Categorize tests/kernels/ based on kernel type (#16799)
Signed-off-by: mgoin <mgoin64@gmail.com>
This commit is contained in:
Michael Goin
565
tests/kernels/moe/test_moe.py
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565
tests/kernels/moe/test_moe.py
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# SPDX-License-Identifier: Apache-2.0
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"""Tests for the MOE layers.
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Run `pytest tests/kernels/test_moe.py`.
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"""
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import pytest
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import torch
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from torch.nn import Parameter
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from torch.nn import functional as F
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from transformers import MixtralConfig
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from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
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import vllm.model_executor.layers.fused_moe # noqa
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from tests.kernels.utils import (opcheck, stack_and_dev, torch_moe,
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torch_moe_single)
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from vllm.model_executor.layers.fused_moe import fused_moe
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from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
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from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
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fused_moe as iterative_moe)
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from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
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awq_marlin_quantize, marlin_quantize)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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quantize_weights)
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from vllm.model_executor.models.mixtral import MixtralMoE
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from vllm.platforms import current_platform
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from vllm.scalar_type import scalar_types
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NUM_EXPERTS = [8, 64]
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EP_SIZE = [1, 4]
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TOP_KS = [2, 6]
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@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
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@pytest.mark.parametrize("n", [128, 1024, 2048])
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@pytest.mark.parametrize("k", [128, 511, 1024])
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@pytest.mark.parametrize("e", NUM_EXPERTS)
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@pytest.mark.parametrize("topk", TOP_KS)
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@pytest.mark.parametrize("ep_size", EP_SIZE)
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("padding", [True, False])
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def test_fused_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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ep_size: int,
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dtype: torch.dtype,
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padding: bool,
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):
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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) / 10
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w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
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score = torch.randn((m, e), device="cuda", dtype=dtype)
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if ep_size > 1:
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local_e = e // ep_size
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e_ids = torch.randint(0,
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e, (local_e, ),
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device="cuda",
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dtype=torch.int32)
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e_map = torch.full((e, ), -1, device="cuda", dtype=torch.int32)
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e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
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w1 = w1[e_ids]
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w2 = w2[e_ids]
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else:
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e_map = None
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torch_output = torch_moe(a, w1, w2, score, topk, e_map)
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iterative_output = iterative_moe(a,
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w1,
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w2,
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score,
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topk,
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global_num_experts=e,
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expert_map=e_map,
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renormalize=False)
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# Pad the weight if moe padding is enabled
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if padding:
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w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
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torch.cuda.empty_cache()
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w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
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torch.cuda.empty_cache()
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triton_output = fused_moe(a,
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w1,
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w2,
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score,
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topk,
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global_num_experts=e,
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expert_map=e_map,
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renormalize=False)
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torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
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torch.testing.assert_close(iterative_output,
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torch_output,
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atol=2e-2,
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rtol=0)
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@pytest.mark.parametrize("m", [1, 32, 222])
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@pytest.mark.parametrize("n", [128, 1024, 2048])
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@pytest.mark.parametrize("k", [128, 1024])
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@pytest.mark.parametrize("e", NUM_EXPERTS)
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@pytest.mark.parametrize("topk", TOP_KS)
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@pytest.mark.parametrize("ep_size", EP_SIZE)
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("group_size", [64, 128])
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@pytest.mark.parametrize("has_zp", [True, False])
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@pytest.mark.parametrize("weight_bits", [4, 8])
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def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
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ep_size: int, dtype: torch.dtype, group_size: int,
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has_zp: bool, weight_bits: int):
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print(m, n, k, e, topk, dtype, group_size, has_zp, weight_bits)
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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) / 10
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w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
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score = torch.randn((m, e), device="cuda", dtype=dtype)
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if weight_bits == 4:
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pack_factor = 2
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quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
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elif weight_bits == 8:
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pack_factor = 1
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quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128
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w1_ref = w1.clone()
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w2_ref = w2.clone()
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w1_qweight = torch.empty((e, 2 * n, k // pack_factor),
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device="cuda",
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dtype=torch.uint8)
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w2_qweight = torch.empty((e, k, n // pack_factor),
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device="cuda",
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dtype=torch.uint8)
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w1_scales = torch.empty((e, 2 * n, k // group_size),
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device="cuda",
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dtype=dtype)
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w2_scales = torch.empty((e, k, n // group_size),
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device="cuda",
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dtype=dtype)
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w1_qzeros = torch.empty((e, 2 * n // pack_factor, k // group_size),
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device="cuda",
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dtype=torch.uint8)
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w2_qzeros = torch.empty((e, k // pack_factor, n // group_size),
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device="cuda",
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dtype=torch.uint8)
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for i in range(e * 2):
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expert_id = i % e
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if i // e == 0:
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w, w_ref, w_qweight, w_scales, w_qzeros = \
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w1, w1_ref, w1_qweight, w1_scales, w1_qzeros
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else:
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w, w_ref, w_qweight, w_scales, w_qzeros = \
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w2, w2_ref, w2_qweight, w2_scales, w2_qzeros
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weight, qweight, scales, qzeros = quantize_weights(
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w[expert_id].T, quant_type, group_size, has_zp, False)
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weight = weight.T
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qweight = qweight.T.contiguous().to(torch.uint8)
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scales = scales.T
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if has_zp:
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qzeros = qzeros.T.contiguous().to(torch.uint8)
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if weight_bits == 4:
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qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
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if has_zp:
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qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
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w_ref[expert_id] = weight
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w_qweight[expert_id] = qweight
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w_scales[expert_id] = scales
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if has_zp:
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w_qzeros[expert_id] = qzeros
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if ep_size > 1:
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local_e = e // ep_size
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e_ids = torch.randint(0,
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e, (local_e, ),
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device="cuda",
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dtype=torch.int32)
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e_map = torch.full((e, ), -1, device="cuda", dtype=torch.int32)
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e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
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w1_ref = w1_ref[e_ids]
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w2_ref = w2_ref[e_ids]
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w1_qweight = w1_qweight[e_ids]
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w2_qweight = w2_qweight[e_ids]
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w1_scales = w1_scales[e_ids]
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w2_scales = w2_scales[e_ids]
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w1_qzeros = w1_qzeros[e_ids]
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w2_qzeros = w2_qzeros[e_ids]
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else:
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e_map = None
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triton_output = fused_moe(a,
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w1_qweight,
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w2_qweight,
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score,
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topk,
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renormalize=False,
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use_int4_w4a16=weight_bits == 4,
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use_int8_w8a16=weight_bits == 8,
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global_num_experts=e,
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expert_map=e_map,
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w1_scale=w1_scales,
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w2_scale=w2_scales,
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w1_zp=w1_qzeros if has_zp else None,
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w2_zp=w2_qzeros if has_zp else None,
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block_shape=[0, group_size])
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torch_output = torch_moe(a, w1_ref, w2_ref, score, topk, e_map)
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torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
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@pytest.mark.parametrize("dtype",
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[torch.float32, torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("padding", [True, False])
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@pytest.mark.parametrize(
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"use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
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@torch.inference_mode()
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def test_mixtral_moe(dtype: torch.dtype, padding: bool, use_rocm_aiter: bool,
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monkeypatch):
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"""Make sure our Mixtral MoE implementation agrees with the one from
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huggingface."""
