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fix FlashDecoding change's regression in intel platform (#2161) 

install triton because GPTQParams needs it.

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
Wang, Yi 2024-07-02 17:56:07 +08:00 committed by GitHub
parent 022f6515a4
commit 5d97e0c4a3
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2 changed files with 7 additions and 4 deletions
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2
Dockerfile_intel
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@ -62,6 +62,7 @@ ENV HUGGINGFACE_HUB_CACHE=/data \
WORKDIR /usr/src
RUN wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/torch-2.1.0.post1%2Bcxx11.abi-cp310-cp310-linux_x86_64.whl && pip install torch-2.1.0.post1+cxx11.abi-cp310-cp310-linux_x86_64.whl
RUN pip install https://github.com/intel/intel-xpu-backend-for-triton/releases/download/v2.1.0/triton-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b distributed origin/dev/distributed
# Install server
@ -132,6 +133,7 @@ RUN conda install -c conda-forge gperftools mkl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torch-2.4.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchvision-0.19.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchaudio-2.4.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
RUN pip install triton
WORKDIR /usr/src

9
server/text_generation_server/layers/attention/ipex.py
View File

@ -1,6 +1,7 @@
import intel_extension_for_pytorch as ipex
import torch
from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
from text_generation_server.layers.attention import Seqlen
SUPPORTS_WINDOWING = False
@ -55,11 +56,10 @@ def paged_attention(
kv_head_mapping: torch.Tensor,
softmax_scale: float,
block_tables: torch.Tensor,
cu_seqlen_q: torch.Tensor,
cu_seqlen_k: torch.Tensor,
seqlen: Seqlen,
max_s: int,
):
return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
out,
query,
key_cache,
@ -67,8 +67,9 @@ def paged_attention(
kv_head_mapping,
softmax_scale,
block_tables,
cu_seqlen_q,
seqlen.input_lengths,
BLOCK_SIZE,
max_s,
None,
)
return out