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Update ROCM libs and improvements (#2579) 

* style

* update torch

* ix issues

* fix clone

* revert mkl

* added custom PA

* style

* fix style

* style

* hide env vart

* fix mixtral model

* add skinny kernel and merge fixes

* fixed style

* fix issue for sliding window models

* addressed review comments

* fix import

* improved error messag

* updated default value

* remove import

* fix imports after rebase

* float16 dep

* improve dockerfile

* cleaned dockerfile
This commit is contained in:
Mohit Sharma 2024-09-30 14:24:32 +05:30 committed by GitHub
parent e790cfc0e4
commit f9e561eced
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16 changed files with 418 additions and 117 deletions
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175
Dockerfile_amd
View File

@ -41,7 +41,7 @@ COPY launcher launcher
RUN cargo build --profile release-opt
# Text Generation Inference base image for RoCm
FROM rocm/dev-ubuntu-22.04:6.1.1_hip_update AS base
FROM rocm/dev-ubuntu-22.04:6.2 AS base
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
build-essential \
@ -50,33 +50,34 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
curl \
git \
make \
libmsgpack-dev \
libssl-dev \
llvm-dev \
g++ \
# Needed to build VLLM & flash.
rocthrust-dev \
hipsparse-dev \
hipblas-dev \
hipblaslt-dev \
hipcub-dev \
rocblas-dev \
hiprand-dev \
hipfft-dev \
rocrand-dev \
miopen-hip-dev \
hipfft-dev \
hipcub-dev \
hipsolver-dev \
rccl-dev \
cmake \
python3.11-dev && \
python3.11-venv && \
rm -rf /var/lib/apt/lists/*
# Keep in sync with `server/pyproject.toml
ARG MAMBA_VERSION=23.1.0-1
ARG PYTORCH_VERSION='2.3.0'
ARG ROCM_VERSION='6.0.2'
ARG PYTHON_VERSION='3.11.10'
# Automatically set by buildx
ARG TARGETPLATFORM
ENV PATH /opt/conda/bin:$PATH
ENV PATH=/opt/conda/bin:$PATH
ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
# TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda.
# Install mamba
@ -100,41 +101,132 @@ RUN case ${TARGETPLATFORM} in \
/opt/conda/bin/conda install -y "python=${PYTHON_VERSION}" ;; \
esac && \
/opt/conda/bin/conda clean -ya
# Install flash-attention, torch dependencies
RUN pip install numpy einops ninja --no-cache-dir
RUN python3 -m pip install --upgrade pip && pip install numpy einops ninja joblib msgpack cmake --no-cache-dir && rm -rf /var/lib/apt/lists/*
RUN pip uninstall -y triton && \
git clone --depth 1 --single-branch https://github.com/ROCm/triton.git && \
cd triton/python && \
pip install .
RUN conda install mkl=2021
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/opt/conda/lib/python3.11/site-packages/torch/lib:/opt/conda/lib/
RUN git clone --depth 1 --recursive --single-branch --branch 2.3-patched https://github.com/fxmarty/pytorch.git pytorch && cd pytorch && pip install -r requirements.txt --no-cache-dir
