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GPTQ updates #2235
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GPTQ updates #2235
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0d374d7
GPTQ updates
HDCharles 83379fe
checking if this fixes the tests
HDCharles 4e5d77c
trying to fix the adam stuff now
HDCharles 2f5fd5b
fix wanda error
HDCharles e4b1ca9
fix adam attempt
HDCharles bb9132b
fix CI
HDCharles 5bab3ab
figured out issue i think
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import unittest | ||
from pathlib import Path | ||
|
||
import torch | ||
from torch.testing._internal.common_utils import TestCase | ||
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||
from torchao._models.llama.model import ( | ||
ModelArgs, | ||
Transformer, | ||
prepare_inputs_for_model, | ||
) | ||
from torchao._models.llama.tokenizer import get_tokenizer | ||
from torchao.quantization import Int4WeightOnlyConfig, quantize_ | ||
from torchao.quantization.utils import compute_error | ||
from torchao.utils import ( | ||
TORCH_VERSION_AT_LEAST_2_4, | ||
) | ||
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||
torch.manual_seed(0) | ||
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class TestGPTQ(TestCase): | ||
@unittest.skip("skipping until we get checkpoints for gpt-fast") | ||
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_gptq_quantizer_int4_weight_only(self): | ||
from torchao._models._eval import ( | ||
LMEvalInputRecorder, | ||
TransformerEvalWrapper, | ||
) | ||
from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer | ||
|
||
precision = torch.bfloat16 | ||
device = "cuda" | ||
checkpoint_path = Path( | ||
"../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth" | ||
) | ||
model = Transformer.from_name(checkpoint_path.parent.name) | ||
checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) | ||
model.load_state_dict(checkpoint, assign=True) | ||
model = model.to(dtype=precision, device="cpu") | ||
model.eval() | ||
|
||
tokenizer_path = checkpoint_path.parent / "tokenizer.model" | ||
assert tokenizer_path.is_file(), tokenizer_path | ||
tokenizer = get_tokenizer( # pyre-ignore[28] | ||
tokenizer_path, | ||
"Llama-2-7b-chat-hf", | ||
) | ||
groupsize = 64 | ||
blocksize = 128 | ||
percdamp = 0.01 | ||
calibration_tasks = ["wikitext"] | ||
calibration_limit = 1 | ||
calibration_seq_length = 100 | ||
input_prep_func = prepare_inputs_for_model | ||
pad_calibration_inputs = False | ||
inputs = ( | ||
LMEvalInputRecorder( | ||
tokenizer, | ||
calibration_seq_length, | ||
input_prep_func, | ||
model.config.vocab_size, | ||
pad_calibration_inputs, | ||
device="cpu", | ||
) | ||
.record_inputs( | ||
calibration_tasks, | ||
calibration_limit, | ||
) | ||
.get_recorded_inputs() | ||
) | ||
|
||
quantizer = Int4WeightOnlyGPTQQuantizer( | ||
groupsize, | ||
blocksize, | ||
percdamp, | ||
) | ||
model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) | ||
|
||
model = quantizer.quantize(model, *inputs).cuda() | ||
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model.reset_caches() | ||
with torch.device("cuda"): | ||
model.setup_caches(max_batch_size=1, max_seq_length=model.config.block_size) | ||
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limit = 1 | ||
result = TransformerEvalWrapper( | ||
model.cuda(), | ||
tokenizer, | ||
model.config.block_size, | ||
prepare_inputs_for_model, | ||
device, | ||
).run_eval( | ||
["wikitext"], | ||
limit, | ||
) | ||
|
||
assert result["results"]["wikitext"]["word_perplexity,none"] < 7.77, ( | ||
f"accuracy regressed from 7.76 to {result['results']['wikitext']['word_perplexity,none']}" | ||
) | ||
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||
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class TestMultiTensorFlow(TestCase): | ||
@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") | ||
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_multitensor_add_tensors(self): | ||
from torchao.quantization.GPTQ import MultiTensor | ||
|
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tensor1 = torch.randn(3, 3) | ||
tensor2 = torch.randn(3, 3) | ||
mt = MultiTensor(tensor1) | ||
