Ignore Dense sparsity config

[rahul-tuli]

Previously, a no-operation (no-op) dense compression
configuration was being used as the sparsity_config for sparse-quantized models

However, since we do not yet support sparse compression for sparse-quantized models, the sparsity_config should ideally be left empty. This PR addresses that by ensuring the sparsity_config remains empty until sparse compression support is implemented for sparse-quantized models

Before this PR the compression_config from config.json was:

  "compression_config": {
    "config_groups": {
      "group_0": {
        "input_activations": null,
        "output_activations": null,
        "targets": [
          "Linear"
        ],
        "weights": {
          "actorder": null,
          "block_structure": null,
          "dynamic": false,
          "group_size": 128,
          "num_bits": 4,
          "observer": "minmax",
          "observer_kwargs": {},
          "strategy": "group",
          "symmetric": true,
          "type": "int"
        }
      }
    },
    "format": "pack-quantized",
    "global_compression_ratio": 1.883165566487463,
    "ignore": [
      "lm_head"
    ],
    "kv_cache_scheme": null,
    "quant_method": "compressed-tensors",
    "quantization_status": "compressed",
    "sparsity_config": {
      "format": "dense",
      "global_sparsity": 0.14297135853065746,
      "ignore": [
        "model.layers.0.self_attn.o_proj",
        "model.layers.0.mlp.gate_proj",
        "model.layers.0.mlp.up_proj",
        "model.layers.0.mlp.down_proj",
        "model.layers.1.self_attn.v_proj",
        "model.layers.1.self_attn.o_proj",
        "model.layers.1.mlp.gate_proj",
        "model.layers.1.mlp.up_proj",
        "model.layers.1.mlp.down_proj",
        "model.layers.2.self_attn.q_proj",
        "model.layers.2.self_attn.k_proj",
        "model.layers.2.self_attn.v_proj",
        "model.layers.2.self_attn.o_proj",
        "model.layers.2.mlp.gate_proj",
        "model.layers.2.mlp.up_proj",
        "model.layers.2.mlp.down_proj",
        "model.layers.3.self_attn.q_proj",
        "model.layers.3.self_attn.k_proj",
        "model.layers.3.self_attn.v_proj",
        "model.layers.3.self_attn.o_proj",
        "model.layers.3.mlp.gate_proj",
        "model.layers.3.mlp.up_proj",
        "model.layers.3.mlp.down_proj",
        "model.layers.4.self_attn.q_proj",
        "model.layers.4.self_attn.k_proj",
        "model.layers.4.self_attn.v_proj",
        "model.layers.4.self_attn.o_proj",
        "model.layers.4.mlp.gate_proj",
        "model.layers.4.mlp.up_proj",
        "model.layers.4.mlp.down_proj",
        "model.layers.5.self_attn.q_proj",
        "model.layers.5.self_attn.k_proj",
        "model.layers.5.self_attn.v_proj",
        "model.layers.5.self_attn.o_proj",
        "model.layers.5.mlp.gate_proj",
        "model.layers.5.mlp.up_proj",
        "model.layers.5.mlp.down_proj",
        "model.layers.6.self_attn.q_proj",
        "model.layers.6.self_attn.k_proj",
        "model.layers.6.self_attn.v_proj",
        "model.layers.6.self_attn.o_proj",
        "model.layers.6.mlp.gate_proj",
        "model.layers.6.mlp.up_proj",
        "model.layers.6.mlp.down_proj",
        "model.layers.7.self_attn.q_proj",
        "model.layers.7.self_attn.k_proj",
        "model.layers.7.self_attn.v_proj",
        "model.layers.7.self_attn.o_proj",
        "model.layers.7.mlp.gate_proj",
        "model.layers.7.mlp.up_proj",
        "model.layers.7.mlp.down_proj",
        "model.layers.8.self_attn.q_proj",
        "model.layers.8.self_attn.k_proj",
        "model.layers.8.self_attn.v_proj",
        "model.layers.8.self_attn.o_proj",
        "model.layers.8.mlp.gate_proj",
        "model.layers.8.mlp.up_proj",
        "model.layers.8.mlp.down_proj",
        "model.layers.9.self_attn.q_proj",
        "model.layers.9.self_attn.k_proj",
        "model.layers.9.self_attn.v_proj",
        "model.layers.9.self_attn.o_proj",
        "model.layers.9.mlp.gate_proj",
        "model.layers.9.mlp.up_proj",
        "model.layers.9.mlp.down_proj",
        "model.layers.10.self_attn.q_proj",
        "model.layers.10.self_attn.k_proj",
        "model.layers.10.self_attn.v_proj",
        "model.layers.10.self_attn.o_proj",
        "model.layers.10.mlp.gate_proj",
        "model.layers.10.mlp.up_proj",
        "model.layers.10.mlp.down_proj",
        "model.layers.11.self_attn.q_proj",
        "model.layers.11.self_attn.k_proj",
        "model.layers.11.self_attn.v_proj",
        "model.layers.11.self_attn.o_proj",
        "model.layers.11.mlp.gate_proj",
        "model.layers.11.mlp.up_proj",
        "model.layers.11.mlp.down_proj",
        "model.layers.12.self_attn.q_proj",
        "model.layers.12.self_attn.k_proj",
        "model.layers.12.self_attn.v_proj",
        "model.layers.12.self_attn.o_proj",
        "model.layers.12.mlp.gate_proj",
        "model.layers.12.mlp.up_proj",
        "model.layers.12.mlp.down_proj",
        "model.layers.13.self_attn.q_proj",
        "model.layers.13.self_attn.k_proj",
        "model.layers.13.self_attn.v_proj",
        "model.layers.13.self_attn.o_proj",
        "model.layers.13.mlp.gate_proj",
        "model.layers.13.mlp.up_proj",
        "model.layers.13.mlp.down_proj",
        "model.layers.14.self_attn.q_proj",
        "model.layers.14.self_attn.k_proj",
        "model.layers.14.self_attn.v_proj",
        "model.layers.14.self_attn.o_proj",
        "model.layers.14.mlp.gate_proj",
        "model.layers.14.mlp.up_proj",
        "model.layers.14.mlp.down_proj",
        "model.layers.15.self_attn.q_proj",
        "model.layers.15.self_attn.k_proj",
        "model.layers.15.self_attn.v_proj",
        "model.layers.15.self_attn.o_proj",
        "model.layers.15.mlp.gate_proj",
        "model.layers.15.mlp.up_proj",
        "model.layers.15.mlp.down_proj",
        "model.layers.16.self_attn.q_proj",
        "model.layers.16.self_attn.k_proj",
        "model.layers.16.self_attn.v_proj",
        "model.layers.16.self_attn.o_proj",
        "model.layers.16.mlp.gate_proj",
        "model.layers.16.mlp.up_proj",
        "model.layers.16.mlp.down_proj",
        "model.layers.17.self_attn.q_proj",
        "model.layers.17.self_attn.k_proj",
        "model.layers.17.self_attn.v_proj",
        "model.layers.17.self_attn.o_proj",
        "model.layers.17.mlp.gate_proj",
        "model.layers.17.mlp.up_proj",
        "model.layers.17.mlp.down_proj",
        "model.layers.18.self_attn.q_proj",
        "model.layers.18.self_attn.k_proj",
        "model.layers.18.self_attn.v_proj",
        "model.layers.18.self_attn.o_proj",
        "model.layers.18.mlp.gate_proj",
        "model.layers.18.mlp.up_proj",
        "model.layers.18.mlp.down_proj",
        "model.layers.19.self_attn.q_proj",
        "model.layers.19.self_attn.k_proj",
        "model.layers.19.self_attn.v_proj",
        "model.layers.19.self_attn.o_proj",
        "model.layers.19.mlp.gate_proj",
        "model.layers.19.mlp.up_proj",
        "model.layers.19.mlp.down_proj",
        "model.layers.20.self_attn.q_proj",
        "model.layers.20.self_attn.k_proj",
        "model.layers.20.self_attn.v_proj",
        "model.layers.20.self_attn.o_proj",
        "model.layers.20.mlp.gate_proj",
        "model.layers.20.mlp.up_proj",
        "model.layers.20.mlp.down_proj",
        "model.layers.21.self_attn.q_proj",
        "model.layers.21.self_attn.k_proj",
        "model.layers.21.self_attn.v_proj",
        "model.layers.21.self_attn.o_proj",
        "model.layers.21.mlp.gate_proj",
        "model.layers.21.mlp.up_proj",
        "model.layers.21.mlp.down_proj",
        "lm_head"
      ],
      "registry_requires_subclass": false,
      "sparsity_structure": "unstructured",
      "targets": [
        "model.layers.0.self_attn.q_proj",
        "model.layers.0.self_attn.k_proj",
        "model.layers.0.self_attn.v_proj",
        "model.layers.1.self_attn.q_proj",
        "model.layers.1.self_attn.k_proj"
      ]
    },
    "version": "0.6.0.20240926"
  }

