Sync from upstream llama.cpp repository
This commit is contained in:
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gguf-py/tests/__init__.py
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gguf-py/tests/__init__.py
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from .test_metadata import *
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238
gguf-py/tests/test_metadata.py
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238
gguf-py/tests/test_metadata.py
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#!/usr/bin/env python3
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import unittest
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from pathlib import Path
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import os
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import sys
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# Necessary to load the local gguf package
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if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import gguf
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class TestMetadataMethod(unittest.TestCase):
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def test_id_to_title(self):
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self.assertEqual(gguf.Metadata.id_to_title("Mixtral-8x7B-Instruct-v0.1"), "Mixtral 8x7B Instruct v0.1")
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self.assertEqual(gguf.Metadata.id_to_title("Meta-Llama-3-8B"), "Meta Llama 3 8B")
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self.assertEqual(gguf.Metadata.id_to_title("hermes-2-pro-llama-3-8b-DPO"), "Hermes 2 Pro Llama 3 8b DPO")
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def test_get_model_id_components(self):
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# This is the basic standard form with organization marker
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self.assertEqual(gguf.Metadata.get_model_id_components("Mistral/Mixtral-8x7B-Instruct-v0.1"),
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('Mixtral-8x7B-Instruct-v0.1', "Mistral", 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
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# Similar to basic standard form but without organization marker
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct-v0.1"),
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('Mixtral-8x7B-Instruct-v0.1', None, 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
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# Missing version
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct"),
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('Mixtral-8x7B-Instruct', None, 'Mixtral', 'Instruct', None, '8x7B'))
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# Missing finetune
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-v0.1"),
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('Mixtral-8x7B-v0.1', None, 'Mixtral', None, 'v0.1', '8x7B'))
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# Base name and size label only
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B"),
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('Mixtral-8x7B', None, 'Mixtral', None, None, '8x7B'))
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# Base name and version only
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-v0.1"),
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('Mixtral-v0.1', None, 'Mixtral', None, 'v0.1', None))
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## Edge Cases ##
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# This is too ambiguous... best to err on caution and output nothing
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral"),
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('Mixtral', None, None, None, None, None))
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# Basename has numbers mixed in and also size label provided. Must avoid capturing number in basename
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self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
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('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
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# Non standard naming
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
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('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
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# Capture 'sub size labels' e.g. A14B in '57B-A14B' usually refers to activated params/weight count
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-57B-A14B-Instruct"),
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('Qwen2-57B-A14B-Instruct', None, 'Qwen2', 'Instruct', None, '57B-A14B'))
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# Check that it can handle a real model id with no version code
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# Note that 4k in this string is non standard and microsoft were referring to context length rather than weight count
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self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Phi-3-mini-4k-instruct", 4 * 10**9),
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('Phi-3-mini-4k-instruct', 'microsoft', 'Phi-3', '4k-instruct', None, 'mini'))
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# There is some legitimate models with only thousands of parameters
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self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
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('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
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# Non standard and not easy to disambiguate
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self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
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('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
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# This is a real model_id where they append 2DPO to refer to Direct Preference Optimization
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self.assertEqual(gguf.Metadata.get_model_id_components("crestf411/daybreak-kunoichi-2dpo-7b"),
