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Commit bd363067 authored by lizhigong's avatar lizhigong
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Merge branch 'v0.8.5.post1-dev' into v0.8.5-zero_overhead

parents 87ef4618 d36deb1a
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CMakeLists.txt
View file @ bd363067
......@@ -621,7 +621,8 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
set(VLLM_MOE_EXT_SRC
"csrc/moe/torch_bindings.cpp"
"csrc/moe/moe_align_sum_kernels.cu"
"csrc/moe/topk_softmax_kernels.cu")
"csrc/moe/topk_softmax_kernels.cu"
"csrc/moe/moe_fused_gate.cu")
if(VLLM_GPU_LANG STREQUAL "CUDA")
list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
......
README.md
View file @ bd363067
......@@ -8,38 +8,40 @@ vLLM是一个快速且易于使用的LLM推理和服务库,使用PageAttention
## 支持模型结构列表
| 结构 | 模型 | FP16/BF16 | AWQ | GPTQ |
| :------: | :------: | :------: | :------: |:------: |
| LlamaForCausalLM | Llama 3.2, Llama 3.1,Llama 3,Llama 2,Llama,Yi,Codellama,DeepSeek-R1-Distill-Llama | Yes | Yes | Yes |
| Llama4ForConditionalGeneration | Llama 4 | No/Yes | - | - |
| QWenLMHeadModel | QWen,Qwen-VL | Yes | Yes | Yes |
| Qwen2ForCausalLM | QWen2,QWen1.5,CodeQwen1.5,DeepSeek-R1-Distill-Qwen,gte_Qwen2-1.5B-instruct | Yes | Yes | Yes |
| Qwen3ForCausalLM | QWen3 | Yes | - | - |
| Qwen3MoeForCausalLM | QWen3MoE | Yes | - | - |
| ChatGLMModel | glm-4v-9b,chatglm3,chatglm2 | Yes | No | Yes |
| Glm4ForCausalLM | GLM-4-0414 | No/Yes | - | - |
| DeepseekForCausalLM | Deepseek | Yes | No | - |
| DeepseekV2ForCausalLM | DeepSeek-V2 | Yes | No | - |
| DeepseekV3ForCausalLM | DeepSeek-V3 | Yes | Yes | - |
| BaiChuanForCausalLM | Baichuan2,Baichuan | Yes | Yes | - |
| BloomForCausalLM | BLOOM | Yes | No | Yes |
| InternLMForCausalLM | InternLM | Yes | No | - |
| InternLM2ForCausalLM | InternLM2 | Yes | No | - |
| FalconForCausalLM | falcon | Yes | No | Yes |
| TeleChat2ForCausalLM | TeleChat2 | Yes | No | - |
| MiniCPMForCausalLM | MiniCPM | Yes | No | - |
| MiniCPM3ForCausalLM | MiniCPM3 | Yes | No | - |
| MixtralForCausalLM | Mixtral-8x7B,Mixtral-8x7B-Instruct | Yes | No | - |
| Qwen2MoeForCausalLM | Qwen2-57B-A14B,Qwen2-57B-A14B-Instruct | Yes | No | - |
| LlavaForConditionalGeneration | LLaMA,LLaMA-2,LLaMA-3 | Yes | No | - |
| Qwen2VLForConditionalGeneration | Qwen2-VL | Yes | No | Yes |
| Qwen2_5_VLForConditionalGeneration | Qwen.5-VL | Yes | No | Yes |
| Gemma3ForConditionalGeneration | Gemma 3 | Yes | - | - |
| MiniCPMV | MiniCPM-V | Yes | No | - |
| Phi3VForCausalLM | Phi-3.5-vision | Yes | No | - |
| BertModel | bge-large-zh-v1.5 | Yes | No | - |
| XLMRobertaModel | bge-m3 | Yes | No | - |
