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Unverified Commit 4172235a authored by Woosuk Kwon's avatar Woosuk Kwon Committed by GitHub
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[V0 deprecation] Deprecate V0 Neuron backend (#21159) 


Signed-off-by: default avatarWoosuk Kwon <woosuk.kwon@berkeley.edu>
parent 848562bd
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.buildkite/release-pipeline.yaml
View file @ 4172235a
...@@ -149,19 +149,3 @@ steps: ...@@ -149,19 +149,3 @@ steps:
- "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)" - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
env: env:
DOCKER_BUILDKIT: "1" DOCKER_BUILDKIT: "1"
- block: "Build Neuron release image"
key: block-neuron-release-image-build
depends_on: ~
- label: "Build and publish Neuron release image"
depends_on: block-neuron-release-image-build
agents:
queue: neuron-postmerge
commands:
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
- "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest"
- "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
env:
DOCKER_BUILDKIT: "1"
.buildkite/scripts/hardware_ci/run-neuron-test.sh deleted 100644 → 0
View file @ 848562bd
#!/bin/bash
# This script build the Neuron docker image and run the API server inside the container.
# It serves a sanity check for compilation and basic model usage.
set -e
set -v
image_name="neuron/vllm-ci"
container_name="neuron_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
HF_CACHE="$(realpath ~)/huggingface"
mkdir -p "${HF_CACHE}"
HF_MOUNT="/root/.cache/huggingface"
HF_TOKEN=$(aws secretsmanager get-secret-value --secret-id "ci/vllm-neuron/hf-token" --region us-west-2 --query 'SecretString' --output text | jq -r .VLLM_NEURON_CI_HF_TOKEN)
NEURON_COMPILE_CACHE_URL="$(realpath ~)/neuron_compile_cache"
mkdir -p "${NEURON_COMPILE_CACHE_URL}"
NEURON_COMPILE_CACHE_MOUNT="/root/.cache/neuron_compile_cache"
# Try building the docker image
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws
# prune old image and containers to save disk space, and only once a day
# by using a timestamp file in tmp.
if [ -f /tmp/neuron-docker-build-timestamp ]; then
last_build=$(cat /tmp/neuron-docker-build-timestamp)
current_time=$(date +%s)
if [ $((current_time - last_build)) -gt 86400 ]; then
# Remove dangling images (those that are not tagged and not used by any container)
docker image prune -f
# Remove unused volumes / force the system prune for old images as well.
docker volume prune -f && docker system prune -f
echo "$current_time" > /tmp/neuron-docker-build-timestamp
fi
else
date "+%s" > /tmp/neuron-docker-build-timestamp
fi
docker build -t "${image_name}" -f docker/Dockerfile.neuron .
# Setup cleanup
remove_docker_container() {
docker image rm -f "${image_name}" || true;
}
trap remove_docker_container EXIT
# Run the image
docker run --rm -it --device=/dev/neuron0 --network bridge \
-v "${HF_CACHE}:${HF_MOUNT}" \
-e "HF_HOME=${HF_MOUNT}" \
-e "HF_TOKEN=${HF_TOKEN}" \
-v "${NEURON_COMPILE_CACHE_URL}:${NEURON_COMPILE_CACHE_MOUNT}" \
-e "NEURON_COMPILE_CACHE_URL=${NEURON_COMPILE_CACHE_MOUNT}" \
--name "${container_name}" \
${image_name} \
/bin/bash -c "
set -e; # Exit on first error
python3 /workspace/vllm/examples/offline_inference/neuron.py;
python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
for f in /workspace/vllm/tests/neuron/2_core/*.py; do
echo \"Running test file: \$f\";
python3 -m pytest \$f -v --capture=tee-sys;
done
"
\ No newline at end of file
MANIFEST.in
View file @ 4172235a
...@@ -2,7 +2,6 @@ include LICENSE ...@@ -2,7 +2,6 @@ include LICENSE
include requirements/common.txt include requirements/common.txt
include requirements/cuda.txt include requirements/cuda.txt
include requirements/rocm.txt include requirements/rocm.txt
include requirements/neuron.txt
include requirements/cpu.txt include requirements/cpu.txt
include CMakeLists.txt include CMakeLists.txt
......
docker/Dockerfile.neuron deleted 100644 → 0
View file @ 848562bd
# default base image
# https://gallery.ecr.aws/neuron/pytorch-inference-neuronx
ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.6.0-neuronx-py310-sdk2.23.0-ubuntu22.04"
FROM $BASE_IMAGE
RUN echo "Base image is $BASE_IMAGE"
# Install some basic utilities
RUN apt-get update && \
apt-get install -y \
git \
python3 \
python3-pip \
ffmpeg libsm6 libxext6 libgl1
### Mount Point ###
# When launching the container, mount the code directory to /workspace
ARG APP_MOUNT=/workspace
VOLUME [ ${APP_MOUNT} ]
WORKDIR ${APP_MOUNT}/vllm
RUN python3 -m pip install --upgrade pip
RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas tenacity
RUN python3 -m pip install neuronx-cc==2.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
RUN python3 -m pip install pytest
# uninstall transformers-neuronx package explicitly to avoid version conflict
RUN python3 -m pip uninstall -y transformers-neuronx
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
RUN python3 -m pip install -U \
'cmake>=3.26.1' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
-r requirements/neuron.txt
ENV VLLM_TARGET_DEVICE neuron
RUN --mount=type=bind,source=.git,target=.git \
pip install --no-build-isolation -v -e .
# install development dependencies (for testing)
RUN python3 -m pip install -e tests/vllm_test_utils
# install transformers-neuronx package as an optional dependencies (for V0)
# FIXME: `--no-deps` argument is temporarily added to resolve transformers package version conflict
RUN python3 -m pip install transformers-neuronx==0.13.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U --no-deps
RUN python3 -m pip install sentencepiece transformers==4.48.0 -U
# overwrite entrypoint to run bash script
RUN echo "import subprocess; import sys; subprocess.check_call(sys.argv[1:])" > /usr/local/bin/dockerd-entrypoint.py
CMD ["/bin/bash"]
examples/offline_inference/neuron.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm import LLM, SamplingParams
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
def main():
# Create an LLM.
llm = LLM(
model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
max_num_seqs=8,
# The max_model_len and block_size arguments are required to be same as
# max sequence length when targeting neuron device.
# Currently, this is a known limitation in continuous batching support
# in transformers-neuronx.
# TODO(liangfu): Support paged-attention in transformers-neuronx.
max_model_len=1024,
block_size=1024,
# ruff: noqa: E501
# The device can be automatically detected when AWS Neuron SDK is installed.
# The device argument can be either unspecified for automated detection,
# or explicitly assigned.
device="neuron",
tensor_parallel_size=2,
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
print("-" * 50)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
print("-" * 50)
if __name__ == "__main__":
main()
examples/offline_inference/neuron_eagle.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to run offline inference with an EAGLE speculative
decoding model on neuron. To use EAGLE speculative decoding, you must use
a draft model that is specifically fine-tuned for EAGLE speculation.
