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[CI] Add unit test framework (#1201)

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This PR added the unit test framework to enable ut for vLLM Ascend. Unit
test runs on CPU machines. It'll be ran once lint check is passed the
same as e2e test.

For unit test, this PR created a new folder called `ut` under `tests`
module. All the test file in `ut` should keep the same with the code in
`vllm-ascend`. The file name should be start with `test_` prefix. For
example, in this PR. the `test_ascend_config.py` is added for
`ascend_config.py` test.

A new fille `worker/test_worker_v1.py` is also added as the placeholder.
This file should be the unit test for `vllm-ascend/worker/worker_v1.py`.

Additional, a new `fake_weight` folder is added, it contains the
config.json from `facebook/opt-125m`, so that the test will not always
visit huggingface.

TODO:
We should add all the unit test file one by one in the future.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-06-16 18:32:28 +08:00
committed by GitHub
parent 966557a2a3
commit 69b817ed65
57 changed files with 396 additions and 267 deletions
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0
tests/e2e/singlecard/ops/__init__.py Normal file
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tests/e2e/singlecard/ops/test_fused_moe.py Normal file
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# SPDX-License-Identifier: Apache-2.0
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/kernels/test_moe.py
"""Tests for the MOE layers.
Run `pytest tests/ops/test_fused_moe.py`.
"""
# fused moe ops test will hit the infer_schema error, we need add the patch
# here to make the test pass.
import vllm_ascend.patch.worker.patch_common.patch_utils # type: ignore[import] # isort: skip # noqa
import pytest
import torch
from vllm.model_executor.layers.activation import SiluAndMul
from vllm_ascend.ops.fused_moe import fused_experts
NUM_EXPERTS = [8, 64]
EP_SIZE = [1, 4]
TOP_KS = [2, 6]
DEVICE = ["npu"]
def torch_moe(a, w1, w2, topk_weights, topk_ids, topk, expert_map):
B, D = a.shape
a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
topk_weights = topk_weights.view(-1)
topk_ids = topk_ids.view(-1)
if expert_map is not None:
topk_ids = expert_map[topk_ids]
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
out[mask] = SiluAndMul()(
a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
return (out.view(B, -1, w2.shape[1]) *
topk_weights.view(B, -1, 1).to(out.dtype)).sum(dim=1)
@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("device", DEVICE)
def test_fused_experts(
m: int,
n: int,
k: int,
e: int,
topk: int,
ep_size: int,
dtype: torch.dtype,
device: str,
):
a = torch.randn((m, k), device=device, dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device=device, dtype=dtype) / 10
w2 = torch.randn((e, k, n), device=device, dtype=dtype) / 10
score = torch.randn((m, e), device=device, dtype=dtype)
if ep_size > 1:
local_e = e // ep_size
e_ids = torch.randint(0,
e, (local_e, ),
device=device,
dtype=torch.int32)
e_map = torch.full((e, ), -1, device=device, dtype=torch.int32)
e_map[e_ids] = torch.arange(local_e, device=device, dtype=torch.int32)
w1 = w1[e_ids]
w2 = w2[e_ids]
else:
e_map = None
score = torch.softmax(score, dim=-1, dtype=dtype)
topk_weights, topk_ids = torch.topk(score, topk)
topk_ids = topk_ids.to(torch.int32)
output = fused_experts(a, w1, w2, topk_weights, topk_ids, topk, e_map)
torch_output = torch_moe(a, w1, w2, topk_weights, topk_ids, topk, e_map)
# TODO: The native params are: atol=2e-2, rtol=0, maybe related to the nan problem
torch.testing.assert_close(output, torch_output, atol=4e-2, rtol=1)
torch.npu.empty_cache()

