From 792c39a0aed8a511e66578c9f5ff7045d597b927 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ond=C5=99ej=20=C4=8Cert=C3=ADk?= Date: Thu, 14 Mar 2024 11:57:10 -0600 Subject: [PATCH] gguf : add support for I64 and F64 arrays GGML currently does not support I64 or F64 arrays and they are not often used in machine learning, however if in the future the need arises, it would be nice to add them now, so that the types are next to the other types I8, I16, I32 in the enums, and it also reserves their type number. Furthermore, with this addition the GGUF format becomes very usable for most computational applications of NumPy (being compatible with the most common NumPy dtypes: i8, i16, i32, i64, f32, f64), providing a faster, and more versatile alternative to the `npz` format, and a simpler alternative to the `hdf5` format. The change in this PR seems small, not significantly increasing the maintenance burden. I tested this from Python using GGUFWriter/Reader and `gguf-dump`, as well as from C, everything seems to work. --- ggml.c | 13 +++++++++++++ ggml.h | 2 ++ gguf-py/gguf/constants.py | 4 ++++ gguf-py/gguf/gguf_reader.py | 14 ++++++++++---- gguf-py/gguf/gguf_writer.py | 12 ++++++++---- 5 files changed, 37 insertions(+), 8 deletions(-) diff --git a/ggml.c b/ggml.c index fbc66f65b..c47d49f47 100644 --- a/ggml.c +++ b/ggml.c @@ -470,6 +470,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(int32_t), .is_quantized = false, }, + [GGML_TYPE_I64] = { + .type_name = "i64", + .blck_size = 1, + .type_size = sizeof(int64_t), + .is_quantized = false, + }, + [GGML_TYPE_F64] = { + .type_name = "f64", + .blck_size = 1, + .type_size = sizeof(double), + .is_quantized = false, + .nrows = 1, + }, [GGML_TYPE_F32] = { .type_name = "f32", .blck_size = 1, diff --git a/ggml.h b/ggml.h index ab26c8f59..c937d4a53 100644 --- a/ggml.h +++ b/ggml.h @@ -366,6 +366,8 @@ extern "C" { GGML_TYPE_I8 = 24, GGML_TYPE_I16 = 25, GGML_TYPE_I32 = 26, + GGML_TYPE_I64 = 27, + GGML_TYPE_F64 = 28, GGML_TYPE_COUNT, }; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2d7cf16c1..458a641dc 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -665,6 +665,8 @@ class GGMLQuantizationType(IntEnum): I8 = 24 I16 = 25 I32 = 26 + I64 = 27 + F64 = 28 class GGUFEndian(IntEnum): @@ -734,6 +736,8 @@ GGML_QUANT_SIZES = { GGMLQuantizationType.I8: (1, 1), GGMLQuantizationType.I16: (1, 2), GGMLQuantizationType.I32: (1, 4), + GGMLQuantizationType.I64: (1, 8), + GGMLQuantizationType.F64: (1, 8), } diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 1c10f5753..33afac552 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -242,12 +242,15 @@ class GGUFReader: n_bytes = n_elems * type_size // block_size data_offs = int(start_offs + offset_tensor[0]) item_type: npt.DTypeLike - if ggml_type == GGMLQuantizationType.F32: - item_count = n_elems - item_type = np.float32 - elif ggml_type == GGMLQuantizationType.F16: + if ggml_type == GGMLQuantizationType.F16: item_count = n_elems item_type = np.float16 + elif ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F64: + item_count = n_elems + item_type = np.float64 elif ggml_type == GGMLQuantizationType.I8: item_count = n_elems item_type = np.int8 @@ -257,6 +260,9 @@ class GGUFReader: elif ggml_type == GGMLQuantizationType.I32: item_count = n_elems item_type = np.int32 + elif ggml_type == GGMLQuantizationType.I64: + item_count = n_elems + item_type = np.int64 else: item_count = n_bytes item_type = np.uint8 diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 81b2eb884..1967b633c 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -204,18 +204,22 @@ class GGUFWriter: for i in range(n_dims): self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: - if tensor_dtype == np.float32: - dtype = GGMLQuantizationType.F32 - elif tensor_dtype == np.float16: + if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 + elif tensor_dtype == np.float32: + dtype = GGMLQuantizationType.F32 + elif tensor_dtype == np.float64: + dtype = GGMLQuantizationType.F64 elif tensor_dtype == np.int8: dtype = GGMLQuantizationType.I8 elif tensor_dtype == np.int16: dtype = GGMLQuantizationType.I16 elif tensor_dtype == np.int32: dtype = GGMLQuantizationType.I32 + elif tensor_dtype == np.int64: + dtype = GGMLQuantizationType.I64 else: - raise ValueError("Only F32, F16, I8, I16, I32 tensors are supported for now") + raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") else: dtype = raw_dtype self.ti_data += self._pack("I", dtype)