[3/n] chore: decouple AWQ implementation from vLLM dependency (#8113)
Co-authored-by: AniZpZ <zhuangsen.zp@antgroup.com>
This commit is contained in:
Hongbo Xu
286
python/sglang/test/test_marlin_moe.py
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286
python/sglang/test/test_marlin_moe.py
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import types
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from typing import Optional
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import pytest
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import torch
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from sgl_kernel import fused_marlin_moe
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
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from sglang.test.test_marlin_utils import awq_marlin_quantize, marlin_quantize
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def stack_and_dev(tensors: list[torch.Tensor]):
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dev = tensors[0].device
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return torch.stack(tensors, dim=0).to(dev)
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def torch_experts(
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a: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weight: torch.Tensor,
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topk_ids: torch.Tensor,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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quant_dtype: Optional[torch.dtype] = None,
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apply_router_weights_on_input: bool = False,
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) -> torch.Tensor:
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assert (
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global_num_experts == -1
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or (global_num_experts == w1.shape[0] and expert_map is None)
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or (expert_map is not None and global_num_experts == expert_map.shape[0])
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)
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M, K = a.shape
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topk = topk_ids.shape[1]
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print("quant_dtype", quant_dtype)
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# exit(0)
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if apply_router_weights_on_input:
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assert topk == 1
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a = a * topk_weight.to(a.dtype)
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a = a.view(M, -1, K).repeat(1, topk, 1).reshape(-1, K)
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out = torch.zeros(M * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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num_experts = w1.shape[0]
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topk_ids = topk_ids.view(-1)
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if expert_map is not None:
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topk_ids = expert_map[topk_ids]
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f32 = torch.float32
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for i in range(num_experts):
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mask = topk_ids == i
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if mask.sum():
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if quant_dtype is None:
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tmp1 = a[mask] @ w1[i].transpose(0, 1)
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tmp2 = SiluAndMul()(tmp1)
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out[mask] = tmp2 @ w2[i].transpose(0, 1)
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if apply_router_weights_on_input:
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return out
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else:
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return (
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(out.view(M, -1, w2.shape[1]).to(f32) * topk_weight.view(M, -1, 1))
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.sum(dim=1)
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.to(out.dtype)
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)
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def torch_moe(
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a: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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score: torch.Tensor,
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topk: int,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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score = torch.softmax(score, dim=-1, dtype=torch.float32)
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topk_weight, topk_ids = torch.topk(score, topk)
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return torch_experts(
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a, w1, w2, topk_weight, topk_ids, global_num_experts, expert_map
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)
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def marlin_moe_generate_valid_test_cases():
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import itertools
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m_list = [1, 123, 666]
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n_list = [128, 1024]
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k_list = [256, 2048]
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e_list = [4, 12]
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topk_list = [2, 3]
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dtype_list = [torch.half, torch.bfloat16]
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group_size_list = [128]
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act_order_list = [True, False]
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quant_type_list = [
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scalar_types.uint4,
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scalar_types.uint4b8,
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]
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is_k_full_list = [True, False]
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all_combinations = itertools.product(
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m_list,
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n_list,
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k_list,
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e_list,
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topk_list,
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dtype_list,
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group_size_list,
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act_order_list,
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quant_type_list,
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is_k_full_list,
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)
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def is_invalid(
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m, n, k, e, topk, dtype, group_size, act_order, quant_type, is_k_full
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):
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# Filter act_order
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if act_order:
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if group_size in (-1, k, n):
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return False
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if quant_type not in [scalar_types.uint4b8]:
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return False
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elif not is_k_full:
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return False
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return True
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cases = []
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for case in all_combinations:
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if is_invalid(*case):
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cases.append(case)
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return cases
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@pytest.mark.flaky(reruns=2)