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if use_rocm_aiter:
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monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
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# Instantiate our and huggingface's MoE blocks
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config = MixtralConfig()
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hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
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vllm_moe = MixtralMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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params_dtype=dtype,
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tp_size=1,
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dp_size=1,
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).cuda()
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# Load the weights
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vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
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for i in range(config.num_local_experts):
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weights = (hf_moe.experts[i].w1.weight.data,
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hf_moe.experts[i].w3.weight.data)
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vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
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vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
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# Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
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hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
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# vLLM uses 1D query [num_tokens, hidden_dim]
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vllm_inputs = hf_inputs.flatten(0, 1)
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# Pad the weight if moe padding is enabled
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if padding:
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vllm_moe.experts.w13_weight = Parameter(F.pad(
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vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[..., 0:-128],
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requires_grad=False)
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torch.cuda.empty_cache()
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vllm_moe.experts.w2_weight = Parameter(F.pad(
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vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
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requires_grad=False)
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torch.cuda.empty_cache()
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# Run forward passes for both MoE blocks
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hf_states, _ = hf_moe.forward(hf_inputs)
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vllm_states = vllm_moe.forward(vllm_inputs)
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mixtral_moe_tol = {
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torch.float32: 1e-3,
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torch.float16: 1e-3,
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torch.bfloat16: 1e-2,
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}
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if use_rocm_aiter:
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# The values of rtol and atol are set based on the tests in ROCM AITER package. # noqa: E501
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# https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174 # noqa: E501
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torch.testing.assert_close(hf_states.flatten(0, 1),
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vllm_states,
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rtol=0.01,
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atol=100)
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else:
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torch.testing.assert_close(hf_states.flatten(0, 1),
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vllm_states,
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rtol=mixtral_moe_tol[dtype],
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atol=mixtral_moe_tol[dtype])
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@pytest.mark.parametrize("m", [1, 33, 123])
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@pytest.mark.parametrize("n", [128, 1024])
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@pytest.mark.parametrize("k", [256, 2048])
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@pytest.mark.parametrize("e", [4, 12])
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@pytest.mark.parametrize("topk", [2, 3])
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@pytest.mark.parametrize("ep_size", [1, 4])
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("group_size", [-1, 32, 128])
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@pytest.mark.parametrize("act_order", [True, False])
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@pytest.mark.parametrize("num_bits", [4, 8])
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@pytest.mark.parametrize("has_zp", [True, False])
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@pytest.mark.parametrize("is_k_full", [True, False])
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@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
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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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ep_size: 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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num_bits: int,
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has_zp: bool,
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is_k_full: bool,
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):
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current_platform.seed_everything(7)
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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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if has_zp:
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# we don't build kernel for int8 with zero
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if num_bits == 8:
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return
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quant_type = scalar_types.uint4 if num_bits == 4 else scalar_types.uint8
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else:
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quant_type = scalar_types.uint4b8 \
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if num_bits == 4 else scalar_types.uint8b128
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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) / 10
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w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
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if ep_size > 1:
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local_e = e // ep_size
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e_ids = torch.randperm(e, device="cuda", dtype=torch.int32)[:local_e]
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e_map = torch.full((e, ), -1, device="cuda", dtype=torch.int32)
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e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
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w1 = w1[e_ids]
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w2 = w2[e_ids]
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else:
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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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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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quant_res = marlin_quantize(w1[i].transpose(1, 0), quant_type,
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group_size, act_order, test_perm)
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w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = quant_res
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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
|
||||
sort_indices1 = stack_and_dev(sort_indices1_l) if sort_indices1_l else None
|
||||
|
||||
w_ref2_l = []
|
||||
qweight2_l = []
|
||||
scales2_l = []
|
||||
zeros2_l = []
|
||||
g_idx2_l = []
|
||||
sort_indices2_l = []
|
||||
|
||||
for i in range(w2.shape[0]):
|
||||
if has_zp:
|
||||
w_ref2, qweight2, scales2, zeros2 = awq_marlin_quantize(
|
||||
w2[i].transpose(1, 0), quant_type, group_size)
|
||||
|
||||
w_ref2_l.append(w_ref2.T)
|
||||
qweight2_l.append(qweight2)
|
||||
scales2_l.append(scales2)
|
||||
zeros2_l.append(zeros2)
|
||||
else:
|
||||
test_perm = torch.randperm(n)
|
||||
quant_res = marlin_quantize(w2[i].transpose(1, 0), quant_type,
|
||||
group_size, act_order, test_perm)
|
||||
w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = quant_res
|
||||
|
||||
w_ref2_l.append(w_ref2.T)
|
||||
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) if g_idx2_l else None
|
||||
zeros2 = stack_and_dev(zeros2_l) if zeros2_l else None
|
||||
sort_indices2 = stack_and_dev(sort_indices2_l) if sort_indices2_l else None
|
||||
|
||||
score = torch.randn((m, e), device="cuda", dtype=dtype)
|
||||
|
||||
topk_weights, topk_ids = fused_topk(a, score, topk, False)
|
||||
|
||||
torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, e_map)
|
||||
|
||||
marlin_output = torch.ops.vllm.fused_marlin_moe(
|
||||
a,
|
||||
qweight1,
|
||||
qweight2,
|
||||
scales1,
|
||||
scales2,
|
||||
score,
|
||||
topk_weights,
|
||||
topk_ids,
|
||||
global_num_experts=e,
|
||||
expert_map=e_map,
|
||||
g_idx1=g_idx1,
|
||||
g_idx2=g_idx2,
|
||||
sort_indices1=sort_indices1,
|
||||
sort_indices2=sort_indices2,
|
||||
w1_zeros=zeros1,
|
||||
w2_zeros=zeros2,
|
||||
num_bits=num_bits,
|
||||
is_k_full=is_k_full)
|
||||
|
||||
torch.testing.assert_close(marlin_output, torch_output, atol=2e-2, rtol=0)
|
||||
|
||||
|
||||
@pytest.mark.skip("This test is here for the sake of debugging, "
|
||||
"don't run it in automated tests.")
