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Bump version to 1.0.0
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README.md

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@@ -38,7 +38,7 @@ and experiment with. The notations in the Triton implementation are also closer
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to what's used in our paper.
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## Beta release (0.2).
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## Installation and features
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Requirements:
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- CUDA 11.4 and above.

setup.py

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@@ -162,7 +162,7 @@ def append_nvcc_threads(nvcc_extra_args):
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setup(
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name="flash_attn",
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version="0.2.8",
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version="1.0.0",
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packages=find_packages(
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exclude=("build", "csrc", "include", "tests", "dist", "docs", "benchmarks", "flash_attn.egg-info",)
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),

training/Dockerfile

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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/[email protected]
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# Install FlashAttention
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RUN pip install flash-attn==0.2.8
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RUN pip install flash-attn==1.0.0
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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 v0.2.8 \
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&& cd flash-attention && git checkout v1.0.0 \
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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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