ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Support arc fp4 (#9266)

Browse source 
* support arc fp4

* fix style

* fix style
This commit is contained in:
Yina Chen 2023-10-25 15:42:48 +08:00 committed by GitHub
parent ab40607b87
commit e2264e8845
3 changed files with 15 additions and 10 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
python/llm/src/bigdl/llm/ggml/quantize.py
View file

@ -33,7 +33,8 @@ ggml_tensor_qtype = {"sym_int4": 2, # q4_0 in ggml
"nf4": 10,
"nf3": 11,
"fp16": 12,
"fp8": 15}
"fp8": 15,
"fp4": 16}
_llama_quantize_type = {"q4_0": 2,
"q4_1": 3,

11
python/llm/src/bigdl/llm/transformers/low_bit_linear.py
View file

@ -65,6 +65,7 @@ SYM_INT8 = ggml_tensor_qtype["sym_int8"]
NF4 = ggml_tensor_qtype["nf4"]
NF3 = ggml_tensor_qtype["nf3"]
FP8 = ggml_tensor_qtype["fp8"]
FP4 = ggml_tensor_qtype["fp4"]
def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
@ -108,7 +109,7 @@ def ggml_q_format_convet_cpu2xpu(tensor: torch.Tensor, num_elem: int, qtype: int
src = ctypes.c_void_p(tensor.data.data_ptr())
if qtype in [SYM_INT4, SYM_INT8, NF4, NF3]:
if qtype in [SYM_INT4, SYM_INT8, NF4, NF3, FP4]:
dst_tensor = torch.empty_like(tensor)
elif qtype == ggml_tensor_qtype["sym_int5"]:
QK = ggml.ggml_qk_size(qtype)
@ -133,7 +134,7 @@ def ggml_q_format_convet_xpu2cpu(tensor: torch.Tensor, num_elem: int, qtype: int
src = ctypes.c_void_p(tensor.data.data_ptr())
if qtype in [SYM_INT4, SYM_INT8, NF4, NF3]:
if qtype in [SYM_INT4, SYM_INT8, NF4, NF3, FP4]:
dst_tensor = torch.empty_like(tensor)
elif qtype == ggml_tensor_qtype["sym_int5"]:
QK = ggml.ggml_qk_size(ggml_tensor_qtype["asym_int5"])
@ -387,8 +388,10 @@ class LowBitLinear(nn.Linear):
else:
# CPU logic
# todo may need to set a different number on different platforms
invalidInputError(self.qtype != NF3 and self.qtype != NF4 and self.qtype != FP8,
"NF3, NF4 and FP8 quantization are currently not supported on CPU")
invalidInputError(self.qtype != NF3 and self.qtype != NF4 and self.qtype != FP8
and self.qtype != FP4,
"NF3, NF4, FP4 and FP8 quantization are currently not"
" supported on CPU")
if IS_SERVER and (not IS_SPR) and \
self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)

11
python/llm/src/bigdl/llm/transformers/model.py
View file

@ -60,9 +60,10 @@ class _BaseAutoModelClass:
:param load_in_4bit: boolean value, True means load linear's weight to symmetric int 4.
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 or fp16. 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.
, sym_int8, nf3, nf4, fp4, fp8 or fp16. 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.
:param modules_to_not_convert: list of str value, modules (nn.Module) that are skipped when
@ -106,8 +107,8 @@ class _BaseAutoModelClass:
from .convert import ggml_convert_low_bit
invalidInputError(q_k in ggml_tensor_qtype,
f"Unknown load_in_low_bit value: {q_k}, expected:"
f" sym_int4, asym_int4, sym_int5, asym_int5, sym_int8, nf3, nf4 "
"or fp16.")
f" sym_int4, asym_int4, sym_int5, asym_int5, sym_int8, nf3, nf4, "
"fp4, fp8 or fp16.")
qtype = ggml_tensor_qtype[q_k]
# In case it needs a second try,