And now it is:

  "compression_config": {
    "config_groups": {
      "group_0": {
        "input_activations": null,
        "output_activations": null,
        "targets": [
          "Linear"
        ],
        "weights": {
          "actorder": null,
          "block_structure": null,
          "dynamic": false,
          "group_size": 128,
          "num_bits": 4,
          "observer": "minmax",
          "observer_kwargs": {},
          "strategy": "group",
          "symmetric": true,
          "type": "int"
        }
      }
    },
    "format": "pack-quantized",
    "global_compression_ratio": 2.1743527231963227,
    "ignore": [
      "lm_head"
    ],
    "kv_cache_scheme": null,
    "quant_method": "compressed-tensors",
    "quantization_status": "compressed",
    "version": "0.6.0.20240926"
  },

Test script Compressed a model using llmcompressor:

from llmcompressor.modifiers.obcq import SparseGPTModifier 
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.modifiers.pruning import ConstantPruningModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers import SparseAutoModelForCausalLM


MODEL_ID = "Xenova/llama2.c-stories110M"
DATASET_ID, SPLIT = "open_platypus", {"calibration": "train[:1%]"}
NUM_CALIBRATION_SAMPLES = 16
OUTPUT_DIR = MODEL_ID.split("/")[-1] + f"-{NUM_CALIBRATION_SAMPLES}-quantized"
MAX_SEQUENCE_LENGTH = 512

targets = ["Linear"]
ignore = ["lm_head"]
scheme = "W4A16"

recipe = [
    QuantizationModifier(scheme=scheme, targets=targets, ignore=ignore),
]

model = SparseAutoModelForCausalLM.from_pretrained(
    MODEL_ID,  device_map="auto", torch_dtype="auto"
)


oneshot(
    model=model,
    dataset=DATASET_ID,
    splits=SPLIT,
    recipe=recipe,
    save_compressed=True,
    output_dir=OUTPUT_DIR,
    overwrite_output_dir=True,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)

print("Compression Done, Model Saved at:", OUTPUT_DIR)