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support q4_0_rtn (#11477)

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* q4_0_rtn
This commit is contained in:
Zhao Changmin 2024-07-02 16:57:02 +08:00 committed by GitHub
parent 6352c718f3
commit 6a0134a9b2
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GPG key ID: B5690EEEBB952194
2 changed files with 11 additions and 6 deletions
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13
python/llm/src/ipex_llm/transformers/low_bit_linear.py
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@ -81,7 +81,12 @@ Q4_K = ggml_tensor_qtype["q4_k"]
Q6_K = ggml_tensor_qtype["q6_k"] Q6_K = ggml_tensor_qtype["q6_k"]
Q5_K = ggml_tensor_qtype["q5_k"] Q5_K = ggml_tensor_qtype["q5_k"]
FP6_K = ggml_tensor_qtype["fp6_k"] FP6_K = ggml_tensor_qtype["fp6_k"]
SYM_INT4_RTN = ggml_tensor_qtype["sym_int4_rtn"]
SYM_INT8_RTN = ggml_tensor_qtype["sym_int8_rtn"] SYM_INT8_RTN = ggml_tensor_qtype["sym_int8_rtn"]
RTN_DTYPE = {
SYM_INT4_RTN: torch.uint8,
SYM_INT8_RTN: torch.int8,
}
# For sym_int4 # For sym_int4
@ -217,8 +222,8 @@ def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
f"Last dim of input tensor must be multiple of {QK}") f"Last dim of input tensor must be multiple of {QK}")
dst_size = (n // QK) * block_size_in_bytes dst_size = (n // QK) * block_size_in_bytes
if qtype in [SYM_INT8_RTN]: if qtype in [SYM_INT8_RTN, SYM_INT4_RTN]:
dst_tensor = torch.empty(dst_size, dtype=torch.int8, dst_tensor = torch.empty(dst_size, dtype=RTN_DTYPE[qtype],
device=device) device=device)
scale = torch.empty(n // k, dtype=torch.float32, scale = torch.empty(n // k, dtype=torch.float32,
device=device) device=device)
@ -230,11 +235,11 @@ def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
dst = ctypes.c_void_p(dst_tensor.data.data_ptr()) dst = ctypes.c_void_p(dst_tensor.data.data_ptr())
hist = (ctypes.c_int64 * 16)() hist = (ctypes.c_int64 * 16)()
if qtype not in [IQ2_XXS, IQ2_XS, Q2_K, IQ1_S, Q4_K, Q6_K, Q5_K, FP6_K]: if qtype not in [IQ2_XXS, IQ2_XS, Q2_K, IQ1_S, Q4_K, Q6_K, Q5_K, FP6_K]:
if qtype in [SYM_INT8_RTN]: if qtype in [SYM_INT8_RTN, SYM_INT4_RTN]:
scale_ptr = ctypes.cast(scale.data.data_ptr(), ctypes.POINTER(ctypes.c_float)) scale_ptr = ctypes.cast(scale.data.data_ptr(), ctypes.POINTER(ctypes.c_float))
ggml.ggml_quantize_tensor_rtn(src, dst, scale_ptr, qtype, n, ggml.ggml_quantize_tensor_rtn(src, dst, scale_ptr, qtype, n,
k, hist, enable_scale_search) k, hist, enable_scale_search)
dst_tensor = dst_tensor.reshape_as(tensor) dst_tensor = dst_tensor.reshape(tensor.shape[0], tensor.shape[-1] // QK)
return dst_tensor, scale.type(torch.float16) return dst_tensor, scale.type(torch.float16)
else: else:
ggml.ggml_quantize_tensor(src, dst, qtype, n, k, hist, enable_scale_search) ggml.ggml_quantize_tensor(src, dst, qtype, n, k, hist, enable_scale_search)

4
python/llm/src/ipex_llm/transformers/npu_model.py
View file

@ -76,7 +76,7 @@ class _BaseAutoModelClass:
# for intel_npu_acceleration_library >= 1.1.0 # for intel_npu_acceleration_library >= 1.1.0
from intel_npu_acceleration_library.dtypes import int8, int4 from intel_npu_acceleration_library.dtypes import int8, int4
qtype_map = { qtype_map = {
'sym_int4': int4, 'sym_int4': "sym_int4_rtn",
'sym_int8': "sym_int8_rtn", 'sym_int8': "sym_int8_rtn",
'fp16': torch.half, 'fp16': torch.half,
'fp32': torch.float, 'fp32': torch.float,
@ -119,7 +119,7 @@ class _BaseAutoModelClass:
from intel_npu_acceleration_library.compiler import create_npu_kernels from intel_npu_acceleration_library.compiler import create_npu_kernels
with torch.no_grad(): with torch.no_grad():
optimize_llm(model) optimize_llm(model)
if qtype == "sym_int8_rtn": if qtype in ["sym_int8_rtn", "sym_int4_rtn"]:
cls.load_convert(qtype, model, *args, **kwargs) cls.load_convert(qtype, model, *args, **kwargs)
else: else:
if not qtype.is_floating_point: if not qtype.is_floating_point: