diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_7b_chat.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..2c108cc137ed15f8c79de5ea229844900a8f17f0 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_7b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-7b-chat-turbomind', + path='deepseek-ai/deepseek-llm-7b-chat', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9), + max_seq_len=8192, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_70b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_70b.py new file mode 100644 index 0000000000000000000000000000000000000000..1471be9b32832111dd9fff735d682e014e183ac7 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_70b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-llama-70b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Llama-70B', + engine_config=dict(session_len=32768, max_batch_size=8, tp=8), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=8, + run_cfg=dict(num_gpus=8), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_8b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..46b521f2aa3e48e50537d6b093f74d5748f9fc15 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_llama_8b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-llama-8b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Llama-8B', + engine_config=dict(session_len=32768, max_batch_size=8, tp=1), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=8, + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_14b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..401299cdb1cbcb39eb5ad3c01cf4f30459a225fa --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_14b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-qwen-14b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=16, + run_cfg=dict(num_gpus=2), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_1_5b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_1_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..d19ace4e9f71cfdfd1d28157e55f61fdfa3cdd44 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_1_5b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-qwen-1_5b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=16, + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_32b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..2ddef1d421c1f0292403d55bdf7d67073c6c35ec --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_32b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-qwen-32b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=16, + run_cfg=dict(num_gpus=4), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_7b.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..69f9e50f36f92d104ef41f857bf41873f81c9374 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_r1_distill_qwen_7b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-r1-distill-qwen-7b-turbomind', + path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', + engine_config=dict(session_len=32768, max_batch_size=8, tp=1), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=8, + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_series.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_series.py new file mode 100644 index 0000000000000000000000000000000000000000..5060b0c0bc1eb9366541645bbcf146c7abb90611 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_series.py @@ -0,0 +1,23 @@ +from opencompass.models import LmdeployPytorchModel + +settings = [ + ('deepseek-7b-base-hf', 'deepseek-ai/deepseek-llm-7b-base', 1), + ('deepseek-67b-base-hf', 'deepseek-ai/deepseek-llm-67b-base', 4), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=LmdeployPytorchModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=2048, max_batch_size=16, tp=num_gpus), + gen_config=dict(top_k=1, temperature=1, top_p=0.9, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=2048, + batch_size=16, + concurrency=16, + run_cfg=dict(num_gpus=num_gpus), + ) + ) diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..a535003ecd2f07840714ede6d146007e2a7cb817 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2.py @@ -0,0 +1,18 @@ +# flake8: noqa +from mmengine.config import read_base +from opencompass.models import ( + TurboMindModel, +) +lmdeploy_deepseek_v2_model = [ + dict( + type=TurboMindModel, + abbr='deepseek-v2-turbomind', + path='deepseek-ai/DeepSeek-V2', + engine_config=dict(session_len=7168, max_batch_size=4, tp=8, cache_max_entry_count=0.7), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9), + max_seq_len=7168, + max_out_len=2048, + batch_size=4, + run_cfg=dict(num_gpus=8), + ) +] \ No newline at end of file diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5.py new file mode 100644 index 0000000000000000000000000000000000000000..f0864516352c4faebc8980318c7f1dde26e7aed9 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5.py @@ -0,0 +1,21 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-v2_5-turbomind', + path='deepseek-ai/DeepSeek-V2.5', + backend='pytorch', + engine_config=dict( + session_len=7168, + max_batch_size=4, + tp=8, + cache_max_entry_count=0.7, + ), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9), + max_seq_len=7168, + max_out_len=2048, + batch_size=4, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5_1210.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5_1210.py new file mode 100644 index 0000000000000000000000000000000000000000..bbb9f4b2354e2e5e4e7e38f9359c49cedffd96cd --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_5_1210.py @@ -0,0 +1,21 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-v2_5-1210-turbomind', + path='deepseek-ai/DeepSeek-V2.5-1210', + backend='pytorch', + engine_config=dict( + session_len=7168, + max_batch_size=4, + tp=8, + cache_max_entry_count=0.7, + ), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9), + max_seq_len=7168, + max_out_len=2048, + batch_size=4, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_lite.py b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_lite.py new file mode 100644 index 0000000000000000000000000000000000000000..0623ee02f6abfefbed76c458cc09a6665013f7a3 --- /dev/null +++ b/opencompass/configs/models/deepseek/lmdeploy_deepseek_v2_lite.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='deepseek-v2_lite-chat-turbomind', + path='deepseek-ai/DeepSeek-V2-Lite-Chat', + engine_config=dict( + session_len=7168, + max_batch_size=4, + tp=2, + cache_max_entry_count=0.7, + ), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9), + max_seq_len=7168, + max_out_len=2048, + batch_size=4, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/deepseek/vllm_deepseek_67b_chat.py b/opencompass/configs/models/deepseek/vllm_deepseek_67b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..3c4d1fec0907f03e2c53d8a6aadcb4f14878b789 --- /dev/null +++ b/opencompass/configs/models/deepseek/vllm_deepseek_67b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='deepseek-67b-chat-vllm', + path='deepseek-ai/deepseek-llm-67b-chat', + max_out_len=1024, + batch_size=16, + model_kwargs=dict(tensor_parallel_size=4), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/deepseek/vllm_deepseek_7b_chat.py b/opencompass/configs/models/deepseek/vllm_deepseek_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..21308ae6142f5deb6be956d87e042d8d446084b1 --- /dev/null +++ b/opencompass/configs/models/deepseek/vllm_deepseek_7b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='deepseek-7b-chat-vllm', + path='deepseek-ai/deepseek-llm-7b-chat', + max_out_len=1024, + batch_size=16, + model_kwargs=dict(tensor_parallel_size=1), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_base.py b/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_base.py new file mode 100644 index 0000000000000000000000000000000000000000..36fcf5cf850212d03f30422e54d13c7712c8f773 --- /dev/null +++ b/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_base.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='deepseek-moe-16b-base-vllm', + path='deepseek-ai/deepseek-moe-16b-base', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.6), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_chat.py b/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..e88fa99265a7e9e5fb29bdf17b02310c70b135bb --- /dev/null +++ b/opencompass/configs/models/deepseek/vllm_deepseek_moe_16b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='deepseek-moe-16b-chat-vllm', + path='deepseek-ai/deepseek-moe-16b-chat', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.6), + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/deltanet/delta_net-1.3B.py b/opencompass/configs/models/deltanet/delta_net-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..c33d3b9242e1f8c7c43524efc73b9cdbe5065548 --- /dev/null +++ b/opencompass/configs/models/deltanet/delta_net-1.3B.py @@ -0,0 +1,27 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/delta_net-1.3B-100B', + tokenizer_path='/mnt/jfzn/msj/delta_net-1.3B-100B', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + abbr='delta_net', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] + + + + diff --git a/opencompass/configs/models/deltanet/gdn-1.3B.py b/opencompass/configs/models/deltanet/gdn-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..ff46f9a2fba01fc5fb277d2b2d5bd7ad01206277 --- /dev/null +++ b/opencompass/configs/models/deltanet/gdn-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/train_exp/gdn_1B_a800', + tokenizer_path='/mnt/jfzn/msj/train_exp/gdn_1B_a800', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='gated_deltanet', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/gla-1.3B.py b/opencompass/configs/models/deltanet/gla-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..40b47969f287cd0efe29eef206054e3055c1d7a0 --- /dev/null +++ b/opencompass/configs/models/deltanet/gla-1.3B.py @@ -0,0 +1,28 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/gla-1.3B-100B', + tokenizer_path='/mnt/jfzn/msj/gla-1.3B-100B', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='gla', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] + + + + diff --git a/opencompass/configs/models/deltanet/hgrn2-1.3B.py b/opencompass/configs/models/deltanet/hgrn2-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..1a5d43578aa578f5232887ab5a265581ad68aa1f --- /dev/null +++ b/opencompass/configs/models/deltanet/hgrn2-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/download_model/hgrn2-1.3B-100B', + tokenizer_path='/mnt/jfzn/msj/download_model/hgrn2-1.3B-100B', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='hgrn2', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/mask_deltanet-1.3B.py b/opencompass/configs/models/deltanet/mask_deltanet-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..018fd883810f0ad975419f12c2e237e1eadaf58f --- /dev/null +++ b/opencompass/configs/models/deltanet/mask_deltanet-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/train_exp/mask_deltanet_1B_rank4', + tokenizer_path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr-rank4', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='mask_deltanet', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/mask_gdn-1.3B.py b/opencompass/configs/models/deltanet/mask_gdn-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..a0c5f1cc823bc9d35c281ee981fbe643288b670b --- /dev/null +++ b/opencompass/configs/models/deltanet/mask_gdn-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr-rank4', + tokenizer_path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr-rank4', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='mask_gdn', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/mask_gdn_t-1.3B.py b/opencompass/configs/models/deltanet/mask_gdn_t-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..dc4efdb9b3315c64ff05f1ff1b4506b4f8639856 --- /dev/null +++ b/opencompass/configs/models/deltanet/mask_gdn_t-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr4_byt', + tokenizer_path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr4_byt', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='mask_gdn', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/retnet-1.3B.py b/opencompass/configs/models/deltanet/retnet-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..2659004e3972b3ef0d1a07f8567e4dd50588e186 --- /dev/null +++ b/opencompass/configs/models/deltanet/retnet-1.3B.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/download_model/retnet-1.3B-100B', + tokenizer_path='/mnt/jfzn/msj/download_model/retnet-1.3B-100B', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='retnet', + batch_size=128, + # max_out_len=48, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] diff --git a/opencompass/configs/models/deltanet/transformer++-1.3B.py b/opencompass/configs/models/deltanet/transformer++-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..66525afc9b40f6a80085053d637035f77571672e --- /dev/null +++ b/opencompass/configs/models/deltanet/transformer++-1.3B.py @@ -0,0 +1,28 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + # HuggingFaceCasualLM + path='/mnt/jfzn/msj/transformer-1.3B-100B', + tokenizer_path='/mnt/jfzn/msj/transformer-1.3B-100B', + tokenizer_kwargs=dict( + padding_side='left', truncation_side='left', + ), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + device_map='auto', + trust_remote_code=True, + ), + max_seq_len=2048, + # opencompass + abbr='transformer', + batch_size=128, + # max_out_len=100, + run_cfg=dict(num_gpus=1), # 模型部署在几个GPU上 + ) +] + + + + diff --git a/opencompass/configs/models/falcon/hf_falcon_40b.py b/opencompass/configs/models/falcon/hf_falcon_40b.py new file mode 100644 index 0000000000000000000000000000000000000000..d5cf827badfc8571bb19b4b0340f1e12e451e455 --- /dev/null +++ b/opencompass/configs/models/falcon/hf_falcon_40b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='falcon-40b-hf', + path='tiiuae/falcon-40b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/falcon/hf_falcon_7b.py b/opencompass/configs/models/falcon/hf_falcon_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..6c42355cdfa7c0278dcf013a53d69bdbf4661aea --- /dev/null +++ b/opencompass/configs/models/falcon/hf_falcon_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='falcon-7b-hf', + path='tiiuae/falcon-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemini/gemini_1_5_flash.py b/opencompass/configs/models/gemini/gemini_1_5_flash.py new file mode 100644 index 0000000000000000000000000000000000000000..79eea6a7d0bb28da06590437daf5bc0bb7a3f740 --- /dev/null +++ b/opencompass/configs/models/gemini/gemini_1_5_flash.py @@ -0,0 +1,22 @@ +from opencompass.models import Gemini + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='gemini-1.5-flash', + type=Gemini, + path='gemini-1.5-flash', + key= + 'ENV', # The key will be obtained from $GEMINI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=15, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + temperature=1, + ) +] diff --git a/opencompass/configs/models/gemini/gemini_1_5_pro.py b/opencompass/configs/models/gemini/gemini_1_5_pro.py new file mode 100644 index 0000000000000000000000000000000000000000..1734849b49a75e293b2b72b332cfcc573f250dff --- /dev/null +++ b/opencompass/configs/models/gemini/gemini_1_5_pro.py @@ -0,0 +1,22 @@ +from opencompass.models import Gemini + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='gemini-1.5-pro', + type=Gemini, + path='gemini-1.5-pro', + key= + 'ENV', # The key will be obtained from $GEMINI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=2, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + temperature=1, + ) +] diff --git a/opencompass/configs/models/gemini/gemini_pro.py b/opencompass/configs/models/gemini/gemini_pro.py new file mode 100644 index 0000000000000000000000000000000000000000..ff2124bce4db9eb809cc4e662d35f7265f79a1c0 --- /dev/null +++ b/opencompass/configs/models/gemini/gemini_pro.py @@ -0,0 +1,22 @@ +from opencompass.models import Gemini + + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], +) + +models = [ + dict(abbr='gemini', + type=Gemini, + path='gemini-pro', + key='ENV', # The key will be obtained from $GEMINI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=16, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + temperature=1,) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_27b.py b/opencompass/configs/models/gemma/hf_gemma2_27b.py new file mode 100644 index 0000000000000000000000000000000000000000..e3e2a36fed33cb48b1dc12fabe4b72d664daec61 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_27b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='gemma2-27b-hf', + path='google/gemma-2-27b', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=2), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_27b_it.py b/opencompass/configs/models/gemma/hf_gemma2_27b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..8d0895e180b5f9b7ebcdf2a3e40e8bc4de7b6b79 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_27b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='gemma2-27b-it-hf', + path='google/gemma-2-27b-it', + max_out_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=2), + stop_words=[''], + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ) + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_2b.py b/opencompass/configs/models/gemma/hf_gemma2_2b.py new file mode 100644 index 0000000000000000000000000000000000000000..498e46bbd371da79b39a726b5b03775d7cc5564a --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_2b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='gemma2-2b-hf', + path='google/gemma-2-2b', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=1), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_2b_it.py b/opencompass/configs/models/gemma/hf_gemma2_2b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..4064245f0acbbe098524d551202210d6c1cf0dae --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_2b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='gemma2-2b-it-hf', + path='google/gemma-2-2b-it', + max_out_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=1), + stop_words=[''], + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ) + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_9b.py b/opencompass/configs/models/gemma/hf_gemma2_9b.py new file mode 100644 index 0000000000000000000000000000000000000000..cbfb775c5c806e73dac93fc6d2f828f474cd8318 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_9b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='gemma2-9b-hf', + path='google/gemma-2-9b', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=1), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma2_9b_it.py b/opencompass/configs/models/gemma/hf_gemma2_9b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..1f5103d94573f68e44acf5669b8dfa8043068c22 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma2_9b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='gemma2-9b-it-hf', + path='google/gemma-2-9b-it', + max_out_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=1), + stop_words=[''], + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ) + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma_2b.py b/opencompass/configs/models/gemma/hf_gemma_2b.py new file mode 100644 index 0000000000000000000000000000000000000000..966ff397f0e38ea8f91d91dcc35a088a1b672485 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma_2b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='gemma-2b-hf', + path='google/gemma-2b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma_2b_it.py b/opencompass/configs/models/gemma/hf_gemma_2b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..d2a83b8fae26ec66f56b0f9de7c0faf044653b05 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma_2b_it.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='gemma-2b-it-hf', + path='google/gemma-2b-it', + max_out_len=1024, + batch_size=1, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma_7b.py b/opencompass/configs/models/gemma/hf_gemma_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c3b6d4875378d61937c6caa7eb9d92baaaf3d6 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='gemma-7b-hf', + path='google/gemma-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/hf_gemma_7b_it.py b/opencompass/configs/models/gemma/hf_gemma_7b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..5d56ef9acefe892f13867a43071eccf60a2823c6 --- /dev/null +++ b/opencompass/configs/models/gemma/hf_gemma_7b_it.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='gemma-7b-it-hf', + path='google/gemma-7b-it', + max_out_len=1024, + batch_size=1, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/lmdeploy_gemma_27b.py b/opencompass/configs/models/gemma/lmdeploy_gemma_27b.py new file mode 100644 index 0000000000000000000000000000000000000000..27867ef97a3d5faeccb3d1284ed2995174bd0ba8 --- /dev/null +++ b/opencompass/configs/models/gemma/lmdeploy_gemma_27b.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='gemma-2-27b-turbomind', + path='google/gemma-2-27b', + engine_config=dict(session_len=16384, max_batch_size=16, tp=2), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/gemma/lmdeploy_gemma_27b_it.py b/opencompass/configs/models/gemma/lmdeploy_gemma_27b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..c2cb48e3873da571acfab1aabc4e02962c8b8e85 --- /dev/null +++ b/opencompass/configs/models/gemma/lmdeploy_gemma_27b_it.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='gemma-2-27b-it-turbomind', + path='google/gemma-2-27b-it', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/lmdeploy_gemma_9b.py b/opencompass/configs/models/gemma/lmdeploy_gemma_9b.py new file mode 100644 index 0000000000000000000000000000000000000000..44ac35016785b1b417d8ee4dfa3307e156d90095 --- /dev/null +++ b/opencompass/configs/models/gemma/lmdeploy_gemma_9b.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='gemma-2-9b-turbomind', + path='google/gemma-2-9b', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/lmdeploy_gemma_9b_it.py b/opencompass/configs/models/gemma/lmdeploy_gemma_9b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..b33a5d523acb62647544bb9048935b8dfc5287ac --- /dev/null +++ b/opencompass/configs/models/gemma/lmdeploy_gemma_9b_it.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='gemma-2-9b-it-turbomind', + path='google/gemma-2-9b-it', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_2b.py b/opencompass/configs/models/gemma/vllm_gemma_2b.py new file mode 100644 index 0000000000000000000000000000000000000000..75faea2a50bd5b52df7970b1f8fe671854dba643 --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_2b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='gemma-2b-vllm', + path='google/gemma-2b', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_2b_it.py b/opencompass/configs/models/gemma/vllm_gemma_2b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..347a39fcaf34a5a4a32f69c277b72b20f54917e4 --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_2b_it.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-2b-it-vllm', + path='google/gemma-2b-it', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..2914640f915a21996605c6765005f201ffe365e1 --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-12b-it-vllm', + path='google/gemma-3-12b-it', + model_kwargs=dict(tensor_parallel_size=4, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..b6f4b93bc2c9f9947837e60f67a8455c5febc89a --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-27b-it-vllm', + path='google/gemma-3-27b-it', + model_kwargs=dict(tensor_parallel_size=4, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..22516ff75718a902d4a21ef468f613e6b2f7913d --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py @@ -0,0 +1,17 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-4b-it-vllm', + path='google/gemma-3-4b-it', + model_kwargs=dict(tensor_parallel_size=2, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_seq_len=140000, + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_7b.py b/opencompass/configs/models/gemma/vllm_gemma_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..818f18ebeac5e96efeabe8ba31d1d65e4394176b --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='gemma-7b-vllm', + path='google/gemma-7b', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_7b_it.py b/opencompass/configs/models/gemma/vllm_gemma_7b_it.py new file mode 100644 index 0000000000000000000000000000000000000000..934aad8af581c765c14c054f66838817459689ff --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_7b_it.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-7b-it-vllm', + path='google/gemma-7b-it', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/README.md b/opencompass/configs/models/hf_internlm/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7f691785cc7352c9543ced13142823b692f2bfc6 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/README.md @@ -0,0 +1,124 @@ +# InternLM Model Details + +## InternLM + +InternLM is pre-trained on a large corpora with 1.6T tokens with a multi-phase progressive process, and then fine-tuned to align with human preferences. We also developed a training system called Uniscale-LLM for efficient large language model training. The evaluation on a number of benchmarks shows that InternLM achieves state-of-the-art performance in multiple aspects, including knowledge understanding, reading comprehension, mathematics, and coding. With such well-rounded capabilities, InternLM achieves outstanding performances on comprehensive exams, including MMLU, AGIEval, C-Eval and GAOKAO-Bench, without resorting to external tools. On these benchmarks, InternLM not only significantly outperforms open-source models, but also obtains superior performance compared to ChatGPT. Also, InternLM demonstrates excellent capability of understanding Chinese language and Chinese culture, which makes it a suitable foundation model to support Chinese-oriented language applications. + +## InternLM2 + +The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k "Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution. + +# Evaluation Command + +## Base Models + +```bash +python3 run.py --models hf_internlm2_7b --datasets mmlu_ppl_ac766d --debug +python3 run.py --models hf_internlm2_7b --datasets cmmlu_ppl_041cbf --debug +python3 run.py --models hf_internlm2_7b --datasets ceval_internal_ppl_93e5ce --debug +python3 run.py --models hf_internlm2_7b --datasets GaokaoBench_no_subjective_gen_d21e37 --debug +python3 run.py --models hf_internlm2_7b --datasets triviaqa_wiki_1shot_gen_20a989 --debug +python3 run.py --models hf_internlm2_7b --datasets nq_open_1shot_gen_20a989 --debug +python3 run.py --models hf_internlm2_7b --datasets race_ppl_abed12 --debug +python3 run.py --models hf_internlm2_7b --datasets winogrande_5shot_ll_252f01 --debug +python3 run.py --models hf_internlm2_7b --datasets hellaswag_10shot_ppl_59c85e --debug +python3 run.py --models hf_internlm2_7b --datasets bbh_gen_98fba6 --debug +python3 run.py --models hf_internlm2_7b --datasets gsm8k_gen_17d0dc --debug +python3 run.py --models hf_internlm2_7b --datasets math_4shot_base_gen_db136b --debug +python3 run.py --models hf_internlm2_7b --datasets TheoremQA_5shot_gen_6f0af8 --debug +python3 run.py --models hf_internlm2_7b --datasets deprecated_humaneval_gen_d2537e --debug +python3 run.py --models hf_internlm2_7b --datasets sanitized_mbpp_gen_742f0c --debug +python3 run.py --models hf_internlm2_7b --datasets lcbench_gen_5ff288 --debug +python3 run.py --models hf_internlm2_7b --datasets gpqa_ppl_6bf57a --debug +``` + +## Chat Models + +```bash +python3 run.py --models hf_internlm2_chat_7b --datasets mmlu_gen_4d595a --debug +python3 run.py --models hf_internlm2_chat_7b --datasets cmmlu_gen_c13365 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets ceval_internal_gen_2daf24 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets GaokaoBench_no_subjective_gen_4c31db --debug +python3 run.py --models hf_internlm2_chat_7b --datasets triviaqa_wiki_1shot_gen_eaf81e --debug +python3 run.py --models hf_internlm2_chat_7b --datasets nq_open_1shot_gen_01cf41 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets race_gen_69ee4f --debug +python3 run.py --models hf_internlm2_chat_7b --datasets winogrande_5shot_gen_b36770 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets hellaswag_10shot_gen_e42710 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets bbh_gen_5b92b0 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets gsm8k_gen_1d7fe4 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets math_0shot_gen_393424 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets TheoremQA_5shot_gen_6f0af8 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets humaneval_gen_8e312c --debug +python3 run.py --models hf_internlm2_chat_7b --datasets sanitized_mbpp_mdblock_gen_a447ff --debug +python3 run.py --models hf_internlm2_chat_7b --datasets lcbench_gen_5ff288 --debug +python3 run.py --models hf_internlm2_chat_7b --datasets gpqa_gen_4baadb --debug +python3 run.py --models hf_internlm2_chat_7b --datasets IFEval_gen_3321a3 --debug +``` + +# Benchmarks + +We provide reference results for the classifical models, you can reproduce these results by following the aforementioned commands. + +## Base Models + +| dataset | internlm2-1.8b-turbomind | internlm2-7b-turbomind | internlm2-20b-turbomind | +|:------------:|---------------------------:|-------------------------:|--------------------------:| +| mmlu | 45.99 | 65.84 | 67.58 | +| cmmlu | 45.27 | 66.17 | 68.28 | +| ceval-test | 44.79 | 63.54 | 67.28 | +| GaokaoBench | 23.78 | 41.41 | 58.99 | +| triviaqa | 38.42 | 69.15 | 74.03 | +| nq | 20.66 | 41.05 | 43.55 | +| race-high | 64.72 | 72.56 | 72.90 | +| winogrande | 66.77 | 83.50 | 84.69 | +| hellaswag | 44.86 | 89.52 | 91.41 | +| bbh | 36.03 | 63.56 | 71.29 | +| gsm8k | 30.40 | 69.98 | 76.80 | +| math | 9.42 | 25.16 | 32.24 | +| TheoremQA | 10.50 | 21.88 | 26.00 | +| humaneval | 30.49 | 48.17 | 51.83 | +| mbpp | 30.74 | 54.47 | 59.92 | +| lcbench | 4.34 | 12.16 | 18.46 | +| GPQA_diamond | 24.24 | 28.28 | 31.31 | + +## Chat Models + +| dataset | internlm2-chat-1.8b-hf | internlm2-chat-1.8b-sft-hf | internlm2-chat-7b-hf | internlm2-chat-7b-sft-hf | internlm2-chat-20b-hf | internlm2-chat-20b-sft-hf | +|:------------:|-------------------------:|-----------------------------:|-----------------------:|---------------------------:|------------------------:|----------------------------:| +| mmlu | 47.58 | 47.44 | 63.05 | 63.33 | 67.37 | 67.34 | +| cmmlu | 46.11 | 46.27 | 62.10 | 62.38 | 66.26 | 66.39 | +| ceval-test | 47.04 | 47.19 | 58.75 | 58.96 | 63.12 | 63.16 | +| GaokaoBench | 29.73 | 28.79 | 54.54 | 55.39 | 57.95 | 57.62 | +| triviaqa | 46.69 | 46.50 | 69.54 | 70.75 | 75.53 | 75.90 | +| nq | 19.09 | 18.14 | 28.73 | 30.78 | 28.75 | 34.10 | +| race-high | 73.87 | 73.81 | 84.51 | 84.88 | 88.02 | 88.11 | +| winogrande | 57.62 | 57.93 | 73.56 | 73.80 | 81.06 | 81.37 | +| hellaswag | 60.47 | 61.58 | 84.80 | 85.21 | 88.48 | 88.95 | +| bbh | 37.69 | 37.12 | 57.83 | 57.19 | 68.24 | 69.38 | +| gsm8k | 39.73 | 36.85 | 69.90 | 69.83 | 75.21 | 76.95 | +| math | 14.06 | 13.10 | 28.08 | 27.60 | 34.68 | 32.54 | +| TheoremQA | 13.63 | 12.88 | 18.50 | 18.75 | 23.00 | 25.12 | +| humaneval | 33.54 | 34.15 | 56.71 | 61.59 | 67.68 | 67.68 | +| mbpp | 39.69 | 36.19 | 57.59 | 55.64 | 68.87 | 69.65 | +| lcbench | 4.52 | 3.56 | 14.60 | 14.34 | 19.64 | 20.55 | +| GPQA_diamond | 25.76 | 26.26 | 28.28 | 27.27 | 30.30 | 29.29 | +| IFEval | 18.30 | 18.67 | 34.75 | 39.19 | 36.41 | 44.55 | + +# Citation + +```BibTeX +@misc{2023internlm, + title={InternLM: A Multilingual Language Model with Progressively Enhanced Capabilities}, + author={InternLM Team}, + howpublished = {\url{https://github.com/InternLM/InternLM-techreport}}, + year={2023} +} +@misc{cai2024internlm2, + title={InternLM2 Technical Report}, + author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, + year={2024}, + eprint={2403.17297}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_1_8b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..de1070beced05955b651810ccceb65f52e74568b --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_1_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-1.8b-hf', + path='internlm/internlm2-1_8b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..d6c9fdeb8fcc8f19f6167fbc1d134b7217ae1789 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-20b-hf', + path='internlm/internlm2-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_5_1_8b_chat.py b/opencompass/configs/models/hf_internlm/hf_internlm2_5_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..d1875e7d3ec836dfb54d9005beb777431c55c8bb --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_5_1_8b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2_5-1_8b-chat-hf', + path='internlm/internlm2_5-1_8b-chat', + max_out_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_5_20b_chat.py b/opencompass/configs/models/hf_internlm/hf_internlm2_5_20b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..eee8e15531855c7ae7d0593de80762da569193dd --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_5_20b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2_5-20b-chat-hf', + path='internlm/internlm2_5-20b-chat', + max_out_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..cafaded18b532125cd7dcd73e509c0a371bdcc26 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2_5-7b-hf', + path='internlm/internlm2_5-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b_chat.py b/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..c5491aaaf1a3de94cd99af3c4727f5a623804f83 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_5_7b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2_5-7b-chat-hf', + path='internlm/internlm2_5-7b-chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..f1f075b9197b4e6bfd3944518e1a55c9178c8557 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-7b-hf', + path='internlm/internlm2-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_base_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_base_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a954e6ab8ea83574897e73a547cf0ab2c0ae58 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_base_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-base-20b-hf', + path='internlm/internlm2-base-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_base_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_base_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..e06031caef7ccbfce1ac386553f058ec447d3c6c --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_base_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-base-7b-hf', + path='internlm/internlm2-base-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..d2385ab5c6df1e83081f2de859d7d12fe47f2d82 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-1.8b-hf', + path='internlm/internlm2-chat-1_8b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b_sft.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..e72c02e86131406d3f1c6f655bea16bd03694dbf --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_1_8b_sft.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-1.8b-sft-hf', + path='internlm/internlm2-chat-1_8b-sft', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..1c3cbcdf3b988456c78ed0639f38fd331dc32d53 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-20b-hf', + path='internlm/internlm2-chat-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_sft.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..76beb7e1e29885db9054dea54bbf1c8c41c92661 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_sft.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-20b-sft-hf', + path='internlm/internlm2-chat-20b-sft', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_with_system.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_with_system.py new file mode 100644 index 0000000000000000000000000000000000000000..c181bc52db5a474ff2ba39da75d90a62bcbde611 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_20b_with_system.py @@ -0,0 +1,37 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='SYSTEM', begin='<|im_start|>system\n', end='<|im_end|>\n'), + dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'), + dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm2-chat-20b-hf', + path='internlm/internlm2-chat-20b', + tokenizer_path='internlm/internlm2-chat-20b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=2, num_procs=1), + end_str='<|im_end|>', + generation_kwargs = {'eos_token_id': [2, 92542]}, + batch_padding=True, + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..c93f290759dc307a77aae8656b152bd369427774 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-7b-hf', + path='internlm/internlm2-chat-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_sft.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..43708d9af70ba0766fadd7a647c93fc0676a0844 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_sft.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-7b-sft-hf', + path='internlm/internlm2-chat-7b-sft', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_with_system.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_with_system.py new file mode 100644 index 0000000000000000000000000000000000000000..bea5566229fe45b7a537617f5b2f672728eb7a09 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_7b_with_system.py @@ -0,0 +1,37 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='SYSTEM', begin='<|im_start|>system\n', end='<|im_end|>\n'), + dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'), + dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm2-chat-7b-hf', + path='internlm/internlm2-chat-7b', + tokenizer_path='internlm/internlm2-chat-7b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='<|im_end|>', + generation_kwargs = {'eos_token_id': [2, 92542]}, + batch_padding=True, + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..77a9bd05ec3662decda07621b39f1c88df6b2037 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-math-20b-hf', + path='internlm/internlm2-math-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + stop_words=['', '<|im_end|>'], + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b_with_system.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b_with_system.py new file mode 100644 index 0000000000000000000000000000000000000000..f77b51db73bb3834c395653319e1e9dce3ec99a4 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_20b_with_system.py @@ -0,0 +1,35 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'), + dict(role='SYSTEM', begin='<|im_start|>system\n', end='<|im_end|>\n'), + dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm2-chat-math-20b-hf', + path='internlm/internlm2-math-20b', + tokenizer_path='internlm/internlm2-math-20b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=2, num_procs=1), + end_str='<|im_end|>', + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..181e8e1c12a6bbed2be8a9e78786402e2f6e45b0 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm2-chat-math-7b-hf', + path='internlm/internlm2-math-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['', '<|im_end|>'], + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b_with_system.py b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b_with_system.py new file mode 100644 index 0000000000000000000000000000000000000000..87cc545f6e66abe4d60a5b0b38a52bf482ed0f85 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_chat_math_7b_with_system.py @@ -0,0 +1,35 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'), + dict(role='SYSTEM', begin='<|im_start|>system\n', end='<|im_end|>\n'), + dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm2-chat-math-7b-hf', + path='internlm/internlm2-math-7b', + tokenizer_path='internlm/internlm2-math-7b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='<|im_end|>', + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_math_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_math_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..5678524f36382c73ca82a4f923b951f95d5a4fb6 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_math_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-math-20b-hf', + path='internlm/internlm2-math-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm2_math_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm2_math_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..0491a73ce2ef2c67932608587ea1bbe0180e5991 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm2_math_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm2-math-7b-hf', + path='internlm/internlm2-math-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm3_8b_instruct.py b/opencompass/configs/models/hf_internlm/hf_internlm3_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..146618a82cd14949f6166674d9c2167d642a808a --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm3_8b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='internlm3-8b-instruct-hf', + path='internlm/internlm3-8b-instruct', + max_out_len=8192, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..b81bb8e39d2c64e2b6a81348e9dac76fef73e62b --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm-20b-hf', + path='internlm/internlm-20b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2489a48ff4e8a0f1206ec55cd5e5cc4f41032d --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='internlm-7b-hf', + path='internlm/internlm-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm_chat_20b.py b/opencompass/configs/models/hf_internlm/hf_internlm_chat_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..4d98e6dbecd0c3436748b0c2da65056c7c6f335d --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm_chat_20b.py @@ -0,0 +1,34 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|User|>:', end='\n'), + dict(role='BOT', begin='<|Bot|>:', end='\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm-chat-20b-hf', + path='internlm/internlm-chat-20b', + tokenizer_path='internlm/internlm-chat-20b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=2, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/hf_internlm/hf_internlm_chat_7b.py b/opencompass/configs/models/hf_internlm/hf_internlm_chat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..385d81456c15d3842e15a1ee11e3616934d548d3 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/hf_internlm_chat_7b.py @@ -0,0 +1,34 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|User|>:', end='\n'), + dict(role='BOT', begin='<|Bot|>:', end='\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='internlm-chat-7b-hf', + path='internlm/internlm-chat-7b', + tokenizer_path='internlm/internlm-chat-7b', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_1_8b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..fed3b5b8be32c7bfd8b485eec3f1fb6ea4070c16 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2-1.8b-turbomind', + path='internlm/internlm2-1_8b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_20b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..e3441a051d63fbc4f3799808bba0c772d9b5263c --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_20b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2-20b-turbomind', + path='internlm/internlm2-20b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_1_8b_chat.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..cf4691f16178f7729be7d1ad44622164e4ffe218 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_1_8b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2_5-1_8b-chat-turbomind', + path='internlm/internlm2_5-1_8b-chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_20b_chat.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_20b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..7fb5216186a940e2177e79b4df63c41b446f87cb --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_20b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2_5-20b-chat-turbomind', + path='internlm/internlm2_5-20b-chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..c2bb220c7a441d9d48481868135f54c36345a7e4 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2_5-7b-turbomind', + path='internlm/internlm2_5-7b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..8dce26843c9a30b3fc0da83e6818fb24a7705997 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2_5-7b-chat-turbomind', + path='internlm/internlm2_5-7b-chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat_1m.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat_1m.py new file mode 100644 index 0000000000000000000000000000000000000000..6d96de285b24d834a4e7a87e4c930e5512f4b8be --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_5_7b_chat_1m.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2_5-7b-chat-1m-turbomind', + path='internlm/internlm2_5-7b-chat-1m', + engine_config=dict(rope_scaling_factor=2.5, session_len=1048576, max_batch_size=1, cache_max_entry_count=0.7, tp=4), # 1M context length + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=1048576, + max_out_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f23b135ffe17b04e0bfa2559a718a317a6cf6c --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2-7b-turbomind', + path='internlm/internlm2-7b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_20b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..a66e68a3975fa62126ccd54229b57fac4772f175 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_20b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2-base-20b-turbomind', + path='internlm/internlm2-base-20b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..5577935ebbfb695d192a44c3ef8fe3eb95a345e8 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_base_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm2-base-7b-turbomind', + path='internlm/internlm2-base-7b', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..f5df7926d4d3c82f053f2357147ddb131a83acf0 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2-chat-1.8b-turbomind', + path='internlm/internlm2-chat-1_8b', + engine_config=dict(session_len=8192, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=8192, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b_sft.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..47c163a3af33a348362e495520bbcb2006c8be21 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_1_8b_sft.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2-chat-1.8b-sft-turbomind', + path='internlm/internlm2-chat-1_8b-sft', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..23f35636c293598b8bd153198e1d1fe431ecce72 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2-chat-20b-turbomind', + path='internlm/internlm2-chat-20b', + engine_config=dict(session_len=8192, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=8192, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b_sft.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..fa8ede0a043dbb4764003dacd43940049b8f0aeb --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_20b_sft.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2-chat-20b-sft-turbomind', + path='internlm/internlm2-chat-20b-sft', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..38ea39d7dff65da5e192c79125423b107f40fcc5 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b.py @@ -0,0 +1,24 @@ +from opencompass.models import TurboMindModelwithChatTemplate + + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr=f'internlm2-chat-7b-lmdeploy', + path='internlm/internlm2-chat-7b', + # inference backend of LMDeploy. It can be either 'turbomind' or 'pytorch'. + # If the model is not supported by 'turbomind', it will fallback to + # 'pytorch' + backend='turbomind', + # For the detailed engine config and generation config, please refer to + # https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/messages.py + engine_config=dict(tp=1), + gen_config=dict(do_sample=False), + max_seq_len=8192, + max_out_len=4096, + # the max number of prompts that LMDeploy receives + # in `generate` function + batch_size=5000, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b_sft.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..09c69758793ad76eb1d1f0a8286ba828341b9246 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_chat_7b_sft.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm2-chat-7b-sft-turbomind', + path='internlm/internlm2-chat-7b-sft', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_series.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_series.py new file mode 100644 index 0000000000000000000000000000000000000000..6c286834089c848bcbf0758c8ad176eef6efcb8c --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm2_series.py @@ -0,0 +1,26 @@ +from opencompass.models import TurboMindModel + +settings = [ + ('internlm2-1.8b-turbomind', 'internlm/internlm2-1_8b', 1), + ('internlm2-7b-turbomind', 'internlm/internlm2-7b', 1), + ('internlm2-base-7b-turbomind', 'internlm/internlm2-base-7b', 1), + ('internlm2-20b-turbomind', 'internlm/internlm2-20b', 2), + ('internlm2-base-20b-turbomind', 'internlm/internlm2-base-20b', 2), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=TurboMindModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=2048, max_batch_size=16, tp=num_gpus), + gen_config=dict(top_k=1, temperature=1, top_p=0.9, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=2048, + batch_size=16, + concurrency=16, + run_cfg=dict(num_gpus=num_gpus), + ) + ) diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..c905db44a8aa4344d4e64c0ef62e0f83290b5256 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm3-8b-instruct-turbomind', + path='internlm/internlm3-8b-instruct', + engine_config=dict(session_len=32768, max_batch_size=16, tp=1), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192 + ), + max_seq_len=32768, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py new file mode 100644 index 0000000000000000000000000000000000000000..1cc4e25107974fb5d28292c59f88987211465119 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py @@ -0,0 +1,19 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm3-8b-instruct-turbomind', + path='internlm/internlm3-8b-instruct', + engine_config=dict(session_len=142000, max_batch_size=1, tp=2, + # for long context + rope_scaling_factor=6.0), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192 + ), + max_seq_len=142000, + max_out_len=8192, + batch_size=1, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm_20b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..761e90cfa90044a113743ea5c4edafacf3d378cc --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_20b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm-20b-turbomind', + path='internlm/internlm-20b', + engine_config=dict(session_len=4096, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=4096, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..6f2f4cc0774ee5b3387db62b892d22ac419a2451 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='internlm-7b-turbomind', + path='internlm/internlm-7b', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=4096, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_20b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..e9af5578bebd853b2cbc095a64c73f778ccb3781 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_20b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm-chat-20b-turbomind', + path='internlm/internlm-chat-20b', + engine_config=dict(session_len=4096, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_7b.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..50656a5f894ea33054346e83e5f3aa99aeb91b88 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm_chat_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm-chat-7b-turbomind', + path='internlm/internlm-chat-7b', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py b/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..1d10bd9414f5b679184cb8216374fa16b7360543 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='OREAL-32B', + path='internlm/OREAL-32B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=16, + run_cfg=dict(num_gpus=4), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..d438cdcd0696fd7ad9a0421402762575ce7e6b49 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-1.8b-vllm', + path='internlm/internlm2-chat-1_8b', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b_sft.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..b9dd1a6226c4024e152150a18287f0fc9a930a2f --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_1_8b_sft.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-1.8b-sft-vllm', + path='internlm/internlm2-chat-1_8b-sft', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b.py new file mode 100644 index 0000000000000000000000000000000000000000..d025fa278d3d5282e8dfcbe4821feec05098d9a1 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-20b-vllm', + path='internlm/internlm2-chat-20b', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b_sft.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..f2b373e17d18445451f126c280a0282a52e0982f --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_20b_sft.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-20b-sft-vllm', + path='internlm/internlm2-chat-20b-sft', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..50a413eabb4ec3dc72af5019f2f44e6b4165380d --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-7b-vllm', + path='internlm/internlm2-chat-7b', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b_sft.