Bump to v2.1.2
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8
.github/workflows/publish.yml
vendored
8
.github/workflows/publish.yml
vendored
@ -63,11 +63,17 @@ jobs:
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# Pytorch <= 2.0 only supports CUDA <= 11.8
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- torch-version: '1.12.1'
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cuda-version: '12.1.0'
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- torch-version: '1.12.1'
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cuda-version: '12.2.0'
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- torch-version: '1.13.1'
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cuda-version: '12.1.0'
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- torch-version: '1.13.1'
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cuda-version: '12.2.0'
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- torch-version: '2.0.1'
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cuda-version: '12.1.0'
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# Pytorch >= 2.1 only supports CUDA 12.1
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- torch-version: '2.0.1'
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cuda-version: '12.2.0'
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# Pytorch >= 2.1 only supports CUDA >= 12.1
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- torch-version: '2.1.0.dev20230731'
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cuda-version: '11.6.2'
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- torch-version: '2.1.0.dev20230731'
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@ -947,7 +947,7 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params ¶ms, cons
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for (int mi = 0; mi < size<0>(acc_o_rowcol); ++mi) {
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float sum = scores_sum(mi);
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float inv_sum = (sum == 0.f || sum != sum) ? 1.f : 1.f / sum;
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lse(mi) = (sum == 0.f || sum != sum) ? INFINITY : scores_max(mi) * params.scale_softmax + __logf(sum);
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lse(mi) = (sum == 0.f || sum != sum) ? -INFINITY : scores_max(mi) * params.scale_softmax + __logf(sum);
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float scale = inv_sum;
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#pragma unroll
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for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) { acc_o_rowcol(mi, ni) *= scale; }
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@ -1,4 +1,4 @@
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__version__ = "2.1.1"
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__version__ = "2.1.2"
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from flash_attn.flash_attn_interface import (
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flash_attn_func,
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@ -85,11 +85,11 @@ RUN pip install transformers==4.25.1 datasets==2.8.0 pytorch-lightning==1.8.6 tr
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RUN pip install git+https://github.com/mlcommons/logging.git@2.1.0
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# Install FlashAttention
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RUN pip install flash-attn==2.1.1
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RUN pip install flash-attn==2.1.2
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# Install CUDA extensions for cross-entropy, fused dense, layer norm
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RUN git clone https://github.com/HazyResearch/flash-attention \
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&& cd flash-attention && git checkout v2.1.1 \
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&& cd flash-attention && git checkout v2.1.2 \
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&& cd csrc/fused_softmax && pip install . && cd ../../ \
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&& cd csrc/rotary && pip install . && cd ../../ \
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&& cd csrc/xentropy && pip install . && cd ../../ \
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