sgl-project--sglang/sgl-kernel/tests/test_gguf.py

# SPDX-License-Identifier: Apache-2.0

import random
import sys
from pathlib import Path

import numpy as np
import pytest
import torch
from gguf import GGMLQuantizationType, GGUFReader, ReaderTensor, dequantize
from huggingface_hub import snapshot_download
from sgl_kernel import (
    ggml_dequantize,
    ggml_moe_a8,
    ggml_moe_a8_vec,
    ggml_moe_get_block_size,
    ggml_mul_mat_a8,
    ggml_mul_mat_vec_a8,
)

GGUF_SAMPLE = snapshot_download("Isotr0py/test-gguf-sample")
GGUF_SAMPLE_MOE = snapshot_download("SzymonOzog/test-gguf-moe-sample")


def get_gguf_sample_tensors(
    hidden_size: int, quant_type: GGMLQuantizationType
) -> list[ReaderTensor]:
    sample_dir = GGUF_SAMPLE
    filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
    sample_file = Path(sample_dir) / filename
    return GGUFReader(sample_file).tensors


def get_gguf_MoE_tensors(
    hidden_size: int, quant_type: GGMLQuantizationType
) -> list[ReaderTensor]:
    sample_dir = GGUF_SAMPLE_MOE
    filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
    sample_file = Path(sample_dir) / filename
    return GGUFReader(sample_file).tensors


DTYPES = [torch.bfloat16]  # [torch.half, torch.bfloat16, torch.float32]
# Hidden_size for testing, must match the sample file in HF repo,
# we have `hidden_size = 256, 1024` for test in HF repo currently.
HIDDEN_SIZES = [256, 1024]
NUM_TOKENS = [7, 2050]  # Arbitrary values for testing
SEEDS = [0]
QUANT_TYPES = [
    # i-matrix
    GGMLQuantizationType.IQ1_M,
    GGMLQuantizationType.IQ1_S,
    GGMLQuantizationType.IQ2_S,
    GGMLQuantizationType.IQ2_XS,
    GGMLQuantizationType.IQ3_S,
    GGMLQuantizationType.IQ3_XXS,
    GGMLQuantizationType.IQ4_NL,
    GGMLQuantizationType.IQ4_XS,
    # k-quants
    GGMLQuantizationType.Q2_K,
    GGMLQuantizationType.Q3_K,
    GGMLQuantizationType.Q4_K,
    GGMLQuantizationType.Q5_K,
    GGMLQuantizationType.Q6_K,
    # standard quantization
    GGMLQuantizationType.Q4_0,
    GGMLQuantizationType.Q5_0,
    GGMLQuantizationType.Q8_0,
]


@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("quant_type", QUANT_TYPES)
@torch.inference_mode()
def test_dequantize(
    hidden_size: int, dtype: torch.dtype, quant_type: GGMLQuantizationType
):
    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
    for tensor in tensors:
        shape_str = tensor.name.split("_")[-1]
        shape = map(int, shape_str.split("x"))

        ref_output = torch.tensor(
            dequantize(tensor.data, quant_type), device="cuda"
        ).to(dtype)
        output = ggml_dequantize(
            torch.tensor(tensor.data, device="cuda"), quant_type, *list(shape), dtype
        )

        torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=4e-2)


@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("quant_type", QUANT_TYPES)
@torch.inference_mode()
def test_mmvq(hidden_size: int, dtype: torch.dtype, quant_type: GGMLQuantizationType):

    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
    x = torch.rand((1, hidden_size), dtype=dtype, device="cuda")
    for tensor in tensors:
        weight = torch.tensor(dequantize(tensor.data, quant_type), device="cuda").to(
            dtype
        )
        ref_output = x @ weight.T

        qweight = torch.tensor(tensor.data, device="cuda")
        output = ggml_mul_mat_vec_a8(qweight, x, quant_type, qweight.shape[0]).to(dtype)

        # NOTE(FlamingoPg): There can be occasional errors, Loosen the granularity of gguf bf16 verification.
        atols = {torch.half: 1, torch.bfloat16: 1.5, torch.float: 1}
        rtols = {torch.half: 1e-1, torch.bfloat16: 3e1, torch.float: 1e-1}

        torch.testing.assert_close(
            output, ref_output, atol=atols[dtype], rtol=rtols[dtype]
        )


@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize(
    "quant_type",
    [
        # k-quants
        GGMLQuantizationType.Q2_K,
        GGMLQuantizationType.Q3_K,
        GGMLQuantizationType.Q4_K,
        GGMLQuantizationType.Q5_K,
        GGMLQuantizationType.Q6_K,
        # standard quants
        GGMLQuantizationType.Q4_0,
        GGMLQuantizationType.Q5_0,
        GGMLQuantizationType.Q8_0,
    ],
)
@torch.inference_mode()
def test_mmq(
    num_tokens: int,
    hidden_size: int,
    dtype: torch.dtype,
    quant_type: GGMLQuantizationType,
):

    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
    x = torch.rand((num_tokens, hidden_size), dtype=dtype, device="cuda")
    for tensor in tensors:
        weight = torch.tensor(dequantize(tensor.data, quant_type), device="cuda").to(
            dtype
        )
        ref_output = x @ weight.T

        qweight = torch.tensor(tensor.data, device="cuda")
        output = ggml_mul_mat_a8(qweight, x, quant_type, qweight.shape[0])
        atols = {torch.half: 1, torch.bfloat16: 1.5, torch.float: 1.2}
        # test matrix has inputs centered around 0 and lower precision from
        # bfloat16 tends to accumulate and can greatly inflate rtol
        # since outputs are also very close to 0
        rtols = {torch.half: 1e-1, torch.bfloat16: 1e4, torch.float: 2e1}
        torch.testing.assert_close(
            output, ref_output, atol=atols[dtype], rtol=rtols[dtype]
        )


if __name__ == "__main__":
    sys.exit(pytest.main([__file__]))