Additionally, to use EAGLE with NxD Inference, the draft model must include
the LM head weights from the target model. These weights are shared between
the draft and target model.
"""
from vllm import LLM, SamplingParams
# Sample prompts.
prompts = [
"What is annapurna labs?",
]
def main():
# Create a sampling params object.
sampling_params = SamplingParams(top_k=1, max_tokens=500, ignore_eos=True)
# Create an LLM.
llm = LLM(
model="/home/ubuntu/model_hf/Meta-Llama-3.1-70B-Instruct",
speculative_config={
"model": "/home/ubuntu/model_hf/Llama-3.1-70B-Instruct-EAGLE-Draft",
"num_speculative_tokens": 5,
"max_model_len": 2048,
},
max_num_seqs=4,
# The max_model_len and block_size arguments are required to be same as
# max sequence length when targeting neuron device.
# Currently, this is a known limitation in continuous batching support
# in neuronx-distributed-inference.
max_model_len=2048,
block_size=2048,
# The device can be automatically detected when AWS Neuron SDK is installed.
# The device argument can be either unspecified for automated detection,
# or explicitly assigned.
device="neuron",
tensor_parallel_size=32,
override_neuron_config={
"enable_eagle_speculation": True,
"enable_fused_speculation": True,
},
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, \n\n\n Generated text: {generated_text!r}")
if __name__ == "__main__":
main()
examples/offline_inference/neuron_int8_quantization.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from vllm import LLM, SamplingParams
# creates XLA hlo graphs for all the context length buckets.
os.environ["NEURON_CONTEXT_LENGTH_BUCKETS"] = "128,512,1024,2048"
# creates XLA hlo graphs for all the token gen buckets.
os.environ["NEURON_TOKEN_GEN_BUCKETS"] = "128,512,1024,2048"
# Quantizes neuron model weight to int8 ,
# The default config for quantization is int8 dtype.
os.environ["NEURON_QUANT_DTYPE"] = "s8"
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
def main():
# Create an LLM.
llm = LLM(
model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
max_num_seqs=8,
# The max_model_len and block_size arguments are required to be same as
# max sequence length when targeting neuron device.
# Currently, this is a known limitation in continuous batching support
# in transformers-neuronx.
# TODO(liangfu): Support paged-attention in transformers-neuronx.
max_model_len=2048,
block_size=2048,
# ruff: noqa: E501
# The device can be automatically detected when AWS Neuron SDK is installed.
# The device argument can be either unspecified for automated detection,
# or explicitly assigned.
device="neuron",
quantization="neuron_quant",
override_neuron_config={
"cast_logits_dtype": "bfloat16",
},
tensor_parallel_size=2,
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
print("-" * 50)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
print("-" * 50)
if __name__ == "__main__":
main()
examples/offline_inference/neuron_multimodal.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import requests
import torch
from neuronx_distributed_inference.models.mllama.utils import add_instruct
from PIL import Image
from vllm import LLM, SamplingParams, TextPrompt
def get_image(image_url):
image = Image.open(requests.get(image_url, stream=True).raw)
return image
# Model Inputs
PROMPTS = [
"What is in this image? Tell me a story",
"What is the recipe of mayonnaise in two sentences?",
"Describe this image",
"What is the capital of Italy famous for?",
]
IMAGES = [
get_image(
"https://images.pexels.com/photos/1108099/pexels-photo-1108099.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
),
None,
get_image(
"https://images.pexels.com/photos/1108099/pexels-photo-1108099.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
),
None,
]
SAMPLING_PARAMS = [
dict(top_k=1, temperature=1.0, top_p=1.0, max_tokens=16)
for _ in range(len(PROMPTS))
]
def get_VLLM_mllama_model_inputs(prompt, single_image, sampling_params):
# Prepare all inputs for mllama generation, including:
# 1. put text prompt into instruct chat template
# 2. compose single text and single image prompt into Vllm's prompt class
# 3. prepare sampling parameters
input_image = single_image
has_image = torch.tensor([1])
if isinstance(single_image, torch.Tensor) and single_image.numel() == 0:
has_image = torch.tensor([0])
instruct_prompt = add_instruct(prompt, has_image)
inputs = TextPrompt(prompt=instruct_prompt)
if input_image is not None:
inputs["multi_modal_data"] = {"image": input_image}
sampling_params = SamplingParams(**sampling_params)
return inputs, sampling_params
def print_outputs(outputs):
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
def main():
assert (
len(PROMPTS) == len(IMAGES) == len(SAMPLING_PARAMS)
), f"""Text, image prompts and sampling parameters should have the
same batch size; but got {len(PROMPTS)}, {len(IMAGES)},
and {len(SAMPLING_PARAMS)}"""
# Create an LLM.
llm = LLM(
model="meta-llama/Llama-3.2-11B-Vision-Instruct",
max_num_seqs=1,
max_model_len=4096,
block_size=4096,
device="neuron",
tensor_parallel_size=32,
override_neuron_config={
"sequence_parallel_enabled": False,
"skip_warmup": True,
"save_sharded_checkpoint": True,
"on_device_sampling_config": {
"global_topk": 1,
"dynamic": False,
"deterministic": False,
},
},
)
batched_inputs = []
batched_sample_params = []
for pmpt, img, params in zip(PROMPTS, IMAGES, SAMPLING_PARAMS):
inputs, sampling_params = get_VLLM_mllama_model_inputs(pmpt, img, params)
# test batch-size = 1
outputs = llm.generate(inputs, sampling_params)
print_outputs(outputs)
batched_inputs.append(inputs)
batched_sample_params.append(sampling_params)
# test batch-size = 4
outputs = llm.generate(batched_inputs, batched_sample_params)
print_outputs(outputs)
if __name__ == "__main__":
main()
examples/offline_inference/neuron_speculation.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to run offline inference with a speculative
decoding model on neuron.
"""
import os
from vllm import LLM, SamplingParams
# Sample prompts.
prompts = [
"Hello, I am a language model and I can help",
"The president of the United States is",
"The capital of France is",
]
def config_buckets():
"""Configure context length and token gen buckets."""