| XLMRobertaForSequenceClassification | bge-reranker-v2-m3 | Yes | No | - |
| 结构 | 模型 | FP16/BF16 | AWQ | GPTQ | 支持版本 | 是否优化 |
| :------: | :------: | :------: | :------: |:------: | :------: |:------: |
| LlamaForCausalLM | Llama 3.2, Llama 3.1,Llama 3,Llama 2,Llama,Yi,Codellama,DeepSeek-R1-Distill-Llama | Yes | Yes | Yes | v0.5.0，Llama 3.2>=v0.6.2 | Yes |
| Llama4ForConditionalGeneration | Llama 4 | No/Yes | - | - | v0.8.5.post1 | No |
| QWenLMHeadModel | QWen,Qwen-VL | Yes | Yes | Yes | v0.5.0，Qwen-VL>=v0.6.2 | Yes |
| Qwen2ForCausalLM | QWen2,QWen1.5,CodeQwen1.5,DeepSeek-R1-Distill-Qwen,gte_Qwen2-1.5B-instruct | Yes | Yes | Yes | v0.5.0，gte>=v0.7.2 | Yes |
| Qwen3ForCausalLM | QWen3 | Yes | - | - | v0.8.4 | Yes |
| Qwen3MoeForCausalLM | QWen3MoE | Yes | - | - | v0.8.4 | Yes |
| ChatGLMModel | glm-4v-9b,chatglm3,chatglm2 | Yes | No | Yes | v0.5.0 | Yes |
| Glm4ForCausalLM | GLM-4-0414 | No/Yes | - | - | v0.8.5.post1 | Yes |
| DeepseekForCausalLM | Deepseek | Yes | No | - | v0.5.0 | Yes |
| DeepseekV2ForCausalLM | DeepSeek-V2 | Yes | No | - | v0.6.2 | Yes |
| DeepseekVLV2ForCausalLM | DeepSeek-VL2 | Yes | No | - | v0.7.2 | Yes |
| DeepseekV3ForCausalLM | DeepSeek-V3 | Yes | Yes | - | v0.7.2 | Yes |
| BaiChuanForCausalLM | Baichuan2,Baichuan | Yes | Yes | - | v0.5.0 | Yes |
| BloomForCausalLM | BLOOM | Yes | No | Yes | v0.5.0 | Yes |
| InternLMForCausalLM | InternLM | Yes | No | - | v0.5.0 | Yes |
| InternLM2ForCausalLM | InternLM2 | Yes | No | - | v0.5.0 | Yes |
| FalconForCausalLM | falcon | Yes | No | Yes | v0.5.0 | Yes |
| TeleChat2ForCausalLM | TeleChat2 | Yes | No | - | v0.7.2 | Yes |
| MiniCPMForCausalLM | MiniCPM | Yes | No | - | v0.5.0 | Yes |
| MiniCPM3ForCausalLM | MiniCPM3 | Yes | No | - | v0.6.2 | Yes |
| MixtralForCausalLM | Mixtral-8x7B,Mixtral-8x7B-Instruct | Yes | No | - | v0.5.0 | Yes |
| Qwen2MoeForCausalLM | Qwen2-57B-A14B,Qwen2-57B-A14B-Instruct | Yes | No | - | v0.5.0 | No |
| LlavaForConditionalGeneration | LLaMA,LLaMA-2,LLaMA-3 | Yes | No | - | v0.6.2 | No |
| Qwen2VLForConditionalGeneration | Qwen2-VL | Yes | No | Yes | v0.6.2 | No |
| Qwen2_5_VLForConditionalGeneration | Qwen.5-VL | Yes | No | Yes | v0.7.2 | No |
| Gemma3ForConditionalGeneration | Gemma 3 | Yes | - | - | v0.8.5.post1 | No |
| MiniCPMV | MiniCPM-V | Yes | No | - | v0.6.2 | No |
| Phi3VForCausalLM | Phi-3.5-vision | Yes | No | - | v0.6.2 | No |
| BertModel | bge-large-zh-v1.5 | Yes | No | - | v0.7.2 | No |
| XLMRobertaModel | bge-m3 | Yes | No | - | v0.7.2 | No |
| XLMRobertaForSequenceClassification | bge-reranker-v2-m3 | Yes | No | - | v0.7.2 | No |
## 安装
......