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('daybreak-kunoichi-2dpo-7b', 'crestf411', 'daybreak-kunoichi', '2dpo', None, '7B'))
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# This is a real model id where the weight size has a decimal point
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-0.5B-Instruct"),
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('Qwen2-0.5B-Instruct', None, 'Qwen2', 'Instruct', None, '0.5B'))
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# Uses an underscore in the size label
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self.assertEqual(gguf.Metadata.get_model_id_components("smallcloudai/Refact-1_6B-fim"),
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('Refact-1_6B-fim', 'smallcloudai', 'Refact', 'fim', None, '1.6B'))
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# Uses Iter3 for the version
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self.assertEqual(gguf.Metadata.get_model_id_components("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"),
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('Gemma-2-9B-It-SPPO-Iter3', 'UCLA-AGI', 'Gemma-2', 'It-SPPO', 'Iter3', '9B'))
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# Has two potential versions in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Hermes-2-Theta-Llama-3-8B"),
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('Hermes-2-Theta-Llama-3-8B', 'NousResearch', 'Hermes-2-Theta-Llama-3', None, None, '8B'))
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# Potential version in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("SeaLLMs/SeaLLMs-v3-7B-Chat"),
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('SeaLLMs-v3-7B-Chat', 'SeaLLMs', 'SeaLLMs-v3', 'Chat', None, '7B'))
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# Underscore in the basename, and 1m for the context size
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self.assertEqual(gguf.Metadata.get_model_id_components("internlm/internlm2_5-7b-chat-1m", 7 * 10**9),
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('internlm2_5-7b-chat-1m', 'internlm', 'internlm2_5', 'chat-1m', None, '7B'))
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# Version before the finetune name
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self.assertEqual(gguf.Metadata.get_model_id_components("pszemraj/jamba-900M-v0.13-KIx2"),
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('jamba-900M-v0.13-KIx2', 'pszemraj', 'jamba', 'KIx2', 'v0.13', '900M'))
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# TODO: hf suffix which could be ignored but isn't
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self.assertEqual(gguf.Metadata.get_model_id_components("state-spaces/mamba-2.8b-hf"),
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('mamba-2.8b-hf', 'state-spaces', 'mamba', 'hf', None, '2.8B'))
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# Two sizes, don't merge them, the other is the number of tokens on which it was trained
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self.assertEqual(gguf.Metadata.get_model_id_components("abacaj/llama-161M-100B", 161 * 10**6),
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('llama-161M-100B', 'abacaj', 'llama', '100b', None, '161M'))
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# It's a trap, there is no size label
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self.assertEqual(gguf.Metadata.get_model_id_components("SparseLLM/relu-100B", 1340 * 10**6),
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('relu-100B', 'SparseLLM', 'relu', '100b', None, None))
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# Weird size notation
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self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
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('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
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# Ignore full-text size labels when there are number-based ones, and deduplicate size labels
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self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"),
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('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B'))
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# Instruct in a name without a size label
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self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"),
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('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None))
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# Non-obvious splitting relying on 'chat' keyword
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self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"),
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('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None))
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# Multiple versions
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self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"),
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('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B'))
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# TODO: DPO in the name
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self.assertEqual(gguf.Metadata.get_model_id_components("jondurbin/bagel-dpo-2.8b-v0.2"),
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('bagel-dpo-2.8b-v0.2', 'jondurbin', 'bagel-dpo', None, 'v0.2', '2.8B'))
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# DPO in name, but can't be used for the finetune to keep 'LLaMA-3' in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("voxmenthe/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized"),
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('SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized', 'voxmenthe', 'SFR-Iterative-DPO-LLaMA-3', 'R-unquantized', None, '8B'))
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# Too ambiguous
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# TODO: should "base" be a 'finetune' or 'size_label'?