ARG _GLIBCXX_USE_CXX11_ABI="1"
ARG CMAKE_PREFIX_PATH="/opt/conda"
ARG COMMON_WORKDIR=/
WORKDIR ${COMMON_WORKDIR}
# Install HIPBLASLt
FROM base AS build_hipblaslt
ARG HIPBLASLT_BRANCH="e6da924"
RUN git clone https://github.com/ROCm/hipBLASLt.git \
&& cd hipBLASLt \
&& git checkout ${HIPBLASLT_BRANCH} \
&& SCCACHE_IDLE_TIMEOUT=1800 ./install.sh --architecture ${PYTORCH_ROCM_ARCH} --legacy_hipblas_direct \
&& cd build/release \
&& make package
FROM scratch AS export_hipblaslt
ARG COMMON_WORKDIR
COPY --from=build_hipblaslt ${COMMON_WORKDIR}/hipBLASLt/build/release/*.deb /
# RCCL build stages
FROM base AS build_rccl
ARG RCCL_BRANCH="rocm-6.2.0"
RUN git clone https://github.com/ROCm/rccl \
&& cd rccl \
&& git checkout ${RCCL_BRANCH} \
&& ./install.sh -p --amdgpu_targets ${PYTORCH_ROCM_ARCH}
FROM scratch AS export_rccl
ARG COMMON_WORKDIR
COPY --from=build_rccl ${COMMON_WORKDIR}/rccl/build/release/*.deb /
# Triton build stages
FROM base AS build_triton
ARG TRITON_BRANCH="e192dba"
ARG TRITON_REPO="https://github.com/triton-lang/triton.git"
RUN python3 -m pip install ninja cmake wheel pybind11 && git clone ${TRITON_REPO} \
&& cd triton \
&& git checkout ${TRITON_BRANCH} \
&& cd python \
&& python3 setup.py bdist_wheel --dist-dir=dist
FROM scratch AS export_triton
ARG COMMON_WORKDIR
COPY --from=build_triton ${COMMON_WORKDIR}/triton/python/dist/*.whl /
# # AMD-SMI build stages
FROM base AS build_amdsmi
RUN cd /opt/rocm/share/amd_smi \
&& pip wheel . --wheel-dir=dist
FROM scratch AS export_amdsmi
COPY --from=build_amdsmi /opt/rocm/share/amd_smi/dist/*.whl /
FROM base as build_pytorch
RUN --mount=type=bind,from=export_hipblaslt,src=/,target=/install \
if ls /install/*.deb; then \
dpkg -i /install/*.deb \
&& sed -i 's/, hipblaslt-dev \(.*\), hipcub-dev/, hipcub-dev/g' /var/lib/dpkg/status \
&& sed -i 's/, hipblaslt \(.*\), hipfft/, hipfft/g' /var/lib/dpkg/status; \
fi
ARG BUILD_ENVIRONMENT=pytorch-linux-jammy-rocm6.2-py3.11
ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
ARG BUILD_CAFFE2="0" \
BUILD_CAFFE2_OPS="0" \
USE_CUDA="0" \
USE_ROCM="1" \
BUILD_TEST="0" \
USE_FBGEMM="0" \
USE_NNPACK="0" \
USE_QNNPACK="0" \
USE_XNNPACK="0" \
USE_FLASH_ATTENTION="1" \
USE_MEM_EFF_ATTENTION="0"
RUN cd pytorch && python tools/amd_build/build_amd.py && python setup.py install
# A commit to fix the output scaling factor issue in _scaled_mm
# Not yet in 2.5.0-rc1
ARG PYTORCH_BRANCH="cedc116"
ARG PYTORCH_VISION_BRANCH="v0.19.1"
ARG PYTORCH_REPO="https://github.com/ROCm/pytorch.git"
# Set AS recommended: https://github.com/ROCm/triton/wiki/A-script-to-set-program-execution-environment-in-ROCm
ENV HIP_FORCE_DEV_KERNARG=1
RUN git clone ${PYTORCH_REPO} pytorch \
&& cd pytorch && git checkout ${PYTORCH_BRANCH} && git submodule update --init --recursive \
&& pip install -r requirements.txt --no-cache-dir \