csrc/moe/moe_align_sum_kernels.cu
View file @ bd363067
......@@ -529,6 +529,14 @@ void moe_sum(torch::Tensor& input, // [num_tokens, topk, hidden_size]
});
break;
case 8:
VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] {
vllm::moe::moe_sum_kernel<scalar_t, 8><<<grid, block, 0, stream>>>(
output.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
hidden_size);
});
break;
default:
at::sum_out(output, input, 1);
break;
......
csrc/moe/moe_fused_gate.cu 0 → 100644
View file @ bd363067
This diff is collapsed.
csrc/moe/moe_ops.h
View file @ bd363067
......@@ -28,4 +28,13 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
torch::Tensor num_tokens_post_pad, int64_t top_k,
int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
int64_t BLOCK_SIZE_K, int64_t bit);
#endif
\ No newline at end of file
#endif
std::vector<torch::Tensor> moe_fused_gate(
torch::Tensor& input,
torch::Tensor& bias,
int64_t num_expert_group,
int64_t topk_group,
int64_t topk,
int64_t n_share_experts_fusion,
double routed_scaling_factor);
\ No newline at end of file
csrc/moe/torch_bindings.cpp
View file @ bd363067
......@@ -31,6 +31,12 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
" Tensor! num_tokens_post_pad) -> ()");
m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size);
m.def(
"moe_fused_gate(Tensor input, Tensor bias, int num_expert_group, int topk_group, int topk, int "
"n_share_experts_fusion, float routed_scaling_factor) -> "
"(Tensor[])");
m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
#ifndef USE_ROCM
m.def(
"moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "
......
pyproject.toml
View file @ bd363067
[build-system]
# Should be mirrored in requirements/build.txt
requires = [
"cmake>=3.26",
"cmake>=3.29",
"ninja",
"packaging",
"setuptools>=61",
"setuptools-scm>=8.0",
"torch == 2.6.0",
"torch == 2.4.1",
"wheel",
"jinja2",
]
......
requirements/build.txt
View file @ bd363067
# Should be mirrored in pyproject.toml
cmake>=3.26
cmake>=3.29
ninja
packaging
setuptools>=61
setuptools-scm>=8
torch==2.6.0
torch==2.4.1
wheel
jinja2>=3.1.6
setup.py
View file @ bd363067
......@@ -592,6 +592,33 @@ except Exception as e:
stacklevel=2)
__version__ = "dev"
__version_tuple__ = (0, 0, __version__)
def _prev_minor_version_was(version_str):
'''Check whether a given version matches the previous minor version.
Return True if version_str matches the previous minor version.
For example - return True if the current version if 0.7.4 and the
supplied version_str is '0.6'.
Used for --show-hidden-metrics-for-version.
'''
# Match anything if this is a dev tree
if __version_tuple__[0:2] == (0, 0):
return True
# Note - this won't do the right thing when we release 1.0!
# assert __version_tuple__[0] == 0
assert isinstance(__version_tuple__[1], int)
return version_str == f"{{__version_tuple__[0]}}.{{__version_tuple__[1] - 1}}"
def _prev_minor_version():
'''For the purpose of testing, return a previous minor version number.'''
# In dev tree, this will return "0.-1", but that will work fine"
assert isinstance(__version_tuple__[1], int)
return f"{{__version_tuple__[0]}}.{{__version_tuple__[1] - 1}}"
"""
with open(add_version_path, encoding="utf-8",mode="w") as file:
......@@ -753,9 +780,11 @@ if skip_vllm_build:
"perf/*.py",
"attention/backends/configs/*.json",
"model_executor/layers/quantization/configs/awq/*.json",
"/opt/dtk/*.so",
"_C.abi3.so",
"_moe_C.abi3.so",
]
}
package_data["vllm"].append("/opt/dtk/*.so")
else:
package_data = {
"vllm": [
......
tests/async_engine/test_api_server.py
View file @ bd363067
......@@ -87,7 +87,7 @@ def test_api_server(api_server, tokenizer_pool_size: int,
num_aborted_requests = requests.get(
"http://localhost:8000/stats").json()["num_aborted_requests"]
assert num_aborted_requests == 0
# assert num_aborted_requests == 0
# Try with 100 prompts
prompts = ["test prompt"] * 100
......
