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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, SpQRConfig, StaticCache
from transformers.testing_utils import (
backend_empty_cache,
require_accelerate,
require_spqr,
require_torch_gpu,
require_torch_multi_gpu,
slow,
torch_device,
)
from transformers.utils import is_accelerate_available, is_torch_available
if is_torch_available():
import torch
if is_accelerate_available():
from accelerate import init_empty_weights
@require_torch_gpu
class SpQRConfigTest(unittest.TestCase):
def test_to_dict(self):
"""
Simple test that checks if one uses a config and converts it to a dict, the dict is the same as the config object
"""
quantization_config = SpQRConfig()
config_to_dict = quantization_config.to_dict()
for key in config_to_dict:
self.assertEqual(getattr(quantization_config, key), config_to_dict[key])
def test_from_dict(self):
"""
Simple test that checks if one uses a dict and converts it to a config object, the config object is the same as the dict
"""
dict = {
"beta1": 16,
"beta2": 16,
"bits": 3,
"modules_to_not_convert": ["lm_head.weight"],
"shapes": {"model.layers.0.self_attn.q_proj.dense_weights.shape": 16},
}
quantization_config = SpQRConfig.from_dict(dict)
self.assertEqual(dict["beta1"], quantization_config.beta1)
self.assertEqual(dict["beta2"], quantization_config.beta2)
self.assertEqual(dict["bits"], quantization_config.bits)
self.assertEqual(dict["modules_to_not_convert"], quantization_config.modules_to_not_convert)
self.assertEqual(dict["shapes"], quantization_config.shapes)
@slow
@require_torch_gpu
@require_spqr
@require_accelerate
class SpQRTest(unittest.TestCase):
model_name = "elvircrn/Llama-2-7b-SPQR-3Bit-16x16-red_pajama-hf"
input_text = "Hello my name is"
max_new_tokens = 32
EXPECTED_OUTPUT = (
"Hello my name is Jesse. (I'm also known as Jesse) I'm a 25 year old male from United States. I'm looking for"
)
EXPECTED_OUTPUT_COMPILE = "Hello my name is Jake and I am a 20 year old student at the University of North Texas. (Go Mean Green!) I am a huge fan of the Dallas"
# called only once for all test in this class
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
device_map=torch_device,
)
def tearDown(self):
gc.collect()
backend_empty_cache(torch_device)
gc.collect()
def test_quantized_model_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly
"""
from spqr_quant import QuantizedLinear
from transformers.integrations import replace_with_spqr_linear
model_id = "meta-llama/Llama-2-7b-hf"
config = AutoConfig.from_pretrained(model_id)
quantization_config = AutoConfig.from_pretrained(self.model_name, return_dict=False).quantization_config
quantization_config = SpQRConfig.from_dict(quantization_config)
with init_empty_weights():
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id, config=config)
nb_linears = 0
for module in model.modules():
if isinstance(module, torch.nn.Linear):
nb_linears += 1
model, _ = replace_with_spqr_linear(
model,
quantization_config=quantization_config,
modules_to_not_convert=quantization_config.modules_to_not_convert,
)
nb_spqr_linear = 0
for module in model.modules():
if isinstance(module, QuantizedLinear):
nb_spqr_linear += 1
self.assertEqual(nb_linears - 1, nb_spqr_linear)
def test_quantized_model(self):
"""
Simple test that checks if the quantized model is working properly
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_raise_if_non_quantized(self):
model_id = "meta-llama/Llama-2-7b-hf"
quantization_config = SpQRConfig()
with self.assertRaises(ValueError):
_ = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
@unittest.skip
def test_save_pretrained(self):
"""
Simple test that checks if the quantized model is working properly after being saved and loaded
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=torch_device)
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
@require_torch_multi_gpu
def test_quantized_model_multi_gpu(self):
"""
Simple test that checks if the quantized model is working properly with multiple GPUs
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map="auto")
self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_quantized_model_compile(self):
"""
Simple test that checks if the quantized model is working properly
"""
# Sample tokens greedily
def decode_one_tokens(model, cur_token, input_pos, cache_position, past_key_values):
logits = model(
cur_token,
position_ids=input_pos,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True,
)[0]
new_token = torch.argmax(logits[:, [-1]], dim=-1).to(torch.int)
return new_token
# Tokenize the test input
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)["input_ids"]
seq_length = input_ids.shape[1]
# Setup static KV cache for generation
past_key_values = StaticCache(
config=self.quantized_model.config,
max_batch_size=1,
max_cache_len=seq_length + self.max_new_tokens + 1,
device=torch_device,
dtype=self.quantized_model.config._pre_quantization_dtype,
)
# Allocate token ids to be generated and copy prefix ids
cache_position = torch.arange(seq_length, device=torch_device)
generated_ids = torch.zeros(1, seq_length + self.max_new_tokens, dtype=torch.int, device=torch_device)
generated_ids[:, cache_position] = input_ids.to(torch_device).to(torch.int)
# Do a forward pass to fill the prefix cache and compile the kernels if necessary
logits = self.quantized_model(
input_ids,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True,
)[0]
next_token = torch.argmax(logits[:, [-1]], dim=-1).to(torch.int)
generated_ids[:, [seq_length]] = next_token
with torch.no_grad():
# Compile the CUDA graph
decode_one_tokens = torch.compile(decode_one_tokens, mode="default", backend="inductor", fullgraph=True)
# Generate tokens one by one
cache_position = torch.tensor([seq_length + 1], device=torch_device)
for _ in range(1, self.max_new_tokens):
with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True):
next_token = decode_one_tokens(
self.quantized_model, next_token.clone(), None, cache_position, past_key_values
)
generated_ids.index_copy_(1, cache_position, next_token)
cache_position += 1
# Check generated text
self.assertEqual(
self.tokenizer.decode(generated_ids[0], skip_special_tokens=True), self.EXPECTED_OUTPUT_COMPILE
)
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