156e6b20
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5 changed files with 82 additions and 35 deletions
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5
python/llm/src/ipex_llm/ggml/quantize.py
View file

@ -47,8 +47,13 @@ ggml_tensor_qtype = {"sym_int4": 2, # q4_0 in ggml
"gguf_iq1_m": 25,
"q6_k": 26,
"q4_k": 27,
"q5_k": 28,
"fp6": 29}
# mixed precison from llama.cpp
gguf_mixed_qtype = {"gguf_q4k_s": 101,
"gguf_q4k_m": 102}
_llama_quantize_type = {"q4_0": 2,
"q4_1": 3,
"q5_0": 8,

30
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -42,7 +42,7 @@ from accelerate import init_empty_weights
import warnings
import transformers
import importlib.util
from ipex_llm.ggml.quantize import ggml_tensor_qtype
from ipex_llm.ggml.quantize import ggml_tensor_qtype, gguf_mixed_qtype
from .utils import logger, get_cur_qtype_and_imatrix
from typing import Union
import numpy as np
@ -337,15 +337,6 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
if in_features % 64 != 0:
# now our kernel requires in_features is a multiple of 64
continue
new_linear = LowBitLinear(
in_features,
out_features,
qtype,
module.bias is not None,
mp_group=mp_group,
enable_xetla=enable_xetla,
optimize_lm_head=optimize_lm_head
)
cur_qtype, cur_imatrix = get_cur_qtype_and_imatrix(qtype,
full_module_name,
imatrix_data,
@ -355,6 +346,16 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
if cur_qtype in [ggml_tensor_qtype["sym_int4"],
ggml_tensor_qtype["asym_int4"]]:
cur_qtype = ggml_tensor_qtype["sym_int8"]
new_linear = LowBitLinear(
in_features,
out_features,
cur_qtype,
module.bias is not None,
mp_group=mp_group,
enable_xetla=enable_xetla,
optimize_lm_head=optimize_lm_head
)
device = module.weight.data.device
# Copy the weights
paramsLowBit = FP4Params(data=module.weight.data,
@ -766,8 +767,15 @@ def ggml_convert_low_bit(model, qtype, optimize_model=True,
embedding_qtype=None,
enable_xetla=False,
mixed_precision=False):
if qtype in ggml_tensor_qtype.values():
index = list(ggml_tensor_qtype.values()).index(qtype)
logger.info(f"Converting the current model to "
f"{list(ggml_tensor_qtype.keys())[list(ggml_tensor_qtype.values()).index(qtype)]} "
f"{list(ggml_tensor_qtype.keys())[index]} "
f"format......")
else:
index = list(gguf_mixed_qtype.values()).index(qtype)
logger.info(f"Converting the current model to "
f"{list(gguf_mixed_qtype.keys())[index]} "
f"format......")
modules_to_not_convert = [] if modules_to_not_convert is None else modules_to_not_convert

3
python/llm/src/ipex_llm/transformers/low_bit_linear.py
View file

@ -79,6 +79,7 @@ Q2_K = ggml_tensor_qtype["q2_k"]
IQ1_S = ggml_tensor_qtype["gguf_iq1_s"]
Q4_K = ggml_tensor_qtype["q4_k"]
Q6_K = ggml_tensor_qtype["q6_k"]
Q5_K = ggml_tensor_qtype["q5_k"]
# For sym_int4
@ -219,7 +220,7 @@ def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
if not convert_shape_only and device != 'meta':
dst = ctypes.c_void_p(dst_tensor.data.data_ptr())
hist = (ctypes.c_int64 * 16)()
if qtype not in [IQ2_XXS, IQ2_XS, Q2_K, IQ1_S, Q4_K, Q6_K]:
if qtype not in [IQ2_XXS, IQ2_XS, Q2_K, IQ1_S, Q4_K, Q6_K, Q5_K]:
ggml.ggml_quantize_tensor(src, dst, qtype, n, k, hist)
else:
if imatrix is not None:

