Add chatglm

[mengker33]

What does this PR do?

This PR adds the chatglm model (a custom model), including chatglm2-6b, chatglm3-6b.
The inference test and pretrain example/test are also available.

Fixes # (issue)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

[xuguangxin]

@libinta @sywangyi , pls help review, thanks.

[emascarenhas]

@mengker33 ,
Please run "pip install -U ruff; make style" and check for errors.
Also run tests/ci/fast_tests.sh and all the slow tests related to test generation for chatglm that you added e.g., "GAUDI2_CI=1 RUN_SLOW=1 python -m pytest test_text_generation_example.py" and check no new errors.

Don't you also need to add a test for the language modeling part?

Does this PR need to be included in this release or can it wait for the next release?

[phoenixdna]

Hi, I am trying to use Guadi card do some inference work on Chatglm3-6b , but I continue to have with the following problem although I use the PR 1478.
The following is the script I copied from your instruction:

GLM=3 python3 run_generation.py \
--model_name_or_path /data/ZhipuAI/chatglm3-6b \
--use_hpu_graphs \
--use_kv_cache \
--do_sample \
--bf16 \
--trim_logits \
--batch_size 1 \
--max_input_tokens 1024 \
--max_new_tokens 512 \
--reuse_cache \
--use_flash_attention

however , I still got the following errors:

[WARNING|utils.py:225] 2024-11-24 21:54:40,419 >> optimum-habana v1.15.0.dev0 has been validated for SynapseAI v1.18.0 but the driver version is v1.17.0, this could lead to undefined behavior!
/root/habanalabs-venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:366: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
  warnings.warn(
/root/habanalabs-venv/lib/python3.10/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
  warnings.warn(
11/24/2024 21:54:41 - INFO - __main__ - Single-device run.
ChatGLMForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00,  1.73it/s]
============================= HABANA PT BRIDGE CONFIGURATION =========================== 
 PT_HPU_LAZY_MODE = 1
 PT_RECIPE_CACHE_PATH = 
 PT_CACHE_FOLDER_DELETE = 0
 PT_HPU_RECIPE_CACHE_CONFIG = 
 PT_HPU_MAX_COMPOUND_OP_SIZE = 9223372036854775807
 PT_HPU_LAZY_ACC_PAR_MODE = 1
 PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 28
CPU RAM       : 123577836 KB
------------------------------------------------------------------------------
[WARNING|tokenization_chatglm.py:174] 2024-11-24 21:54:50,850 >> Setting eos_token is not supported, use the default one.
[WARNING|tokenization_chatglm.py:170] 2024-11-24 21:54:50,850 >> Setting pad_token is not supported, use the default one.
[WARNING|tokenization_chatglm.py:166] 2024-11-24 21:54:50,850 >> Setting unk_token is not supported, use the default one.
11/24/2024 21:54:51 - INFO - __main__ - Args: Namespace(device='hpu', model_name_or_path='/data/ZhipuAI/chatglm3-6b', bf16=True, max_new_tokens=512, max_input_tokens=1024, batch_size=1, warmup=3, n_iterations=5, local_rank=0, use_kv_cache=True, use_hpu_graphs=True, dataset_name=None, column_name=None, do_sample=True, num_beams=1, top_k=None, penalty_alpha=None, trim_logits=True, seed=27, profiling_warmup_steps=0, profiling_steps=0, profiling_record_shapes=False, prompt=None, bad_words=None, force_words=None, assistant_model=None, peft_model=None, num_return_sequences=1, token=None, model_revision='main', attn_softmax_bf16=False, output_dir=None, bucket_size=-1, bucket_internal=False, dataset_max_samples=-1, limit_hpu_graphs=False, show_graphs_count=False, reuse_cache=True, verbose_workers=False, simulate_dyn_prompt=None, reduce_recompile=False, use_flash_attention=True, flash_attention_recompute=False, flash_attention_causal_mask=False, flash_attention_fast_softmax=True, book_source=False, torch_compile=False, ignore_eos=True, temperature=1.0, top_p=1.0, const_serialization_path=None, trust_remote_code=False, parallel_strategy='none', input_embeds=False, run_partial_dataset=False, load_quantized_model_with_autogptq=False, disk_offload=False, load_quantized_model_with_inc=False, local_quantized_inc_model_path=None, quant_config='', world_size=0, global_rank=0)
11/24/2024 21:54:51 - INFO - __main__ - device: hpu, n_hpu: 0, bf16: True
11/24/2024 21:54:51 - INFO - __main__ - Model initialization took 10.588s
11/24/2024 21:54:51 - INFO - __main__ - Graph compilation...
Warming up iteration 1/3
Traceback (most recent call last):
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 758, in <module>
    main()
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 523, in main
    generate(None, args.reduce_recompile)
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 494, in generate
    outputs = model.generate(
  File "/root/habanalabs-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/root/jupyter/optimum-habana/optimum/habana/transformers/generation/utils.py", line 1008, in generate
    self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
  File "/root/habanalabs-venv/lib/python3.10/site-packages/transformers/generation/utils.py", line 1676, in _prepare_special_tokens
    eos_token_tensor is not None
RuntimeError: Graph compile failed. synStatus=synStatus 26 [Generic failure].

