137 lines
3.9 KiB
Python
137 lines
3.9 KiB
Python
import numpy as np
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import pytest
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import torch
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from sgl_kernel import awq_marlin_repack
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from sgl_kernel.scalar_type import scalar_types
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from sglang.srt.layers.quantization.utils import (
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get_pack_factor,
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pack_cols,
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quantize_weights,
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)
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GPTQ_MARLIN_TILE = 16
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def awq_pack(
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q_w: torch.Tensor,
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num_bits: int,
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size_k: int,
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size_n: int,
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):
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assert q_w.shape == (size_k, size_n)
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# Interleave column dim (for the dequantize code) and pack it to int32
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if num_bits == 4:
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interleave = np.array([0, 2, 4, 6, 1, 3, 5, 7])
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elif num_bits == 8:
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interleave = np.array([0, 2, 1, 3])
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else:
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raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
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q_w = q_w.reshape((-1, len(interleave)))[:, interleave].ravel()
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q_w = q_w.reshape((-1, size_n)).contiguous()
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return pack_cols(q_w, num_bits, size_k, size_n)
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def marlin_permute_weights(q_w, size_k, size_n, perm, tile=GPTQ_MARLIN_TILE):
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assert q_w.shape == (size_k, size_n)
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assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}"
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assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}"
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# Permute weights to 16x64 marlin tiles
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q_w = q_w.reshape((size_k // tile, tile, size_n // tile, tile))
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q_w = q_w.permute((0, 2, 1, 3))
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q_w = q_w.reshape((size_k // tile, size_n * tile))
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q_w = q_w.reshape((-1, perm.numel()))[:, perm].reshape(q_w.shape)
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return q_w
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def marlin_weights(q_w, size_k, size_n, num_bits, perm):
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# Permute
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q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
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# Pack
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pack_factor = get_pack_factor(num_bits)
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orig_device = q_w.device
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q_w = q_w.cpu().numpy().astype(np.uint32)
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q_packed = np.zeros((q_w.shape[0], q_w.shape[1] // pack_factor), dtype=np.uint32)
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for i in range(pack_factor):
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q_packed |= q_w[:, i::pack_factor] << num_bits * i
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q_packed = torch.from_numpy(q_packed.astype(np.int32)).to(orig_device)
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return q_packed
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def get_weight_perm(num_bits: int):
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perm_list: list[int] = []
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for i in range(32):
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perm1: list[int] = []
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col = i // 4
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for block in [0, 1]:
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for row in [
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2 * (i % 4),
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2 * (i % 4) + 1,
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2 * (i % 4 + 4),
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2 * (i % 4 + 4) + 1,
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]:
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perm1.append(16 * row + col + 8 * block)
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for j in range(4):
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perm_list.extend([p + 256 * j for p in perm1])
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perm = np.array(perm_list)
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if num_bits == 4:
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interleave = np.array([0, 2, 4, 6, 1, 3, 5, 7])
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elif num_bits == 8:
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interleave = np.array([0, 2, 1, 3])
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else:
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raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
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perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
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perm = torch.from_numpy(perm)
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return perm
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@pytest.mark.parametrize("num_bits", [4, 8])
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@pytest.mark.parametrize("k_tiles,n_tiles", [(1, 1), (2, 2)])
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@pytest.mark.parametrize("group_size", [16, 32])
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def test_awq_marlin_repack_correct(num_bits, k_tiles, n_tiles, group_size):
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tile_k, tile_n = 16, 64
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size_k = k_tiles * tile_k
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size_n = n_tiles * tile_n
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pack_factor = 32 // num_bits
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b_weight = torch.randn((size_k, size_n), dtype=torch.float16, device="cuda")
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w_ref, q_w, s, zp = quantize_weights(
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b_weight, scalar_types.uint4, group_size, zero_points=True
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)
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q_w_awq = awq_pack(q_w, num_bits, size_k, size_n)
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weight_perm = get_weight_perm(num_bits)
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q_w_marlin = marlin_weights(q_w, size_k, size_n, num_bits, weight_perm)
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out_gpu = awq_marlin_repack(q_w_awq, size_k, size_n, num_bits)
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assert out_gpu.is_cuda and out_gpu.dtype == torch.int32
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expected_cols = size_n * tile_k // pack_factor
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assert list(out_gpu.shape) == [size_k // tile_k, expected_cols]
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torch.cuda.synchronize()
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torch.testing.assert_close(out_gpu, q_w_marlin)
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if __name__ == "__main__":
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import subprocess
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subprocess.call(["pytest", "--tb=short", str(__file__)])
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