44
python/llm/src/ipex_llm/transformers/model.py
View file

@ -42,7 +42,7 @@ from transformers.configuration_utils import PretrainedConfig
from .utils import extract_local_archive_file, \
load_state_dict, \
get_local_shard_files, load_imatrix_data
from ipex_llm.ggml.quantize import ggml_tensor_qtype
from ipex_llm.ggml.quantize import ggml_tensor_qtype, gguf_mixed_qtype
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.gguf.api import load_gguf_model
import torch
@ -117,12 +117,12 @@ class _BaseAutoModelClass:
Default to be ``False``.
:param load_in_low_bit: str value, options are ``'sym_int4'``, ``'asym_int4'``,
``'sym_int5'``, ``'asym_int5'``, ``'sym_int8'``, ``'nf3'``,
``'nf4'``, ``'fp4'``, ``'fp6'`` ``'fp8'``, ``'fp8_e4m3'``,
``'fp8_e5m2'``, ``'gguf_iq2_xxs'``, ``'gguf_iq2_xs'``,
``'gguf_iq1_s'``, ``'fp16'``, ``'bf16'``, ``'q4_k'`` or
``'q6_k'``, ``'sym_int4'`` means symmetric int 4,
``'asym_int4'`` means asymmetric int 4,
``'nf4'`` means 4-bit NormalFloat, etc.
``'nf4'``, ``'fp4'``, ``'fp8'``, ``'fp8_e4m3'``, ``'fp8_e5m2'``,
``'fp6'``, ``'gguf_iq2_xxs'``, ``'gguf_iq2_xs'``,
``'gguf_iq1_s'``, ``'gguf_q4k_m'``, ``'gguf_q4k_s'``,
``'fp16'``, ``'bf16'``,
``'sym_int4'`` means symmetric int 4, ``'asym_int4'`` means
asymmetric int 4, ``'nf4'`` means 4-bit NormalFloat, etc.
Relevant low bit optimizations will be applied to the model.
:param optimize_model: boolean value, Whether to further optimize the low_bit llm model.
Default to be ``True``.
@ -139,8 +139,9 @@ class _BaseAutoModelClass:
added to llama.cpp.
:param model_hub: str value, options are ``'huggingface'`` and ``'modelscope'``,
specify the model hub. Default to be ``'huggingface'``.
:param embedding_qtype: str value, options are ``'q2_k'`` now. Default to be None.
Relevant low bit optimizations will be applied to nn.Embedding layer.
:param embedding_qtype: str value, options are ``'q2_k'``, ``'q4_k'`` now.
Default to be None. Relevant low bit optimizations will be applied to
``nn.Embedding`` layer.
:param mixed_precision: boolean value, Whether to use mixed precision quantization.
Default to be False. If set to True, we will use sym_int8 for lm_head when
load_in_low_bit is sym_int4 or asym_int4.
@ -321,10 +322,12 @@ class _BaseAutoModelClass:
"For gguf_iq2 and gguf_iq1 quantization,"
"imatrix is needed.")
cpu_embedding = kwargs.get("cpu_embedding", False)
# for 2bit, default use embedding_quantization
if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s", "q2_k"] and \
not cpu_embedding and embedding_qtype is None:
# for iq2/k-quants, default use embedding_quantization
if not cpu_embedding and embedding_qtype is None:
if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s", "q2_k"]:
embedding_qtype = "q2_k"
elif q_k in ["gguf_q4k_s", "gguf_q4k_m"]:
embedding_qtype = "q4_k"
if imatrix_file is not None:
imatrix_data = load_imatrix_data(imatrix_file)
kwargs["imatrix_data"] = imatrix_data
@ -376,12 +379,16 @@ class _BaseAutoModelClass:
@classmethod
def load_convert(cls, q_k, optimize_model, *args, **kwargs):
from .convert import ggml_convert_low_bit
invalidInputError(q_k in ggml_tensor_qtype,
invalidInputError(q_k in ggml_tensor_qtype or q_k in gguf_mixed_qtype,
f"Unknown load_in_low_bit value: {q_k}, expected:"
f" sym_int4, asym_int4, sym_int5, asym_int5, sym_int8, nf3, nf4, "
f"fp4, fp6, fp8, fp8_e4m3, fp8_e5m2, fp16, bf16, gguf_iq2_xxs, "
f"gguf_iq2_xs, gguf_iq1_s, q2_k, q4_k, q6_k, mixed_fp4 or mixed_fp8.")
f"gguf_iq2_xs, gguf_iq1_s, q2_k, q4_k, q5_k, q6_k, "
f"gguf_q4k_s, gguf_q4k_m, mixed_fp4 or mixed_fp8.")
if q_k in ggml_tensor_qtype:
qtype = ggml_tensor_qtype[q_k]
else:
qtype = gguf_mixed_qtype[q_k]
# In case it needs a second try,
# `from_pretrained`` may pop items out in dict
@ -550,17 +557,24 @@ class _BaseAutoModelClass:
" with load_in_4bit or load_in_low_bit to get a low-bit model , and "
" serialize the model using save_low_bit first.")
invalidInputError(bigdl_transformers_low_bit in ggml_tensor_qtype,
invalidInputError(bigdl_transformers_low_bit in ggml_tensor_qtype or
bigdl_transformers_low_bit in gguf_mixed_qtype,
f"Unknown bigdl_transformers_low_bit value: {bigdl_transformers_low_bit},"
f" expected: sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8.")
# set default optimize_model=True
optimize_model = kwargs.pop("optimize_model", True)
if bigdl_transformers_low_bit in ggml_tensor_qtype:
qtype = ggml_tensor_qtype[bigdl_transformers_low_bit]
else:
qtype = gguf_mixed_qtype[bigdl_transformers_low_bit]
if bigdl_transformers_low_bit in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s", "q2_k"] and \
not cpu_embedding:
embedding_qtype = "q2_k"
elif bigdl_transformers_low_bit in ["gguf_q4k_s", "gguf_q4k_m"] and \
not cpu_embedding:
embedding_qtype = "q4_k"
if embedding_qtype is not None:
embedding_qtype = ggml_tensor_qtype[embedding_qtype]

