[Model] Add support for GPTJ architecture

[tlopex]

This PR supports GPTJ architecture.

The model conversation demonstration is here:

tlopex@tlopex-OMEN-by-HP-Laptop-17-ck1xxx:~/mlc-llm$ mlc_llm chat dist/gpt-j-6b-q4f16_1-MLC              --device "cuda:0" --overrides context_window_size=2048              --model ./dist/libs/gpt-j-6b-q4f16_1-cuda.so
[2024-11-04 21:35:57] INFO auto_device.py:79: Found device: cuda:0
[2024-11-04 21:35:57] INFO engine_base.py:143: Using library model: ./dist/libs/gpt-j-6b-q4f16_1-cuda.so
[21:35:58] /home/tlopex/mlc-llm/cpp/serve/config.cc:688: Under mode "local", max batch size will be set to 4, max KV cache token capacity will be set to 2048, prefill chunk size will be set to 2048. 
[21:35:58] /home/tlopex/mlc-llm/cpp/serve/config.cc:688: Under mode "interactive", max batch size will be set to 1, max KV cache token capacity will be set to 2048, prefill chunk size will be set to 2048. 
[21:35:58] /home/tlopex/mlc-llm/cpp/serve/config.cc:688: Under mode "server", max batch size will be set to 128, max KV cache token capacity will be set to 20800, prefill chunk size will be set to 2048. 
[21:35:58] /home/tlopex/mlc-llm/cpp/serve/config.cc:769: The actual engine mode is "interactive". So max batch size is 1, max KV cache token capacity is 2048, prefill chunk size is 2048.
[21:35:58] /home/tlopex/mlc-llm/cpp/serve/config.cc:774: Estimated total single GPU memory usage: 5395.686 MB (Parameters: 3247.127 MB. KVCache: 1008.268 MB. Temporary buffer: 1140.291 MB). The actual usage might be slightly larger than the estimated number.
You can use the following special commands:
  /help               print the special commands
  /exit               quit the cli
  /stats              print out stats of last request (token/sec)
  /metrics            print out full engine metrics
  /reset              restart a fresh chat
  /set [overrides]    override settings in the generation config. For example,
                      `/set temperature=0.5;top_p=0.8;seed=23;max_tokens=100;stop=str1,str2`
                      Note: Separate stop words in the `stop` option with commas (,).
  Multi-line input: Use escape+enter to start a new line.

>>> hi
How may I help you?

I found that I have to change code in position_embedding of relax to run locally. I wonder if I still need a update there.

[MasterJH5574]

@tlopex Thanks! Do you mind fixing the lint errors as shown in CI?

[tlopex]

@MasterJH5574 Sorry for being late. I thought I solved the lint issue yesterday.
Now there seems something wrong with Model Compilation,

[2024-11-05 10:28:28] INFO compile.py:185: Registering metadata: {'model_type': 'gptj', 'quantization': 'q4f32_1', 'context_window_size': 2048, 'sliding_window_size': -1, 'attention_sink_size': -1, 'prefill_chunk_size': 2048, 'tensor_parallel_shards': 1, 'pipeline_parallel_stages': 1, 'kv_state_kind': 'kv_cache', 'max_batch_size': 1}

error: Unsupported RoPE scaling type: gptj

 --> /Users/catalyst/Workspace/miniforge3/envs/mlc-llm-ci/lib/python3.8/site-packages/tvm/relax/frontend/nn/llm/kv_cache.py:708:53

     |  

 708 |                                                      _rope(q, q_rope_position[cur_L], d, rope_theta, rope_scale, (cur_L, cur_H_qo, j), dtype, rope_scaling),

     |                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 

Compiling with arguments:

  --config          GPTJConfig(vocab_size=50400, n_embd=4096, n_layer=28, n_head=16, layer_norm_epsilon=1e-05, rotary_dim=64, activation_function='gelu_new', n_inner=None, rope_scaling={'rope_type': 'gptj'}, context_window_size=2048, prefill_chunk_size=2048, tensor_parallel_shards=1, max_batch_size=1, head_dim=0, kwargs={})

  --quantization    GroupQuantize(name='q4f32_1', kind='group-quant', group_size=32, quantize_dtype='int4', storage_dtype='uint32', model_dtype='float32', linear_weight_layout='NK', quantize_embedding=True, quantize_final_fc=True, num_elem_per_storage=8, num_storage_per_group=4, max_int_value=7, tensor_parallel_shards=0)

  --model-type      gptj

  --target          {"thread_warp_size": runtime.BoxInt(32), "host": {"mtriple": "arm64-apple-darwin22.1.0", "tag": "", "kind": "llvm", "mcpu": "apple-m1", "keys": ["arm_cpu", "cpu"]}, "max_threads_per_block": runtime.BoxInt(1024), "max_function_args": runtime.BoxInt(31), "max_num_threads": runtime.BoxInt(256), "kind": "metal", "max_shared_memory_per_block": runtime.BoxInt(32768), "tag": "", "keys": ["metal", "gpu"]}

  --opt             flashinfer=0;cublas_gemm=0;faster_transformer=0;cudagraph=0;cutlass=0;ipc_allreduce_strategy=NONE

  --system-lib-prefix ""

  --output          /var/folders/n1/5d_r6z251v39vwpj8hj_z1vc0000gp/T/tmpl4pq_51h/lib328.dylib

  --overrides       context_window_size=None;sliding_window_size=None;prefill_chunk_size=None;attention_sink_size=None;max_batch_size=None;tensor_parallel_shards=1;pipeline_parallel_stages=None

note: run with `TVM_BACKTRACE=1` environment variable to display a backtrace.

[10:28:28] /Users/catalyst/Workspace/mlc-ai-package-self-runner/_work/package/package/tvm/src/relax/ir/block_builder.cc:65: Warning: BlockBuilder destroyed with remaining blocks!

It is the same problem I met in my own device when I did not update position_embedding in tvm. So I think maybe I need to pull a request there.

[MasterJH5574]

@tlopex It looks to me that we will need to first send the positional embedding changes to apache/tvm in the file https://github.com/apache/tvm/blob/main/python/tvm/relax/frontend/nn/llm/position_embedding.py. Could you try send your changes to position_embedding.py there? After we merge the PR there we can bump tvm and follow up on this PR.

[tlopex]

@MasterJH5574 Sure, I've already done that. Please take a look at it: apache/tvm#17506
I am just wondering why it could not pass the CI.

[MasterJH5574]

@tlopex Hey sorry for the late response. I might have fixed the CI. Could you please help rebase this branch? We can see if it can pass CI now.

[tlopex]

Okay. Thanks! @MasterJH5574

[tlopex]

I think it can be merged. Thanks so much!
cc @MasterJH5574 @tqchen