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# SPDX-License-Identifier: Apache-2.0
"""Tests for the MOE layers.
Run `pytest tests/kernels/test_moe.py`.
"""
from typing import List
import pytest
import torch
from moe._ops import ops
from moe.fused_moe import fused_moe, fused_topk, moe_align_block_size
from moe.fused_marlin_moe import fused_marlin_moe
from moe.platforms import current_platform
from moe.scalar_type import scalar_types
from moe.utils.marlin_utils_test import marlin_quantize, quantize_weights
from .utils import compute_max_diff, opcheck, torch_moe
from torch.nn import Parameter
from torch.nn import functional as F
def stack_and_dev(tensors: List[torch.Tensor]):
dev = tensors[0].device
return torch.stack(tensors, dim=0).to(dev)
NUM_EXPERTS = [8, 64]
EP_SIZE = [1, 4]
TOP_KS = [2, 6]
@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 511, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("padding", [True, False])
def test_fused_moe(
m: int,
n: int,
k: int,
e: int,
topk: int,
ep_size: int,
dtype: torch.dtype,
padding: bool,
):
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
score = torch.randn((m, e), device="cuda", dtype=dtype)
if ep_size > 1:
local_e = e // ep_size
e_ids = torch.randint(0,
e, (local_e, ),
device="cuda",
dtype=torch.int32)
e_map = torch.full((e, ), -1, device="cuda", dtype=torch.int32)
e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
w1 = w1[e_ids]
w2 = w2[e_ids]
else:
e_map = None
torch_output = torch_moe(a, w1, w2, score, topk, e_map)
if padding:
w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
torch.cuda.empty_cache()
w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
torch.cuda.empty_cache()
triton_output = fused_moe(a,
w1,
w2,
score,
topk,
global_num_experts=e,
expert_map=e_map,
renormalize=False)
torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
@pytest.mark.parametrize("m", [1, 32, 222])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.parametrize("has_zp", [True, False])
@pytest.mark.parametrize("weight_bits", [4, 8])
def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
ep_size: int, dtype: torch.dtype, group_size: int,
has_zp: bool, weight_bits: int):
print(m, n, k, e, topk, dtype, group_size, has_zp, weight_bits)
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
score = torch.randn((m, e), device="cuda", dtype=dtype)
if weight_bits == 4:
pack_factor = 2
quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
elif weight_bits == 8:
pack_factor = 1
quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128
w1_ref = w1.clone()
w2_ref = w2.clone()
w1_qweight = torch.empty((e, 2 * n, k // pack_factor),
device="cuda",
dtype=torch.uint8)
w2_qweight = torch.empty((e, k, n // pack_factor),
device="cuda",
dtype=torch.uint8)
w1_scales = torch.empty((e, 2 * n, k // group_size),
device="cuda",
dtype=dtype)
w2_scales = torch.empty((e, k, n // group_size),
device="cuda",
dtype=dtype)
w1_qzeros = torch.empty((e, 2 * n // pack_factor, k // group_size),
device="cuda",
dtype=torch.uint8)
w2_qzeros = torch.empty((e, k // pack_factor, n // group_size),
device="cuda",
dtype=torch.uint8)
for i in range(e * 2):
expert_id = i % e
if i // e == 0:
w, w_ref, w_qweight, w_scales, w_qzeros = \
w1, w1_ref, w1_qweight, w1_scales, w1_qzeros
else:
w, w_ref, w_qweight, w_scales, w_qzeros = \
w2, w2_ref, w2_qweight, w2_scales, w2_qzeros
weight, qweight, scales, qzeros = quantize_weights(
w[expert_id].T, quant_type, group_size, has_zp, False)
weight = weight.T
qweight = qweight.T.contiguous().to(torch.uint8)
scales = scales.T
if has_zp:
qzeros = qzeros.T.contiguous().to(torch.uint8)
if weight_bits == 4:
qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
if has_zp:
qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
w_ref[expert_id] = weight
w_qweight[expert_id] = qweight
w_scales[expert_id] = scales
if has_zp:
w_qzeros[expert_id] = qzeros
if ep_size > 1:
local_e = e // ep_size
e_ids = torch.randint(0,
e, (local_e, ),
device="cuda",
dtype=torch.int32)
e_map = torch.full((e, ), -1, device="cuda", dtype=torch.int32)
e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
w1_ref = w1_ref[e_ids]