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@pytest.mark.parametrize(
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("m, n, k, e, topk, dtype, group_size," "act_order, quant_type, is_k_full"),
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marlin_moe_generate_valid_test_cases(),
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)
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def test_fused_marlin_moe(
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m: int,
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n: int,
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k: int,
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e: int,
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topk: int,
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dtype: torch.dtype,
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group_size: int,
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act_order: bool,
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quant_type: ScalarType,
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is_k_full: bool,
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):
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if not torch.cuda.is_available():
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pytest.skip("CUDA device not available")
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torch.manual_seed(0)
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has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
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# Filter act_order
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if act_order:
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if group_size == -1:
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return
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if group_size in (k, n):
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return
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if has_zp:
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return
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else:
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if not is_k_full:
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return
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a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
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w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 20
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w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 20
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e_map = None
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w_ref1_l = []
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qweight1_l = []
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scales1_l = []
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zeros1_l = []
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g_idx1_l = []
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sort_indices1_l = []
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for i in range(w1.shape[0]):
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if has_zp:
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w_ref1, qweight1, scales1, zeros1 = awq_marlin_quantize(
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w1[i].transpose(1, 0), quant_type, group_size
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)
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w_ref1_l.append(w_ref1.T)
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qweight1_l.append(qweight1)
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scales1_l.append(scales1)
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zeros1_l.append(zeros1)
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else:
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test_perm = torch.randperm(k)
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w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = marlin_quantize(
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w1[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
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)
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w_ref1_l.append(w_ref1.T)
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qweight1_l.append(qweight1)
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scales1_l.append(scales1)
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g_idx1_l.append(g_idx1)
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sort_indices1_l.append(sort_indices1)
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w_ref1 = stack_and_dev(w_ref1_l)
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qweight1 = stack_and_dev(qweight1_l).contiguous()
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scales1 = stack_and_dev(scales1_l)
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g_idx1 = stack_and_dev(g_idx1_l) if g_idx1_l else None
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zeros1 = stack_and_dev(zeros1_l) if zeros1_l else None
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sort_indices1 = stack_and_dev(sort_indices1_l) if sort_indices1_l else None
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w_ref2_l = []
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qweight2_l = []
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scales2_l = []
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zeros2_l = []
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g_idx2_l = []
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sort_indices2_l = []
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for i in range(w2.shape[0]):
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if has_zp:
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w_ref2, qweight2, scales2, zeros2 = awq_marlin_quantize(
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w2[i].transpose(1, 0), quant_type, group_size
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)
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w_ref2_l.append(w_ref2.T)
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qweight2_l.append(qweight2)
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scales2_l.append(scales2)
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zeros2_l.append(zeros2)
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else:
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test_perm = torch.randperm(n)
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w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = marlin_quantize(
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w2[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
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)
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w_ref2_l.append(w_ref2.T)
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qweight2_l.append(qweight2)
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scales2_l.append(scales2)
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g_idx2_l.append(g_idx2)
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sort_indices2_l.append(sort_indices2)
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w_ref2 = stack_and_dev(w_ref2_l)
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qweight2 = stack_and_dev(qweight2_l).contiguous()
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scales2 = stack_and_dev(scales2_l)
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g_idx2 = stack_and_dev(g_idx2_l) if g_idx2_l else None
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zeros2 = stack_and_dev(zeros2_l) if zeros2_l else None
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sort_indices2 = stack_and_dev(sort_indices2_l) if sort_indices2_l else None
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score = torch.randn((m, e), device="cuda", dtype=dtype)
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from sglang.srt.layers.moe.topk import fused_topk_torch_native
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topk_weights, topk_ids = fused_topk_torch_native(a, score, topk, False)
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torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, expert_map=e_map)
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marlin_output = fused_marlin_moe(
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a,
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qweight1,