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tests/e2e/singlecard/ops/test_multi_step.py Normal file
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# Copyright (c) China Merchants Bank Co., Ltd. 2025. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#/
# to run this test, you need to cd to the upper package which is 'tests',
# and run with command 'pytest -s ops/test_multi_step.py'
import torch
import torch_npu # noqa: F401
DTYPES = [torch.int32, torch.int64]
DEVICES = [f"npu:{0}"]
# Set tolerance to 0 for equals
DEFAULT_ATOL = 0
DEFAULT_RTOL = 0
# test custom ops of https://github.com/vllm-project/vllm-ascend/tree/main/csrc/kernels/advance_step.cpp
@torch.inference_mode()
def test_single_generation_multi_step() -> None:
input_tokens_data = [2926]
input_tokens_ascendc = torch.tensor(input_tokens_data, device='npu:0')
input_tokens_python = torch.tensor(input_tokens_data, device='npu:0')
sampled_token_ids_data = [[13]]
sampled_token_ids = torch.tensor(sampled_token_ids_data, device='npu:0')
input_positions_data = [5]
input_positions_ascendc = torch.tensor(input_positions_data,
device='npu:0')
input_positions_python = torch.tensor(input_positions_data, device='npu:0')
seq_lens_data = [6]
seq_lens_ascendc = torch.tensor(seq_lens_data,
device='npu:0',
dtype=torch.int32)
seq_lens_python = torch.tensor(seq_lens_data,
device='npu:0',
dtype=torch.int32)
slot_mapping_data = [5]
slot_mapping_ascendc = torch.tensor(slot_mapping_data,
device='npu:0',
dtype=torch.int32)
slot_mapping_python = torch.tensor(slot_mapping_data,
device='npu:0',
dtype=torch.int32)
block_tables_data = [[0]]
block_tables = torch.tensor(block_tables_data,
device='npu:0',
dtype=torch.int32)
torch.ops._C.advance_step_flashattn_ascendc(
1, 1, 128, input_tokens_ascendc, sampled_token_ids,
input_positions_ascendc, seq_lens_ascendc, slot_mapping_ascendc,
block_tables)
normal(1, 1, 128, input_tokens_python, sampled_token_ids,
input_positions_python, seq_lens_python, slot_mapping_python,
block_tables)
# Compare the results.
torch.testing.assert_close(input_tokens_ascendc,
input_tokens_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(input_positions_ascendc,
input_positions_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(seq_lens_ascendc,
seq_lens_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(slot_mapping_ascendc,
slot_mapping_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
@torch.inference_mode()
def test_multi_result_generation_multi_step() -> None:
input_tokens_data = [2926, 279, 12095, 1588]
input_tokens_ascendc = torch.tensor(input_tokens_data, device='npu:0')
input_tokens_python = torch.tensor(input_tokens_data, device='npu:0')
sampled_token_ids_data = [[13], [1968], [13], [13]]
sampled_token_ids = torch.tensor(sampled_token_ids_data, device='npu:0')
input_positions_data = [5, 7, 5, 5]
input_positions_ascendc = torch.tensor(input_positions_data,
device='npu:0')
input_positions_python = torch.tensor(input_positions_data, device='npu:0')
seq_lens_data = [6, 8, 6, 6]
seq_lens_ascendc = torch.tensor(seq_lens_data,
device='npu:0',
dtype=torch.int32)
seq_lens_python = torch.tensor(seq_lens_data,
device='npu:0',
dtype=torch.int32)
slot_mapping_data = [5, 135, 261, 389]
slot_mapping_ascendc = torch.tensor(slot_mapping_data,
device='npu:0',
dtype=torch.int32)
slot_mapping_python = torch.tensor(slot_mapping_data,
device='npu:0',
dtype=torch.int32)
block_tables_data = [[0], [1], [2], [3]]
block_tables = torch.tensor(block_tables_data,
device='npu:0',
dtype=torch.int32)
torch.ops._C.advance_step_flashattn_ascendc(
4, 4, 128, input_tokens_ascendc, sampled_token_ids,
input_positions_ascendc, seq_lens_ascendc, slot_mapping_ascendc,
block_tables)
normal(4, 4, 128, input_tokens_python, sampled_token_ids,
input_positions_python, seq_lens_python, slot_mapping_python,
block_tables)
# Compare the results.
torch.testing.assert_close(input_tokens_ascendc,
input_tokens_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(input_positions_ascendc,
input_positions_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(seq_lens_ascendc,
seq_lens_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(slot_mapping_ascendc,
slot_mapping_python,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
def normal(num_seqs: int, num_queries: int, block_size: int,
input_tokens: torch.Tensor, sampled_token_ids: torch.Tensor,
input_positions: torch.Tensor, seq_lens_tensor: torch.Tensor,
slot_mapping: torch.Tensor, block_tables: torch.Tensor) -> None:
sampled_token_ids_list = sampled_token_ids[:num_queries].squeeze(-1)
input_tokens[:num_queries] = sampled_token_ids_list
# get seq_lens and input_positions
seq_lens = seq_lens_tensor[:num_queries]
next_seq_lens = seq_lens + 1
next_input_pos = next_seq_lens - 1
# update seq_lens and input_positions
seq_lens_tensor[:num_queries] = next_seq_lens
input_positions[:num_queries] = next_input_pos # type: ignore
# get block index and offset
block_idx = next_input_pos // block_size
block_offset = next_input_pos % block_size
current_block_table = block_tables.gather(
1, block_idx.unsqueeze(-1)).squeeze(-1)
slot_num = current_block_table * block_size + block_offset
# update slot_mapping
slot_mapping[:num_queries] = slot_num

