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fix test - use automodelforcausallm decompress #974

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fix test - use automodelforcausallm decompress #974

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30 changes: 11 additions & 19 deletions tests/llmcompressor/transformers/sparsification/test_compress_tensor_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from compressed_tensors.quantization import QuantizationStatus
from compressed_tensors.utils import get_offloaded_device, update_prefix_dict
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.utils.quantization_config import CompressedTensorsConfig

from llmcompressor.core import reset_session
from llmcompressor.pytorch.utils.helpers import tensor_sparsity
Expand Down Expand Up @@ -171,9 +172,8 @@ def test_quant_model_reload(format, dtype, tmp_path):
device = "cpu"
dataset = "open_platypus"
concatenate_data = False
num_calibration_samples = 64
num_calibration_samples = 1
splits = {"calibration": "train[:10%]"}
empty_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype)

# create a quantized model
oneshot(
Expand All @@ -191,7 +191,7 @@ def test_quant_model_reload(format, dtype, tmp_path):
# Fetch the oneshot model
model = get_session_model()
og_state_dict = model.state_dict()
path = tmp_path / "compressed"
save_path_compressed = tmp_path / "compressed"

for _, module in model.named_modules():
if hasattr(module, "quantization_scheme"):
Expand All @@ -200,32 +200,24 @@ def test_quant_model_reload(format, dtype, tmp_path):

# Save to disk
model.save_pretrained(
path,
save_path_compressed,
quantization_format=format,
save_compressed=True,
)

# Verify config on disk
config = AutoConfig.from_pretrained(path)
config = AutoConfig.from_pretrained(save_path_compressed)
compression_config = getattr(config, QUANTIZATION_CONFIG_NAME, None)
quant_config = ModelCompressor.parse_quantization_config(compression_config)
assert quant_config["format"] == format

# As HFQuantizer doesn't decompress the model, use the compressor to decompress
# the model instead
compressor = ModelCompressor.from_compression_config(compression_config)
compressor.quantization_config.quantization_status = QuantizationStatus.FROZEN
compressor.decompress(model_path=path, model=empty_model)

# eventually use this pathway once HFQuant Decompression works
"""
dense_model = SparseAutoModelForCausalLM.from_pretrained(
"compress_out", torch_dtype="auto", device_map=device
decompressed_model = AutoModelForCausalLM.from_pretrained(
save_path_compressed,
torch_dtype=dtype,
quantization_config=CompressedTensorsConfig(run_compressed=False),
)
"""
# Verify the abs difference between the decompressed model
# and the original model
reconstructed_state_dict = empty_model.state_dict()

reconstructed_state_dict = decompressed_model.state_dict()
assert len(og_state_dict) == len(reconstructed_state_dict)
for key in og_state_dict.keys():
dense_tensor = og_state_dict[key].to(device)
Expand Down
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