&& python tools/amd_build/build_amd.py \
&& CMAKE_PREFIX_PATH=$(python3 -c 'import sys; print(sys.prefix)') python3 setup.py bdist_wheel --dist-dir=dist
FROM scratch as export_pytorch
ARG COMMON_WORKDIR
COPY --from=build_pytorch ${COMMON_WORKDIR}/pytorch/dist/*.whl /
# On MI250 and MI300, performances for flash with Triton FA are slightly better than CK.
# However, Triton requires a tunning for each prompt length, which is prohibitive.
ENV ROCM_USE_FLASH_ATTN_V2_TRITON=0
FROM base AS install_deps
FROM base AS kernel-builder
ARG COMMON_WORKDIR
# Install hipblaslt
RUN --mount=type=bind,from=export_hipblaslt,src=/,target=/install \
if ls /install/*.deb; then \
dpkg -i /install/*.deb \
&& sed -i 's/, hipblaslt-dev \(.*\), hipcub-dev/, hipcub-dev/g' /var/lib/dpkg/status \
&& sed -i 's/, hipblaslt \(.*\), hipfft/, hipfft/g' /var/lib/dpkg/status; \
fi
RUN --mount=type=bind,from=export_rccl,src=/,target=/install \
if ls /install/*.deb; then \
dpkg -i /install/*.deb \
# RCCL needs to be installed twice
&& dpkg -i /install/*.deb \
&& sed -i 's/, rccl-dev \(.*\), rocalution/, rocalution/g' /var/lib/dpkg/status \
&& sed -i 's/, rccl \(.*\), rocalution/, rocalution/g' /var/lib/dpkg/status; \
fi
RUN --mount=type=bind,from=export_triton,src=/,target=/install \
if ls /install/*.whl; then \
# Preemptively uninstall to prevent pip same-version no-installs
pip uninstall -y triton \
&& pip install /install/*.whl; \
fi
RUN --mount=type=bind,from=export_amdsmi,src=/,target=/install \
# Preemptively uninstall to prevent pip same-version no-installs
pip uninstall -y amdsmi \
&& pip install /install/*.whl;
RUN --mount=type=bind,from=export_pytorch,src=/,target=/install \
if ls /install/*.whl; then \
# Preemptively uninstall to prevent pip same-version no-installs
pip uninstall -y torch torchvision \
&& pip install /install/*.whl; \
fi
FROM install_deps AS kernel-builder
# # Build vllm kernels
FROM kernel-builder AS vllm-builder
@ -174,7 +266,7 @@ COPY server/exllamav2_kernels/ .
RUN python setup.py build
FROM base AS base-copy
FROM install_deps AS base-copy
# Text Generation Inference base env
ENV HF_HOME=/data \
@ -224,6 +316,19 @@ ENTRYPOINT ["./entrypoint.sh"]
# Final image
FROM base-copy
# Set AS recommended: https://github.com/ROCm/triton/wiki/A-script-to-set-program-execution-environment-in-ROCm
ENV HIP_FORCE_DEV_KERNARG=1
# On MI250 and MI300, performances for flash with Triton FA are slightly better than CK.
# However, Triton requires a tunning for each prompt length, which is prohibitive.
ENV ROCM_USE_FLASH_ATTN_V2_TRITON=0
ENV ROCM_USE_CUSTOM_PAGED_ATTN=1
ENV PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP=0
ENV VLLM_MOE_PADDING=0
ENV ATTENTION=paged
ENV USE_PREFIX_CACHING=0
ENV ROCM_USE_SKINNY_GEMM=1
COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh
RUN chmod +x /tgi-entrypoint.sh

