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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
454 lines
16 KiB
Python
454 lines
16 KiB
Python
# 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 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 (compute_max_diff, opcheck, stack_and_dev,
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torch_moe, torch_moe_single)
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from vllm import _custom_ops as ops
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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 (
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fused_topk, moe_align_block_size)
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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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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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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("dtype", [torch.float16, torch.bfloat16])
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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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dtype: torch.dtype,
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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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triton_output = fused_moe(a, w1, w2, score, topk, renormalize=False)
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torch_output = torch_moe(a, w1, w2, score, topk)
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torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
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iterative_output = iterative_moe(a, w1, w2, score, topk, renormalize=False)
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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("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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dtype: torch.dtype, group_size: int, has_zp: bool,
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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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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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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)
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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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@torch.inference_mode()
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def test_mixtral_moe(dtype: torch.dtype):
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"""Make sure our Mixtral MoE implementation agrees with the one from
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huggingface."""
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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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).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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# 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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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, 64, 222])
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@pytest.mark.parametrize("n", [128, 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("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("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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group_size: int,
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act_order: bool,
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num_bits: int,
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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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else:
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if not is_k_full:
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return
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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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dtype = torch.float16
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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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w_ref1_l = []
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qweight1_l = []
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scales1_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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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,
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test_perm)
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w_ref1_l.append(w_ref1)
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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)
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sort_indices1 = stack_and_dev(sort_indices1_l)
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w_ref2_l = []
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qweight2_l = []
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scales2_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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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,
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test_perm)
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w_ref2_l.append(w_ref2)
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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)
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sort_indices2 = stack_and_dev(sort_indices2_l)
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score = torch.randn((m, e), device="cuda", dtype=dtype)
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topk_weights, topk_ids = fused_topk(a, score, topk, False)
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triton_output = fused_moe(
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a,
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w_ref1.transpose(1, 2).contiguous(),
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w_ref2.transpose(1, 2).contiguous(),
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score,
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topk,
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renormalize=False,
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)
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marlin_output = torch.ops.vllm.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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num_bits=num_bits,
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is_k_full=is_k_full,
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)
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assert compute_max_diff(marlin_output, triton_output) < 4e-2
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if ops.supports_moe_ops:
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token_expert_indicies = torch.empty(m,
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topk,
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dtype=torch.int32,
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device=a.device)
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opcheck(torch.ops._moe_C.topk_softmax, (
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topk_weights,
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topk_ids,
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token_expert_indicies,
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score.float(),
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))
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block_size_m = 4
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sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m,
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e)
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max_workspace_size = ((m + 255) // 256) * (max(2 * n, k) // 64) * 16
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workspace = torch.zeros(max_workspace_size,
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dtype=torch.int,
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device="cuda",
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requires_grad=False)
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zp = torch.empty((0, 0),
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dtype=dtype,
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device="cuda",
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requires_grad=False)
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opcheck(torch.ops._moe_C.marlin_gemm_moe,
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(a, qweight1, sorted_token_ids, topk_weights, topk_ids,
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scales1, zp, g_idx1, sort_indices1, workspace, quant_type.id,
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m, 2 * n, k, True, e, topk, block_size_m, True, False))
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@pytest.mark.skip("This test is here for the sake of debugging, "
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"don't run it in automated tests.")
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@pytest.mark.parametrize("m", [64, 512, 222, 33, 1])
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@pytest.mark.parametrize("n", [128, 2048, 256, 1024])
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@pytest.mark.parametrize("k", [128, 1024, 512])
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@pytest.mark.parametrize("e", [8, 64])
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@pytest.mark.parametrize("topk", [2, 6])
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@pytest.mark.parametrize("group_size", [-1, 32, 64, 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("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_single_marlin_moe_multiply(
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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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group_size: int,
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act_order: bool,
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num_bits: int,
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is_k_full: bool,
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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 == -1:
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return
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if group_size == k:
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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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quant_type = (scalar_types.uint4b8
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if num_bits == 4 else scalar_types.uint8b128)
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dtype = torch.float16
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a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
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w = torch.randn((e, n, k), device="cuda", dtype=dtype) / 10
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w_ref_l = []
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qweights_l = []
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scales_l = []
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g_idx_l = []
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sort_indices_l = []
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for i in range(w.shape[0]):
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test_perm = torch.randperm(k)
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w_ref, qweight, scales, g_idx, sort_indices, _ = marlin_quantize(
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w[i].transpose(1, 0), quant_type, group_size, act_order, test_perm)
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w_ref_l.append(w_ref)
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qweights_l.append(qweight)
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|
scales_l.append(scales)
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g_idx_l.append(g_idx)
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|
sort_indices_l.append(sort_indices)
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|
|
|
w_ref = stack_and_dev(w_ref_l)
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qweight = stack_and_dev(qweights_l).contiguous()
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|
scales = stack_and_dev(scales_l)
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|
g_idx = stack_and_dev(g_idx_l)
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|
sort_indices = stack_and_dev(sort_indices_l)
|
|
|
|
score = torch.randn((m, e), device="cuda", dtype=dtype)
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|
marlin_output = torch.ops.vllm.single_marlin_moe(
|
|
a,
|
|
qweight,
|
|
scales,
|
|
score,
|
|
topk,
|
|
renormalize=False,
|
|
g_idx=g_idx,
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|
sort_indices=sort_indices,
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|
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(torch.ops._moe_C.moe_align_block_size,
|
|
(topk_ids, num_experts, block_size, sorted_ids, expert_ids,
|
|
num_tokens_post_pad))
|