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[GPTQ] Fix test (#28018)

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* fix test

* reduce length

* smaller model
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Marc Sun 2024-01-15 11:22:54 -05:00 committed by GitHub
parent 366c03271e
commit 7c8dd88d13
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1 changed files with 13 additions and 14 deletions
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27
tests/quantization/gptq/test_gptq.py
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@ -217,7 +217,9 @@ class GPTQTest(unittest.TestCase):
with tempfile.TemporaryDirectory() as tmpdirname: with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname) self.quantized_model.save_pretrained(tmpdirname)
if not self.use_exllama: if not self.use_exllama:
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname).to(0) quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, quantization_config=GPTQConfig(use_exllama=False, bits=4)
).to(0)
self.check_quantized_layers_type(quantized_model_from_saved, "cuda-old") self.check_quantized_layers_type(quantized_model_from_saved, "cuda-old")
else: else:
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel # we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
@ -242,12 +244,11 @@ class GPTQTest(unittest.TestCase):
with tempfile.TemporaryDirectory() as tmpdirname: with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname) self.quantized_model.save_pretrained(tmpdirname)
if not self.use_exllama: if not self.use_exllama:
self.assertEqual(self.quantized_model.config.quantization_config.use_exllama, False) self.check_quantized_layers_type(self.quantized_model, "cuda-old")
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel # we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained( quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, quantization_config=GPTQConfig(use_exllama=True, bits=4), device_map={"": 0} tmpdirname, quantization_config=GPTQConfig(use_exllama=True, bits=4), device_map={"": 0}
) )
self.assertEqual(quantized_model_from_saved.config.quantization_config.use_exllama, True)
self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits) self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits)
self.check_quantized_layers_type(quantized_model_from_saved, "exllama") self.check_quantized_layers_type(quantized_model_from_saved, "exllama")
self.check_inference_correctness(quantized_model_from_saved) self.check_inference_correctness(quantized_model_from_saved)
@ -279,10 +280,10 @@ class GPTQTestActOrderExllama(unittest.TestCase):
""" """
EXPECTED_OUTPUTS = set() EXPECTED_OUTPUTS = set()
EXPECTED_OUTPUTS.add("Hello my name is Katie and I am a 20 year") EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
model_name = "hf-internal-testing/Llama-2-7B-GPTQ" # 4bit + act_order + 128g
revision = "gptq-4bit-128g-actorder_True" model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
input_text = "Hello my name is" input_text = "Hello, how are you ?"
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
@ -292,7 +293,6 @@ class GPTQTestActOrderExllama(unittest.TestCase):
cls.quantization_config = GPTQConfig(bits=4, max_input_length=4028) cls.quantization_config = GPTQConfig(bits=4, max_input_length=4028)
cls.quantized_model = AutoModelForCausalLM.from_pretrained( cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name, cls.model_name,
revision=cls.revision,
torch_dtype=torch.float16, torch_dtype=torch.float16,
device_map={"": 0}, device_map={"": 0},
quantization_config=cls.quantization_config, quantization_config=cls.quantization_config,
@ -336,7 +336,7 @@ class GPTQTestActOrderExllama(unittest.TestCase):
self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3) self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
self.assertTrue("temp_state buffer is too small" in str(cm.exception)) self.assertTrue("temp_state buffer is too small" in str(cm.exception))
prompt = "I am in Paris and" * 500 prompt = "I am in Paris and"
inp = self.tokenizer(prompt, return_tensors="pt").to(0) inp = self.tokenizer(prompt, return_tensors="pt").to(0)
self.assertTrue(inp["input_ids"].shape[1] < 4028) self.assertTrue(inp["input_ids"].shape[1] < 4028)
self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3) self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
@ -355,10 +355,10 @@ class GPTQTestExllamaV2(unittest.TestCase):
""" """
EXPECTED_OUTPUTS = set() EXPECTED_OUTPUTS = set()
EXPECTED_OUTPUTS.add("Hello my name is Katie and I am a 20 year") EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
model_name = "hf-internal-testing/Llama-2-7B-GPTQ" # 4bit + act_order + 128g
revision = "gptq-4bit-128g-actorder_True" model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
input_text = "Hello my name is" input_text = "Hello, how are you ?"
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
@ -368,7 +368,6 @@ class GPTQTestExllamaV2(unittest.TestCase):
cls.quantization_config = GPTQConfig(bits=4, exllama_config={"version": 2}) cls.quantization_config = GPTQConfig(bits=4, exllama_config={"version": 2})
cls.quantized_model = AutoModelForCausalLM.from_pretrained( cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name, cls.model_name,
revision=cls.revision,
torch_dtype=torch.float16, torch_dtype=torch.float16,
device_map={"": 0}, device_map={"": 0},
quantization_config=cls.quantization_config, quantization_config=cls.quantization_config,