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..9f07c93e3a56d21ab8af017b3bfdaa5c03e611da --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_chat_7b_sft.py @@ -0,0 +1,13 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='internlm2-chat-7b-sft-vllm', + path='internlm/internlm2-chat-7b-sft', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=32768, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_internlm/vllm_internlm2_series.py b/opencompass/configs/models/hf_internlm/vllm_internlm2_series.py new file mode 100644 index 0000000000000000000000000000000000000000..aee5a68583b4818ff012f004d4b7703763f39a79 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/vllm_internlm2_series.py @@ -0,0 +1,25 @@ +from opencompass.models import VLLM + +settings = [ + ('internlm2-1.8b-vllm', 'internlm/internlm2-1_8b', 1), + ('internlm2-7b-vllm', 'internlm/internlm2-7b', 1), + ('internlm2-base-7b-vllm', 'internlm/internlm2-base-7b', 1), + ('internlm2-20b-vllm', 'internlm/internlm2-20b', 2), + ('internlm2-base-20b-vllm', 'internlm/internlm2-base-20b', 2), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=VLLM, + abbr=abbr, + path=path, + model_kwargs=dict(tensor_parallel_size=num_gpus), + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/hf_llama/hf_llama2_13b.py b/opencompass/configs/models/hf_llama/hf_llama2_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..9d51ac0c6f3aa1c388092da7bd56dc185664add6 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_13b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-2-13b-hf', + path='meta-llama/Llama-2-13b-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama2_13b_chat.py b/opencompass/configs/models/hf_llama/hf_llama2_13b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..bd2b54be48e3dad3c035009aed61218fa0a5fa0c --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_13b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-2-13b-chat-hf', + path='meta-llama/Llama-2-13b-chat-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama2_70b.py b/opencompass/configs/models/hf_llama/hf_llama2_70b.py new file mode 100644 index 0000000000000000000000000000000000000000..0aad6e66ad3346d40d0bdfe0a2b917f313de1937 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_70b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-2-70b-hf', + path='meta-llama/Llama-2-70b-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama2_70b_chat.py b/opencompass/configs/models/hf_llama/hf_llama2_70b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6351fa30fc33b113a8597cf458cdd8e33f0ee4 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_70b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-2-70b-chat-hf', + path='meta-llama/Llama-2-70b-chat-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama2_7b.py b/opencompass/configs/models/hf_llama/hf_llama2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..beb4d667deff14a1dd0fae7a11675f81651dab5b --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-2-7b-hf', + path='meta-llama/Llama-2-7b-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama2_7b_chat.py b/opencompass/configs/models/hf_llama/hf_llama2_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..e1f95331027cd33c08965127a7ef482161346cdd --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama2_7b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-2-7b-chat-hf', + path='meta-llama/Llama-2-7b-chat-hf', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_1_70b_instruct.py b/opencompass/configs/models/hf_llama/hf_llama3_1_70b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..c7527bb53d976f637d5757560280c7522975a3f8 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_1_70b_instruct.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-3_1-70b-instruct-hf', + path='meta-llama/Meta-Llama-3.1-70B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_1_8b.py b/opencompass/configs/models/hf_llama/hf_llama3_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..a41e1ddfc70fe21b0aab0e94dbc779cbf64a5d5b --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_1_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-3_1-8b-hf', + path='meta-llama/Meta-Llama-3.1-8B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_1_8b_instruct.py b/opencompass/configs/models/hf_llama/hf_llama3_1_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2f955767b3096c07ea411b5e92235a7a5eabef --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_1_8b_instruct.py @@ -0,0 +1,22 @@ +from opencompass.models import HuggingFacewithChatTemplate + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], + reserved_roles=[dict(role='SYSTEM', api_role='SYSTEM')], +) + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-3_1-8b-instruct-hf', + path='meta-llama/Meta-Llama-3.1-8B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + meta_template=api_meta_template, + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_2_3b_instruct.py b/opencompass/configs/models/hf_llama/hf_llama3_2_3b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..2197b6ce11e27841a3e2990cab3b8e53f5b5f6c4 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_2_3b_instruct.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-3_2-3b-instruct-hf', + path='meta-llama/Llama-3.2-3B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_70b.py b/opencompass/configs/models/hf_llama/hf_llama3_70b.py new file mode 100644 index 0000000000000000000000000000000000000000..32ab4f5532367ed58211d93c8f3fa4e1bb1748fc --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_70b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-3-70b-hf', + path='meta-llama/Meta-Llama-3-70B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_70b_instruct.py b/opencompass/configs/models/hf_llama/hf_llama3_70b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7e85545abe7ad23d5f5ecf9c012a08ad8be3e1 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_70b_instruct.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-3-70b-instruct-hf', + path='meta-llama/Meta-Llama-3-70B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_8b.py b/opencompass/configs/models/hf_llama/hf_llama3_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..3ae9f2c36cba7fb07773686336ffe0d977e95390 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-3-8b-hf', + path='meta-llama/Meta-Llama-3-8B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama3_8b_instruct.py b/opencompass/configs/models/hf_llama/hf_llama3_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2fd8f0aeb452ab6ae3e50fb6f0c419c3724a90 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama3_8b_instruct.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='llama-3-8b-instruct-hf', + path='meta-llama/Meta-Llama-3-8B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama_13b.py b/opencompass/configs/models/hf_llama/hf_llama_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6976140e277fa10447b68e7cdae7f6f79dd472 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama_13b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-13b-hf', + path='huggyllama/llama-13b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama_30b.py b/opencompass/configs/models/hf_llama/hf_llama_30b.py new file mode 100644 index 0000000000000000000000000000000000000000..50d0f80d16eff7dd55b76c20c4ae9a116a3232af --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama_30b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-30b-hf', + path='huggyllama/llama-30b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama_65b.py b/opencompass/configs/models/hf_llama/hf_llama_65b.py new file mode 100644 index 0000000000000000000000000000000000000000..a57612999ae9daa161d42fed1de18d6495fb1171 --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama_65b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-65b-hf', + path='huggyllama/llama-65b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/hf_llama/hf_llama_7b.py b/opencompass/configs/models/hf_llama/hf_llama_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..1100f1196722e26aa141a13d293dc550e4031e3b --- /dev/null +++ b/opencompass/configs/models/hf_llama/hf_llama_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='llama-7b-hf', + path='huggyllama/llama-7b', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..379a6513b3d9925ea03b040948356d312856206d --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-2-13b-turbomind', + path='meta-llama/Llama-2-13b-hf', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=4096, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b_chat.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..cacdec9a57c30d344ca931b5bce259316911a6cf --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_13b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-2-13b-chat-turbomind', + path='meta-llama/Llama-2-13b-chat-hf', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b.py new file mode 100644 index 0000000000000000000000000000000000000000..90ad556d9e51acd753c02e8ceb53a78853412b13 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-2-70b-turbomind', + path='meta-llama/Llama-2-70b-hf', + engine_config=dict(session_len=4096, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=4096, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b_chat.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..b850106b3053da7a931a4e5a10456dc0261bbb3a --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_70b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-2-70b-chat-turbomind', + path='meta-llama/Llama-2-70b-chat-hf', + engine_config=dict(max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2941df6f7a3638efd88902dfb2a856babdad59 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-2-7b-turbomind', + path='meta-llama/Llama-2-7b-hf', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=4096, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b_chat.py b/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..aa34524884a6b59140d2a195698de0853922d4e1 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama2_7b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-2-7b-chat-turbomind', + path='meta-llama/Llama-2-7b-chat-hf', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_70b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_70b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..9674169f5e4222053d23a61bfbb480c7177614dc --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_70b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3_1-70b-instruct-turbomind', + path='meta-llama/Meta-Llama-3.1-70B-Instruct', + engine_config=dict(max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..a0b3cc5e1c4243eb41b6a8e9373f73cda1700d8c --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-3.1-8b-turbomind', + path='meta-llama/Meta-Llama-3.1-8B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..2754eb835fbf1ed64f986ac28c7f20e55804e763 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_1_8b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3_1-8b-instruct-turbomind', + path='meta-llama/Meta-Llama-3.1-8B-Instruct', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_2_3b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_2_3b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..611746dcfce931277835df552e96e3e35021960b --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_2_3b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3_2-3b-instruct-turbomind', + path='meta-llama/Llama-3.2-3B-Instruct', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_3_70b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_3_70b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..045d57225aa67494911a370664c0bcd1b892ed3b --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_3_70b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3_3-70b-instruct-turbomind', + path='meta-llama/Llama-3.3-70B-Instruct', + engine_config=dict(max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192), + max_seq_len=16384, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>', '<|eom_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b.py new file mode 100644 index 0000000000000000000000000000000000000000..f16dc221ef46a3dfb26859bee20ab17948ba3bea --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-3-70b-turbomind', + path='meta-llama/Meta-Llama-3-70B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=4, cache_max_entry_count=0.7), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..12fc944c75cd936f41b8cf1e346e5ccaa4a3334f --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_70b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3-70b-instruct-turbomind', + path='meta-llama/Meta-Llama-3-70B-Instruct', + engine_config=dict(max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..141e6b350929b95e60ea1c99f9040dedda6f77e7 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-3-8b-turbomind', + path='meta-llama/Meta-Llama-3-8B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py b/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..5a6431b7a143726260cd68d9a091fe5c1703e324 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='llama-3-8b-instruct-turbomind', + path='meta-llama/Meta-Llama-3-8B-Instruct', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama_13b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..bd7f91cb8e96a479b3c0d999389dd0dc53044bdc --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama_13b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-13b-turbomind', + path='huggyllama/llama-13b', + engine_config=dict(session_len=2048, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=2048, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama_30b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama_30b.py new file mode 100644 index 0000000000000000000000000000000000000000..375ff9e7c2bf625d90e0c2a194cd7a24cc39d43c --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama_30b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-30b-turbomind', + path='huggyllama/llama-30b', + engine_config=dict(session_len=2048, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=2048, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama_65b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama_65b.py new file mode 100644 index 0000000000000000000000000000000000000000..e91cb0a0e4f60a8bc1405a4a5a5accaa245a8e0d --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama_65b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-65b-turbomind', + path='huggyllama/llama-65b', + engine_config=dict(session_len=2048, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=2048, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/hf_llama/lmdeploy_llama_7b.py b/opencompass/configs/models/hf_llama/lmdeploy_llama_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..f215215bf81d72d33bd83cb050161c7fad9c93c5 --- /dev/null +++ b/opencompass/configs/models/hf_llama/lmdeploy_llama_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='llama-7b-turbomind', + path='huggyllama/llama-7b', + engine_config=dict(session_len=2048, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=2048, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/hf_llama/vllm_llama_series.py b/opencompass/configs/models/hf_llama/vllm_llama_series.py new file mode 100644 index 0000000000000000000000000000000000000000..5f777abf077089739e14c3f3f9f44be7bc1953c5 --- /dev/null +++ b/opencompass/configs/models/hf_llama/vllm_llama_series.py @@ -0,0 +1,32 @@ +from opencompass.models import VLLM + +settings = [ + ('llama-7b-vllm', 'huggyllama/llama-7b', 1), + ('llama-13b-vllm', 'huggyllama/llama-13b', 1), + ('llama-30b-vllm', 'huggyllama/llama-30b', 2), + ('llama-65b-vllm', 'huggyllama/llama-65b', 4), + ('llama-2-7b-vllm', 'meta-llama/Llama-2-7b-hf', 1), + ('llama-2-13b-vllm', 'meta-llama/Llama-2-13b-hf', 1), + ('llama-2-70b-vllm', 'meta-llama/Llama-2-70b-hf', 4), + ('llama-3-8b-vllm', 'meta-llama/Meta-Llama-3-8B', 1), + ('llama-3-70b-vllm', 'meta-llama/Meta-Llama-3-70B', 4), + ('llama-3.1-8b-instruct-vllm', 'meta-llama/Meta-Llama-3.1-8B-Instruct', 1), + ('llama-3.1-70b-instruct-vllm', 'meta-llama/Meta-Llama-3.1-70B-Instruct', 4), + ('llama-3.1-405b-fp8-instruct-vllm', 'meta-llama/Meta-Llama-3.1-405B-Instruct-FP8', 8), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=VLLM, + abbr=abbr, + path=path, + model_kwargs=dict(tensor_parallel_size=num_gpus), + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..d5ffbf6e9cfa59c0d19f658d82b75b4e62b88584 --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py @@ -0,0 +1,17 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt2-13b-hf', + path='FreedomIntelligence/HuatuoGPT2-13B', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=True,), + max_out_len=1024, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..98d29ad2eeda0f6ca7367b96089cf4e97dd6a99e --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt2-7b-hf', + path='FreedomIntelligence/HuatuoGPT2-7B', + max_out_len=1024, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..db1130e1272e54782a0ea5ec7f3691edb79b7946 --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFacewithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt-o1-7b-hf', + path='FreedomIntelligence/HuatuoGPT-o1-7B', + max_out_len=2048, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content, think_start_token='## Thinking', think_end_token='## Final Response'), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..ba2e2c1de2d72bb4688d40ca1ee73abfc8ca6cb1 --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFacewithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt-o1-8b-hf', + path='FreedomIntelligence/HuatuoGPT-o1-8B', + max_out_len=2048, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content, think_start_token='## Thinking', think_end_token='## Final Response'), + ) +] diff --git a/opencompass/configs/models/internlm/internlm_7b.py b/opencompass/configs/models/internlm/internlm_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..90a84b6df1710f629de34f4399efffac6081942d --- /dev/null +++ b/opencompass/configs/models/internlm/internlm_7b.py @@ -0,0 +1,14 @@ +from opencompass.models import InternLM + + +models = [ + dict( + type=InternLM, + path='./internData/', + tokenizer_path='./internData/V7.model', + model_config='./internData/model_config.py', + max_out_len=100, + max_seq_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1, num_procs=1)) +] diff --git a/opencompass/configs/models/interns1/intern_s1.py b/opencompass/configs/models/interns1/intern_s1.py new file mode 100644 index 0000000000000000000000000000000000000000..88ae29705909538c6055a1988c916432ecfde8af --- /dev/null +++ b/opencompass/configs/models/interns1/intern_s1.py @@ -0,0 +1,33 @@ +from opencompass.models import OpenAISDK +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], +), + +models = [ + dict( + abbr='intern-s1', + key='YOUR_API_KEY', + openai_api_base='YOUR_API_BASE', + type=OpenAISDK, + path='internlm/Intern-S1', + temperature=0.7, + meta_template=api_meta_template, + query_per_second=1, + batch_size=8, + max_out_len=64000, + max_seq_len=65536, + openai_extra_kwargs={ + 'top_p': 0.95, + }, + retry=10, + extra_body={ + 'chat_template_kwargs': {'enable_thinking': True} + }, + pred_postprocessor=dict(type=extract_non_reasoning_content), + ), +] \ No newline at end of file diff --git a/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_38b.py b/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_38b.py new file mode 100644 index 0000000000000000000000000000000000000000..98713696b4648899502aaad9d8061a6cd7d30850 --- /dev/null +++ b/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_38b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internvl2_5-38b-turbomind', + path='OpenGVLab/InternVL2_5-38B', + engine_config=dict(session_len=8192, max_batch_size=8, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=8192, + max_out_len=8192, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_8b.py b/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..3541249c61f036dcfce8d52f74ff5c0e76650b71 --- /dev/null +++ b/opencompass/configs/models/internvl/lmdeploy_internvl_2_5_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internvl2_5-8b-turbomind', + path='OpenGVLab/InternVL2_5-8B', + engine_config=dict(session_len=8192, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192), + max_seq_len=8192, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/judge_llm/auto_j/hf_autoj_bilingual_6b.py b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_bilingual_6b.py new file mode 100644 index 0000000000000000000000000000000000000000..8e26b717a0a17aa8e7b78bb84bf3f385e91fbd10 --- /dev/null +++ b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_bilingual_6b.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceCausalLM + +''' +This is a bilingual 6B version of Auto-J. +It is trained on both the original training data +and its Chinese translation, which can be find in +https://huggingface.co/GAIR/autoj-bilingual-6b +''' + +models = [dict( + type=HuggingFaceCausalLM, + abbr='autoj-bilingual-6b', + path='GAIR/autoj-bilingual-6b', + tokenizer_path='GAIR/autoj-bilingual-6b', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b.py b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..7cc047477ee27706e0c1c223f1c8ef377f9bc9cb --- /dev/null +++ b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='autoj-13b', + path='GAIR/autoj-13b', + tokenizer_path='GAIR/autoj-13b', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b_4bit.py b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b_4bit.py new file mode 100644 index 0000000000000000000000000000000000000000..4d89114ed122bfe5fdd7a46a7f045536c17a4fbf --- /dev/null +++ b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_eng_13b_4bit.py @@ -0,0 +1,23 @@ +from opencompass.models import HuggingFaceCausalLM + +''' +#This is a 4bits quantized version of Auto-J by using AutoGPTQ, +which is available on huggingface-hub: +https://huggingface.co/GAIR/autoj-13b-GPTQ-4bits +''' + +models = [dict( + type=HuggingFaceCausalLM, + abbr='autoj-13b-GPTQ-4bits', + path='GAIR/autoj-13b-GPTQ-4bits', + tokenizer_path='GAIR/autoj-13b-GPTQ-4bits', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/auto_j/hf_autoj_scen_classifier.py b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_scen_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..ee833bc18fbe0720ad12fea0daf12e7884ee4ac0 --- /dev/null +++ b/opencompass/configs/models/judge_llm/auto_j/hf_autoj_scen_classifier.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='autoj-scenario-classifier', + path='GAIR/autoj-scenario-classifier', + tokenizer_path='GAIR/autoj-scenario-classifier', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_13b_v1.py b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_13b_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..aad7005c774b6ea1e81f9e1527c904cf3c6c6ef7 --- /dev/null +++ b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_13b_v1.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='judgelm-13b-v1-hf', + path='BAAI/JudgeLM-13B-v1.0', + tokenizer_path='BAAI/JudgeLM-13B-v1.0', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_33b_v1.py b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_33b_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..c674b3a9ca358d1d1c2d00a56fc36c215ce29ea2 --- /dev/null +++ b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_33b_v1.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='judgelm-33b-v1-hf', + path='BAAI/JudgeLM-33B-v1.0', + tokenizer_path='BAAI/JudgeLM-33B-v1.0', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=4, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_7b_v1.py b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_7b_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..5d5b8256433bb539c1ea6fd58d025ba245db74c2 --- /dev/null +++ b/opencompass/configs/models/judge_llm/judgelm/hf_judgelm_7b_v1.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='judgelm-7b-v1-hf', + path='BAAI/JudgeLM-7B-v1.0', + tokenizer_path='BAAI/JudgeLM-7B-v1.0', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/pandalm/hf_alpaca_pandalm_7b_v1.py b/opencompass/configs/models/judge_llm/pandalm/hf_alpaca_pandalm_7b_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..84fb131a85aa519bf9b5b18ba374a4160fb61418 --- /dev/null +++ b/opencompass/configs/models/judge_llm/pandalm/hf_alpaca_pandalm_7b_v1.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='alpaca-pandalm-7b-v1-hf', + path='WeOpenML/PandaLM-Alpaca-7B-v1', + tokenizer_path='WeOpenML/PandaLM-Alpaca-7B-v1', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/judge_llm/pandalm/hf_pandalm_7b_v1.py b/opencompass/configs/models/judge_llm/pandalm/hf_pandalm_7b_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..e73ececcf18139661c1b8ecced81d8b6062bba51 --- /dev/null +++ b/opencompass/configs/models/judge_llm/pandalm/hf_pandalm_7b_v1.py @@ -0,0 +1,18 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [dict( + type=HuggingFaceCausalLM, + abbr='pandalm-7b-v1-hf', + path='WeOpenML/PandaLM-7B-v1', + tokenizer_path='WeOpenML/PandaLM-7B-v1', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + max_out_len=1024, + max_seq_len=4096, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + )] diff --git a/opencompass/configs/models/lemur/lemur_70b_chat.py b/opencompass/configs/models/lemur/lemur_70b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..080ea45bf30e89552e986b73be2ff8544104ec2d --- /dev/null +++ b/opencompass/configs/models/lemur/lemur_70b_chat.py @@ -0,0 +1,30 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n<|im_start|>user\n', end='<|im_end|>'), + dict(role='BOT', begin='\n<|im_start|>assistant\n', end='<|im_end|>', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='lemur-70b-chat-v1', + path='OpenLemur/lemur-70b-chat-v1', + tokenizer_path='OpenLemur/lemur-70b-chat-v1', + # tokenizer_kwargs=dict( + # padding_side='left', + # truncation_side='left', + # trust_remote_code=True, + # use_fast=False,), + # pad_token_id=151643, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=4, num_procs=1), + ) +] diff --git a/opencompass/configs/models/lingowhale/hf_lingowhale_8b.py b/opencompass/configs/models/lingowhale/hf_lingowhale_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..45544e75d2671569acd24ef11c6da4828e1119a8 --- /dev/null +++ b/opencompass/configs/models/lingowhale/hf_lingowhale_8b.py @@ -0,0 +1,25 @@ +from opencompass.models import HuggingFace + + +models = [ + dict( + type=HuggingFace, + abbr='lingowhale-8b-hf', + path='deeplang-ai/LingoWhale-8B', + tokenizer_path='deeplang-ai/LingoWhale-8B', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + torch_dtype='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_ministral_8b_instruct_2410.py b/opencompass/configs/models/mistral/hf_ministral_8b_instruct_2410.py new file mode 100644 index 0000000000000000000000000000000000000000..4abcfe4b69ebb9ed443b36f39e6ab35018fa3a65 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_ministral_8b_instruct_2410.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='ministral-8B-instruct-2410-hf', + path='mistralai/Ministral-8B-Instruct-2410', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_1.py b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..cb0f1a65a618d4e452c772c948aa636d3bf531cd --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_1.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mistral-7b-instruct-v0.1-hf', + path='mistralai/Mistral-7B-Instruct-v0.1', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_2.py b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_2.py new file mode 100644 index 0000000000000000000000000000000000000000..188698c771f297827d50b82f8c310918ea271341 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_2.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mistral-7b-instruct-v0.2-hf', + path='mistralai/Mistral-7B-Instruct-v0.2', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_3.py b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_3.py new file mode 100644 index 0000000000000000000000000000000000000000..fdc51c8d6a4e39326ad10252da0f86b3cb98afa7 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_instruct_v0_3.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mistral-7b-instruct-v0.3-hf', + path='mistralai/Mistral-7B-Instruct-v0.3', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_v0_1.py b/opencompass/configs/models/mistral/hf_mistral_7b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..3446cf37c8b1e70f7af2e9b788d9a2a73daa1083 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_v0_1.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='mistral-7b-v0.1-hf', + path='mistralai/Mistral-7B-v0.1', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_v0_2.py b/opencompass/configs/models/mistral/hf_mistral_7b_v0_2.py new file mode 100644 index 0000000000000000000000000000000000000000..df696e4287dc6a2637efea1fbe5a7f77dcc4514b --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_v0_2.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='mistral-7b-v0.2-hf', + path='mistral-community/Mistral-7B-v0.2', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_7b_v0_3.py b/opencompass/configs/models/mistral/hf_mistral_7b_v0_3.py new file mode 100644 index 0000000000000000000000000000000000000000..90d9a829ee2010cca6713a0e00424166245d8d67 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_7b_v0_3.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFaceBaseModel + + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='mistral-7b-v0.3-hf', + path='mistralai/Mistral-7B-v0.3', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_nemo_instruct_2407.py b/opencompass/configs/models/mistral/hf_mistral_nemo_instruct_2407.py new file mode 100644 index 0000000000000000000000000000000000000000..6c90769e3281c8d9c0751e3fbcc60da10c6fa39c --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_nemo_instruct_2407.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mistral-nemo-instruct-2407-hf', + path='mistralai/Mistral-Nemo-Instruct-2407', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mistral_small_instruct_2409.py b/opencompass/configs/models/mistral/hf_mistral_small_instruct_2409.py new file mode 100644 index 0000000000000000000000000000000000000000..b9810c3e21c4fdc213411fd94148beadde5fc4a4 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mistral_small_instruct_2409.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mistral-small-instruct-2409-hf', + path='mistralai/Mistral-Small-Instruct-2409', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mixtral_8x22b_instruct_v0_1.py b/opencompass/configs/models/mistral/hf_mixtral_8x22b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..588c18a700408ac0c45048ef6afc3b2277b7cbde --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mixtral_8x22b_instruct_v0_1.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mixtral-8x22b-instruct-v0.1-hf', + path='mistralai/Mixtral-8x22B-Instruct-v0.1', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mixtral_8x22b_v0_1.py b/opencompass/configs/models/mistral/hf_mixtral_8x22b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..d84021ab053c9b5d778ad627373716bf7e28ff70 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mixtral_8x22b_v0_1.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='mixtral-8x22b-v0.1-hf', + path='mistralai/Mixtral-8x22B-v0.1', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mixtral_8x7b_instruct_v0_1.py b/opencompass/configs/models/mistral/hf_mixtral_8x7b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..c910d210045c81120e3f96867c0fea5bdf50baf5 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mixtral_8x7b_instruct_v0_1.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='mixtral-8x7b-instruct-v0.1-hf', + path='mistralai/Mixtral-8x7B-Instruct-v0.1', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/mistral/hf_mixtral_8x7b_v0_1.py b/opencompass/configs/models/mistral/hf_mixtral_8x7b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..2520422644a29265351e82309f89c150f48b5b01 --- /dev/null +++ b/opencompass/configs/models/mistral/hf_mixtral_8x7b_v0_1.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='mixtral-8x7b-v0.1-hf', + path='mistralai/Mixtral-8x7B-v0.1', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_ministral_8b_instruct_2410.py b/opencompass/configs/models/mistral/lmdeploy_ministral_8b_instruct_2410.py new file mode 100644 index 0000000000000000000000000000000000000000..cef8d980d0fcb0240daa40ad60679f82a5f42bbb --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_ministral_8b_instruct_2410.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='ministral-8B-instruct-2410-turbomind', + path='mistralai/Ministral-8B-Instruct-2410', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mistral_7b_instruct_v0_3.py b/opencompass/configs/models/mistral/lmdeploy_mistral_7b_instruct_v0_3.py new file mode 100644 index 0000000000000000000000000000000000000000..4c867b602766c4c3c6a588d9b1eb6739c641a8c8 --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mistral_7b_instruct_v0_3.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mistral-7b-instruct-v0.3-turbomind', + path='mistralai/Mistral-7B-Instruct-v0.3', + engine_config=dict(session_len=32768, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=32768, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mistral_large_instruct_2411.py b/opencompass/configs/models/mistral/lmdeploy_mistral_large_instruct_2411.py new file mode 100644 index 0000000000000000000000000000000000000000..205dc27b76e2fc2acde977f8d000cd515afb96a5 --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mistral_large_instruct_2411.py @@ -0,0 +1,22 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mixtral-large-instruct-2411-turbomind', + path='mistralai/Mistral-Large-Instruct-2411', + engine_config=dict( + session_len=32768, + max_batch_size=16, + tp=4, + cache_max_entry_count=0.7, + ), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=32768, + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mistral_nemo_instruct_2407.py b/opencompass/configs/models/mistral/lmdeploy_mistral_nemo_instruct_2407.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3c27f47a1579fd9a40bb0a97948f5b04ef97bc --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mistral_nemo_instruct_2407.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mistral-nemo-instruct-2407-turbomind', + path='mistralai/Mistral-Nemo-Instruct-2407', + engine_config=dict(session_len=32768, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=32768, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mistral_small_instruct_2409.py b/opencompass/configs/models/mistral/lmdeploy_mistral_small_instruct_2409.py new file mode 100644 index 0000000000000000000000000000000000000000..b6c07212e3a6d0faa3eb477f6978c70499026c30 --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mistral_small_instruct_2409.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mistral-small-instruct-2409-turbomind', + path='mistralai/Mistral-Small-Instruct-2409', + engine_config=dict(session_len=32768, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=32768, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mixtral_8x22b_instruct_v0_1.py b/opencompass/configs/models/mistral/lmdeploy_mixtral_8x22b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..1ffef2569c69ea59c2a6f475a00bc635d20510fd --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mixtral_8x22b_instruct_v0_1.py @@ -0,0 +1,22 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mixtral-8x22b-instruct-v0.1-turbomind', + path='mistralai/Mixtral-8x22B-Instruct-v0.1', + engine_config=dict( + session_len=32768, + max_batch_size=16, + tp=8, + cache_max_entry_count=0.7, + ), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=32768, + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/mistral/lmdeploy_mixtral_large_instruct_2407.py b/opencompass/configs/models/mistral/lmdeploy_mixtral_large_instruct_2407.py new file mode 100644 index 0000000000000000000000000000000000000000..6dcdce83e578e277308ec98e0fa0817daf2b394a --- /dev/null +++ b/opencompass/configs/models/mistral/lmdeploy_mixtral_large_instruct_2407.py @@ -0,0 +1,22 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='mixtral-large-instruct-2407-turbomind', + path='mistralai/Mistral-Large-Instruct-2407', + engine_config=dict( + session_len=32768, + max_batch_size=16, + tp=4, + cache_max_entry_count=0.7, + ), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=32768, + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/mistral/mixtral_8x7b_32k.py b/opencompass/configs/models/mistral/mixtral_8x7b_32k.py new file mode 100644 index 0000000000000000000000000000000000000000..e1375f74e1e77751e5126c599fafcec4985dd501 --- /dev/null +++ b/opencompass/configs/models/mistral/mixtral_8x7b_32k.py @@ -0,0 +1,19 @@ +from opencompass.models import Mixtral + +# Please follow the instruction in https://github.com/open-compass/MixtralKit +# to download the model weights and install the requirements + + +models = [ + dict( + abbr='mixtral-8x7b-32k', + type=Mixtral, + path='./models/mixtral/mixtral-8x7b-32kseqlen', + tokenizer_path='./models/mixtral/mixtral-8x7b-32kseqlen/tokenizer.model', + max_out_len=100, + max_seq_len=2048, + batch_size=8, + num_gpus=2, + run_cfg=dict(num_gpus=2, num_procs=1), + ), +] diff --git a/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_1.py b/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..140afa0615f4e4c1f84578f53d377bc655a69acf --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLMwithChatTemplate + + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='mistral-7b-instruct-v0.1-vllm', + path='mistralai/Mistral-7B-Instruct-v0.1', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=256, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_2.py b/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_2.py new file mode 100644 index 0000000000000000000000000000000000000000..ddaa0dd78b6dca51f00cf17eef9a1c0f9bac6e9b --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mistral_7b_instruct_v0_2.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLMwithChatTemplate + + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='mistral-7b-instruct-v0.2-vllm', + path='mistralai/Mistral-7B-Instruct-v0.2', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=256, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mistral_7b_v0_1.py b/opencompass/configs/models/mistral/vllm_mistral_7b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..7cf9406ccc408f9e2c294b26d2428984604d212a --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mistral_7b_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='mistral-7b-v0.1-vllm', + path='mistralai/Mistral-7B-v0.1', + model_kwargs=dict(dtype='bfloat16', tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mistral_7b_v0_2.py b/opencompass/configs/models/mistral/vllm_mistral_7b_v0_2.py new file mode 100644 index 0000000000000000000000000000000000000000..f7af965dc4eae9405914317547280b7657ad83ef --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mistral_7b_v0_2.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='mistral-7b-v0.2-vllm', + path='mistral-community/Mistral-7B-v0.2', + model_kwargs=dict(dtype='bfloat16', tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mixtral_8x22b_instruct_v0_1.py b/opencompass/configs/models/mistral/vllm_mixtral_8x22b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..99a1cd28878ddbf9c17c641dbde83efdff8c1e56 --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mixtral_8x22b_instruct_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLMwithChatTemplate + + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='mixtral-8x22b-instruct-v0.1-vllm', + path='mistralai/Mixtral-8x22B-Instruct-v0.1', + model_kwargs=dict(tensor_parallel_size=8), + max_out_len=256, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mixtral_8x22b_v0_1.py b/opencompass/configs/models/mistral/vllm_mixtral_8x22b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..5f5536bacf3391b001ad7ec81cc5967a7be46d96 --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mixtral_8x22b_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='mixtral-8x22b-v0.1-vllm', + path='mistralai/Mixtral-8x22B-v0.1', + model_kwargs=dict(dtype='bfloat16', tensor_parallel_size=8), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mixtral_8x7b_instruct_v0_1.py b/opencompass/configs/models/mistral/vllm_mixtral_8x7b_instruct_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..56759dd6e98042c39ae9e69a5fd7c65167d687c6 --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mixtral_8x7b_instruct_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLMwithChatTemplate + + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='mixtral-8x7b-instruct-v0.1-vllm', + path='mistralai/Mixtral-8x7B-Instruct-v0.1', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=256, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mixtral_8x7b_v0_1.py b/opencompass/configs/models/mistral/vllm_mixtral_8x7b_v0_1.py new file mode 100644 index 0000000000000000000000000000000000000000..76a10bb2cba76a34d75f818a2a9e1933cd94e04d --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mixtral_8x7b_v0_1.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='mixtral-8x7b-v0.1-vllm', + path='mistralai/Mixtral-8x7B-v0.1', + model_kwargs=dict(dtype='bfloat16', tensor_parallel_size=2), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/mistral/vllm_mixtral_large_instruct_2407.py b/opencompass/configs/models/mistral/vllm_mixtral_large_instruct_2407.py new file mode 100644 index 0000000000000000000000000000000000000000..5f67f294b839a2c1d4716433ee2c237e0a97da1f --- /dev/null +++ b/opencompass/configs/models/mistral/vllm_mixtral_large_instruct_2407.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLMwithChatTemplate + + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='mixtral-large-instruct-2407-vllm', + path='mistralai/Mistral-Large-Instruct-2407', + model_kwargs=dict(tensor_parallel_size=8), + max_out_len=256, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/moonshot/kimi_k2.py b/opencompass/configs/models/moonshot/kimi_k2.py new file mode 100644 index 0000000000000000000000000000000000000000..dd6000b8b4656a94bec6417851a8c53dec898160 --- /dev/null +++ b/opencompass/configs/models/moonshot/kimi_k2.py @@ -0,0 +1,22 @@ +from opencompass.models import OpenAISDK + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='kimi-k2-0711-preview', + type=OpenAISDK, + path='kimi-k2-0711-preview', + key='your-api-key-here', # Set your API key here + meta_template=api_meta_template, + query_per_second=1, + openai_api_base='https://api.moonshot.cn/v1', + batch_size=1, + temperature=1, + max_seq_len=131072, + retry=10, + ), +] diff --git a/opencompass/configs/models/moonshot/kimi_k2_streaming.py b/opencompass/configs/models/moonshot/kimi_k2_streaming.py new file mode 100644 index 0000000000000000000000000000000000000000..452f6a6a8e35ab5833ac35ca6c0e72e3fea96b4f --- /dev/null +++ b/opencompass/configs/models/moonshot/kimi_k2_streaming.py @@ -0,0 +1,25 @@ +from opencompass.models import OpenAISDKStreaming + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='kimi-k2-0711-preview-streaming', + type=OpenAISDKStreaming, + path='kimi-k2-0711-preview', + key='your-api-key-here', # Set your API key here + meta_template=api_meta_template, + query_per_second=1, + openai_api_base='https://api.moonshot.cn/v1', + batch_size=1, + temperature=1, + max_seq_len=131072, + retry=10, + stream=True, # Enable streaming output + verbose=True, # Enable detailed logging to see real-time streaming output + stream_chunk_size=1, # Streaming chunk size + ), +] \ No newline at end of file diff --git a/opencompass/configs/models/moss/hf_moss_moon_003_base.py b/opencompass/configs/models/moss/hf_moss_moon_003_base.py new file mode 100644 index 0000000000000000000000000000000000000000..fc43d0ebd353230d02fb1132857d1df91cf61648 --- /dev/null +++ b/opencompass/configs/models/moss/hf_moss_moon_003_base.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='moss-moon-003-base-hf', + path='fnlp/moss-moon-003-base', + tokenizer_path='fnlp/moss-moon-003-base', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto', revision='5e406ca0ebbdea11cc3b12aa5932995c692568ac'), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/moss/hf_moss_moon_003_sft.py b/opencompass/configs/models/moss/hf_moss_moon_003_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..b0248df03b78eef79c065fd331b2e7b431d5315a --- /dev/null +++ b/opencompass/configs/models/moss/hf_moss_moon_003_sft.py @@ -0,0 +1,28 @@ +from opencompass.models import HuggingFaceCausalLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|Human|>: ', end='\n'), + dict(role='BOT', begin='<|MOSS|>: ', end='\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='moss-moon-003-sft-hf', + path='fnlp/moss-moon-003-sft', + tokenizer_path='fnlp/moss-moon-003-sft', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto', revision='7119d446173035561f40977fb9cb999995bb7517'), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/mpt/hf_mpt_7b.py b/opencompass/configs/models/mpt/hf_mpt_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..0eadbad07f01436f7c0680bedd4f23bf9f94b68f --- /dev/null +++ b/opencompass/configs/models/mpt/hf_mpt_7b.py @@ -0,0 +1,27 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='mpt-7b-hf', + path='mosaicml/mpt-7b', + tokenizer_path='mosaicml/mpt-7b', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=True + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + max_seq_len=4096, + revision='68e1a8e0ebb9b30f3c45c1ef6195980f29063ae2', + ), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/mpt/hf_mpt_instruct_7b.py b/opencompass/configs/models/mpt/hf_mpt_instruct_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..0d406a922703a7b7495a5690c6b6ebbc246fee5c --- /dev/null +++ b/opencompass/configs/models/mpt/hf_mpt_instruct_7b.py @@ -0,0 +1,27 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='mpt-instruct-7b-hf', + path='mosaicml/mpt-7b-instruct', + tokenizer_path='mosaicml/mpt-7b-instruct', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=True + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + max_seq_len=4096, + revision='68e1a8e0ebb9b30f3c45c1ef6195980f29063ae2', + ), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/ms_internlm/ms_internlm_chat_7b_8k.py b/opencompass/configs/models/ms_internlm/ms_internlm_chat_7b_8k.py new file mode 100644 index 0000000000000000000000000000000000000000..4f6ed0910bde037be4d8ed4408b71e6d4b6c0bfc --- /dev/null +++ b/opencompass/configs/models/ms_internlm/ms_internlm_chat_7b_8k.py @@ -0,0 +1,30 @@ +from opencompass.models import ModelScopeCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|User|>:', end='\n'), + dict(role='BOT', begin='<|Bot|>:', end='\n', generate=True), + ], +) + +models = [ + dict( + type=ModelScopeCausalLM, + abbr='internlm-chat-7b-8k-ms', + path='Shanghai_AI_Laboratory/internlm-chat-7b-8k', + tokenizer_path='Shanghai_AI_Laboratory/internlm-chat-7b-8k', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + use_fast=False, + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/nanbeige/hf_nanbeige2_16b_chat.py b/opencompass/configs/models/nanbeige/hf_nanbeige2_16b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..4660f3299ec1ca491e2a42a8cbe6f523e8ef5166 --- /dev/null +++ b/opencompass/configs/models/nanbeige/hf_nanbeige2_16b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='nanbeige2-16b-chat-hf', + path='Nanbeige/Nanbeige2-16B-Chat', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/nanbeige/hf_nanbeige2_8b_chat.py b/opencompass/configs/models/nanbeige/hf_nanbeige2_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..cb9dd4649483ccaf7260f4fdc4aa8637be3c12a4 --- /dev/null +++ b/opencompass/configs/models/nanbeige/hf_nanbeige2_8b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='nanbeige2-8b-chat-hf', + path='Nanbeige/Nanbeige2-8B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/nanbeige/hf_nanbeige_16b_chat.py b/opencompass/configs/models/nanbeige/hf_nanbeige_16b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..6146da55e2120759b442ada5040ca754e1536a60 --- /dev/null +++ b/opencompass/configs/models/nanbeige/hf_nanbeige_16b_chat.py @@ -0,0 +1,35 @@ +from opencompass.models import HuggingFaceCausalLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='### Human: \n', end='\n\n'), + dict(role='BOT', begin='### Assistant: ', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='nanbeige-16b-chat-hf', + path='Nanbeige/Nanbeige-16B-Chat', + tokenizer_path='Nanbeige/Nanbeige-16B-Chat', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + torch_dtype='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False, + ), + meta_template=_meta_template, + batch_padding=False, + max_out_len=100, + max_seq_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=2, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/nvidia/lmdeploy_nemotron_70b_instruct_hf.py b/opencompass/configs/models/nvidia/lmdeploy_nemotron_70b_instruct_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2ed9f56e13bb13f4577065656b17991083a21702 --- /dev/null +++ b/opencompass/configs/models/nvidia/lmdeploy_nemotron_70b_instruct_hf.py @@ -0,0 +1,18 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='nvidia-3_1-Nemotron-70b-instruct-HF-turbomind', + path='nvidia/Llama-3.1-Nemotron-70B-Instruct-HF', + engine_config=dict(max_batch_size=16, tp=4), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096 + ), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/openai/gpt_3_5_turbo.py b/opencompass/configs/models/openai/gpt_3_5_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..453e0493b05d2da5749261d1ec216607e05fa2df --- /dev/null +++ b/opencompass/configs/models/openai/gpt_3_5_turbo.py @@ -0,0 +1,18 @@ +from opencompass.models import OpenAI + + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], +) + +models = [ + dict(abbr='GPT-3.5-turbo-0613', + type=OpenAI, path='gpt-3.5-turbo-0613', + key='ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + max_out_len=2048, max_seq_len=4096, batch_size=8), +] diff --git a/opencompass/configs/models/openai/gpt_3_5_turbo_0125.py b/opencompass/configs/models/openai/gpt_3_5_turbo_0125.py new file mode 100644 index 0000000000000000000000000000000000000000..efab611b46d4b091997a611a64ec56490c901097 --- /dev/null +++ b/opencompass/configs/models/openai/gpt_3_5_turbo_0125.py @@ -0,0 +1,20 @@ +from opencompass.models import OpenAI + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='GPT-3.5-turbo-0125', + type=OpenAI, + path='gpt-3.5-turbo-0125', + key= + 'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + max_out_len=2048, + max_seq_len=4096, + batch_size=8), +] diff --git a/opencompass/configs/models/openai/gpt_4.py b/opencompass/configs/models/openai/gpt_4.py new file mode 100644 index 0000000000000000000000000000000000000000..453e0493b05d2da5749261d1ec216607e05fa2df --- /dev/null +++ b/opencompass/configs/models/openai/gpt_4.py @@ -0,0 +1,18 @@ +from opencompass.models import OpenAI + + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], +) + +models = [ + dict(abbr='GPT-3.5-turbo-0613', + type=OpenAI, path='gpt-3.5-turbo-0613', + key='ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + max_out_len=2048, max_seq_len=4096, batch_size=8), +] diff --git a/opencompass/configs/models/openai/gpt_4o_2024_05_13.py b/opencompass/configs/models/openai/gpt_4o_2024_05_13.py new file mode 100644 index 0000000000000000000000000000000000000000..e742f8a41ff362439b8c091db44362d2906589e6 --- /dev/null +++ b/opencompass/configs/models/openai/gpt_4o_2024_05_13.py @@ -0,0 +1,20 @@ +from opencompass.models import OpenAI + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='GPT-4o-2024-05-13', + type=OpenAI, + path='gpt-4o-2024-05-13', + key= + 'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + max_out_len=2048, + max_seq_len=4096, + batch_size=8), +] diff --git a/opencompass/configs/models/openai/o1_mini_2024_09_12.py b/opencompass/configs/models/openai/o1_mini_2024_09_12.py new file mode 100644 index 0000000000000000000000000000000000000000..331ecf31976df1070994242f75550b1074f05800 --- /dev/null +++ b/opencompass/configs/models/openai/o1_mini_2024_09_12.py @@ -0,0 +1,20 @@ +from opencompass.models import OpenAISDK + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='o1-mini-2024-09-12', + type=OpenAISDK, + path='o1-mini-2024-09-12', + key= + 'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + batch_size=1, + temperature=1, + max_completion_tokens=8192), # you can change it for large reasoning inference cost, according to: https://platform.openai.com/docs/guides/reasoning +] diff --git a/opencompass/configs/models/openai/o1_preview_2024_09_12.py b/opencompass/configs/models/openai/o1_preview_2024_09_12.py new file mode 100644 index 0000000000000000000000000000000000000000..9dff10371840c4b0385ecd9f0693eb45087df240 --- /dev/null +++ b/opencompass/configs/models/openai/o1_preview_2024_09_12.py @@ -0,0 +1,20 @@ +from opencompass.models import OpenAISDK + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +], ) + +models = [ + dict( + abbr='o1-preview-2024-09-12', + type=OpenAISDK, + path='o1-preview-2024-09-12', + key= + 'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well + meta_template=api_meta_template, + query_per_second=1, + batch_size=1, + temperature=1, + max_completion_tokens=8192), # you can change it for large reasoning inference cost, according to: https://platform.openai.com/docs/guides/reasoning +] diff --git a/opencompass/configs/models/openbmb/hf_minicpm3_4b.py b/opencompass/configs/models/openbmb/hf_minicpm3_4b.py new file mode 100644 index 0000000000000000000000000000000000000000..730324ea4e4a8e7428157df450984031683fa14b --- /dev/null +++ b/opencompass/configs/models/openbmb/hf_minicpm3_4b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='MiniCPM3-4B-hf', + path='openbmb/MiniCPM3-4B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + model_kwargs=dict( + torch_dtype='torch.bfloat16', + ), + ) +] diff --git a/opencompass/configs/models/openbmb/hf_minicpm_2b_dpo_fp32.py b/opencompass/configs/models/openbmb/hf_minicpm_2b_dpo_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..d09690290886d3337af656f65f2c36c240a3df7e --- /dev/null +++ b/opencompass/configs/models/openbmb/hf_minicpm_2b_dpo_fp32.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='minicpm-2b-dpo-fp32-hf', + path='openbmb/MiniCPM-2B-dpo-fp32', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_bf16.py b/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_bf16.py new file mode 100644 index 0000000000000000000000000000000000000000..43303b2462fc9e9066c32407ea4698d6dab9283d --- /dev/null +++ b/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_bf16.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='minicpm-2b-sft-bf16-hf', + path='openbmb/MiniCPM-2B-sft-bf16', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_fp32.py b/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..a13fbcd6d01cde2a62ea1ede064efa05d91e0cf8 --- /dev/null +++ b/opencompass/configs/models/openbmb/hf_minicpm_2b_sft_fp32.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='minicpm-2b-sft-fp32-hf', + path='openbmb/MiniCPM-2B-sft-fp32', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/opt/hf_opt_125m.py b/opencompass/configs/models/opt/hf_opt_125m.py new file mode 100644 index 0000000000000000000000000000000000000000..b813d28e6e041274f965019dd9497b91cd8d7a14 --- /dev/null +++ b/opencompass/configs/models/opt/hf_opt_125m.