6
docs/source/installation_amd.md
View File

@ -31,6 +31,12 @@ Two implementations of Flash Attention are available for ROCm, the first is [ROC
By default, the Composable Kernel implementation is used. However, the Triton implementation has slightly lower latency on MI250 and MI300, but requires a warmup which can be prohibitive as it needs to be done again for each new prompt length. If needed, FA Triton impelmentation can be enabled with `--env ROCM_USE_FLASH_ATTN_V2_TRITON="0"` when launching TGI's docker container.
## Custom PagedAttention
For better performance on ROCm, a custom Paged Attention kernel is available and is enabled by default. To disable it and fall back to the PagedAttention v2 kernel, set the environment variable `ROCM_USE_CUSTOM_PAGED_ATTN=0`.
The custom kernel supports bf16 and fp16 data types, block size of 16, head size of 128, a maximum context length of 16k, and GQA ratios between 1 and 16. For other configurations, we use the PagedAttention v2 kernel.
## Unsupported features
The following features are currently not supported in the ROCm version of TGI, and the supported may be extended in the future:

4
server/Makefile-flash-att-v2
View File

@ -1,5 +1,5 @@
flash_att_v2_commit_cuda := v2.6.1
flash_att_v2_commit_rocm := 2554f490101742ccdc56620a938f847f61754be6
flash_att_v2_commit_rocm := 2092111b9f975b3347c652ff7fabd431130256c4
build-flash-attention-v2-cuda:
pip install -U packaging wheel
@ -11,7 +11,7 @@ install-flash-attention-v2-cuda: build-flash-attention-v2-cuda
build-flash-attention-v2-rocm:
if [ ! -d 'flash-attention-v2' ]; then \
pip install -U packaging ninja --no-cache-dir && \
git clone https://github.com/ROCm/flash-attention.git flash-attention-v2 && \
git clone https://github.com/mht-sharma/flash-attention.git flash-attention-v2 && \
cd flash-attention-v2 && git fetch && git checkout $(flash_att_v2_commit_rocm) && \
git submodule update --init --recursive && GPU_ARCHS="gfx90a;gfx942" PYTORCH_ROCM_ARCH="gfx90a;gfx942" python setup.py build; \
fi

4
server/Makefile-vllm
View File

@ -1,5 +1,5 @@
commit_cuda := d243e9dc7e2c9c2e36a4150ec8e64809cb55c01b
commit_rocm := c6ee53b1be97e3bbc791b95f22827501297f8921
commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247
build-vllm-cuda:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
@ -13,7 +13,7 @@ install-vllm-cuda: build-vllm-cuda
build-vllm-rocm:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
git clone https://github.com/fxmarty/rocm-vllm.git vllm; \
git clone https://github.com/mht-sharma/vllm.git vllm; \
fi
cd vllm && git fetch && git checkout $(commit_rocm) && \
PYTORCH_ROCM_ARCH="gfx90a;gfx942" python setup.py build

13
server/exllama_kernels/setup.py
View File

@ -1,5 +1,17 @@
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
extra_cuda_cflags = []
extra_cflags = []
if torch.version.hip:
extra_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_cuda_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_compile_args = {
"cxx": extra_cflags,
"nvcc": extra_cuda_cflags,
}
setup(
name="exllama_kernels",
@ -13,6 +25,7 @@ setup(
"exllama_kernels/cuda_func/q4_matmul.cu",
"exllama_kernels/cuda_func/q4_matrix.cu",
],
extra_compile_args=extra_compile_args,
)
],
cmdclass={"build_ext": BuildExtension},

6
server/exllamav2_kernels/setup.py
View File

@ -3,11 +3,13 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
extra_cuda_cflags = ["-lineinfo", "-O3"]
extra_cflags = []
if torch.version.hip:
extra_cuda_cflags += ["-DHIPBLAS_USE_HIP_HALF"]
extra_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_cuda_cflags += ["-DHIPBLAS_USE_HIP_HALF", "-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_compile_args = {
"cxx": extra_cflags,
"nvcc": extra_cuda_cflags,
}

3
server/text_generation_server/layers/attention/common.py
View File

@ -1,4 +1,5 @@
from dataclasses import dataclass
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models.globals import ATTENTION
import torch
from typing import Optional
@ -65,5 +66,7 @@ else:
max_k: int
def clamp(self, max):
if SYSTEM == "rocm":
return self
raise NotImplementedError("Not implemented seqlen for paged")
return Seqlen(torch.clamp(self.input_lengths, max=max))

