Failed to collect training data for sparsity predictor

[PaulWang0513]

Hi,
I was trying to train the sparsity predictor by following the instructions in the README file, but encountered some error when running ./run_infer_opt_175b_collect_sp_data.sh.

Following is the printed message:

start running ./c4_train/c4_train.jsonl
Initialize NCCLCommunicator: < pipeline_group_0 >; rank: 0
<get_request_processor>: None
<RequestProcessor> dir: ./c4_train
<RequestProcessor> file: c4_train.jsonl
<RequestProcessor>, output file: ./c4_train/output_c4_train.jsonl
input seq length: 2048
=======Initialize Dist Inference(Sync).=======
=======Gpipe use FP16=======
loading layer 0
loading layer 1
loading layer 2
loading layer 3
loading layer 4
loading layer 5
loading layer 6
loading layer 7
loading layer 8
loading layer 9
loading layer 10
loading layer 11
temperature is 0, should be deterministic (greedy).
temperature is 0, should be deterministic (greedy).
<inference_batch> rank-<0> Enter!
<inference_batch> rank-<0> after first barrier!
<inference_batch> rank-<0> after first _init_cached_seqs_and_attentions!
<inference_batch> rank-<0> after second barrier!
<inference_batch> rank-<0> enter computation!
Compute prompt seq< 0 >.
_copy_initial_token_emb
_copy_initial_token_emb 0/1
Traceback (most recent call last):
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/dist_inference_runner.py", line 111, in <module>
    main()
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/dist_inference_runner.py", line 97, in main
    distributed_inference_mask_iter(args, pipe, device, request_processor)
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/utils/dist_inference_utils.py", line 58, in distributed_inference_mask_iter
    current_iter_time = pipeline.inference_batch(input_ids, output_ids_list, attention_mask=attention_mask)
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/pipeline_parallel/dist_pipeline_inference_mask_greedy_token_pipe_sync.py", line 822, in inference_batch
    self.forward_seq_pipeline_stage(
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/pipeline_parallel/dist_pipeline_inference_mask_greedy_token_pipe_sync.py", line 652, in forward_seq_pipeline_stage
    self._forward_compute_prompt_seq(
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/pipeline_parallel/dist_pipeline_inference_mask_greedy_token_pipe_sync.py", line 470, in _forward_compute_prompt_seq
    self._generate_echo_token_logprobs(index, indices=seq)
  File "/mnt/c/Users/Paul/Documents/GitHub/DejaVu/Decentralized_FM_alpha/pipeline_parallel/dist_pipeline_inference_mask_greedy_token_pipe_sync.py", line 485, in _generate_echo_token_logprobs
    self.ret_tokens[
RuntimeError: The expanded size of the tensor (2048) must match the existing size (2047) at non-singleton dimension 1.  Target sizes: [1, 2048].  Tensor sizes: [2047]

I used WSL2 with Ubuntu 20.04 and CUDA 11.3.
Due to storage limitations, I changed some setting to use a smaller pretrained model (opt-125m).
In DejaVu/Decentralized_FM_alpha/c4_train/get_data.py:

data = {
    "best_of": 1,
    "echo": True,
    "logprobs": 1,
    "max_tokens": 0,
    "model": "opt-125m",
    "n": 1,
    "prompt": doc["text"],
    "request_type": "language-model-inference",
    "stop": None,
    "temperature": 0,
    "top_p": 1,
}

In DejaVu/Decentralized_FM_alpha/convert_opt_checkpoint.py:

parser.add_argument('--model-name', type=str, default='facebook/opt-125m', 
                        help='model-name')
parser.add_argument('--save-path', type=str, default='/mnt/c/Users/Paul/Documents/GitHub/DejaVu/pretrained_models/opt-125m/checkpoint', 
                        help='model-name')

In DejaVu/Decentralized_FM_alpha/run_infer_opt_175b_collect_sp_data.sh:

ARGS="--model-name /mnt/c/Users/Paul/Documents/GitHub/DejaVu/pretrained_models/opt-125m/checkpoint \
--model-type opt-save \
--seed 42 \
--fp16 \
--num-layers 12 \
--max-layers 12 \
--budget 22800 \
--num-iters 2000 \
--dist-url tcp://127.0.0.1:9032 \
--token-micro-batch-size 1 \
--world-size 1 --pipeline-group-size 1 --data-group-size 1 \
--pp-mode pipe_sync_sample_mask_token_pipe \
--infer-data ${file}"

(trap 'kill 0' SIGINT; \
python dist_inference_runner.py $(echo ${ARGS}) --cuda-id 0 --rank 0 \
    & \
wait)

The att_sp_x_0.mmap ~ att_sp_x_11.mmap, mlp_sp_x_0.mmap ~ mlp_sp_x_11.mmap, mlp_label_0.mmap ~ mlp_label_11.mmap, and score_norm_0 ~ score_norm_11.mmap are created successfully before the error occured.
Can you think of any possible reason or solution?
Thanks!

[simonsimanta]

Hello Authors,

Encountered the above RuntimeError related to tensor size mismatch while trying to train the sparsity predictor as per the instructions in the README. The issue arises when running ./run_infer_opt_175b_collect_sp_data.sh. All relevant files are created successfully until the script hits a tensor size error.