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tests/e2e/singlecard/ops/test_rotary_embedding.py Normal file
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# Copyright 2023 The vLLM team.
# Copyright (c) Huawei Technologies Co., Ltd. 2024-2025. All rights reserved.
# Adapted from
# https://github.com/vllm-project/vllm/blob/main/vllm/tests/kernels/test_rotary_embedding.py
from typing import Optional, Tuple, Union
import pytest
import torch
import torch.nn as nn
import vllm_ascend.platform # noqa: F401
# Only Neox style true scenario is supported for now
IS_NEOX_STYLE = [True]
DTYPES = [torch.half]
HEAD_SIZES = [64, 96, 128, 256]
ROTARY_DIMS = [None, 32] # None means rotary dim == head size
NUM_HEADS = [17] # Arbitrary values for testing
BATCH_SIZES = [5] # Arbitrary values for testing
SEQ_LENS = [11, 4096] # Arbitrary values for testing
SEEDS = [0]
DEVICES = [f"npu:{0}"]
# Set tolerance to 1 for quant ops
DEFAULT_ATOL = 1e-3
DEFAULT_RTOL = 1e-3
def _apply_rotary_emb(
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
is_neox_style: bool,
) -> torch.Tensor:
"""
Args:
x: [num_tokens, num_heads, head_size]
cos: [num_tokens, head_size // 2]
sin: [num_tokens, head_size // 2]
is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
positional embeddings.
"""
cos = cos.unsqueeze(-2).to(x.dtype)
sin = sin.unsqueeze(-2).to(x.dtype)
if is_neox_style:
x1, x2 = torch.chunk(x, 2, dim=-1)
else:
x1 = x[..., ::2]
x2 = x[..., 1::2]
o1 = x1 * cos - x2 * sin
o2 = x2 * cos + x1 * sin
if is_neox_style:
return torch.cat((o1, o2), dim=-1)
else:
return torch.stack((o1, o2), dim=-1).flatten(-2)
# adapted from https://github.com/vllm-project/vllm/vllm/model_executor/layers/rotary_embedding.py
class RotaryEmbedding(nn.Module):
"""Original rotary positional embedding."""
def __init__(
self,
head_size: int,
rotary_dim: int,
max_position_embeddings: int,
base: int,
is_neox_style: bool,
dtype: torch.dtype,
) -> None:
super().__init__()
self.head_size = head_size
self.rotary_dim = rotary_dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.is_neox_style = is_neox_style
self.dtype = dtype
cache = self._compute_cos_sin_cache()
cache = cache.to(dtype)
self.cos_sin_cache: torch.Tensor
self.register_buffer("cos_sin_cache", cache, persistent=False)
def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
"""Compute the inverse frequency."""
# NOTE(woosuk): To exactly match the HF implementation, we need to
# use CPU to compute the cache and then move it to GPU. However, we
# create the cache on GPU for faster initialization. This may cause
# a slight numerical difference between the HF implementation and ours.
inv_freq = 1.0 / (base**(torch.arange(
0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim))
return inv_freq
def _compute_cos_sin_cache(self) -> torch.Tensor:
"""Compute the cos and sin cache."""
inv_freq = self._compute_inv_freq(self.base)
t = torch.arange(self.max_position_embeddings, dtype=torch.float)
freqs = torch.einsum("i,j -> ij", t, inv_freq)
cos = freqs.cos()
sin = freqs.sin()
cache = torch.cat((cos, sin), dim=-1)
return cache
def forward_native(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""A PyTorch-native implementation of forward()."""
if offsets is not None:
positions = positions + offsets
positions = positions.flatten()
num_tokens = positions.shape[0]
cos_sin = self.cos_sin_cache.index_select(0, positions)
cos, sin = cos_sin.chunk(2, dim=-1)
query_shape = query.shape
query = query.view(num_tokens, -1, self.head_size)
query_rot = query[..., :self.rotary_dim]
query_pass = query[..., self.rotary_dim:]
query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
key_shape = key.shape
key = key.view(num_tokens, -1, self.head_size)
key_rot = key[..., :self.rotary_dim]
key_pass = key[..., self.rotary_dim:]
key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
return query, key
# test with leading dimension and merge seqlen and batch_size as num_tokens
@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
@pytest.mark.parametrize("seq_len", SEQ_LENS)
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", DEVICES)
@torch.inference_mode()
def test_rotary_embedding_quant_with_leading_dim(
is_neox_style: bool,
batch_size: int,
seq_len: int,
num_heads: int,
head_size: int,
rotary_dim: Optional[int],
dtype: torch.dtype,
seed: int,
device: str,
max_position: int = 8192,
base: int = 10000,
) -> None:
if rotary_dim is None:
rotary_dim = head_size
torch.set_default_device(device)
if rotary_dim is None:
rotary_dim = head_size
rope = RotaryEmbedding(head_size, rotary_dim, max_position, base,
is_neox_style, dtype)
rope = rope.to(dtype=dtype)
num_tokens = batch_size * seq_len
positions = torch.randint(0, max_position, (batch_size * seq_len, ))
qkv_tensor = torch.randn(num_tokens,
num_heads * head_size * 3,
dtype=dtype)
query, key, _ = qkv_tensor.split(
[num_heads * head_size, num_heads * head_size, num_heads * head_size],
dim=-1,
)
ref_query, ref_key = rope.forward_native(positions, query, key)
query, key = torch.ops._C.rotary_embedding(
positions,
query,
key,
rope.head_size,
rope.cos_sin_cache,
rope.is_neox_style,
)
# Compare the results.
torch.testing.assert_close(query.view(ref_query.size()),
ref_query,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)
torch.testing.assert_close(key.view(ref_key.size()),
ref_key,
atol=DEFAULT_ATOL,
rtol=DEFAULT_RTOL)

