gguf-py : add support for I8, I16 and I32 (ggml-org#6045)

* Refactor dtype handling to be extensible

This code is equivalent as before, but now it is prepared to easily add
more NumPy dtypes.

* Add support for I8, I16 and I32

These types are allowed in the GGUF specification.

* Add support for I8, I16 and I32 to gguf_writer

* Add support for I8, I16, I32 to gguf_reader

Author: certik

gguf-py/gguf/constants.py

@@ -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):
@@ -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),
 }
 
 

gguf-py/gguf/gguf_reader.py

@@ -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

gguf-py/gguf/gguf_writer.py

@@ -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
@@ -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)