178
server/text_generation_server/layers/attention/rocm.py
View File

@ -1,4 +1,5 @@
import os
from typing import Optional
import torch
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models.globals import ATTENTION
@ -8,16 +9,28 @@ from loguru import logger
major, minor = torch.cuda.get_device_capability()
is_sm75 = major == 7 and minor == 5
_PARTITION_SIZE = 512
_PARTITION_SIZE_V1V2 = 512
_PARTITION_SIZE_CUSTOM = 256
use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"}
ENGINE = "triton" if use_triton else "ck"
PREFILL_IN_KV_CACHE = False
use_rocm_custom_paged_attn = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0"
try:
from vllm._C import cache_ops
if use_rocm_custom_paged_attn:
from vllm._custom_C import paged_attention_custom
except ImportError as e:
log_master(
logger.info,
f"Custom Paged Attention not available. Complete error: {e}",
)
use_rocm_custom_paged_attn = False
try:
import vllm._custom_ops as ops
except Exception as e:
raise ImportError(
f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
@ -36,9 +49,7 @@ def reshape_and_cache(
key_cache.view(-1, shape[-2], shape[-1])[slots] = key
value_cache.view(-1, shape[-2], shape[-1])[slots] = value
else:
cache_ops.reshape_and_cache(
key, value, key_cache, value_cache, slots, "auto", 1.0
)
ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
def paged_attention(
@ -48,8 +59,9 @@ def paged_attention(
kv_head_mapping: torch.Tensor,
softmax_scale: float,
block_tables: torch.Tensor,
input_lengths: Seqlen,
seqlen: Seqlen,
max_s: int,
softcap: Optional[float] = None,
):
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
# Copyright 2023 The vLLM team. All rights
@ -68,11 +80,31 @@ def paged_attention(
# limitations under the License.
#
if softcap is not None:
raise RuntimeError("Paged attention doesn't support softcapping")
# value_cache => [num_blocks, num_heads, head_size, block_size]
block_size = value_cache.shape[3]
num_seqs, num_heads, head_size = query.shape
num_kv_heads = key_cache.shape[1]
gqa_ratio = num_heads // num_kv_heads
use_custom = (
use_rocm_custom_paged_attn
and (query.dtype == torch.half or query.dtype == torch.bfloat16)
and (head_size == 128 or head_size == 64)
and (block_size == 16 or block_size == 32)
and (gqa_ratio >= 1 and gqa_ratio <= 16)
and max_s <= 32768
)
if not use_custom:
_PARTITION_SIZE = _PARTITION_SIZE_V1V2
else:
_PARTITION_SIZE = _PARTITION_SIZE_CUSTOM
max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
input_lengths = input_lengths.input_lengths
input_lengths = seqlen.input_lengths
out = torch.empty_like(query)
@ -81,9 +113,13 @@ def paged_attention(
# V1 to avoid the overhead of reduction. Also, if the number of
# sequences or heads is large, we use V1 since there is enough work
# to parallelize.
from vllm._C import ops
import vllm._custom_ops as ops
use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512)
use_v1 = (
max_s <= 8192
and (max_num_partitions == 1 or num_seqs * num_heads > 512)
and not use_custom
)
if use_v1:
ops.paged_attention_v1(
out,
@ -115,24 +151,44 @@ def paged_attention(
)
max_logits = torch.empty_like(exp_sums)
ops.paged_attention_v2(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
1.0,
)
if not use_custom:
ops.paged_attention_v2(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
1.0,
)
else:
paged_attention_custom(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
)
return out
@ -175,13 +231,14 @@ if ENGINE == "ck":
def attention(
q,
k,
v,
cu_seqlens,
max_s,
softmax_scale,
window_size_left=-1,
causal=True,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale: float,
window_size_left: int = -1,
causal: bool = True,
softcap: float = 0.0,
):
if window_size_left <= 0 and window_size_left != -1:
raise ValueError("`window_size_left` must be > 0 or -1")
@ -191,46 +248,57 @@ if ENGINE == "ck":
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
return flash_attn_2_cuda.varlen_fwd(
q,
k,
v,
key_cache,
value_cache,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_q,
None,
None,
None,
None,
seqlen.max_q,
seqlen.max_k,
0.0,
softmax_scale,
False,
causal,
window_size_left,
0,
softcap,
False,
None,
)
)[0]
elif ENGINE == "triton":
from .flash_attn_triton import triton_attention
def attention(
q,
k,
v,
cu_seqlens,
max_s,
softmax_scale,
window_size_left=-1,
causal=True,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale: float,
window_size_left: int = -1,
causal: bool = True,
softcap: Optional[float] = None,
):
if softcap is not None:
raise NotImplementedError("softcap is only available with CK flash attn")
out = torch.empty_like(q)
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
output, _ = triton_attention(
q,
k,
v,
key_cache,
value_cache,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_q,
seqlen.max_q,
seqlen.max_k,
causal,
softmax_scale,
)