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# (in this case it should be a size label, but other models use it to signal that they are not finetuned)
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self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"),
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('Florence-2-base', 'microsoft', None, None, None, None))
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## Invalid cases ##
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# Start with a dash and has dashes in rows
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self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
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('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
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## LoRA ##
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self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"),
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('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B'))
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# Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix
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self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234),
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('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B'))
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def test_apply_metadata_heuristic_from_model_card(self):
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model_card = {
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'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
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'model-index': [{'name': 'Mixtral-8x7B-Instruct-v0.1', 'results': []}],
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'language': ['en'],
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'datasets': ['teknium/OpenHermes-2.5'],
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'widget': [{'example_title': 'Hermes 2 Pro', 'messages': [{'role': 'system', 'content': 'You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.'}, {'role': 'user', 'content': 'Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.'}]}],
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'base_model': ["EmbeddedLLM/Mistral-7B-Merge-14-v0", "janai-hq/trinity-v1"]
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}
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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expect = gguf.Metadata()
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expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
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expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
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expect.languages=['en']
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expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]
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self.assertEqual(got, expect)
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# Base Model spec is inferred from model id
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model_card = {'base_models': 'teknium/OpenHermes-2.5'}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Base Model spec is only url
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model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Base Model spec is given directly
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model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is inferred from model id
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model_card = {'datasets': 'teknium/OpenHermes-2.5'}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is only url
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model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is given directly
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model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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def test_apply_metadata_heuristic_from_hf_parameters(self):
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hf_params = {"_name_or_path": "./hermes-2-pro-llama-3-8b-DPO"}
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=hf_params, model_path=None)
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expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
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self.assertEqual(got, expect)
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def test_apply_metadata_heuristic_from_model_dir(self):
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model_dir_path = Path("./hermes-2-pro-llama-3-8b-DPO")
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=None, model_path=model_dir_path)
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expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
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self.assertEqual(got, expect)
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if __name__ == "__main__":
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unittest.main()
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247
gguf-py/tests/test_quants.py
Executable file
247
gguf-py/tests/test_quants.py
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#!/usr/bin/env python3
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# Test gguf.quants so that it exactly matches the C implementation of the (de)quantization