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +opt125m = dict( + type=HuggingFaceBaseModel, + abbr='opt-125m-hf', + path='facebook/opt-125m', + max_out_len=1024, + batch_size=64, + run_cfg=dict(num_gpus=1), +) + +models = [opt125m] diff --git a/opencompass/configs/models/opt/hf_opt_350m.py b/opencompass/configs/models/opt/hf_opt_350m.py new file mode 100644 index 0000000000000000000000000000000000000000..57aa609cb454720bcbaa78dee16e9efefcfa8997 --- /dev/null +++ b/opencompass/configs/models/opt/hf_opt_350m.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +opt350m = dict( + type=HuggingFaceBaseModel, + abbr='opt-350m-hf', + path='facebook/opt-350m', + max_out_len=1024, + batch_size=32, + run_cfg=dict(num_gpus=1), +) + +models = [opt350m] diff --git a/opencompass/configs/models/others/hf_abel_7b_001.py b/opencompass/configs/models/others/hf_abel_7b_001.py new file mode 100644 index 0000000000000000000000000000000000000000..12c761e09228bcb915582a01f74ebf15fb21aede --- /dev/null +++ b/opencompass/configs/models/others/hf_abel_7b_001.py @@ -0,0 +1,31 @@ +from opencompass.models import HuggingFaceCausalLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='Question:\n', end='\n'), + dict(role='BOT', begin='Answer:\n', end='\n', generate=True), + ], +) + +models = [ + dict( + abbr='abel-7b-001', + type=HuggingFaceCausalLM, + path='GAIR/Abel-7B-001', + tokenizer_path='GAIR/Abel-7B-001', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_abel_7b_002.py b/opencompass/configs/models/others/hf_abel_7b_002.py new file mode 100644 index 0000000000000000000000000000000000000000..89abf183473bb37e8d919dcaff48eacf3dadc9cc --- /dev/null +++ b/opencompass/configs/models/others/hf_abel_7b_002.py @@ -0,0 +1,31 @@ +from opencompass.models import HuggingFaceCausalLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='Question:\n', end='\n'), + dict(role='BOT', begin='Answer:\n', end='\n', generate=True), + ], +) + +models = [ + dict( + abbr='abel-7b-002', + type=HuggingFaceCausalLM, + path='GAIR/Abel-7B-002', + tokenizer_path='GAIR/Abel-7B-002', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_arithmo_mistral_7b.py b/opencompass/configs/models/others/hf_arithmo_mistral_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..f3f15c0cd19effe9430863f0d36a813064377448 --- /dev/null +++ b/opencompass/configs/models/others/hf_arithmo_mistral_7b.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='', + round=[ + dict(role='HUMAN', begin='Question: ', end='\n\n'), + dict(role='BOT', begin='Answer: ', end='\n\n', generate=True), + ], +) + +models = [ + dict( + abbr='arithmo-mistral-7b-hf', + type=HuggingFaceCausalLM, + path='akjindal53244/Arithmo-Mistral-7B', + tokenizer_path='akjindal53244/Arithmo-Mistral-7B', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_command_r_plus.py b/opencompass/configs/models/others/hf_command_r_plus.py new file mode 100644 index 0000000000000000000000000000000000000000..bdbf924f6a009b5b4ef33a81a1e2abfcda3cee2c --- /dev/null +++ b/opencompass/configs/models/others/hf_command_r_plus.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='command-r-plus-hf', + path='CohereForAI/c4ai-command-r-plus', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/others/hf_dbrx_base.py b/opencompass/configs/models/others/hf_dbrx_base.py new file mode 100644 index 0000000000000000000000000000000000000000..985e6add3c87eb14ff4a9d69c8a48811fb1487e5 --- /dev/null +++ b/opencompass/configs/models/others/hf_dbrx_base.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='dbrx-base-hf', + path='databricks/dbrx-base', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/others/hf_dbrx_instruct.py b/opencompass/configs/models/others/hf_dbrx_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..a207f3bb92ce9669cbd173ca537765a0a2536e89 --- /dev/null +++ b/opencompass/configs/models/others/hf_dbrx_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='dbrx-instruct-hf', + path='databricks/dbrx-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/others/hf_dolphin_21_mistral_7b.py b/opencompass/configs/models/others/hf_dolphin_21_mistral_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..9d1ee9dd15079d7ffc10cb6429d0de9d81129cec --- /dev/null +++ b/opencompass/configs/models/others/hf_dolphin_21_mistral_7b.py @@ -0,0 +1,32 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'), + dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True), + ], +) + +models = [ + dict( + abbr='dolphin-2.2.1-mistral-7b-hf', + type=HuggingFaceCausalLM, + path='ehartford/dolphin-2.2.1-mistral-7b', + tokenizer_path='ehartford/dolphin-2.2.1-mistral-7b', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_fashiongpt_70b_v11.py b/opencompass/configs/models/others/hf_fashiongpt_70b_v11.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd8060886a015f938e28abb3443e8cd79992333 --- /dev/null +++ b/opencompass/configs/models/others/hf_fashiongpt_70b_v11.py @@ -0,0 +1,32 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='### User:\n', end='\n'), + dict(role='BOT', begin='### Assistant:\n', generate=True), + ], +) + +models = [ + dict( + abbr='fashiongpt-70b-v11-hf', + type=HuggingFaceCausalLM, + path='ICBU-NPU/FashionGPT-70B-V1.1', + tokenizer_path='ICBU-NPU/FashionGPT-70B-V1.1', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=8, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_gsm8k_rft_llama7b2_u13b.py b/opencompass/configs/models/others/hf_gsm8k_rft_llama7b2_u13b.py new file mode 100644 index 0000000000000000000000000000000000000000..75cc2554332b04e0a3c076b305fc1fc787b85dd4 --- /dev/null +++ b/opencompass/configs/models/others/hf_gsm8k_rft_llama7b2_u13b.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n', + round=[ + dict(role='HUMAN', begin='### Instruction:\n', end='\n\n'), + dict(role='BOT', begin='### Response:', end='\n\n', generate=True), + ], +) + +models = [ + dict( + abbr='gsm8k-rft-llama7b2-u13b', + type=HuggingFaceCausalLM, + path='OFA-Sys/gsm8k-rft-llama7b2-u13b', + tokenizer_path='OFA-Sys/gsm8k-rft-llama7b2-u13b', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_metamath_7b_v1_0.py b/opencompass/configs/models/others/hf_metamath_7b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..c5af55424a84d0186f22f4425c16aca8d59592f5 --- /dev/null +++ b/opencompass/configs/models/others/hf_metamath_7b_v1_0.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n', + round=[ + dict(role='HUMAN', begin='### Instruction:\n', end='\n\n'), + dict(role='BOT', begin='### Response: ', end='\n\n', generate=True), + ], +) + +models = [ + dict( + abbr='metamath-7b-v1.0-hf', + type=HuggingFaceCausalLM, + path='meta-math/MetaMath-7B-V1.0', + tokenizer_path='meta-math/MetaMath-7B-V1.0', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_metamath_llemma_7b.py b/opencompass/configs/models/others/hf_metamath_llemma_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..115af0dc16f6cdbe94f75b6232309ee6e3a7aa44 --- /dev/null +++ b/opencompass/configs/models/others/hf_metamath_llemma_7b.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n', + round=[ + dict(role='HUMAN', begin='### Instruction:\n', end='\n\n'), + dict(role='BOT', begin='### Response: ', end='\n\n', generate=True), + ], +) + +models = [ + dict( + abbr='metamath-llemma-7b-hf', + type=HuggingFaceCausalLM, + path='meta-math/MetaMath-Llemma-7B', + tokenizer_path='meta-math/MetaMath-Llemma-7B', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_metamath_mistral_7b.py b/opencompass/configs/models/others/hf_metamath_mistral_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..39e5d81172b2ed5c414d9750440e41f0c3e2d69e --- /dev/null +++ b/opencompass/configs/models/others/hf_metamath_mistral_7b.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n', + round=[ + dict(role='HUMAN', begin='### Instruction:\n', end='\n\n'), + dict(role='BOT', begin='### Response: ', end='\n\n', generate=True), + ], +) + +models = [ + dict( + abbr='metamath-mistral-7b-hf', + type=HuggingFaceCausalLM, + path='meta-math/MetaMath-Mistral-7B', + tokenizer_path='meta-math/MetaMath-Mistral-7B', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_openchat_35_0106.py b/opencompass/configs/models/others/hf_openchat_35_0106.py new file mode 100644 index 0000000000000000000000000000000000000000..63248355d90d36241b35eca5fe6b135016167c51 --- /dev/null +++ b/opencompass/configs/models/others/hf_openchat_35_0106.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='GPT4 Correct User: ', end='<|end_of_turn|>'), + dict(role='BOT', begin='GPT4 Correct Assistant: ', end='<|end_of_turn|>', generate=True), + ], +) + +models = [ + dict( + abbr='openchat-3.5-0106-hf', + type=HuggingFaceCausalLM, + path='openchat/openchat-3.5-0106', + tokenizer_path='openchat/openchat-3.5-0106', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='<|end_of_turn|>', + ) +] diff --git a/opencompass/configs/models/others/hf_openchat_35_1210.py b/opencompass/configs/models/others/hf_openchat_35_1210.py new file mode 100644 index 0000000000000000000000000000000000000000..da9b8951f11513b00411406c55b45638ddf5334c --- /dev/null +++ b/opencompass/configs/models/others/hf_openchat_35_1210.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='GPT4 Correct User: ', end='<|end_of_turn|>'), + dict(role='BOT', begin='GPT4 Correct Assistant: ', end='<|end_of_turn|>', generate=True), + ], +) + +models = [ + dict( + abbr='openchat-3.5-1210-hf', + type=HuggingFaceCausalLM, + path='openchat/openchat-3.5-1210', + tokenizer_path='openchat/openchat-3.5-1210', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='<|end_of_turn|>', + ) +] diff --git a/opencompass/configs/models/others/hf_orionstar_14b_base.py b/opencompass/configs/models/others/hf_orionstar_14b_base.py new file mode 100644 index 0000000000000000000000000000000000000000..f0cf8a03014841acbf0385bb9d3be359b0178037 --- /dev/null +++ b/opencompass/configs/models/others/hf_orionstar_14b_base.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + dict( + abbr='orionstar-14b-base-hf', + type=HuggingFaceCausalLM, + path='OrionStarAI/Orion-14B-Base', + tokenizer_path='OrionStarAI/Orion-14B-Base', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + min_out_len=1, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=2, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_orionstar_yi_34b_chat.py b/opencompass/configs/models/others/hf_orionstar_yi_34b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec943a07787db52525328f37089a163d1ab0aee --- /dev/null +++ b/opencompass/configs/models/others/hf_orionstar_yi_34b_chat.py @@ -0,0 +1,34 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + begin='<|startoftext|>', + round=[ + dict(role='HUMAN', begin='Human: ', end='\n\n'), + dict(role='BOT', begin='Assistant: <|endoftext|>', end='<|endoftext|>', generate=True), + ], +) + +models = [ + dict( + abbr='orionstar-yi-34b-chat-hf', + type=HuggingFaceCausalLM, + path='OrionStarAI/OrionStar-Yi-34B-Chat', + tokenizer_path='OrionStarAI/OrionStar-Yi-34B-Chat', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=4, num_procs=1), + end_str='<|endoftext|>', + ) +] diff --git a/opencompass/configs/models/others/hf_phi_2.py b/opencompass/configs/models/others/hf_phi_2.py new file mode 100644 index 0000000000000000000000000000000000000000..b2cfa439335874c0babb27811c807de7c63dc857 --- /dev/null +++ b/opencompass/configs/models/others/hf_phi_2.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='phi-2-hf', + path='microsoft/phi-2', + tokenizer_path='microsoft/phi-2', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + min_out_len=3, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/hf_telechat_12b_v2.py b/opencompass/configs/models/others/hf_telechat_12b_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..f4476cdde7416af4a5b638549d7841bca1ae9a21 --- /dev/null +++ b/opencompass/configs/models/others/hf_telechat_12b_v2.py @@ -0,0 +1,26 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<_user>'), + dict(role='BOT', begin='<_bot>', end='<_end>', generate=True), + ], +) + +models = [ + dict( + abbr='telechat-12b-v2-hf', + type=HuggingFaceCausalLM, + path='Tele-AI/TeleChat-12B-v2', + model_kwargs=dict(device_map='auto', torch_dtype='torch.bfloat16', trust_remote_code=True), + tokenizer_kwargs=dict(padding_side='left', truncation_side='left', trust_remote_code=True), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=2), + end_str='<_end>', + batch_padding=True, + ) +] diff --git a/opencompass/configs/models/others/hf_telechat_52b.py b/opencompass/configs/models/others/hf_telechat_52b.py new file mode 100644 index 0000000000000000000000000000000000000000..2585dbccc59f36f0524d2c47071b2cd47688779b --- /dev/null +++ b/opencompass/configs/models/others/hf_telechat_52b.py @@ -0,0 +1,26 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<_user>'), + dict(role='BOT', begin='<_bot>', end='<_end>', generate=True), + ], +) + +models = [ + dict( + abbr='telechat-52b-hf', + type=HuggingFaceCausalLM, + path='Tele-AI/TeleChat-52B', + model_kwargs=dict(device_map='auto', torch_dtype='torch.bfloat16', trust_remote_code=True), + tokenizer_kwargs=dict(padding_side='left', truncation_side='left', trust_remote_code=True), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + run_cfg=dict(num_gpus=4), + end_str='<_end>', + batch_padding=True, + ) +] diff --git a/opencompass/configs/models/others/hf_telechat_7b.py b/opencompass/configs/models/others/hf_telechat_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..1fbfaa623c29cf1bb39f6118b3f9352575242e58 --- /dev/null +++ b/opencompass/configs/models/others/hf_telechat_7b.py @@ -0,0 +1,25 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<_user>'), + dict(role='BOT', begin='<_bot>', end='<_end>', generate=True), + ], +) + +models = [ + dict( + abbr='telechat-7b-hf', + type=HuggingFaceCausalLM, + path='Tele-AI/telechat-7B', + model_kwargs=dict(device_map='auto', trust_remote_code=True), + tokenizer_kwargs=dict(padding_side='left', truncation_side='left', trust_remote_code=True), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='<_end>', + ) +] diff --git a/opencompass/configs/models/others/hf_yayi2_30b_base.py b/opencompass/configs/models/others/hf_yayi2_30b_base.py new file mode 100644 index 0000000000000000000000000000000000000000..bc7668a82481b48f2b5507c8ab06302163cba7fb --- /dev/null +++ b/opencompass/configs/models/others/hf_yayi2_30b_base.py @@ -0,0 +1,25 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + abbr='yayi2-30b-hf', + type=HuggingFaceCausalLM, + path='wenge-research/yayi2-30b', + tokenizer_path='wenge-research/yayi2-30b', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + min_out_len=1, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=4, num_procs=1), + ) +] diff --git a/opencompass/configs/models/others/vllm_dbrx_instruct.py b/opencompass/configs/models/others/vllm_dbrx_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..70d2736fbe78ceb66ac37bd071667eec26500d64 --- /dev/null +++ b/opencompass/configs/models/others/vllm_dbrx_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='dbrx-instruct-vllm', + path='databricks/dbrx-instruct', + model_kwargs=dict(tensor_parallel_size=8), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/others/vllm_orionstar_14b_longchat.py b/opencompass/configs/models/others/vllm_orionstar_14b_longchat.py new file mode 100644 index 0000000000000000000000000000000000000000..2c90f7a3de21bac0e13a78e1173aa644cb449da8 --- /dev/null +++ b/opencompass/configs/models/others/vllm_orionstar_14b_longchat.py @@ -0,0 +1,26 @@ +from opencompass.models import VLLM + + +_meta_template = dict( + begin='', + round=[ + dict(role='HUMAN', begin='Human: ', end='\n'), + dict(role='BOT', begin='Assistant: ', end='', generate=True), + ], +) + +models = [ + dict( + abbr='orionstar-14b-longchat-vllm', + type=VLLM, + path='OrionStarAI/Orion-14B-LongChat', + model_kwargs=dict(tensor_parallel_size=4), + generation_kwargs=dict(temperature=0), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=4096, + batch_size=32, + run_cfg=dict(num_gpus=4, num_procs=1), + stop_words=['<|endoftext|>'], + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_3_5_MoE_instruct.py b/opencompass/configs/models/phi/hf_phi_3_5_MoE_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..40153f8c37db622b207e948d2fc732558014c1a4 --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_3_5_MoE_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-3-5-MoE-instruct-hf', + path='microsoft/Phi-3.5-MoE-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_3_5_mini_instruct.py b/opencompass/configs/models/phi/hf_phi_3_5_mini_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..f02e4c6f090b2068d554ea3fd47939a49142bf84 --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_3_5_mini_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-3-5-mini-instruct-hf', + path='microsoft/Phi-3.5-mini-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_3_medium_4k_instruct.py b/opencompass/configs/models/phi/hf_phi_3_medium_4k_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..444f5ae0c0dccb84e483f24ee25925160cfbebce --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_3_medium_4k_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-3-medium-4k-instruct-hf', + path='microsoft/Phi-3-medium-4k-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_3_mini_4k_instruct.py b/opencompass/configs/models/phi/hf_phi_3_mini_4k_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c4544ce86de3ba1149aac6ef42a6030305cd86 --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_3_mini_4k_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-3-mini-4k-instruct-hf', + path='microsoft/Phi-3-mini-4k-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_3_small_8k_instruct.py b/opencompass/configs/models/phi/hf_phi_3_small_8k_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..89b0fa76d90ee4fef6195a7b419a6ffdfc374deb --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_3_small_8k_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-3-small-8k-instruct-hf', + path='microsoft/Phi-3-small-8k-instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/phi/hf_phi_4.py b/opencompass/configs/models/phi/hf_phi_4.py new file mode 100644 index 0000000000000000000000000000000000000000..1f4f6754aec425ca9c1eb57e1b17df7f31135c5b --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_4.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-4', + path='microsoft/phi-4', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/pulse/hf_pulse_7b.py b/opencompass/configs/models/pulse/hf_pulse_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..de966bc518a9a877baf54a7b03504afce7a5f587 --- /dev/null +++ b/opencompass/configs/models/pulse/hf_pulse_7b.py @@ -0,0 +1,23 @@ +from opencompass.models import HuggingFaceCausalLM +import torch + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='PULSE-7bv5', + path='OpenMEDLab/PULSE-7bv5', + tokenizer_path='OpenMEDLab/PULSE-7bv5', + model_kwargs=dict( + trust_remote_code=True, + torch_dtype=torch.bfloat16, + ## load_in_4bit=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + trust_remote_code=True, + ), + max_out_len=1024, + batch_size=1, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/README.md b/opencompass/configs/models/qwen/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3db35a2646e1b27906624a258a63e35bf8a88d13 --- /dev/null +++ b/opencompass/configs/models/qwen/README.md @@ -0,0 +1,142 @@ +# Qwen Model Details + +## Qwen + +Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models. + +## Qwen1.5 + +Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: + +- 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; +- Significant performance improvement in human preference for chat models; +- Multilingual support of both base and chat models; +- Stable support of 32K context length for models of all sizes +- No need of trust_remote_code. + +# Evaluation Command + +## Base Models + +```bash +python3 run.py --models hf_qwen1_5_7b --datasets mmlu_ppl_ac766d --debug +python3 run.py --models hf_qwen1_5_7b --datasets cmmlu_ppl_041cbf --debug +python3 run.py --models hf_qwen1_5_7b --datasets ceval_internal_ppl_93e5ce --debug +python3 run.py --models hf_qwen1_5_7b --datasets GaokaoBench_no_subjective_gen_d21e37 --debug +python3 run.py --models hf_qwen1_5_7b --datasets triviaqa_wiki_1shot_gen_20a989 --debug +python3 run.py --models hf_qwen1_5_7b --datasets nq_open_1shot_gen_20a989 --debug +python3 run.py --models hf_qwen1_5_7b --datasets race_ppl_abed12 --debug +python3 run.py --models hf_qwen1_5_7b --datasets winogrande_5shot_ll_252f01 --debug +python3 run.py --models hf_qwen1_5_7b --datasets hellaswag_10shot_ppl_59c85e --debug +python3 run.py --models hf_qwen1_5_7b --datasets bbh_gen_98fba6 --debug +python3 run.py --models hf_qwen1_5_7b --datasets gsm8k_gen_17d0dc --debug +python3 run.py --models hf_qwen1_5_7b --datasets math_4shot_base_gen_db136b --debug +python3 run.py --models hf_qwen1_5_7b --datasets TheoremQA_5shot_gen_6f0af8 --debug +python3 run.py --models hf_qwen1_5_7b --datasets deprecated_humaneval_gen_d2537e --debug +python3 run.py --models hf_qwen1_5_7b --datasets sanitized_mbpp_gen_742f0c --debug +python3 run.py --models hf_qwen1_5_7b --datasets lcbench_gen_5ff288 --debug +python3 run.py --models hf_qwen1_5_7b --datasets gpqa_ppl_6bf57a --debug +``` + +## Chat Models + +```bash +python3 run.py --models hf_qwen1_5_7b_chat --datasets mmlu_gen_4d595a --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets cmmlu_gen_c13365 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets ceval_internal_gen_2daf24 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets GaokaoBench_no_subjective_gen_4c31db --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets triviaqa_wiki_1shot_gen_eaf81e --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets nq_open_1shot_gen_01cf41 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets race_gen_69ee4f --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets winogrande_5shot_gen_b36770 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets hellaswag_10shot_gen_e42710 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets bbh_gen_5b92b0 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets gsm8k_gen_1d7fe4 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets math_0shot_gen_393424 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets TheoremQA_5shot_gen_6f0af8 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets humaneval_gen_8e312c --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets sanitized_mbpp_mdblock_gen_a447ff --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets lcbench_gen_5ff288 --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets gpqa_gen_4baadb --debug +python3 run.py --models hf_qwen1_5_7b_chat --datasets IFEval_gen_3321a3 --debug +``` + +# Benchmarks + +We provide reference results for the classifical models, you can reproduce these results by following the aforementioned commands. + +## Base Models + +| dataset | qwen-1.8b-turbomind | qwen-7b-turbomind | qwen-14b-turbomind | qwen-72b-turbomind | +|:------------:|----------------------:|--------------------:|---------------------:|---------------------:| +| mmlu | 46.61 | 59.75 | 67.85 | 77.36 | +| cmmlu | 51.98 | 62.10 | 70.05 | 83.32 | +| ceval-test | 54.24 | 62.06 | 70.33 | 83.25 | +| GaokaoBench | 22.11 | 35.32 | 54.07 | 77.56 | +| triviaqa | 22.76 | 53.61 | 49.72 | 79.13 | +| nq | 5.68 | 17.87 | 13.77 | 18.20 | +| race-high | 63.09 | 80.30 | 88.11 | 90.62 | +| winogrande | 61.25 | 72.06 | 72.45 | 82.56 | +| hellaswag | 38.04 | 64.62 | 85.88 | 90.40 | +| bbh | 22.53 | 45.89 | 56.75 | 63.35 | +| gsm8k | 23.73 | 54.36 | 61.64 | 79.68 | +| math | 6.30 | 15.56 | 30.38 | 44.18 | +| TheoremQA | 9.38 | 15.00 | 21.62 | 27.12 | +| humaneval | 16.46 | 23.78 | 23.78 | 66.46 | +| mbpp | 2.72 | 46.69 | 55.64 | 65.76 | +| lcbench | 1.82 | 4.95 | 8.86 | 16.86 | +| GPQA_diamond | 28.79 | 24.75 | 27.78 | 31.31 | + +| dataset | qwen1.5-0.5b-hf | qwen1.5-1.8b-hf | qwen1.5-4b-hf | qwen1.5-7b-hf | qwen1.5-14b-hf | qwen1.5-32b-hf | qwen1.5-72b-hf | +|:------------:|------------------:|------------------:|----------------:|----------------:|-----------------:|-----------------:|-----------------:| +| mmlu | 39.98 | 47.14 | 57.03 | 62.15 | 69.10 | 73.88 | 77.02 | +| cmmlu | 46.05 | 57.45 | 66.38 | 71.86 | 76.95 | 81.58 | 83.00 | +| ceval-test | 48.36 | 58.67 | 66.55 | 72.49 | 76.93 | 82.50 | 83.03 | +| GaokaoBench | 30.67 | 35.66 | 54.31 | 65.99 | 66.60 | 79.01 | 80.26 | +| triviaqa | 21.24 | 34.32 | 44.59 | 56.60 | 59.96 | 56.20 | 77.81 | +| nq | 6.01 | 10.28 | 15.73 | 18.61 | 16.07 | 21.75 | 20.53 | +| race-high | 54.66 | 67.27 | 78.50 | 82.73 | 87.99 | 90.57 | 90.45 | +| winogrande | 57.38 | 60.46 | 65.90 | 70.01 | 72.93 | 78.69 | 80.74 | +| hellaswag | 29.19 | 42.32 | 55.89 | 68.51 | 83.86 | 87.28 | 90.41 | +| bbh | 20.54 | 27.01 | 34.81 | 39.87 | 50.38 | 67.47 | 58.81 | +| gsm8k | 13.27 | 34.87 | 47.61 | 54.36 | 63.53 | 72.71 | 79.53 | +| math | 4.16 | 11.32 | 17.50 | 17.34 | 36.18 | 45.74 | 41.56 | +| TheoremQA | 5.88 | 12.00 | 13.75 | 4.25 | 12.62 | 26.62 | 26.62 | +| humaneval | 8.54 | 23.17 | 41.46 | 53.05 | 57.32 | 70.12 | 65.85 | +| mbpp | 5.06 | 15.95 | 45.91 | 52.14 | 52.14 | 59.14 | 61.09 | +| lcbench | 0.87 | 2.00 | 5.65 | 6.69 | 12.69 | 14.34 | 15.29 | +| GPQA_diamond | 23.74 | 28.79 | 23.23 | 20.71 | 32.32 | 30.81 | 31.82 | + +## Chat Models + +| dataset | qwen1.5-0.5b-chat-hf | qwen1.5-1.8b-chat-hf | qwen1.5-4b-chat-hf | qwen1.5-7b-chat-hf | qwen1.5-14b-chat-hf | qwen1.5-32b-chat-hf | qwen1.5-72b-chat-hf | qwen1.5-110b-chat-hf | +|:------------:|-----------------------:|-----------------------:|---------------------:|---------------------:|----------------------:|----------------------:|----------------------:|-----------------------:| +| mmlu | 35.32 | 45.62 | 55.90 | 61.79 | 67.96 | 75.36 | 77.24 | 77.95 | +| cmmlu | 31.55 | 48.93 | 58.53 | 68.78 | 75.07 | 80.39 | 82.48 | 86.46 | +| ceval-test | 36.88 | 55.17 | 61.54 | 68.71 | 74.80 | 80.47 | 81.53 | 87.33 | +| GaokaoBench | 21.51 | 46.19 | 59.11 | 70.55 | 80.39 | 86.15 | 88.58 | 89.59 | +| triviaqa | 19.84 | 35.81 | 48.93 | 53.65 | 62.58 | 74.72 | 83.25 | 86.20 | +| nq | 7.42 | 10.22 | 19.31 | 16.87 | 20.53 | 25.26 | 35.21 | 36.98 | +| race-high | 49.03 | 66.24 | 73.53 | 83.28 | 87.51 | 91.22 | 91.11 | 92.31 | +| winogrande | 50.51 | 51.07 | 57.54 | 65.27 | 70.09 | 77.90 | 80.82 | 82.32 | +| hellaswag | 29.60 | 41.71 | 60.45 | 71.58 | 79.70 | 88.56 | 89.37 | 91.11 | +| bbh | 24.12 | 26.82 | 43.15 | 38.12 | 55.38 | 69.28 | 72.97 | 71.04 | +| gsm8k | 8.79 | 27.60 | 47.61 | 56.25 | 64.90 | 79.91 | 77.03 | 79.53 | +| math | 0.56 | 4.94 | 7.34 | 22.14 | 32.22 | 41.80 | 45.22 | 54.38 | +| TheoremQA | 9.00 | 9.25 | 13.88 | 12.25 | 13.63 | 19.25 | 22.75 | 17.50 | +| humaneval | 9.15 | 15.85 | 30.49 | 40.85 | 50.00 | 57.93 | 60.37 | 65.24 | +| mbpp | 11.28 | 22.57 | 43.58 | 50.58 | 56.03 | 65.37 | 66.93 | 68.48 | +| lcbench | 0.00 | 1.65 | 5.56 | 8.78 | 14.42 | 10.78 | 18.77 | 34.58 | +| GPQA_diamond | 19.70 | 29.80 | 25.25 | 31.82 | 30.30 | 31.31 | 32.83 | 35.86 | +| IFEval | 13.12 | 16.08 | 25.51 | 38.82 | 42.51 | 49.54 | 51.02 | 55.08 | + +# Citation + +```BibTeX +@article{qwen, + title={Qwen Technical Report}, + author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, + journal={arXiv preprint arXiv:2309.16609}, + year={2023} +} +``` diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_0_5b.py b/opencompass/configs/models/qwen/hf_qwen1_5_0_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..60014be656c7c08a348cfdacf0ddf12fb8280dad --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_0_5b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-0.5b-hf', + path='Qwen/Qwen1.5-0.5B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_0_5b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_0_5b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..98d2eb7f5a2587d787732237e3c9f2f83a2423ba --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_0_5b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-0.5b-chat-hf', + path='Qwen/Qwen1.5-0.5B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_110b.py b/opencompass/configs/models/qwen/hf_qwen1_5_110b.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb23cecc65ccdac7795ddd4472d566c199cd5d7 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_110b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-110b-hf', + path='Qwen/Qwen1.5-110B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_110b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_110b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..dc150bdbb22aa427f0343ccaee547815d4eb866a --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_110b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-110b-chat-hf', + path='Qwen/Qwen1.5-110B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_14b.py b/opencompass/configs/models/qwen/hf_qwen1_5_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..4109fb734dc782ee63a087d47e1f2e1cc8c6dbc4 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_14b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-14b-hf', + path='Qwen/Qwen1.5-14B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_14b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..35055aa8c3bd44bc7489a50a8d61b8e50018d743 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_14b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-14b-chat-hf', + path='Qwen/Qwen1.5-14B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_1_8b.py b/opencompass/configs/models/qwen/hf_qwen1_5_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..5caf3efabfb74065f7c95c56efb6ea117911111f --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_1_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-1.8b-hf', + path='Qwen/Qwen1.5-1.8B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_1_8b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..37607b1d25f8642f5b64f3f01202aa033d96bc16 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_1_8b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-1.8b-chat-hf', + path='Qwen/Qwen1.5-1.8B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_32b.py b/opencompass/configs/models/qwen/hf_qwen1_5_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..e886873da13f06b6b1669a6e872ea5c4c0e59c7e --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_32b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-32b-hf', + path='Qwen/Qwen1.5-32B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_32b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_32b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..51f452cf2a363e5cddb271cd8b519ab9b952a0d3 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_32b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-32b-chat-hf', + path='Qwen/Qwen1.5-32B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_4b.py b/opencompass/configs/models/qwen/hf_qwen1_5_4b.py new file mode 100644 index 0000000000000000000000000000000000000000..e63eaec4227fae2bd4490a59d15b3c9a660be72a --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_4b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-4b-hf', + path='Qwen/Qwen1.5-4B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_4b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_4b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f5157c9b775e177f365367335cba0da35e08559c --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_4b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-4b-chat-hf', + path='Qwen/Qwen1.5-4B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_72b.py b/opencompass/configs/models/qwen/hf_qwen1_5_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..ca1a585c0c18d5dbabe5fcd7d8829a65c3873c4f --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_72b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-72b-hf', + path='Qwen/Qwen1.5-72B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_72b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..1e293676e3ca5f1ad8c8c71664c0f878e194afd5 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_72b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-72b-chat-hf', + path='Qwen/Qwen1.5-72B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_7b.py b/opencompass/configs/models/qwen/hf_qwen1_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..2649ffff2516091884a9a59336f6511602a56dec --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-7b-hf', + path='Qwen/Qwen1.5-7B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_7b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..0ac8254f64b8d81b2b073d8d485cbadfeb7cef40 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_7b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-7b-chat-hf', + path='Qwen/Qwen1.5-7B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b.py b/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..0e112c8bf641a93e045cc512c80ea6ef9d1e0a7a --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen1.5-moe-a2.7b-hf', + path='Qwen/Qwen1.5-MoE-A2.7B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b_chat.py b/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..011846933564a639e96b2d8db9aef9ed2e8bb193 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen1_5_moe_a2_7b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen1.5-moe-a2.7b-chat-hf', + path='Qwen/Qwen1.5-MoE-A2.7B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_0_5b.py b/opencompass/configs/models/qwen/hf_qwen2_0_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..22305d6fc1c5dbdc9201b9ec40c2dbe7234adc3b --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_0_5b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2-0.5b-hf', + path='Qwen/Qwen2-0.5B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_0_5b_instruct.py b/opencompass/configs/models/qwen/hf_qwen2_0_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..76e0dbbaf270ec361fd2e41a8514e734b8432fad --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_0_5b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2-0.5b-instruct-hf', + path='Qwen/Qwen2-0.5B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_1_5b.py b/opencompass/configs/models/qwen/hf_qwen2_1_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..2c9d488a3f09a2dcb05d241b3fa4a91d4d88c4d2 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_1_5b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2-1.5b-hf', + path='Qwen/Qwen2-1.5B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_1_5b_instruct.py b/opencompass/configs/models/qwen/hf_qwen2_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..89fb082c131c5e90465eab909d5306464d20c8b0 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_1_5b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2-1.5b-instruct-hf', + path='Qwen/Qwen2-1.5B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_57b_a14b.py b/opencompass/configs/models/qwen/hf_qwen2_57b_a14b.py new file mode 100644 index 0000000000000000000000000000000000000000..1e5f51a778414e9f2de515de54cc6e39709ef3e1 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_57b_a14b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2-57b-a14b-hf', + path='Qwen/Qwen2-57B-A14B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_72b.py b/opencompass/configs/models/qwen/hf_qwen2_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..97cc74f6ffda2464acf269db2ad21a2498d1248d --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_72b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2-72b-hf', + path='Qwen/Qwen2-72B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_7b.py b/opencompass/configs/models/qwen/hf_qwen2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..a861151f89ee63a32e0c9a0531d71f412048342e --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2-7b-hf', + path='Qwen/Qwen2-7B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen2_7b_instruct.py b/opencompass/configs/models/qwen/hf_qwen2_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..490caeb1b11ec8f76ed292e8aa95104baf6b496d --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen2_7b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2-7b-instruct-hf', + path='Qwen/Qwen2-7B-Instruct', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_14b.py b/opencompass/configs/models/qwen/hf_qwen_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..ac09858331a5020de180d220a0cd0c793a49b25e --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_14b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen-14b-hf', + path='Qwen/Qwen-14B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_14b_chat.py b/opencompass/configs/models/qwen/hf_qwen_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..640eb5781af573063ef8470202fff50130d596f3 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_14b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen-14b-chat-hf', + path='Qwen/Qwen-14B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_1_8b.py b/opencompass/configs/models/qwen/hf_qwen_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..f82d3db70059c42719b71612d6ac0d928b9ce4f4 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_1_8b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen-1.8b-hf', + path='Qwen/Qwen-1_8B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_1_8b_chat.py b/opencompass/configs/models/qwen/hf_qwen_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..3303e2e7f54627c96893e0de42e9f7798240a619 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_1_8b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen-1.8b-chat-hf', + path='Qwen/Qwen-1_8B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_72b.py b/opencompass/configs/models/qwen/hf_qwen_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..63eacfefaf92863859cff1da676f7d286b4d6945 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_72b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen-72b-hf', + path='Qwen/Qwen-72B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_72b_chat.py b/opencompass/configs/models/qwen/hf_qwen_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..c1dbf5b3d4ef9394161ff1e2bac3bef839dedef2 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_72b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen-72b-chat-hf', + path='Qwen/Qwen-72B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_7b.py b/opencompass/configs/models/qwen/hf_qwen_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..17ba5b47b23d8f4d9e2888bc8babd3a445abab7e --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen-7b-hf', + path='Qwen/Qwen-7B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/hf_qwen_7b_chat.py b/opencompass/configs/models/qwen/hf_qwen_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f94d7e83457ab20dfbf00b58d226d0e6e0217de9 --- /dev/null +++ b/opencompass/configs/models/qwen/hf_qwen_7b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen-7b-chat-hf', + path='Qwen/Qwen-7B-Chat', + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b.py new file mode 100644 index 0000000000000000000000000000000000000000..13b3c0b55caec2d01ee2d1c063b041db6bc78f0c --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-110b-turbomind', + path='Qwen/Qwen1.5-110B', + engine_config=dict(session_len=7168, max_batch_size=8, tp=8, cache_max_entry_count=0.6), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..bc123b4050091351ba07202ab90280e0d8dee694 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_110b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-110b-chat-turbomind', + path='Qwen/Qwen1.5-110B-Chat', + engine_config=dict(session_len=16834, max_batch_size=8, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16834, + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=4), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..459ad1c00017037dce1940367adc7e820ad13996 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-14b-turbomind', + path='Qwen/Qwen1.5-14B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5f0d54b969da5f7ad8811952e358a003ab2b832e --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_14b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-14b-chat-turbomind', + path='Qwen/Qwen1.5-14B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..89dbb51db84664eee859666ca282bf35c80d3faa --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-1.8b-turbomind', + path='Qwen/Qwen1.5-1.8B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..803ff3336d67183ce1fb28001c97b9a9bad6bb07 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_1_8b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-1.8b-chat-turbomind', + path='Qwen/Qwen1.5-1.8B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..511461000f808ce1e9df7635ed877ae7e92129a4 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-32b-turbomind', + path='Qwen/Qwen1.5-32B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2, cache_max_entry_count=0.7), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..96fd1e43c9de6ac0c70b6cacc6b1c07a2b782913 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_32b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-32b-chat-turbomind', + path='Qwen/Qwen1.5-32B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b.py new file mode 100644 index 0000000000000000000000000000000000000000..298152fa463edd4ef9633523c51e100ed003b838 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-4b-turbomind', + path='Qwen/Qwen1.5-4B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f9fcc3fb90767486c6a9c737aaaf769c7bda68f2 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_4b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-4b-chat-turbomind', + path='Qwen/Qwen1.5-4B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..892e18256b9ec8a6094a19b666157eeaf8181941 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-72b-turbomind', + path='Qwen/Qwen1.5-72B', + engine_config=dict(session_len=7168, max_batch_size=4, tp=4, cache_max_entry_count=0.7), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..64a5f7cb6095aa962202af9f1fc0e8e55ce51825 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_72b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-72b-chat-turbomind', + path='Qwen/Qwen1.5-72B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..b42908060fbd99f4aceb40945dc6ca8ded59929a --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen1.5-7b-turbomind', + path='Qwen/Qwen1.5-7B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..1ab3930363017a1f34ad58c1e498509b2f7f0855 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_7b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen1.5-7b-chat-turbomind', + path='Qwen/Qwen1.5-7B-Chat', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen1_5_series.py b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_series.py new file mode 100644 index 0000000000000000000000000000000000000000..fc49582ed0a80e7d52ed7c08adbf8a9229720bbb --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen1_5_series.py @@ -0,0 +1,30 @@ +from opencompass.models import TurboMindModel + +settings = [ + # ('qwen1.5-0.5b-turbomind', 'Qwen/Qwen1.5-0.5B', 1), + ('qwen1.5-1.8b-turbomind', 'Qwen/Qwen1.5-1.8B', 1), + ('qwen1.5-4b-turbomind', 'Qwen/Qwen1.5-4B', 1), + ('qwen1.5-7b-turbomind', 'Qwen/Qwen1.5-7B', 1), + ('qwen1.5-14b-turbomind', 'Qwen/Qwen1.5-14B', 1), + ('qwen1.5-32b-turbomind', 'Qwen/Qwen1.5-32B', 2), + ('qwen1.5-72b-turbomind', 'Qwen/Qwen1.5-72B', 4), + ('qwen1.5-110b-turbomind', 'Qwen/Qwen1.5-110B', 4), + ('qwen1.5-moe-a2.7b-turbomind', 'Qwen/Qwen1.5-MoE-A2.7B', 1), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=TurboMindModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=7168, max_batch_size=8, tp=num_gpus), + gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=7168, + batch_size=8, + concurrency=8, + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..6da4573bac0351293a299c57bacb9503c78eb32f --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2-1.5b-turbomind', + path='Qwen/Qwen2-1.5B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b_instruct.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..f050ca382eceb77f7e589aaf4121de0316ba36aa --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_1_5b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2-1.5b-instruct-turbomind', + path='Qwen/Qwen2-1.5B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_72b.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..87c2a0271ad1e6b97d90acef841d6c808db9b1fd --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2-72b-turbomind', + path='Qwen/Qwen2-72B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=4, cache_max_entry_count=0.7), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_72b_instruct.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_72b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..c29482b5b0baaa3a97b1608ed589a25236d5da7b --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_72b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2-72b-instruct-turbomind', + path='Qwen/Qwen2-72B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_7b.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..fb410af6b9e830aad119d1fc481569f2606b7c8c --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2-7b-turbomind', + path='Qwen/Qwen2-7B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_7b_instruct.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..05fa25c5ed67ce69c6eadef7630c80803ea53939 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_7b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2-7b-instruct-turbomind', + path='Qwen/Qwen2-7B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen2_series.py b/opencompass/configs/models/qwen/lmdeploy_qwen2_series.py new file mode 100644 index 0000000000000000000000000000000000000000..dc5c19205c876cbcf2fef72d0edcd382e42403b7 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen2_series.py @@ -0,0 +1,26 @@ +from opencompass.models import TurboMindModel + +settings = [ + # ('qwen2-0.5b-turbomind', 'Qwen/Qwen2-0.5B', 1), + ('qwen2-1.5b-turbomind', 'Qwen/Qwen2-1.5B', 1), + ('qwen2-7b-turbomind', 'Qwen/Qwen2-7B', 1), + ('qwen2-72b-turbomind', 'Qwen/Qwen2-72B', 4), + ('qwen2-57b-a14b-turbomind', 'Qwen/Qwen2-57B-A14B', 2), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=TurboMindModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=7168, max_batch_size=8, tp=num_gpus), + gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=7168, + batch_size=8, + concurrency=8, + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_14b.py b/opencompass/configs/models/qwen/lmdeploy_qwen_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..c7f0f5476d337cfdc65ab7714af62156bf5fde35 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_14b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen-14b-turbomind', + path='Qwen/Qwen-14B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_14b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..ccaee6a7ff064b4d67cd377c27d8ecfc1811db76 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_14b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen-14b-chat-turbomind', + path='Qwen/Qwen-14B-Chat', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b.py b/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..b8b1fa4e1c58ca5d4c9a6b190b578c1fd2393abd --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen-1.8b-turbomind', + path='Qwen/Qwen-1_8B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..8126bdce7d9f37302a6e63452869e7c6b33658ed --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_1_8b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen-1.8b-chat-turbomind', + path='Qwen/Qwen-1_8B-Chat', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_72b.py b/opencompass/configs/models/qwen/lmdeploy_qwen_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..e338fee1ba3a7cc47b3bbc9be0f5a83df34fae7c --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen-72b-turbomind', + path='Qwen/Qwen-72B', + engine_config=dict(session_len=7168, max_batch_size=4, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_72b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f7faf6276e92efcd20bb882bd483677807bc955d --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_72b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen-72b-chat-turbomind', + path='Qwen/Qwen-72B-Chat', + engine_config=dict(session_len=7168, max_batch_size=4, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=4, + run_cfg=dict(num_gpus=4), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_7b.py b/opencompass/configs/models/qwen/lmdeploy_qwen_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..5d95e7197297b66aa61e75216163a5f4408ea29f --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen-7b-turbomind', + path='Qwen/Qwen-7B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_7b_chat.py b/opencompass/configs/models/qwen/lmdeploy_qwen_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..27776127e58f197bac3054a11757aa802f0857b0 --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_7b_chat.