As I understand this PR is exactly for chatglm3-6b，but I don't understand why this happens after tried a lots of times. So please help to give some suggestion!
with regards.

[mengker33]

tests/ci/fast_tests.sh

Hi, I have tried with fast_tests.sh and test_text_generation_example.py, and no errors occurred.
I also added tests for the language modeling part.

[mengker33]

Hi, I am trying to use Guadi card do some inference work on Chatglm3-6b , but I continue to have with the following problem although I use the PR 1478. The following is the script I copied from your instruction:

GLM=3 python3 run_generation.py \
--model_name_or_path /data/ZhipuAI/chatglm3-6b \
--use_hpu_graphs \
--use_kv_cache \
--do_sample \
--bf16 \
--trim_logits \
--batch_size 1 \
--max_input_tokens 1024 \
--max_new_tokens 512 \
--reuse_cache \
--use_flash_attention

however , I still got the following errors:

[WARNING|utils.py:225] 2024-11-24 21:54:40,419 >> optimum-habana v1.15.0.dev0 has been validated for SynapseAI v1.18.0 but the driver version is v1.17.0, this could lead to undefined behavior!
/root/habanalabs-venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:366: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
  warnings.warn(
/root/habanalabs-venv/lib/python3.10/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
  warnings.warn(
11/24/2024 21:54:41 - INFO - __main__ - Single-device run.
ChatGLMForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00,  1.73it/s]
============================= HABANA PT BRIDGE CONFIGURATION =========================== 
 PT_HPU_LAZY_MODE = 1
 PT_RECIPE_CACHE_PATH = 
 PT_CACHE_FOLDER_DELETE = 0
 PT_HPU_RECIPE_CACHE_CONFIG = 
 PT_HPU_MAX_COMPOUND_OP_SIZE = 9223372036854775807
 PT_HPU_LAZY_ACC_PAR_MODE = 1
 PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 28
CPU RAM       : 123577836 KB
------------------------------------------------------------------------------
[WARNING|tokenization_chatglm.py:174] 2024-11-24 21:54:50,850 >> Setting eos_token is not supported, use the default one.
[WARNING|tokenization_chatglm.py:170] 2024-11-24 21:54:50,850 >> Setting pad_token is not supported, use the default one.
[WARNING|tokenization_chatglm.py:166] 2024-11-24 21:54:50,850 >> Setting unk_token is not supported, use the default one.
11/24/2024 21:54:51 - INFO - __main__ - Args: Namespace(device='hpu', model_name_or_path='/data/ZhipuAI/chatglm3-6b', bf16=True, max_new_tokens=512, max_input_tokens=1024, batch_size=1, warmup=3, n_iterations=5, local_rank=0, use_kv_cache=True, use_hpu_graphs=True, dataset_name=None, column_name=None, do_sample=True, num_beams=1, top_k=None, penalty_alpha=None, trim_logits=True, seed=27, profiling_warmup_steps=0, profiling_steps=0, profiling_record_shapes=False, prompt=None, bad_words=None, force_words=None, assistant_model=None, peft_model=None, num_return_sequences=1, token=None, model_revision='main', attn_softmax_bf16=False, output_dir=None, bucket_size=-1, bucket_internal=False, dataset_max_samples=-1, limit_hpu_graphs=False, show_graphs_count=False, reuse_cache=True, verbose_workers=False, simulate_dyn_prompt=None, reduce_recompile=False, use_flash_attention=True, flash_attention_recompute=False, flash_attention_causal_mask=False, flash_attention_fast_softmax=True, book_source=False, torch_compile=False, ignore_eos=True, temperature=1.0, top_p=1.0, const_serialization_path=None, trust_remote_code=False, parallel_strategy='none', input_embeds=False, run_partial_dataset=False, load_quantized_model_with_autogptq=False, disk_offload=False, load_quantized_model_with_inc=False, local_quantized_inc_model_path=None, quant_config='', world_size=0, global_rank=0)
11/24/2024 21:54:51 - INFO - __main__ - device: hpu, n_hpu: 0, bf16: True
11/24/2024 21:54:51 - INFO - __main__ - Model initialization took 10.588s
11/24/2024 21:54:51 - INFO - __main__ - Graph compilation...
Warming up iteration 1/3
Traceback (most recent call last):
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 758, in <module>
    main()
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 523, in main
    generate(None, args.reduce_recompile)
  File "/root/jupyter/optimum-habana/examples/text-generation/run_generation.py", line 494, in generate
    outputs = model.generate(
  File "/root/habanalabs-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/root/jupyter/optimum-habana/optimum/habana/transformers/generation/utils.py", line 1008, in generate
    self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
  File "/root/habanalabs-venv/lib/python3.10/site-packages/transformers/generation/utils.py", line 1676, in _prepare_special_tokens
    eos_token_tensor is not None
RuntimeError: Graph compile failed. synStatus=synStatus 26 [Generic failure].