|
||||
@pytest.mark.parametrize("m", [1, 33, 123])
|
||||
@pytest.mark.parametrize("n", [128, 1024])
|
||||
@pytest.mark.parametrize("k", [256, 2048])
|
||||
@pytest.mark.parametrize("e", [4, 12])
|
||||
@pytest.mark.parametrize("topk", [2, 3])
|
||||
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
|
||||
@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("has_zp", [True, False])
|
||||
@pytest.mark.parametrize("is_k_full", [True, False])
|
||||
def test_single_marlin_moe_multiply(m: int, n: int, k: int, e: int, topk: int,
|
||||
dtype: torch.dtype, group_size: int,
|
||||
act_order: bool, num_bits: int,
|
||||
has_zp: bool, is_k_full: bool):
|
||||
# Filter act_order
|
||||
if act_order:
|
||||
if group_size == -1:
|
||||
return
|
||||
if group_size in (k, n):
|
||||
return
|
||||
if has_zp:
|
||||
return
|
||||
else:
|
||||
if not is_k_full:
|
||||
return
|
||||
|
||||
if has_zp:
|
||||
quant_type = scalar_types.uint4 if num_bits == 4 else scalar_types.uint8
|
||||
else:
|
||||
quant_type = scalar_types.uint4b8 \
|
||||
if num_bits == 4 else scalar_types.uint8b128
|
||||
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
|
||||
w = torch.randn((e, n, k), device="cuda", dtype=dtype) / 10
|
||||
|
||||
w_ref_l = []
|
||||
qweight_l = []
|
||||
scales_l = []
|
||||
zeros_l = []
|
||||
g_idx_l = []
|
||||
sort_indices_l = []
|
||||
|
||||
for i in range(w.shape[0]):
|
||||
if has_zp:
|
||||
w_ref, qweight, scales, zeros = awq_marlin_quantize(
|
||||
w[i].transpose(1, 0), quant_type, group_size)
|
||||
|
||||
w_ref_l.append(w_ref.T)
|
||||
qweight_l.append(qweight)
|
||||
scales_l.append(scales)
|
||||
zeros_l.append(zeros)
|
||||
else:
|
||||
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.T)
|
||||
qweight_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(qweight_l).contiguous()
|
||||
scales = stack_and_dev(scales_l)
|
||||
g_idx = stack_and_dev(g_idx_l) if g_idx_l else None
|
||||
zeros = stack_and_dev(zeros_l) if zeros_l else None
|
||||
sort_indices = stack_and_dev(sort_indices_l) if sort_indices_l else None
|
||||
|
||||
score = torch.randn((m, e), device="cuda", dtype=dtype)
|
||||
marlin_output = torch.ops.vllm.single_marlin_moe(
|
||||
a,
|
||||
qweight,
|
||||
scales,
|
||||
score,
|
||||
topk,
|
||||
renormalize=False,
|
||||
g_idx=g_idx,
|
||||
sort_indices=sort_indices,
|
||||
w_zeros=zeros,
|
||||
num_bits=num_bits,
|
||||
is_k_full=is_k_full,
|
||||
)
|
||||
|
||||
torch_output = torch_moe_single(a, w_ref, score, topk)
|
||||
|
||||
torch.testing.assert_close(marlin_output, torch_output, atol=2e-2, rtol=0)
|
||||
|
||||
|
||||
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(torch.ops._moe_C.moe_align_block_size,
|
||||
(topk_ids, num_experts, block_size, sorted_ids, expert_ids,
|
||||
num_tokens_post_pad))
|
||||
Reference in New Issue
Block a user