25
python/llm/src/ipex_llm/transformers/utils.py
View file

@ -41,7 +41,7 @@
# SOFTWARE.
import os
from transformers.modeling_utils import _add_variant
from ipex_llm.ggml.quantize import ggml_tensor_qtype
from ipex_llm.ggml.quantize import ggml_tensor_qtype, gguf_mixed_qtype
from ..utils.common import invalidInputError
from typing import Union, Optional
import torch
@ -271,10 +271,12 @@ def module_name_process(full_module_name):
def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data, model_config=None):
cur_qtype = qtype
cur_imatrix = None
if model_config is not None:
model_type = getattr(model_config, "model_type", None)
else:
model_dtype = None
if qtype in [ggml_tensor_qtype["gguf_iq2_xxs"], ggml_tensor_qtype["gguf_iq2_xs"],
ggml_tensor_qtype["gguf_iq1_s"]]:
# For quantization which needs importance matrix
@ -306,7 +308,6 @@ def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data, model_confi
cur_imatrix = None
if new_module_name == 'lm_head':
cur_qtype = ggml_tensor_qtype['sym_int8']
return cur_qtype, cur_imatrix
elif qtype == ggml_tensor_qtype["q2_k"]:
new_module_name, layer, cur_module = module_name_process(full_module_name)
if cur_module == 'v' or (cur_module == 'down' and int(layer) in [0, 1, 10, 11]):
@ -319,8 +320,26 @@ def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data, model_confi
cur_imatrix = None
if new_module_name == 'lm_head':
cur_qtype = ggml_tensor_qtype['sym_int8']
elif qtype > 100:
# gguf mixed precision
new_module_name, layer, cur_module = module_name_process(full_module_name)
num_hidden_layers = getattr(model_config, "num_hidden_layers", None)
if qtype in [gguf_mixed_qtype["gguf_q4k_s"], gguf_mixed_qtype["gguf_q4k_m"]] and \
new_module_name == 'lm_head':
cur_qtype = ggml_tensor_qtype['q6_k']
elif qtype == gguf_mixed_qtype["gguf_q4k_m"]:
if int(layer) < int(num_hidden_layers/2) and cur_module in ['v', 'down']:
cur_qtype = ggml_tensor_qtype['q6_k']
else:
return qtype, None
cur_qtype = ggml_tensor_qtype['q4_k']
elif qtype == gguf_mixed_qtype["gguf_q4k_s"]:
if int(layer) < int(num_hidden_layers/8) and cur_module in ['v', 'down']:
cur_qtype = ggml_tensor_qtype['q5_k']
else:
cur_qtype = ggml_tensor_qtype['q4_k']
else:
pass
return cur_qtype, cur_imatrix
def get_modelscope_hf_config(model_id_or_path: str,