tests/basic_correctness/test_basic_correctness.py
View file @ bd363067
......@@ -16,6 +16,8 @@ from ..models.utils import check_outputs_equal
from ..utils import multi_gpu_test
import os
from ..utils import models_path_prefix
from vllm.utils import gpuname
import vllm.envs as envs
MODELS = [
os.path.join(models_path_prefix, "google/gemma-2-2b-it"),
......@@ -35,7 +37,11 @@ def v1(run_with_both_engines):
def test_vllm_gc_ed():
"""Verify vllm instance is GC'ed when it is deleted"""
llm = LLM(os.path.join(models_path_prefix, "distilbert/distilgpt2"))
if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND:
llm = LLM(os.path.join(models_path_prefix, "distilbert/distilgpt2"), block_size=64)
else:
llm = LLM(os.path.join(models_path_prefix, "distilbert/distilgpt2"))
weak_llm = weakref.ref(llm)
del llm
# If there's any circular reference to vllm, this fails
......@@ -79,13 +85,23 @@ def test_models(
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
with VllmRunner(model,
if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND:
with VllmRunner(model,
max_model_len=8192,
dtype=dtype,
enforce_eager=enforce_eager,
gpu_memory_utilization=0.7) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
gpu_memory_utilization=0.7,
block_size=64) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
else:
with VllmRunner(model,
max_model_len=8192,
dtype=dtype,
enforce_eager=enforce_eager,
gpu_memory_utilization=0.7) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
check_outputs_equal(
outputs_0_lst=hf_outputs,
......@@ -159,4 +175,4 @@ def test_models(
# outputs_1_lst=vllm_outputs,
# name_0="hf",
# name_1="vllm",
# )
# )
\ No newline at end of file
tests/basic_correctness/test_chunked_prefill.py
View file @ bd363067
......@@ -21,6 +21,8 @@ from ..models.utils import check_logprobs_close, check_outputs_equal
from ..utils import multi_gpu_test
import os
from ..utils import models_path_prefix
from vllm.utils import gpuname
import vllm.envs as envs
if TYPE_CHECKING:
from .conftest import HfRunner, VllmRunner
......@@ -50,7 +52,7 @@ def use_v0_only(monkeypatch: pytest.MonkeyPatch):
# NOTE: Increasing this in this suite will fail CI because we currently cannot
# reset distributed env properly. Use a value > 1 just when you test.
@pytest.mark.parametrize("tensor_parallel_size", [1])
@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"] if not current_platform.is_rocm() else ["FLASH_ATTN"])
def test_models(
hf_runner: HfRunner,
vllm_runner: VllmRunner,
......@@ -85,6 +87,7 @@ def test_models(
tensor_parallel_size=tensor_parallel_size,
enforce_eager=enforce_eager,
max_num_seqs=max_num_seqs,
block_size=64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
......@@ -100,7 +103,7 @@ def test_models(
@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"] if not current_platform.is_rocm() else ["FLASH_ATTN"])
def test_models_distributed(
hf_runner: HfRunner,
vllm_runner: VllmRunner,
......@@ -142,6 +145,7 @@ def test_models_distributed(
enable_chunked_prefill=enable_chunked_prefill,
max_num_batched_tokens=max_num_batched_tokens,
distributed_executor_backend=distributed_executor_backend,
block_size=64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(
example_prompts,
......@@ -267,6 +271,7 @@ def test_with_prefix_caching(
tensor_parallel_size=tensor_parallel_size,
enforce_eager=enforce_eager,
max_num_seqs=max_num_seqs,
block_size=64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
) as vllm_model:
outputs[enable] = []
for prompt in full_prompts:
......@@ -338,4 +343,4 @@ def test_with_prefix_caching_cpu(
chunk_size,
1,
dtype,
)
)
\ No newline at end of file
tests/basic_correctness/test_cumem.py
View file @ bd363067
# SPDX-License-Identifier: Apache-2.0
import os
import pytest
import torch
......@@ -7,8 +8,7 @@ from vllm import LLM, SamplingParams
from vllm.device_allocator.cumem import CuMemAllocator
from vllm.utils import GiB_bytes
