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gguf-py: add support for I8, I16 and I32 #6045

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merged 4 commits into from
Mar 14, 2024
Merged

gguf-py: add support for I8, I16 and I32 #6045

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b7e9d5c
Refactor dtype handling to be extensible
certik Mar 13, 2024
dc0e4d8
Add support for I8, I16 and I32
certik Mar 13, 2024
c542375
Add support for I8, I16 and I32 to gguf_writer
certik Mar 13, 2024
fc5d6e6
Add support for I8, I16, I32 to gguf_reader
certik Mar 13, 2024
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6 changes: 6 additions & 0 deletions gguf-py/gguf/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -661,6 +661,9 @@ class GGMLQuantizationType(IntEnum):
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
I8 = 24
I16 = 25
I32 = 26


class GGUFEndian(IntEnum):
Expand Down Expand Up @@ -727,6 +730,9 @@ def get_type(val: Any) -> GGUFValueType:
GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64),
GGMLQuantizationType.I8: (1, 1),
GGMLQuantizationType.I16: (1, 2),
GGMLQuantizationType.I32: (1, 4),
}


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9 changes: 9 additions & 0 deletions gguf-py/gguf/gguf_reader.py
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Expand Up @@ -248,6 +248,15 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
elif ggml_type == GGMLQuantizationType.F16:
item_count = n_elems
item_type = np.float16
elif ggml_type == GGMLQuantizationType.I8:
item_count = n_elems
item_type = np.int8
elif ggml_type == GGMLQuantizationType.I16:
item_count = n_elems
item_type = np.int16
elif ggml_type == GGMLQuantizationType.I32:
item_count = n_elems
item_type = np.int32
else:
item_count = n_bytes
item_type = np.uint8
Expand Down
16 changes: 12 additions & 4 deletions gguf-py/gguf/gguf_writer.py
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Expand Up @@ -196,9 +196,6 @@ def add_tensor_info(
if self.state is not WriterState.EMPTY:
raise ValueError(f'Expected output file to be empty, got {self.state}')

if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
raise ValueError("Only F32 and F16 tensors are supported for now")

encoded_name = name.encode("utf8")
self.ti_data += self._pack("Q", len(encoded_name))
self.ti_data += encoded_name
Expand All @@ -207,7 +204,18 @@ def add_tensor_info(
for i in range(n_dims):
self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
if tensor_shape == np.float32:
dtype = GGMLQuantizationType.F32
elif tensor_dtype == np.float16:
dtype = GGMLQuantizationType.F16
elif tensor_dtype == np.int8:
dtype = GGMLQuantizationType.I8
elif tensor_dtype == np.int16:
dtype = GGMLQuantizationType.I16
elif tensor_dtype == np.int32:
dtype = GGMLQuantizationType.I32
else:
raise ValueError("Only F32, F16, I8, I16, I32 tensors are supported for now")
else:
dtype = raw_dtype
self.ti_data += self._pack("I", dtype)
Expand Down
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