# creates XLA hlo graphs for all the context length buckets.
os.environ["NEURON_CONTEXT_LENGTH_BUCKETS"] = "128,512,1024,2048"
# creates XLA hlo graphs for all the token gen buckets.
os.environ["NEURON_TOKEN_GEN_BUCKETS"] = "128,512,1024,2048"
def initialize_llm():
"""Create an LLM with speculative decoding."""
return LLM(
model="openlm-research/open_llama_7b",
speculative_config={
"model": "openlm-research/open_llama_3b",
"num_speculative_tokens": 4,
"max_model_len": 2048,
},
max_num_seqs=4,
max_model_len=2048,
block_size=2048,
device="neuron",
tensor_parallel_size=32,
)
def process_requests(llm: LLM, sampling_params: SamplingParams):
"""Generate texts from prompts and print them."""
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
def main():
"""Main function that sets up the llm and processes prompts."""
config_buckets()
llm = initialize_llm()
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, top_k=1)
process_requests(llm, sampling_params)
if __name__ == "__main__":
main()
requirements/neuron.txt deleted 100644 → 0
View file @ 848562bd
# Common dependencies
-r common.txt
# Dependencies for Neuron devices
packaging>=24.2
setuptools>=77.0.3,<80.0.0
torch-neuronx >= 2.5.0
neuronx-cc>=2.0.0a0
torchvision # Required for Llama3.2 multimodal image preprocessing
setup.py
View file @ 4172235a
...@@ -413,8 +413,7 @@ def _no_device() -> bool: ...@@ -413,8 +413,7 @@ def _no_device() -> bool:
def _is_cuda() -> bool: def _is_cuda() -> bool:
has_cuda = torch.version.cuda is not None has_cuda = torch.version.cuda is not None
return (VLLM_TARGET_DEVICE == "cuda" and has_cuda return (VLLM_TARGET_DEVICE == "cuda" and has_cuda and not _is_tpu())
and not (_is_neuron() or _is_tpu()))
def _is_hip() -> bool: def _is_hip() -> bool:
...@@ -422,10 +421,6 @@ def _is_hip() -> bool: ...@@ -422,10 +421,6 @@ def _is_hip() -> bool:
or VLLM_TARGET_DEVICE == "rocm") and torch.version.hip is not None or VLLM_TARGET_DEVICE == "rocm") and torch.version.hip is not None
def _is_neuron() -> bool:
return VLLM_TARGET_DEVICE == "neuron"
def _is_tpu() -> bool: def _is_tpu() -> bool:
return VLLM_TARGET_DEVICE == "tpu" return VLLM_TARGET_DEVICE == "tpu"
...@@ -470,25 +465,6 @@ def get_rocm_version(): ...@@ -470,25 +465,6 @@ def get_rocm_version():
return None return None
def get_neuronxcc_version():
import sysconfig
site_dir = sysconfig.get_paths()["purelib"]
version_file = os.path.join(site_dir, "neuronxcc", "version",
"__init__.py")
# Check if the command was executed successfully
with open(version_file) as fp:
content = fp.read()
# Extract the version using a regular expression
match = re.search(r"__version__ = '(\S+)'", content)
if match:
# Return the version string
return match.group(1)
else:
raise RuntimeError("Could not find Neuron version in the output")
def get_nvcc_cuda_version() -> Version: def get_nvcc_cuda_version() -> Version:
"""Get the CUDA version from nvcc. """Get the CUDA version from nvcc.
...@@ -541,12 +517,6 @@ def get_vllm_version() -> str: ...@@ -541,12 +517,6 @@ def get_vllm_version() -> str:
rocm_version = get_rocm_version() or torch.version.hip rocm_version = get_rocm_version() or torch.version.hip
if rocm_version and rocm_version != MAIN_CUDA_VERSION: if rocm_version and rocm_version != MAIN_CUDA_VERSION:
version += f"{sep}rocm{rocm_version.replace('.', '')[:3]}" version += f"{sep}rocm{rocm_version.replace('.', '')[:3]}"
elif _is_neuron():
# Get the Neuron version
neuron_version = str(get_neuronxcc_version())
if neuron_version != MAIN_CUDA_VERSION:
neuron_version_str = neuron_version.replace(".", "")[:3]
version += f"{sep}neuron{neuron_version_str}"
elif _is_tpu(): elif _is_tpu():
version += f"{sep}tpu" version += f"{sep}tpu"
elif _is_cpu(): elif _is_cpu():
...@@ -591,8 +561,6 @@ def get_requirements() -> list[str]: ...@@ -591,8 +561,6 @@ def get_requirements() -> list[str]:
requirements = modified_requirements requirements = modified_requirements
elif _is_hip(): elif _is_hip():
requirements = _read_requirements("rocm.txt") requirements = _read_requirements("rocm.txt")
elif _is_neuron():
requirements = _read_requirements("neuron.txt")
elif _is_tpu(): elif _is_tpu():
requirements = _read_requirements("tpu.txt") requirements = _read_requirements("tpu.txt")
elif _is_cpu(): elif _is_cpu():
...@@ -601,7 +569,7 @@ def get_requirements() -> list[str]: ...@@ -601,7 +569,7 @@ def get_requirements() -> list[str]:
requirements = _read_requirements("xpu.txt") requirements = _read_requirements("xpu.txt")
else: else:
raise ValueError( raise ValueError(
"Unsupported platform, please use CUDA, ROCm, Neuron, or CPU.") "Unsupported platform, please use CUDA, ROCm, or CPU.")
return requirements return requirements
......
tests/engine/test_arg_utils.py
View file @ 4172235a
...@@ -287,15 +287,6 @@ def test_prefix_cache_default(): ...@@ -287,15 +287,6 @@ def test_prefix_cache_default():
}, },
"mm-processor-kwargs" "mm-processor-kwargs"
), ),
(
'{"cast_logits_dtype":"bfloat16","sequence_parallel_norm":true,"sequence_parallel_norm_threshold":2048}',
{
"cast_logits_dtype": "bfloat16",
"sequence_parallel_norm": True,
"sequence_parallel_norm_threshold": 2048,
},
"override-neuron-config"
),
]) ])
# yapf: enable # yapf: enable
def test_composite_arg_parser(arg, expected, option): def test_composite_arg_parser(arg, expected, option):
......