As I understand this PR is exactly for chatglm3-6b，but I don't understand why this happens after tried a lots of times. So please help to give some suggestion! with regards.

Hi, I didn't see any inference/pretraining error from my local test, please check if your test goes through the correct glm modeling path in optimum-habana.

[phoenixdna]

Hi, I am trying to use Guadi card do some inference work on Chatglm3-6b , but I continue to have with the following problem although I use the PR 1478. The following is the script I copied from your instruction:

As I understand this PR is exactly for chatglm3-6b，but I don't understand why this happens after tried a lots of times. So please help to give some suggestion! with regards.

Hi, I didn't see any inference/pretraining error from my local test, please check if your test goes through the correct glm modeling path in optimum-habana.

Thanks for you reply, I download the chatglm3-6b from the modelscope, I don't know what's your mean by "go throught the correct glm modeling path in optimum-habana", could you kindly expain this?

[mengker33]

Hi, I am trying to use Guadi card do some inference work on Chatglm3-6b , but I continue to have with the following problem although I use the PR 1478. The following is the script I copied from your instruction:

As I understand this PR is exactly for chatglm3-6b，but I don't understand why this happens after tried a lots of times. So please help to give some suggestion! with regards.

Hi, I didn't see any inference/pretraining error from my local test, please check if your test goes through the correct glm modeling path in optimum-habana.

Thanks for you reply, I download the chatglm3-6b from the modelscope, I don't know what's your mean by "go throught the correct glm modeling path in optimum-habana", could you kindly expain this?

You need to check if the model is initialized correctly by going through optimum/habana/transformers/models/chatglm/modeling_chatglm.py instead of the one in your downloaded modeling codes.

[emascarenhas]

@mengker33 ,

I tried to run this test and got an error.
optimum-habana# GAUDI2_CI=1 RUN_SLOW=1 python -m pytest tests/test_text_generation_example.py

__________________________________________________________ ERROR collecting tests/test_text_generation_example.py __________________________________________________________
tests/test_text_generation_example.py::test_text_generation_bf16_1x: in "parametrize" the number of names (5):
['model_name', 'batch_size', 'reuse_cache', 'baseline', 'check_output']
must be equal to the number of values (4):
('THUDM/glm-4-9b-chat', 1, True, 105)

[mengker33]

@mengker33 ,

I tried to run this test and got an error. optimum-habana# GAUDI2_CI=1 RUN_SLOW=1 python -m pytest tests/test_text_generation_example.py

__________________________________________________________ ERROR collecting tests/test_text_generation_example.py __________________________________________________________ tests/test_text_generation_example.py::test_text_generation_bf16_1x: in "parametrize" the number of names (5): ['model_name', 'batch_size', 'reuse_cache', 'baseline', 'check_output'] must be equal to the number of values (4): ('THUDM/glm-4-9b-chat', 1, True, 105)

I think you are using the old version of this PR, please rebase to the latest and try again, thanks!

[emascarenhas]

@mengker33 ,

I think you are using the old version of this PR, please rebase to the latest and try again, thanks!

Yes. This was the case. I am able to run the examples in the readme successfully after rebasing.
This test command is giving an error.
GAUDI2_CI=1 RUN_SLOW=1 python -m pytest tests/test_examples.py -s -v -k chatglm
E FileNotFoundError: [Errno 2] No such file or directory: '/home/optimum-habana/tests/baselines/chatglm3_6b.json'

Is that file required?

[phoenixdna]

Thanks for you reply, I download the chatglm3-6b from the modelscope, I don't know what's your mean by "go throught the correct glm modeling path in optimum-habana", could you kindly expain this?

You need to check if the model is initialized correctly by going through optimum/habana/transformers/models/chatglm/modeling_chatglm.py instead of the one in your downloaded modeling codes.

ok, thx for your reply and will give a try

[mengker33]

GAUDI2_CI=1 RUN_SLOW=1 python -m pytest tests/test_examples.py -s -v -k chatglm

Sorry, my bad... I had this baselines/chatglm3_6b.json file locally but forgot to push it to this PR. Really appreciate your test!

[regisss]

Please add this model to the table in the README and in the doc:

• optimum-habana/README.md

  Line 192 in 899b364

  | Architecture | Training | Inference | <center>Tasks</center> |

• optimum-habana/docs/source/index.mdx

  Line 59 in 899b364

  | Architecture | Training | Inference | Tasks |

[regisss]

What's the difference between ChatGLM-2 and ChatGLM-3 exactly? To know if we really need to test both

[mengker33]

I don't think there is modeling functional difference, the only difference lies in some customized tokenizer methods' implementation. I removed the test for chatglm2.

[mengker33]

Please add this model to the table in the README and in the doc:

• optimum-habana/README.md

  Line 192 in 899b364

  | Architecture | Training | Inference | <center>Tasks</center> |

• optimum-habana/docs/source/index.mdx

  Line 59 in 899b364

  | Architecture | Training | Inference | Tasks |

Done, thanks!

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.