w2_ref = w2_ref[e_ids]
w1_qweight = w1_qweight[e_ids]
w2_qweight = w2_qweight[e_ids]
w1_scales = w1_scales[e_ids]
w2_scales = w2_scales[e_ids]
w1_qzeros = w1_qzeros[e_ids]
w2_qzeros = w2_qzeros[e_ids]
else:
e_map = None
triton_output = fused_moe(a,
w1_qweight,
w2_qweight,
score,
topk,
renormalize=False,
use_int4_w4a16=weight_bits == 4,
use_int8_w8a16=weight_bits == 8,
global_num_experts=e,
expert_map=e_map,
w1_scale=w1_scales,
w2_scale=w2_scales,
w1_zp=w1_qzeros if has_zp else None,
w2_zp=w2_qzeros if has_zp else None,
block_shape=[0, group_size])
torch_output = torch_moe(a, w1_ref, w2_ref, score, topk, e_map)
torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
@pytest.mark.parametrize("m", [1, 33, 64, 222])
@pytest.mark.parametrize("n", [128, 2048])
@pytest.mark.parametrize("k", [128, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("group_size", [-1, 32, 128])
@pytest.mark.parametrize("act_order", [True, False])
@pytest.mark.parametrize("num_bits", [4, 8])
@pytest.mark.parametrize("is_k_full", [True, False])
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
def test_fused_marlin_moe(
m: int,
n: int,
k: int,
e: int,
topk: int,
group_size: int,
act_order: bool,
num_bits: int,
is_k_full: bool,
):
current_platform.seed_everything(7)
# Filter act_order
if act_order:
if group_size == -1:
return
if group_size in (k, n):
return
else:
if not is_k_full:
return
quant_type = (scalar_types.uint4b8
if num_bits == 4 else scalar_types.uint8b128)
dtype = torch.float16
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
w_ref1_l = []
qweight1_l = []
scales1_l = []
g_idx1_l = []
sort_indices1_l = []
for i in range(w1.shape[0]):
test_perm = torch.randperm(k)
w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = marlin_quantize(
w1[i].transpose(1, 0), quant_type, group_size, act_order,
test_perm)
w_ref1_l.append(w_ref1)
qweight1_l.append(qweight1)
scales1_l.append(scales1)
g_idx1_l.append(g_idx1)
sort_indices1_l.append(sort_indices1)
w_ref1 = stack_and_dev(w_ref1_l)
qweight1 = stack_and_dev(qweight1_l).contiguous()
scales1 = stack_and_dev(scales1_l)
g_idx1 = stack_and_dev(g_idx1_l)
sort_indices1 = stack_and_dev(sort_indices1_l)
w_ref2_l = []
qweight2_l = []
scales2_l = []
g_idx2_l = []
sort_indices2_l = []
for i in range(w2.shape[0]):
test_perm = torch.randperm(n)
w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = marlin_quantize(
w2[i].transpose(1, 0), quant_type, group_size, act_order,
test_perm)
w_ref2_l.append(w_ref2)
qweight2_l.append(qweight2)
scales2_l.append(scales2)
g_idx2_l.append(g_idx2)
sort_indices2_l.append(sort_indices2)
w_ref2 = stack_and_dev(w_ref2_l)
qweight2 = stack_and_dev(qweight2_l).contiguous()
scales2 = stack_and_dev(scales2_l)
g_idx2 = stack_and_dev(g_idx2_l)
sort_indices2 = stack_and_dev(sort_indices2_l)
score = torch.randn((m, e), device="cuda", dtype=dtype)
topk_weights, topk_ids = fused_topk(a, score, topk, False)
triton_output = fused_moe(
a,
w_ref1.transpose(1, 2).contiguous(),
w_ref2.transpose(1, 2).contiguous(),
score,
topk,
renormalize=False,
)
marlin_output = fused_marlin_moe(
a,
qweight1,
qweight2,
scales1,
scales2,
score,
topk_weights,
topk_ids,
g_idx1=g_idx1,
g_idx2=g_idx2,
sort_indices1=sort_indices1,
sort_indices2=sort_indices2,
num_bits=num_bits,
is_k_full=is_k_full,
)
assert compute_max_diff(marlin_output, triton_output) < 4e-2
token_expert_indicies = torch.empty(m,
topk,
dtype=torch.int32,
device=a.device)
opcheck(ops.topk_softmax, (
topk_weights,
topk_ids,
token_expert_indicies,
score.float(),
))
block_size_m = 4
sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m,
e)
max_workspace_size = ((m + 255) // 256) * (max(2 * n, k) // 64) * 16
workspace = torch.zeros(max_workspace_size,
dtype=torch.int,
device="cuda",
requires_grad=False)
zp = torch.empty((0, 0),
dtype=dtype,
device="cuda",
requires_grad=False)
opcheck(ops.marlin_gemm_moe,
(a, qweight1, sorted_token_ids, topk_weights, topk_ids,
scales1, zp, g_idx1, sort_indices1, workspace, quant_type.id,
m, 2 * n, k, True, e, topk, block_size_m, True, False))
@pytest.mark.skip("This test is here for the sake of debugging, "
"don't run it in automated tests.")