tests/neuron/1_core/test_activation.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
from vllm.model_executor.layers.activation import FastGELU, SiluAndMul
from vllm.platforms import current_platform
@pytest.mark.parametrize("activation", ["silu_and_mul", "gelu_fast"])
@pytest.mark.parametrize("num_tokens,d,dtype", [
(7, 512, torch.half),
(7, 512, torch.float),
(83, 512, torch.half),
])
@torch.inference_mode()
def test_act_and_mul(
activation: str,
num_tokens: int,
d: int,
dtype: torch.dtype,
) -> None:
import torch_xla.core.xla_model as xm
device = xm.xla_device()
current_platform.seed_everything(0)
torch.set_default_device("cpu")
x = torch.randn(num_tokens, 2 * d, dtype=dtype).to(device=device)
if activation == "silu_and_mul":
layer = SiluAndMul()
fn = layer.forward_native
elif activation == "gelu_fast":
layer = FastGELU()
fn = F.gelu
else:
raise NotImplementedError(
f"activation {activation} is not implemented.")
assert x.is_xla, "input tensor under testing is expected to be XLA tensor."
out = layer.to(device=device).forward_neuron(x)
ref_out = fn(x.cpu())
torch.testing.assert_close(out.cpu(), ref_out, atol=0.01, rtol=0.0)
tests/neuron/1_core/test_block_table.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import neuronxcc.nki.language as nl
import pytest
import torch
import torch.nn.functional as F
from neuronxcc import nki
from vllm.attention.ops.nki_flash_attn import (
load_block_tables, transform_block_tables_for_indirect_load)
def is_power_of_2(n):
return n > 0 and (n & (n - 1) == 0)
def nki_load_and_transform_block_tables(
block_tables,
num_tiles,
num_blocks_per_tile,
num_head,
head_id,
block_size_tiling_factor,
):
assert is_power_of_2(
num_blocks_per_tile), f"{num_blocks_per_tile=} must be power of 2"
block_tables_sbuf = load_block_tables(block_tables, num_tiles,
num_blocks_per_tile)
# we need to pass an Index as head_id
head_id = nl.arange(1)[None, :] + head_id
block_tables_transposed = transform_block_tables_for_indirect_load(
block_tables_sbuf, block_size_tiling_factor, num_head, head_id)
B_P_SIZE = 128
assert block_tables_transposed.shape[1] == B_P_SIZE
out = nl.ndarray(
block_tables_transposed.shape,
dtype=nl.int32,
buffer=nl.shared_hbm,
)
for i in nl.affine_range(block_tables_transposed.shape[0]):
nl.store(dst=out[i], value=block_tables_transposed[i])
return out
def ref_block_tables_transform(
block_tables,
num_tiles,
num_blocks_per_tile,
num_head,
head_id,
block_size_tiling_factor,
):
assert block_tables.numel() == num_tiles * num_blocks_per_tile
block_tables = block_tables.view(num_tiles, num_blocks_per_tile)
B_F_SIZE = 128
num_tiles_padded = (num_tiles + B_F_SIZE - 1) // B_F_SIZE * B_F_SIZE
block_tables = F.pad(
block_tables,
(0, 0, 0, num_tiles_padded - num_tiles),
"constant",
0,
)
block_tables = block_tables * num_head + head_id
block_tables = block_tables.view(num_tiles_padded, num_blocks_per_tile, 1)
offset = torch.arange(0, block_size_tiling_factor).view(1, 1, -1)
block_tables = block_tables * block_size_tiling_factor + offset
block_tables_transposed = block_tables.view(num_tiles_padded, -1).t()
num_blocks_per_tile = block_tables_transposed.shape[0]
assert num_blocks_per_tile % B_F_SIZE == 0
return block_tables_transposed.view(num_blocks_per_tile // B_F_SIZE,
B_F_SIZE, num_tiles_padded)
@pytest.mark.parametrize(
"q_head_per_kv_head,head_id",
[
(1, 0),
(3, 1),
],
)
@pytest.mark.parametrize(
"num_tiles,num_blocks_per_tile",
[
(1, 1),
(13, 16),
(17, 128),
(35, 512),
(128, 128),
(130, 64),
(280, 256),
(315, 1),
],
)
@torch.inference_mode()
def test_load_and_transform_block_tables(
monkeypatch: pytest.MonkeyPatch,
num_tiles,
num_blocks_per_tile,
q_head_per_kv_head,
head_id,
) -> None:
import torch_xla.core.xla_model as xm
device = xm.xla_device()
compiler_flags_str = " ".join([
"-O1",
"--retry_failed_compilation",
])
with monkeypatch.context() as m:
m.setenv("NEURON_CC_FLAGS", compiler_flags_str)
torch.manual_seed(10000)
torch.set_printoptions(sci_mode=False)
# On Neuron, we need B_P_SIZE = 128 blocks to make DMA efficient
B_P_SIZE = 128
if num_blocks_per_tile < B_P_SIZE:
assert B_P_SIZE % num_blocks_per_tile == 0
block_size_tiling_factor = B_P_SIZE // num_blocks_per_tile
else:
block_size_tiling_factor = 1
max_num_blocks = 100000
block_tables = torch.randint(
0,
max_num_blocks,
(num_tiles * num_blocks_per_tile, ),
dtype=torch.int32,
)
nki_out = nki.jit(nki_load_and_transform_block_tables)[1, 1](
block_tables.to(device=device),
num_tiles,
num_blocks_per_tile,
q_head_per_kv_head,
head_id,
block_size_tiling_factor,
).cpu()
ref_out = ref_block_tables_transform(
block_tables,
num_tiles,
num_blocks_per_tile,
q_head_per_kv_head,
head_id,
block_size_tiling_factor,
)
assert (nki_out.shape == ref_out.shape
), f"{nki_out.shape=} != {ref_out.shape=}"
assert torch.all(nki_out == ref_out)
tests/neuron/1_core/test_cache.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.attention.ops.nki_flash_attn import reshape_and_cache
@pytest.mark.parametrize(
"num_tokens, n_kv_head, d_head, num_blocks, block_size",
[
# Small model configuration (e.g., GPT-2 small)
(32, 12, 64, 4, 128), # Typical sequence processing
(1, 12, 64, 4, 128), # Single token update
(128, 12, 64, 4, 128), # Longer sequence
# Medium model configuration (e.g., GPT-2 medium)
(64, 16, 96, 8, 256), # Standard batch
(256, 16, 96, 8, 256), # Large batch
# Large model configuration (e.g., GPT-3 style)
(48, 32, 128, 16, 512), # Typical processing window
