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Update transformers version (#96)
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* fix

* fix
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goliaro authored Feb 12, 2025
1 parent 10f3085 commit 91c0e43
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Showing 6 changed files with 8 additions and 8 deletions.
2 changes: 1 addition & 1 deletion conda/flexflow.yml
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Expand Up @@ -20,7 +20,7 @@ dependencies:
- torchvision>=0.14.1
- regex
- onnx
- transformers>=4.31.0
- transformers>=4.47.1
- sentencepiece
- einops
- requests
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2 changes: 1 addition & 1 deletion docker/flexflow-environment/Dockerfile
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Expand Up @@ -121,7 +121,7 @@ RUN if [ "$FF_GPU_BACKEND" == "cuda" ] ; then \
fi
RUN rm /usr/local/bin/install_pytorch.sh
# Various dependencies
RUN pip3 install transformers>=4.31.0 sentencepiece einops
RUN pip3 install transformers>=4.47.1 sentencepiece einops
RUN pip3 install tensorflow notebook
# PEFT-related
RUN pip3 install scipy bitsandbytes datasets accelerate loralib triton peft
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2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -11,7 +11,7 @@ torch>=1.13.1
torchaudio>=0.13.1
torchvision>=0.14.1
onnx
transformers>=4.31.0
transformers>=4.47.1
sentencepiece
einops
pip
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2 changes: 1 addition & 1 deletion src/ops/kernels/softmax.cu
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Expand Up @@ -328,7 +328,7 @@ void peft_bwd_kernel(SoftmaxMeta const *m,
token_ids[j] = bc->tokensInfo[j + tokens_previous_requests + 1].token_id;
}

DT scale_factor = 1.0 / (bc->requestsInfo[i].num_tokens_in_batch - 1);
DT scale_factor = 1.0 / (bc->requestsInfo[i].num_tokens_in_batch );
// ignore last token
checkCUDA(cudaMemsetAsync(
input_grad_ptr + (tokens_previous_requests +
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4 changes: 2 additions & 2 deletions tests/peft/hf_finetune.py
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Expand Up @@ -14,9 +14,9 @@
import argparse
import transformers

if transformers.__version__ < "4.31.0":
if transformers.__version__ < "4.47.1":
raise RuntimeError(
"Please update the transformers library version to 4.31.0 or above"
"Please update the transformers library version to 4.47.1 or above"
)
from datasets import load_dataset

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4 changes: 2 additions & 2 deletions tests/peft/peft_alignment_test.py
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Expand Up @@ -746,8 +746,8 @@ def compare(hf_tensor, ff_tensor, label="", tolerance=1e-4):
ff_gradient_name = convert_hf_filename_to_ff(hf_gradient_name)
ff_gradient = get_ff_tensor(ff_gradient_name, hf_gradient.shape, tp_type=TPType.REPLICATE)

lora_low_rank_activation_fwd_path = f"/usr/.cache/flexflow/debug/flexflow/fwd/step_{step_idx}/shard_0/layers.{i}.layers.{i}.mlp.down_proj.lora.low_rank_activation"
lora_low_rank_activation_bwd_path = f"/usr/.cache/flexflow/debug/flexflow/bwd/step_{step_idx}/shard_0/layers.{i}.layers.{i}.mlp.down_proj.lora.low_rank_activation"
lora_low_rank_activation_fwd_path = f"fwd/step_{step_idx}/shard_0/layers.{i}.layers.{i}.mlp.down_proj.lora.low_rank_activation"
lora_low_rank_activation_bwd_path = f"bwd/step_{step_idx}/shard_0/layers.{i}.layers.{i}.mlp.down_proj.lora.low_rank_activation"
lora_low_rank_activation_fwd = load_ff_tensor(lora_low_rank_activation_fwd_path, [16, 128])[:,:self.num_tokens]
lora_low_rank_activation_fwd = torch.from_numpy(lora_low_rank_activation_fwd)
lora_low_rank_activation_bwd = load_ff_tensor(lora_low_rank_activation_bwd_path, [16, 24])
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