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qweight2,
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scales1,
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scales2,
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score,
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topk_weights,
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topk_ids,
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g_idx1=g_idx1,
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g_idx2=g_idx2,
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sort_indices1=sort_indices1,
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sort_indices2=sort_indices2,
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w1_zeros=zeros1,
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w2_zeros=zeros2,
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num_bits=4,
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is_k_full=is_k_full,
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)
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torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)
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if __name__ == "__main__":
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# Run the specific test function directly
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pytest.main([__file__])
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171
python/sglang/test/test_marlin_utils.py
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171
python/sglang/test/test_marlin_utils.py
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"""
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Adapted from
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https://github.com/vllm-project/vllm/blob/020f58abcdea65302225663130d08fd8f4dd755a/vllm/model_executor/layers/quantization/utils/marlin_utils_test.py
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"""
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# SPDX-License-Identifier: Apache-2.0
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"""Utility functions used for tests and benchmarks"""
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from typing import Optional
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import numpy as np
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import torch
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from sglang.srt.layers.quantization.marlin_utils import (
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GPTQ_MARLIN_TILE,
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marlin_permute_scales,
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marlin_zero_points,
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)
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from sglang.srt.layers.quantization.scalar_type import ScalarType
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from sglang.srt.layers.quantization.utils import (
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get_pack_factor,
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gptq_quantize_weights,
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quantize_weights,
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sort_weights,
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)
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class MarlinWorkspace:
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def __init__(self, out_features, min_thread_n, max_parallel):
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assert (
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out_features % min_thread_n == 0
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), "out_features = {} is undivisible by min_thread_n = {}".format(
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out_features, min_thread_n
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)
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max_workspace_size = (out_features // min_thread_n) * max_parallel
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self.scratch = torch.zeros(max_workspace_size, dtype=torch.int, device="cuda")
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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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def marlin_quantize(
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w: torch.Tensor,
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quant_type: ScalarType,
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group_size: int,
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act_order: bool,
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test_perm: Optional[torch.Tensor] = None,
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):
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size_k, size_n = w.shape
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num_bits = quant_type.size_bits
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# Normalize group_size
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if group_size == -1:
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group_size = size_k
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assert group_size <= size_k
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# Quantize (and apply act_order if provided)
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w_ref, q_w, s, g_idx, rand_perm = gptq_quantize_weights(
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w, quant_type, group_size, act_order, test_perm
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)
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# For act_order, sort the "weights" and "g_idx" so that group ids are
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# increasing
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sort_indices = torch.empty(0, dtype=torch.int, device=w.device)
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if act_order:
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q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)
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# Reformat to marlin
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weight_perm = get_weight_perm(num_bits)
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marlin_q_w = marlin_weights(q_w, size_k, size_n, num_bits, weight_perm)
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marlin_s = marlin_permute_scales(s, size_k, size_n, group_size)
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# Create result
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res_list = [w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, rand_perm]
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for i in range(len(res_list)):
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res_list[i] = res_list[i].to(w.device)
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return res_list
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def awq_marlin_quantize(w: torch.Tensor, quant_type: ScalarType, group_size: int):
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size_k, size_n = w.shape
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# Normalize group_size
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if group_size == -1:
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group_size = size_k
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assert group_size <= size_k
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# Detect num groups
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assert size_k % group_size == 0
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num_groups = size_k // group_size
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# Quantize with zp
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w_ref, q_w, s, zp = quantize_weights(w, quant_type, group_size, zero_points=True)
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# Reformat to marlin
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weight_perm = get_weight_perm(quant_type.size_bits)
|
||||
marlin_q_w = marlin_weights(q_w, size_k, size_n, quant_type.size_bits, weight_perm)
|
||||
marlin_s = marlin_permute_scales(s, size_k, size_n, group_size)
|
||||
marlin_zp = marlin_zero_points(zp, num_groups, size_n, quant_type.size_bits)
|
||||
|
||||
# Create result
|
||||
res_list = [w_ref, marlin_q_w, marlin_s, marlin_zp]
|
||||
for i in range(len(res_list)):
|
||||
res_list[i] = res_list[i].to(w.device)
|
||||
|
||||
return res_list
|
||||
Reference in New Issue
Block a user