(512, 32, 128, 16, 512), # Full context window
# Edge cases and stress tests
(1024, 8, 32, 32, 32), # Many tokens, small heads
(16, 64, 256, 4, 64), # Few tokens, many heads
(2048, 24, 128, 64, 128), # Large scale test
# Minimal configurations for debugging
(4, 2, 16, 2, 16), # Tiny test case
(1, 1, 8, 1, 8), # Minimal possible
])
def test_reshape_and_cache(num_tokens, n_kv_head, d_head, num_blocks,
block_size):
# Set random seed for reproducibility
torch.manual_seed(42)
# Create CPU tensors for reference implementation
key_cpu = torch.randn(num_tokens, n_kv_head, d_head) / torch.sqrt(
torch.tensor(d_head))
value_cpu = torch.randn(num_tokens, n_kv_head, d_head) / torch.sqrt(
torch.tensor(d_head))
key_cache_cpu = torch.zeros(num_blocks, n_kv_head, block_size, d_head)
value_cache_cpu = torch.zeros(num_blocks, n_kv_head, block_size, d_head)
slot_mapping_cpu = torch.randperm(num_blocks * block_size)[:num_tokens]
# Run reference implementation on CPU
block_indices = torch.div(slot_mapping_cpu,
block_size,
rounding_mode="floor")
block_offsets = slot_mapping_cpu % block_size
for i in range(num_tokens):
block_idx = block_indices[i]
block_offset = block_offsets[i]
key_cache_cpu[block_idx, :, block_offset, :] = key_cpu[i]
value_cache_cpu[block_idx, :, block_offset, :] = value_cpu[i]
# Create XLA device tensors
device = torch.device('xla')
key = key_cpu.to(device)
value = value_cpu.to(device)
key_cache = torch.zeros_like(key_cache_cpu, device=device)
value_cache = torch.zeros_like(value_cache_cpu, device=device)
slot_mapping = slot_mapping_cpu.to(device)
kv_cache = torch.stack([key_cache, value_cache])
# Run vectorized implementation on XLA device
reshape_and_cache(key, value, kv_cache, slot_mapping)
key_cache, value_cache = torch.unbind(kv_cache, dim=0)
# Move results back to CPU for comparison
key_cache_result = key_cache.cpu()
value_cache_result = value_cache.cpu()
# Assert results match
torch.testing.assert_close(key_cache_result,
key_cache_cpu,
rtol=1e-5,
atol=1e-5)
torch.testing.assert_close(value_cache_result,
value_cache_cpu,
rtol=1e-5,
atol=1e-5)
tests/neuron/1_core/test_layernorm.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.platforms import current_platform
@pytest.mark.parametrize("num_tokens,hidden_size,add_residual,dtype", [
(7, 8, False, torch.half),
(83, 768, False, torch.half),
(83, 768, True, torch.half),
(83, 768, True, torch.bfloat16),
(83, 768, True, torch.float32),
])
@torch.inference_mode()
def test_rms_norm(
num_tokens: int,
hidden_size: int,
add_residual: bool,
dtype: torch.dtype,
) -> None:
import torch_xla.core.xla_model as xm
device = xm.xla_device()
current_platform.seed_everything(0)
torch.set_default_device("cpu")
layer = RMSNorm(hidden_size).to(dtype=dtype)
layer.weight.data.normal_(mean=1.0, std=0.1)
scale = 1 / (2 * hidden_size)
x = torch.randn(num_tokens, hidden_size, dtype=dtype).to(device=device)
x *= scale
residual = torch.randn_like(x) * scale if add_residual else None
residual_cpu = residual.cpu() if add_residual else None
ref_out = layer.to(device="cpu").forward_native(x.cpu(), residual_cpu)
assert x.is_xla, "input tensor under testing is expected to be XLA tensor."
out = layer.to(device=device)(x, residual)
# NOTE(woosuk): LayerNorm operators (including RMS) typically have larger
# numerical errors than other operators because they involve reductions.
# Therefore, we use a larger tolerance.
if add_residual:
assert out[0].is_xla, "output tensor is expected to be XLA tensor"
torch.testing.assert_close(out[0].cpu(),
ref_out[0],
atol=1e-2,
rtol=1e-2)
torch.testing.assert_close(out[1].cpu(),
ref_out[1],
atol=1e-2,
rtol=1e-2)
else:
assert out.is_xla, "output tensor is expected to be XLA tensor"
torch.testing.assert_close(out.cpu(), ref_out, atol=1e-2, rtol=1e-2)
tests/neuron/1_core/test_logits_processor.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import random
from unittest.mock import patch
import pytest
import torch
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_random_seed
from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
from vllm.utils import is_pin_memory_available
class MockLogitsProcessor(LogitsProcessor):
def __init__(self, vocab_size: int, scale: float,
fake_logits: torch.Tensor):
super().__init__(vocab_size=vocab_size, scale=scale)
self.fake_logits = fake_logits.clone()
def forward(self, *args, **kwargs):
with patch(
"vllm.model_executor.layers.logits_processor._prune_hidden_states",
lambda x, y: x
), patch(
"vllm.model_executor.layers.logits_processor.LogitsProcessor._get_logits",
lambda *args, **kwargs: self.fake_logits):
return super().forward(*args, **kwargs)
def _prepare_test(
batch_size: int
) -> tuple[torch.Tensor, torch.Tensor, MockLogitsProcessor]:
vocab_size = 32000
input_tensor = torch.rand((batch_size, 1024), dtype=torch.float16)
fake_logits = torch.full((batch_size, vocab_size),
1e-2,
dtype=input_tensor.dtype)
logits_processor = MockLogitsProcessor(32000, 0.5, fake_logits)
return input_tensor, fake_logits, logits_processor
RANDOM_SEEDS = list(range(8))
@pytest.mark.parametrize("seed", RANDOM_SEEDS)
def test_logits_processors(seed: int):
import torch_xla.core.xla_model as xm
device = xm.xla_device()
set_random_seed(seed)
torch.set_default_device("cpu")
batch_size = random.randint(1, 256)
input_tensor, fake_logits, logits_processor = _prepare_test(batch_size)