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen-7b-chat-turbomind', + path='Qwen/Qwen-7B-Chat', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|im_end|>', '<|im_start|>'], + ) +] diff --git a/opencompass/configs/models/qwen/lmdeploy_qwen_series.py b/opencompass/configs/models/qwen/lmdeploy_qwen_series.py new file mode 100644 index 0000000000000000000000000000000000000000..33d78b4bb04d400fd0d21f1c5714a8d0b6290cce --- /dev/null +++ b/opencompass/configs/models/qwen/lmdeploy_qwen_series.py @@ -0,0 +1,26 @@ +from opencompass.models import TurboMindModel + +settings = [ + ('qwen-1.8b-turbomind', 'Qwen/Qwen-1_8B', 1), + ('qwen-7b-turbomind', 'Qwen/Qwen-7B', 1), + ('qwen-14b-turbomind', 'Qwen/Qwen-14B', 1), + ('qwen-72b-turbomind', 'Qwen/Qwen-72B', 4), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=TurboMindModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=7168, max_batch_size=16, tp=num_gpus), + gen_config=dict(top_k=1, temperature=1, top_p=0.9, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=7168, + batch_size=16, + concurrency=16, + run_cfg=dict(num_gpus=num_gpus), + stop_words=['<|im_end|>', '<|im_start|>'], + ) + ) diff --git a/opencompass/configs/models/qwen/ms_qwen_7b_chat.py b/opencompass/configs/models/qwen/ms_qwen_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..b0191946309250aac4d68cc0f9046b707ba55f76 --- /dev/null +++ b/opencompass/configs/models/qwen/ms_qwen_7b_chat.py @@ -0,0 +1,30 @@ +from opencompass.models import ModelScopeCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n<|im_start|>user\n', end='<|im_end|>'), + dict(role='BOT', begin='\n<|im_start|>assistant\n', end='<|im_end|>', generate=True), + ], +) + +models = [ + dict( + type=ModelScopeCausalLM, + abbr='qwen-7b-chat-ms', + path='qwen/Qwen-7B-Chat', + tokenizer_path='qwen/Qwen-7B-Chat', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False,), + pad_token_id=151643, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..4822de65bdf12d3f84dbadeb611e99faf25a900d --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-0.5b-vllm', + path='Qwen/Qwen1.5-0.5B', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..60ff8707a2102e003749b8b6ccbaf83cf6f7c17f --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_0_5b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-0.5b-chat-vllm', + path='Qwen/Qwen1.5-0.5B-Chat', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_110b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_110b.py new file mode 100644 index 0000000000000000000000000000000000000000..1c302a9d79427dd398919a5bbd5be84f3db1b3a1 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_110b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-110b-vllm', + path='Qwen/Qwen1.5-110B', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_110b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_110b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..0466e59c8d8e2c90c5785e0d7f2cb7a6ab43ef8c --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_110b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-110b-chat-vllm', + path='Qwen/Qwen1.5-110B-Chat', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_14b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..f1aef793e77508bbd6b6208db6f2d9f860ad5f2b --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_14b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-14b-vllm', + path='Qwen/Qwen1.5-14B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_14b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..72aafaaed2581b104255f2bdedd285a1ee828a0b --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_14b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-14b-chat-vllm', + path='Qwen/Qwen1.5-14B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..6cde3122185e72b58bcff3b0bb05185d1469b352 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-1.8b-vllm', + path='Qwen/Qwen1.5-1.8B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..25c48174b39c56eed91eeb0b70abf65620c633ea --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_1_8b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-1.8b-chat-vllm', + path='Qwen/Qwen1.5-1.8B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_32b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..e0d6e528dd49573a2baf72c5db755c516cd2c39b --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_32b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-32b-vllm', + path='Qwen/Qwen1.5-32B', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_32b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_32b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5684d0d72e70253b840cfafce63bd1013cddcf3a --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_32b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-32b-chat-vllm', + path='Qwen/Qwen1.5-32B-Chat', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_4b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_4b.py new file mode 100644 index 0000000000000000000000000000000000000000..bddbbdb9130611180da1b0674d2eb4200f367dba --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_4b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-4b-vllm', + path='Qwen/Qwen1.5-4B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_4b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_4b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..2ff3719a663778b7e53bff6463feca4cc17523bf --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_4b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-4b-chat-vllm', + path='Qwen/Qwen1.5-4B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_72b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..43681778321936a8eff70090ccd202d8c27deeff --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-72b-vllm', + path='Qwen/Qwen1.5-72B', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_72b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b70b7efb39a2651489b829e030a364aeab3718 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_72b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-72b-chat-vllm', + path='Qwen/Qwen1.5-72B-Chat', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_7b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..af3588673da0427da59002ef3682ca70331cfea1 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-7b-vllm', + path='Qwen/Qwen1.5-7B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_7b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..af36ec0446cb19dede3a73571fef113e0f1ec9c6 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_7b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-7b-chat-vllm', + path='Qwen/Qwen1.5-7B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b.py b/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..4ca4c48a071a7de6d5918fffd503227bd081db94 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen1.5-moe-a2.7b-vllm', + path='Qwen/Qwen1.5-MoE-A2.7B', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b_chat.py b/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..55e65a4b0a2509ed963a5b0dd32465909fa5b702 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_moe_a2_7b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen1.5-moe-a2.7b-chat-vllm', + path='Qwen/Qwen1.5-MoE-A2.7B-Chat', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen1_5_series.py b/opencompass/configs/models/qwen/vllm_qwen1_5_series.py new file mode 100644 index 0000000000000000000000000000000000000000..d9b6fc6c70fd8cfbd8362a3496b7f1003f4f87b8 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen1_5_series.py @@ -0,0 +1,29 @@ +from opencompass.models import VLLM + +settings = [ + ('qwen1.5-0.5b-vllm', 'Qwen/Qwen1.5-0.5B', 1), + ('qwen1.5-1.8b-vllm', 'Qwen/Qwen1.5-1.8B', 1), + ('qwen1.5-4b-vllm', 'Qwen/Qwen1.5-4B', 1), + ('qwen1.5-7b-vllm', 'Qwen/Qwen1.5-7B', 1), + ('qwen1.5-14b-vllm', 'Qwen/Qwen1.5-14B', 1), + ('qwen1.5-32b-vllm', 'Qwen/Qwen1.5-32B', 2), + ('qwen1.5-72b-vllm', 'Qwen/Qwen1.5-72B', 4), + ('qwen1.5-110b-vllm', 'Qwen/Qwen1.5-110B', 4), + ('qwen1.5-moe-a2.7b-vllm', 'Qwen/Qwen1.5-MoE-A2.7B', 1), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=VLLM, + abbr=abbr, + path=path, + model_kwargs=dict(tensor_parallel_size=num_gpus), + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/qwen/vllm_qwen2_0_5b.py b/opencompass/configs/models/qwen/vllm_qwen2_0_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..877dc5bd019f82ad237d864c3c05b3350a9560be --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_0_5b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen2-0.5b-vllm', + path='Qwen/Qwen2-0.5B', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_0_5b_instruct.py b/opencompass/configs/models/qwen/vllm_qwen2_0_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..48dd41b6ed629ae688dda4aebd7f932f625345c6 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_0_5b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2-0.5b-instruct-vllm', + path='Qwen/Qwen2-0.5B-Instruct', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_1_5b.py b/opencompass/configs/models/qwen/vllm_qwen2_1_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1e01f6b370334d1559b08a6eadd73707e8ddbe --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_1_5b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen2-1.5b-vllm', + path='Qwen/Qwen2-1.5B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_1_5b_instruct.py b/opencompass/configs/models/qwen/vllm_qwen2_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..fb173d3127cc1831fe3d62193859a5e961cfe6da --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_1_5b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2-1.5b-instruct-vllm', + path='Qwen/Qwen2-1.5B-Instruct', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_57b_a14b_instruct.py b/opencompass/configs/models/qwen/vllm_qwen2_57b_a14b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..6b586c74db2dbadf02ae510d2d59448bacf6aef7 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_57b_a14b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2-57b-a14b-instruct-vllm', + path='Qwen/Qwen2-57B-A14B-Instruct', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_72b.py b/opencompass/configs/models/qwen/vllm_qwen2_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f0b709dd61d4ecd87d1811fcbb81d85eb26572 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen2-72b-vllm', + path='Qwen/Qwen2-72B', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_72b_instruct.py b/opencompass/configs/models/qwen/vllm_qwen2_72b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..fd53bc443ef6b6f41e232a1436b895fafeef96ed --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_72b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2-72b-instruct-vllm', + path='Qwen/Qwen2-72B-Instruct', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_7b.py b/opencompass/configs/models/qwen/vllm_qwen2_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..1ed1bb8404a64180b0a2cc4c79c12592e6b73427 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen2-7b-vllm', + path='Qwen/Qwen2-7B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_7b_instruct.py b/opencompass/configs/models/qwen/vllm_qwen2_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..0c4b6255cee3a3ab2cfa9bbfd94d4c1d4b46babc --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_7b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2-7b-instruct-vllm', + path='Qwen/Qwen2-7B-Instruct', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen2_series.py b/opencompass/configs/models/qwen/vllm_qwen2_series.py new file mode 100644 index 0000000000000000000000000000000000000000..b65d09935d9df0c9d71246ad9f84751f972eb738 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen2_series.py @@ -0,0 +1,25 @@ +from opencompass.models import VLLM + +settings = [ + ('qwen2-0.5b-vllm', 'Qwen/Qwen2-0.5B', 1), + ('qwen2-1.5b-vllm', 'Qwen/Qwen2-1.5B', 1), + ('qwen2-7b-vllm', 'Qwen/Qwen2-7B', 1), + ('qwen2-72b-vllm', 'Qwen/Qwen2-72B', 4), + ('qwen2-57b-a14b-vllm', 'Qwen/Qwen2-57B-A14B', 2), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=VLLM, + abbr=abbr, + path=path, + model_kwargs=dict(tensor_parallel_size=num_gpus), + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/qwen/vllm_qwen_14b.py b/opencompass/configs/models/qwen/vllm_qwen_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..4c369c2ea01137516dc13bf7501bca231f9ea3ca --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_14b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen-14b-vllm', + path='Qwen/Qwen-14B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_14b_chat.py b/opencompass/configs/models/qwen/vllm_qwen_14b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..967d942bfddb34fb55c90ca5e4025ea3e40eddd3 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_14b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen-14b-chat-vllm', + path='Qwen/Qwen-14B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_1_8b.py b/opencompass/configs/models/qwen/vllm_qwen_1_8b.py new file mode 100644 index 0000000000000000000000000000000000000000..38f2fd8ac79633f557d97514d5f1e67b0433fef4 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen-1.8b-vllm', + path='Qwen/Qwen-1_8B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_1_8b_chat.py b/opencompass/configs/models/qwen/vllm_qwen_1_8b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f3bba88399d122436768a0bdbcd988c6fc2483fb --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_1_8b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen-1.8b-chat-vllm', + path='Qwen/Qwen-1_8B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_72b.py b/opencompass/configs/models/qwen/vllm_qwen_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..5611703eb312d52d5607c84311468099557a7e35 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_72b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen-72b-vllm', + path='Qwen/Qwen-72B', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_72b_chat.py b/opencompass/configs/models/qwen/vllm_qwen_72b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..f28353f9d28a54e885dd4c6ded9f0d77ce466d23 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_72b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen-72b-chat-vllm', + path='Qwen/Qwen-72B-Chat', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_7b.py b/opencompass/configs/models/qwen/vllm_qwen_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..7dae26191c6569e775418b79090eca08a4a177e0 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import VLLM + +models = [ + dict( + type=VLLM, + abbr='qwen-7b-vllm', + path='Qwen/Qwen-7B', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + max_seq_len=8192, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_7b_chat.py b/opencompass/configs/models/qwen/vllm_qwen_7b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..55016ffed20be7fcdd13cb8c08c49c1bad7058d9 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_7b_chat.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen-7b-chat-vllm', + path='Qwen/Qwen-7B-Chat', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=1024, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen/vllm_qwen_series.py b/opencompass/configs/models/qwen/vllm_qwen_series.py new file mode 100644 index 0000000000000000000000000000000000000000..f01ef5ab6e9e11c957624dab6e2643ef0d3b23c7 --- /dev/null +++ b/opencompass/configs/models/qwen/vllm_qwen_series.py @@ -0,0 +1,24 @@ +from opencompass.models import VLLM + +settings = [ + ('qwen-1.8b-vllm', 'Qwen/Qwen-1_8B', 1), + ('qwen-7b-vllm', 'Qwen/Qwen-7B', 1), + ('qwen-14b-vllm', 'Qwen/Qwen-14B', 1), + ('qwen-72b-vllm', 'Qwen/Qwen-72B', 4), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=VLLM, + abbr=abbr, + path=path, + model_kwargs=dict(tensor_parallel_size=num_gpus), + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=num_gpus, num_procs=1), + ) + ) diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_0_5b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_0_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..35289bb1082c55b1a1f641d7b10483bcb4ad12aa --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_0_5b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-0.5b-instruct-hf', + path='Qwen/Qwen2.5-0.5B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_14b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_14b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..3680544be14c1e8982ffbc5b22d4bf8feb656e03 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_14b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-14b-instruct-hf', + path='Qwen/Qwen2.5-14B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_1_5b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..52da52895605a136268d2852b00239fb446b830c --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_1_5b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-1.5b-instruct-hf', + path='Qwen/Qwen2.5-1.5B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_32b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_32b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..3993a113b0a5931a89ec1e1ee0d2a0b961d48d60 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_32b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-32b-instruct-hf', + path='Qwen/Qwen2.5-32B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_3b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_3b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..88a101994f105bef8268e0eb7b013830829ddebf --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_3b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-3b-instruct-hf', + path='Qwen/Qwen2.5-3B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_72b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_72b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..4ece03b95c91feb67532db060da60dde2828cacc --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_72b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-72b-instruct-hf', + path='Qwen/Qwen2.5-72B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen2_5_7b_instruct.py b/opencompass/configs/models/qwen2_5/hf_qwen2_5_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..4551c7253fe3fec624ded867c80d1b0519a898e4 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen2_5_7b_instruct.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='qwen2.5-7b-instruct-hf', + path='Qwen/Qwen2.5-7B-Instruct', + max_out_len=4096, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen_2_5_14b.py b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..2f64872f1a4b09bd7be0da595aca0d5b1f2755bb --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_14b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2.5-14b-hf', + path='Qwen/Qwen2.5-14B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/hf_qwen_2_5_32b.py b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..ddd27f7f181023478a95735cafbf2046196e5de7 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_32b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2.5-32b-hf', + path='Qwen/Qwen2.5-32B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] \ No newline at end of file diff --git a/opencompass/configs/models/qwen2_5/hf_qwen_2_5_7b.py b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..579950c6db916e9bea86ad57fdc2f6b55ab0a417 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/hf_qwen_2_5_7b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='qwen2.5-7b-hf', + path='Qwen/Qwen2.5-7B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] \ No newline at end of file diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_0_5b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_0_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..145549630b8ee726511603c3a193cc9d9eae5755 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_0_5b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-0.5b-instruct-turbomind', + path='Qwen/Qwen2.5-0.5B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b.py new file mode 100644 index 0000000000000000000000000000000000000000..a5f63e5430e69e761ddcc1d31be40d66f4d0dbfb --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2.5-14b-turbomind', + path='Qwen/Qwen2.5-14B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..36469028823e5cee7050336a7438afb807d4b9ce --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_14b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-14b-instruct-turbomind', + path='Qwen/Qwen2.5-14B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b.py new file mode 100644 index 0000000000000000000000000000000000000000..a2661c9fd453c01383e710f268630799e3d1af8b --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2.5-1.5b-turbomind', + path='Qwen/Qwen2.5-1.5B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..9b2f6c5fdf0a4895c83bb4c313c0e2797949857e --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_1_5b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-1.5b-instruct-turbomind', + path='Qwen/Qwen2.5-1.5B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..bf0c0c15f04b77e0a2453899bb4122fbc6703f75 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2.5-32b-turbomind', + path='Qwen/Qwen2.5-32B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..81e0cb0f13d620e47d4491098faeddeca0a7bc91 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_32b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-32b-instruct-turbomind', + path='Qwen/Qwen2.5-32B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_3b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_3b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..bab4b32ea8377105618b1b5c94ff13514cf4f58c --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_3b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-3b-instruct-turbomind', + path='Qwen/Qwen2.5-3B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b.py new file mode 100644 index 0000000000000000000000000000000000000000..0bee0557e68263739649bdab063397560ed473e4 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2.5-72b-turbomind', + path='Qwen/Qwen2.5-72B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=4), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024 + ), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..8b61daa8a910d4b76f47a939b45515d984838a4f --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_72b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-72b-instruct-turbomind', + path='Qwen/Qwen2.5-72B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b.py new file mode 100644 index 0000000000000000000000000000000000000000..b2d7aa0c5c3424b9881ec80e8159c37b4259e280 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='qwen2.5-7b-turbomind', + path='Qwen/Qwen2.5-7B', + engine_config=dict(session_len=7168, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=1024), + max_seq_len=7168, + max_out_len=1024, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b_instruct.py b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..54732521f39fe1a1623fa5f1ccf0dbee52d75c0b --- /dev/null +++ b/opencompass/configs/models/qwen2_5/lmdeploy_qwen2_5_7b_instruct.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen2.5-7b-instruct-turbomind', + path='Qwen/Qwen2.5-7B-Instruct', + engine_config=dict(session_len=16384, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=4096, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_0_5b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_0_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..effd120181c0fea66dd8e0b4c4a064dc79b6d422 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_0_5b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-0.5b-instruct-vllm', + path='Qwen/Qwen2.5-0.5B-Instruct', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..438279f1509157158763fac28f3ca37e0a549091 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-14b-instruct-vllm', + path='Qwen/Qwen2.5-14B-Instruct', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py new file mode 100644 index 0000000000000000000000000000000000000000..6dec374338db223b4fb0a31a8b7d928698fb0982 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-14b-instruct-vllm', + path='Qwen/Qwen2.5-14B-Instruct', + model_kwargs=dict( + tensor_parallel_size=4, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_1_5b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_1_5b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..16b7f809b1a28da7ddaca11d29e81ce8155e48cc --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_1_5b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-1.5b-instruct-vllm', + path='Qwen/Qwen2.5-1.5B-Instruct', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..58d518459a4f76f5b7667f2d7be07ac45a007802 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-32b-instruct-vllm', + path='Qwen/Qwen2.5-32B-Instruct', + model_kwargs=dict(tensor_parallel_size=2), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py new file mode 100644 index 0000000000000000000000000000000000000000..5c326734e55fa948f18fe92e8c6e35ca0792497d --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-32b-instruct-vllm', + path='Qwen/Qwen2.5-32B-Instruct', + model_kwargs=dict( + tensor_parallel_size=8, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_3b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_3b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..e24c7c1f82e6bd600fd5fbbf4db700539dc789ed --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_3b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-3b-instruct-vllm', + path='Qwen/Qwen2.5-3B-Instruct', + model_kwargs=dict(tensor_parallel_size=1, gpu_memory_utilization=0.5), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..c1a557901b7f9f425715f88a3e2b48fc03f6af9a --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2_5-72b-instruct-vllm', + path='Qwen/Qwen2.5-72B-Instruct', + model_kwargs=dict(tensor_parallel_size=4), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py new file mode 100644 index 0000000000000000000000000000000000000000..2a4a52fa0a60643bf78f7ae1eeea54d96a83f070 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2_5-72b-instruct-vllm', + path='Qwen/Qwen2.5-72B-Instruct', + model_kwargs=dict( + tensor_parallel_size=8, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..25c38d0f8ce5b1d6da36b2bd66e51c06cb8c44ae --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct.py @@ -0,0 +1,14 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-7b-instruct-vllm', + path='Qwen/Qwen2.5-7B-Instruct', + model_kwargs=dict(tensor_parallel_size=1), + max_out_len=4096, + batch_size=16, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py new file mode 100644 index 0000000000000000000000000000000000000000..db21f73040d2dd0eeb625235243b5ce866b1e899 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-7b-instruct-vllm', + path='Qwen/Qwen2.5-7B-Instruct', + model_kwargs=dict( + tensor_parallel_size=4, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen3/lmdeploy_qwen3_0_6b.py b/opencompass/configs/models/qwen3/lmdeploy_qwen3_0_6b.py new file mode 100644 index 0000000000000000000000000000000000000000..ea1c66992eed9559b5f66cbde0502e39d046be55 --- /dev/null +++ b/opencompass/configs/models/qwen3/lmdeploy_qwen3_0_6b.py @@ -0,0 +1,19 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='qwen_3_0.6b_thinking-turbomind', + path='Qwen/Qwen3-0.6B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=1), + gen_config=dict( + top_k=20, temperature=0.6, top_p=0.95, do_sample=True, enable_thinking=True + ), + max_seq_len=32768, + max_out_len=32000, + batch_size=16, + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ), +] \ No newline at end of file diff --git a/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py b/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py new file mode 100644 index 0000000000000000000000000000000000000000..6c2bf07818d023620d3dd70d653c452cc55965a9 --- /dev/null +++ b/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='QwQ-32B', + path='Qwen/QwQ-32B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192), + max_seq_len=32768, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=2), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] \ No newline at end of file diff --git a/opencompass/configs/models/qwq/lmdeploy_qwq_32b_preview.py b/opencompass/configs/models/qwq/lmdeploy_qwq_32b_preview.py new file mode 100644 index 0000000000000000000000000000000000000000..db30c2ee673ccf766c5aaa49cc6dc0f63922e635 --- /dev/null +++ b/opencompass/configs/models/qwq/lmdeploy_qwq_32b_preview.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='QwQ-32B-Preview', + path='Qwen/QwQ-32B-Preview', + engine_config=dict(session_len=32768, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192), + max_seq_len=32768, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/rwkv/rwkv5_3b.py b/opencompass/configs/models/rwkv/rwkv5_3b.py new file mode 100644 index 0000000000000000000000000000000000000000..ca2899cb11ae3235cff3d36f5b888ee3a462062b --- /dev/null +++ b/opencompass/configs/models/rwkv/rwkv5_3b.py @@ -0,0 +1,25 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='rwkv-5-3b', + path='RWKV/rwkv-5-world-3b', + tokenizer_path='RWKV/rwkv-5-world-3b', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=False, + ), + max_out_len=100, + max_seq_len=2048, + batch_padding=True, + batch_size=16, + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/skywork/hf_skywork_13b.py b/opencompass/configs/models/skywork/hf_skywork_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..1b56c3a6e742aecc60eef3e7ba9fda74d64dd671 --- /dev/null +++ b/opencompass/configs/models/skywork/hf_skywork_13b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='skywork-13b-hf', + path='Skywork/Skywork-13B-base', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/skywork/lmdeploy_skywork_o1_open_llama3_1_8b_instruct.py b/opencompass/configs/models/skywork/lmdeploy_skywork_o1_open_llama3_1_8b_instruct.py new file mode 100644 index 0000000000000000000000000000000000000000..bc6cf47cf17edcba45b2358bc888bc2ab1289c83 --- /dev/null +++ b/opencompass/configs/models/skywork/lmdeploy_skywork_o1_open_llama3_1_8b_instruct.py @@ -0,0 +1,16 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='Skywork-o1-Open-Llama-3_1-8B-turbomind', + path='Skywork/Skywork-o1-Open-Llama-3.1-8B', + engine_config=dict(max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096), + max_seq_len=16384, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=1), + stop_words=['<|end_of_text|>', '<|eot_id|>'], + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v1.py b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..12566f6958607f2023ca5b8031d5907135b13134 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v1.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-13b-base-v1-hf', + path='TigerResearch/tigerbot-13b-base-v1', + tokenizer_path='TigerResearch/tigerbot-13b-base-v1', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ), +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v2.py b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..ebd2ca582e254f908c58e32fd266a4698458b553 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_base_v2.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-13b-base-v2-hf', + path='TigerResearch/tigerbot-13b-base', + tokenizer_path='TigerResearch/tigerbot-13b-base', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ), +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v1.py b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..1766ff8ec39a579adf8a602ccbff349514fd7559 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v1.py @@ -0,0 +1,29 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n'), + dict(role='BOT', begin='\n\n### Response:\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-13b-chat-v1-hf', + path='TigerResearch/tigerbot-13b-chat-v1', + tokenizer_path='TigerResearch/tigerbot-13b-chat-v1', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v2.py b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..fa3fc0e988c6db7d5b61bf17670fc71efadf7bfc --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_13b_chat_v2.py @@ -0,0 +1,29 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n'), + dict(role='BOT', begin='\n\n### Response:\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-13b-chat-v2-hf', + path='TigerResearch/tigerbot-13b-chat', + tokenizer_path='TigerResearch/tigerbot-13b-chat', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_70b_base.py b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_base.py new file mode 100644 index 0000000000000000000000000000000000000000..12cfd4d05686f972dac29d5248fa02dd6a5ccf21 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_base.py @@ -0,0 +1,24 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-70b-base-v1-hf', + path='TigerResearch/tigerbot-70b-base', + tokenizer_path='TigerResearch/tigerbot-70b-base', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=4, num_procs=1), + ), +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v2.py b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..0fed03f4e9b69f38a2861abd67f5475e44ab1833 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v2.py @@ -0,0 +1,29 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n'), + dict(role='BOT', begin='\n\n### Response:\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-70b-chat-v2-hf', + path='TigerResearch/tigerbot-70b-chat-v2', + tokenizer_path='TigerResearch/tigerbot-70b-chat-v2', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=4, num_procs=1), + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v3.py b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..c05457b32642ab9f614ceb58b691fff6e3cfe5e0 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_70b_chat_v3.py @@ -0,0 +1,32 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n'), + dict(role='BOT', begin='\n\n### Response:\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-70b-chat-v3-hf', + path='TigerResearch/tigerbot-70b-chat-v3', + tokenizer_path='TigerResearch/tigerbot-70b-chat-v3', + model_kwargs=dict( + trust_remote_code=True, + device_map='auto', + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=4, num_procs=1), + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base.py b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base.py new file mode 100644 index 0000000000000000000000000000000000000000..7bebf108d43407e550a35447676fe7b46f49c273 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-base-7b-hf', + path='TigerResearch/tigerbot-7b-base', + tokenizer_path='TigerResearch/tigerbot-7b-base', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ), +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base_v3.py b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..65f5130d3702c91a555dd382f49eaca859795126 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_base_v3.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-7b-base-v3-hf', + path='TigerResearch/tigerbot-7b-base', + tokenizer_path='TigerResearch/tigerbot-7b-base', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ), +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_7b_chat_v3.py b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_chat_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..1902af80a7c4beb74f22075632a71632883746e7 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_chat_v3.py @@ -0,0 +1,29 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n'), + dict(role='BOT', begin='\n\n### Response:\n', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-7b-chat-v3-hf', + path='TigerResearch/tigerbot-7b-chat', + tokenizer_path='TigerResearch/tigerbot-7b-chat', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/tigerbot/hf_tigerbot_7b_sft.py b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..5105b9b310b993502aa02bd92f9236dc6d2ae926 --- /dev/null +++ b/opencompass/configs/models/tigerbot/hf_tigerbot_7b_sft.py @@ -0,0 +1,29 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='\n\n### Instruction:\n:'), + dict(role='BOT', begin='\n\n### Response:\n:', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='tigerbot-sft-7b-hf', + path='TigerResearch/tigerbot-7b-sft', + tokenizer_path='TigerResearch/tigerbot-7b-sft', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=100, + max_seq_len=2048, + batch_size=8, + meta_template=_meta_template, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_13b_v13.py b/opencompass/configs/models/vicuna/hf_vicuna_13b_v13.py new file mode 100644 index 0000000000000000000000000000000000000000..74f4e147cec814fcdc6b1707095f839595ee0fb9 --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_13b_v13.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-13b-v1.3-hf', + path='lmsys/vicuna-13b-v1.3', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_13b_v15.py b/opencompass/configs/models/vicuna/hf_vicuna_13b_v15.py new file mode 100644 index 0000000000000000000000000000000000000000..28366ea9b8f18b9327e7249c4c68d11a8193a905 --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_13b_v15.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-13b-v1.5-hf', + path='lmsys/vicuna-13b-v1.5', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_13b_v15_16k.py b/opencompass/configs/models/vicuna/hf_vicuna_13b_v15_16k.py new file mode 100644 index 0000000000000000000000000000000000000000..3caf3f5711900fd726b6113f61008c9b53816e41 --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_13b_v15_16k.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-13b-v1.5-16k-hf', + path='lmsys/vicuna-13b-v1.5-16k', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_33b_v13.py b/opencompass/configs/models/vicuna/hf_vicuna_33b_v13.py new file mode 100644 index 0000000000000000000000000000000000000000..036cbc638c5525b67eb63298c181138c4ba01b49 --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_33b_v13.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-33b-v1.3-hf', + path='lmsys/vicuna-33b-v1.3', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_7b_v13.py b/opencompass/configs/models/vicuna/hf_vicuna_7b_v13.py new file mode 100644 index 0000000000000000000000000000000000000000..396264554c8d7351bad5a4b4c71d6944d0f22bed --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_7b_v13.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-7b-v1.3-hf', + path='lmsys/vicuna-7b-v1.3', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_7b_v15.py b/opencompass/configs/models/vicuna/hf_vicuna_7b_v15.py new file mode 100644 index 0000000000000000000000000000000000000000..b7888f04c5f19a301ade7c57a2454fab150a4a4d --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_7b_v15.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-7b-v1.5-hf', + path='lmsys/vicuna-7b-v1.5', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/hf_vicuna_7b_v15_16k.py b/opencompass/configs/models/vicuna/hf_vicuna_7b_v15_16k.py new file mode 100644 index 0000000000000000000000000000000000000000..c8b557ab51bbfe6b34695d36b8734c8e6ada1b5c --- /dev/null +++ b/opencompass/configs/models/vicuna/hf_vicuna_7b_v15_16k.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='vicuna-7b-v1.5-16k-hf', + path='lmsys/vicuna-7b-v1.5-16k', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + fastchat_template='vicuna', + ) +] diff --git a/opencompass/configs/models/vicuna/vllm_vicuna_13b_v15_16k.py b/opencompass/configs/models/vicuna/vllm_vicuna_13b_v15_16k.py new file mode 100644 index 0000000000000000000000000000000000000000..e26b3ff18a6ce615ba8e0f3e5afee6dc5f543a3a --- /dev/null +++ b/opencompass/configs/models/vicuna/vllm_vicuna_13b_v15_16k.py @@ -0,0 +1,23 @@ +from opencompass.models import VLLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: '), + dict(role='BOT', begin=' ASSISTANT:', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='vicuna-13b-v1.5-16k-vllm', + path='lmsys/vicuna-13b-v1.5-16k', + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=2, num_procs=1), + ) +] diff --git a/opencompass/configs/models/vicuna/vllm_vicuna_7b_v15_16k.py b/opencompass/configs/models/vicuna/vllm_vicuna_7b_v15_16k.py new file mode 100644 index 0000000000000000000000000000000000000000..ca261f1327084b3362d12ffaf483b6e92b8af61d --- /dev/null +++ b/opencompass/configs/models/vicuna/vllm_vicuna_7b_v15_16k.py @@ -0,0 +1,23 @@ +from opencompass.models import VLLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: '), + dict(role='BOT', begin=' ASSISTANT:', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='vicuna-7b-v1.5-16k-vllm', + path='lmsys/vicuna-7b-v1.5-16k', + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/wizardcoder/hf_wizardcoder_15b.py b/opencompass/configs/models/wizardcoder/hf_wizardcoder_15b.py new file mode 100644 index 0000000000000000000000000000000000000000..8593ecd38b50d3513b5ecb01a6470de7046691dd --- /dev/null +++ b/opencompass/configs/models/wizardcoder/hf_wizardcoder_15b.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + # WizardCoder 15B + dict( + type=HuggingFaceCausalLM, + abbr='WizardCoder-15B-V1.0', + path='WizardLM/WizardCoder-15B-V1.0', + tokenizer_path='WizardLM/WizardCoder-15B-V1.0', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=1024, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ), +] diff --git a/opencompass/configs/models/wizardcoder/hf_wizardcoder_1b.py b/opencompass/configs/models/wizardcoder/hf_wizardcoder_1b.py new file mode 100644 index 0000000000000000000000000000000000000000..650fdd35a62894526245c847c4f11e5f95da32be --- /dev/null +++ b/opencompass/configs/models/wizardcoder/hf_wizardcoder_1b.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + # WizardCoder 1B + dict( + type=HuggingFaceCausalLM, + abbr='WizardCoder-1B-V1.0', + path='WizardLM/WizardCoder-1B-V1.0', + tokenizer_path='WizardLM/WizardCoder-1B-V1.0', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=1024, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ), +] diff --git a/opencompass/configs/models/wizardcoder/hf_wizardcoder_3b.py b/opencompass/configs/models/wizardcoder/hf_wizardcoder_3b.py new file mode 100644 index 0000000000000000000000000000000000000000..650fdd35a62894526245c847c4f11e5f95da32be --- /dev/null +++ b/opencompass/configs/models/wizardcoder/hf_wizardcoder_3b.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + # WizardCoder 1B + dict( + type=HuggingFaceCausalLM, + abbr='WizardCoder-1B-V1.0', + path='WizardLM/WizardCoder-1B-V1.0', + tokenizer_path='WizardLM/WizardCoder-1B-V1.0', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=1024, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=1, num_procs=1), + ), +] diff --git a/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_13b.py b/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_13b.py new file mode 100644 index 0000000000000000000000000000000000000000..e4bb0c1cca83ec1ebc1265340ddd1bacc9e7454a --- /dev/null +++ b/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_13b.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + # WizardCoder Python 13B + dict( + type=HuggingFaceCausalLM, + abbr='WizardCoder-Python-13B-V1.0', + path='WizardLM/WizardCoder-Python-13B-V1.0', + tokenizer_path='WizardLM/WizardCoder-Python-13B-V1.0', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=1024, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=2, num_procs=1), + ), +] diff --git a/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_34b.py b/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_34b.py new file mode 100644 index 0000000000000000000000000000000000000000..f254ba51f93db1d521257a3213edb5679b57bd75 --- /dev/null +++ b/opencompass/configs/models/wizardcoder/hf_wizardcoder_python_34b.py @@ -0,0 +1,21 @@ +from opencompass.models import HuggingFaceCausalLM + +models = [ + # WizardCoder Python 34B + dict( + type=HuggingFaceCausalLM, + abbr='WizardCoder-Python-34B-V1.0', + path='WizardLM/WizardCoder-Python-34B-V1.0', + tokenizer_path='WizardLM/WizardCoder-Python-34B-V1.0', + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + max_out_len=1024, + max_seq_len=2048, + batch_size=8, + model_kwargs=dict(trust_remote_code=True, device_map='auto'), + run_cfg=dict(num_gpus=4, num_procs=1), + ), +] diff --git a/opencompass/configs/models/wizardlm/hf_wizardlm_13b_v1_2.py b/opencompass/configs/models/wizardlm/hf_wizardlm_13b_v1_2.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0bbc386c4c7bac9b2b11fc4391cfff51e36177 --- /dev/null +++ b/opencompass/configs/models/wizardlm/hf_wizardlm_13b_v1_2.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: ', end=' '), + dict(role='BOT', begin='ASSISTANT: ', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='wizardlm-13b-v1.2-hf', + path='WizardLM/WizardLM-13B-V1.2', + tokenizer_path='WizardLM/WizardLM-13B-V1.2', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=2, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/wizardlm/hf_wizardlm_70b_v1_0.py b/opencompass/configs/models/wizardlm/hf_wizardlm_70b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..aa44f7c9686da0cf1cc930bb29f9019154652d12 --- /dev/null +++ b/opencompass/configs/models/wizardlm/hf_wizardlm_70b_v1_0.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: ', end=' '), + dict(role='BOT', begin='ASSISTANT: ', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='wizardlm-70b-v1.0-hf', + path='WizardLM/WizardLM-70B-V1.0', + tokenizer_path='WizardLM/WizardLM-70B-V1.0', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=4, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/wizardlm/hf_wizardlm_7b_v1_0.py b/opencompass/configs/models/wizardlm/hf_wizardlm_7b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..00d4a3ea34434d0e39acee3d1017267f0c749725 --- /dev/null +++ b/opencompass/configs/models/wizardlm/hf_wizardlm_7b_v1_0.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', end='\n\n'), + dict(role='BOT', begin='### Response:', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='wizardlm-7b-v1.0-hf', + path='WizardLM/WizardLM-7B-V1.0', + tokenizer_path='WizardLM/WizardLM-7B-V1.0', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_0.py b/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..b622a593cfdd2832c49a5861ec6ab434b5f12574 --- /dev/null +++ b/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_0.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', end='\n\n'), + dict(role='BOT', begin='### Response:', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='wizardmath-7b-v1.0-hf', + path='WizardLM/WizardMath-7B-V1.0', + tokenizer_path='WizardLM/WizardMath-7B-V1.0', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_1.py b/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_1.py new file mode 100644 index 0000000000000000000000000000000000000000..926c5cdc832902d863c227e283d806b63938970a --- /dev/null +++ b/opencompass/configs/models/wizardlm/hf_wizardmath_7b_v1_1.py @@ -0,0 +1,33 @@ +from opencompass.models import HuggingFaceCausalLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', end='\n\n'), + dict(role='BOT', begin='### Response:', end='', generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr='wizardmath-7b-v1.1-hf', + path='WizardLM/WizardMath-7B-V1.1', + tokenizer_path='WizardLM/WizardMath-7B-V1.1', + model_kwargs=dict( + device_map='auto', + trust_remote_code=True, + ), + tokenizer_kwargs=dict( + padding_side='left', + truncation_side='left', + trust_remote_code=True, + ), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + end_str='', + ) +] diff --git a/opencompass/configs/models/wizardlm/vllm_wizardlm_13b_v1_2.py b/opencompass/configs/models/wizardlm/vllm_wizardlm_13b_v1_2.py new file mode 100644 index 0000000000000000000000000000000000000000..2e7fafeb683ebae4bcb31e0c6da9f9d01aab906b --- /dev/null +++ b/opencompass/configs/models/wizardlm/vllm_wizardlm_13b_v1_2.py @@ -0,0 +1,24 @@ +from opencompass.models import VLLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: ', end=' '), + dict(role='BOT', begin='ASSISTANT: ', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='wizardlm-13b-v1.2-vllm', + path='WizardLM/WizardLM-13B-V1.2', + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=1, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/wizardlm/vllm_wizardlm_70b_v1_0.py b/opencompass/configs/models/wizardlm/vllm_wizardlm_70b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..a722593a6c297eb2e480daa0decd57dccfb9ce08 --- /dev/null +++ b/opencompass/configs/models/wizardlm/vllm_wizardlm_70b_v1_0.py @@ -0,0 +1,25 @@ +from opencompass.models import VLLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='USER: ', end=' '), + dict(role='BOT', begin='ASSISTANT: ', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='wizardlm-70b-v1.0-vllm', + path='WizardLM/WizardLM-70B-V1.0', + model_kwargs=dict(tensor_parallel_size=4), + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=4, num_procs=1), + ) +] diff --git a/opencompass/configs/models/wizardlm/vllm_wizardlm_7b_v1_0.py b/opencompass/configs/models/wizardlm/vllm_wizardlm_7b_v1_0.py new file mode 100644 index 0000000000000000000000000000000000000000..60b33c865ca5ee627265b37dbab3dc52f649c7c5 --- /dev/null +++ b/opencompass/configs/models/wizardlm/vllm_wizardlm_7b_v1_0.py @@ -0,0 +1,24 @@ +from opencompass.models import VLLM + + +_meta_template = dict( + round=[ + dict(role='HUMAN', end='\n\n'), + dict(role='BOT', begin='### Response:', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='wizardlm-7b-v1.0-vllm', + path='WizardLM/WizardLM-7B-V1.0', + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_34b.py b/opencompass/configs/models/yi/hf_yi_1_5_34b.py new file mode 100644 index 0000000000000000000000000000000000000000..38cd6424509aab4b3847ae6629c0de8adca0d688 --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_34b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='yi-1.5-34b-hf', + path='01-ai/Yi-1.5-34B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_34b_chat.py b/opencompass/configs/models/yi/hf_yi_1_5_34b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..b1742ee89c888666dd3d7fe15794f4fc98be3267 --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_34b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='yi-1.5-34b-chat-hf', + path='01-ai/Yi-1.5-34B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_6b.py b/opencompass/configs/models/yi/hf_yi_1_5_6b.py new file mode 100644 index 0000000000000000000000000000000000000000..df4fd23c4169567ec89b0a8bd09a73b3a3476506 --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_6b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='yi-1.5-6b-hf', + path='01-ai/Yi-1.5-6B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_6b_chat.py b/opencompass/configs/models/yi/hf_yi_1_5_6b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..015df536cee857f886171da6d2b1cda823693e94 --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_6b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='yi-1.5-6b-chat-hf', + path='01-ai/Yi-1.5-6B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_9b.py b/opencompass/configs/models/yi/hf_yi_1_5_9b.py new file mode 100644 index 0000000000000000000000000000000000000000..c38506a4aaa95b81dfa59e2f80f2bc233cfb6efd --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_9b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='yi-1.5-9b-hf', + path='01-ai/Yi-1.5-9B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_1_5_9b_chat.py b/opencompass/configs/models/yi/hf_yi_1_5_9b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7e566d6ec349d8cab73e6511aeb55916ee4bcc --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_1_5_9b_chat.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='yi-1.5-9b-chat-hf', + path='01-ai/Yi-1.5-9B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_34b.py b/opencompass/configs/models/yi/hf_yi_34b.py new file mode 100644 index 0000000000000000000000000000000000000000..7fc59d4643b59eb8ec14f52c4ce6e651da064d5e --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_34b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='yi-34b-hf', + path='01-ai/Yi-34B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_34b_chat.py b/opencompass/configs/models/yi/hf_yi_34b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..16a3a2acef8181358d29b4c94e9e277a46a81409 --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_34b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='yi-34b-chat-hf', + path='01-ai/Yi-34B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + stop_words=['<|endoftext|>', '<|im_end|>'], + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_6b.py b/opencompass/configs/models/yi/hf_yi_6b.py new file mode 100644 index 0000000000000000000000000000000000000000..6ce3b13476f257f5d6c4b56c938921f805c62cab --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_6b.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='yi-6b-hf', + path='01-ai/Yi-6B', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/hf_yi_6b_chat.py b/opencompass/configs/models/yi/hf_yi_6b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5210553e186b29a987ab9659a6e7f78a79072f4b --- /dev/null +++ b/opencompass/configs/models/yi/hf_yi_6b_chat.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='yi-6b-chat-hf', + path='01-ai/Yi-6B-Chat', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + stop_words=['<|endoftext|>', '<|im_end|>'], + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_1_5_34b_chat.py b/opencompass/configs/models/yi/lmdeploy_yi_1_5_34b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..d296a1008443d7b88578331d7c16b7b528098d9c --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_1_5_34b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='yi-1.5-34b-chat-turbomind', + path='01-ai/Yi-1.5-34B-Chat', + engine_config=dict(session_len=4096, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_1_5_6b_chat.py b/opencompass/configs/models/yi/lmdeploy_yi_1_5_6b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..eeaf8ea2514d53e4717859165444687f78c86fef --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_1_5_6b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='yi-1.5-6b-chat-turbomind', + path='01-ai/Yi-1.5-6B-Chat', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b.py b/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b.py new file mode 100644 index 0000000000000000000000000000000000000000..5780ec2e3b5beedc83d91de4603ed18733c4fc9c --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModel + +models = [ + dict( + type=TurboMindModel, + abbr='yi-1.5-9b-turbomind', + path='01-ai/Yi-1.5-9B', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b_chat.py b/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..4e33ba2328d40c282cc59ab1e3e59afd20ee7346 --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_1_5_9b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='yi-1.5-9b-chat-turbomind', + path='01-ai/Yi-1.5-9B-Chat', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_34b_chat.py b/opencompass/configs/models/yi/lmdeploy_yi_34b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5ed603a6dcb22c0d9239c4c7c39deb7bc4012134 --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_34b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='yi-34b-chat-turbomind', + path='01-ai/Yi-34B-Chat', + engine_config=dict(session_len=4096, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_6b_chat.py b/opencompass/configs/models/yi/lmdeploy_yi_6b_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..5c75bfa50c9a816767f0b79bb5270eb665bf8ce0 --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_6b_chat.py @@ -0,0 +1,15 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='yi-6b-chat-turbomind', + path='01-ai/Yi-6B-Chat', + engine_config=dict(session_len=4096, max_batch_size=16, tp=1), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048), + max_seq_len=4096, + max_out_len=2048, + batch_size=16, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/yi/lmdeploy_yi_series.py b/opencompass/configs/models/yi/lmdeploy_yi_series.py new file mode 100644 index 0000000000000000000000000000000000000000..f19476b7ea35d9b563574b7c2c4b0f5c6d5ab8cf --- /dev/null +++ b/opencompass/configs/models/yi/lmdeploy_yi_series.py @@ -0,0 +1,23 @@ +from opencompass.models import LmdeployPytorchModel + +settings = [ + ('yi-6b-pytorch', '01-ai/Yi-6B', 1), + ('yi-34b-pytorch', '01-ai/Yi-34B', 2), +] + +models = [] +for abbr, path, num_gpus in settings: + models.append( + dict( + type=LmdeployPytorchModel, + abbr=abbr, + path=path, + engine_config=dict(session_len=2048, max_batch_size=16, tp=num_gpus), + gen_config=dict(top_k=1, temperature=1, top_p=0.9, max_new_tokens=1024), + max_out_len=1024, + max_seq_len=2048, + batch_size=16, + concurrency=16, + run_cfg=dict(num_gpus=num_gpus), + ) + ) diff --git a/opencompass/configs/models/zephyr/hf_zephyr_7b_beta.py b/opencompass/configs/models/zephyr/hf_zephyr_7b_beta.py new file mode 100644 index 0000000000000000000000000000000000000000..da58c31eecd784e91aa45f0563d67fe724a8284e --- /dev/null +++ b/opencompass/configs/models/zephyr/hf_zephyr_7b_beta.