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tests/e2e/singlecard/ops/test_vocabparallelembedding.py Normal file
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from typing import Tuple
import pytest
import torch
import torch_npu # noqa: F401
import vllm_ascend.platform # noqa: F401
# Test parameters
DTYPES = [torch.int32]
#SHAPES = [(100,), (5, 20), (3, 4, 5)] # Various tensor shapes
#SHAPES = [(3, 4, 8), (3, 4, 5)] # Various tensor shapes
SHAPES = [(3, 4, 3)]
DEVICES = [f"npu:{0}"]
SEEDS = [0]
def get_masked_input_and_mask_ref(
input_: torch.Tensor, org_vocab_start_index: int,
org_vocab_end_index: int, num_org_vocab_padding: int,
added_vocab_start_index: int,
added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""Reference implementation for verification"""
org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ <
org_vocab_end_index)
added_vocab_mask = (input_ >= added_vocab_start_index) & (
input_ < added_vocab_end_index)
added_offset = added_vocab_start_index - (
org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding
valid_offset = (org_vocab_start_index *
org_vocab_mask) + (added_offset * added_vocab_mask)
vocab_mask = org_vocab_mask | added_vocab_mask
masked_input = vocab_mask * (input_ - valid_offset)
return masked_input, ~vocab_mask
@pytest.mark.parametrize("shape", SHAPES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("seed", SEEDS)
@torch.inference_mode()
def test_get_masked_input_and_mask(
shape: Tuple[int, ...],
dtype: torch.dtype,
device: str,
seed: int,
) -> None:
# Set random seed
torch.manual_seed(seed)
torch.set_default_device(device)
# Generate random input tensor
input_tensor = torch.randint(0, 1000, shape, dtype=dtype)
# Test parameters
test_case = {
"org_start": 100,
"org_end": 200,
"padding": 0,
"added_start": 300,
"added_end": 400,
}
# Get reference result
ref_masked_input, ref_mask = get_masked_input_and_mask_ref(
input_tensor, test_case["org_start"], test_case["org_end"],
test_case["padding"], test_case["added_start"], test_case["added_end"])
# Get custom op result
print("input_tensor:", input_tensor)
custom_masked_input, custom_mask = torch.ops._C.get_masked_input_and_mask(
input_tensor, test_case["org_start"], test_case["org_end"],
test_case["padding"], test_case["added_start"], test_case["added_end"])
ref_masked_input = ref_masked_input.to(dtype)
print("custom_masked_input:", custom_masked_input)
print("ref_masked_input:", ref_masked_input)
print("custom_mask:", custom_mask)
print("ref_mask:", ref_mask)
# Compare results
torch.testing.assert_close(
custom_masked_input,
ref_masked_input,
rtol=1e-5,
atol=1e-5,
msg=f"Masked input mismatch for case: {test_case}")
torch.testing.assert_close(custom_mask,
ref_mask,
rtol=1e-5,
atol=1e-5,
msg=f"Mask mismatch for case: {test_case}")