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# NOTE: this is kind of a mess, but at least it worked for initially testing the Python implementations.
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from __future__ import annotations
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import argparse
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from math import prod
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import os
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import sys
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from pathlib import Path
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import ctypes
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import logging
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import numpy as np
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# Necessary to load the local gguf package
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if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import gguf
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from gguf.constants import GGMLQuantizationType
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logger = logging.getLogger("test-quants")
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c_float_p = ctypes.POINTER(ctypes.c_float)
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class ggml_init_params(ctypes.Structure):
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_fields_ = [
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("mem_size", ctypes.c_size_t),
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("mem_buffer", ctypes.c_void_p),
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("no_alloc", ctypes.c_bool),
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]
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class GGMLQuants:
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libggml: ctypes.CDLL
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def __init__(self, libggml: Path):
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self.libggml = ctypes.CDLL(str(libggml))
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self.libggml.ggml_quantize_chunk.restype = ctypes.c_size_t
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# enum ggml_type type,
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# const float * src,
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# void * dst,
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# int64_t start,
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# int64_t nrows,
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# int64_t n_per_row,
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# const float * imatrix) {
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self.libggml.ggml_quantize_chunk.argtypes = (
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ctypes.c_int,
|
||||
ctypes.POINTER(ctypes.c_float),
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_int64,
|
||||
ctypes.c_int64,
|
||||
ctypes.c_int64,
|
||||
ctypes.POINTER(ctypes.c_float),
|
||||
)
|
||||
|
||||
self.libggml.ggml_quantize_requires_imatrix.restype = ctypes.c_bool
|
||||
self.libggml.ggml_quantize_requires_imatrix.argtypes = (ctypes.c_int,)
|
||||
|
||||
for t in (
|
||||
"q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
|
||||
"q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
|
||||
"tq1_0", "tq2_0",
|
||||
"mxfp4",
|
||||
"iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
|
||||
"iq4_nl", "iq4_xs",
|
||||
):
|
||||
dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t)
|
||||
dequant_func.restype = None
|
||||
dequant_func.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
|
||||
|
||||
self.libggml.ggml_fp16_to_fp32_row.restype = None
|
||||
self.libggml.ggml_fp16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
|
||||
self.libggml.ggml_bf16_to_fp32_row.restype = None
|
||||
self.libggml.ggml_bf16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
|
||||
|
||||
self.libggml.ggml_init.argtypes = (ggml_init_params,)
|
||||
|
||||
self.libggml.ggml_init(ggml_init_params(1 * 1024 * 1024, 0, False))
|
||||
|
||||
def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
|
||||
result = np.zeros(gguf.quant_shape_from_byte_shape(tensor.shape, qtype), dtype=np.float32, order="C")
|
||||
if qtype == GGMLQuantizationType.F32:
|
||||
# no-op
|
||||
result = tensor.view(np.float32)
|
||||
elif qtype == GGMLQuantizationType.F16:
|
||||
self.libggml.ggml_fp16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
|
||||
elif qtype == GGMLQuantizationType.BF16:
|
||||
self.libggml.ggml_bf16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
|
||||
else:
|
||||
lw_qname = qtype.name.lower()
|
||||
if lw_qname[-1] == "k":
|
||||
lw_qname = lw_qname[:-1] + "K"
|
||||
dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + lw_qname)
|
||||
dequant_func(tensor.ctypes.data_as(ctypes.c_void_p), result.ctypes.data_as(c_float_p), result.size)
|
||||
return result
|
||||
|
||||
def quantize(self, data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
|
||||
result = np.zeros(gguf.quant_shape_to_byte_shape(data.shape, qtype), dtype=np.uint8, order="C")
|
||||
if self.libggml.ggml_quantize_requires_imatrix(qtype.value):
|
||||
# TODO: is a column-wise sum of squares appropriate?
|
||||
qw = np.sum((data * data).reshape((-1, data.shape[-1])), axis=0).ctypes.data_as(c_float_p)