@pytest.mark.parametrize("m", [64, 512, 222, 33, 1])
@pytest.mark.parametrize("n", [128, 2048, 256, 1024])
@pytest.mark.parametrize("k", [128, 1024, 512])
@pytest.mark.parametrize("e", [8, 64])
@pytest.mark.parametrize("topk", [2, 6])
@pytest.mark.parametrize("group_size", [-1, 32, 64, 128])
@pytest.mark.parametrize("act_order", [True, False])
@pytest.mark.parametrize("num_bits", [4, 8])
@pytest.mark.parametrize("is_k_full", [True, False])
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
def test_single_marlin_moe_multiply(
m: int,
n: int,
k: int,
e: int,
topk: int,
group_size: int,
act_order: bool,
num_bits: int,
is_k_full: bool,
):
# Filter act_order
if act_order:
if group_size == -1:
return
if group_size == k:
return
else:
if not is_k_full:
return
quant_type = (scalar_types.uint4b8
if num_bits == 4 else scalar_types.uint8b128)
dtype = torch.float16
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w = torch.randn((e, n, k), device="cuda", dtype=dtype) / 10
w_ref_l = []
qweights_l = []
scales_l = []
g_idx_l = []
sort_indices_l = []
for i in range(w.shape[0]):
test_perm = torch.randperm(k)
w_ref, qweight, scales, g_idx, sort_indices, _ = marlin_quantize(
w[i].transpose(1, 0), quant_type, group_size, act_order, test_perm)
w_ref_l.append(w_ref)
qweights_l.append(qweight)
scales_l.append(scales)
g_idx_l.append(g_idx)
sort_indices_l.append(sort_indices)
w_ref = stack_and_dev(w_ref_l)
qweight = stack_and_dev(qweights_l).contiguous()
scales = stack_and_dev(scales_l)
g_idx = stack_and_dev(g_idx_l)
sort_indices = stack_and_dev(sort_indices_l)
score = torch.randn((m, e), device="cuda", dtype=dtype)
marlin_output = ops.single_marlin_moe(
a,
qweight,
scales,
score,
topk,
renormalize=False,
g_idx=g_idx,
sort_indices=sort_indices,
num_bits=num_bits,
is_k_full=is_k_full,
)
torch_output = torch_moe_single(a, w_ref.transpose(1, 2), score, topk)
assert compute_max_diff(marlin_output, torch_output) < 1e-2
def test_moe_align_block_size_opcheck():
num_experts = 4
block_size = 4
topk_ids = torch.randint(0,
num_experts, (3, 4),
dtype=torch.int32,
device='cuda')
max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
sorted_ids = torch.empty((max_num_tokens_padded, ),
dtype=torch.int32,
device=topk_ids.device)
sorted_ids.fill_(topk_ids.numel())
max_num_m_blocks = max_num_tokens_padded // block_size
expert_ids = torch.empty((max_num_m_blocks, ),
dtype=torch.int32,
device=topk_ids.device)
num_tokens_post_pad = torch.empty((1),
dtype=torch.int32,
device=topk_ids.device)
opcheck(ops.moe_align_block_size,
(topk_ids, num_experts, block_size, sorted_ids, expert_ids,
num_tokens_post_pad))
|