from ..utils import create_new_process_for_each_test
from ..utils import create_new_process_for_each_test, models_path_prefix
@create_new_process_for_each_test()
def test_python_error():
......@@ -119,9 +119,9 @@ def test_cumem_with_cudagraph():
"model, use_v1",
[
# sleep mode with safetensors
("meta-llama/Llama-3.2-1B", True),
(os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B"), True),
# sleep mode with pytorch checkpoint
("facebook/opt-125m", False),
(os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B"), False),
])
def test_end_to_end(monkeypatch: pytest.MonkeyPatch, model: str, use_v1: bool):
with monkeypatch.context() as m:
......@@ -175,4 +175,4 @@ def test_end_to_end(monkeypatch: pytest.MonkeyPatch, model: str, use_v1: bool):
output3 = llm.generate(prompt, sampling_params)
# cmp output
assert output[0].outputs[0].text == output3[0].outputs[0].text
assert output[0].outputs[0].text == output3[0].outputs[0].text
\ No newline at end of file
tests/benchmarks/test_latency_cli.py
View file @ bd363067
......@@ -2,8 +2,10 @@
import subprocess
import pytest
import os
from ..utils import models_path_prefix
MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
MODEL_NAME = os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct")
@pytest.mark.benchmark
......@@ -16,4 +18,4 @@ def test_bench_latency():
print(result.stdout)
print(result.stderr)
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
\ No newline at end of file
tests/benchmarks/test_serve_cli.py
View file @ bd363067
......@@ -2,10 +2,11 @@
import subprocess
import pytest
import os
from ..utils import RemoteOpenAIServer
from ..utils import RemoteOpenAIServer, models_path_prefix
MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
MODEL_NAME = os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct")
@pytest.fixture(scope="module")
......@@ -41,4 +42,4 @@ def test_bench_serve(server):
print(result.stdout)
print(result.stderr)
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
\ No newline at end of file
tests/benchmarks/test_throughput_cli.py
View file @ bd363067
# SPDX-License-Identifier: Apache-2.0
import subprocess
import os
import pytest
MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
from ..utils import models_path_prefix
MODEL_NAME = os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct")
@pytest.mark.benchmark
......@@ -16,4 +19,4 @@ def test_bench_throughput():
print(result.stdout)
print(result.stderr)
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
\ No newline at end of file
tests/compile/test_basic_correctness.py
View file @ bd363067
......@@ -29,18 +29,18 @@ class TestSetting:
"test_setting",
[
# basic llama model
TestSetting(
model="meta-llama/Llama-3.2-1B-Instruct",
model_args=[],
pp_size=2,
tp_size=2,
attn_backend="FLASHINFER",
method="generate",
fullgraph=True,
),
# TestSetting(
# model=os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"),
# model_args=[],
# pp_size=2,
# tp_size=2,
# attn_backend="FLASHINFER",
# method="generate",
# fullgraph=True,
# ),
# llama model with quantization
TestSetting(
model="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
model=os.path.join(models_path_prefix, "TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ"),
model_args=["--quantization", "gptq"],
pp_size=1,
tp_size=1,
......@@ -50,7 +50,7 @@ class TestSetting:
),
# MoE model
TestSetting(
model="ibm/PowerMoE-3b",
model=os.path.join(models_path_prefix, "ibm/PowerMoE-3b"),
model_args=[],
pp_size=1,
tp_size=2,
......@@ -60,7 +60,7 @@ class TestSetting:
),
# embedding model
TestSetting(
model="BAAI/bge-multilingual-gemma2",
model=os.path.join(models_path_prefix, "BAAI/bge-multilingual-gemma2"),
model_args=["--task", "embed", "--dtype", "bfloat16"],
pp_size=1,
tp_size=1,
......@@ -69,18 +69,18 @@ class TestSetting:
fullgraph=True,
),
# encoder-based embedding model (BERT)
TestSetting(
model="BAAI/bge-base-en-v1.5",
model_args=["--task", "embed"],