# This sample logits processor gives infinite score to the i-th token,
# where i is the length of the input sequence.
# We therefore expect the output token sequence to be [0, 1, 2, ...]
def pick_ith(token_ids, logits):
logits[len(token_ids)] = float("inf")
return logits
seq_group_metadata_list = []
seq_lens = []
for i in range(batch_size):
seq_group_metadata_list.append(
SequenceGroupMetadata(
request_id=f"test_{i}",
is_prompt=True,
seq_data={0: SequenceData.from_seqs([1, 2, 3])},
sampling_params=SamplingParams(temperature=0,
logits_processors=[pick_ith]),
block_tables={0: [1]},
))
seq_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
sampling_metadata = SamplingMetadata.prepare(
seq_group_metadata_list,
seq_lens,
query_lens=seq_lens,
device=device,
pin_memory=is_pin_memory_available())
logits_processor_output = logits_processor(
lm_head=None,
hidden_states=input_tensor,
sampling_metadata=sampling_metadata)
fake_logits *= logits_processor.scale
torch.testing.assert_close(logits_processor_output[:, 1],
fake_logits[:, 1],
rtol=1e-4,
atol=0.0)
tests/neuron/1_core/test_neuron_model_runner.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from unittest.mock import MagicMock
from vllm.config import VllmConfig
from vllm.engine.arg_utils import EngineArgs
from vllm.platforms import current_platform
from vllm.platforms.neuron import NeuronFramework
from vllm.sampling_params import SamplingParams
from vllm.sequence import SequenceData, SequenceGroupMetadata
from vllm.worker.neuron_model_runner import NeuronModelRunner
os.environ[
'VLLM_NEURON_FRAMEWORK'] = NeuronFramework.TRANSFORMERS_NEURONX.value
def _create_neuron_model_runner(model: str, *args,
**kwargs) -> NeuronModelRunner:
engine_args = EngineArgs(model, *args, **kwargs)
engine_config = engine_args.create_engine_config()
vllm_config = VllmConfig(
model_config=engine_config.model_config,
parallel_config=engine_config.parallel_config,
scheduler_config=engine_config.scheduler_config,
device_config=engine_config.device_config,
)
neuron_model_runner = NeuronModelRunner(vllm_config=vllm_config)
return neuron_model_runner
def test_update_neuron_sampling_params_not_full_batch():
os.environ["NEURON_ON_DEVICE_SAMPLING_DISABLED"] = "0"
model_runner = _create_neuron_model_runner(
"facebook/opt-125m",
seed=0,
dtype="float16",
max_num_seqs=2,
)
assert not model_runner._on_device_sampling_disabled
# Test sampling param updating only when TNx is framework
# NxDI handles sampling parameter updating inside model
if current_platform.use_transformers_neuronx():
model_mock = MagicMock()
model_runner.model = model_mock
seq_group_metadata_list = [
SequenceGroupMetadata(
request_id="test_0",
is_prompt=True,
seq_data={0: SequenceData.from_seqs([1, 2, 3])},
sampling_params=SamplingParams(temperature=0.5,
top_k=1,
top_p=0.5),
block_tables={0: [1]},
)
]
model_runner.prepare_model_input(seq_group_metadata_list)
# Index neuron sampling parameters based on block_tables indices.
# The first block_id of the sequence 0 is 1, so its parameters are
# placed at index 1. So the sampling parameters will be:
# Index 0: default sampling parameters
# Index 1: sequecne 0's sampling parameters.
neuron_sampling_params = (
model_runner.model_config.neuron_sampling_params)
assert neuron_sampling_params.temperature == [1.0, 0.5]
assert neuron_sampling_params.top_k == [
model_runner._MAX_NEURON_SAMPLING_TOP_K, 1
]
assert neuron_sampling_params.top_p == [1.0, 0.5]
model_mock.model.update_generation_config.assert_called_once_with(
neuron_sampling_params)
def test_update_neuron_sampling_params_full_batch():
os.environ["NEURON_ON_DEVICE_SAMPLING_DISABLED"] = "0"
model_runner = _create_neuron_model_runner(
"facebook/opt-125m",
seed=0,
dtype="float16",
max_num_seqs=2,
)
assert not model_runner._on_device_sampling_disabled
# Test sampling param updating only when TNx is framework
# NxDI handles sampling parameter updating inside model
if current_platform.use_transformers_neuronx():
model_mock = MagicMock()
model_runner.model = model_mock
seq_group_metadata_list = [
SequenceGroupMetadata(
request_id="test_0",
is_prompt=True,
seq_data={0: SequenceData.from_seqs([1, 2, 3])},
sampling_params=SamplingParams(temperature=0.5,
top_k=1,
top_p=0.5),
block_tables={0: [1]},
),
SequenceGroupMetadata(
request_id="test_0",
is_prompt=True,
seq_data={1: SequenceData.from_seqs([4, 5, 6])},
sampling_params=SamplingParams(temperature=0.2,
top_k=2,
top_p=0.2),
block_tables={1: [0]},
)
]
model_runner.prepare_model_input(seq_group_metadata_list)
# Index neuron sampling parameters based on block_tables indices.
# The first block_id of the sequence 0 is 1, so its parameters are
# placed at index 1. So the sampling parameters will be:
# Index 0: sequence 1's sampling parameters
# Index 1: sequecne 0's sampling parameters.
neuron_sampling_params = (
model_runner.model_config.neuron_sampling_params)
assert neuron_sampling_params.temperature == [0.2, 0.5]
assert neuron_sampling_params.top_k == [2, 1]
assert neuron_sampling_params.top_p == [0.2, 0.5]
model_mock.model.update_generation_config.assert_called_once_with(
neuron_sampling_params)
tests/neuron/1_core/test_neuron_quant.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm.model_executor.layers.quantization.neuron_quant import (
NeuronQuantConfig)
def test_get_supported_act_dtypes():
neuron_quant_config = NeuronQuantConfig()
supported_act_dtypes = neuron_quant_config.get_supported_act_dtypes()
target_list = ["any_dtype1", "any_dtype2"]
for dtype in target_list:
assert dtype in supported_act_dtypes
tests/neuron/1_core/test_prefix_prefill.py deleted 100644 → 0
View file @ 848562bd
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import pytest
import torch
import torch.nn.functional as F
from vllm.utils import cdiv
class BlockDiagonalCausalFromBottomRightMask:
@staticmethod
def _from_seqlens(query_lens, seq_lens, block_size=None):
from torch import logical_and, logical_or
contexted = block_size is None
context_lens = torch.tensor(seq_lens) - torch.tensor(query_lens)
n_queries = sum(query_lens)