mt.add_tensors(tensor2) | ||
self.assertEqual(mt.count, 2) | ||
self.assertTrue(torch.equal(mt.values[0], tensor1)) | ||
self.assertTrue(torch.equal(mt.values[1], tensor2)) | ||
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@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") | ||
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_multitensor_pad_unpad(self): | ||
from torchao.quantization.GPTQ import MultiTensor | ||
|
||
tensor1 = torch.randn(3, 3) | ||
mt = MultiTensor(tensor1) | ||
mt.pad_to_length(3) | ||
self.assertEqual(mt.count, 3) | ||
mt.unpad() | ||
self.assertEqual(mt.count, 1) | ||
|
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@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") | ||
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_multitensor_inplace_operation(self): | ||
from torchao.quantization.GPTQ import MultiTensor | ||
|
||
tensor1 = torch.ones(3, 3) | ||
mt = MultiTensor(tensor1) | ||
mt += 1 # In-place addition | ||
self.assertTrue(torch.equal(mt.values[0], torch.full((3, 3), 2))) | ||
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class TestMultiTensorInputRecorder(TestCase): | ||
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_multitensor_input_recorder(self): | ||
from torchao.quantization.GPTQ import MultiTensor, MultiTensorInputRecorder | ||
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input_recorder = MultiTensorInputRecorder() | ||
in1 = ([1], torch.randn(3, 3), (1, "dog", torch.randn(3, 3)), torch.float) | ||
in2 = ([1], torch.randn(3, 3), (1, "dog", torch.randn(3, 3)), torch.float) | ||
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input_recorder(*in1) | ||
input_recorder(*in2) | ||
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MT_input = input_recorder.get_recorded_inputs() | ||
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self.assertEqual(MT_input[0], [1]) | ||
self.assertTrue(isinstance(MT_input[1], MultiTensor)) | ||
self.assertTrue(isinstance(MT_input[2], tuple)) | ||
self.assertEqual(MT_input[2][0], 1) | ||
self.assertEqual(MT_input[2][1], "dog") | ||
self.assertTrue(isinstance(MT_input[2][2], MultiTensor)) | ||
self.assertEqual(MT_input[3], torch.float) | ||
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@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") | ||
def test_gptq_with_input_recorder(self): | ||
from torchao.quantization.GPTQ import ( | ||
Int4WeightOnlyGPTQQuantizer, | ||
MultiTensorInputRecorder, | ||
) | ||
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torch.set_default_dtype(torch.bfloat16) | ||
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config = ModelArgs(n_layer=2) | ||
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with torch.device("cuda"): | ||
model = Transformer(config) | ||
model.setup_caches(max_batch_size=2, max_seq_length=100) | ||
idx = torch.randint(1, 10000, (10, 2, 50)).to(torch.int32) | ||
test_input = prepare_inputs_for_model(idx[0]) | ||
import copy | ||
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model2 = copy.deepcopy(model) | ||
out = model(*test_input) | ||
quantize_(model2, Int4WeightOnlyConfig()) | ||
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outq = model2(*test_input) | ||
del model2 | ||
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input_recorder = MultiTensorInputRecorder() | ||
for i in range(10): | ||
input = prepare_inputs_for_model(idx[i]) | ||
input_recorder(*input) | ||
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args = input_recorder.get_recorded_inputs() | ||
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quantizer = Int4WeightOnlyGPTQQuantizer() | ||
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quantizer.quantize(model, *args) | ||
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outgptq = model(*test_input) | ||
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self.assertGreater(compute_error(outgptq, out), 30) | ||
self.assertGreater(compute_error(outgptq, out), compute_error(outq, out)) | ||
torch.set_default_dtype(torch.float32) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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