Model-Optimizer/tests/gpu/torch/quantization/test_quantize_cuda.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""High-level tests for quantization."""

import pytest
from _test_utils.torch.quantization.models import SimpleConv, SimpleConvLinear, SimpleLinear
from _test_utils.torch.quantization.quantize_common import (
    FP4_SVDQUANT_CFG,
    INT4_AWQ_CLIP_CFG,
    INT4_AWQ_FULL_CFG,
    quantize_model_and_forward,
    save_restore_test,
)

import modelopt.torch.quantization as mtq
from modelopt.torch.quantization.extensions import get_cuda_ext_mx

NVFP4_WEIGHT_ACT_MSE_CFG = {
    "quant_cfg": {
        "*weight_quantizer": {
            "num_bits": (2, 1),
            "block_sizes": {-1: 16, "type": "static", "scale_bits": (4, 3)},
            "axis": None,
            "enable": True,
        },
        "*input_quantizer": {
            "num_bits": (2, 1),
            "block_sizes": {-1: 16, "type": "dynamic", "scale_bits": (4, 3)},
            "axis": None,
            "enable": True,
        },
    },
    "algorithm": {
        "method": "mse",
        "step_size": 0.25,
        "start_multiplier": 0.25,
        "stop_multiplier": 2.0,
    },
}

NVFP4_WEIGHT_MSE_FP8_SWEEP_CFG = {
    "quant_cfg": {
        "*weight_quantizer": {
            "num_bits": (2, 1),
            "block_sizes": {-1: 16, "type": "static", "scale_bits": (4, 3)},
            "axis": None,
            "enable": True,
        },
        "*input_quantizer": {
            "enable": False,
        },
    },
    "algorithm": {
        "method": "mse",
        "fp8_scale_sweep": True,
    },
}


@pytest.mark.parametrize("model_cls", [SimpleLinear, SimpleConv, SimpleConvLinear])
@pytest.mark.parametrize(
    "config",
    [
        mtq.INT8_DEFAULT_CFG,
        mtq.FP8_DEFAULT_CFG,
        mtq.W4A8_AWQ_BETA_CFG,
        mtq.INT8_SMOOTHQUANT_CFG,
        mtq.INT4_BLOCKWISE_WEIGHT_ONLY_CFG,
        mtq.INT4_AWQ_CFG,
        INT4_AWQ_CLIP_CFG,
        INT4_AWQ_FULL_CFG,
        mtq.NVFP4_DEFAULT_CFG,
        FP4_SVDQUANT_CFG,
        mtq.NVFP4_AWQ_LITE_CFG,
        mtq.NVFP4_AWQ_CLIP_CFG,
        mtq.NVFP4_AWQ_FULL_CFG,
        mtq.MXFP8_DEFAULT_CFG,
        mtq.MXFP6_DEFAULT_CFG,
        mtq.MXFP4_DEFAULT_CFG,
        mtq.MXINT8_DEFAULT_CFG,
        mtq.NVFP4_KV_ROTATE_CFG,
        mtq.FP8_2D_BLOCKWISE_WEIGHT_ONLY_CFG,
        NVFP4_WEIGHT_ACT_MSE_CFG,
        NVFP4_WEIGHT_MSE_FP8_SWEEP_CFG,
    ],
)
def test_quantize(model_cls, config):
    """Test quantize function can run without problems."""
    if config in [
        mtq.NVFP4_DEFAULT_CFG,
        FP4_SVDQUANT_CFG,
        mtq.NVFP4_AWQ_LITE_CFG,
        mtq.NVFP4_AWQ_CLIP_CFG,
        mtq.NVFP4_AWQ_FULL_CFG,
        mtq.MXFP8_DEFAULT_CFG,
        mtq.MXFP6_DEFAULT_CFG,
        mtq.MXFP4_DEFAULT_CFG,
        mtq.MXINT8_DEFAULT_CFG,
        mtq.NVFP4_KV_ROTATE_CFG,
        mtq.FP8_2D_BLOCKWISE_WEIGHT_ONLY_CFG,
        NVFP4_WEIGHT_ACT_MSE_CFG,
        NVFP4_WEIGHT_MSE_FP8_SWEEP_CFG,
    ]:
        if get_cuda_ext_mx() is None:
            pytest.skip("cuda_ext_mx is not available")
        if model_cls in [SimpleConv, SimpleConvLinear]:
            pytest.skip("Conv weight quantization will fail as the kernel_size < FP4 blocksize")

    if config == mtq.FP8_2D_BLOCKWISE_WEIGHT_ONLY_CFG:
        # reduce block sizes for simple testing models
        config["quant_cfg"]["*weight_quantizer"]["block_sizes"] = {-1: 8, -2: 8}
    model = model_cls().cuda()
    calib_data = [model.get_input().cuda() for _ in range(8)]
    quantize_model_and_forward(model, config, calib_data)


@pytest.mark.parametrize(
    ("model_cls", "quant_config"),
    [
        (SimpleLinear, mtq.INT8_SMOOTHQUANT_CFG),
        (SimpleLinear, mtq.W4A8_AWQ_BETA_CFG),
        (SimpleConvLinear, mtq.INT8_DEFAULT_CFG),
    ],
)
def test_save_restore(model_cls, quant_config):
    save_restore_test(model_cls, "cuda", quant_config)