num_seqs = len(query_lens)
if contexted:
key_lens_blockaligned = seq_lens
else:
n_blocks_per_seq = (context_lens + block_size - 1) // block_size
offset_per_seq = n_blocks_per_seq * block_size
key_lens_blockaligned = offset_per_seq[:num_seqs].tolist()
n_keys = sum(key_lens_blockaligned)
a = (torch.arange(n_queries).reshape(n_queries,
1).expand(n_queries, n_keys))
b = torch.arange(n_keys).reshape(1, n_keys).expand(n_queries, n_keys)
q_cumsum = torch.tensor([0] + query_lens).cumsum(dim=0)
k_cumsum = torch.tensor([0] + key_lens_blockaligned).cumsum(dim=0)
prior_mask = torch.zeros(n_queries, n_keys)
new_masks: list[torch.Tensor] = []
for seq_id in range(num_seqs):
ri = q_cumsum[seq_id]
ci = k_cumsum[seq_id]
nr = query_lens[seq_id]
if contexted:
nc = seq_lens[seq_id]
a_offset = ci + nc - ri - nr
new_mask = (a + a_offset) >= b
else:
nc = context_lens[seq_id]
a_offset = ci + nc - 1
new_mask = a_offset >= b
left_mask = b >= ci
top_mask = a >= ri
bottom_mask = a < (ri + nr)
new_mask = logical_and(
logical_and(logical_and(new_mask, left_mask), top_mask),
bottom_mask,
)
prior_mask = logical_or(prior_mask, new_mask)
new_masks = new_masks + [new_mask]
return prior_mask
@staticmethod
def from_seqlens(query_lens, seq_lens, block_size=None):
contexted = block_size is None
if contexted:
prior_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
query_lens, seq_lens)
active_mask = None
else:
prior_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
query_lens, seq_lens, block_size)
active_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
query_lens, query_lens)
return prior_mask, active_mask
def ref_softmax(x: torch.Tensor,
dim: int,
mixed_precision=False,
return_max_reduce=False):
max_value = torch.amax(x, dim=dim, keepdims=True)
exp = torch.exp(x - max_value)
if mixed_precision:
sum_value = torch.sum(exp.astype(torch.float32),
dim=dim,
keepdims=True).astype(x.dtype)
else:
sum_value = torch.sum(exp, dim=dim, keepdims=True)
if return_max_reduce:
return exp / sum_value, max_value, torch.reciprocal(sum_value)
return exp / sum_value
def ref_masked_attention(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
scale: float,
attn_mask: Optional[torch.Tensor] = None,
return_max_reduce: Optional[bool] = False,
) -> torch.Tensor:
scaled_qk = scale * torch.einsum("qhd,khd->hqk", query, key).float()
if attn_mask is not None:
masked_score = scaled_qk + attn_mask.float()
if return_max_reduce:
norm_score, cached_max, cached_sum_reciprocal = ref_softmax(
masked_score, dim=-1, return_max_reduce=True)
else:
norm_score = ref_softmax(masked_score, dim=-1)
out = torch.einsum("hqk,khd->qhd", norm_score.to(value.dtype), value)
if return_max_reduce:
return (
out,
cached_max,
cached_sum_reciprocal,
norm_score,
masked_score,
scaled_qk,
)
else:
return (out, )
def ref_context_attention(
query,
key,
value,
query_lens,
seq_lens,
head_size,
num_queries_per_kv,
return_max_reduce=False,
):
scale = float(1.0 / (head_size**0.5))
if num_queries_per_kv > 1:
# Handle MQA and GQA
key = torch.repeat_interleave(key, num_queries_per_kv, dim=1)
value = torch.repeat_interleave(value, num_queries_per_kv, dim=1)
attn_mask, _ = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
query_lens, seq_lens)
# convert binary mask to -inf values
attn_mask = torch.logical_not(attn_mask)
attn_mask = attn_mask.float() * -30000
output, *debug_tensors = ref_masked_attention(
query,
key,
value,
scale,
attn_mask,
return_max_reduce=return_max_reduce,
)
output = output.unsqueeze(1)
if return_max_reduce:
cached_max, cached_sum_reciprocal, lse, masked_score, scaled_qk = (
debug_tensors)
return (
output,
cached_max,
cached_sum_reciprocal,
lse,
masked_score,
scaled_qk,
)
else:
return output
def sample_inputs(
prefill_batch_size,
decode_batch_size,
min_query_len,
max_query_len,
min_ctx_len,
max_ctx_len,
block_size,
num_heads,
num_kv_heads,
head_size,
dtype,
):
batch_size = prefill_batch_size + decode_batch_size
max_model_len = (max_query_len + max_ctx_len) * 4
max_block_per_request = max_model_len // block_size
cache_size = (batch_size * max_block_per_request) + 2
prefill_ctx_lens = torch.randint(min_ctx_len,
max_ctx_len + 1, (prefill_batch_size, ),
dtype=torch.long).tolist()
decode_ctx_lens = torch.randint(min_ctx_len,
max_ctx_len + 1, (decode_batch_size, ),
dtype=torch.long).tolist()
ctx_lens = prefill_ctx_lens + decode_ctx_lens
query_lens = torch.randint(
min_query_len,
max_query_len + 1,
(prefill_batch_size, ),
dtype=torch.long,
).tolist() + [1 for _ in range(decode_batch_size)]
seq_lens = [a + b for a, b in zip(query_lens, ctx_lens)]
num_tokens = sum(query_lens)
query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
query.uniform_(-1, 1)
torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype)
kv.uniform_(-1, 1)
key, value = kv.unbind(dim=1)
k_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
v_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
values = torch.arange(0, cache_size, dtype=torch.long)
values = values[torch.randperm(cache_size)]
block_table = values[:batch_size * max_block_per_request].view(
batch_size, max_block_per_request)
b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long)
b_start_loc = torch.cumsum(torch.tensor([0] + query_lens[:-1],
dtype=torch.long),
dim=0)
# copy kv to cache
b_seq_start_loc = torch.cumsum(torch.tensor([0] + seq_lens[:-1],
dtype=torch.long),
dim=0)
for i in range(batch_size):
for j in range(query_lens[i]):
k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
j])
v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
b_ctx_len[i] + j])
cur_ctx = 0
block_id = 0
while cur_ctx < b_ctx_len[i]:
start_loc = b_seq_start_loc[i] + cur_ctx
if cur_ctx + block_size > b_ctx_len[i]:
end_loc = b_seq_start_loc[i] + b_ctx_len[i]
else:
end_loc = start_loc + block_size
start_slot = block_table[i, block_id] * block_size
end_slot = start_slot + end_loc - start_loc
k_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
key[start_loc:end_loc])
v_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
value[start_loc:end_loc])
cur_ctx += block_size
block_id += 1
