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	gguf : fix writing tensors
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							| @@ -13,9 +13,9 @@ import numpy as np | ||||
|  | ||||
|  | ||||
| class GGMLQuantizationType(IntEnum): | ||||
|     F32  = 0 | ||||
|     F16  = 1 | ||||
|     QR_0 = 2 | ||||
|     F32 = 0 | ||||
|     F16 = 1 | ||||
|     Q4_0 = 2 | ||||
|     Q4_1 = 3 | ||||
|     # Q4_2 = 4 # support has been removed | ||||
|     # Q4_3 = 5 # support has been removed | ||||
| @@ -32,16 +32,16 @@ class GGMLQuantizationType(IntEnum): | ||||
|  | ||||
|  | ||||
| class GGUFValueType(IntEnum): | ||||
|     UINT8   = 0 | ||||
|     INT8    = 1 | ||||
|     UINT16  = 2 | ||||
|     INT16   = 3 | ||||
|     UINT32  = 4 | ||||
|     INT32   = 5 | ||||
|     UINT8 = 0 | ||||
|     INT8 = 1 | ||||
|     UINT16 = 2 | ||||
|     INT16 = 3 | ||||
|     UINT32 = 4 | ||||
|     INT32 = 5 | ||||
|     FLOAT32 = 6 | ||||
|     BOOL    = 7 | ||||
|     STRING  = 8 | ||||
|     ARRAY   = 9 | ||||
|     BOOL = 7 | ||||
|     STRING = 8 | ||||
|     ARRAY = 9 | ||||
|  | ||||
|     @staticmethod | ||||
|     def get_type(val): | ||||
| @@ -75,7 +75,9 @@ class GGUFWriter: | ||||
|         return cls(f) | ||||
|  | ||||
|     def write_key(self, key: str): | ||||
|         self.write_val(key, GGUFValueType.STRING) | ||||
|         encoded_key = key.encode("utf8") | ||||
|         self.fout.write(struct.pack("<I", len(encoded_key))) | ||||
|         self.fout.write(encoded_key) | ||||
|  | ||||
|     def write_uint8(self, key: str, val: int): | ||||
|         self.write_key(key) | ||||
| @@ -158,29 +160,35 @@ class GGUFWriter: | ||||
|         return ((x + n - 1) // n) * n | ||||
|  | ||||
|     def write_tensor_info(self, name: str, tensor: np.ndarray): | ||||
|         self.write_val(name, GGUFValueType.STRING) | ||||
|         self.write_key(name) | ||||
|         n_dims = len(tensor.shape) | ||||
|         self.write_val(n_dims, GGUFValueType.INT32) | ||||
|         self.fout.write(struct.pack("<i", n_dims)) | ||||
|         for i in range(n_dims): | ||||
|             self.write_val(tensor.shape[n_dims - 1 - i], GGUFValueType.INT32) | ||||
|             self.fout.write(struct.pack("<i", tensor.shape[n_dims - 1 - i])) | ||||
|  | ||||
|         assert tensor.dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" | ||||
|         dtype = GGMLQuantizationType.F32 if tensor.dtype == np.float32 else GGMLQuantizationType.F16 | ||||
|         self.write_val(dtype, GGUFValueType.INT32) | ||||
|         self.fout.write(struct.pack("<i", dtype)) | ||||
|         self.fout.write(struct.pack("<Q", self.offset_tensor)) | ||||
|         self.offset_tensor += GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) | ||||
|  | ||||
|         offset_data = GGUFWriter.ggml_pad(self.fout.tell(), constants.GGUF_DEFAULT_ALIGNMENT) | ||||
|         pad = offset_data - self.fout.tell() | ||||
|         self.fout.write(bytes([0] * pad)) | ||||
|         self.flush() | ||||
|  | ||||
|         self.tensors.append(tensor) | ||||
|  | ||||
|     def write_tensors(self): | ||||
|         offset_data = GGUFWriter.ggml_pad(self.fout.tell(), constants.GGUF_DEFAULT_ALIGNMENT) | ||||
|         pad = offset_data - self.fout.tell() | ||||
|         print(f"pad: {pad}") | ||||
|         if pad != 0: | ||||
|             self.fout.write(bytes([0] * pad)) | ||||
|  | ||||
|         for tensor in self.tensors: | ||||
|             tensor.tofile(self.fout) | ||||
|             pad = GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) - tensor.nbytes | ||||
|             self.fout.write(bytes([0] * pad)) | ||||
|             print(f"pad: {pad}") | ||||
|             if pad != 0: | ||||
|                 self.fout.write(bytes([0] * pad)) | ||||
|  | ||||
|     def flush(self): | ||||
|         self.fout.flush() | ||||
| @@ -274,10 +282,10 @@ if __name__ == "__main__": | ||||
|     gguf_writer.write_architecture("llama") | ||||
|     gguf_writer.write_uint32("answer", 42)  # Write a 32-bit integer | ||||
|     gguf_writer.write_float32("answer_in_float", 42.0)  # Write a 32-bit float | ||||
|     tensor1 = np.random.random(size=(7, 10)).astype(np.float32) | ||||
|     tensor2 = np.random.random(size=(16, 12)).astype(np.float16) | ||||
|     tensor1 = np.ones((7, 8, 3), dtype=np.float32) | ||||
|     tensor2 = np.ones((7, 8, 3), dtype=np.float32) | ||||
|     gguf_writer.write_tensor_info("tensor1", tensor1) | ||||
|     gguf_writer.write_tensor_info("tensor2", tensor2) | ||||
|     gguf_writer.write_tensors() | ||||
|  | ||||
| gguf_writer.close() | ||||
|     gguf_writer.close() | ||||
|   | ||||
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	 M. Yusuf Sarıgöz
					M. Yusuf Sarıgöz