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110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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from transformers import (
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AutoModelForCausalLM,
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AutoProcessor,
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GemmaQuantizationConfig,
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)
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from transformers.testing_utils import (
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backend_empty_cache,
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require_accelerate,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available
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if is_torch_available():
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import torch
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# Fill in once the released hub repo is published.
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MODEL_ID = ""
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class GemmaQuantizationConfigTest(unittest.TestCase):
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def test_to_dict_round_trip(self):
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cfg = GemmaQuantizationConfig(num_bits=8, quantize_embeddings=True)
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d = cfg.to_dict()
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for key, value in d.items():
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self.assertEqual(getattr(cfg, key), value)
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self.assertEqual(d["quant_method"], "gemma")
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class ReplaceWithQuantLayersTest(unittest.TestCase):
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def test_replaces_linear_and_embedding(self):
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from transformers.integrations.gemma_quant import (
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QuantizedEmbedding,
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QuantizedLinear,
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replace_with_quant_layers,
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)
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class Model(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.lin = torch.nn.Linear(8, 4, bias=False)
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self.emb = torch.nn.Embedding(16, 8)
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model = Model()
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cfg = GemmaQuantizationConfig(quantize_embeddings=True)
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replace_with_quant_layers(model, quantization_config=cfg)
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self.assertIsInstance(model.lin, QuantizedLinear)
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self.assertIsInstance(model.emb, QuantizedEmbedding)
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@slow
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@require_torch_accelerator
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@require_accelerate
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@unittest.skipUnless(MODEL_ID, "MODEL_ID is empty — fill in once the released hub repo is published.")
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class GemmaQuantInferenceTest(unittest.TestCase):
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"""End-to-end smoke test against a freshly-converted local checkpoint."""
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@classmethod
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def setUpClass(cls):
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cls.processor = AutoProcessor.from_pretrained(MODEL_ID)
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cls.model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype=torch.bfloat16, device_map=torch_device)
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cls.model.eval()
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@classmethod
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def tearDownClass(cls):
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del cls.model
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gc.collect()
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backend_empty_cache(torch_device)
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gc.collect()
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def test_quantized_linears_installed(self):
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from transformers.integrations.gemma_quant import QuantizedLinear
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q_proj = self.model.get_submodule("model.language_model.layers.0.self_attn.q_proj")
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self.assertIsInstance(q_proj, QuantizedLinear)
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def test_greedy_generation_capital_of_france(self):
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messages = [{"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}]
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inputs = self.processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(self.model.device)
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with torch.inference_mode():
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gen = self.model.generate(**inputs, max_new_tokens=16, do_sample=False, num_beams=1)
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text = self.processor.tokenizer.decode(gen[0, inputs["input_ids"].shape[-1] :], skip_special_tokens=True)
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self.assertIn("Paris", text)
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