kv_cache = torch.stack([k_cache, v_cache])
return (
query,
k,
v,
kv_cache,
block_table,
key,
value,
query_lens,
seq_lens,
)
def get_active_block_tables(block_tables, query_lens, seq_lens, block_size,
num_blocks):
context_lens = seq_lens - query_lens
blocks_per_seq = (context_lens + block_size - 1) // block_size
num_seqs = len(seq_lens)
active_blocks: list[int] = []
for seq_id in range(num_seqs):
active_blocks = (
active_blocks +
block_tables[seq_id, :blocks_per_seq[seq_id]].tolist())
return F.pad(
torch.tensor(active_blocks, dtype=torch.int32),
(0, num_blocks - len(active_blocks)),
"constant",
0,
)
@pytest.mark.parametrize(
"prefill_batch_size,decode_batch_size,block_size,large_tile_size,num_heads,num_queries_per_kv,head_size,mixed_precision",
[
# Test minimal configurations (small block size)
(1, 199, 1, 512, 4, 2, 8, False
), # minimal block size, small dimensions
(1, 199, 1, 512, 4, 2, 8, True), # same with mixed precision
# Test common/medium configurations
(4, 12, 32, 2048, 32, 8, 64, False), # common case, larger heads
(4, 12, 32, 2048, 16, 4, 32,
True), # medium size, mixed precision, grouped-query attention (GQA)
# Test large configurations
(4, 12, 256, 8192, 8, 1, 128, False), # large blocks, large head size
(4, 12, 256, 8192, 64, 8, 64, True), # large blocks, many heads
# Test asymmetric configurations
(2, 24, 64, 4096, 12, 4, 96, False), # varied batch sizes
(8, 8, 128, 2048, 24, 2, 48, True), # balanced batches
# Test edge cases
(1, 128, 16, 1024, 4, 2, 16, False), # large decode batch
(16, 4, 8, 1024, 4, 2, 128, True), # large prefill batch
(4, 12, 32, 2048, 16, 1, 32, True), # multi-head attention (MHA)
(4, 12, 32, 2048, 16, 16, 32, True), # multi-query attention (MQA)
])
@torch.inference_mode()
def test_contexted_kv_attention(
monkeypatch: pytest.MonkeyPatch,
prefill_batch_size: int,
decode_batch_size: int,
num_heads: int,
num_queries_per_kv: int,
head_size: int,
block_size: int,
large_tile_size,
mixed_precision: bool,
) -> None:
import torch_xla.core.xla_model as xm
from vllm.attention.ops.nki_flash_attn import (flash_attn_varlen_nkifunc,
reorder_context_mask)
assert large_tile_size % block_size == 0
device = xm.xla_device()
compiler_flags_str = " ".join([
"-O1",
"--retry_failed_compilation",
])
with monkeypatch.context() as m:
m.setenv("NEURON_CC_FLAGS", compiler_flags_str)
torch.manual_seed(0)
torch.set_printoptions(sci_mode=False)
torch.set_default_device("cpu")
dtype = torch.float32
min_ctx_len = 32
max_ctx_len = 1024
min_query_len = 16
max_query_len = 512
num_kv_heads = num_heads // num_queries_per_kv
(
query,
k_active,
v_active,
kv_cache,
block_table,
key,
value,
query_lens,
seq_lens,
) = sample_inputs(
prefill_batch_size=prefill_batch_size,
decode_batch_size=decode_batch_size,
min_query_len=min_query_len,
max_query_len=max_query_len,
min_ctx_len=min_ctx_len,
max_ctx_len=max_ctx_len,
block_size=block_size,
num_heads=num_heads,
num_kv_heads=num_kv_heads,
head_size=head_size,
dtype=dtype,
)
output_ref = ref_context_attention(
query,
key,
value,
query_lens,
seq_lens,
head_size,
num_queries_per_kv,
return_max_reduce=False,
)
# build neuron program
B_P_SIZE = 128
assert (large_tile_size >= B_P_SIZE
), f"Expect {large_tile_size=} to be larger than {B_P_SIZE=}"
def pad_to_multiple(a, b):
return cdiv(a, b) * b
def pad_to_next_power_of_2(a):
assert a > 0
return 2**int(a - 1).bit_length()
# calculate input shapes
max_num_queries = pad_to_next_power_of_2(sum(query_lens))
context_lens = torch.tensor(seq_lens) - torch.tensor(query_lens)
num_active_blocks = cdiv(context_lens, block_size).sum().item()
num_active_blocks = pad_to_multiple(num_active_blocks,
large_tile_size // block_size)
context_kv_len = num_active_blocks * block_size
assert (
context_kv_len %
large_tile_size == 0), f"invalid context_kv_len={context_kv_len}"
# pad QKV tensors
pad_dims = (
0,
0,
0,
0,
0,
max_num_queries - query.shape[0],
)
query = F.pad(query, pad_dims, "constant", 0)
k = F.pad(k_active, pad_dims, "constant", 0)
v = F.pad(v_active, pad_dims, "constant", 0)
# permute QKV tensors
# query: (1, n_heads, d, seq_q)
# key: (1, n_kv_heads, d, seq_k)
# value: (1, n_kv_heads, seq_v, d)
query = query.unsqueeze(0).permute(0, 2, 3, 1).contiguous()
k = k.unsqueeze(0).permute(0, 2, 3, 1).contiguous()
v = v.unsqueeze(0).permute(0, 2, 1, 3).contiguous()
kv_cache = kv_cache.permute(0, 1, 3, 2, 4).contiguous()
# transform block table
active_block_table = get_active_block_tables(
block_table.cpu(),
torch.tensor(query_lens).cpu(),
torch.tensor(seq_lens).cpu(),
block_size,
num_active_blocks,
)
# Build attention masks
prior_mask, active_mask = (
BlockDiagonalCausalFromBottomRightMask.from_seqlens(
query_lens, seq_lens, block_size=block_size))
prior_mask_padded = F.pad(
prior_mask,
(
0,
context_kv_len - prior_mask.shape[1],
0,
max_num_queries - prior_mask.shape[0],
),
"constant",
0,
).bool()
active_mask_padded = F.pad(
active_mask,
(
0,
max_num_queries - active_mask.shape[1],
0,
max_num_queries - active_mask.shape[0],
),
"constant",
0,
).bool()
attn_mask = torch.concat([prior_mask_padded, active_mask_padded],
dim=1)
attn_mask = reorder_context_mask(attn_mask, large_tile_size,
block_size)
input_args = (
query.to(device=device),
k.to(device=device),
v.to(device=device),
kv_cache.to(device=device),
active_block_table.to(device=device),
attn_mask.to(device=device),
)
input_kwargs = dict(
n_kv_head=num_kv_heads,
head_size=head_size,
mixed_precision=mixed_precision,
LARGE_TILE_SZ=large_tile_size,
)
output_nki = flash_attn_varlen_nkifunc(*input_args, **input_kwargs)
num_actual_tokens = sum(query_lens)
# - o: shape (bs, n_heads, seq_q, d) -> (bs, seq_q, n_heads, d)
output_nki = output_nki.cpu().permute(0, 2, 1, 3)
output_nki = output_nki[0, :num_actual_tokens, :, :]
output_ref_padded = F.pad(
output_ref,
(0, 0, 0, 0, 0, 0, 0, max_num_queries - output_ref.shape[0]),
"constant",
0,
)
output_ref = output_ref_padded.transpose(
0, 1)[0, :num_actual_tokens, :, :]
torch.testing.assert_close(output_nki, output_ref, atol=1e-2, rtol=0)
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