48
server/text_generation_server/layers/linear.py
View File

@ -1,12 +1,21 @@
import torch
from text_generation_server.utils.import_utils import SYSTEM
from torch.nn import functional as F
import os
if SYSTEM == "rocm":
try:
from vllm import _custom_C
except Exception as e:
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
"true",
"1",
)
if ROCM_USE_SKINNY_GEMM:
try:
from vllm import _custom_C
except Exception as e:
raise ImportError(
f"Could not load `vllm._custom_C` for ROCm skinny gemm. Full error: {e}"
)
class FastLinear(torch.nn.Module):
@ -48,6 +57,14 @@ class FastLinearROCm(torch.nn.Module):
else:
self.bias = None
self.cu_count = torch.cuda.get_device_properties(
device="cuda"
).multi_processor_count
self.use_skinny_gemm = (
ROCM_USE_SKINNY_GEMM
and "gfx1" not in torch.cuda.get_device_properties("cuda").gcnArchName
)
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
@ -61,7 +78,11 @@ class FastLinearROCm(torch.nn.Module):
weight = self.weight
bias = self.bias
if SYSTEM == "rocm" and inp.numel() // inp.shape[-1] == 1:
if (
self.use_skinny_gemm
and inp.dtype == torch.float16
and inp.shape[-1] % 8 == 0
):
batched = False
inp_shape = inp.shape
@ -69,13 +90,16 @@ class FastLinearROCm(torch.nn.Module):
inp = inp.view(-1, inp_shape[-1])
batched = True
m, k = weight.shape[0], inp_shape[1]
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device="cuda"
)
if (k == 8192 and (m == 1280 or m == 7168)) or (k == 3584 and m == 8192):
_custom_C.LLMM1(weight, inp, out, 8)
elif k <= 8192 and k % 8 == 0 and m % 4 == 0:
m, n, k = weight.shape[0], inp_shape[0], inp_shape[1]
if m > 8 and n <= 4:
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
)
_custom_C.wvSpltK(weight, inp, out, n, self.cu_count)
elif m % 4 == 0 and n == 1 and k <= 8192:
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
)
_custom_C.LLMM1(weight, inp, out, 4)
else:
out = F.linear(inp, weight)

5
server/text_generation_server/layers/moe/__init__.py
View File

@ -19,7 +19,10 @@ from text_generation_server.utils.weights import (
Weights,
)
if SYSTEM != "ipex":
if SYSTEM == "rocm":
from .fused_moe_rocm import grouped_topk
from vllm.model_executor.layers.fused_moe import fused_topk
elif SYSTEM != "ipex":
from moe_kernels.fused_moe import fused_topk, grouped_topk

52
server/text_generation_server/layers/moe/fused_moe_rocm.py Normal file
View File

@ -0,0 +1,52 @@
# coding=utf-8
# Copyright 2023, 2024 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple
import torch
import torch.distributed
# TODO: Remove the functions once moe_kernel are built for ROCM
def grouped_topk(
hidden_states: torch.Tensor,
gating_output: torch.Tensor,
topk: int,
renormalize: bool,
num_expert_group: int = 0,
topk_group: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
scores = torch.softmax(gating_output, dim=-1)
num_token = scores.shape[0]
group_scores = (
scores.view(num_token, num_expert_group, -1).max(dim=-1).values
) # [n, n_group]
group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
1
] # [n, top_k_group]
group_mask = torch.zeros_like(group_scores) # [n, n_group]
group_mask.scatter_(1, group_idx, 1) # [n, n_group]
score_mask = (
group_mask.unsqueeze(-1)
.expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
.reshape(num_token, -1)
) # [n, e]
tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
if renormalize:
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
return topk_weights, topk_ids

