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vllm/tests/lora/test_punica_ops_sizes.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628) 
- **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>
2025-02-02 11:58:18 -08:00

402 lines
9.4 KiB
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# SPDX-License-Identifier: Apache-2.0
"""
This script is mainly used to tests various hidden_sizes. We have collected the
hidden_sizes included in the LoRA models currently supported by vLLM. It tests
whether the corresponding Triton kernel can run normally when tensor parallelism
is set to [1, 2, 4, 8, 16, 32, 64].
"""
from threading import Lock
import pytest
import torch
import vllm.lora.ops.triton_ops # noqa: F401
from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
bgmv_shrink, sgmv_expand,
sgmv_expand_slice, sgmv_shrink)
from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
from vllm.platforms import current_platform
from .utils import (assert_close, generate_data,
generate_data_for_expand_nslices,
generate_data_for_nslices)
HIDDEN_SIZES = [
128,
256,
512,
896,
1024,
1152,
1216,
1280,
1536,
1664,
2048,
2240,
2304,
2368,
2432,
2560,
2752,
3072,
3328,
3456,
3584,
3712,
4096,
4480,
4608,
4736,
4864,
5120,
5504,
5632,
5888,
6144,
6400,
6848,
6912,
7168,
7424,
8192,
8960,
9216,
9472,
10240,
11008,
11264,
13824,
14336,
14784,
14848,
15360,
18944,
22016,
22528,
24576,
27392,
27648,
29568,
29696,
32000,
32256,
32512,
32768,
33024,
36864,
43264,
49152,
49408,
60544,
60672,
64000,
64256,
102400,
102656,
128000,
128256,
]
#The size of TP
divisibility = [1, 2, 8, 16, 64]
all_hidden_size = []
for div in divisibility:
for hidden_size in HIDDEN_SIZES:
all_hidden_size.append(hidden_size // div)
HIDDEN_SIZES = list(set(all_hidden_size))
BATCHES = [4]
NUM_LORA = [4]
DTYPES = [torch.float16, torch.bfloat16]
MAX_RANKS = [32]
SCALES = [0.5]
SEED = [0]
DEVICES = [f"cuda:{0}"]
_dict_lock = Lock()
@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("scaling", SCALES)
@pytest.mark.parametrize("nslices", [1, 2, 3])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", DEVICES)
def test_punica_sgmv(
batches: int,
num_loras: int,
rank: int,
hidden_size: int,
scaling: float,
nslices: int,
dtype: torch.dtype,
op_type: str,
seed: int,
device: str,
):
torch.set_default_device(device)
current_platform.seed_everything(seed)
seq_length = 128
(
inputs_tensor,
lora_weights_lst,
our_out_tensor,
ref_out_tensor,
b_seq_start_loc,
lora_indices_tensor,
seq_len_tensor,
indices,
) = generate_data_for_nslices(
batches,
hidden_size,
num_loras,
rank,
seq_length,
nslices,
dtype,
op_type,
device,
)
max_seq_length = seq_len_tensor.max()
token_nums = seq_len_tensor.sum().item()
if isinstance(max_seq_length, tuple):
max_seq_length = max_seq_length[0].item()
else:
max_seq_length = max_seq_length.item()
if op_type == "shrink":
# Preventing cache error pointer.
with _dict_lock:
_LORA_A_PTR_DICT.clear()
torch.ops.vllm.sgmv_shrink(
inputs_tensor,
lora_weights_lst,
our_out_tensor,
b_seq_start_loc,
seq_len_tensor,
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
scaling,
)
for index in range(nslices):
sgmv_shrink(
inputs_tensor,
lora_weights_lst[index],
ref_out_tensor[index],
b_seq_start_loc,
seq_len_tensor,
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
scaling,
)
else:
with _dict_lock:
_LORA_B_PTR_DICT.clear()
torch.ops.vllm.sgmv_expand(
inputs_tensor,
lora_weights_lst,
our_out_tensor,
b_seq_start_loc,
seq_len_tensor,
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
offset_start=0,
add_inputs=True,
)
if nslices == 1:
# Verify the torch's sgmv_expand op
sgmv_expand(
inputs_tensor[0],
lora_weights_lst[0],
ref_out_tensor,
b_seq_start_loc,
seq_len_tensor,
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
add_inputs=True,
)
else:
slice_offset = 0
for index in range(nslices):
lora_weights = lora_weights_lst[index]
sgmv_expand_slice(
inputs_tensor[index],
lora_weights,
ref_out_tensor,
b_seq_start_loc,
seq_len_tensor,
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
slice_offset,
hidden_size,
add_inputs=True,
)
slice_offset += hidden_size
assert_close(our_out_tensor, ref_out_tensor)
@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("scaling", SCALES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", DEVICES)
def test_punica_bgmv(
batches: int,
num_loras: int,
rank: int,
hidden_size: int,
scaling: float,
dtype: torch.dtype,
op_type: str,
seed: int,
device: str,
):
torch.set_default_device(device)
current_platform.seed_everything(seed)
seq_length = 1
(
inputs_tensor,
lora_weights,
our_out_tensor,
ref_out_tensor,
b_seq_start_loc,
lora_indices_tensor,
seq_len_tensor,
indices,
) = generate_data(
batches,
hidden_size,
num_loras,
rank,
seq_length,
dtype,
op_type,
device,
)
if op_type == "shrink":
torch.ops.vllm.bgmv_shrink(
inputs_tensor,
lora_weights,
our_out_tensor,
indices,
scaling,
)
bgmv_shrink(
inputs_tensor,
lora_weights,
ref_out_tensor,
indices,
scaling,
)
else:
torch.ops.vllm.bgmv_expand(
inputs_tensor,
lora_weights,
our_out_tensor,
indices,
add_inputs=True,
)
bgmv_expand(
inputs_tensor,
lora_weights,
ref_out_tensor,
indices,
add_inputs=True,
)
if op_type == "shrink":
ref_out_tensor = ref_out_tensor.to(torch.float32)
assert_close(our_out_tensor, ref_out_tensor)
@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("nslices", [2, 3])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", DEVICES)
def test_punica_bgmv_expand_nslices(
batches: int,
num_loras: int,
rank: int,
hidden_size: int,
nslices: int,
dtype: torch.dtype,
seed: int,
device: str,
):
torch.set_default_device(device)
current_platform.seed_everything(seed)
seq_length = 1
(
inputs_tensor,
lora_weights_lst,
our_outputs,
ref_outputs,
b_seq_start_loc,
lora_indices_tensor,
seq_len_tensor,
indices,
) = generate_data_for_expand_nslices(
batches,
hidden_size,
num_loras,
rank,
seq_length,
dtype,
nslices,
device,
)
slice_offset = 0
for index in range(nslices):
lora_weights = lora_weights_lst[index]
torch.ops.vllm.bgmv_expand_slice(
inputs_tensor,
lora_weights,
our_outputs,
indices,
slice_offset,
slice_size=hidden_size,
add_inputs=True,
)
bgmv_expand_slice(
inputs_tensor,
lora_weights,
ref_outputs,
indices,
slice_offset,
slice_size=hidden_size,
add_inputs=True,
)
slice_offset += hidden_size
assert_close(our_outputs, ref_outputs)
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