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='zephyr-7b-beta-hf', + path='HuggingFaceH4/zephyr-7b-beta', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/zephyr/vllm_zephyr_7b_beta.py b/opencompass/configs/models/zephyr/vllm_zephyr_7b_beta.py new file mode 100644 index 0000000000000000000000000000000000000000..499b58b6dec54e09d9140c1235b8595a07909b17 --- /dev/null +++ b/opencompass/configs/models/zephyr/vllm_zephyr_7b_beta.py @@ -0,0 +1,23 @@ +from opencompass.models import VLLM + +_meta_template = dict( + round=[ + dict(role='HUMAN', begin='<|user|>\n', end=''), + dict(role='BOT', begin='<|assistant|>\n', end='', generate=True), + ], +) + +models = [ + dict( + type=VLLM, + abbr='zephyr-7b-beta-vllm', + path='HuggingFaceH4/zephyr-7b-beta', + meta_template=_meta_template, + max_out_len=100, + max_seq_len=2048, + batch_size=32, + generation_kwargs=dict(temperature=0), + stop_words=[''], + run_cfg=dict(num_gpus=1, num_procs=1), + ) +] diff --git a/opencompass/configs/summarizers/groups/GaokaoBench.py b/opencompass/configs/summarizers/groups/GaokaoBench.py new file mode 100644 index 0000000000000000000000000000000000000000..30c6beed5b8d4b958e284b826850553c582b74e2 --- /dev/null +++ b/opencompass/configs/summarizers/groups/GaokaoBench.py @@ -0,0 +1,6 @@ +GaokaoBench_summary_groups = [] + +# gaokao-bench +_GaokaoBench_weights = {'2010-2022_Math_II_MCQs': 1090, '2010-2022_Math_I_MCQs': 1070, '2010-2022_History_MCQs': 1148, '2010-2022_Biology_MCQs': 900, '2010-2022_Political_Science_MCQs': 1280, '2010-2022_Physics_MCQs': 384, '2010-2022_Chemistry_MCQs': 744, '2010-2013_English_MCQs': 105, '2010-2022_Chinese_Modern_Lit': 261, '2010-2022_English_Fill_in_Blanks': 900.0, '2012-2022_English_Cloze_Test': 260, '2010-2022_Geography_MCQs': 380, '2010-2022_English_Reading_Comp': 940, '2010-2022_Chinese_Lang_and_Usage_MCQs': 240} +_GaokaoBench_weights = {'GaokaoBench_' + k: v for k, v in _GaokaoBench_weights.items()} +GaokaoBench_summary_groups.append({'name': 'GaokaoBench', 'subsets': list(_GaokaoBench_weights.keys()), 'weights': _GaokaoBench_weights}) diff --git a/opencompass/configs/summarizers/groups/MMLUArabic.py b/opencompass/configs/summarizers/groups/MMLUArabic.py new file mode 100644 index 0000000000000000000000000000000000000000..335ea4d7bc4ec3e783febfc407a543be861bb184 --- /dev/null +++ b/opencompass/configs/summarizers/groups/MMLUArabic.py @@ -0,0 +1,50 @@ +sub_categories = { + 'math': ['abstract_algebra', 'college_mathematics', 'elementary_mathematics', 'high_school_mathematics', 'high_school_statistics'], + 'health': ['anatomy', 'clinical_knowledge', 'college_medicine', 'human_aging', 'medical_genetics', 'nutrition', 'professional_medicine', 'virology'], + 'physics': ['astronomy', 'college_physics', 'conceptual_physics', 'high_school_physics'], + 'business': ['business_ethics', 'management', 'marketing'], + 'biology': ['college_biology', 'high_school_biology'], + 'chemistry': ['college_chemistry', 'high_school_chemistry'], + 'computer science': ['college_computer_science', 'computer_security', 'high_school_computer_science', 'machine_learning'], + 'economics': ['econometrics', 'high_school_macroeconomics', 'high_school_microeconomics'], + 'engineering': ['electrical_engineering'], + 'philosophy': ['formal_logic', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'world_religions'], + 'other': ['global_facts', 'miscellaneous', 'professional_accounting'], + 'history': ['high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'prehistory'], + 'geography': ['high_school_geography'], + 'politics': ['high_school_government_and_politics', 'public_relations', 'security_studies', 'us_foreign_policy'], + 'psychology': ['high_school_psychology', 'professional_psychology'], + 'culture': ['human_sexuality', 'sociology'], + 'law': ['international_law', 'jurisprudence', 'professional_law'] +} + +categories = { + 'STEM': ['physics', 'chemistry', 'biology', 'computer science', 'math', 'engineering'], + 'humanities': ['history', 'philosophy', 'law'], + 'social_sciences': ['politics', 'culture', 'economics', 'geography', 'psychology'], + 'other': ['other', 'business', 'health'], +} + +category2subject = {} +for k, v in categories.items(): + for subject, subcat in sub_categories.items(): + if subject in v: + for c in subcat: + category2subject.setdefault(k, []).append(c) + +MMLUArabic_summary_groups = [] + +_MMLUArabic_stem = ['acegpt_MMLUArabic_' + s for s in category2subject['STEM']] +MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_STEM', 'subsets': _MMLUArabic_stem}) + +_MMLUArabic_humanities = ['acegpt_MMLUArabic_' + s for s in category2subject['humanities']] +MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_humanities', 'subsets': _MMLUArabic_humanities}) + +_MMLUArabic_social_science = ['acegpt_MMLUArabic_' + s for s in category2subject['social_sciences']] +MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_social_science', 'subsets': _MMLUArabic_social_science}) + +_MMLUArabic_other = ['acegpt_MMLUArabic_' + s for s in category2subject['other']] +MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_other', 'subsets': _MMLUArabic_other}) + +_MMLUArabic_all = _MMLUArabic_stem + _MMLUArabic_humanities + _MMLUArabic_social_science + _MMLUArabic_other +MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic', 'subsets': _MMLUArabic_all}) diff --git a/opencompass/configs/summarizers/groups/OlympiadBench.py b/opencompass/configs/summarizers/groups/OlympiadBench.py new file mode 100644 index 0000000000000000000000000000000000000000..e30831fffff9a5f28a0071211b79b0bd5bf9665a --- /dev/null +++ b/opencompass/configs/summarizers/groups/OlympiadBench.py @@ -0,0 +1,32 @@ +categories = [ + 'OE_TO_maths_en_COMP', # OpenEnded - TextOnly - maths - COMP + 'OE_TO_maths_zh_COMP', # OpenEnded - TextOnly - maths - COMP + 'OE_TO_maths_zh_CEE', # OpenEnded - TextOnly - maths - CEE + 'OE_TO_physics_en_COMP', # OpenEnded - TextOnly - physics - COMP + 'OE_TO_physics_zh_CEE' # OpenEnded - TextOnly - physics - CEE +] + +OlympiadBench_summary_groups = [ + {'name': 'OlympiadBench', 'subsets': ['OlympiadBench_' + c.replace(' ', '_') for c in categories]}, +] + +math_categories = [ + 'OE_TO_maths_en_COMP', # OpenEnded - TextOnly - maths - COMP + 'OE_TO_maths_zh_COMP', # OpenEnded - TextOnly - maths - COMP + 'OE_TO_maths_zh_CEE', # OpenEnded - TextOnly - maths - CEE +] + +physics_categories = [ + 'OE_TO_physics_en_COMP', # OpenEnded - TextOnly - physics - COMP + 'OE_TO_physics_zh_CEE' # OpenEnded - TextOnly - physics - CEE +] + + +OlympiadBenchMath_summary_groups = [ + {'name': 'OlympiadBenchMath', 'subsets': ['OlympiadBench_' + c.replace(' ', '_') for c in math_categories]}, +] + + +OlympiadBenchPhysics_summary_groups = [ + {'name': 'OlympiadBenchPhysics', 'subsets': ['OlympiadBench_' + c.replace(' ', '_') for c in physics_categories]}, +] diff --git a/opencompass/configs/summarizers/groups/PHYSICS.py b/opencompass/configs/summarizers/groups/PHYSICS.py new file mode 100644 index 0000000000000000000000000000000000000000..eff80721ea6a6f672956387a3a8956832937a648 --- /dev/null +++ b/opencompass/configs/summarizers/groups/PHYSICS.py @@ -0,0 +1,14 @@ +physics_summary_groups = [] + +# bbh +_physcis = [ + 'atomic_dataset_textonly', + 'electro_dataset_textonly', + 'mechanics_dataset_textonly', + 'optics_dataset_textonly', + 'quantum_dataset_textonly', + 'statistics_dataset_textonly', +] + +_physcis = ['PHYSICS_' + s for s in _physcis] +physics_summary_groups.append({'name': 'PHYSICS', 'subsets': _physcis}) \ No newline at end of file diff --git a/opencompass/configs/summarizers/groups/PMMEval.py b/opencompass/configs/summarizers/groups/PMMEval.py new file mode 100644 index 0000000000000000000000000000000000000000..6f5976d0ab1429da32810be9f54ca3ec54a87cc6 --- /dev/null +++ b/opencompass/configs/summarizers/groups/PMMEval.py @@ -0,0 +1,41 @@ +NATURAL_LANGUAGE_FULLNAMES = ['English', 'Chinese', 'Arabic', 'Spanish', 'French', 'Japanese', 'Korean', 'Portuguese', 'Thai', 'Vietnamese'] +NATURAL_LANGUAGE_FULLNAMES_FLORES = ['Chinese', 'Arabic', 'Spanish', 'French', 'Japanese', 'Korean', 'Portuguese', 'Thai', 'Vietnamese'] +NATURAL_LANGUAGE_CODES = ['en', 'zh', 'ar', 'es', 'fr', 'ja', 'ko', 'pt', 'th', 'vi'] +NATURAL_LANGUAGE_CODES_MMMLU = ['EN-US', 'ZH-CN', 'AR-XY', 'ES-LA', 'FR-FR', 'JA-JP', 'KO-KR', 'PT-BR', 'TH-TL', 'VI-VT'] + +PMMEval_summary_groups = [ + { + 'name': 'flores', + 'subsets': [f'flores-{lang_fullname}' for lang_fullname in NATURAL_LANGUAGE_FULLNAMES_FLORES] + }, + { + 'name': 'humanevalxl', + 'subsets': [f'humanevalxl-python-{lang_fullname}' for lang_fullname in NATURAL_LANGUAGE_FULLNAMES] + \ + [f'humanevalxl-java-{lang_fullname}' for lang_fullname in NATURAL_LANGUAGE_FULLNAMES] + \ + [f'humanevalxl-javascript-{lang_fullname}' for lang_fullname in NATURAL_LANGUAGE_FULLNAMES] + }, + { + 'name': 'mgsm', + 'subsets': [f'mgsm-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES] + }, + { + 'name': 'mhellaswag', + 'subsets': [f'mhellaswag-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES] + }, + { + 'name': 'mifeval', + 'subsets': [f'mifeval-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES] + }, + { + 'name': 'mlogiqa', + 'subsets': [f'mlogiqa-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES] + }, + { + 'name': 'mmmlu', + 'subsets': [f'mmmlu-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES_MMMLU] + }, + { + 'name': 'xnli', + 'subsets': [f'xnli-{lang_code}' for lang_code in NATURAL_LANGUAGE_CODES] + } +] diff --git a/opencompass/configs/summarizers/groups/agieval.py b/opencompass/configs/summarizers/groups/agieval.py new file mode 100644 index 0000000000000000000000000000000000000000..0334cd46c62884ed51073db66b7322243ad8c6bd --- /dev/null +++ b/opencompass/configs/summarizers/groups/agieval.py @@ -0,0 +1,17 @@ +agieval_summary_groups = [] + +_agieval_chinese_sets = ['gaokao-chinese', 'gaokao-english', 'gaokao-geography', 'gaokao-history', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-physics', 'gaokao-mathqa', 'logiqa-zh', 'jec-qa-kd', 'jec-qa-ca', 'gaokao-mathcloze'] +_agieval_chinese_sets = ['agieval-' + s for s in _agieval_chinese_sets] +agieval_summary_groups.append({'name': 'agieval-chinese', 'subsets': _agieval_chinese_sets}) + +_agieval_english_sets = ['lsat-ar', 'lsat-lr', 'lsat-rc', 'logiqa-en', 'sat-math', 'sat-en', 'sat-en-without-passage', 'aqua-rat', 'math'] +_agieval_english_sets = ['agieval-' + s for s in _agieval_english_sets] +agieval_summary_groups.append({'name': 'agieval-english', 'subsets': _agieval_english_sets}) + +_agieval_gaokao_sets = ['gaokao-chinese', 'gaokao-english', 'gaokao-geography', 'gaokao-history', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-physics', 'gaokao-mathqa', 'gaokao-mathcloze'] +_agieval_gaokao_sets = ['agieval-' + s for s in _agieval_gaokao_sets] +agieval_summary_groups.append({'name': 'agieval-gaokao', 'subsets': _agieval_gaokao_sets}) + +_agieval_all = ['gaokao-chinese', 'gaokao-english', 'gaokao-geography', 'gaokao-history', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-physics', 'gaokao-mathqa', 'logiqa-zh', 'lsat-ar', 'lsat-lr', 'lsat-rc', 'logiqa-en', 'sat-math', 'sat-en', 'sat-en-without-passage', 'aqua-rat', 'jec-qa-kd', 'jec-qa-ca', 'gaokao-mathcloze', 'math'] +_agieval_all = ['agieval-' + s for s in _agieval_all] +agieval_summary_groups.append({'name': 'agieval', 'subsets': _agieval_all}) diff --git a/opencompass/configs/summarizers/groups/babilong.py b/opencompass/configs/summarizers/groups/babilong.py new file mode 100644 index 0000000000000000000000000000000000000000..9b1b39eef3ce39ca8e4db195421ee0826ad77ed3 --- /dev/null +++ b/opencompass/configs/summarizers/groups/babilong.py @@ -0,0 +1,37 @@ +default_babilong_tasks = [ + 'qa1', + 'qa2', + 'qa3', + 'qa4', + 'qa5', + 'qa6', + 'qa7', + 'qa8', + 'qa9', + 'qa10', +] +context_window_sizes = [ + '0k', + '1k', + '2k', + '4k', + '8k', + '16k', + '32k', + '64k', + '128k', + '256k', + '512k', + '1m', +] +babilong_summary_groups = [] +for context_window_size in context_window_sizes: + babilong_summary_groups.append( + { + 'name': f'babilong_{context_window_size}', + 'subsets': [ + f'babilong_{task}_{context_window_size}' + for task in default_babilong_tasks + ], + } + ) diff --git a/opencompass/configs/summarizers/groups/bbeh.py b/opencompass/configs/summarizers/groups/bbeh.py new file mode 100644 index 0000000000000000000000000000000000000000..95697144f98e3b6c5dca9a13a518bef3f69e5512 --- /dev/null +++ b/opencompass/configs/summarizers/groups/bbeh.py @@ -0,0 +1,13 @@ +bbeh_summary_groups = [] + +# bbeh +_bbeh = [ + 'bbeh_boolean_expressions', 'bbeh_disambiguation_qa', 'bbeh_geometric_shapes', 'bbeh_hyperbaton', + 'bbeh_movie_recommendation', 'bbeh_nycc', 'bbeh_shuffled_objects', 'bbeh_boardgame_qa', + 'bbeh_buggy_tables', 'bbeh_causal_understanding', 'bbeh_dyck_languages', 'bbeh_linguini', + 'bbeh_multistep_arithmetic', 'bbeh_object_counting', 'bbeh_object_properties', 'bbeh_sarc_triples', + 'bbeh_spatial_reasoning', 'bbeh_sportqa', 'bbeh_temporal_sequence', 'bbeh_time_arithmetic', + 'bbeh_web_of_lies', 'bbeh_word_sorting', 'bbeh_zebra_puzzles' +] +bbeh_summary_groups.append({'name': 'bbeh', 'subsets': _bbeh, 'metric':'naive_average'}) +bbeh_summary_groups.append({'name': 'bbeh', 'subsets': _bbeh, 'metric':'harmonic_mean'}) \ No newline at end of file diff --git a/opencompass/configs/summarizers/groups/bbh.py b/opencompass/configs/summarizers/groups/bbh.py new file mode 100644 index 0000000000000000000000000000000000000000..8286c5c1ffde655456f3121b0debd7825ddae4e9 --- /dev/null +++ b/opencompass/configs/summarizers/groups/bbh.py @@ -0,0 +1,6 @@ +bbh_summary_groups = [] + +# bbh +_bbh = ['temporal_sequences', 'disambiguation_qa', 'date_understanding', 'tracking_shuffled_objects_three_objects', 'penguins_in_a_table','geometric_shapes', 'snarks', 'ruin_names', 'tracking_shuffled_objects_seven_objects', 'tracking_shuffled_objects_five_objects','logical_deduction_three_objects', 'hyperbaton', 'logical_deduction_five_objects', 'logical_deduction_seven_objects', 'movie_recommendation','salient_translation_error_detection', 'reasoning_about_colored_objects', 'multistep_arithmetic_two', 'navigate', 'dyck_languages', 'word_sorting', 'sports_understanding','boolean_expressions', 'object_counting', 'formal_fallacies', 'causal_judgement', 'web_of_lies'] +_bbh = ['bbh-' + s for s in _bbh] +bbh_summary_groups.append({'name': 'bbh', 'subsets': _bbh}) diff --git a/opencompass/configs/summarizers/groups/calm.py b/opencompass/configs/summarizers/groups/calm.py new file mode 100644 index 0000000000000000000000000000000000000000..9f52aeead743444929ad0398381b8c96be8bd375 --- /dev/null +++ b/opencompass/configs/summarizers/groups/calm.py @@ -0,0 +1,168 @@ +task_hiearchy_dict = { + # association/ + # correlation/ + 'CORR-B_correlation_CN':'association/correlation/', + 'CORR-B_correlation_EN':'association/correlation/', + # explaining_away_effect/ + 'EAE-B_exp-away_CN':'association/explaining_away_effect/', + 'EAE-B_exp-away_EN':'association/explaining_away_effect/', + # causal_discovery/ + # abstract_reasoning/ + 'AR-B_CaLM-AR_CN':'causal_discovery/abstract_reasoning/', + 'AR-B_CaLM-AR_EN':'causal_discovery/abstract_reasoning/', + # causal_attribution/ + 'CA-B_FA_CN':'causal_discovery/causal_attribution/', + 'CA-B_FA_EN':'causal_discovery/causal_attribution/', + 'CA-B_FP_CN':'causal_discovery/causal_attribution/', + 'CA-B_FP_EN':'causal_discovery/causal_attribution/', + # event_causality_identification/ + 'ECI-B_CTB_CN':'causal_discovery/event_causality_identification/', + 'ECI-B_CTB_EN':'causal_discovery/event_causality_identification/', + 'ECI-B_ESC_CN':'causal_discovery/event_causality_identification/', + 'ECI-B_ESC_EN':'causal_discovery/event_causality_identification/', + 'ECI-B_MAVEN-ERE_CN':'causal_discovery/event_causality_identification/', + 'ECI-B_MAVEN-ERE_EN':'causal_discovery/event_causality_identification/', + # pairwise_causal_discovery/ + 'PCD-B_COPA_CN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-B_COPA_EN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-B_E-CARE_CN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-B_E-CARE_EN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-C_COPA_CN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-C_COPA_EN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-C_E-CARE_CN':'causal_discovery/pairwise_causal_discovery/', + 'PCD-C_E-CARE_EN':'causal_discovery/pairwise_causal_discovery/', + # counterfactual/ + # actual_causality/ + 'AC-B_causal_judgement_CN':'counterfactual/actual_causality/', + 'AC-B_causal_judgement_EN':'counterfactual/actual_causality/', + # causal_explanation_generation/ + 'CEG-O_E-CARE_CN':'counterfactual/causal_explanation_generation/', + 'CEG-O_E-CARE_EN':'counterfactual/causal_explanation_generation/', + # counterfactual_reasoning/ + 'CR-B_det-counterfactual_CN':'counterfactual/counterfactual_reasoning/', + 'CR-B_det-counterfactual_EN':'counterfactual/counterfactual_reasoning/', + 'CR-C_CRASS_CN':'counterfactual/counterfactual_reasoning/', + 'CR-C_CRASS_EN':'counterfactual/counterfactual_reasoning/', + # effect_of_the_treatment_on_the_treated/ + 'ETT-B_ETT-natural_CN':'counterfactual/effect_of_the_treatment_on_the_treated/', + 'ETT-B_ETT-natural_EN':'counterfactual/effect_of_the_treatment_on_the_treated/', + 'ETT-P_ETT-basic_CN':'counterfactual/effect_of_the_treatment_on_the_treated/', + 'ETT-P_ETT-basic_EN':'counterfactual/effect_of_the_treatment_on_the_treated/', + 'ETT-P_ETT-hard_CN':'counterfactual/effect_of_the_treatment_on_the_treated/', + 'ETT-P_ETT-hard_EN':'counterfactual/effect_of_the_treatment_on_the_treated/', + # natural_direct_effect/ + 'NDE-B_NDE-natural_CN':'counterfactual/natural_direct_effect/', + 'NDE-B_NDE-natural_EN':'counterfactual/natural_direct_effect/', + 'NDE-P_NDE-basic_CN':'counterfactual/natural_direct_effect/', + 'NDE-P_NDE-basic_EN':'counterfactual/natural_direct_effect/', + 'NDE-P_NDE-hard_CN':'counterfactual/natural_direct_effect/', + 'NDE-P_NDE-hard_EN':'counterfactual/natural_direct_effect/', + # natural_indirect_effect/ + 'NIE-B_NIE-natural_CN':'counterfactual/natural_indirect_effect/', + 'NIE-B_NIE-natural_EN':'counterfactual/natural_indirect_effect/', + 'NIE-P_NIE-basic_CN':'counterfactual/natural_indirect_effect/', + 'NIE-P_NIE-basic_EN':'counterfactual/natural_indirect_effect/', + 'NIE-P_NIE-hard_CN':'counterfactual/natural_indirect_effect/', + 'NIE-P_NIE-hard_EN':'counterfactual/natural_indirect_effect/', + # probability_of_necessity/ + 'PN-P_PN-basic_CN':'counterfactual/probability_of_necessity/', + 'PN-P_PN-basic_EN':'counterfactual/probability_of_necessity/', + 'PN-P_PN-hard_CN':'counterfactual/probability_of_necessity/', + 'PN-P_PN-hard_EN':'counterfactual/probability_of_necessity/', + # probability_of_sufficiency/ + 'PS-P_PS-basic_CN':'counterfactual/probability_of_sufficiency/', + 'PS-P_PS-basic_EN':'counterfactual/probability_of_sufficiency/', + 'PS-P_PS-hard_CN':'counterfactual/probability_of_sufficiency/', + 'PS-P_PS-hard_EN':'counterfactual/probability_of_sufficiency/', + # intervention/ + # average_treatment_effect/ + 'ATE-B_ATE-natural_CN':'intervention/average_treatment_effect/', + 'ATE-B_ATE-natural_EN':'intervention/average_treatment_effect/', + 'ATE-P_ATE-basic_CN':'intervention/average_treatment_effect/', + 'ATE-P_ATE-basic_EN':'intervention/average_treatment_effect/', + 'ATE-P_ATE-hard_CN':'intervention/average_treatment_effect/', + 'ATE-P_ATE-hard_EN':'intervention/average_treatment_effect/', + # backdoor_adjustment_set/ + 'BAS-B_backadj_CN':'intervention/backdoor_adjustment_set/', + 'BAS-B_backadj_EN':'intervention/backdoor_adjustment_set/', + 'BAS-C_max-BAS_CN':'intervention/backdoor_adjustment_set/', + 'BAS-C_max-BAS_EN':'intervention/backdoor_adjustment_set/', + 'BAS-C_min-BAS_CN':'intervention/backdoor_adjustment_set/', + 'BAS-C_min-BAS_EN':'intervention/backdoor_adjustment_set/', + 'BAS-C_mix-BAS_CN':'intervention/backdoor_adjustment_set/', + 'BAS-C_mix-BAS_EN':'intervention/backdoor_adjustment_set/', + # causal_effect_identification/ + 'CEI-B_0.2-UC_CN':'intervention/causal_effect_identification/', + 'CEI-B_0.2-UC_EN':'intervention/causal_effect_identification/', + 'CEI-B_0.4-UC_CN':'intervention/causal_effect_identification/', + 'CEI-B_0.4-UC_EN':'intervention/causal_effect_identification/', + 'CEI-B_0.6-UC_CN':'intervention/causal_effect_identification/', + 'CEI-B_0.6-UC_EN':'intervention/causal_effect_identification/', + 'CEI-B_0.8-UC_CN':'intervention/causal_effect_identification/', + 'CEI-B_0.8-UC_EN':'intervention/causal_effect_identification/', + # collider_bias/ + 'CB-B_collider-bias_CN':'intervention/collider_bias/', + 'CB-B_collider-bias_EN':'intervention/collider_bias/', + # controlled_direct_effect/ + 'CDE-B_CDE-natural_CN':'intervention/controlled_direct_effect/', + 'CDE-B_CDE-natural_EN':'intervention/controlled_direct_effect/', + 'CDE-P_CDE-basic_CN':'intervention/controlled_direct_effect/', + 'CDE-P_CDE-basic_EN':'intervention/controlled_direct_effect/', + 'CDE-P_CDE-hard_CN':'intervention/controlled_direct_effect/', + 'CDE-P_CDE-hard_EN':'intervention/controlled_direct_effect/', + # frontdoor_adjustment_set/ + 'FAS-C_FAS_CN':'intervention/frontdoor_adjustment_set/', + 'FAS-C_FAS_EN':'intervention/frontdoor_adjustment_set/', + # instrumental_variable/ + 'IV-C_CaLM-IV_CN':'intervention/instrumental_variable/', + 'IV-C_CaLM-IV_EN':'intervention/instrumental_variable/',} +dict_keys = list(task_hiearchy_dict.keys()) +error_dict = {'Same response to all questions':[], + 'Language inconsistency':[], + 'Limitation of instruction-following':[], + 'Repetition':[], + 'Empty response':[],} + +for error in error_dict: + for key in dict_keys: + if 'CEG-O_E-CARE' in key: + continue + error_dict[error].append([f'calm_{key}', error]) + +English_avg = [] +Chinese_avg = [] +for key in dict_keys: + if key.endswith('EN'): + English_avg.append([f'calm_{key}', 'Accuracy']) + else: + assert key.endswith('CN') + Chinese_avg.append([f'calm_{key}', 'Accuracy']) + +calm_summary_groups = [ + # English Average + {'name': 'English Average', 'subsets': English_avg}, + + # Chinese Average + {'name': 'Chinese Average', 'subsets': Chinese_avg}, + + # Accuracy Average + {'name': 'Accuracy Average', 'subsets': ['English Average', 'Chinese Average']}, +] +for error in error_dict: + calm_summary_groups.append({'name': error+' Average', 'subsets': error_dict[error]}) + +summarizer = dict( + dataset_abbrs = [ + '###### CALM-Lite Accuracy ######', + 'Accuracy Average', + 'English Average', + 'Chinese Average', + '###### CALM-Lite Errors ######', + 'Same response to all questions Average', + 'Language inconsistency Average', + 'Limitation of instruction-following Average', + 'Repetition Average', + 'Empty response Average', + ], + summary_groups=calm_summary_groups, +) diff --git a/opencompass/configs/summarizers/groups/ceval.py b/opencompass/configs/summarizers/groups/ceval.py new file mode 100644 index 0000000000000000000000000000000000000000..ecd62d6554b5859c704895bcda010256c4ec74d9 --- /dev/null +++ b/opencompass/configs/summarizers/groups/ceval.py @@ -0,0 +1,47 @@ +ceval_summary_groups = [] + +_ceval_stem = ['computer_network', 'operating_system', 'computer_architecture', 'college_programming', 'college_physics', 'college_chemistry', 'advanced_mathematics', 'probability_and_statistics', 'discrete_mathematics', 'electrical_engineer', 'metrology_engineer', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry', 'high_school_biology', 'middle_school_mathematics', 'middle_school_biology', 'middle_school_physics', 'middle_school_chemistry', 'veterinary_medicine'] +_ceval_stem = ['ceval-' + s for s in _ceval_stem] +ceval_summary_groups.append({'name': 'ceval-stem', 'subsets': _ceval_stem}) + +_ceval_social_science = ['college_economics', 'business_administration', 'marxism', 'mao_zedong_thought', 'education_science', 'teacher_qualification', 'high_school_politics', 'high_school_geography', 'middle_school_politics', 'middle_school_geography'] +_ceval_social_science = ['ceval-' + s for s in _ceval_social_science] +ceval_summary_groups.append({'name': 'ceval-social-science', 'subsets': _ceval_social_science}) + +_ceval_humanities = ['modern_chinese_history', 'ideological_and_moral_cultivation', 'logic', 'law', 'chinese_language_and_literature', 'art_studies', 'professional_tour_guide', 'legal_professional', 'high_school_chinese', 'high_school_history', 'middle_school_history'] +_ceval_humanities = ['ceval-' + s for s in _ceval_humanities] +ceval_summary_groups.append({'name': 'ceval-humanities', 'subsets': _ceval_humanities}) + +_ceval_other = ['civil_servant', 'sports_science', 'plant_protection', 'basic_medicine', 'clinical_medicine', 'urban_and_rural_planner', 'accountant', 'fire_engineer', 'environmental_impact_assessment_engineer', 'tax_accountant', 'physician'] +_ceval_other = ['ceval-' + s for s in _ceval_other] +ceval_summary_groups.append({'name': 'ceval-other', 'subsets': _ceval_other}) + +_ceval_hard = ['advanced_mathematics', 'discrete_mathematics', 'probability_and_statistics', 'college_chemistry', 'college_physics', 'high_school_mathematics', 'high_school_chemistry', 'high_school_physics'] +_ceval_hard = ['ceval-' + s for s in _ceval_hard] +ceval_summary_groups.append({'name': 'ceval-hard', 'subsets': _ceval_hard}) + +_ceval_all = _ceval_stem + _ceval_social_science + _ceval_humanities + _ceval_other +ceval_summary_groups.append({'name': 'ceval', 'subsets': _ceval_all}) + +_ceval_stem = ['computer_network', 'operating_system', 'computer_architecture', 'college_programming', 'college_physics', 'college_chemistry', 'advanced_mathematics', 'probability_and_statistics', 'discrete_mathematics', 'electrical_engineer', 'metrology_engineer', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry', 'high_school_biology', 'middle_school_mathematics', 'middle_school_biology', 'middle_school_physics', 'middle_school_chemistry', 'veterinary_medicine'] +_ceval_stem = ['ceval-test-' + s for s in _ceval_stem] +ceval_summary_groups.append({'name': 'ceval-test-stem', 'subsets': _ceval_stem}) + +_ceval_social_science = ['college_economics', 'business_administration', 'marxism', 'mao_zedong_thought', 'education_science', 'teacher_qualification', 'high_school_politics', 'high_school_geography', 'middle_school_politics', 'middle_school_geography'] +_ceval_social_science = ['ceval-test-' + s for s in _ceval_social_science] +ceval_summary_groups.append({'name': 'ceval-test-social-science', 'subsets': _ceval_social_science}) + +_ceval_humanities = ['modern_chinese_history', 'ideological_and_moral_cultivation', 'logic', 'law', 'chinese_language_and_literature', 'art_studies', 'professional_tour_guide', 'legal_professional', 'high_school_chinese', 'high_school_history', 'middle_school_history'] +_ceval_humanities = ['ceval-test-' + s for s in _ceval_humanities] +ceval_summary_groups.append({'name': 'ceval-test-humanities', 'subsets': _ceval_humanities}) + +_ceval_other = ['civil_servant', 'sports_science', 'plant_protection', 'basic_medicine', 'clinical_medicine', 'urban_and_rural_planner', 'accountant', 'fire_engineer', 'environmental_impact_assessment_engineer', 'tax_accountant', 'physician'] +_ceval_other = ['ceval-test-' + s for s in _ceval_other] +ceval_summary_groups.append({'name': 'ceval-test-other', 'subsets': _ceval_other}) + +_ceval_hard = ['advanced_mathematics', 'discrete_mathematics', 'probability_and_statistics', 'college_chemistry', 'college_physics', 'high_school_mathematics', 'high_school_chemistry', 'high_school_physics'] +_ceval_hard = ['ceval-test-' + s for s in _ceval_hard] +ceval_summary_groups.append({'name': 'ceval-test-hard', 'subsets': _ceval_hard}) + +_ceval_all = _ceval_stem + _ceval_social_science + _ceval_humanities + _ceval_other +ceval_summary_groups.append({'name': 'ceval-test', 'subsets': _ceval_all}) diff --git a/opencompass/configs/summarizers/groups/charm_reason.py b/opencompass/configs/summarizers/groups/charm_reason.py new file mode 100644 index 0000000000000000000000000000000000000000..3d1f4c1993e9e3c4ca104c08f58431baeef40cc4 --- /dev/null +++ b/opencompass/configs/summarizers/groups/charm_reason.py @@ -0,0 +1,35 @@ +charm_tasks = [ + 'Anachronisms_Judgment', + 'Movie_and_Music_Recommendation', + 'Natural_Language_Inference', + 'Reading_Comprehension', + 'Sequence_Understanding', + 'Sport_Understanding', + 'Time_Understanding', +] +regions = [ + 'Chinese', + 'Global', +] +prompts = [ + 'Direct', + 'ZH-CoT', + 'EN-CoT', + 'XLT', + 'Translate-EN', +] + + +charm_reason_summary_groups = [] +for prompt in prompts: + for region in regions: + subsets = ['charm-reason-' + region + '_' + task + '_' + prompt for task in charm_tasks] + charm_reason_summary_groups.append({'name': 'charm-reason-' + region + '_' + prompt, 'subsets': subsets}) + +for prompt in prompts: + subsets = ['charm-reason-' + region + '_' + prompt for region in regions] + charm_reason_summary_groups.append({'name': 'charm-reason-' + prompt, 'subsets': subsets}) + +charm_reason_summary_groups.append( + {'name': 'charm-reason-CoT', 'subsets': ['charm-reason-ZH-CoT', 'charm-reason-EN-CoT']} +) diff --git a/opencompass/configs/summarizers/groups/cibench.py b/opencompass/configs/summarizers/groups/cibench.py new file mode 100644 index 0000000000000000000000000000000000000000..8c6650c266543d8577179fdbe83bee3ac8e74167 --- /dev/null +++ b/opencompass/configs/summarizers/groups/cibench.py @@ -0,0 +1,395 @@ + +_cibench_generation_modules = ['pandas', 'matplotlib', 'opencv', 'scipy', 'seaborn', 'pytorch'] +_cibench_generation = ['cibench_generation/' + i for i in _cibench_generation_modules] +cibench_summary_groups = [] +_cibench_generation_weight = { + 'matplotlib': [175, 2, 1, 156], + 'pandas': [200, 45, 45, 38], + 'pytorch': [69, 0, 8, 11], + 'seaborn': [130, 0, 2, 106], + 'opencv': [177, 21, 6, 106], + 'scipy': [161, 94, 14, 49], +} +cibench_summary_groups.extend([ + { + 'name': 'cibench_generation:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_generation], + 'weights': {'cibench_generation/' + k : v[0] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation:executable', + 'subsets': [[i, 'executable'] for i in _cibench_generation], + 'weights': {'cibench_generation/' + k : v[0] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_generation], + 'weights': {'cibench_generation/' + k : v[1] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_generation], + 'weights': {'cibench_generation/' + k : v[2] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_generation], + 'weights': {'cibench_generation/' + k : v[3] for k,v in _cibench_generation_weight.items()}, + }, +]) + +_cibench_generation = ['cibench_generation_oracle/' + i for i in _cibench_generation_modules] +cibench_summary_groups.extend([ + { + 'name': 'cibench_generation_oracle:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_generation], + 'weights': {'cibench_generation_oracle/' + k : v[0] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation_oracle:executable', + 'subsets': [[i, 'executable'] for i in _cibench_generation], + 'weights': {'cibench_generation_oracle/' + k : v[0] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation_oracle:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_generation], + 'weights': {'cibench_generation_oracle/' + k : v[1] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation_oracle:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_generation], + 'weights': {'cibench_generation_oracle/' + k : v[2] for k,v in _cibench_generation_weight.items()}, + }, + { + 'name': 'cibench_generation_oracle:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_generation], + 'weights': {'cibench_generation_oracle/' + k : v[3] for k,v in _cibench_generation_weight.items()}, + }, +]) + +_cibench_template_modules = ['lightgbm', 'matplotlib', 'nltk', 'opencv', 'pandas', 'pytorch', + 'scipy', 'seaborn', 'sklearn', 'tensorflow'] +_cibench_template = ['cibench_template/' + i for i in _cibench_template_modules] +# number of total exec questions in this module +_cibench_template_weight = { + 'lightgbm': [30, 15, 0, 0], + 'matplotlib': [42, 0, 0, 36], + 'nltk': [70, 30, 20, 10], + 'opencv': [60, 10, 0, 40], + 'pandas': [60, 40, 0, 10], + 'pytorch': [28, 0, 0, 0], + 'scipy': [60, 40, 0, 0], + 'seaborn': [42, 0, 0, 35], + 'sklearn': [42, 6, 0, 18], + 'tensorflow': [36, 6, 0, 12], +} +cibench_summary_groups.extend([ + { + 'name': 'cibench_template:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + +_cibench_template_oracle = ['cibench_template_oracle/' + i for i in _cibench_template_modules] +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_oracle:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_template_oracle], + 'weights': {'cibench_template_oracle/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_oracle:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template_oracle], + 'weights': {'cibench_template_oracle/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_oracle:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template_oracle], + 'weights': {'cibench_template_oracle/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_oracle:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template_oracle], + 'weights': {'cibench_template_oracle/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_oracle:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template_oracle], + 'weights': {'cibench_template_oracle/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + + +## chinese +_cibench_template_cn_modules = ['lightgbm', 'matplotlib', 'nltk', 'opencv', 'pandas', 'pytorch', + 'scipy', 'seaborn', 'sklearn', 'tensorflow'] +_cibench_template_cn = ['cibench_template_chinese/' + i for i in _cibench_template_cn_modules] +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_cn:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + +_cibench_template_cn_oracle = ['cibench_template_oracle_chinese/' + i for i in _cibench_template_cn_modules] +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_cn_oracle:tool_rate', + 'subsets': [[i, 'tool_rate'] for i in _cibench_template_cn_oracle], + 'weights': {'cibench_template_oracle_chinese/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn_oracle:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template_cn_oracle], + 'weights': {'cibench_template_oracle_chinese/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn_oracle:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template_cn_oracle], + 'weights': {'cibench_template_oracle_chinese/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn_oracle:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template_cn_oracle], + 'weights': {'cibench_template_oracle_chinese/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn_oracle:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template_cn_oracle], + 'weights': {'cibench_template_oracle_chinese/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + + +########### New summerizer for Category metric + +cibench_data_manipulation = [ + ['cibench_generation/pandas', 'numeric_correct', _cibench_generation_weight['pandas'][1]], + ['cibench_generation/pandas', 'text_score', _cibench_generation_weight['pandas'][2]], + ['cibench_generation/pandas', 'vis_sim', _cibench_generation_weight['pandas'][3]], + ['cibench_template/pandas', 'numeric_correct', _cibench_template_weight['pandas'][1]], + ['cibench_template/pandas', 'text_score', _cibench_template_weight['pandas'][2]], + ['cibench_template/pandas', 'vis_sim', _cibench_template_weight['pandas'][3]], +] +cibench_data_visualization = [ + ['cibench_generation/matplotlib', 'numeric_correct', _cibench_generation_weight['matplotlib'][1]], + ['cibench_generation/matplotlib', 'text_score', _cibench_generation_weight['matplotlib'][2]], + ['cibench_generation/matplotlib', 'vis_sim', _cibench_generation_weight['matplotlib'][3]], + ['cibench_generation/seaborn', 'numeric_correct', _cibench_generation_weight['seaborn'][1]], + ['cibench_generation/seaborn', 'text_score', _cibench_generation_weight['seaborn'][2]], + ['cibench_generation/seaborn', 'vis_sim', _cibench_generation_weight['seaborn'][3]], + ['cibench_template/matplotlib', 'numeric_correct', _cibench_template_weight['matplotlib'][1]], + ['cibench_template/matplotlib', 'text_score', _cibench_template_weight['matplotlib'][2]], + ['cibench_template/matplotlib', 'vis_sim', _cibench_template_weight['matplotlib'][3]], + ['cibench_template/seaborn', 'numeric_correct', _cibench_template_weight['seaborn'][1]], + ['cibench_template/seaborn', 'text_score', _cibench_template_weight['seaborn'][2]], + ['cibench_template/seaborn', 'vis_sim', _cibench_template_weight['seaborn'][3]], +] +cibench_modeling = [ + ['cibench_generation/pytorch', 'numeric_correct', _cibench_generation_weight['pytorch'][1]], + ['cibench_generation/pytorch', 'text_score', _cibench_generation_weight['pytorch'][2]], + ['cibench_generation/pytorch', 'vis_sim', _cibench_generation_weight['pytorch'][3]], + ['cibench_template/pytorch', 'numeric_correct', _cibench_template_weight['pytorch'][1]], + ['cibench_template/pytorch', 'text_score', _cibench_template_weight['pytorch'][2]], + ['cibench_template/pytorch', 'vis_sim', _cibench_template_weight['pytorch'][3]], + ['cibench_template/sklearn', 'numeric_correct', _cibench_template_weight['sklearn'][1]], + ['cibench_template/sklearn', 'text_score', _cibench_template_weight['sklearn'][2]], + ['cibench_template/sklearn', 'vis_sim', _cibench_template_weight['sklearn'][3]], + ['cibench_template/tensorflow', 'numeric_correct', _cibench_template_weight['tensorflow'][1]], + ['cibench_template/tensorflow', 'text_score', _cibench_template_weight['tensorflow'][2]], + ['cibench_template/tensorflow', 'vis_sim', _cibench_template_weight['tensorflow'][3]], + ['cibench_template/lightgbm', 'numeric_correct', _cibench_template_weight['lightgbm'][1]], + ['cibench_template/lightgbm', 'text_score', _cibench_template_weight['lightgbm'][2]], + ['cibench_template/lightgbm', 'vis_sim', _cibench_template_weight['lightgbm'][3]], +] +cibench_nlp = [ + ['cibench_template/nltk', 'numeric_correct', _cibench_template_weight['nltk'][1]], + ['cibench_template/nltk', 'text_score', _cibench_template_weight['nltk'][2]], + ['cibench_template/nltk', 'vis_sim', _cibench_template_weight['nltk'][3]], +] +cibench_ip = [ + ['cibench_generation/opencv', 'numeric_correct', _cibench_generation_weight['opencv'][1]], + ['cibench_generation/opencv', 'text_score', _cibench_generation_weight['opencv'][2]], + ['cibench_generation/opencv', 'vis_sim', _cibench_generation_weight['opencv'][3]], + ['cibench_template/opencv', 'numeric_correct', _cibench_template_weight['opencv'][1]], + ['cibench_template/opencv', 'text_score', _cibench_template_weight['opencv'][2]], + ['cibench_template/opencv', 'vis_sim', _cibench_template_weight['opencv'][3]], +] +cibench_math = [ + ['cibench_generation/scipy', 'numeric_correct', _cibench_generation_weight['scipy'][1]], + ['cibench_generation/scipy', 'text_score', _cibench_generation_weight['scipy'][2]], + ['cibench_generation/scipy', 'vis_sim', _cibench_generation_weight['scipy'][3]], + ['cibench_template/scipy', 'numeric_correct', _cibench_template_weight['scipy'][1]], + ['cibench_template/scipy', 'text_score', _cibench_template_weight['scipy'][2]], + ['cibench_template/scipy', 'vis_sim', _cibench_template_weight['scipy'][3]], +] +cibench_summary_groups.extend([ + { + 'name': 'cibench_data_manipulation:scores', + 'subsets': [i[:2] for i in cibench_data_manipulation], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_data_manipulation}, + }, + { + 'name': 'cibench_data_visualization:scores', + 'subsets': [i[:2] for i in cibench_data_visualization], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_data_visualization}, + }, + { + 'name': 'cibench_modeling:scores', + 'subsets': [i[:2] for i in cibench_modeling], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_modeling}, + }, + { + 'name': 'cibench_nlp:scores', + 'subsets': [i[:2] for i in cibench_nlp], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_nlp}, + }, + { + 'name': 'cibench_ip:scores', + 'subsets': [i[:2] for i in cibench_ip], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_ip}, + }, + { + 'name': 'cibench_math:scores', + 'subsets': [i[:2] for i in cibench_math], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_math}, + }, +]) + + +########### New summerizer for Category metric oracle + +cibench_data_manipulation = [ + ['cibench_generation_oracle/pandas', 'numeric_correct', _cibench_generation_weight['pandas'][1]], + ['cibench_generation_oracle/pandas', 'text_score', _cibench_generation_weight['pandas'][2]], + ['cibench_generation_oracle/pandas', 'vis_sim', _cibench_generation_weight['pandas'][3]], + ['cibench_template_oracle/pandas', 'numeric_correct', _cibench_template_weight['pandas'][1]], + ['cibench_template_oracle/pandas', 'text_score', _cibench_template_weight['pandas'][2]], + ['cibench_template_oracle/pandas', 'vis_sim', _cibench_template_weight['pandas'][3]], +] +cibench_data_visualization = [ + ['cibench_generation_oracle/matplotlib', 'numeric_correct', _cibench_generation_weight['matplotlib'][1]], + ['cibench_generation_oracle/matplotlib', 'text_score', _cibench_generation_weight['matplotlib'][2]], + ['cibench_generation_oracle/matplotlib', 'vis_sim', _cibench_generation_weight['matplotlib'][3]], + ['cibench_generation_oracle/seaborn', 'numeric_correct', _cibench_generation_weight['seaborn'][1]], + ['cibench_generation_oracle/seaborn', 'text_score', _cibench_generation_weight['seaborn'][2]], + ['cibench_generation_oracle/seaborn', 'vis_sim', _cibench_generation_weight['seaborn'][3]], + ['cibench_template_oracle/matplotlib', 'numeric_correct', _cibench_template_weight['matplotlib'][1]], + ['cibench_template_oracle/matplotlib', 'text_score', _cibench_template_weight['matplotlib'][2]], + ['cibench_template_oracle/matplotlib', 'vis_sim', _cibench_template_weight['matplotlib'][3]], + ['cibench_template_oracle/seaborn', 'numeric_correct', _cibench_template_weight['seaborn'][1]], + ['cibench_template_oracle/seaborn', 'text_score', _cibench_template_weight['seaborn'][2]], + ['cibench_template_oracle/seaborn', 'vis_sim', _cibench_template_weight['seaborn'][3]], +] +cibench_modeling = [ + ['cibench_generation_oracle/pytorch', 'numeric_correct', _cibench_generation_weight['pytorch'][1]], + ['cibench_generation_oracle/pytorch', 'text_score', _cibench_generation_weight['pytorch'][2]], + ['cibench_generation_oracle/pytorch', 'vis_sim', _cibench_generation_weight['pytorch'][3]], + ['cibench_template_oracle/pytorch', 'numeric_correct', _cibench_template_weight['pytorch'][1]], + ['cibench_template_oracle/pytorch', 'text_score', _cibench_template_weight['pytorch'][2]], + ['cibench_template_oracle/pytorch', 'vis_sim', _cibench_template_weight['pytorch'][3]], + ['cibench_template_oracle/sklearn', 'numeric_correct', _cibench_template_weight['sklearn'][1]], + ['cibench_template_oracle/sklearn', 'text_score', _cibench_template_weight['sklearn'][2]], + ['cibench_template_oracle/sklearn', 'vis_sim', _cibench_template_weight['sklearn'][3]], + ['cibench_template_oracle/tensorflow', 'numeric_correct', _cibench_template_weight['tensorflow'][1]], + ['cibench_template_oracle/tensorflow', 'text_score', _cibench_template_weight['tensorflow'][2]], + ['cibench_template_oracle/tensorflow', 'vis_sim', _cibench_template_weight['tensorflow'][3]], + ['cibench_template_oracle/lightgbm', 'numeric_correct', _cibench_template_weight['lightgbm'][1]], + ['cibench_template_oracle/lightgbm', 'text_score', _cibench_template_weight['lightgbm'][2]], + ['cibench_template_oracle/lightgbm', 'vis_sim', _cibench_template_weight['lightgbm'][3]], +] +cibench_nlp = [ + ['cibench_template_oracle/nltk', 'numeric_correct', _cibench_template_weight['nltk'][1]], + ['cibench_template_oracle/nltk', 'text_score', _cibench_template_weight['nltk'][2]], + ['cibench_template_oracle/nltk', 'vis_sim', _cibench_template_weight['nltk'][3]], +] +cibench_ip = [ + ['cibench_generation_oracle/opencv', 'numeric_correct', _cibench_generation_weight['opencv'][1]], + ['cibench_generation_oracle/opencv', 'text_score', _cibench_generation_weight['opencv'][2]], + ['cibench_generation_oracle/opencv', 'vis_sim', _cibench_generation_weight['opencv'][3]], + ['cibench_template_oracle/opencv', 'numeric_correct', _cibench_template_weight['opencv'][1]], + ['cibench_template_oracle/opencv', 'text_score', _cibench_template_weight['opencv'][2]], + ['cibench_template_oracle/opencv', 'vis_sim', _cibench_template_weight['opencv'][3]], +] +cibench_math = [ + ['cibench_generation_oracle/scipy', 'numeric_correct', _cibench_generation_weight['scipy'][1]], + ['cibench_generation_oracle/scipy', 'text_score', _cibench_generation_weight['scipy'][2]], + ['cibench_generation_oracle/scipy', 'vis_sim', _cibench_generation_weight['scipy'][3]], + ['cibench_template_oracle/scipy', 'numeric_correct', _cibench_template_weight['scipy'][1]], + ['cibench_template_oracle/scipy', 'text_score', _cibench_template_weight['scipy'][2]], + ['cibench_template_oracle/scipy', 'vis_sim', _cibench_template_weight['scipy'][3]], +] +cibench_summary_groups.extend([ + { + 'name': 'cibench_data_manipulation_oracle:scores', + 'subsets': [i[:2] for i in cibench_data_manipulation], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_data_manipulation}, + }, + { + 'name': 'cibench_data_visualization_oracle:scores', + 'subsets': [i[:2] for i in cibench_data_visualization], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_data_visualization}, + }, + { + 'name': 'cibench_modeling_oracle:scores', + 'subsets': [i[:2] for i in cibench_modeling], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_modeling}, + }, + { + 'name': 'cibench_nlp_oracle:scores', + 'subsets': [i[:2] for i in cibench_nlp], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_nlp}, + }, + { + 'name': 'cibench_ip_oracle:scores', + 'subsets': [i[:2] for i in cibench_ip], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_ip}, + }, + { + 'name': 'cibench_math_oracle:scores', + 'subsets': [i[:2] for i in cibench_math], + 'weights': {f'{k[0]}@{k[1]}': k[-1] for k in cibench_math}, + }, +]) diff --git a/opencompass/configs/summarizers/groups/cmmlu.py b/opencompass/configs/summarizers/groups/cmmlu.py new file mode 100644 index 0000000000000000000000000000000000000000..90d69f0bc721643ed139a869e82a5b0e75435c6c --- /dev/null +++ b/opencompass/configs/summarizers/groups/cmmlu.py @@ -0,0 +1,104 @@ +subcategories = { + 'agronomy': ['other'], + 'anatomy': ['biology'], + 'ancient_chinese': ['linguistics','china specific'], + 'arts': ['arts'], + 'astronomy': ['physics'], + 'business_ethics': ['business'], + 'chinese_civil_service_exam': ['politics','china specific'], + 'chinese_driving_rule': ['other','china specific'], + 'chinese_food_culture': ['culture','china specific'], + 'chinese_foreign_policy': ['politics','china specific'], + 'chinese_history':['history','china specific'], + 'chinese_literature': ['literature','china specific'], + 'chinese_teacher_qualification': ['education','china specific'], + 'college_actuarial_science':['math'], + 'college_education':['education'], + 'college_engineering_hydrology': ['engineering'], + 'college_law': ['law'], + 'college_mathematics': ['math'], + 'college_medical_statistics':['statistics'], + 'clinical_knowledge': ['other'], + 'college_medicine': ['other'], + 'computer_science': ['computer science'], + 'computer_security': ['other'], + 'conceptual_physics': ['physics'], + 'construction_project_management': ['other','china specific'], + 'economics': ['economics'], + 'education': ['education'], + 'elementary_chinese':['linguistics','china specific'], + 'elementary_commonsense':['other','china specific'], + 'elementary_information_and_technology': ['other'], + 'electrical_engineering': ['engineering'], + 'elementary_mathematics': ['math'], + 'ethnology': ['culture','china specific'], + 'food_science': ['other'], + 'genetics': ['biology'], + 'global_facts': ['global'], + 'high_school_biology': ['biology'], + 'high_school_chemistry': ['chemistry'], + 'high_school_geography': ['geography'], + 'high_school_mathematics': ['math'], + 'high_school_physics': ['physics'], + 'high_school_politics': ['politics','china specific'], + 'human_sexuality': ['other'], + 'international_law': ['law'], + 'journalism': ['sociology'], + 'jurisprudence': ['law'], + 'legal_and_moral_basis': ['other'], + 'logical': ['philosophy'], + 'machine_learning': ['computer science'], + 'management': ['business'], + 'marketing': ['business'], + 'marxist_theory': ['philosophy'], + 'modern_chinese': ['linguistics','china specific'], + 'nutrition': ['other'], + 'philosophy': ['philosophy'], + 'professional_accounting': ['business'], + 'professional_law': ['law'], + 'professional_medicine': ['other'], + 'professional_psychology': ['psychology'], + 'public_relations': ['politics'], + 'security_study': ['politics'], + 'sociology': ['culture'], + 'sports_science': ['other'], + 'traditional_chinese_medicine': ['other','china specific'], + 'virology': ['biology'], + 'world_history':['history'], + 'world_religions': ['global'], +} + +categories = { + 'STEM': ['physics', 'chemistry', 'biology', 'computer science', 'math', 'engineering', 'statistics'], + 'Humanities': ['history', 'philosophy', 'law', 'arts', 'literature', 'global'], + 'Social Science': ['linguistics','business', 'politics', 'culture', 'economics', 'geography', 'psychology', 'education', 'sociology'], + 'Other':['other'], + 'China specific': ['china specific'], +} + +category2subject = {} +for k, v in categories.items(): + for subject, subcat in subcategories.items(): + for c in subcat: + if c in v: + category2subject.setdefault(k, []).append(subject) + +cmmlu_summary_groups = [] + +_cmmlu_humanities = ['cmmlu-' + s for s in category2subject['Humanities']] +cmmlu_summary_groups.append({'name': 'cmmlu-humanities', 'subsets': _cmmlu_humanities}) + +_cmmlu_stem = ['cmmlu-' + s for s in category2subject['STEM']] +cmmlu_summary_groups.append({'name': 'cmmlu-stem', 'subsets': _cmmlu_stem}) + +_cmmlu_social_science = ['cmmlu-' + s for s in category2subject['Social Science']] +cmmlu_summary_groups.append({'name': 'cmmlu-social-science', 'subsets': _cmmlu_social_science}) + +_cmmlu_other = ['cmmlu-' + s for s in category2subject['Other']] +cmmlu_summary_groups.append({'name': 'cmmlu-other', 'subsets': _cmmlu_other}) + +_cmmlu_china_specific = ['cmmlu-' + s for s in category2subject['China specific']] +cmmlu_summary_groups.append({'name': 'cmmlu-china-specific', 'subsets': _cmmlu_china_specific}) + +_cmmlu_all = ['cmmlu-' + s for s in subcategories.keys()] +cmmlu_summary_groups.append({'name': 'cmmlu', 'subsets': _cmmlu_all}) diff --git a/opencompass/configs/summarizers/groups/ds1000.py b/opencompass/configs/summarizers/groups/ds1000.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde89814a181d03b56bcafcd505ce9335ee292d --- /dev/null +++ b/opencompass/configs/summarizers/groups/ds1000.py @@ -0,0 +1,5 @@ +ds1000_summary_groups = [] + +_ds1000_all = ['Pandas', 'Numpy', 'Tensorflow', 'Scipy', 'Sklearn', 'Pytorch', 'Matplotlib'] +_ds1000_all = ['ds1000_' + d for d in _ds1000_all] +ds1000_summary_groups.append({'name': 'ds1000', 'subsets': _ds1000_all}) diff --git a/opencompass/configs/summarizers/groups/flores.py b/opencompass/configs/summarizers/groups/flores.py new file mode 100644 index 0000000000000000000000000000000000000000..79baed790d1426306e5d20f96b7fd37478a3eb10 --- /dev/null +++ b/opencompass/configs/summarizers/groups/flores.py @@ -0,0 +1,31 @@ +flores_summary_groups = [] + +_flores_lang_map = { + 'Indo-European-Germanic': ['afr', 'dan', 'deu', 'isl', 'ltz', 'nld', 'nob', 'swe'], + 'Indo-European-Romance': ['ast', 'cat', 'fra', 'glg', 'oci', 'por', 'ron', 'spa'], + 'Indo-European-Slavic': ['bel', 'bos', 'bul', 'ces', 'hrv', 'mkd', 'pol', 'rus', 'slk', 'slv', 'srp', 'ukr'], + 'Indo-European-Indo-Aryan': ['asm', 'ben', 'guj', 'hin', 'mar', 'npi', 'ory', 'pan', 'snd', 'urd'], + 'Indo-European-Other': ['ckb', 'cym', 'ell', 'fas', 'gle', 'hye', 'ita', 'lav', 'lit', 'pus', 'tgk'], + 'Austronesian': ['ceb', 'ind', 'jav', 'mri', 'msa', 'tgl'], + 'Atlantic-Congo': ['ibo', 'kam', 'kea', 'lin', 'lug', 'nso', 'nya', 'sna', 'swh', 'umb', 'wol', 'xho', 'yor', 'zul'], + 'Afro-Asiatic': ['amh', 'ara', 'ful', 'mlt', 'orm', 'som'], + 'Turkic': ['azj', 'kaz', 'kir', 'tur', 'uzb'], + 'Dravidian': ['kan', 'mal', 'tam', 'tel'], + 'Sino-Tibetan': ['mya', 'zho_simpl', 'zho_trad'], + 'Other': ['est', 'fin', 'hau', 'heb', 'hun', 'jpn', 'kat', 'khm', 'kor', 'lao', 'luo', 'mon', 'tha', 'vie'], +} +for _lang_serie in _flores_lang_map: + flores_summary_groups.append({ + 'name': f'flores_100_{_lang_serie}_English', + 'subsets': [f'flores_100_{lang_name}-eng' for lang_name in _flores_lang_map[_lang_serie]] + }) + flores_summary_groups.append({ + 'name': f'flores_100_English_{_lang_serie}', + 'subsets': [f'flores_100_eng-{lang_name}' for lang_name in _flores_lang_map[_lang_serie]] + }) + +flores_summary_groups.append({ + 'name': 'flores_100', + 'subsets': [f'flores_100_{lang_name}-eng' for lang_name in sum(_flores_lang_map.values(), [])] + \ + [f'flores_100_eng-{lang_name}' for lang_name in sum(_flores_lang_map.values(), [])] +}) diff --git a/opencompass/configs/summarizers/groups/humanevalx.py b/opencompass/configs/summarizers/groups/humanevalx.py new file mode 100644 index 0000000000000000000000000000000000000000..e4c008be5674695395c3daef25315587272c1712 --- /dev/null +++ b/opencompass/configs/summarizers/groups/humanevalx.py @@ -0,0 +1,5 @@ +humanevalx_summary_groups = [] + +_humanevalx_all = ['python', 'cpp', 'go', 'java', 'js'] +_humanevalx_all = ['humanevalx-' + d for d in _humanevalx_all] +humanevalx_summary_groups.append({'name': 'humanevalx', 'subsets': _humanevalx_all}) diff --git a/opencompass/configs/summarizers/groups/infinitebench.py b/opencompass/configs/summarizers/groups/infinitebench.py new file mode 100644 index 0000000000000000000000000000000000000000..7e19b87797c8339b376ef4e80526217eb01091b6 --- /dev/null +++ b/opencompass/configs/summarizers/groups/infinitebench.py @@ -0,0 +1,5 @@ +infinitebench_summary_groups = [] + +_infinitebench = ['codedebug', 'coderun', 'endia', 'enmc', 'enqa', 'ensum', 'mathcalc', 'mathfind', 'retrievekv', 'retrievenumber', 'retrievepasskey', 'zhqa'] +_infinitebench = ['InfiniteBench_' + s for s in _infinitebench] +infinitebench_summary_groups.append({'name': 'InfiniteBench', 'subsets': _infinitebench}) diff --git a/opencompass/configs/summarizers/groups/jigsaw_multilingual.py b/opencompass/configs/summarizers/groups/jigsaw_multilingual.py new file mode 100644 index 0000000000000000000000000000000000000000..6a7fc504d5dbf209cf69271be559a9a66b85ff20 --- /dev/null +++ b/opencompass/configs/summarizers/groups/jigsaw_multilingual.py @@ -0,0 +1,6 @@ +jigsaw_multilingual_summary_groups = [] + +# bbh +_jigsaw_multilingual = ['es', 'fr', 'it', 'pt', 'ru', 'tr'] +_jigsaw_multilingual = ['jigsaw_multilingual_' + s for s in _jigsaw_multilingual] +jigsaw_multilingual_summary_groups.append({'name': 'jigsaw_multilingual', 'subsets': _jigsaw_multilingual}) diff --git a/opencompass/configs/summarizers/groups/korbench.py b/opencompass/configs/summarizers/groups/korbench.py new file mode 100644 index 0000000000000000000000000000000000000000..101fd65dc6ae8eff185c3a3de91391f830717bff --- /dev/null +++ b/opencompass/configs/summarizers/groups/korbench.py @@ -0,0 +1,5 @@ +korbench_summary_groups = [] +categories = ['cipher', 'counterfactual', 'logic', 'operation', 'puzzle'] +mixed_categories = ['Multi-Q', 'Multi-R', 'Multi-RQ'] +korbench_summary_groups.append({'name': 'korbench_single', 'subsets': [f'korbench_{c}' for c in categories]}) +korbench_summary_groups.append({'name': 'korbench_mixed', 'subsets': [f'korbench_{c}' for c in mixed_categories]}) diff --git a/opencompass/configs/summarizers/groups/lawbench.py b/opencompass/configs/summarizers/groups/lawbench.py new file mode 100644 index 0000000000000000000000000000000000000000..217c9a3696fa8c76b6d39b8313f6437a46528718 --- /dev/null +++ b/opencompass/configs/summarizers/groups/lawbench.py @@ -0,0 +1,29 @@ +names = [ + ['1-1', 'article_recitation'], + ['1-2', 'knowledge_question_answering'], + ['2-1', 'document_proofreading'], + ['2-2', 'dispute_focus_identification'], + ['2-3', 'marital_disputes_identification'], + ['2-4', 'issue_topic_identification'], + ['2-5', 'reading_comprehension'], + ['2-6', 'named_entity_recognition'], + ['2-7', 'opinion_summarization'], + ['2-8', 'argument_mining'], + ['2-9', 'event_detection'], + ['2-10', 'trigger_word_extraction'], + ['3-1', 'fact_based_article_prediction'], + ['3-2', 'scene_based_article_prediction'], + ['3-3', 'charge_prediction'], + ['3-4', 'prison_term_prediction_wo_article'], + ['3-5', 'prison_term_prediction_w_article'], + ['3-6', 'case_analysis'], + ['3-7', 'criminal_damages_calculation'], + ['3-8', 'consultation'], +] + +lawbench_summary_groups = [] + +_lawbench_0_shot = ['lawbench-' + index + '-' + name + '-0-shot' for index, name in names] +lawbench_summary_groups.append({'name': 'lawbench-0-shot', 'subsets': _lawbench_0_shot}) +_lawbench_1_shot = ['lawbench-' + index + '-' + name + '-1-shot' for index, name in names] +lawbench_summary_groups.append({'name': 'lawbench-1-shot', 'subsets': _lawbench_1_shot}) diff --git a/opencompass/configs/summarizers/groups/lcbench.py b/opencompass/configs/summarizers/groups/lcbench.py new file mode 100644 index 0000000000000000000000000000000000000000..bfdc17e2704162898c9f48f7be4d5be489f963b8 --- /dev/null +++ b/opencompass/configs/summarizers/groups/lcbench.py @@ -0,0 +1,3 @@ +lcbench_summary_groups = [ + {'name': 'lcbench', 'subsets': ['lcbench_en', 'lcbench_cn']}, +] diff --git a/opencompass/configs/summarizers/groups/legacy/cibench.py b/opencompass/configs/summarizers/groups/legacy/cibench.py new file mode 100644 index 0000000000000000000000000000000000000000..bc2ab94c27886c966318cbb044669227865e9dc1 --- /dev/null +++ b/opencompass/configs/summarizers/groups/legacy/cibench.py @@ -0,0 +1,109 @@ + +_cibench = ['Pandas', 'Matplotlib', 'Opencv', 'SciPy', 'Seaborn', 'PyTorch'] +_cibench = ['cibench_' + i for i in _cibench] +cibench_summary_groups = [{'name': 'cibench', 'subsets': _cibench}] + +_cibench_template = ['lightgbm', 'matplotlib', 'nltk', 'opencv', 'pandas', 'pytorch', + 'scipy', 'seaborn', 'sklearn', 'tensorflow'] +_cibench_template = ['cibench_template/' + i for i in _cibench_template] +# number of total exec questions in this module +_cibench_template_weight = { + 'lightgbm': [30, 15, 0, 0], + 'matplotlib': [42, 0, 0, 36], + 'nltk': [70, 30, 20, 10], + 'opencv': [60, 10, 0, 40], + 'pandas': [60, 40, 0, 10], + 'pytorch': [28, 0, 0, 0], + 'scipy': [60, 40, 0, 0], + 'seaborn': [42, 0, 0, 35], + 'sklearn': [42, 6, 0, 18], + 'tensorflow': [36, 6, 0, 12], +} +cibench_summary_groups.extend([ + { + 'name': 'cibench_template:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template], + 'weights': {'cibench_template/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + + +## chinese +_cibench_template_cn = ['lightgbm', 'matplotlib', 'nltk', 'opencv', 'pandas', 'pytorch', + 'scipy', 'seaborn', 'sklearn', 'tensorflow'] +_cibench_template_cn = ['cibench_template_chinese/' + i for i in _cibench_template_cn] +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_cn:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[0] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[1] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:text_score', + 'subsets': [[i, 'text_score'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[2] for k,v in _cibench_template_weight.items()}, + }, + { + 'name': 'cibench_template_cn:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template_cn], + 'weights': {'cibench_template_chinese/' + k : v[3] for k,v in _cibench_template_weight.items()}, + }, +]) + + +## add more without nltk +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_wo_nltk:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template if 'nltk' not in i], + 'weights': {'cibench_template/' + k : v[0] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, + { + 'name': 'cibench_template_wo_nltk:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template if 'nltk' not in i], + 'weights': {'cibench_template/' + k : v[1] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, + { + 'name': 'cibench_template_wo_nltk:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template if 'nltk' not in i], + 'weights': {'cibench_template/' + k : v[3] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, +]) + +cibench_summary_groups.extend([ + { + 'name': 'cibench_template_cn_wo_nltk:executable', + 'subsets': [[i, 'executable'] for i in _cibench_template_cn if 'nltk' not in i], + 'weights': {'cibench_template_chinese/' + k : v[0] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, + { + 'name': 'cibench_template_cn_wo_nltk:numeric_correct', + 'subsets': [[i, 'numeric_correct'] for i in _cibench_template_cn if 'nltk' not in i], + 'weights': {'cibench_template_chinese/' + k : v[1] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, + { + 'name': 'cibench_template_cn_wo_nltk:vis_sim', + 'subsets': [[i, 'vis_sim'] for i in _cibench_template_cn if 'nltk' not in i], + 'weights': {'cibench_template_chinese/' + k : v[3] for k,v in _cibench_template_weight.items() if 'nltk' not in k}, + }, +]) diff --git a/opencompass/configs/summarizers/groups/leval.py b/opencompass/configs/summarizers/groups/leval.py new file mode 100644 index 0000000000000000000000000000000000000000..e39974c802c4a4367122789001233bd8258e4918 --- /dev/null +++ b/opencompass/configs/summarizers/groups/leval.py @@ -0,0 +1,3 @@ +leval_summary_groups = [ + {'name': 'leval', 'subsets': ['LEval_coursera', 'LEval_gsm100', 'LEval_quality', 'LEval_tpo', 'LEval_topic_retrieval', 'LEval_financialqa', 'LEval_gov_report_summ', 'LEval_legal_contract_qa', 'LEval_meeting_summ', 'LEval_multidocqa', 'LEval_narrativeqa', 'LEval_nq', 'LEval_news_summ', 'LEval_paper_assistant', 'LEval_patent_summ', 'LEval_review_summ', 'LEval_scientificqa', 'LEval_tvshow_summ']}, +] diff --git a/opencompass/configs/summarizers/groups/longbench.py b/opencompass/configs/summarizers/groups/longbench.