|
||||
else:
|
||||
qw = ctypes.cast(0, c_float_p)
|
||||
result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], qw)
|
||||
assert result.size == result_size
|
||||
return result
|
||||
|
||||
|
||||
def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) -> bool:
|
||||
same = np.array_equal(t1, t2)
|
||||
if same:
|
||||
return True
|
||||
else:
|
||||
block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
|
||||
if t1.dtype == np.float32:
|
||||
t1 = t1.reshape((-1, block_size))
|
||||
t2 = t2.reshape((-1, block_size))
|
||||
else:
|
||||
t1 = t1.reshape((-1, type_size))
|
||||
t2 = t2.reshape((-1, type_size))
|
||||
x = t1.view(np.uint8) ^ t2.view(np.uint8)
|
||||
diff_bits = np.count_nonzero(np.unpackbits(x, axis=-1), axis=-1)
|
||||
num_bad_blocks = np.count_nonzero(diff_bits, axis=0)
|
||||
if num_bad_blocks == 0 and t1.shape == t2.shape:
|
||||
logger.debug("Bits are equal, but arrays don't match, likely contains NANs")
|
||||
return True
|
||||
logger.debug(f"{num_bad_blocks} bad blocks ({100 * num_bad_blocks / x.shape[0]:.6f}%)")
|
||||
bad_block_id = np.argmax(diff_bits, axis=0)
|
||||
logger.debug(f"Worst block id: {bad_block_id}")
|
||||
logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
|
||||
|
||||
sum_diff_bits = np.sum(diff_bits)
|
||||
logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)")
|
||||
return False
|
||||
|
||||
|
||||
def do_test(libggml_path: Path, quick: bool = False, user_type: GGMLQuantizationType | None = None):
|
||||
ggml_quants = GGMLQuants(libggml_path)
|
||||
|
||||
np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n})
|
||||
|
||||
r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False)
|
||||
# test zero blocks
|
||||
r[0, 0, :] = 0
|
||||
## Maybe test infinities? (can make NANs, not really useful in practice)
|
||||
# r[0, 1, 0] = np.inf
|
||||
# r[0, 2, 0] = -np.inf
|
||||
# r[0, 3, 0] = np.inf
|
||||
# r[0, 3, 1] = -np.inf
|
||||
|
||||
for qtype in ((GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()) if user_type is None else (user_type,)):
|
||||
has_dequantize = False
|
||||
has_quantize = False
|
||||
|
||||
try:
|
||||
gguf.dequantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][1]), dtype=np.uint8), qtype)
|
||||
has_dequantize = True
|
||||
except (NotImplementedError, AssertionError) as e:
|
||||
if isinstance(e, AssertionError):
|
||||
logger.error(f"Error with {qtype.name}: {e}")
|
||||
raise e
|
||||
try:
|
||||
gguf.quantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][0]), dtype=np.float32), qtype)
|
||||
has_quantize = True
|
||||
except (NotImplementedError, AssertionError) as e:
|
||||
if isinstance(e, AssertionError):
|
||||
logger.error(f"Error with {qtype.name}: {e}")
|
||||
raise e
|
||||
|
||||
if not has_dequantize and not has_quantize:
|
||||
continue
|
||||
|
||||
logger.info(f"Testing {qtype.name}")
|
||||
|
||||
rc = r.copy(order="C")
|
||||
|
||||
pyq = None
|
||||
ggq = None
|
||||
|
||||
if has_quantize:
|
||||
logger.debug(f"Quantizing to {qtype.name} with Python")
|
||||
pyq = gguf.quants.quantize(rc, qtype)
|
||||
|
||||
logger.debug(f"Quantizing to {qtype.name} with C")
|
||||
ggq = ggml_quants.quantize(rc, qtype)
|
||||
|
||||
if qtype == GGMLQuantizationType.F16:
|
||||
pyq = pyq.view(np.uint8)
|
||||
quant_equal = compare_tensors(pyq, ggq, qtype)
|
||||
|
||||
if not quant_equal:
|
||||
logger.error(f"Quantization to {qtype.name} does not match ❌")
|
||||
else:
|
||||
logger.info(f"Quantization to {qtype.name} matches exactly ✅")
|
||||
|
||||
if has_dequantize:
|
||||
if ggq is None and not quick:
|
||||
logger.debug(f"Quantizing to {qtype.name} with C")
|
||||
ggq = ggml_quants.quantize(rc, qtype)
|
||||
|
||||
if ggq is not None:
|
||||
logger.debug(f"Dequantizing from {qtype.name} with Python")
|
||||
pydq = gguf.quants.dequantize(ggq, qtype)
|
||||
logger.debug(f"Dequantizing from {qtype.name} with C")
|
||||
ggdq = ggml_quants.dequantize(ggq, qtype)
|
||||
|
||||
dequant_equal = compare_tensors(pydq, ggdq, qtype)
|
||||
|
||||
if not dequant_equal:
|
||||
logger.error(f"Dequantization from {qtype.name} does not match ❌")
|
||||
else:
|
||||
logger.info(f"Dequantization from {qtype.name} matches exactly ✅")
|
||||
|
||||
rq_shape = gguf.quants.quant_shape_to_byte_shape((8, 1024, 1024 // 2), qtype)
|
||||
rq = np.random.random(rq_shape).astype(np.float16).view(np.uint8)
|
||||
|
||||
logger.debug(f"Dequantizing random f16 data as {qtype.name} with Python")
|
||||
pydq = gguf.quants.dequantize(rq, qtype)
|
||||
logger.debug(f"Dequantizing random f16 data as {qtype.name} with C")
|
||||
ggdq = ggml_quants.dequantize(rq, qtype)
|
||||
|
||||
dequant_equal = compare_tensors(pydq, ggdq, qtype)
|
||||
|
||||
if not dequant_equal:
|
||||
logger.error(f"Dequantization from random f16 data as {qtype.name} does not match ❌")
|
||||
else:
|
||||
logger.info(f"Dequantization from random f16 data as {qtype.name} matches exactly ✅")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation")
|
||||
parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "bin" / "libggml.so", help="The path to libggml.so")
|
||||
parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary")
|
||||
parser.add_argument("--type", type=str, help="The quant type to test (all by default)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
do_test(args.libggml, args.quick, GGMLQuantizationType[args.type.upper()] if args.type is not None else None)
|
||||
Reference in New Issue
Block a user