File size: 4,159 Bytes
a402b9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | import pytest
import torch
from sgl_kernel import gptq_gemm
from sglang.srt.layers.quantization.utils import pack_cols, pack_rows
def torch_dequantize(q_weight, q_zeros, scales, g_idx, use_shuffle, bit, K, N):
assert bit == 4, "Reference dequantization only supports 4-bit"
group_size = K // scales.shape[0]
pack_factor = 32 // bit
# unpack q_weight: (K//pack_factor, N) -> (K, N)
unpacked_q_weight = torch.empty(
q_weight.shape[0] * pack_factor,
q_weight.shape[1],
dtype=torch.uint8,
device=q_weight.device,
)
for i in range(pack_factor):
unpacked_q_weight[i::pack_factor, :] = (q_weight >> (i * 4)) & 0x0F
# unpack q_zeros: (num_groups, N//pack_factor) -> (num_groups, N)
unpacked_q_zeros = torch.empty(
q_zeros.shape[0],
q_zeros.shape[1] * pack_factor,
dtype=torch.uint8,
device=q_zeros.device,
)
for i in range(pack_factor):
unpacked_q_zeros[:, i::pack_factor] = (q_zeros >> (i * 4)) & 0x0F
unpacked_q_zeros += 1
unpacked_q_zeros = unpacked_q_zeros.to(scales.dtype)
scale_zeros = unpacked_q_zeros * scales # (num_groups, N)
current_g_idx = torch.tensor(
[i // group_size for i in range(K)], dtype=torch.int32, device=q_weight.device
)
scale_mat = scales[current_g_idx] # (K, N)
scale_zeros_mat = scale_zeros[current_g_idx] # (K, N)
# dequant: weight * scale - scale_zeros
dequantized_b = unpacked_q_weight.to(scales.dtype) * scale_mat - scale_zeros_mat
return dequantized_b.reshape(K, N)
def torch_gptq_gemm(
a, b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx, use_shuffle, bit
):
K, N = a.shape[1], b_q_weight.shape[1]
b_dequant = torch_dequantize(
b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx, use_shuffle, bit, K, N
)
c = torch.matmul(a, b_dequant)
return c
def _test_gptq_gemm_once(M, N, K, bit, group_size, use_shuffle, dtype, device="cuda"):
b_fp = torch.randn(K, N, dtype=dtype, device=device)
assert K % group_size == 0, "K must be divisible by group_size"
num_groups = K // group_size
if use_shuffle:
return
else:
g_idx = torch.tensor(
[i // group_size for i in range(K)], dtype=torch.int32, device=device
)
b_shuffled = b_fp[g_idx]
b_grouped = b_shuffled.reshape(num_groups, group_size, N)
b_max = torch.max(b_grouped, dim=1, keepdim=True)[0]
b_min = torch.min(b_grouped, dim=1, keepdim=True)[0]
scales = (b_max - b_min) / (2**bit - 1)
scales = scales.clamp(min=1e-6)
zeros_float = (-b_min / scales).round()
q_b = (
(b_grouped / scales + zeros_float).round().clamp(0, 2**bit - 1).to(torch.uint8)
)
q_zeros_unpacked = zeros_float.to(torch.uint8) - 1
b_q_weight = pack_rows(q_b.reshape(K, N), bit, K, N)
q_zeros_unpacked = q_zeros_unpacked.reshape(num_groups, N)
b_gptq_qzeros = pack_cols(q_zeros_unpacked, bit, num_groups, N)
b_gptq_scales = scales.squeeze(1)
a = torch.randn(M, K, dtype=dtype, device=device)
c_ref = torch_gptq_gemm(
a, b_q_weight, b_gptq_qzeros, b_gptq_scales, g_idx, use_shuffle, bit
)
c_out = gptq_gemm(
a, b_q_weight, b_gptq_qzeros, b_gptq_scales, g_idx, use_shuffle, bit
)
rtol = 4e-2
atol = 4e-2
torch.testing.assert_close(c_ref, c_out, rtol=rtol, atol=atol)
print(
f"✅ Test passed: M={M}, N={N}, K={K}, bit={bit}, group_size={group_size}, use_shuffle={use_shuffle}, dtype={dtype}"
)
@pytest.mark.parametrize("M", [1, 8, 128])
@pytest.mark.parametrize("N", [2048, 4096])
@pytest.mark.parametrize("K", [2048, 4096])
@pytest.mark.parametrize("bit", [4])
@pytest.mark.parametrize("group_size", [128])
@pytest.mark.parametrize("use_shuffle", [False])
@pytest.mark.parametrize("dtype", [torch.float16])
def test_gptq_gemm(M, N, K, bit, group_size, use_shuffle, dtype):
if not torch.cuda.is_available():
pytest.skip("CUDA not available")
_test_gptq_gemm_once(M, N, K, bit, group_size, use_shuffle, dtype, "cuda")
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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