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a129f5dcfeb223d0a32d3d8598cd90706bc399 --- /dev/null +++ b/opencompass/configs/summarizers/groups/longbench.py @@ -0,0 +1,22 @@ +longbench_summary_groups = [ + {'name': 'longbench_single-document-qa', 'subsets': ['LongBench_narrativeqa', 'LongBench_qasper', 'LongBench_multifieldqa_en', 'LongBench_multifieldqa_zh']}, + {'name': 'longbench_multi-document-qa', 'subsets': ['LongBench_hotpotqa', 'LongBench_2wikimqa', 'LongBench_musique', 'LongBench_dureader']}, + {'name': 'longbench_summarization', 'subsets': ['LongBench_gov_report', 'LongBench_qmsum', 'LongBench_multi_news', 'LongBench_vcsum']}, + {'name': 'longbench_few-shot-learning', 'subsets': ['LongBench_trec', 'LongBench_triviaqa', 'LongBench_samsum', 'LongBench_lsht']}, + {'name': 'longbench_synthetic-tasks', 'subsets': ['LongBench_passage_count', 'LongBench_passage_retrieval_en', 'LongBench_passage_retrieval_zh']}, + {'name': 'longbench_code-completion', 'subsets': ['LongBench_lcc', 'LongBench_repobench-p']}, + + # code tasks are included in both longbench_zh and longbench_en + {'name': 'longbench_zh', 'subsets': ['LongBench_multifieldqa_zh', 'LongBench_dureader', 'LongBench_vcsum', + 'LongBench_lsht', 'LongBench_passage_retrieval_zh', + 'LongBench_lcc', 'LongBench_repobench-p']}, + {'name': 'longbench_en', 'subsets': [ + 'LongBench_narrativeqa', 'LongBench_qasper', 'LongBench_multifieldqa_en', + 'LongBench_hotpotqa', 'LongBench_2wikimqa', 'LongBench_musique', + 'LongBench_gov_report', 'LongBench_qmsum', 'LongBench_multi_news', + 'LongBench_trec', 'LongBench_triviaqa', 'LongBench_samsum', + 'LongBench_passage_count', 'LongBench_passage_retrieval_en', + 'LongBench_lcc', 'LongBench_repobench-p' + ]}, + {'name': 'longbench', 'subsets': ['longbench_single-document-qa', 'longbench_multi-document-qa', 'longbench_summarization', 'longbench_few-shot-learning', 'longbench_synthetic-tasks', 'longbench_code-completion']}, +] diff --git a/opencompass/configs/summarizers/groups/lveval.py b/opencompass/configs/summarizers/groups/lveval.py new file mode 100644 index 0000000000000000000000000000000000000000..01406cccacde099a7b424ca6bf15cd7d62ff119d --- /dev/null +++ b/opencompass/configs/summarizers/groups/lveval.py @@ -0,0 +1,110 @@ +len_levels = ['16k', '32k', '64k', '128k', '256k'] + +subsets_lveval_loogle_SD_mixup = [ + 'LVEval_loogle_SD_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_cmrc_mixup = [ + 'LVEval_cmrc_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_multifieldqa_en_mixup = [ + 'LVEval_multifieldqa_en_mixup' + '_' + len_level + for len_level in len_levels +] +subsets_lveval_multifieldqa_zh_mixup = [ + 'LVEval_multifieldqa_zh_mixup' + '_' + len_level + for len_level in len_levels +] +subsets_lveval_dureader_mixup = [ + 'LVEval_dureader_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_loogle_CR_mixup = [ + 'LVEval_loogle_CR_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_loogle_MIR_mixup = [ + 'LVEval_loogle_MIR_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_hotpotwikiqa_mixup = [ + 'LVEval_hotpotwikiqa_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_lic_mixup = [ + 'LVEval_lic_mixup' + '_' + len_level for len_level in len_levels +] +subsets_lveval_factrecall_en = [ + 'LVEval_factrecall_en' + '_' + len_level for len_level in len_levels +] +subsets_lveval_factrecall_zh = [ + 'LVEval_factrecall_zh' + '_' + len_level for len_level in len_levels +] + +subsets_lveval_single_hop_qa = ( + subsets_lveval_loogle_SD_mixup + subsets_lveval_cmrc_mixup +) +subsets_lveval_single_hop_cqa = ( + subsets_lveval_multifieldqa_en_mixup + subsets_lveval_multifieldqa_zh_mixup +) +subsets_lveval_multi_hop_qa = ( + subsets_lveval_dureader_mixup + + subsets_lveval_loogle_CR_mixup + + subsets_lveval_loogle_MIR_mixup +) +subsets_lveval_multi_hop_cqa = ( + subsets_lveval_hotpotwikiqa_mixup + subsets_lveval_lic_mixup +) +subsets_lveval_factrecall_cqa = ( + subsets_lveval_factrecall_en + subsets_lveval_factrecall_zh +) + +subsets_lveval_qa = ( + subsets_lveval_single_hop_qa + + subsets_lveval_single_hop_cqa + + subsets_lveval_multi_hop_qa + + subsets_lveval_multi_hop_cqa + + subsets_lveval_factrecall_cqa +) + +lveval_summary_groups = [ + { + 'name': 'LVEval_loogle_SD_mixup', + 'subsets': subsets_lveval_loogle_SD_mixup, + }, + {'name': 'LVEval_cmrc_mixup', 'subsets': subsets_lveval_cmrc_mixup}, + { + 'name': 'LVEval_multifieldqa_en_mixup', + 'subsets': subsets_lveval_multifieldqa_en_mixup, + }, + { + 'name': 'LVEval_multifieldqa_zh_mixup', + 'subsets': subsets_lveval_multifieldqa_zh_mixup, + }, + { + 'name': 'LVEval_dureader_mixup', + 'subsets': subsets_lveval_dureader_mixup, + }, + { + 'name': 'LVEval_loogle_CR_mixup', + 'subsets': subsets_lveval_loogle_CR_mixup, + }, + { + 'name': 'LVEval_loogle_MIR_mixup', + 'subsets': subsets_lveval_loogle_MIR_mixup, + }, + { + 'name': 'LVEval_hotpotwikiqa_mixup', + 'subsets': subsets_lveval_hotpotwikiqa_mixup, + }, + {'name': 'LVEval_lic_mixup', 'subsets': subsets_lveval_lic_mixup}, + {'name': 'LVEval_factrecall_en', 'subsets': subsets_lveval_factrecall_en}, + {'name': 'LVEval_factrecall_zh', 'subsets': subsets_lveval_factrecall_zh}, + {'name': 'LVEval_single_hop_qa', 'subsets': subsets_lveval_single_hop_qa}, + { + 'name': 'LVEval_single_hop_cqa', + 'subsets': subsets_lveval_single_hop_cqa, + }, + {'name': 'LVEval_multi_hop_qa', 'subsets': subsets_lveval_multi_hop_qa}, + {'name': 'LVEval_multi_hop_cqa', 'subsets': subsets_lveval_multi_hop_cqa}, + { + 'name': 'LVEval_factrecall_cqa', + 'subsets': subsets_lveval_factrecall_cqa, + }, + {'name': 'LVEval_qa', 'subsets': subsets_lveval_qa}, +] diff --git a/opencompass/configs/summarizers/groups/mathbench.py b/opencompass/configs/summarizers/groups/mathbench.py new file mode 100644 index 0000000000000000000000000000000000000000..0a95ebb723f5b41bac105f92d132cb1d168c0c26 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench.py @@ -0,0 +1,88 @@ +from copy import deepcopy + +naive_mathbench_summary_groups = [ + { + 'name': 'mathbench-college', + 'subsets': [ + ['mathbench-college-single_choice_cn', 'acc_1'], + ['mathbench-college-cloze_en', 'accuracy'], + ] + }, + { + 'name': 'mathbench-high', + 'subsets': [ + ['mathbench-high-single_choice_cn', 'acc_1'], + ['mathbench-high-single_choice_en', 'acc_1'], + ] + }, + { + 'name': 'mathbench-middle', + 'subsets': [ + ['mathbench-middle-single_choice_cn', 'acc_1'], + ] + }, + { + 'name': 'mathbench-primary', + 'subsets': [ + ['mathbench-primary-cloze_cn', 'accuracy'], + ] + }, + { + 'name': 'mathbench', + 'subsets': [ + 'mathbench-college', + 'mathbench-high', + 'mathbench-middle', + 'mathbench-primary', + ], + }, + { + 'name': 'mathbench-college-circular', + 'subsets': [ + ['mathbench-college-single_choice_cn', 'perf_4'], + ] + }, + { + 'name': 'mathbench-high-circular', + 'subsets': [ + ['mathbench-high-single_choice_cn', 'perf_4'], + ['mathbench-high-single_choice_en', 'perf_4'], + ] + }, + { + 'name': 'mathbench-middle-circular', + 'subsets': [ + ['mathbench-middle-single_choice_cn', 'perf_4'], + ] + }, + { + 'name': 'mathbench-circular', + 'subsets': [ + 'mathbench-college-circular', + 'mathbench-high-circular', + 'mathbench-middle-circular', + ], + }, + { + 'name': 'mathbench-circular-and-cloze', + 'subsets': [ + 'mathbench-college-circular', + 'mathbench-high-circular', + 'mathbench-middle-circular', + 'mathbench-college-cloze_en', + 'mathbench-primary-cloze_cn', + ], + } +] + +agent_mathbench_summary_groups = [] +for item in naive_mathbench_summary_groups: + item = deepcopy(item) + item['name'] = item['name'] + '-agent' + if isinstance(item['subsets'][0], str): + item['subsets'] = [i + '-agent' for i in item['subsets']] + else: + item['subsets'] = [[i[0] + '-agent', i[1]] for i in item['subsets']] + agent_mathbench_summary_groups.append(item) + +mathbench_summary_groups = naive_mathbench_summary_groups + agent_mathbench_summary_groups diff --git a/opencompass/configs/summarizers/groups/mathbench_2024.py b/opencompass/configs/summarizers/groups/mathbench_2024.py new file mode 100644 index 0000000000000000000000000000000000000000..05a11e35f3fc1ebf809b6dc79a1515aa518ffe2c --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench_2024.py @@ -0,0 +1,26 @@ + +mathbench_2024_wocircular_summary_groups = [ + {'name': 'college', 'subsets': ['college-single_choice_cn', 'college-single_choice_en']}, + {'name': 'high', 'subsets': ['high-single_choice_cn', 'high-single_choice_en']}, + {'name': 'middle', 'subsets': ['middle-single_choice_cn', 'middle-single_choice_en']}, + {'name': 'primary', 'subsets': ['primary-cloze_cn', 'primary-cloze_en']}, + {'name': 'cn', 'subsets': ['college-single_choice_cn', 'high-single_choice_cn', 'middle-single_choice_cn', 'primary-cloze_cn']}, + {'name': 'en', 'subsets': ['college-single_choice_en', 'high-single_choice_en', 'middle-single_choice_en', 'primary-cloze_en']}, + {'name': 'a', 'subsets': ['college', 'high', 'middle', 'primary', 'arithmetic-cloze_en']}, + + {'name': 'college_knowledge', 'subsets': ['college_knowledge-single_choice_cn', 'college_knowledge-single_choice_en']}, + {'name': 'high_knowledge', 'subsets': ['high_knowledge-single_choice_cn', 'high_knowledge-single_choice_en']}, + {'name': 'middle_knowledge', 'subsets': ['middle_knowledge-single_choice_cn', 'middle_knowledge-single_choice_en']}, + {'name': 'primary_knowledge', 'subsets': ['primary_knowledge-single_choice_cn', 'primary_knowledge-single_choice_en']}, + {'name': 'knowledge-cn', 'subsets': ['college_knowledge-single_choice_cn', 'high_knowledge-single_choice_cn', 'middle_knowledge-single_choice_cn', 'primary_knowledge-single_choice_cn']}, + {'name': 'knowledge-en', 'subsets': ['college_knowledge-single_choice_en', 'high_knowledge-single_choice_en', 'middle_knowledge-single_choice_en', 'primary_knowledge-single_choice_en']}, + {'name': 't', 'subsets': ['college_knowledge', 'high_knowledge', 'middle_knowledge', 'primary_knowledge']}, + + {'name': 'overall', 'subsets': ['a', 't']}, +] + +for g in mathbench_2024_wocircular_summary_groups: + g['name'] = 'mathbench-wocircular-' + g['name'] + g['subsets'] = ['mathbench-wocircular-' + s for s in g['subsets']] + +mathbench_2024_summary_groups = mathbench_2024_wocircular_summary_groups diff --git a/opencompass/configs/summarizers/groups/mathbench_agent.py b/opencompass/configs/summarizers/groups/mathbench_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..003098b1a6b66663903e27bc232d1cd599e8f4ce --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench_agent.py @@ -0,0 +1,75 @@ + +mathbench_agent_summary_groups = [ + { + 'name': 'mathbench-college-agent', + 'subsets': [ + ['mathbench-college-single_choice_cn-agent', 'acc_1'], + ['mathbench-college-cloze_en-agent', 'accuracy'], + ] + }, + { + 'name': 'mathbench-high-agent', + 'subsets': [ + ['mathbench-high-single_choice_cn-agent', 'acc_1'], + ['mathbench-high-single_choice_en-agent', 'acc_1'], + ] + }, + { + 'name': 'mathbench-middle-agent', + 'subsets': [ + ['mathbench-middle-single_choice_cn-agent', 'acc_1'], + ] + }, + { + 'name': 'mathbench-primary-agent', + 'subsets': [ + ['mathbench-primary-cloze_cn-agent', 'accuracy'], + ] + }, + { + 'name': 'mathbench-agent', + 'subsets': [ + 'mathbench-college-agent', + 'mathbench-high-agent', + 'mathbench-middle-agent', + 'mathbench-primary-agent', + ], + }, + { + 'name': 'mathbench-college-circular-agent', + 'subsets': [ + ['mathbench-college-single_choice_cn-agent', 'perf_4'], + ] + }, + { + 'name': 'mathbench-high-circular-agent', + 'subsets': [ + ['mathbench-high-single_choice_cn-agent', 'perf_4'], + ['mathbench-high-single_choice_en-agent', 'perf_4'], + ] + }, + { + 'name': 'mathbench-middle-circular-agent', + 'subsets': [ + ['mathbench-middle-single_choice_cn-agent', 'perf_4'], + ] + }, + { + 'name': 'mathbench-circular-agent', + 'subsets': [ + 'mathbench-college-circular-agent', + 'mathbench-high-circular-agent', + 'mathbench-middle-circular-agent', + ], + }, + { + 'name': 'mathbench-circular-and-cloze-agent', + 'subsets': [ + 'mathbench-college-circular-agent', + 'mathbench-high-circular-agent', + 'mathbench-middle-circular-agent', + 'mathbench-college-cloze_en-agent', + 'mathbench-primary-cloze_cn-agent', + ], + } +] diff --git a/opencompass/configs/summarizers/groups/mathbench_v1.py b/opencompass/configs/summarizers/groups/mathbench_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..08b241ea454ff7b3feb48fa870974dcad4d1bb6e --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench_v1.py @@ -0,0 +1,13 @@ +mathbench_v1_summary_groups = [ + {'name': 'mathbench-college_application', 'subsets': ['mathbench-college-single_choice_cn', 'mathbench-college-single_choice_en']}, + {'name': 'mathbench-high_application', 'subsets': ['mathbench-high-single_choice_cn', 'mathbench-high-single_choice_en']}, + {'name': 'mathbench-middle_application', 'subsets': ['mathbench-middle-single_choice_cn', 'mathbench-middle-single_choice_en']}, + {'name': 'mathbench-primary_application', 'subsets': ['mathbench-primary-cloze_cn', 'mathbench-primary-cloze_en', 'mathbench-calculate-cloze_en'], 'weights': {'mathbench-primary-cloze_cn': 1, 'mathbench-primary-cloze_en': 1, 'mathbench-calculate-cloze_en': 2}}, + {'name': 'mathbench-college_knowledge', 'subsets': ['mathbench-college_knowledge-single_choice_cn', 'mathbench-college_knowledge-single_choice_en']}, + {'name': 'mathbench-high_knowledge', 'subsets': ['mathbench-high_knowledge-single_choice_cn', 'mathbench-high_knowledge-single_choice_en']}, + {'name': 'mathbench-middle_knowledge', 'subsets': ['mathbench-middle_knowledge-single_choice_cn', 'mathbench-middle_knowledge-single_choice_en']}, + {'name': 'mathbench-primary_knowledge', 'subsets': ['mathbench-primary_knowledge-single_choice_cn', 'mathbench-primary_knowledge-single_choice_en']}, + {'name': 'mathbench_application', 'subsets': ['mathbench-college_application', 'mathbench-high_application', 'mathbench-middle_application', 'mathbench-primary_application']}, + {'name': 'mathbench_knowledge', 'subsets': ['mathbench-college_knowledge', 'mathbench-high_knowledge', 'mathbench-middle_knowledge', 'mathbench-primary_knowledge']}, + {'name': 'mathbench', 'subsets': ['mathbench_application', 'mathbench_knowledge']}, +] diff --git a/opencompass/configs/summarizers/groups/mathbench_v1_2024.py b/opencompass/configs/summarizers/groups/mathbench_v1_2024.py new file mode 100644 index 0000000000000000000000000000000000000000..5a77acc3f79cc9dd3c3ca27756e0874103c77b61 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench_v1_2024.py @@ -0,0 +1,42 @@ + +mathbench_2024_summary_groups = [ + {'name': 'college', 'subsets': [['mathbench-college-single_choice_cn', 'perf_4'], ['mathbench-college-single_choice_en', 'perf_4']]}, + {'name': 'high', 'subsets': [['mathbench-high-single_choice_cn', 'perf_4'], ['mathbench-high-single_choice_en', 'perf_4']]}, + {'name': 'middle', 'subsets': [['mathbench-middle-single_choice_cn', 'perf_4'], ['mathbench-middle-single_choice_en', 'perf_4']]}, + {'name': 'primary', 'subsets': [['mathbench-primary-cloze_cn', 'accuracy'], ['mathbench-primary-cloze_en', 'accuracy']]}, + {'name': 'arithmetic', 'subsets': [['mathbench-arithmetic-cloze_en', 'accuracy']]}, + {'name': 'mathbench-a-cn', 'subsets': ['mathbench-college-single_choice_cn', 'mathbench-high-single_choice_cn', 'mathbench-middle-single_choice_cn', 'mathbench-primary-cloze_cn']}, + {'name': 'mathbench-a-en', 'subsets': ['mathbench-college-single_choice_en', 'mathbench-high-single_choice_en', 'mathbench-middle-single_choice_en', 'mathbench-primary-cloze_en']}, + {'name': 'mathbench-a (average)', 'subsets': ['college', 'high', 'middle', 'primary', 'arithmetic']}, + + {'name': 'college_knowledge', 'subsets': [['mathbench-college_knowledge-single_choice_cn', 'perf_4'], ['mathbench-college_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'high_knowledge', 'subsets': [['mathbench-high_knowledge-single_choice_cn', 'perf_4'], ['mathbench-high_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'middle_knowledge', 'subsets': [['mathbench-middle_knowledge-single_choice_cn', 'perf_4'], ['mathbench-middle_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'primary_knowledge', 'subsets': [['mathbench-primary_knowledge-single_choice_cn', 'perf_4'], ['mathbench-primary_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-t-cn', 'subsets': ['mathbench-college_knowledge-single_choice_cn', 'mathbench-high_knowledge-single_choice_cn', 'mathbench-middle_knowledge-single_choice_cn', 'mathbench-primary_knowledge-single_choice_cn']}, + {'name': 'mathbench-t-en', 'subsets': ['mathbench-college_knowledge-single_choice_en', 'mathbench-high_knowledge-single_choice_en', 'mathbench-middle_knowledge-single_choice_en', 'mathbench-primary_knowledge-single_choice_en']}, + {'name': 'mathbench-t (average)', 'subsets': ['college_knowledge', 'high_knowledge', 'middle_knowledge', 'primary_knowledge']}, + + {'name': 'Overall', 'subsets': ['mathbench-a (average)', 'mathbench-t (average)']}, +] + +summarizer = dict( + dataset_abbrs = [ + '###### MathBench-A: Application Part ######', + 'college', + 'high', + 'middle', + 'primary', + 'arithmetic', + 'mathbench-a (average)', + '###### MathBench-T: Theory Part ######', + 'college_knowledge', + 'high_knowledge', + 'middle_knowledge', + 'primary_knowledge', + 'mathbench-t (average)', + '###### Overall: Average between MathBench-A and MathBench-T ######', + 'Overall', + ], + summary_groups=mathbench_2024_summary_groups, +) diff --git a/opencompass/configs/summarizers/groups/mathbench_v1_2024_lang.py b/opencompass/configs/summarizers/groups/mathbench_v1_2024_lang.py new file mode 100644 index 0000000000000000000000000000000000000000..5c17c1cd05272a6cd6e96c4c34093d4668280ecf --- /dev/null +++ b/opencompass/configs/summarizers/groups/mathbench_v1_2024_lang.py @@ -0,0 +1,112 @@ +# Language specific summarizer groups for MathBench + +mathbench_2024_summary_groups = [ + # mathbench-a average with subsets + {'name': 'college', 'subsets': [['mathbench-college-single_choice_cn', 'perf_4'], ['mathbench-college-single_choice_en', 'perf_4']]}, + {'name': 'high', 'subsets': [['mathbench-high-single_choice_cn', 'perf_4'], ['mathbench-high-single_choice_en', 'perf_4']]}, + {'name': 'middle', 'subsets': [['mathbench-middle-single_choice_cn', 'perf_4'], ['mathbench-middle-single_choice_en', 'perf_4']]}, + {'name': 'primary', 'subsets': [['mathbench-primary-cloze_cn', 'accuracy'], ['mathbench-primary-cloze_en', 'accuracy']]}, + {'name': 'arithmetic', 'subsets': [['mathbench-arithmetic-cloze_en', 'accuracy']]}, + {'name': 'mathbench-a (average)', 'subsets': ['college', 'high', 'middle', 'primary', 'arithmetic']}, + + # mathbench-a language + {'name': 'mathbench-a-college-cn', 'subsets': [['mathbench-college-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-a-college-en', 'ssubsets': [['mathbench-college-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-a-high-cn', 'subsets': [['mathbench-high-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-a-high-en', 'subsets': [['mathbench-high-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-a-middle-cn', 'subsets': [['mathbench-middle-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-a-middle-en', 'subsets': [['mathbench-middle-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-a-primary-cn', 'subsets': [['mathbench-primary-cloze_cn', 'accuracy']]}, + {'name': 'mathbench-a-primary-en', 'subsets': [['mathbench-primary-cloze_en', 'accuracy']]}, + {'name': 'mathbench-a-arithmetic', 'subsets': [['mathbench-arithmetic-cloze_en', 'accuracy']]}, + {'name': 'mathbench-a-cn-average', 'subsets': ['mathbench-a-college-cn', 'mathbench-a-high-cn', 'mathbench-a-middle-cn', 'mathbench-a-primary-cn']}, + {'name': 'mathbench-a-en-average', 'subsets': ['mathbench-a-college-en', 'mathbench-a-high-en', 'mathbench-a-middle-en', 'mathbench-a-primary-en']}, + # mathbench-a average + {'name': 'mathbench-a (average)', 'subsets': ['mathbench-a-cn-average', 'mathbench-a-en-average']}, + + # mathbench-t average with subsets + {'name': 'college_knowledge', 'subsets': [['mathbench-college_knowledge-single_choice_cn', 'perf_4'], ['mathbench-college_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'high_knowledge', 'subsets': [['mathbench-high_knowledge-single_choice_cn', 'perf_4'], ['mathbench-high_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'middle_knowledge', 'subsets': [['mathbench-middle_knowledge-single_choice_cn', 'perf_4'], ['mathbench-middle_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'college_knowledge', 'subsets': [['mathbench-college_knowledge-single_choice_cn', 'perf_4'], ['mathbench-college_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'high_knowledge', 'subsets': [['mathbench-high_knowledge-single_choice_cn', 'perf_4'], ['mathbench-high_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'middle_knowledge', 'subsets': [['mathbench-middle_knowledge-single_choice_cn', 'perf_4'], ['mathbench-middle_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'primary_knowledge', 'subsets': [['mathbench-primary_knowledge-single_choice_cn', 'perf_4'], ['mathbench-primary_knowledge-single_choice_en', 'perf_4']]}, + # mathbench-t language + {'name': 'mathbench-t-college-cn', 'subsets': [['mathbench-college_knowledge-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-t-college-en', 'subsets': [['mathbench-college_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-t-high-cn', 'subsets': [['mathbench-high_knowledge-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-t-high-en', 'subsets': [['mathbench-high_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-t-middle-cn', 'subsets': [['mathbench-middle_knowledge-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-t-middle-en', 'subsets': [['mathbench-middle_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-t-primary-cn', 'subsets': [['mathbench-primary_knowledge-single_choice_cn', 'perf_4']]}, + {'name': 'mathbench-t-primary-en', 'subsets': [['mathbench-primary_knowledge-single_choice_en', 'perf_4']]}, + {'name': 'mathbench-t-cn-average', 'subsets': ['mathbench-t-college-cn', 'mathbench-t-high-cn', 'mathbench-t-middle-cn', 'mathbench-t-primary-cn']}, + {'name': 'mathbench-t-en-average', 'subsets': ['mathbench-t-college-en', 'mathbench-t-high-en', 'mathbench-t-middle-en', 'mathbench-t-primary-en']}, + # mathbench-t average + {'name': 'mathbench-t (average)', 'subsets': ['mathbench-t-cn-average', 'mathbench-t-en-average']}, + + # overall cn + {'name': 'college-cn', 'subsets': ['mathbench-a-college-cn', 'mathbench-t-college-cn']}, + {'name': 'high-cn', 'subsets': ['mathbench-a-high-cn', 'mathbench-t-high-cn']}, + {'name': 'middle-cn', 'subsets': ['mathbench-a-middle-cn', 'mathbench-t-middle-cn']}, + {'name': 'primary-cn', 'subsets': ['mathbench-a-primary-cn', 'mathbench-t-primary-cn']}, + {'name': 'cn-avarage', 'subsets': ['college-cn', 'high-cn', 'middle-cn', 'primary-cn']}, + + # overall en + {'name': 'college-en', 'subsets': ['mathbench-a-college-en', 'mathbench-t-college-en']}, + {'name': 'high-en', 'subsets': ['mathbench-a-high-en', 'mathbench-t-high-en']}, + {'name': 'middle-en', 'subsets': ['mathbench-a-middle-en', 'mathbench-t-middle-en']}, + {'name': 'primary-en', 'subsets': ['mathbench-a-primary-en', 'mathbench-t-primary-en']}, + {'name': 'en-avarage', 'subsets': ['college-en', 'high-en', 'middle-en', 'primary-en']}, + + # overall + {'name': 'Overall', 'subsets': ['mathbench-a (average)', 'mathbench-t (average)']}, +] + + +summarizer = dict( + dataset_abbrs = [ + '########################################################', + '###### MathBench-A-CN: Application Part (Chinese) ######', + 'mathbench-a-college-cn', + 'mathbench-a-high-cn', + 'mathbench-a-middle-cn', + 'mathbench-a-primary-cn', + 'mathbench-a-cn-average', + '###### MathBench-A-EN: Application Part (English) ######', + 'mathbench-a-college-en', + 'mathbench-a-high-en', + 'mathbench-a-middle-en', + 'mathbench-a-primary-en', + 'mathbench-a-en-average', + '#########################################################', + '###### MathBench-T-CN: Theory Part (Chinese) ############', + 'mathbench-t-college-cn', + 'mathbench-t-high-cn', + 'mathbench-t-middle-cn', + 'mathbench-t-primary-cn', + 'mathbench-t-cn-average', + '###### MathBench-T-EN: Theory Part (English) ############', + 'mathbench-t-college-en', + 'mathbench-t-high-en', + 'mathbench-t-middle-en', + 'mathbench-t-primary-en', + 'mathbench-t-en-average', + '#########################################################', + '###### MathBench-CN ############', + 'college-cn', + 'high-cn', + 'middle-cn', + 'primary-cn', + 'cn-avarage', + '###### MathBench-EN ############', + 'college-en', + 'high-en', + 'middle-en', + 'primary-en', + 'en-avarage', + '#########################################################', + ], + summary_groups=mathbench_2024_summary_groups, +) diff --git a/opencompass/configs/summarizers/groups/mgsm.py b/opencompass/configs/summarizers/groups/mgsm.py new file mode 100644 index 0000000000000000000000000000000000000000..207f66f5b0d618d59c607361121f0118f4615714 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mgsm.py @@ -0,0 +1,9 @@ +ALL_LANGUAGES = ['bn', 'de', 'en', 'es', 'fr', 'ja', 'ru', 'sw', 'te', 'th', 'zh'] +LATIN_LANGUAGES = ['de', 'en', 'es', 'fr', 'sw'] +NON_LATIN_LANGUAGES = ['bn', 'ja', 'ru', 'te', 'th', 'zh'] + +mgsm_summary_groups = [ + {'name': 'mgsm_latin', 'subsets': [f'mgsm_{lang}' for lang in LATIN_LANGUAGES]}, + {'name': 'mgsm_non_latin', 'subsets': [f'mgsm_{lang}' for lang in NON_LATIN_LANGUAGES]}, + {'name': 'mgsm', 'subsets': [f'mgsm_{lang}' for lang in ALL_LANGUAGES]}, +] diff --git a/opencompass/configs/summarizers/groups/mmlu.py b/opencompass/configs/summarizers/groups/mmlu.py new file mode 100644 index 0000000000000000000000000000000000000000..e581dba40542ef4417e8ab3d1cdd6dfec2f3ff27 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mmlu.py @@ -0,0 +1,23 @@ +mmlu_summary_groups = [] + +_mmlu_humanities = ['formal_logic', 'high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'international_law', 'jurisprudence', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'prehistory', 'professional_law', 'world_religions'] +_mmlu_humanities = ['lukaemon_mmlu_' + s for s in _mmlu_humanities] +mmlu_summary_groups.append({'name': 'mmlu-humanities', 'subsets': _mmlu_humanities}) + +_mmlu_stem = ['abstract_algebra', 'anatomy', 'astronomy', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_physics', 'computer_security', 'conceptual_physics', 'electrical_engineering', 'elementary_mathematics', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_mathematics', 'high_school_physics', 'high_school_statistics', 'machine_learning'] +_mmlu_stem = ['lukaemon_mmlu_' + s for s in _mmlu_stem] +mmlu_summary_groups.append({'name': 'mmlu-stem', 'subsets': _mmlu_stem}) + +_mmlu_social_science = ['econometrics', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_microeconomics', 'high_school_psychology', 'human_sexuality', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy'] +_mmlu_social_science = ['lukaemon_mmlu_' + s for s in _mmlu_social_science] +mmlu_summary_groups.append({'name': 'mmlu-social-science', 'subsets': _mmlu_social_science}) + +_mmlu_other = ['business_ethics', 'clinical_knowledge', 'college_medicine', 'global_facts', 'human_aging', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'nutrition', 'professional_accounting', 'professional_medicine', 'virology'] +_mmlu_other = ['lukaemon_mmlu_' + s for s in _mmlu_other] +mmlu_summary_groups.append({'name': 'mmlu-other', 'subsets': _mmlu_other}) + +_mmlu_all = _mmlu_humanities + _mmlu_stem + _mmlu_social_science + _mmlu_other +_mmlu_weights = {'college_biology': 144,'college_chemistry': 100,'college_computer_science': 100,'college_mathematics': 100,'college_physics': 102,'electrical_engineering': 145,'astronomy': 152,'anatomy': 135,'abstract_algebra': 100,'machine_learning': 112,'clinical_knowledge': 265,'global_facts': 100,'management': 103,'nutrition': 306,'marketing': 234,'professional_accounting': 282,'high_school_geography': 198,'international_law': 121,'moral_scenarios': 895,'computer_security': 100,'high_school_microeconomics': 238,'professional_law': 1534,'medical_genetics': 100,'professional_psychology': 612,'jurisprudence': 108,'world_religions': 171,'philosophy': 311,'virology': 166,'high_school_chemistry': 203,'public_relations': 110,'high_school_macroeconomics': 390,'human_sexuality': 131,'elementary_mathematics': 378,'high_school_physics': 151,'high_school_computer_science': 100,'high_school_european_history': 165,'business_ethics': 100,'moral_disputes': 346,'high_school_statistics': 216,'miscellaneous': 783,'formal_logic': 126,'high_school_government_and_politics': 193,'prehistory': 324,'security_studies': 245,'high_school_biology': 310,'logical_fallacies': 163,'high_school_world_history': 237,'professional_medicine': 272,'high_school_mathematics': 270,'college_medicine': 173,'high_school_us_history': 204,'sociology': 201,'econometrics': 114,'high_school_psychology': 545,'human_aging': 223,'us_foreign_policy': 100,'conceptual_physics': 235} +_mmlu_weights = {'lukaemon_mmlu_' + k : v for k,v in _mmlu_weights.items()} +mmlu_summary_groups.append({'name': 'mmlu', 'subsets': _mmlu_all}) +mmlu_summary_groups.append({'name': 'mmlu-weighted', 'subsets': _mmlu_all, 'weights': _mmlu_weights}) diff --git a/opencompass/configs/summarizers/groups/mmlu_cf.py b/opencompass/configs/summarizers/groups/mmlu_cf.py new file mode 100644 index 0000000000000000000000000000000000000000..3e0b8b25b739bd29166fd52eab68aac408ed8667 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mmlu_cf.py @@ -0,0 +1,5 @@ +categories = ['Math', 'Physics', 'Chemistry', 'Law', 'Engineering', 'Other', 'Economics', 'Health', 'Psychology', 'Business', 'Biology', 'Philosophy', 'Computer_Science', 'History'] + +mmlu_cf_summary_groups = [ + {'name': 'mmlu_cf', 'subsets': ['mmlu_cf_' + c.replace(' ', '_') for c in categories]}, +] diff --git a/opencompass/configs/summarizers/groups/mmlu_pro.py b/opencompass/configs/summarizers/groups/mmlu_pro.py new file mode 100644 index 0000000000000000000000000000000000000000..ba2d9fc76c25e56f6d6befcaaf47e479ddc1ee07 --- /dev/null +++ b/opencompass/configs/summarizers/groups/mmlu_pro.py @@ -0,0 +1,5 @@ +categories = ['math', 'physics', 'chemistry', 'law', 'engineering', 'other', 'economics', 'health', 'psychology', 'business', 'biology', 'philosophy', 'computer science', 'history'] + +mmlu_pro_summary_groups = [ + {'name': 'mmlu_pro', 'subsets': ['mmlu_pro_' + c.replace(' ', '_') for c in categories]}, +] diff --git a/opencompass/configs/summarizers/groups/mmmlu.py b/opencompass/configs/summarizers/groups/mmmlu.py new file mode 100644 index 0000000000000000000000000000000000000000..4f95396122d46285e4470f717bd9fcc711043e8f --- /dev/null +++ b/opencompass/configs/summarizers/groups/mmmlu.py @@ -0,0 +1,5 @@ +categories = ['mmlu_AR-XY','mmlu_BN-BD','mmlu_DE-DE','mmlu_ES-LA','mmlu_FR-FR','mmlu_HI-IN','mmlu_ID-ID','mmlu_IT-IT','mmlu_JA-JP','mmlu_KO-KR','mmlu_PT-BR','mmlu_SW-KE','mmlu_YO-NG','mmlu_ZH-CN'] + +mmmlu_summary_groups = [ + {'name': 'mmmlu', 'subsets': [f'openai_m{c}' for c in categories]}, +] diff --git a/opencompass/configs/summarizers/groups/multipl_e.py b/opencompass/configs/summarizers/groups/multipl_e.py new file mode 100644 index 0000000000000000000000000000000000000000..1d50c7b632c6722b90c371da2ff531a10666fdfa --- /dev/null +++ b/opencompass/configs/summarizers/groups/multipl_e.py @@ -0,0 +1,6 @@ +multiple_summary_groups = [] + +humaneval_multiple = ['humaneval-multiple-cpp', 'humaneval-multiple-cs', 'humaneval-multiple-go', 'humaneval-multiple-java', 'humaneval-multiple-rb', 'humaneval-multiple-js', 'humaneval-multiple-php', 'humaneval-multiple-r', 'humaneval-multiple-rs', 'humaneval-multiple-sh'] +mbpp_multiple = ['mbpp-multiple-cpp', 'mbpp-multiple-cs', 'mbpp-multiple-go', 'mbpp-multiple-java', 'mbpp-multiple-rb', 'mbpp-multiple-js', 'mbpp-multiple-php', 'mbpp-multiple-r', 'mbpp-multiple-rs', 'mbpp-multiple-sh'] +multiple_summary_groups.append({'name': 'multiple', 'subsets': humaneval_multiple}) +multiple_summary_groups.append({'name':'multiple','subsets': mbpp_multiple}) diff --git a/opencompass/configs/summarizers/groups/musr_average.py b/opencompass/configs/summarizers/groups/musr_average.py new file mode 100644 index 0000000000000000000000000000000000000000..4e1879a5ea8a1ece218a166f8fc6f1241c0a8153 --- /dev/null +++ b/opencompass/configs/summarizers/groups/musr_average.py @@ -0,0 +1,18 @@ +summarizer = dict( + dataset_abbrs=[ + 'musr_murder_mysteries', + 'musr_object_placements', + 'musr_team_allocation', + 'musr_average' + ], + summary_groups=[ + { + 'name': 'musr_average', + 'subsets': [ + 'musr_murder_mysteries', + 'musr_object_placements', + 'musr_team_allocation', + ], + } + ], +) diff --git a/opencompass/configs/summarizers/groups/plugineval.py b/opencompass/configs/summarizers/groups/plugineval.py new file mode 100644 index 0000000000000000000000000000000000000000..994ee1ee85b7d88aec474fe6ba932923bcf59da1 --- /dev/null +++ b/opencompass/configs/summarizers/groups/plugineval.py @@ -0,0 +1,150 @@ +from copy import deepcopy + +_base_summary_groups = [ + { + 'name': 'plugin_eval-instruct_v1', + 'metric': 'format_metric', + 'subsets': [ + ['plugin_eval-instruct_v1', 'string_format_metric'], + ['plugin_eval-instruct_v1', 'json_format_metric'], + ] + }, + { + 'name': 'plugin_eval-instruct_v1', + 'metric': 'args_em_metric', + 'subsets': [ + ['plugin_eval-instruct_v1', 'string_args_em_metric'], + ['plugin_eval-instruct_v1', 'json_args_em_metric'], + ] + }, + { + 'name': 'plugin_eval-instruct_v1', + 'metric': 'string_metric', + 'subsets': [ + ['plugin_eval-instruct_v1', 'string_format_metric'], + ['plugin_eval-instruct_v1', 'string_args_em_metric'], + ] + }, + { + 'name': 'plugin_eval-instruct_v1', + 'metric': 'json_metric', + 'subsets': [ + ['plugin_eval-instruct_v1', 'json_format_metric'], + ['plugin_eval-instruct_v1', 'json_args_em_metric'], + ] + }, + { + 'name': 'copy_plugin_eval-review_str_v1', + 'subsets': [ + ['plugin_eval-review_str_v1', 'review_quality'], + ], + }, + { + 'name': 'plugin_eval_one_review', + 'subsets': [ + ['plugin_eval-instruct_v1', 'format_metric'], + ['plugin_eval-instruct_v1', 'args_em_metric'], + ['plugin_eval-plan_str_v1', 'f1_score'], + ['plugin_eval-plan_json_v1', 'f1_score'], + ['plugin_eval-reason_str_v1', 'thought'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'thought'], + ['plugin_eval-retrieve_str_v1', 'name'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'name'], + ['plugin_eval-understand_str_v1', 'args'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'args'], + ['plugin_eval-review_str_v1', 'review_quality'], + ] + }, + { + 'name': 'plugin_eval', + 'subsets': [ + ['plugin_eval-instruct_v1', 'format_metric'], + ['plugin_eval-instruct_v1', 'args_em_metric'], + ['plugin_eval-plan_str_v1', 'f1_score'], + ['plugin_eval-plan_json_v1', 'f1_score'], + ['plugin_eval-reason_str_v1', 'thought'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'thought'], + ['plugin_eval-retrieve_str_v1', 'name'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'name'], + ['plugin_eval-understand_str_v1', 'args'], + ['plugin_eval-reason_retrieve_understand_json_v1', 'args'], + ['plugin_eval-review_str_v1', 'review_quality'], + ] + }, + + # special treatment for first 10% data points + { + 'name': 'plugin_eval-p10-instruct_v1', + 'metric': 'format_metric', + 'subsets': [ + ['plugin_eval-p10-instruct_v1', 'string_format_metric'], + ['plugin_eval-p10-instruct_v1', 'json_format_metric'], + ] + }, + { + 'name': 'plugin_eval-p10-instruct_v1', + 'metric': 'args_em_metric', + 'subsets': [ + ['plugin_eval-p10-instruct_v1', 'string_args_em_metric'], + ['plugin_eval-p10-instruct_v1', 'json_args_em_metric'], + ] + }, + { + 'name': 'plugin_eval-p10', + 'subsets': [ + ['plugin_eval-p10-instruct_v1', 'format_metric'], + ['plugin_eval-p10-instruct_v1', 'args_em_metric'], + ['plugin_eval-p10-plan_str_v1', 'f1_score'], + ['plugin_eval-p10-plan_json_v1', 'f1_score'], + ['plugin_eval-p10-reason_str_v2', 'thought'], + ['plugin_eval-p10-reason_retrieve_understand_json_v2', 'thought'], + ['plugin_eval-p10-retrieve_str_v2', 'name'], + ['plugin_eval-p10-reason_retrieve_understand_json_v2', 'name'], + ['plugin_eval-p10-understand_str_v2', 'args'], + ['plugin_eval-p10-reason_retrieve_understand_json_v2', 'args'], + ['plugin_eval-p10-review_str_v6', 'review_quality'], + ] + }, +] + +plugineval_summary_groups = [] + +# base +for group in _base_summary_groups: + group = deepcopy(group) + plugineval_summary_groups.append(group) + +# base _zh +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'] + '_zh' + group['subsets'] = [[subset[0] + '_zh', subset[1]] for subset in group['subsets']] + plugineval_summary_groups.append(group) + +# base -p10- +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'].replace('plugin_eval', 'plugin_eval-p10') + group['subsets'] = [[subset[0].replace('plugin_eval', 'plugin_eval-p10'), subset[1]] for subset in group['subsets']] + plugineval_summary_groups.append(group) + +# base -p10- _zh +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'].replace('plugin_eval', 'plugin_eval-p10') + '_zh' + group['subsets'] = [[subset[0].replace('plugin_eval', 'plugin_eval-p10') + '_zh', subset[1]] for subset in group['subsets']] + plugineval_summary_groups.append(group) + +# base -mus-p10- +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'].replace('plugin_eval', 'plugin_eval-mus-p10') + group['subsets'] = [[subset[0].replace('plugin_eval', 'plugin_eval-mus-p10'), subset[1]] for subset in group['subsets']] + plugineval_summary_groups.append(group) + +# base -mus-p10- _zh +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'].replace('plugin_eval', 'plugin_eval-mus-p10') + '_zh' + group['subsets'] = [[subset[0].replace('plugin_eval', 'plugin_eval-mus-p10') + '_zh', subset[1]] for subset in group['subsets']] + plugineval_summary_groups.append(group) diff --git a/opencompass/configs/summarizers/groups/ruler.py b/opencompass/configs/summarizers/groups/ruler.py new file mode 100644 index 0000000000000000000000000000000000000000..84e6c2c82b00913768ab95b0e3dca543c2580931 --- /dev/null +++ b/opencompass/configs/summarizers/groups/ruler.py @@ -0,0 +1,29 @@ +"""RULER summary groups.""" + +default_ruler_tasks = [ + 'ruler_niah_single_1', + 'ruler_niah_single_2', + 'ruler_niah_single_3', + 'ruler_niah_multikey_1', + 'ruler_niah_multikey_2', + 'ruler_niah_multikey_3', + 'ruler_niah_multivalue', + 'ruler_niah_multiquery', + 'ruler_vt', + 'ruler_fwe', + 'ruler_cwe', + 'ruler_qa_squad', + 'ruler_qa_hotpotqa', +] +context_window_sizes = ['4k', '8k', '16k', '32k', '64k', '128k', '256k', '512k', '1m'] + +ruler_summary_groups = [] +for context_window_size in context_window_sizes: + ruler_summary_groups.append( + { + 'name': f'ruler_{context_window_size}', + 'subsets': [ + f'{task}_{context_window_size}' for task in default_ruler_tasks + ], + } + ) diff --git a/opencompass/configs/summarizers/groups/scibench.py b/opencompass/configs/summarizers/groups/scibench.py new file mode 100644 index 0000000000000000000000000000000000000000..07d3d46c4e0d7411980a993287ca66128effd06a --- /dev/null +++ b/opencompass/configs/summarizers/groups/scibench.py @@ -0,0 +1,6 @@ +scibench_summary_groups = [] + +scibench_tasks = ['atkins', 'calculus', 'chemmc', 'class', 'diff', 'fund', 'matter', 'quan', 'stat', 'thermo'] +for suffix in ['', '_zs-cot', '_fs', '_fs-cot']: + subsets = [f'scibench-{subset}{suffix}' for subset in scibench_tasks] + scibench_summary_groups.append({'name': f'scibench{suffix}', 'subsets': subsets}) diff --git a/opencompass/configs/summarizers/groups/scicode.py b/opencompass/configs/summarizers/groups/scicode.py new file mode 100644 index 0000000000000000000000000000000000000000..757b8953f79bd82ac8a7857940a99de4f1839048 --- /dev/null +++ b/opencompass/configs/summarizers/groups/scicode.py @@ -0,0 +1,23 @@ +scicode_summary_groups = [ + { + 'name': 'SciCode', + 'subsets': [ + ['SciCode', 'accuracy'], + ['SciCode', 'sub_accuracy'], + ] + }, + { + 'name': 'SciCode_with_background', + 'subsets': [ + ['SciCode_with_background', 'accuracy'], + ['SciCode_with_background', 'sub_accuracy'], + ] + }, + { + 'name': 'SciCode_wo_background', + 'subsets': [ + ['SciCode_wo_background', 'accuracy'], + ['SciCode_wo_background', 'sub_accuracy'], + ] + } +] diff --git a/opencompass/configs/summarizers/groups/supergpqa.py b/opencompass/configs/summarizers/groups/supergpqa.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb35a9743ddbc78112e343f4b356dc0cc927381 --- /dev/null +++ b/opencompass/configs/summarizers/groups/supergpqa.py @@ -0,0 +1,89 @@ +supergpqa_summary_groups = [] + +# gaokao-bench +supergpqa_weights = { +'Electronic_Science_and_Technology': 246, +'Philosophy': 347, +'Traditional_Chinese_Medicine': 268, +'Applied_Economics': 723, +'Mathematics': 2622, +'Physics': 2845, +'Clinical_Medicine': 1218, +'Computer_Science_and_Technology': 763, +'Information_and_Communication_Engineering': 504, +'Control_Science_and_Engineering': 190, +'Theoretical_Economics': 150, +'Law': 591, +'History': 674, +'Basic_Medicine': 567, +'Education': 247, +'Materials_Science_and_Engineering': 289, +'Electrical_Engineering': 556, +'Systems_Science': 50, +'Power_Engineering_and_Engineering_Thermophysics': 684, +'Military_Science': 205, +'Biology': 1120, +'Business_Administration': 142, +'Language_and_Literature': 440, +'Public_Health_and_Preventive_Medicine': 292, +'Political_Science': 65, +'Chemistry': 1769, +'Hydraulic_Engineering': 218, +'Chemical_Engineering_and_Technology': 410, +'Pharmacy': 278, +'Geography': 133, +'Art_Studies': 603, +'Architecture': 162, +'Forestry_Engineering': 100, +'Public_Administration': 151, +'Oceanography': 200, +'Journalism_and_Communication': 207, +'Nuclear_Science_and_Technology': 107, +'Weapon_Science_and_Technology': 100, +'Naval_Architecture_and_Ocean_Engineering': 138, +'Environmental_Science_and_Engineering': 189, +'Transportation_Engineering': 251, +'Geology': 341, +'Physical_Oceanography': 50, +'Musicology': 426, +'Stomatology': 132, +'Aquaculture': 56, +'Mechanical_Engineering': 176, +'Aeronautical_and_Astronautical_Science_and_Technology': 119, +'Civil_Engineering': 358, +'Mechanics': 908, +'Petroleum_and_Natural_Gas_Engineering': 112, +'Sociology': 143, +'Food_Science_and_Engineering': 109, +'Agricultural_Engineering': 104, +'Surveying_and_Mapping_Science_and_Technology': 168, +'Metallurgical_Engineering': 255, +'Library,_Information_and_Archival_Management': 150, +'Mining_Engineering': 100, +'Astronomy': 405, +'Geological_Resources_and_Geological_Engineering': 50, +'Atmospheric_Science': 203, +'Optical_Engineering': 376, +'Animal_Husbandry': 103, +'Geophysics': 100, +'Crop_Science': 145, +'Management_Science_and_Engineering': 58, +'Psychology': 87, +'Forestry': 131, +'Textile_Science_and_Engineering': 100, +'Veterinary_Medicine': 50, +'Instrument_Science_and_Technology': 50, +'Physical_Education': 150, +} +supergpqa_weights = { + 'supergpqa_' + k: v for k, v in supergpqa_weights.items() +} +supergpqa_summary_groups.append( + { + 'name': 'SuperGPQA', + 'subsets':[[k, 'accuracy'] for k in supergpqa_weights.keys()], + 'weights': supergpqa_weights, + } +) + +print(supergpqa_summary_groups) \ No newline at end of file diff --git a/opencompass/configs/summarizers/groups/teval.py b/opencompass/configs/summarizers/groups/teval.py new file mode 100644 index 0000000000000000000000000000000000000000..af2fcf2c77680d4e89e15d756172d53d91d74296 --- /dev/null +++ b/opencompass/configs/summarizers/groups/teval.py @@ -0,0 +1,73 @@ +from copy import deepcopy + +_base_summary_groups = [ + { + 'name': 'teval-instruct_v1', + 'metric': 'format_metric', + 'subsets': [ + ['teval-instruct_v1', 'string_format_metric'], + ['teval-instruct_v1', 'json_format_metric'], + ] + }, + { + 'name': 'teval-instruct_v1', + 'metric': 'args_em_metric', + 'subsets': [ + ['teval-instruct_v1', 'string_args_em_metric'], + ['teval-instruct_v1', 'json_args_em_metric'], + ] + }, + { + 'name': 'teval-instruct_v1', + 'metric': 'string_metric', + 'subsets': [ + ['teval-instruct_v1', 'string_format_metric'], + ['teval-instruct_v1', 'string_args_em_metric'], + ] + }, + { + 'name': 'teval-instruct_v1', + 'metric': 'json_metric', + 'subsets': [ + ['teval-instruct_v1', 'json_format_metric'], + ['teval-instruct_v1', 'json_args_em_metric'], + ] + }, + { + 'name': 'copy_teval-review_str_v1', + 'subsets': [ + ['teval-review_str_v1', 'review_quality'], + ], + }, + { + 'name': 'teval', + 'subsets': [ + ['teval-instruct_v1', 'format_metric'], + ['teval-instruct_v1', 'args_em_metric'], + ['teval-plan_str_v1', 'f1_score'], + ['teval-plan_json_v1', 'f1_score'], + ['teval-reason_str_v1', 'thought'], + ['teval-reason_retrieve_understand_json_v1', 'thought'], + ['teval-retrieve_str_v1', 'name'], + ['teval-reason_retrieve_understand_json_v1', 'name'], + ['teval-understand_str_v1', 'args'], + ['teval-reason_retrieve_understand_json_v1', 'args'], + ['teval-review_str_v1', 'review_quality'], + ['copy_teval-review_str_v1', 'naive_average'], # a hack for review * 2 + ] + }, +] + +teval_summary_groups = [] + +# base +for group in _base_summary_groups: + group = deepcopy(group) + teval_summary_groups.append(group) + +# base _zh +for group in _base_summary_groups: + group = deepcopy(group) + group['name'] = group['name'] + '_zh' + group['subsets'] = [[subset[0] + '_zh', subset[1]] for subset in group['subsets']] + teval_summary_groups.append(group) diff --git a/opencompass/configs/summarizers/groups/tydiqa.py b/opencompass/configs/summarizers/groups/tydiqa.py new file mode 100644 index 0000000000000000000000000000000000000000..2a22adeabb4e70ce8a53672818badf776a23f4f5 --- /dev/null +++ b/opencompass/configs/summarizers/groups/tydiqa.py @@ -0,0 +1,5 @@ +tydiqa_summary_groups = [] + +_tydiqa = ['arabic', 'bengali', 'english', 'finnish', 'indonesian', 'japanese', 'korean', 'russian', 'swahili', 'telugu', 'thai'] +_tydiqa = ['tydiqa-goldp_' + s for s in _tydiqa] +tydiqa_summary_groups.append({'name': 'tydiqa-goldp', 'subsets': _tydiqa}) diff --git a/opencompass/configs/summarizers/groups/xiezhi.py b/opencompass/configs/summarizers/groups/xiezhi.py new file mode 100644 index 0000000000000000000000000000000000000000..76aceb044a7be17333a68b2c410d9ac3b63c3250 --- /dev/null +++ b/opencompass/configs/summarizers/groups/xiezhi.py @@ -0,0 +1,4 @@ +xiezhi_summary_groups = [] + +_xiezhi = ['xiezhi-spec_eng', 'xiezhi-spec_chn', 'xiezhi-inter_eng', 'xiezhi-inter_chn'] +xiezhi_summary_groups.append({'name': 'xiezhi', 'subsets': _xiezhi}) diff --git a/opencompass/datasets/IFEval/__init__.py b/opencompass/datasets/IFEval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/opencompass/datasets/IFEval/evaluation_main.py b/opencompass/datasets/IFEval/evaluation_main.py new file mode 100644 index 0000000000000000000000000000000000000000..ab9781181a404ffa60b6b1476d0ef34a9dac746e --- /dev/null +++ b/opencompass/datasets/IFEval/evaluation_main.py @@ -0,0 +1,141 @@ +# flake8: noqa +# yapf: disable + +# Copyright 2023 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import dataclasses +from typing import Dict, List, Optional, Union + +from absl import flags + +import opencompass.datasets.IFEval.instructions_registry as instructions_registry + +_INPUT_DATA = flags.DEFINE_string('input_data', + None, + 'path to input data', + required=True) + +_INPUT_RESPONSE_DATA = flags.DEFINE_string('input_response_data', + None, + 'path to input response data', + required=False) + +_OUTPUT_DIR = flags.DEFINE_string( + 'output_dir', + None, + 'Output directory for inference and eval results.', + required=True, +) + + +@dataclasses.dataclass +class InputExample: + key: int + instruction_id_list: List[str] + prompt: str + kwargs: List[Dict[str, Optional[Union[str, int]]]] + + +@dataclasses.dataclass +class OutputExample: + instruction_id_list: List[str] + prompt: str + response: str + follow_all_instructions: bool + follow_instruction_list: List[bool] + + +def test_instruction_following_strict( + inp, + response, +): + """Tests response to see if instrutions are followed.""" + instruction_list = inp.instruction_id_list + is_following_list = [] + + for index, instruction_id in enumerate(instruction_list): + instruction_cls = instructions_registry.INSTRUCTION_DICT[ + instruction_id] + instruction = instruction_cls(instruction_id) + instruction.build_description(**inp.kwargs[index]) + args = instruction.get_instruction_args() + if args and 'prompt' in args: + instruction.build_description(prompt=inp.prompt) + + if response.strip() and instruction.check_following(response): + is_following_list.append(True) + else: + is_following_list.append(False) + + return OutputExample( + instruction_id_list=inp.instruction_id_list, + prompt=inp.prompt, + response=response, + follow_all_instructions=all(is_following_list), + follow_instruction_list=is_following_list, + ) + + +def test_instruction_following_loose( + inp, + response, +): + """Tests response for an upper bound for following instructions.""" + r = response.split('\n') + response_remove_first = '\n'.join(r[1:]).strip() + response_remove_last = '\n'.join(r[:-1]).strip() + response_remove_both = '\n'.join(r[1:-1]).strip() + revised_response = response.replace('*', '') + revised_response_remove_first = response_remove_first.replace('*', '') + revised_response_remove_last = response_remove_last.replace('*', '') + revised_response_remove_both = response_remove_both.replace('*', '') + all_responses = [ + response, + revised_response, + response_remove_first, + response_remove_last, + response_remove_both, + revised_response_remove_first, + revised_response_remove_last, + revised_response_remove_both, + ] + instruction_list = inp.instruction_id_list + is_following_list = [] + + for index, instruction_id in enumerate(instruction_list): + instruction_cls = instructions_registry.INSTRUCTION_DICT[ + instruction_id] + instruction = instruction_cls(instruction_id) + + instruction.build_description(**inp.kwargs[index]) + args = instruction.get_instruction_args() + if args and 'prompt' in args: + instruction.build_description(prompt=inp.prompt) + + is_following = False + for r in all_responses: + if r.strip() and instruction.check_following(r): + is_following = True + break + + is_following_list.append(is_following) + + return OutputExample( + instruction_id_list=inp.instruction_id_list, + prompt=inp.prompt, + response=response, + follow_all_instructions=all(is_following_list), + follow_instruction_list=is_following_list, + ) diff --git a/opencompass/datasets/IFEval/ifeval.py b/opencompass/datasets/IFEval/ifeval.py new file mode 100644 index 0000000000000000000000000000000000000000..521e5a8d73873c2fc6221a50fa873bb8adaf8cbc --- /dev/null +++ b/opencompass/datasets/IFEval/ifeval.py @@ -0,0 +1,97 @@ +import json + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .evaluation_main import (InputExample, test_instruction_following_loose, + test_instruction_following_strict) + + +@LOAD_DATASET.register_module() +class IFEvalDataset(BaseDataset): + + @staticmethod + def load(path): + path = get_data_path(path, local_mode=True) + datasets = [] + with open(path, 'r', encoding='utf-8') as file: + for line in file: + tmp = json.loads(line.strip()) + dataset = dict(prompt=tmp['prompt'], reference=tmp) + datasets.append(dataset) + return Dataset.from_list(datasets) + + +class IFEvaluator(BaseEvaluator): + + def score(self, predictions, references, origin_prompt): + prompt_strict_correct, prompt_strict_total = 0, 0 + inst_strict_correct, inst_strict_total = 0, 0 + prompt_loose_correct, prompt_loose_total = 0, 0 + inst_loose_correct, inst_loose_total = 0, 0 + details = {} + for index, (pred, refer) in enumerate(zip(predictions, references)): + input = InputExample( + key=refer['key'], + instruction_id_list=refer['instruction_id_list'], + prompt=refer['prompt'], + kwargs=refer['kwargs']) + for kwarg in input.kwargs: + for k in list(kwarg.keys()): + if kwarg[k] is None: + kwarg.pop(k, None) + + # strict + example = test_instruction_following_strict(input, pred) + follow_instruction_list = example.follow_instruction_list + instruction_id_list = example.instruction_id_list + prompt_strict_total += 1 + is_strict_correct = all(follow_instruction_list) + prompt_strict_correct += is_strict_correct + inst_strict_total += len(instruction_id_list) + inst_strict_correct += sum(follow_instruction_list) + + # loose + example = test_instruction_following_loose(input, pred) + follow_instruction_list = example.follow_instruction_list + instruction_id_list = example.instruction_id_list + prompt_loose_total += 1 + is_loose_correct = all(follow_instruction_list) + prompt_loose_correct += is_loose_correct + inst_loose_total += len(instruction_id_list) + inst_loose_correct += sum(follow_instruction_list) + + if is_strict_correct: + grade = 'strict' + elif is_loose_correct: + grade = 'loose' + else: + grade = 'none' + + details[str(index)] = { + 'prompt': origin_prompt[index], + 'pred': pred, + 'refer': refer, + 'is_strict_correct': is_strict_correct, + 'is_loose_correct': is_loose_correct, + 'is_correct': is_strict_correct, + 'grade': grade + } + + results = { + 'Prompt-level-strict-accuracy': + prompt_strict_correct / prompt_strict_total * 100, + 'Inst-level-strict-accuracy': + inst_strict_correct / inst_strict_total * 100, + 'Prompt-level-loose-accuracy': + prompt_loose_correct / prompt_loose_total * 100, + 'Inst-level-loose-accuracy': + inst_loose_correct / inst_loose_total * 100, + 'details': + details + } + return results diff --git a/opencompass/datasets/IFEval/instructions.py b/opencompass/datasets/IFEval/instructions.py new file mode 100644 index 0000000000000000000000000000000000000000..5287556e786a6e89581309fbeb602f63cfb323ea --- /dev/null +++ b/opencompass/datasets/IFEval/instructions.py @@ -0,0 +1,1570 @@ +# flake8: noqa +# yapf: disable + +# Copyright 2023 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Library of instructions.""" +import collections +import json +import random +import re +import string +from typing import Dict, Optional, Sequence, Union + +try: + import langdetect +except ImportError: + langdetect = None + +from absl import logging + +import opencompass.datasets.IFEval.instructions_util as instructions_util + +_InstructionArgsDtype = Optional[Dict[str, Union[int, str, Sequence[str]]]] + +_LANGUAGES = instructions_util.LANGUAGE_CODES + +# The relational operation for comparison. +_COMPARISON_RELATION = ('less than', 'at least') + +# The maximum number of sentences. +_MAX_NUM_SENTENCES = 20 + +# The number of placeholders. +_NUM_PLACEHOLDERS = 4 + +# The number of bullet lists. +_NUM_BULLETS = 5 + +# The options of constrained response. +_CONSTRAINED_RESPONSE_OPTIONS = ('My answer is yes.', 'My answer is no.', + 'My answer is maybe.') + +# The options of starter keywords. +_STARTER_OPTIONS = ('I would say', 'My answer is', 'I believe', + 'In my opinion', 'I think', 'I reckon', 'I feel', + 'From my perspective', 'As I see it', 'According to me', + "As far as I'm concerned", 'To my understanding', + 'In my view', 'My take on it is', 'As per my perception') + +# The options of ending keywords. +# TODO(jeffreyzhou) add more ending options +_ENDING_OPTIONS = ('Any other questions?', + 'Is there anything else I can help with?') + +# The number of highlighted sections. +_NUM_HIGHLIGHTED_SECTIONS = 4 + +# The section spliter. +_SECTION_SPLITER = ('Section', 'SECTION') + +# The number of sections. +_NUM_SECTIONS = 5 + +# The number of paragraphs. +_NUM_PARAGRAPHS = 5 + +# The postscript marker. +_POSTSCRIPT_MARKER = ('P.S.', 'P.P.S') + +# The number of keywords. +_NUM_KEYWORDS = 2 + +# The occurrences of a single keyword. +_KEYWORD_FREQUENCY = 3 + +# The occurrences of a single letter. +_LETTER_FREQUENCY = 10 + +# The occurrences of words with all capital letters. +_ALL_CAPITAL_WORD_FREQUENCY = 20 + +# The number of words in the response. +_NUM_WORDS_LOWER_LIMIT = 100 +_NUM_WORDS_UPPER_LIMIT = 500 + + +class Instruction: + """An instruction template.""" + + def __init__(self, instruction_id): + self.id = instruction_id + + def build_description(self, **kwargs): + raise NotImplementedError('`build_description` not implemented.') + + def get_instruction_args(self): + raise NotImplementedError('`get_instruction_args` not implemented.') + + def get_instruction_args_keys(self): + raise NotImplementedError( + '`get_instruction_args_keys` not implemented.') + + def check_following(self, value): + raise NotImplementedError('`check_following` not implemented.') + + +class ResponseLanguageChecker(Instruction): + """Check the language of the entire response.""" + + def build_description(self, *, language=None): + """Build the instruction description. + + Args: + language: A string representing the expected language of the response. The + language has to comply to the 97 types defined in + `langid.py` (https://pypi.org/project/langid/1.1.5/), which follows + ISO 639-1 codes (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes); + for example, `en` for English, `zh` for Chinese, `fr` for French. + + Returns: + A string representing the instruction description. + """ + self._language = language + if self._language is None: + self._language = random.choice(list(_LANGUAGES.keys())) + # TODO(tianjianlu): opens the description generation to more choices. + self._description_pattern = ( + 'Your ENTIRE response should be in {language} language, no other ' + + 'language is allowed.') + return self._description_pattern.format( + language=_LANGUAGES[self._language]) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'language': self._language} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['language'] + + def check_following(self, value): + """Check if the language of the entire response follows the + instruction. + + Args: + value: A string representing the response. + + Returns: + True if the language of `value` follows instruction; otherwise False. + """ + assert isinstance(value, str) + + try: + return langdetect.detect(value) == self._language + except langdetect.LangDetectException as e: + # Count as instruction is followed. + logging.error('Unable to detect language for text %s due to %s', + value, e) # refex: disable=pytotw.037 + return True + + +class NumberOfSentences(Instruction): + """Check the number of sentences.""" + + def build_description(self, *, num_sentences=None, relation=None): + """Build the instruction description. + + Args: + num_sentences: An integer specifying the number of sentences as a + threshold. + relation: A string in (`less than`, `at least`), defining the relational + operator for comparison. + Two relational comparisons are supported for now: + if 'less than', the actual number of sentences < the threshold; + if 'at least', the actual number of sentences >= the threshold. + + Returns: + A string representing the instruction description. + """ + # The number of sentences as a threshold for comparison. + self._num_sentences_threshold = num_sentences + if (self._num_sentences_threshold is None + or self._num_sentences_threshold < 0): + self._num_sentences_threshold = random.randint( + 1, _MAX_NUM_SENTENCES) + + if relation is None: + self._comparison_relation = random.choice(_COMPARISON_RELATION) + elif relation not in _COMPARISON_RELATION: + raise ValueError( + 'The supported relation for comparison must be in ' + f'{_COMPARISON_RELATION}, but {relation} is given.') + else: + self._comparison_relation = relation + + self._description_pattern = ( + 'Your response should contain {relation} {num_sentences} sentences.' + ) + return self._description_pattern.format( + relation=self._comparison_relation, + num_sentences=self._num_sentences_threshold) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'num_sentences': self._num_sentences_threshold, + 'relation': self._comparison_relation + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_sentences', 'relation'] + + def check_following(self, value): + """Check if the number of sentences follows the instruction. + + Args: + value: A string representing the response. + + Returns: + True if the response follows the instruction. + + Raise: + ValueError if the string in `instruction_args` is not in + [`less_than`, `at_least`]. + """ + num_sentences = instructions_util.count_sentences(value) + if self._comparison_relation == _COMPARISON_RELATION[0]: + return num_sentences < self._num_sentences_threshold + elif self._comparison_relation == _COMPARISON_RELATION[1]: + return num_sentences >= self._num_sentences_threshold + + +class PlaceholderChecker(Instruction): + """Check the placeholders in template writing.""" + + def build_description(self, *, num_placeholders=None): + """Build the instruction description. + + Args: + num_placeholders: An integer denoting the minimum number of + placeholders required in the response. + + Returns: + A string representing the instruction description. + """ + self._num_placeholders = num_placeholders + if self._num_placeholders is None or self._num_placeholders < 0: + self._num_placeholders = random.randint(1, _NUM_PLACEHOLDERS) + self._description_pattern = ( + 'The response must contain at least {num_placeholders} placeholders ' + + 'represented by square brackets, such as [address].') + return self._description_pattern.format( + num_placeholders=self._num_placeholders) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'num_placeholders': self._num_placeholders} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_placeholders'] + + def check_following(self, value): + """Check if the number of placeholders follows the instruction. + + Args: + value: A string representing the response. + + Returns: + True if the actual number of placeholders in the response is greater than + or equal to `num_placeholders`; otherwise, False. + """ + placeholders = re.findall(r'\[.*?\]', value) + num_placeholders = len(placeholders) + return num_placeholders >= self._num_placeholders + + +class BulletListChecker(Instruction): + """Checks the bullet list in the prompt.""" + + def build_description(self, *, num_bullets=None): + """Build the instruction description. + + Args: + num_bullets: An integer specifying the exact number of bullet lists + that is required to appear in the response. + + Returns: + A string representing the instruction description. + """ + self._num_bullets = num_bullets + if self._num_bullets is None or self._num_bullets < 0: + self._num_bullets = random.randint(1, _NUM_BULLETS) + self._description_pattern = ( + 'Your answer must contain exactly {num_bullets} bullet points. ' + + 'Use the markdown bullet points such as:\n' + + '* This is point 1. \n' + '* This is point 2') + return self._description_pattern.format(num_bullets=self._num_bullets) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'num_bullets': self._num_bullets} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_bullets'] + + def check_following(self, value): + r"""Check if the number of bullet lists meets the requirement. + + Args: + value: A string representing the response. The response is expected to + contain some bullet lists that start with `\*`. + + Returns: + True if the actual number of bullet lists in the response meets the + requirement. + """ + bullet_lists = re.findall(r'^\s*\*[^\*].*$', value, flags=re.MULTILINE) + bullet_lists_2 = re.findall(r'^\s*-.*$', value, flags=re.MULTILINE) + num_bullet_lists = len(bullet_lists) + len(bullet_lists_2) + return num_bullet_lists == self._num_bullets + + +class ConstrainedResponseChecker(Instruction): + """Checks the constrained response.""" + + def build_description(self): + """Build the instruction description.""" + # A sequence of string(s) representing the options of the expected response. + self._constrained_responses = _CONSTRAINED_RESPONSE_OPTIONS + self._description_pattern = ( + 'Answer with one of the following options: {response_options}') + return self._description_pattern.format( + response_options=self._constrained_responses) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks if the response matches the constrained options. + + Args: + value: A string representing the response. + + Returns: + True if the actual response contains one of the options in the constrained + responses; otherwise False. + """ + value = value.strip() + for constrained_response in self._constrained_responses: + if constrained_response in value: + return True + return False + + +class ConstrainedStartChecker(Instruction): + """Checks the response start.""" + + def build_description(self, *, starter=None): + """Build the instruction description. + + Args: + starter: A string representing the keyword that the response should start + with. + + Returns: + A string representing the instruction description. + """ + self._starter = starter.strip() if isinstance(starter, + str) else starter + if self._starter is None: + self._starter = random.choice(_STARTER_OPTIONS) + self._description_pattern = ( + 'During the conversation, when it is your turn, ' + + 'please always start with {starter}') + return self._description_pattern.format(starter=self._starter) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'starter': self._starter} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['starter'] + + def check_following(self, value): + """Checks if the response starts with the constrained keyword or + phrase. + + Args: + value: A string representing the response. + + Returns: + True if the response starts with the given phrase or keyword that is + contained in `instruction_args`; otherwise, False. + """ + response_pattern = r'^\s*' + self._starter + r'.*$' + response_with_constrained_start = re.search(response_pattern, + value, + flags=re.MULTILINE) + return True if response_with_constrained_start else False + + +class HighlightSectionChecker(Instruction): + """Checks the highlighted section.""" + + def build_description(self, *, num_highlights=None): + """Build the instruction description. + + Args: + num_highlights: An integer specifying the minimum number of highlighted + sections. + + Returns: + A string representing the instruction description. + """ + self._num_highlights = num_highlights + if self._num_highlights is None or self._num_highlights < 0: + self._num_highlights = random.randint(1, _NUM_HIGHLIGHTED_SECTIONS) + + self._description_pattern = ( + 'Highlight at least {num_highlights} sections in your answer with ' + + 'markdown, i.e. *highlighted section*.') + + return self._description_pattern.format( + num_highlights=self._num_highlights) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'num_highlights': self._num_highlights} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_highlights'] + + def check_following(self, value): + """Checks if the number of highlighted sections meets the requirement. + + Args: + value: a string representing the response. The response is expected to + contain highlighted sections in the format of *highlighted*. + + Returns: + True if the actual number of highlighted sections in the format of + *highlighted sections* meets the minimum requirement; otherwise False. + """ + num_highlights = 0 + highlights = re.findall(r'\*[^\n\*]*\*', value) + double_highlights = re.findall(r'\*\*[^\n\*]*\*\*', value) + for highlight in highlights: + if highlight.strip('*').strip(): + num_highlights += 1 + for highlight in double_highlights: + if highlight.removeprefix('**').removesuffix('**').strip(): + num_highlights += 1 + + return num_highlights >= self._num_highlights + + +class SectionChecker(Instruction): + """Checks the sections.""" + + def build_description(self, *, section_spliter=None, num_sections=None): + """Build the instruction description. + + Args: + section_spliter: A string represents the section spliter keyword that + marks a new section, i.e., `Section` or `SECTION`. + num_sections: An integer specifying the number of sections. + + Returns: + A string representing the instruction description. + """ + self._section_spliter = section_spliter.strip() if isinstance( + section_spliter, str) else section_spliter + if self._section_spliter is None: + self._section_spliter = random.choice(_SECTION_SPLITER) + + self._num_sections = num_sections + if self._num_sections is None or self._num_sections < 0: + self._num_sections = random.randint(1, _NUM_SECTIONS) + + self._description_pattern = ( + 'Your response must have {num_sections} sections. Mark the beginning ' + + 'of each section with {section_spliter} X, such as:\n' + + '{section_spliter} 1\n' + '[content of section 1]\n' + + '{section_spliter} 2\n' + '[content of section 2]') + + return self._description_pattern.format( + num_sections=self._num_sections, + section_spliter=self._section_spliter) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'section_spliter': self._section_spliter, + 'num_sections': self._num_sections + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['section_spliter', 'num_sections'] + + def check_following(self, value): + """Checks the response contains multiple sections. + + Args: + value: A string representing the response. The response is expected + to contain multiple sections (number of sections is greater than 1). + A new section starts with `Section 1`, where the number denotes the + section index. + + Returns: + True if the number of sections in the response is greater than or equal to + the minimum number of sections; otherwise, False. + """ + section_splitter_patten = r'\s?' + self._section_spliter + r'\s?\d+\s?' + sections = re.split(section_splitter_patten, value) + num_sections = len(sections) - 1 + return num_sections >= self._num_sections + + +class ParagraphChecker(Instruction): + """Checks the paragraphs.""" + + def build_description(self, *, num_paragraphs=None): + """Build the instruction description. + + Args: + num_paragraphs: An integer specifying the number of paragraphs. + + Returns: + A string representing the instruction description. + """ + self._num_paragraphs = num_paragraphs + if self._num_paragraphs is None or self._num_paragraphs < 0: + self._num_paragraphs = random.randint(1, _NUM_PARAGRAPHS) + + self._description_pattern = ( + 'There should be {num_paragraphs} paragraphs. ' + + 'Paragraphs are separated with the markdown divider: ***') + + return self._description_pattern.format( + num_paragraphs=self._num_paragraphs) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'num_paragraphs': self._num_paragraphs} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_paragraphs'] + + def check_following(self, value): + """Checks the response contains required number of paragraphs. + + Args: + value: A string representing the response. The response may contain + paragraphs that are separated by the markdown divider: `***`. + + Returns: + True if the actual number of paragraphs is the same as required; + otherwise, False. + """ + paragraphs = re.split(r'\s?\*\*\*\s?', value) + num_paragraphs = len(paragraphs) + + for index, paragraph in enumerate(paragraphs): + if not paragraph.strip(): + if index == 0 or index == len(paragraphs) - 1: + num_paragraphs -= 1 + else: + return False + + return num_paragraphs == self._num_paragraphs + + +class PostscriptChecker(Instruction): + """Checks the postscript.""" + + def build_description(self, *, postscript_marker=None): + """Build the instruction description. + + Args: + postscript_marker: A string containing the keyword that marks the start + of the postscript section. + + Returns: + A string representing the instruction description. + """ + self._postscript_marker = postscript_marker.strip() if isinstance( + postscript_marker, str) else postscript_marker + if self._postscript_marker is None: + self._postscript_marker = random.choice(_POSTSCRIPT_MARKER) + + self._description_pattern = ( + 'At the end of your response, please explicitly add a postscript ' + + 'starting with {postscript}') + + return self._description_pattern.format( + postscript=self._postscript_marker) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'postscript_marker': self._postscript_marker} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['postscript_marker'] + + def check_following(self, value): + """Checks if the response follows the postscript format. + + Args: + value: a string representing the response. The response is expected to + contain a postscript section. + + Returns: + True if the response contains a postscript section starting with + the keyword containing in the `instruction_args`; otherwise False. + """ + value = value.lower() + if self._postscript_marker == 'P.P.S': + postscript_pattern = r'\s*p\.\s?p\.\s?s.*$' + elif self._postscript_marker == 'P.S.': + postscript_pattern = r'\s*p\.\s?s\..*$' + else: + postscript_pattern = r'\s*' + self._postscript_marker.lower( + ) + r'.*$' + postscript = re.findall(postscript_pattern, value, flags=re.MULTILINE) + return True if postscript else False + + +class RephraseChecker(Instruction): + """Checks the repharse.""" + + def build_description(self, *, original_message): + """Build the instruction description. + + Args: + original_message: A string representing the original message. The + rephrased response should only change its words/sentences in between + its two asterisks, for example, *change me*. Both original and rephrased + messages should contain the changes in the form of *change me*. + + Returns: + A string representing the instruction description. + """ + if not self.is_change(original_message): + raise ValueError( + f'Message {original_message} does not contain changes ' + 'in the form of *change me*.') + + self._reference_without_change = original_message + self._description = ( + 'Rephrasing: Your rephrased response should only' + + 'change the words/sentences in between two asterisks' + + 'such as *change me*.') + return self._description + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'original_message': self._reference_without_change} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['original_message'] + + def check_following(self, value): + r"""Checks if the rephrasing follows the instruction. + + Args: + value: A string representing the response, which is expected to rephras + the string of `instruction_args`. + + Returns: + True if `value` and `instruction_args` only differ by the words/sentences + in between two asterisks such as *change me*; otherwise, False. + """ + + if not self.is_change(value): + raise ValueError(f'value {value} does not contain ' + 'changes in the form of *change me*.') + + response_without_changes = self.strip_changes(value) + reference_without_changes = self.strip_changes( + self._reference_without_change) + + return response_without_changes == reference_without_changes + + def is_change(self, response): + """Check if there is change in the response in the form of *change + me*.""" + return re.search(r'\*.*\*', response) + + def strip_changes(self, response): + """Strips off the changes.""" + return re.sub(r'\*.*\*', '', response) + + +class KeywordChecker(Instruction): + """Check the exisitence of certain keywords.""" + + def build_description(self, *, keywords=None): + """Build the instruction description. + + Args: + keywords: A sequence of strings representing the keywords that are + expected in the response. + + Returns: + A string representing the instruction description. + """ + + if not keywords: + self._keywords = instructions_util.generate_keywords( + num_keywords=_NUM_KEYWORDS) + else: + self._keywords = keywords + self._keywords = sorted(self._keywords) + + self._description_pattern = ( + 'Include keywords {keywords} in the response.') + + return self._description_pattern.format(keywords=self._keywords) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'keywords': self._keywords} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['keywords'] + + def check_following(self, value): + """Check if the response contain the expected keywords.""" + for keyword in self._keywords: + if not re.search(keyword, value, flags=re.IGNORECASE): + return False + return True + + +class KeywordFrequencyChecker(Instruction): + """Check the keyword frequency.""" + + def build_description(self, + *, + keyword=None, + frequency=None, + relation=None): + """Build the instruction description. + + Args: + keyword: A string representing a keyword that is expected in the response. + frequency: An integer specifying the number of times `keyword` is expected + to appear in the response. + relation: A string in (`less than`, `at least`), defining the relational + operator for comparison. + Two relational comparisons are supported for now: + if 'less than', the actual number of occurrences < frequency; + if 'at least', the actual number of occurrences >= frequency. + + Returns: + A string representing the instruction description. + """ + if not keyword: + self._keyword = instructions_util.generate_keywords( + num_keywords=1)[0] + else: + self._keyword = keyword.strip() + + self._frequency = frequency + if self._frequency is None or self._frequency < 0: + self._frequency = random.randint(1, _KEYWORD_FREQUENCY) + + if relation is None: + self._comparison_relation = random.choice(_COMPARISON_RELATION) + elif relation not in _COMPARISON_RELATION: + raise ValueError( + 'The supported relation for comparison must be in ' + f'{_COMPARISON_RELATION}, but {relation} is given.') + else: + self._comparison_relation = relation + + self._description_pattern = ( + 'In your response, the word {keyword} should appear {relation} ' + + '{frequency} times.') + + return self._description_pattern.format( + keyword=self._keyword, + relation=self._comparison_relation, + frequency=self._frequency) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'keyword': self._keyword, + 'frequency': self._frequency, + 'relation': self._comparison_relation + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['keyword', 'frequency', 'relation'] + + def check_following(self, value): + """Checks if the response contain the keyword with required + frequency.""" + actual_occurrences = len( + re.findall(self._keyword, value, flags=re.IGNORECASE)) + + if self._comparison_relation == _COMPARISON_RELATION[0]: + return actual_occurrences < self._frequency + elif self._comparison_relation == _COMPARISON_RELATION[1]: + return actual_occurrences >= self._frequency + + +class NumberOfWords(Instruction): + """Checks the number of words.""" + + def build_description(self, *, num_words=None, relation=None): + """Build the instruction description. + + Args: + num_words: An integer specifying the number of words contained in the + response. + relation: A string in (`less than`, `at least`), defining the relational + operator for comparison. + Two relational comparisons are supported for now: + if 'less than', the actual number of words < num_words; + if 'at least', the actual number of words >= num_words. + + Returns: + A string representing the instruction description. + """ + + self._num_words = num_words + if self._num_words is None or self._num_words < 0: + self._num_words = random.randint(_NUM_WORDS_LOWER_LIMIT, + _NUM_WORDS_UPPER_LIMIT) + + if relation is None: + self._comparison_relation = random.choice(_COMPARISON_RELATION) + elif relation not in _COMPARISON_RELATION: + raise ValueError( + 'The supported relation for comparison must be in ' + f'{_COMPARISON_RELATION}, but {relation} is given.') + else: + self._comparison_relation = relation + + self._description_pattern = ( + 'Answer with {relation} {num_words} words.') + + return self._description_pattern.format( + relation=self._comparison_relation, num_words=self._num_words) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'num_words': self._num_words, + 'relation': self._comparison_relation + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_words', 'relation'] + + def check_following(self, value): + """Checks if the response contains the expected number of words.""" + num_words = instructions_util.count_words(value) + + if self._comparison_relation == _COMPARISON_RELATION[0]: + return num_words < self._num_words + elif self._comparison_relation == _COMPARISON_RELATION[1]: + return num_words >= self._num_words + + +class JsonFormat(Instruction): + """Check the Json format.""" + + def build_description(self): + self._description_pattern = ( + 'Entire output should be wrapped in JSON format. You can use markdown' + ' ticks such as ```.') + return self._description_pattern + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + value = (value.strip().removeprefix('```json').removeprefix( + '```Json').removeprefix('```JSON').removeprefix( + '```').removesuffix('```').strip()) + try: + json.loads(value) + except ValueError as _: + return False + return True + + +class ParagraphFirstWordCheck(Instruction): + """Check the paragraph and the first word of the nth paragraph.""" + + def build_description(self, + num_paragraphs=None, + nth_paragraph=None, + first_word=None): + r"""Build the instruction description. + + Args: + num_paragraphs: An integer indicating the number of paragraphs expected + in the response. A paragraph is a subset of the string that is + expected to be separated by '\n\n'. + nth_paragraph: An integer indicating the paragraph number that we look at. + Note that n starts from 1. + first_word: A string that represent the first word of the bth paragraph. + + Returns: + A string representing the instruction description. + """ + self._num_paragraphs = num_paragraphs + if self._num_paragraphs is None or self._num_paragraphs < 0: + self._num_paragraphs = random.randint(1, _NUM_PARAGRAPHS) + + self._nth_paragraph = nth_paragraph + if (self._nth_paragraph is None or self._nth_paragraph <= 0 + or self._nth_paragraph > self._num_paragraphs): + self._nth_paragraph = random.randint(1, self._num_paragraphs + 1) + + self._first_word = first_word + if self._first_word is None: + self._first_word = instructions_util.generate_keywords( + num_keywords=1)[0] + self._first_word = self._first_word.lower() + + self._description_pattern = ( + 'There should be {num_paragraphs} paragraphs. ' + + 'Paragraphs and only paragraphs are separated with each other by two ' + + "new lines as if it was '\\n\\n' in python. " + + 'Paragraph {nth_paragraph} must start with word {first_word}.') + + return self._description_pattern.format( + num_paragraphs=self._num_paragraphs, + nth_paragraph=self._nth_paragraph, + first_word=self._first_word) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'num_paragraphs': self._num_paragraphs, + 'nth_paragraph': self._nth_paragraph, + 'first_word': self._first_word + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_paragraphs', 'nth_paragraph', 'first_word'] + + def check_following(self, value): + """Checks for required number of paragraphs and correct first word. + + Args: + value: a string representing the response. The response may contain + paragraphs that are separated by two new lines and the first word of + the nth paragraph will have to match a specified word. + + Returns: + True if the number of paragraphs is the same as required and the first + word of the specified paragraph is the same as required. Otherwise, false. + """ + + paragraphs = re.split(r'\n\n', value) + num_paragraphs = len(paragraphs) + + for paragraph in paragraphs: + if not paragraph.strip(): + num_paragraphs -= 1 + + # check that index doesn't go out of bounds + if self._nth_paragraph <= num_paragraphs: + paragraph = paragraphs[self._nth_paragraph - 1].strip() + if not paragraph: + return False + else: + return False + + first_word = '' + punctuation = {'.', ',', '?', '!', "'", '"'} + + # get first word and remove punctuation + word = paragraph.split()[0].strip() + # TODO(jeffrey): make more complex? + word = word.lstrip("'") + word = word.lstrip('"') + + for letter in word: + if letter in punctuation: + break + first_word += letter.lower() + + return (num_paragraphs == self._num_paragraphs + and first_word == self._first_word) + + +# TODO(jeffrey) add relation - at least/at most? +class KeySentenceChecker(Instruction): + """Check the existence of certain key sentences.""" + + def build_description(self, key_sentences=None, num_sentences=None): + """Build the instruction description. + + Args: + key_sentences: A sequences of strings representing the key sentences that + are expected in the response. + num_sentences: The number of key sentences that are expected to be seen in + the response. + + Returns: + A string representing the instruction description. + """ + + if not key_sentences: + # TODO(jeffrey) make a generate sentences function? wonderwords package + self._key_sentences = set(['For now, this is fine.']) + else: + self._key_sentences = key_sentences + + if not num_sentences: + self._num_sentences = random.randint(1, len(self._key_sentences)) + else: + self._num_sentences = num_sentences + + self._description_pattern = ( + 'Include {num_sentences} of the following sentences {key_sentences}' + ) + + return self._description_pattern.format( + num_sentences=self._num_sentences, + key_sentences=self._key_sentences) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'num_sentences': self._num_sentences, + 'key_sentences': list(self._key_sentences) + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['num_sentences', 'key_sentences'] + + def check_following(self, value): + """Checks if the response contains the expected key sentences.""" + count = 0 + sentences = instructions_util.split_into_sentences(value) + for sentence in self._key_sentences: + if sentence in sentences: + count += 1 + + return count == self._num_sentences + + +class ForbiddenWords(Instruction): + """Checks that specified words are not used in response.""" + + def build_description(self, forbidden_words=None): + """Build the instruction description. + + Args: + forbidden_words: A sequences of strings representing words that are not + allowed in the response. + + Returns: + A string representing the instruction description. + """ + + if not forbidden_words: + self._forbidden_words = instructions_util.generate_keywords( + num_keywords=_NUM_KEYWORDS) + else: + self._forbidden_words = list(set(forbidden_words)) + self._forbidden_words = sorted(self._forbidden_words) + self._description_pattern = ( + 'Do not include keywords {forbidden_words} in the response.') + + return self._description_pattern.format( + forbidden_words=self._forbidden_words) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return {'forbidden_words': self._forbidden_words} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['forbidden_words'] + + def check_following(self, value): + """Check if the response does not contain the expected keywords.""" + for word in self._forbidden_words: + if re.search(r'\b' + word + r'\b', value, flags=re.IGNORECASE): + return False + return True + + +class RephraseParagraph(Instruction): + """Checks that the paragraph is rephrased.""" + + def build_description(self, *, original_paragraph, low, high): + """Builds the instruction description. + + Args: + original_paragraph: A string presenting the original paragraph. The + rephrases response should have betweeb low-high words in common. + low: An integer presenting the lower bound of similar words. + high: An integer representing the upper bound of similar words. + + Returns: + A string representing the instruction description. + """ + # TODO(jeffrey) make more encompassing + self._original_paragraph = original_paragraph + self._low = low + self._high = high + + self._description = ( + 'Rephrase the following paragraph: ' + + '{original_paragraph}\nYour response should have ' + + 'between {low} and {high} of the same words. ' + + 'Words are the same if and only if all of the ' + + 'letters, ignoring cases, are the same. For ' + + "example, 'run' is the same as 'Run' but different " + "to 'ran'.") + + return self._description.format(original_paragraph=original_paragraph, + low=self._low, + high=self._high) + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return { + 'original_paragraph': self._original_paragraph, + 'low': self._low, + 'high': self._high + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['original_paragraph', 'low', 'high'] + + def check_following(self, value): + val_words = re.findall(r'\w+', value.lower()) + original_words = re.findall(r'\w+', self._original_paragraph.lower()) + similar_words = 0 + + dict_val = collections.Counter(val_words) + dict_original = collections.Counter(original_words) + + for word in dict_original: + similar_words += min(dict_original[word], dict_val[word]) + + return similar_words >= self._low and similar_words <= self._high + + +class TwoResponsesChecker(Instruction): + """Check that two responses were given.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'Give two different responses. Responses and only responses should' + ' be separated by 6 asterisk symbols: ******.') + return self._description_pattern + + def get_instruction_args(self): + """Returns the keyword args of `build_description`.""" + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks if the response has two different answers. + + Args: + value: A string representing the response. + + Returns: + True if two responses are detected and false otherwise. + """ + valid_responses = list() + responses = value.split('******') + for index, response in enumerate(responses): + if not response.strip(): + if index != 0 and index != len(responses) - 1: + return False + else: + valid_responses.append(response) + return (len(valid_responses) == 2 + and valid_responses[0].strip() != valid_responses[1].strip()) + + +class RepeatPromptThenAnswer(Instruction): + """Checks that Prompt is first repeated then answered.""" + + def build_description(self, *, prompt_to_repeat=None): + """Build the instruction description. + + Args: + prompt_to_repeat: The prompt that is meant to be repeated. + + Returns: + A string representing the instruction description. + """ + if not prompt_to_repeat: + raise ValueError('prompt_to_repeat must be set.') + else: + self._prompt_to_repeat = prompt_to_repeat + self._description_pattern = ( + 'First repeat the request word for word without change,' + ' then give your answer (1. do not say any words or characters' + ' before repeating the request; 2. the request you need to repeat' + ' does not include this sentence)') + return self._description_pattern + + def get_instruction_args(self): + return {'prompt_to_repeat': self._prompt_to_repeat} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['prompt_to_repeat'] + + def check_following(self, value): + if value.strip().lower().startswith( + self._prompt_to_repeat.strip().lower()): + return True + return False + + +class EndChecker(Instruction): + """Checks that the prompt ends with a given phrase.""" + + def build_description(self, *, end_phrase=None): + """Build the instruction description. + + Args: + end_phrase: A string representing the phrase the response should end with. + + Returns: + A string representing the instruction description. + """ + self._end_phrase = (end_phrase.strip() + if isinstance(end_phrase, str) else end_phrase) + if self._end_phrase is None: + self._end_phrase = random.choice(_ENDING_OPTIONS) + self._description_pattern = ( + 'Finish your response with this exact phrase {ender}. ' + 'No other words should follow this phrase.') + return self._description_pattern.format(ender=self._end_phrase) + + def get_instruction_args(self): + return {'end_phrase': self._end_phrase} + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['end_phrase'] + + def check_following(self, value): + """Checks if the response ends with the expected phrase.""" + value = value.strip().strip("\"").lower() + self._end_phrase = self._end_phrase.strip().lower() + return value.endswith(self._end_phrase) + + +class TitleChecker(Instruction): + """Checks the response for a title.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'Your answer must contain a title, wrapped in double angular brackets,' + ' such as <>.') + return self._description_pattern + + def get_instruction_args(self): + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks if the response contains a title.""" + pattern = r'<<[^\n]+>>' + re_pattern = re.compile(pattern) + titles = re.findall(re_pattern, value) + + for title in titles: + if title.lstrip('<').rstrip('>').strip(): + return True + return False + + +class LetterFrequencyChecker(Instruction): + """Checks letter frequency.""" + + def build_description(self, + *, + letter=None, + let_frequency=None, + let_relation=None): + """Build the instruction description. + + Args: + letter: A string representing a letter that is expected in the response. + let_frequency: An integer specifying the number of times `keyword` is + expected to appear in the response. + let_relation: A string in (`less than`, `at least`), defining the + relational operator for comparison. Two relational comparisons are + supported for now; if 'less than', the actual number of + occurrences < frequency; if 'at least', the actual number of + occurrences >= frequency. + + Returns: + A string representing the instruction description. + """ + if (not letter or len(letter) > 1 or ord(letter.lower()) < 97 + or ord(letter.lower()) > 122): + self._letter = random.choice(list(string.ascii_letters)) + else: + self._letter = letter.strip() + self._letter = self._letter.lower() + + self._frequency = let_frequency + if self._frequency is None or self._frequency < 0: + self._frequency = random.randint(1, _LETTER_FREQUENCY) + + if let_relation is None: + self._comparison_relation = random.choice(_COMPARISON_RELATION) + elif let_relation not in _COMPARISON_RELATION: + raise ValueError( + 'The supported relation for comparison must be in ' + f'{_COMPARISON_RELATION}, but {let_relation} is given.') + else: + self._comparison_relation = let_relation + + self._description_pattern = ( + 'In your response, the letter {letter} should appear {let_relation}' + ' {let_frequency} times.') + + return self._description_pattern.format( + letter=self._letter, + let_frequency=self._frequency, + let_relation=self._comparison_relation, + ) + + def get_instruction_args(self): + """Returns the keyword args of build description.""" + return { + 'letter': self._letter, + 'let_frequency': self._frequency, + 'let_relation': self._comparison_relation + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['letter', 'let_frequency', 'let_relation'] + + def check_following(self, value): + """Checks that the response contains the letter at the right + frequency.""" + value = value.lower() + letters = collections.Counter(value) + + if self._comparison_relation == _COMPARISON_RELATION[0]: + return letters[self._letter] < self._frequency + else: + return letters[self._letter] >= self._frequency + + +class CapitalLettersEnglishChecker(Instruction): + """Checks that the response is in english and is in all capital letters.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'Your entire response should be in English, and in all capital letters.' + ) + return self._description_pattern + + def get_instruction_args(self): + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks that the response is in English and in all capital + letters.""" + assert isinstance(value, str) + + try: + return value.isupper() and langdetect.detect(value) == 'en' + except langdetect.LangDetectException as e: + # Count as instruction is followed. + logging.error('Unable to detect language for text %s due to %s', + value, e) # refex: disable=pytotw.037 + return True + + +class LowercaseLettersEnglishChecker(Instruction): + """Checks that the response is in english and is in all lowercase + letters.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'Your entire response should be in English, and in all lowercase' + ' letters. No capital letters are allowed.') + return self._description_pattern + + def get_instruction_args(self): + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks that the response is in English and in all lowercase + letters.""" + assert isinstance(value, str) + + try: + return value.islower() and langdetect.detect(value) == 'en' + except langdetect.LangDetectException as e: + # Count as instruction is followed. + logging.error('Unable to detect language for text %s due to %s', + value, e) # refex: disable=pytotw.037 + return True + + +class CommaChecker(Instruction): + """Checks the response for no commas.