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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>
318 lines
8.4 KiB
Python
318 lines
8.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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This script is mainly used to test whether trtion kernels can run normally
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under different conditions, including various batches, numbers of LoRA , and
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maximum ranks.
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"""
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from threading import Lock
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import pytest
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import torch
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# Enable custom op register
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import vllm.lora.ops.triton_ops # noqa: F401
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from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
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bgmv_shrink, sgmv_expand,
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sgmv_expand_slice, sgmv_shrink)
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from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
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from vllm.platforms import current_platform
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from .utils import (assert_close, generate_data,
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generate_data_for_expand_nslices,
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generate_data_for_nslices)
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HIDDEN_SIZES = [2049]
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BATCHES = [1, 4, 16, 32]
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NUM_LORA = [1, 8, 32, 128]
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DTYPES = [torch.float16, torch.bfloat16]
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MAX_RANKS = [1, 4, 8, 16, 32, 64, 128, 256]
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SCALES = [0.5]
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SEED = [0]
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DEVICES = [f"cuda:{0}"]
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_dict_lock = Lock()
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@pytest.mark.parametrize("batches", BATCHES)
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@pytest.mark.parametrize("num_loras", NUM_LORA)
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@pytest.mark.parametrize("rank", MAX_RANKS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("scaling", SCALES)
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@pytest.mark.parametrize("nslices", [1, 2, 3])
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("op_type", ["shrink", "expand"])
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@pytest.mark.parametrize("seed", SEED)
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@pytest.mark.parametrize("device", DEVICES)
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def test_punica_sgmv(
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batches: int,
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num_loras: int,
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rank: int,
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hidden_size: int,
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scaling: float,
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nslices: int,
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dtype: torch.dtype,
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op_type: str,
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seed: int,
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device: str,
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):
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torch.set_default_device(device)
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current_platform.seed_everything(seed)
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seq_length = 128
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(
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inputs_tensor,
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lora_weights_lst,
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our_out_tensor,
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ref_out_tensor,
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b_seq_start_loc,
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lora_indices_tensor,
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seq_len_tensor,
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indices,
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) = generate_data_for_nslices(
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batches,
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hidden_size,
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num_loras,
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rank,
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seq_length,
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nslices,
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dtype,
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op_type,
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device,
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)
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max_seq_length = seq_len_tensor.max()
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token_nums = seq_len_tensor.sum().item()
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if isinstance(max_seq_length, tuple):
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max_seq_length = max_seq_length[0].item()
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else:
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max_seq_length = max_seq_length.item()
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if op_type == "shrink":
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# Preventing cache error pointer.
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with _dict_lock:
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_LORA_A_PTR_DICT.clear()
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torch.ops.vllm.sgmv_shrink(
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inputs_tensor,
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lora_weights_lst,
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our_out_tensor,
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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batches,
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max_seq_length,
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token_nums,
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scaling,
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)
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for index in range(nslices):
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sgmv_shrink(
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inputs_tensor,
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lora_weights_lst[index],
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ref_out_tensor[index],
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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batches,
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max_seq_length,
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token_nums,
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scaling,
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)
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else:
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with _dict_lock:
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_LORA_B_PTR_DICT.clear()
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torch.ops.vllm.sgmv_expand(
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inputs_tensor,
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lora_weights_lst,
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our_out_tensor,
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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batches,
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max_seq_length,
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token_nums,
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offset_start=0,
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add_inputs=True,
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)
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slice_offset = 0
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if nslices == 1:
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# Verify the torch's sgmv_expand op
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sgmv_expand(
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inputs_tensor[0],
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lora_weights_lst[0],
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ref_out_tensor,
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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batches,
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max_seq_length,
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token_nums,
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add_inputs=True,
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)
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else:
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for index in range(nslices):
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lora_weights = lora_weights_lst[index]
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sgmv_expand_slice(
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inputs_tensor[index],
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lora_weights,
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ref_out_tensor,
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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batches,
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max_seq_length,
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token_nums,
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slice_offset,
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hidden_size,
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add_inputs=True,
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)
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slice_offset += hidden_size
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assert_close(our_out_tensor, ref_out_tensor)
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@pytest.mark.parametrize("batches", BATCHES)
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@pytest.mark.parametrize("num_loras", NUM_LORA)
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@pytest.mark.parametrize("rank", MAX_RANKS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("scaling", SCALES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("op_type", ["shrink", "expand"])
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@pytest.mark.parametrize("seed", SEED)
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@pytest.mark.parametrize("device", DEVICES)
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def test_punica_bgmv(
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batches: int,
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num_loras: int,
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rank: int,
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hidden_size: int,
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scaling: float,
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dtype: torch.dtype,
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op_type: str,
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seed: int,
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device: str,
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):
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torch.set_default_device(device)
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current_platform.seed_everything(seed)
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seq_length = 1
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(
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inputs_tensor,
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lora_weights,
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our_out_tensor,
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ref_out_tensor,
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b_seq_start_loc,
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lora_indices_tensor,
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seq_len_tensor,
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indices,
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) = generate_data(
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batches,
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hidden_size,
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num_loras,
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rank,
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seq_length,
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dtype,
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op_type,
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device,
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)
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if op_type == "shrink":
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torch.ops.vllm.bgmv_shrink(
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inputs_tensor,
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lora_weights,
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our_out_tensor,
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indices,
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scaling,
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)
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bgmv_shrink(
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inputs_tensor,
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lora_weights,
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ref_out_tensor,
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indices,
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scaling,
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)
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else:
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torch.ops.vllm.bgmv_expand(
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inputs_tensor,
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lora_weights,
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our_out_tensor,
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indices,
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add_inputs=True,
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)
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bgmv_expand(
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inputs_tensor,
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lora_weights,
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ref_out_tensor,
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indices,
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add_inputs=True,
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)
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if op_type == "shrink":
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ref_out_tensor = ref_out_tensor.to(torch.float32)
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assert_close(our_out_tensor, ref_out_tensor)
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@pytest.mark.parametrize("batches", BATCHES)
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@pytest.mark.parametrize("num_loras", NUM_LORA)
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@pytest.mark.parametrize("rank", MAX_RANKS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("nslices", [2, 3])
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEED)
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@pytest.mark.parametrize("device", DEVICES)
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def test_punica_bgmv_expand_nslices(
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batches: int,
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num_loras: int,
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rank: int,
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hidden_size: int,
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nslices: int,
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dtype: torch.dtype,
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seed: int,
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device: str,
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):
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torch.set_default_device(device)
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current_platform.seed_everything(seed)
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seq_length = 1
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(
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inputs_tensor,
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lora_weights_lst,
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our_outputs,
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ref_outputs,
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b_seq_start_loc,
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lora_indices_tensor,
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seq_len_tensor,
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indices,
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) = generate_data_for_expand_nslices(
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batches,
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hidden_size,
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num_loras,
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rank,
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seq_length,
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dtype,
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nslices,
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device,
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)
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slice_offset = 0
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for index in range(nslices):
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lora_weights = lora_weights_lst[index]
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torch.ops.vllm.bgmv_expand_slice(
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inputs_tensor,
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lora_weights,
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our_outputs,
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indices,
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slice_offset,
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slice_size=hidden_size,
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add_inputs=True,
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)
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bgmv_expand_slice(
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inputs_tensor,
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lora_weights,
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ref_outputs,
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indices,
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slice_offset,
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slice_size=hidden_size,
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add_inputs=True,
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)
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slice_offset += hidden_size
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assert_close(our_outputs, ref_outputs)
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