15
server/text_generation_server/layers/moe/unquantized.py
View File

@ -6,7 +6,9 @@ import torch.nn as nn
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.weights import UnquantizedWeight, Weights
if SYSTEM != "ipex":
if SYSTEM == "rocm":
from vllm.model_executor.layers.fused_moe import fused_moe
elif SYSTEM != "ipex":
from moe_kernels.fused_moe import fused_moe
@ -52,6 +54,17 @@ class UnquantizedSparseMoELayer(nn.Module):
)
def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
if SYSTEM == "rocm":
return fused_moe(
x,
self.gate_up_proj,
self.down_proj,
gating_output,
self.topk,
renormalize=self.renormalize,
inplace=True,
)
return fused_moe(
x,
w1=self.gate_up_proj,

1
server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
View File

@ -390,6 +390,7 @@ class DeepseekV2MLP(nn.Module):
if (
SYSTEM == "rocm"
and self.hidden_act == "silu"
and hidden_states.dtype == torch.float16
and hidden_states.shape[0] == 1
and not self.quantize
):

5
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -316,12 +316,17 @@ class LlamaMLP(nn.Module):
# TODO: This is a hotfix to be removed & properly refactored.
self.quantize = config.quantize
self.hidden_size = config.hidden_size
def forward(self, hidden_states, adapter_data):
if (
SYSTEM == "rocm"
and self.hidden_act == "silu"
and hidden_states.dtype == torch.float16
and hidden_states.shape[0] == 1
and not self.quantize
and self.hidden_size
!= 16384 # TODO: Temporary workaround for `LLMM_Silu` kernel not working with LLama3.1 405B; needs refactoring once fixed.
):
out = torch.empty(
hidden_states.shape[0],

1
server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
View File

@ -303,6 +303,7 @@ class MistralMLP(nn.Module):
if (
SYSTEM == "rocm"
and self.hidden_act == "silu"
and hidden_states.dtype == torch.float16
and hidden_states.shape[0] == 1
and not self.quantize
):

19
server/text_generation_server/models/flash_causal_lm.py
View File

@ -1125,12 +1125,12 @@ class FlashCausalLM(Model):
else:
self.kv_cache = [
(
torch.empty(
torch.zeros(
(num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
dtype=dtype,
device=device,
),
torch.empty(
torch.zeros(
(num_blocks, num_heads, head_size, BLOCK_SIZE),
dtype=dtype,
device=device,
@ -1320,8 +1320,7 @@ class FlashCausalLM(Model):
elif CUDA_GRAPHS is not None:
tuning_sequences = CUDA_GRAPHS
else:
# For seqlen = 1, we dispatch to LLMM1 kernel.
tuning_sequences = [2, 3, 4, 5, 6, 7]
tuning_sequences = [1, 2, 3, 4, 5, 6, 7]
tunableop_filepath = os.path.join(
HUGGINGFACE_HUB_CACHE,
@ -1330,7 +1329,11 @@ class FlashCausalLM(Model):
log_master(
logger.info,
f"PyTorch TunableOp (https://github.com/fxmarty/pytorch/tree/2.3-patched/aten/src/ATen/cuda/tunable) is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
f"PyTorch TunableOp is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
)
torch.cuda.tunable.set_filename(
tunableop_filepath, insert_device_ordinal=False
)
if os.path.isfile(tunableop_filepath):
@ -1346,7 +1349,8 @@ class FlashCausalLM(Model):
log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
self.tunableop_warmup(seqlen)
torch.cuda.tunable.write_file(tunableop_filepath)
torch.cuda.tunable.tuning_enable(False)
if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
torch.cuda.tunable.tuning_enable(False)
else:
log_master(
logger.info,
@ -1382,6 +1386,7 @@ class FlashCausalLM(Model):
cu_seqlen_prefill = torch.tensor(
[0, seqlen], device=self.device, dtype=torch.int32
)
max_s = seqlen
seqlen = Seqlen(
input_lengths=input_lengths,
prefix_lengths=prefix_lens_tensor,
@ -1399,7 +1404,7 @@ class FlashCausalLM(Model):
block_tables=None,
seqlen=seqlen,
slots=slots,
max_s=seqlen,
max_s=max_s,
lm_head_indices=None,
prefill_cache_indices=None,
)