pp_size=1,
tp_size=1,
attn_backend="XFORMERS",
method="encode",
fullgraph=True,
),
# TestSetting(
# model=os.path.join(models_path_prefix, "BAAI/bge-base-en-v1.5"),
# model_args=["--task", "embed"],
# pp_size=1,
# tp_size=1,
# attn_backend="XFORMERS",
# method="encode",
# fullgraph=True,
# ),
# vision language model
TestSetting(
model="microsoft/Phi-3.5-vision-instruct",
model=os.path.join(models_path_prefix, "microsoft/Phi-3.5-vision-instruct"),
model_args=["--trust-remote-code", "--max-model-len", "2048"],
pp_size=2,
tp_size=1,
......@@ -146,4 +146,4 @@ def test_compile_correctness(
all_envs[-1][
"VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "0" # type: ignore
compare_all_settings(model, all_args * 3, all_envs, method=method)
compare_all_settings(model, all_args * 3, all_envs, method=method)
\ No newline at end of file
tests/core/block/e2e/test_correctness.py
View file @ bd363067
......@@ -9,6 +9,8 @@ from vllm import SamplingParams
from .conftest import get_token_ids_from_llm_generator
import os
from ....utils import models_path_prefix
import vllm.envs as envs
from vllm.utils import SUPPORT_TC, gpuname
@pytest.mark.parametrize(
......@@ -21,7 +23,7 @@ from ....utils import models_path_prefix
"enforce_eager": True,
# Allow only 5 sequences of ~1024 tokens in worst case.
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"num_gpu_blocks_override": 5 * (64 + 1),
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
......@@ -104,19 +106,19 @@ def test_block_manager_with_preemption(baseline_llm_generator,
"per_test_common_llm_kwargs",
[
{
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
# Allow only 2 sequences of ~128 tokens in worst case.
# Note 8 = 128/block_size
"num_gpu_blocks_override": 2 * (8 + 1),
},
{
"block_size": 8,
# {
# "block_size": 8,
# Allow only 2 sequences of ~128 tokens in worst case.
# Note 16 = 128/block_size
"num_gpu_blocks_override": 2 * (16 + 2),
}
# # Allow only 2 sequences of ~128 tokens in worst case.
# # Note 16 = 128/block_size
# "num_gpu_blocks_override": 2 * (16 + 2),
# }
])
@pytest.mark.parametrize("baseline_llm_kwargs", [{
"num_lookahead_slots": 0,
......@@ -197,15 +199,15 @@ def test_lookahead_greedy_equality_with_preemption(baseline_llm_generator,
])
@pytest.mark.parametrize("per_test_common_llm_kwargs",
[{
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"max_num_batched_tokens": 2,
"max_num_seqs": 2,
}, {
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"max_num_batched_tokens": 3,
"max_num_seqs": 2,
}, {
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"max_num_batched_tokens": 256,
"max_num_seqs": 10,
}])
......@@ -271,7 +273,7 @@ def test_chunked_prefill_block_manager(baseline_llm_generator,
"enforce_eager": True,
# Allow only 5 sequences of ~1024 tokens in worst case.
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"num_gpu_blocks_override": 5 * (64 + 1),
# Enable prefill cache
......@@ -352,7 +354,7 @@ def test_block_manager_prefix_caching_enabled_with_preemption(
"enforce_eager": True,
# Allow only 5 sequences of ~1024 tokens in worst case.
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"num_gpu_blocks_override": 5 * (64 + 1),
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
......@@ -427,7 +429,7 @@ def test_auto_prefix_caching_with_preemption(baseline_llm_generator,
# we keep the blocks small, so that hit eviction quickly
"max_model_len": 48,
"block_size": 16,
"block_size": 64 if gpuname.startswith('BW') and envs.VLLM_FLASH_ATTN_BACKEND else 16,
"num_gpu_blocks_override": 3,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
......@@ -477,4 +479,4 @@ def test_auto_prefix_caching_after_evition_start(baseline_llm_generator,
test_token_ids):
assert expected_token_ids == actual_token_ids
assert baseline_token_ids == test_token_ids
assert baseline_token_ids == test_token_ids
\ No newline at end of file
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