RuntimeError: The expanded size of the tensor (2048) must match the existing size (2047) at non-singleton dimension 1. Target sizes: [1, 2048]. Tensor sizes: [2047]

Can you please provide additional information on how can we resolve the issue ?

Thank You .

[zhaoyang-star]

I met the same error when tp=1. Looking for the author to fix it.
A tricky way to solve it is using tp=2. I have verified it and the error disappears.

[ariellubonja]

Hi everyone! I am facing this issue right now, after trying to use facebook/opt-125m model instead. Anyone found a fix?

@zhaoyang-star Where is this parameter tp=2?

[2455DD]

Same things happens to me too when I'm trying to use facebook/opt-1.7b model in colab notebook. Waiting for any possible solution.

I met the same error when tp=1. Looking for the author to fix it. A tricky way to solve it is using tp=2. I have verified it and the error disappears.

@zhaoyang-star Is tp a library or a parameter? Does it mean the top-p in c4_train/getdata.py ? Thanks for your help!

[ariellubonja]

@2455DD I reached out to the author earlier this month, and she replied with this "I believe there is a temporary work around suggested on GitHub by setting the top_p = 2 in get_data file."

So, you're correct, it is top-p in c4_train/getdata.py

[mailonghua]

I followed the instructions and modified the file DejaVu/Decentralized_FM_alpha/c4_train/getdata.py in the following way:

data = {
"best_of": 1,
"echo": True,
"logprobs": 1,
"max_tokens": 0,
"model": "opt-1.3b",
"n": 1,
"prompt": doc["text"],
"request_type": "language-model-inference",
"stop": None,
"temperature": 0,
"top_p": 2,
}
After making these changes, I deleted the local Hugging Face cache of C4 and the file c4_train.jsonl. I then re-executed getdata.py and ran the script run_infer_opt_175b_collect_sp_data.sh. However, I am still encountering a size mismatch error:
xxx
RuntimeError: The size of tensor a (2047) must match the size of tensor b (2048) at non-singleton dimension 3

[mailonghua]

@2455DD I reached out to the author earlier this month, and she replied with this "I believe there is a temporary work around suggested on GitHub by setting the top_p = 2 in get_data file."

So, you're correct, it is top-p in c4_train/getdata.py

Same things happens to me too when I'm trying to use facebook/opt-1.7b model in colab notebook. Waiting for any possible solution.

I met the same error when tp=1. Looking for the author to fix it. A tricky way to solve it is using tp=2. I have verified it and the error disappears.

@zhaoyang-star Is tp a library or a parameter? Does it mean the top-p in c4_train/getdata.py ? Thanks for your help!

I used the run_infer_opt_1_3_b_collect_sp_data.sh script from your forked repository, but I encountered the same size mismatch error. Did you run the script successfully?

[Philippe-Guyard]

@2455DD I reached out to the author earlier this month, and she replied with this "I believe there is a temporary work around suggested on GitHub by setting the top_p = 2 in get_data file."
So, you're correct, it is top-p in c4_train/getdata.py

Same things happens to me too when I'm trying to use facebook/opt-1.7b model in colab notebook. Waiting for any possible solution.

I met the same error when tp=1. Looking for the author to fix it. A tricky way to solve it is using tp=2. I have verified it and the error disappears.

@zhaoyang-star Is tp a library or a parameter? Does it mean the top-p in c4_train/getdata.py ? Thanks for your help!

I used the run_infer_opt_1_3_b_collect_sp_data.sh script from your forked repository, but I encountered the same size mismatch error. Did you run the script successfully?

Can confirm I am having a similar issue after setting "model": "opt-125m", "top_p":2. Only difference is I get

RuntimeError: The expanded size of the tensor (2048) must match the existing size (2047) at non-singleton dimension 1.  Target sizes: [1, 2048].  Tensor sizes: [2047]

(the difference being that the mismatch happens at dimension 1 and not 3).

[zhaoningyuan]

it works for me if i set pipeline-group-size=2

ARGS="--model-name /data/xxx/DejaVu/Decentralized_FM_alpha/pretrained_models \
--model-type opt-save \
--seed 42 \
--fp16 \
--num-layers 16 \
--max-layers 32 \
--budget 22800 \
--num-iters 2000 \
--dist-url tcp://127.0.0.1:9032 \
--token-micro-batch-size 16 \
--world-size 2 --pipeline-group-size 2 --data-group-size 1 \
--pp-mode pipe_sync_sample_mask_token_pipe \
--infer-data ${file}"

(trap 'kill 0' SIGINT; \
python dist_inference_runner.py $(echo ${ARGS}) --cuda-id 0 --rank 0 \
    &
python dist_inference_runner.py $(echo ${ARGS}) --cuda-id 1 --rank 1 \
    & \
wait)

Hello Authors,

Encountered the above RuntimeError related to tensor size mismatch while trying to train the sparsity predictor as per the instructions in the README. The issue arises when running ./run_infer_opt_175b_collect_sp_data.sh. All relevant files are created successfully until the script hits a tensor size error.

RuntimeError: The expanded size of the tensor (2048) must match the existing size (2047) at non-singleton dimension 1. Target sizes: [1, 2048]. Tensor sizes: [2047]

Can you please provide additional information on how can we resolve the issue ?

Thank You .