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'In your entire response, refrain from the use of any commas.') + return self._description_pattern + + def get_instruction_args(self): + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks that the response does not contain commas.""" + return not re.search(r'\,', value) + + +class CapitalWordFrequencyChecker(Instruction): + """Checks frequency of words with all capital letters.""" + + def build_description( + self, + capital_frequency=None, + capital_relation=None, + ): + """Build the instruction description. + + Args: + capital_frequency: An integer that represents the number of words that + should be in all capital letters. + capital_relation: A string that is 'at least' or 'at most' that refers to + the frequency. + + Returns: + A string representing the instruction description. + """ + self._frequency = capital_frequency + if self._frequency is None: + self._frequency = random.randint(1, _ALL_CAPITAL_WORD_FREQUENCY) + + self._comparison_relation = capital_relation + if capital_relation is None: + self._comparison_relation = random.choice(_COMPARISON_RELATION) + elif capital_relation not in _COMPARISON_RELATION: + raise ValueError( + 'The supported relation for comparison must be in ' + f'{_COMPARISON_RELATION}, but {capital_relation} is given.') + + self._description_pattern = ( + 'In your response, words with all capital letters should appear' + ' {relation} {frequency} times.') + + return self._description_pattern.format( + frequency=self._frequency, relation=self._comparison_relation) + + def get_instruction_args(self): + """Returns the keyword args of build description.""" + return { + 'capital_frequency': self._frequency, + 'capital_relation': self._comparison_relation, + } + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return ['capital_frequency', 'capital_relation'] + + def check_following(self, value): + """Checks the frequency of words with all capital letters.""" + # Hyphenated words will count as one word + words = instructions_util.nltk.word_tokenize(value) + capital_words = [word for word in words if word.isupper()] + + capital_words = len(capital_words) + + if self._comparison_relation == _COMPARISON_RELATION[0]: + return capital_words < self._frequency + else: + return capital_words >= self._frequency + + +class QuotationChecker(Instruction): + """Checks response is wrapped with double quotation marks.""" + + def build_description(self): + """Build the instruction description.""" + self._description_pattern = ( + 'Wrap your entire response with double quotation marks.') + return self._description_pattern + + def get_instruction_args(self): + """Returns the keyword args of build description.""" + return None + + def get_instruction_args_keys(self): + """Returns the args keys of `build_description`.""" + return [] + + def check_following(self, value): + """Checks if the response is wrapped with double quotation marks.""" + value = value.strip() + return len(value) > 1 and value[0] == '"' and value[-1] == '"' diff --git a/opencompass/datasets/IFEval/instructions_registry.py b/opencompass/datasets/IFEval/instructions_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..d4ea39b06943b61d254fd9c94a563fbda475da43 --- /dev/null +++ b/opencompass/datasets/IFEval/instructions_registry.py @@ -0,0 +1,190 @@ +# Copyright 2023 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Registry of all instructions.""" +import opencompass.datasets.IFEval.instructions as instructions + +_KEYWORD = 'keywords:' + +_LANGUAGE = 'language:' + +_LENGTH = 'length_constraints:' + +_CONTENT = 'detectable_content:' + +_FORMAT = 'detectable_format:' + +_MULTITURN = 'multi-turn:' + +_COMBINATION = 'combination:' + +_STARTEND = 'startend:' + +_CHANGE_CASES = 'change_case:' + +_PUNCTUATION = 'punctuation:' + +INSTRUCTION_DICT = { + _KEYWORD + 'existence': + instructions.KeywordChecker, + _KEYWORD + 'frequency': + instructions.KeywordFrequencyChecker, + # TODO(jeffreyzhou): make a proper set of sentences to choose from + # _KEYWORD + "key_sentences": instructions.KeySentenceChecker, + _KEYWORD + 'forbidden_words': + instructions.ForbiddenWords, + _KEYWORD + 'letter_frequency': + instructions.LetterFrequencyChecker, + _LANGUAGE + 'response_language': + instructions.ResponseLanguageChecker, + _LENGTH + 'number_sentences': + instructions.NumberOfSentences, + _LENGTH + 'number_paragraphs': + instructions.ParagraphChecker, + _LENGTH + 'number_words': + instructions.NumberOfWords, + _LENGTH + 'nth_paragraph_first_word': + instructions.ParagraphFirstWordCheck, + _CONTENT + 'number_placeholders': + instructions.PlaceholderChecker, + _CONTENT + 'postscript': + instructions.PostscriptChecker, + _FORMAT + 'number_bullet_lists': + instructions.BulletListChecker, + # TODO(jeffreyzhou): Pre-create paragraph or use prompt to replace + # _CONTENT + "rephrase_paragraph": instructions.RephraseParagraph, + _FORMAT + 'constrained_response': + instructions.ConstrainedResponseChecker, + _FORMAT + 'number_highlighted_sections': + (instructions.HighlightSectionChecker), + _FORMAT + 'multiple_sections': + instructions.SectionChecker, + # TODO(tianjianlu): Re-enable rephrasing with preprocessing the message. + # _FORMAT + "rephrase": instructions.RephraseChecker, + _FORMAT + 'json_format': + instructions.JsonFormat, + _FORMAT + 'title': + instructions.TitleChecker, + # TODO(tianjianlu): Re-enable with specific prompts. + # _MULTITURN + "constrained_start": instructions.ConstrainedStartChecker, + _COMBINATION + 'two_responses': + instructions.TwoResponsesChecker, + _COMBINATION + 'repeat_prompt': + instructions.RepeatPromptThenAnswer, + _STARTEND + 'end_checker': + instructions.EndChecker, + _CHANGE_CASES + 'capital_word_frequency': + instructions.CapitalWordFrequencyChecker, + _CHANGE_CASES + 'english_capital': + instructions.CapitalLettersEnglishChecker, + _CHANGE_CASES + 'english_lowercase': + instructions.LowercaseLettersEnglishChecker, + _PUNCTUATION + 'no_comma': + instructions.CommaChecker, + _STARTEND + 'quotation': + instructions.QuotationChecker, +} + +INSTRUCTION_CONFLICTS = { + _KEYWORD + 'existence': {_KEYWORD + 'existence'}, + _KEYWORD + 'frequency': {_KEYWORD + 'frequency'}, + # TODO(jeffreyzhou): make a proper set of sentences to choose from + # _KEYWORD + "key_sentences": instructions.KeySentenceChecker, + _KEYWORD + 'forbidden_words': {_KEYWORD + 'forbidden_words'}, + _KEYWORD + 'letter_frequency': {_KEYWORD + 'letter_frequency'}, + _LANGUAGE + 'response_language': { + _LANGUAGE + 'response_language', + _FORMAT + 'multiple_sections', + _KEYWORD + 'existence', + _KEYWORD + 'frequency', + _KEYWORD + 'forbidden_words', + _STARTEND + 'end_checker', + _CHANGE_CASES + 'english_capital', + _CHANGE_CASES + 'english_lowercase', + }, + _LENGTH + 'number_sentences': {_LENGTH + 'number_sentences'}, + _LENGTH + 'number_paragraphs': { + _LENGTH + 'number_paragraphs', + _LENGTH + 'nth_paragraph_first_word', + _LENGTH + 'number_sentences', + _LENGTH + 'nth_paragraph_first_word', + }, + _LENGTH + 'number_words': {_LENGTH + 'number_words'}, + _LENGTH + 'nth_paragraph_first_word': { + _LENGTH + 'nth_paragraph_first_word', + _LENGTH + 'number_paragraphs', + }, + _CONTENT + 'number_placeholders': {_CONTENT + 'number_placeholders'}, + _CONTENT + 'postscript': {_CONTENT + 'postscript'}, + _FORMAT + 'number_bullet_lists': {_FORMAT + 'number_bullet_lists'}, + # TODO(jeffreyzhou): Pre-create paragraph or use prompt to replace + # _CONTENT + "rephrase_paragraph": instructions.RephraseParagraph, + _FORMAT + 'constrained_response': + set(INSTRUCTION_DICT.keys()), + _FORMAT + 'number_highlighted_sections': + {_FORMAT + 'number_highlighted_sections'}, + _FORMAT + 'multiple_sections': { + _FORMAT + 'multiple_sections', + _LANGUAGE + 'response_language', + _FORMAT + 'number_highlighted_sections', + }, + # TODO(tianjianlu): Re-enable rephrasing with preprocessing the message. + # _FORMAT + "rephrase": instructions.RephraseChecker, + _FORMAT + 'json_format': + set(INSTRUCTION_DICT.keys()).difference( + {_KEYWORD + 'forbidden_words', _KEYWORD + 'existence'}), + _FORMAT + 'title': {_FORMAT + 'title'}, + # TODO(tianjianlu): Re-enable with specific prompts. + # _MULTITURN + "constrained_start": instructions.ConstrainedStartChecker, + _COMBINATION + 'two_responses': + set(INSTRUCTION_DICT.keys()).difference({ + _KEYWORD + 'forbidden_words', _KEYWORD + 'existence', + _LANGUAGE + 'response_language', _FORMAT + 'title', + _PUNCTUATION + 'no_comma' + }), + _COMBINATION + 'repeat_prompt': + set(INSTRUCTION_DICT.keys()).difference( + {_KEYWORD + 'existence', _FORMAT + 'title', + _PUNCTUATION + 'no_comma'}), + _STARTEND + 'end_checker': {_STARTEND + 'end_checker'}, + _CHANGE_CASES + 'capital_word_frequency': { + _CHANGE_CASES + 'capital_word_frequency', + _CHANGE_CASES + 'english_lowercase', + _CHANGE_CASES + 'english_capital', + }, + _CHANGE_CASES + 'english_capital': {_CHANGE_CASES + 'english_capital'}, + _CHANGE_CASES + 'english_lowercase': { + _CHANGE_CASES + 'english_lowercase', + _CHANGE_CASES + 'english_capital', + }, + _PUNCTUATION + 'no_comma': {_PUNCTUATION + 'no_comma'}, + _STARTEND + 'quotation': {_STARTEND + 'quotation', _FORMAT + 'title'}, +} + + +def conflict_make(conflicts): + """Makes sure if A conflicts with B, B will conflict with A. + + Args: + conflicts: Dictionary of potential conflicts where key is instruction id + and value is set of instruction ids that it conflicts with. + + Returns: + Revised version of the dictionary. All instructions conflict with + themselves. If A conflicts with B, B will conflict with A. + """ + for key in conflicts: + for k in conflicts[key]: + conflicts[k].add(key) + conflicts[key].add(key) + return conflicts diff --git a/opencompass/datasets/IFEval/instructions_util.py b/opencompass/datasets/IFEval/instructions_util.py new file mode 100644 index 0000000000000000000000000000000000000000..fbccee8097ae58b726a7140c5a19d86b82d66492 --- /dev/null +++ b/opencompass/datasets/IFEval/instructions_util.py @@ -0,0 +1,145 @@ +# flake8: noqa +# yapf: disable + +# Copyright 2023 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utility library of instructions.""" + +import functools +import random +import re + +import nltk + +WORD_LIST = ['western', 'sentence', 'signal', 'dump', 'spot', 'opposite', 'bottom', 'potato', 'administration', 'working', 'welcome', 'morning', 'good', 'agency', 'primary', 'wish', 'responsibility', 'press', 'problem', 'president', 'steal', 'brush', 'read', 'type', 'beat', 'trainer', 'growth', 'lock', 'bone', 'case', 'equal', 'comfortable', 'region', 'replacement', 'performance', 'mate', 'walk', 'medicine', 'film', 'thing', 'rock', 'tap', 'total', 'competition', 'ease', 'south', 'establishment', 'gather', 'parking', 'world', 'plenty', 'breath', 'claim', 'alcohol', 'trade', 'dear', 'highlight', 'street', 'matter', 'decision', 'mess', 'agreement', 'studio', 'coach', 'assist', 'brain', 'wing', 'style', 'private', 'top', 'brown', 'leg', 'buy', 'procedure', 'method', 'speed', 'high', 'company', 'valuable', 'pie', 'analyst', 'session', 'pattern', 'district', 'pleasure', 'dinner', 'swimming', 'joke', 'order', 'plate', 'department', 'motor', 'cell', 'spend', 'cabinet', 'difference', 'power', 'examination', 'engine', 'horse', 'dimension', 'pay', 'toe', 'curve', 'literature', 'bother', 'fire', 'possibility', 'debate', 'activity', 'passage', 'hello', 'cycle', 'background', 'quiet', 'author', 'effect', 'actor', 'page', 'bicycle', 'error', 'throat', 'attack', 'character', 'phone', 'tea', 'increase', 'outcome', 'file', 'specific', 'inspector', 'internal', 'potential', 'staff', 'building', 'employer', 'shoe', 'hand', 'direction', 'garden', 'purchase', 'interview', 'study', 'recognition', 'member', 'spiritual', 'oven', 'sandwich', 'weird', 'passenger', 'particular', 'response', 'reaction', 'size', 'variation', 'a', 'cancel', 'candy', 'exit', 'guest', 'condition', 'fly', 'price', 'weakness', 'convert', 'hotel', 'great', 'mouth', 'mind', 'song', 'sugar', 'suspect', 'telephone', 'ear', 'roof', 'paint', 'refrigerator', 'organization', 'jury', 'reward', 'engineering', 'day', 'possession', 'crew', 'bar', 'road', 'description', 'celebration', 'score', 'mark', 'letter', 'shower', 'suggestion', 'sir', 'luck', 'national', 'progress', 'hall', 'stroke', 'theory', 'offer', 'story', 'tax', 'definition', 'history', 'ride', 'medium', 'opening', 'glass', 'elevator', 'stomach', 'question', 'ability', 'leading', 'village', 'computer', 'city', 'grand', 'confidence', 'candle', 'priest', 'recommendation', 'point', 'necessary', 'body', 'desk', 'secret', 'horror', 'noise', 'culture', 'warning', 'water', 'round', 'diet', 'flower', 'bus', 'tough', 'permission', 'week', 'prompt', 'connection', 'abuse', 'height', 'save', 'corner', 'border', 'stress', 'drive', 'stop', 'rip', 'meal', 'listen', 'confusion', 'girlfriend', 'living', 'relation', 'significance', 'plan', 'creative', 'atmosphere', 'blame', 'invite', 'housing', 'paper', 'drink', 'roll', 'silver', 'drunk', 'age', 'damage', 'smoke', 'environment', 'pack', 'savings', 'influence', 'tourist', 'rain', 'post', 'sign', 'grandmother', 'run', 'profit', 'push', 'clerk', 'final', 'wine', 'swim', 'pause', 'stuff', 'singer', 'funeral', 'average', 'source', 'scene', 'tradition', 'personal', 'snow', 'nobody', 'distance', 'sort', 'sensitive', 'animal', 'major', 'negotiation', 'click', 'mood', 'period', 'arrival', 'expression', 'holiday', 'repeat', 'dust', 'closet', 'gold', 'bad', 'sail', 'combination', 'clothes', 'emphasis', 'duty', 'black', 'step', 'school', 'jump', 'document', 'professional', 'lip', 'chemical', 'front', 'wake', 'while', 'inside', 'watch', 'row', 'subject', 'penalty', 'balance', 'possible', 'adult', 'aside', 'sample', 'appeal', 'wedding', 'depth', 'king', 'award', 'wife', 'blow', 'site', 'camp', 'music', 'safe', 'gift', 'fault', 'guess', 'act', 'shame', 'drama', 'capital', 'exam', 'stupid', 'record', 'sound', 'swing', 'novel', 'minimum', 'ratio', 'machine', 'shape', 'lead', 'operation', 'salary', 'cloud', 'affair', 'hit', 'chapter', 'stage', 'quantity', 'access', 'army', 'chain', 'traffic', 'kick', 'analysis', 'airport', 'time', 'vacation', 'philosophy', 'ball', 'chest', 'thanks', 'place', 'mountain', 'advertising', 'red', 'past', 'rent', 'return', 'tour', 'house', 'construction', 'net', 'native', 'war', 'figure', 'fee', 'spray', 'user', 'dirt', 'shot', 'task', 'stick', 'friend', 'software', 'promotion', 'interaction', 'surround', 'block', 'purpose', 'practice', 'conflict', 'routine', 'requirement', 'bonus', 'hole', 'state', 'junior', 'sweet', 'catch', 'tear', 'fold', 'wall', 'editor', 'life', 'position', 'pound', 'respect', 'bathroom', 'coat', 'script', 'job', 'teach', 'birth', 'view', 'resolve', 'theme', 'employee', 'doubt', 'market', 'education', 'serve', 'recover', 'tone', 'harm', 'miss', 'union', 'understanding', 'cow', 'river', 'association', 'concept', 'training', 'recipe', 'relationship', 'reserve', 'depression', 'proof', 'hair', 'revenue', 'independent', 'lift', 'assignment', 'temporary', 'amount', 'loss', 'edge', 'track', 'check', 'rope', 'estimate', 'pollution', 'stable', 'message', 'delivery', 'perspective', 'mirror', 'assistant', 'representative', 'witness', 'nature', 'judge', 'fruit', 'tip', 'devil', 'town', 'emergency', 'upper', 'drop', 'stay', 'human', 'neck', 'speaker', 'network', 'sing', 'resist', 'league', 'trip', 'signature', 'lawyer', 'importance', 'gas', 'choice', 'engineer', 'success', 'part', 'external', 'worker', 'simple', 'quarter', 'student', 'heart', 'pass', 'spite', 'shift', 'rough', 'lady', 'grass', 'community', 'garage', 'youth', 'standard', 'skirt', 'promise', 'blind', 'television', 'disease', 'commission', 'positive', 'energy', 'calm', 'presence', 'tune', 'basis', 'preference', 'head', 'common', 'cut', 'somewhere', 'presentation', 'current', 'thought', 'revolution', 'effort', 'master', 'implement', 'republic', 'floor', 'principle', 'stranger', 'shoulder', 'grade', 'button', 'tennis', 'police', 'collection', 'account', 'register', 'glove', 'divide', 'professor', 'chair', 'priority', 'combine', 'peace', 'extension', 'maybe', 'evening', 'frame', 'sister', 'wave', 'code', 'application', 'mouse', 'match', 'counter', 'bottle', 'half', 'cheek', 'resolution', 'back', 'knowledge', 'make', 'discussion', 'screw', 'length', 'accident', 'battle', 'dress', 'knee', 'log', 'package', 'it', 'turn', 'hearing', 'newspaper', 'layer', 'wealth', 'profile', 'imagination', 'answer', 'weekend', 'teacher', 'appearance', 'meet', 'bike', 'rise', 'belt', 'crash', 'bowl', 'equivalent', 'support', 'image', 'poem', 'risk', 'excitement', 'remote', 'secretary', 'public', 'produce', 'plane', 'display', 'money', 'sand', 'situation', 'punch', 'customer', 'title', 'shake', 'mortgage', 'option', 'number', 'pop', 'window', 'extent', 'nothing', 'experience', 'opinion', 'departure', 'dance', 'indication', 'boy', 'material', 'band', 'leader', 'sun', 'beautiful', 'muscle', 'farmer', 'variety', 'fat', 'handle', 'director', 'opportunity', 'calendar', 'outside', 'pace', 'bath', 'fish', 'consequence', 'put', 'owner', 'go', 'doctor', 'information', 'share', 'hurt', 'protection', 'career', 'finance', 'force', 'golf', 'garbage', 'aspect', 'kid', 'food', 'boot', 'milk', 'respond', 'objective', 'reality', 'raw', 'ring', 'mall', 'one', 'impact', 'area', 'news', 'international', 'series', 'impress', 'mother', 'shelter', 'strike', 'loan', 'month', 'seat', 'anything', 'entertainment', 'familiar', 'clue', 'year', 'glad', 'supermarket', 'natural', 'god', 'cost', 'conversation', 'tie', 'ruin', 'comfort', 'earth', 'storm', 'percentage', 'assistance', 'budget', 'strength', 'beginning', 'sleep', 'other', 'young', 'unit', 'fill', 'store', 'desire', 'hide', 'value', 'cup', 'maintenance', 'nurse', 'function', 'tower', 'role', 'class', 'camera', 'database', 'panic', 'nation', 'basket', 'ice', 'art', 'spirit', 'chart', 'exchange', 'feedback', 'statement', 'reputation', 'search', 'hunt', 'exercise', 'nasty', 'notice', 'male', 'yard', 'annual', 'collar', 'date', 'platform', 'plant', 'fortune', 'passion', 'friendship', 'spread', 'cancer', 'ticket', 'attitude', 'island', 'active', 'object', 'service', 'buyer', 'bite', 'card', 'face', 'steak', 'proposal', 'patient', 'heat', 'rule', 'resident', 'broad', 'politics', 'west', 'knife', 'expert', 'girl', 'design', 'salt', 'baseball', 'grab', 'inspection', 'cousin', 'couple', 'magazine', 'cook', 'dependent', 'security', 'chicken', 'version', 'currency', 'ladder', 'scheme', 'kitchen', 'employment', 'local', 'attention', 'manager', 'fact', 'cover', 'sad', 'guard', 'relative', 'county', 'rate', 'lunch', 'program', 'initiative', 'gear', 'bridge', 'breast', 'talk', 'dish', 'guarantee', 'beer', 'vehicle', 'reception', 'woman', 'substance', 'copy', 'lecture', 'advantage', 'park', 'cold', 'death', 'mix', 'hold', 'scale', 'tomorrow', 'blood', 'request', 'green', 'cookie', 'church', 'strip', 'forever', 'beyond', 'debt', 'tackle', 'wash', 'following', 'feel', 'maximum', 'sector', 'sea', 'property', 'economics', 'menu', 'bench', 'try', 'language', 'start', 'call', 'solid', 'address', 'income', 'foot', 'senior', 'honey', 'few', 'mixture', 'cash', 'grocery', 'link', 'map', 'form', 'factor', 'pot', 'model', 'writer', 'farm', 'winter', 'skill', 'anywhere', 'birthday', 'policy', 'release', 'husband', 'lab', 'hurry', 'mail', 'equipment', 'sink', 'pair', 'driver', 'consideration', 'leather', 'skin', 'blue', 'boat', 'sale', 'brick', 'two', 'feed', 'square', 'dot', 'rush', 'dream', 'location', 'afternoon', 'manufacturer', 'control', 'occasion', 'trouble', 'introduction', 'advice', 'bet', 'eat', 'kill', 'category', 'manner', 'office', 'estate', 'pride', 'awareness', 'slip', 'crack', 'client', 'nail', 'shoot', 'membership', 'soft', 'anybody', 'web', 'official', 'individual', 'pizza', 'interest', 'bag', 'spell', 'profession', 'queen', 'deal', 'resource', 'ship', 'guy', 'chocolate', 'joint', 'formal', 'upstairs', 'car', 'resort', 'abroad', 'dealer', 'associate', 'finger', 'surgery', 'comment', 'team', 'detail', 'crazy', 'path', 'tale', 'initial', 'arm', 'radio', 'demand', 'single', 'draw', 'yellow', 'contest', 'piece', 'quote', 'pull', 'commercial', 'shirt', 'contribution', 'cream', 'channel', 'suit', 'discipline', 'instruction', 'concert', 'speech', 'low', 'effective', 'hang', 'scratch', 'industry', 'breakfast', 'lay', 'join', 'metal', 'bedroom', 'minute', 'product', 'rest', 'temperature', 'many', 'give', 'argument', 'print', 'purple', 'laugh', 'health', 'credit', 'investment', 'sell', 'setting', 'lesson', 'egg', 'middle', 'marriage', 'level', 'evidence', 'phrase', 'love', 'self', 'benefit', 'guidance', 'affect', 'you', 'dad', 'anxiety', 'special', 'boyfriend', 'test', 'blank', 'payment', 'soup', 'obligation', 'reply', 'smile', 'deep', 'complaint', 'addition', 'review', 'box', 'towel', 'minor', 'fun', 'soil', 'issue', 'cigarette', 'internet', 'gain', 'tell', 'entry', 'spare', 'incident', 'family', 'refuse', 'branch', 'can', 'pen', 'grandfather', 'constant', 'tank', 'uncle', 'climate', 'ground', 'volume', 'communication', 'kind', 'poet', 'child', 'screen', 'mine', 'quit', 'gene', 'lack', 'charity', 'memory', 'tooth', 'fear', 'mention', 'marketing', 'reveal', 'reason', 'court', 'season', 'freedom', 'land', 'sport', 'audience', 'classroom', 'law', 'hook', 'win', 'carry', 'eye', 'smell', 'distribution', 'research', 'country', 'dare', 'hope', 'whereas', 'stretch', 'library', 'if', 'delay', 'college', 'plastic', 'book', 'present', 'use', 'worry', 'champion', 'goal', 'economy', 'march', 'election', 'reflection', 'midnight', 'slide', 'inflation', 'action', 'challenge', 'guitar', 'coast', 'apple', 'campaign', 'field', 'jacket', 'sense', 'way', 'visual', 'remove', 'weather', 'trash', 'cable', 'regret', 'buddy', 'beach', 'historian', 'courage', 'sympathy', 'truck', 'tension', 'permit', 'nose', 'bed', 'son', 'person', 'base', 'meat', 'usual', 'air', 'meeting', 'worth', 'game', 'independence', 'physical', 'brief', 'play', 'raise', 'board', 'she', 'key', 'writing', 'pick', 'command', 'party', 'yesterday', 'spring', 'candidate', 'physics', 'university', 'concern', 'development', 'change', 'string', 'target', 'instance', 'room', 'bitter', 'bird', 'football', 'normal', 'split', 'impression', 'wood', 'long', 'meaning', 'stock', 'cap', 'leadership', 'media', 'ambition', 'fishing', 'essay', 'salad', 'repair', 'today', 'designer', 'night', 'bank', 'drawing', 'inevitable', 'phase', 'vast', 'chip', 'anger', 'switch', 'cry', 'twist', 'personality', 'attempt', 'storage', 'being', 'preparation', 'bat', 'selection', 'white', 'technology', 'contract', 'side', 'section', 'station', 'till', 'structure', 'tongue', 'taste', 'truth', 'difficulty', 'group', 'limit', 'main', 'move', 'feeling', 'light', 'example', 'mission', 'might', 'wait', 'wheel', 'shop', 'host', 'classic', 'alternative', 'cause', 'agent', 'consist', 'table', 'airline', 'text', 'pool', 'craft', 'range', 'fuel', 'tool', 'partner', 'load', 'entrance', 'deposit', 'hate', 'article', 'video', 'summer', 'feature', 'extreme', 'mobile', 'hospital', 'flight', 'fall', 'pension', 'piano', 'fail', 'result', 'rub', 'gap', 'system', 'report', 'suck', 'ordinary', 'wind', 'nerve', 'ask', 'shine', 'note', 'line', 'mom', 'perception', 'brother', 'reference', 'bend', 'charge', 'treat', 'trick', 'term', 'homework', 'bake', 'bid', 'status', 'project', 'strategy', 'orange', 'let', 'enthusiasm', 'parent', 'concentrate', 'device', 'travel', 'poetry', 'business', 'society', 'kiss', 'end', 'vegetable', 'employ', 'schedule', 'hour', 'brave', 'focus', 'process', 'movie', 'illegal', 'general', 'coffee', 'ad', 'highway', 'chemistry', 'psychology', 'hire', 'bell', 'conference', 'relief', 'show', 'neat', 'funny', 'weight', 'quality', 'club', 'daughter', 'zone', 'touch', 'tonight', 'shock', 'burn', 'excuse', 'name', 'survey', 'landscape', 'advance', 'satisfaction', 'bread', 'disaster', 'item', 'hat', 'prior', 'shopping', 'visit', 'east', 'photo', 'home', 'idea', 'father', 'comparison', 'cat', 'pipe', 'winner', 'count', 'lake', 'fight', 'prize', 'foundation', 'dog', 'keep', 'ideal', 'fan', 'struggle', 'peak', 'safety', 'solution', 'hell', 'conclusion', 'population', 'strain', 'alarm', 'measurement', 'second', 'train', 'race', 'due', 'insurance', 'boss', 'tree', 'monitor', 'sick', 'course', 'drag', 'appointment', 'slice', 'still', 'care', 'patience', 'rich', 'escape', 'emotion', 'royal', 'female', 'childhood', 'government', 'picture', 'will', 'sock', 'big', 'gate', 'oil', 'cross', 'pin', 'improvement', 'championship', 'silly', 'help', 'sky', 'pitch', 'man', 'diamond', 'most', 'transition', 'work', 'science', 'committee', 'moment', 'fix', 'teaching', 'dig', 'specialist', 'complex', 'guide', 'people', 'dead', 'voice', 'original', 'break', 'topic', 'data', 'degree', 'reading', 'recording', 'bunch', 'reach', 'judgment', 'lie', 'regular', 'set', 'painting', 'mode', 'list', 'player', 'bear', 'north', 'wonder', 'carpet', 'heavy', 'officer', 'negative', 'clock', 'unique', 'baby', 'pain', 'assumption', 'disk', 'iron', 'bill', 'drawer', 'look', 'double', 'mistake', 'finish', 'future', 'brilliant', 'contact', 'math', 'rice', 'leave', 'restaurant', 'discount', 'sex', 'virus', 'bit', 'trust', 'event', 'wear', 'juice', 'failure', 'bug', 'context', 'mud', 'whole', 'wrap', 'intention', 'draft', 'pressure', 'cake', 'dark', 'explanation', 'space', 'angle', 'word', 'efficiency', 'management', 'habit', 'star', 'chance', 'finding', 'transportation', 'stand', 'criticism', 'flow', 'door', 'injury', 'insect', 'surprise', 'apartment'] # pylint: disable=line-too-long + +# ISO 639-1 codes to language names. +LANGUAGE_CODES = { + 'en': 'English', + 'es': 'Spanish', + 'pt': 'Portuguese', + 'ar': 'Arabic', + 'hi': 'Hindi', + 'fr': 'French', + 'ru': 'Russian', + 'de': 'German', + 'ja': 'Japanese', + 'it': 'Italian', + 'bn': 'Bengali', + 'uk': 'Ukrainian', + 'th': 'Thai', + 'ur': 'Urdu', + 'ta': 'Tamil', + 'te': 'Telugu', + 'bg': 'Bulgarian', + 'ko': 'Korean', + 'pl': 'Polish', + 'he': 'Hebrew', + 'fa': 'Persian', + 'vi': 'Vietnamese', + 'ne': 'Nepali', + 'sw': 'Swahili', + 'kn': 'Kannada', + 'mr': 'Marathi', + 'gu': 'Gujarati', + 'pa': 'Punjabi', + 'ml': 'Malayalam', + 'fi': 'Finnish', +} + +_ALPHABETS = '([A-Za-z])' +_PREFIXES = '(Mr|St|Mrs|Ms|Dr)[.]' +_SUFFIXES = '(Inc|Ltd|Jr|Sr|Co)' +_STARTERS = r'(Mr|Mrs|Ms|Dr|Prof|Capt|Cpt|Lt|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)' +_ACRONYMS = '([A-Z][.][A-Z][.](?:[A-Z][.])?)' +_WEBSITES = '[.](com|net|org|io|gov|edu|me)' +_DIGITS = '([0-9])' +_MULTIPLE_DOTS = r'\.{2,}' + + +def split_into_sentences(text): + """Split the text into sentences. + + Args: + text: A string that consists of more than or equal to one sentences. + + Returns: + A list of strings where each string is a sentence. + """ + text = ' ' + text + ' ' + text = text.replace('\n', ' ') + text = re.sub(_PREFIXES, '\\1', text) + text = re.sub(_WEBSITES, '\\1', text) + text = re.sub(_DIGITS + '[.]' + _DIGITS, '\\1\\2', text) + text = re.sub( + _MULTIPLE_DOTS, + lambda match: '' * len(match.group(0)) + '', + text, + ) + if 'Ph.D' in text: + text = text.replace('Ph.D.', 'PhD') + text = re.sub(r'\s' + _ALPHABETS + '[.] ', ' \\1 ', text) + text = re.sub(_ACRONYMS + ' ' + _STARTERS, '\\1 \\2', text) + text = re.sub( + _ALPHABETS + '[.]' + _ALPHABETS + '[.]' + _ALPHABETS + '[.]', + '\\1\\2\\3', + text, + ) + text = re.sub(_ALPHABETS + '[.]' + _ALPHABETS + '[.]', '\\1\\2', + text) + text = re.sub(' ' + _SUFFIXES + '[.] ' + _STARTERS, ' \\1 \\2', text) + text = re.sub(' ' + _SUFFIXES + '[.]', ' \\1', text) + text = re.sub(' ' + _ALPHABETS + '[.]', ' \\1', text) + if '”' in text: + text = text.replace('.”', '”.') + if '"' in text: + text = text.replace('."', '".') + if '!' in text: + text = text.replace('!"', '"!') + if '?' in text: + text = text.replace('?"', '"?') + text = text.replace('.', '.') + text = text.replace('?', '?') + text = text.replace('!', '!') + text = text.replace('', '.') + sentences = text.split('') + sentences = [s.strip() for s in sentences] + if sentences and not sentences[-1]: + sentences = sentences[:-1] + return sentences + + +def count_words(text): + """Counts the number of words.""" + tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+') + tokens = tokenizer.tokenize(text) + num_words = len(tokens) + return num_words + + +@functools.lru_cache(maxsize=None) +def _get_sentence_tokenizer(): + return nltk.data.load('nltk:tokenizers/punkt/english.pickle') + + +def count_sentences(text): + """Count the number of sentences.""" + tokenizer = _get_sentence_tokenizer() + tokenized_sentences = tokenizer.tokenize(text) + return len(tokenized_sentences) + + +def generate_keywords(num_keywords): + """Randomly generates a few keywords.""" + return random.sample(WORD_LIST, k=num_keywords) diff --git a/opencompass/datasets/NPHardEval/__init__.py b/opencompass/datasets/NPHardEval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dcaabd11c8b67680d9e2d43eefbf841c425e4f2b --- /dev/null +++ b/opencompass/datasets/NPHardEval/__init__.py @@ -0,0 +1,9 @@ +from .cmp_GCP_D import * # noqa: F401, F403 +from .cmp_KSP import * # noqa: F401, F403 +from .cmp_TSP_D import * # noqa: F401, F403 +from .hard_GCP import * # noqa: F401, F403 +from .hard_MSP import * # noqa: F401, F403 +from .hard_TSP import * # noqa: F401, F403 +from .p_BSP import * # noqa: F401, F403 +from .p_EDP import * # noqa: F401, F403 +from .p_SPP import * # noqa: F401, F403 diff --git a/opencompass/datasets/NPHardEval/cmp_GCP_D.py b/opencompass/datasets/NPHardEval/cmp_GCP_D.py new file mode 100644 index 0000000000000000000000000000000000000000..5090fb50c823067de90803bf89e62336a1481b66 --- /dev/null +++ b/opencompass/datasets/NPHardEval/cmp_GCP_D.py @@ -0,0 +1,167 @@ +import ast + +try: + import networkx as nx +except ImportError: + nx = None + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import gcp_dPrompts + + +def q2text(q, p=gcp_dPrompts): + number_of_colors = q.split('\n')[0].split()[-2] # last character of the first line + number_of_vertices = q.split('\n')[1].split(' ')[2] # third word of the second line + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(total_vertices=number_of_vertices, number_of_colors=number_of_colors) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + '\n' + \ + '\n The graph is below: \n' + for line in q.split('\n')[2:]: + vertex_list = line.split(' ') + this_line = 'Vertex {} is connected to vertex {}.'.format( + vertex_list[1], vertex_list[2]) + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class CMP_GCP_D_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + for file_num in range(10): + with open(data_path + 'decision_data_GCP_{}.txt'.format(file_num)) as f: + data = f.read() + sample = data.split('\n\n')[:-1] + all_data += zip([file_num + 1] * len(sample), sample) + for (level, q) in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + q, + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class CMP_GCP_D_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + details = {} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = q.split('####\n')[-1] + try: + number_of_colors = int(q.split('\n')[0].split()[-2]) + output, reasoning = self.parse_xml_to_dict(output) + output_dict['output'] = output + output_dict['correctness'], _ = self.gcp_decision_check(q, output, number_of_colors) + except Exception as e: + print(f'Attempt failed: {e}') + output_dict['correctness'] = False + output_dict['reasoning'] = reasoning + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + details[str(index)] = {'q': q, 'output': output, 'result': r} + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + result['details'] = details + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + except Exception: + final_answer_element = '' + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def read_dimacs_format(self, dimacs_str): + lines = dimacs_str.strip().split('\n') + p_line = next(line for line in lines if line.startswith('p')) + _, _, num_vertices, num_edges = p_line.split() + num_vertices, num_edges = int(num_vertices), int(num_edges) + + adjacency_list = {i: set() for i in range(1, num_vertices + 1)} + for line in lines: + if line.startswith('e'): + _, vertex1, vertex2 = line.split() + vertex1, vertex2 = int(vertex1), int(vertex2) + if vertex1 in adjacency_list and vertex2 in adjacency_list: + adjacency_list[vertex1].add(vertex2) + adjacency_list[vertex2].add(vertex1) + + return num_vertices, adjacency_list + + def gcp_greedy_solution(self, adjacency_list): + """Provides a greedy solution to the GCP problem. + + :param adjacency_list: A dictionary of the adjacency list. + :return: A tuple of (num_colors, coloring). + """ + G = nx.Graph() + G.add_nodes_from(adjacency_list.keys()) + for vertex, neighbors in adjacency_list.items(): + for neighbor in neighbors: + G.add_edge(vertex, neighbor) + coloring = nx.coloring.greedy_color(G, strategy='largest_first') + num_colors = max(coloring.values()) + 1 + return num_colors, coloring + + def gcp_decision_check(self, dimacs_str, answer, k_colors): + """Check if the given GCP instance is feasible with k_colors. + + :param dimacs_str: The DIMACS format string of the GCP instance. + :param answer: The answer returned by the model. + :param k_colors: The target number of colors. + :return: A tuple of (is_correct, message). + """ + num_vertices, adjacency_list = self.read_dimacs_format(dimacs_str) + try: + is_feasible = answer.get('Feasible', 'no').lower() == 'yes' + except Exception: + return False, 'Feasible key not found' + num_colors, coloring = self.gcp_greedy_solution(adjacency_list) + exist_optimal = num_colors <= k_colors + if is_feasible != exist_optimal: + if exist_optimal: + return False, f'Feasibility mismatch: {coloring}' + else: + return False, f'Feasibility mismatch: {is_feasible} vs {exist_optimal}' + return True, 'Feasible' if is_feasible else 'Infeasible' diff --git a/opencompass/datasets/NPHardEval/cmp_KSP.py b/opencompass/datasets/NPHardEval/cmp_KSP.py new file mode 100644 index 0000000000000000000000000000000000000000..d1277a79fd7c028d2b3aad119cabd3806a1a4ade --- /dev/null +++ b/opencompass/datasets/NPHardEval/cmp_KSP.py @@ -0,0 +1,185 @@ +import ast +import json + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import kspPrompts + + +def q2text(q, p=kspPrompts): + knapsack_capacity = q['knapsack_capacity'] + items = q['items'] + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(knapsack_capacity=knapsack_capacity) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + \ + '\n The items details are as below: \n' + for item in items: + this_line = f"Item {item['id']} has weight {item['weight']} and value {item['value']}." + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class CMP_KSP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + with open(data_path + 'ksp_instances.json', 'r') as f: + data = json.load(f) + for sample in data: + level = len(sample['items']) - 3 + all_data.append((level, sample)) + for (level, q) in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class CMP_KSP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + details = {} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + try: + llm_string = q + output, reasoning = self.parse_xml_to_dict(llm_string) + output_dict['output'] = output + output_dict['correctness'], _ = self.kspCheck(q, output) + output_dict['reasoning'] = reasoning + output_dict['level'] = level + except Exception as e: + print(f'Attempt failed: {e}') + if output_dict: + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + else: + print(f'Failed to run {q}') + r = 'fail' + + result[r] += level + details[str(index)] = {'q': q, 'output': output, 'result': r} + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + result['details'] = details + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + except Exception: + final_answer_element = '' + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def ksp_optimal_solution(self, knapsacks, capacity): + """Provides the optimal solution for the KSP instance with dynamic + programming. + + :param knapsacks: A dictionary of the knapsacks. + :param capacity: The capacity of the knapsack. + :return: The optimal value. + """ + # num_knapsacks = len(knapsacks) + + # Create a one-dimensional array to store intermediate solutions + dp = [0] * (capacity + 1) + + for itemId, (weight, value) in knapsacks.items(): + for w in range(capacity, weight - 1, -1): + dp[w] = max(dp[w], value + dp[w - weight]) + + return dp[capacity] + + # KSP + def kspCheck(self, instance, solution): + """Validates the solution for the KSP instance. + + :param instance: A dictionary of the KSP instance. + :param solution: A dictionary of the solution. + :return: A tuple of (is_correct, message). + """ + # Change string key to integer key and value to boolean + items = instance.get('items', []) + knapsacks = { + item['id']: (item['weight'], item['value']) + for item in items + } + + ksp_optimal_value = self.ksp_optimal_solution( + knapsacks, instance['knapsack_capacity']) + + try: + is_feasible = (solution.get('Feasible', '').lower() == 'yes') + except Exception: + return False, f'Output format is incorrect.' + if is_feasible != (ksp_optimal_value > 0): + return False, f'The solution is {is_feasible} but the optimal solution is {ksp_optimal_value > 0}.' + + total_value = int(solution.get('TotalValue', -1)) + selectedItems = list(map(int, solution.get('SelectedItemIds', []))) + + if len(set(selectedItems)) != len(selectedItems): + return False, f'Duplicate items are selected.' + + total_weight = 0 + cum_value = 0 + + # Calculate total weight and value of selected items + for item in selectedItems: + if knapsacks.get(item, False): + weight, value = knapsacks[item] + total_weight += weight + cum_value += value + else: + return False, f'Item {item} does not exist.' + + # Check if the item weight exceeds the knapsack capacity + if total_weight > instance['knapsack_capacity']: + return False, f"Total weight {total_weight} exceeds knapsack capacity {instance['knapsack_capacity']}." + + if total_value != cum_value: + return False, f'The total value {total_value} does not match the cumulative value {cum_value} of the selected items.' + + if total_value != ksp_optimal_value: + return False, f'The total value {total_value} does not match the optimal value {ksp_optimal_value}.' + + return True, f'The solution is valid with total weight {total_weight} and total value {total_value}.' diff --git a/opencompass/datasets/NPHardEval/cmp_TSP_D.py b/opencompass/datasets/NPHardEval/cmp_TSP_D.py new file mode 100644 index 0000000000000000000000000000000000000000..bff15260ae11f49b2c5aaa7ab7bcc3104dd4b016 --- /dev/null +++ b/opencompass/datasets/NPHardEval/cmp_TSP_D.py @@ -0,0 +1,156 @@ +import ast +import json + +try: + import networkx as nx +except ImportError: + nx = None + +import pandas as pd +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import tsp_dPrompts + + +def q2text(adj_matrix, distance_limit, p=tsp_dPrompts): + total_cities = adj_matrix.shape[0] # exclude the last row + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(total_cities=total_cities, distance_limit=distance_limit) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + '\n' + \ + 'The distances between cities are below: \n' + + for i in range(adj_matrix.shape[0]): + for j in range(adj_matrix.shape[1]): + if i < j: # only use the upper triangle + this_line = 'The distance between City {} and City {} is {}.'.format(i, j, adj_matrix[i, j]) + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class CMP_TSP_D_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + for level in range(10): + for file_num in range(10): + df = pd.read_csv(data_path + 'decision_data_TSP_level_{}_instance_{}.csv'.format(level, file_num + 1), + header=None, + index_col=False) + all_data.append((level + 1, df)) + + for (level, q) in all_data: + threshold = q.iloc[-1, 0] # therashold is the last row + distance_matrix = q.iloc[: + -1].values # distance matrix is the rest of the rows + prompt = q2text(distance_matrix, threshold) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q.to_json()), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class CMP_TSP_D_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + details = {} + tsp_d_Results = [] + for index, (q, llm_string) in enumerate(zip(references, predictions)): + output_dict = {} + output, reasoning = self.parse_xml_to_dict(llm_string) + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + q = pd.DataFrame(eval(q)) + threshold = q.iloc[-1, 0] # therashold is the last row + distance_matrix = q.iloc[:-1].values # distance matrix is the rest of the rows + output_dict['output'] = output + try: + output_dict['correctness'], _ = self.tsp_decision_check(distance_matrix, threshold, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['reasoning'] = reasoning + output_dict['level'] = level + if output_dict: + tsp_d_Results.append(output_dict) + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + + result[r] += level + details[str(index)] = {'q': q, 'output': output, 'result': r} + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + result['details'] = details + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + except Exception: + final_answer_element = '' + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def tsp_approx(self, distance_matrix): + """Returns an approximate solution to the TSP problem. + + :param distance_matrix: A 2D numpy array representing the distance matrix. + :return: A list of the cities in the order they were visited. + """ + G = nx.from_numpy_array(distance_matrix) + return nx.approximation.traveling_salesman_problem(G) + + def tsp_decision_check(self, distance_matrix, threshold, tour): + """Checks if a given TSP tour is valid and within the threshold + distance. + + :param distance_matrix: A 2D numpy array representing the distance matrix. + :param threshold: The maximum distance allowed. + :param tour: A dictionary containing the feasibility. + """ + try: + is_feasible = tour.get('Feasible', 'no').lower() == 'yes' + except Exception: + return False, 'Output format incorrect' + + # Calculate the approxed distance of the tour + tours = self.tsp_approx(distance_matrix) + tour_distance = sum(distance_matrix[tours[i], tours[i + 1]] for i in range(len(tours) - 1)) + distance_matrix[tours[-1], tours[0]] + + if is_feasible != (tour_distance <= threshold): + return False, f'Feasibility mismatch: {is_feasible} vs {tour_distance} > {threshold}' + return True, 'Feasible: {} <= {}'.format(tour_distance, threshold) diff --git a/opencompass/datasets/NPHardEval/hard_GCP.py b/opencompass/datasets/NPHardEval/hard_GCP.py new file mode 100644 index 0000000000000000000000000000000000000000..24cc6fc5e721fbb371919ad5fdc25d3e9d3b4993 --- /dev/null +++ b/opencompass/datasets/NPHardEval/hard_GCP.py @@ -0,0 +1,191 @@ +import ast +import xml.etree.ElementTree as ET + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import gcpPrompts + + +def q2text(q, p=gcpPrompts): # q is the data for the HP-hard question, p is the prompt + # print(q) + chromatic_number = q.split('\n')[0][-1] # last character of the first line + number_of_vertices = q.split('\n')[1].split(' ')[2] # third word of the second line + prompt_text = p['Intro'] + '\n' \ + + p['Initial_question'].format(max_vertices=number_of_vertices,max_colors=chromatic_number) + '\n' \ + + p['Output_content'] + '\n' \ + + p['Output_format'] + \ + '\n The graph is below: \n' + for line in q.split('\n')[2:]: + vertex_list = line.split(' ') + this_line = 'Vertex {} is connected to vertex {}.'.format(vertex_list[1], vertex_list[2]) + prompt_text += this_line + '\n' + + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class HardGCPDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + for file_num in range(10): + with open(data_path + 'synthesized_data_GCP_{}.txt'.format(file_num)) as f: + data = f.read() + sample = data.split('\n\n')[:-1] + all_data += zip([file_num + 1] * len(sample), sample) + for (level, q) in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + q, + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class HardGCPEvaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + details = {} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = q.split('####\n')[-1] + + output_dict['output'] = output + try: + output_dict['correctness'] = self.gcpCheck(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['level'] = level + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + details[str(index)] = {'q': q, 'output': output, 'result': r} + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + result['details'] = details + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + # Parse the XML string + root = ET.fromstring(xml_string) + + # Find the 'final_answer' tag + final_answer_element = root.find('final_answer') + + # Find the 'reasoning' tag + reasoning_element = root.find('reasoning') + except Exception: + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end] + reasoning_element = xml_string[reasoning_start:reasoning_end] + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def gcpCheck(self, dimacs_str, answer_str): + num_vertices, adjacency_list = self.read_dimacs_format(dimacs_str) + answer_colors = self.parse_answer(answer_str) + # print(adjacency_list) + # print(answer_colors) + + # Check if all colors in the answer are valid + for vertex, neighbors in adjacency_list.items(): + for neighbor in neighbors: + try: + if answer_colors[vertex] == answer_colors[neighbor]: + print(f'Invalid coloring: Vertex {vertex} and {neighbor} have the same color.') + return False + except: + print(f'Invalid input.') # dealing with hullucination + return False + + print(f'Valid coloring found with {len(set(answer_colors.values()))} colors: {answer_colors}') + return True + + def read_dimacs_format(self, dimacs_str): + lines = dimacs_str.strip().split('\n') + # Read the number of vertices and edges + p_line = next(line for line in lines if line.startswith('p')) + _, _, num_vertices, num_edges = p_line.split() + num_vertices, num_edges = int(num_vertices), int(num_edges) + + # Create adjacency list + adjacency_list = {i: set() for i in range(1, num_vertices + 1)} + + # Read the edges and ignore those that reference non-existing vertices + for line in lines: + if line.startswith('e'): + _, vertex1, vertex2 = line.split() + vertex1, vertex2 = int(vertex1), int(vertex2) + if vertex1 in adjacency_list and vertex2 in adjacency_list: + adjacency_list[vertex1].add(vertex2) + adjacency_list[vertex2].add(vertex1) + + return num_vertices, adjacency_list + + def parse_answer(self, llm_string): + # # Convert the answer string to a dictionary + # answer_dict = {} + # # Remove the braces and split the string by commas + # entries = answer_str.strip("}{").split(', ') + # for entry in entries: + # vertex, color = entry.split(':') + # answer_dict[int(vertex)] = color + # return answer_dict + + all_answers, reasoning_element = self.parse_xml_to_dict(llm_string) + + if all_answers == '': + return {} + elif all_answers is None: + return {} + else: + if isinstance(all_answers, str): + try: + all_answers = ast.literal_eval(all_answers) + except Exception: + try: + all_answers = ast.literal_eval('{' + all_answers + '}') + except Exception: + return {} + else: + all_answers = ast.literal_eval(all_answers.text) + # answer_dict = {} + # for pair in all_answers: + # vertex, color = pair.split(":") + # answer_dict[int(vertex)] = color + # convert key type to int + all_answers = {int(k): v for k, v in all_answers.items()} + return all_answers # answer_dict diff --git a/opencompass/datasets/NPHardEval/hard_MSP.py b/opencompass/datasets/NPHardEval/hard_MSP.py new file mode 100644 index 0000000000000000000000000000000000000000..7c4f4db336fcce708d1253ef0725e53c4e23bda4 --- /dev/null +++ b/opencompass/datasets/NPHardEval/hard_MSP.py @@ -0,0 +1,205 @@ +import ast +import json +import xml.etree.ElementTree as ET + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import mspPrompts + + +def q2text(q, p=mspPrompts): # q is the data for the HP-hard question, p is the prompt + total_participants = q['participants'] + total_timeslots = q['time_slots'] + prompt_text = p['Intro'] + '\n' \ + + p['Initial_question'].format(total_participants=total_participants,total_timeslots=total_timeslots) + '\n' \ + + p['Output_content'] + '\n' \ + + p['Output_format'] + \ + '\n The meetings and participants details are as below: \n' + meetings = q['meetings'] + participants = q['participants'] + for meeting in meetings: + this_line = 'Meeting {} is with duration {}.'.format(meeting['id'], meeting['duration']) + prompt_text += this_line + '\n' + for j in participants.keys(): + this_line = 'Participant {} is available at time slots {} and has meetings {}.'.format(j, participants[j]['available_slots'], participants[j]['meetings']) + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class Hard_MSP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + with open(data_path + 'msp_instances.json', 'r') as f: + data = json.load(f) + all_data = zip([int(d['complexity_level']) for d in data], data) + + for (level, q) in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class Hard_MSP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + + output_dict['output'] = output + output_dict['level'] = level + try: + output_dict['correctness'], _ = self.mspCheck(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def mspCheck(self, instance, llm_string): + """Validate the MSP solution. + + Parameters: + - instance: The MSP instance as a dictionary. + - solution: A dictionary with meeting ids as keys and lists of scheduled time slots as values. + + Returns: + - A tuple (is_valid, message). is_valid is True if the solution is valid, False otherwise. + message contains information about the validity of the solution. + """ + # print(llm_string) + solution, reasoning_element = self.parse_xml_to_dict(llm_string) + # print(solution.text) + + # convert solution to dictionary + if solution == '': + return False, None + elif solution is None: + return False, None + else: + if isinstance(solution, str): + try: + solution = ast.literal_eval(solution) + if solution is None: + return False, None + except Exception: + try: + solution = ast.literal_eval('{' + solution + '}') + if solution is None: + return False, None + except Exception: + return False, None + else: + try: + solution = ast.literal_eval(solution.text) + if solution is None: + return False, None + except Exception: + return False, None + # convert key type to int + if isinstance(solution, dict): + print(solution) + solution = {int(k): v for k, v in solution.items()} + else: + return False, None + + # Check if all meetings are scheduled within the available time slots + for meeting in instance['meetings']: + m_id = meeting['id'] + duration = meeting['duration'] + scheduled_slots = solution.get(m_id, None) + + # Check if the meeting is scheduled + if scheduled_slots is None: + return False, f'Meeting {m_id} is not scheduled.' + + # Check if the meeting fits within the number of total time slots + if any(slot >= instance['time_slots'] for slot in scheduled_slots): + return False, f'Meeting {m_id} does not fit within the available time slots.' + + # Check if the scheduled slots are contiguous and fit the meeting duration + if len(scheduled_slots) != duration or not all(scheduled_slots[i] + 1 == scheduled_slots[i + 1] + for i in range(len(scheduled_slots) - 1)): + return False, f'Meeting {m_id} is not scheduled in contiguous time slots fitting its duration.' + + # Check if all participants are available at the scheduled time + for p_id, participant in instance['participants'].items(): + if m_id in participant['meetings']: + if not all(slot in participant['available_slots'] for slot in scheduled_slots): + return False, f'Participant {p_id} is not available for meeting {m_id} at the scheduled time.' + + # Check if any participant is double-booked + participants_schedule = {p_id: [] for p_id in instance['participants']} + for m_id, time_slots in solution.items(): + try: + duration = next(meeting['duration'] for meeting in instance['meetings'] if meeting['id'] == m_id) + if len(time_slots) != duration: + return False, f'Meeting {m_id} duration does not match the number of scheduled time slots.' + for p_id, participant in instance['participants'].items(): + if m_id in participant['meetings']: + participants_schedule[p_id].extend(time_slots) + except Exception: + return False, f'Meeting {m_id} is not in the instance or program error.' + + for p_id, slots in participants_schedule.items(): + if len(slots) != len(set(slots)): + return False, f'Participant {p_id} is double-booked.' + + return True, 'The solution is valid.' + + def parse_xml_to_dict(self, xml_string): + try: + # Parse the XML string + root = ET.fromstring(xml_string) + + # Find the 'final_answer' tag + final_answer_element = root.find('final_answer') + + # Find the 'reasoning' tag + reasoning_element = root.find('reasoning') + except: + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end] + reasoning_element = xml_string[reasoning_start:reasoning_end] + except: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element diff --git a/opencompass/datasets/NPHardEval/hard_TSP.py b/opencompass/datasets/NPHardEval/hard_TSP.py new file mode 100644 index 0000000000000000000000000000000000000000..fd74a17a18e0b433989d4b1e39300d176efcdfe8 --- /dev/null +++ b/opencompass/datasets/NPHardEval/hard_TSP.py @@ -0,0 +1,213 @@ +import ast +import json +import xml.etree.ElementTree as ET + +import numpy as np +import pandas as pd +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import tspPrompts + + +def q2text(q, p=tspPrompts): # q is the data for the HP-hard question, p is the prompt + total_cities = q.shape[0] + prompt_text = p['Intro'] + '\n' \ + + p['Initial_question'].format(total_cities=total_cities) + '\n' \ + + p['Output_content'] + '\n' \ + + p['Output_format'] + \ + '\n The distances between cities are below: \n' + for i in range(q.shape[0]): + for j in range(q.shape[1]): + if i < j: # only use the upper triangle + this_line = 'The path between City {} and City {} is with distance {}.'.format(i, j, q.iloc[i, j]) + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class Hard_TSP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + for level in range(10): + for file_num in range(10): + # read np array + df = pd.read_csv(data_path + 'synthesized_data_TSP_level_{}_instance_{}.csv'.format(level, file_num + 1), + header=None, + index_col=False) + # transform df to + all_data.append((level + 1, df)) + for (level, q) in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q.to_json()), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class Hard_TSP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + q = pd.DataFrame(eval(q)) + + output_dict['output'] = output + try: + output_dict['correctness'], _ = self.tspCheck(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['level'] = level + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + # Parse the XML string + root = ET.fromstring(xml_string) + + # Find the 'final_answer' tag + final_answer_element = root.find('final_answer') + + # Find the 'reasoning' tag + reasoning_element = root.find('reasoning') + except: + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end] + reasoning_element = xml_string[reasoning_start:reasoning_end] + except: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def tspCheck(self, distance_matrix, llm_string): + """Check if the TSP solution is complete and if the distance matches + the greedy solution. + + :param tour_string: String representing the TSP tour in the format "0->1->2->...->N->0" + :param distance_matrix: 2D numpy array representing the distances between cities + :return: Boolean indicating whether the tour is complete and matches the greedy distance + """ + # convert distance_matrix to numpy array + distance_matrix = np.array(distance_matrix) + + # Convert the tour string to a list of integers + # print(llm_string) + final_answer_element, reasoning_element = self.parse_xml_to_dict(llm_string) + # convert solution to dictionary + if final_answer_element == '': + return False, '' + elif final_answer_element is None: + return False, '' + else: + if isinstance(final_answer_element, str): + try: + tour_string = ast.literal_eval(final_answer_element)['Path'] + if tour_string is None: + return False, '' + except Exception: + try: + tour_string = ast.literal_eval('{' + final_answer_element + '}')['Path'] + if tour_string is None: + return False, '' + except Exception: + return False, '' + else: + try: + tour_string = ast.literal_eval(final_answer_element.text)['Path'] + if tour_string is None: + return False, '' + except Exception: + return False, '' + try: + tour = list(map(int, tour_string.split('->'))) + except Exception: + return False, '' + # we could also prinpt `reasoning_element` to see the reasoning of the answer + # we could also print the final distance of the tour by `final_answer_element['Distance']` + + # Check if tour is a cycle + if tour[0] != tour[-1]: + return False, 'The tour must start and end at the same city.' + + # Check if all cities are visited + if len(tour) != len(distance_matrix) + 1: + return False, 'The tour does not visit all cities exactly once.' + + # Calculate the distance of the provided tour + tour_distance = sum(distance_matrix[tour[i]][tour[i + 1]] + for i in range(len(tour) - 1)) + + # Find the greedy tour distance for comparison + greedy_tour, greedy_distance = self.greedy_tsp(distance_matrix) + + # Check if the provided tour distance is equal to the greedy tour distance + if tour_distance != greedy_distance: + return False, f'The tour distance ({tour_distance}) does not match the greedy solution ({greedy_distance}).' + + return True, 'The solution is complete and matches the greedy solution distance.' + + def greedy_tsp(self, distance_matrix): + """Solve the Traveling Salesman Problem using a greedy algorithm. + + :param distance_matrix: 2D numpy array where the element at [i, j] is the distance between city i and j + :return: A tuple containing a list of the cities in the order they were visited and the total distance + """ + num_cities = distance_matrix.shape[0] + unvisited_cities = set(range(num_cities)) + current_city = np.random.choice(list(unvisited_cities)) + tour = [current_city] + total_distance = 0 + + while unvisited_cities: + unvisited_cities.remove(current_city) + if unvisited_cities: + # Find the nearest unvisited city + distances_to_unvisited = distance_matrix[current_city][list(unvisited_cities)] + nearest_city = list(unvisited_cities)[np.argmin(distances_to_unvisited)] + tour.append(nearest_city) + # Update the total distance + total_distance += distance_matrix[current_city, nearest_city] + current_city = nearest_city + + # Return to start + total_distance += distance_matrix[current_city, tour[0]] + tour.append(tour[0]) + + return tour, total_distance diff --git a/opencompass/datasets/NPHardEval/p_BSP.py b/opencompass/datasets/NPHardEval/p_BSP.py new file mode 100644 index 0000000000000000000000000000000000000000..f59c6c5dfbfb2f90e7ab81fd389373b05cada7bc --- /dev/null +++ b/opencompass/datasets/NPHardEval/p_BSP.py @@ -0,0 +1,126 @@ +import ast +import json + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import bspPrompts + + +def q2text(q, p=bspPrompts): + target_value = q['target'] + # TO-DO: fix data not being sorted + array = sorted(q['array']) + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(target_value=target_value) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + \ + '\n The sorted array elements are: ' + ', '.join(map(str, array)) + '\n' + + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class P_BSP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data, newdata = [], [] + with open(data_path + 'bsp_instances.json', 'r') as f: + data = json.load(f) + for sample in data: + level = len(sample['array']) - 2 + all_data.append((level, sample)) + + for level, q in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class P_BSP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + output, reasoning = self.parse_xml_to_dict(output) + output_dict['output'] = output + try: + output_dict['correctness'], _ = self.bsp_check(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['reasoning'] = reasoning + output_dict['level'] = level + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + except Exception: + final_answer_element = '' + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def bsp_check(self, instance, solution): + """Check if the binary search solution is valid. + + :param instance: The instance dictionary with array and target value. + :param solution: The solution dictionary with the position of the target value. + :return: A tuple of (is_correct, message). + """ + array = sorted(instance['array']) + target_value = instance['target'] + solution, reasoning = self.parse_xml_to_dict(solution) + if isinstance(solution, str): + return False, f'The solution is invalid.' + try: + position = int(solution['Position']) + except Exception: + return False, f'The solution is invalid.' + if position == -1 or position >= len(array): + return False, f'The solution is invalid.' + elif array[position] != target_value: + return False, f'The target index is incorrect.' + return True, 'The solution is valid.' diff --git a/opencompass/datasets/NPHardEval/p_EDP.py b/opencompass/datasets/NPHardEval/p_EDP.py new file mode 100644 index 0000000000000000000000000000000000000000..f94116aa4088e52914f3245b93bc11a7e5a2a443 --- /dev/null +++ b/opencompass/datasets/NPHardEval/p_EDP.py @@ -0,0 +1,147 @@ +import ast +import json + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import edpPrompts + + +def q2text(q, p=edpPrompts): + string_a = q['string_a'] + string_b = q['string_b'] + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(string_a=string_a, string_b=string_b) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class P_EDP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + with open(data_path + 'edp_instances.json', 'r') as f: + data = json.load(f) + for sample in data: + level = len(sample['string_a']) - 2 + all_data.append((level, sample)) + + for level, q in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class P_EDP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + output, reasoning = self.parse_xml_to_dict(output) + output_dict['output'] = output + try: + output_dict['correctness'], _ = self.edp_check(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['reasoning'] = reasoning + output_dict['level'] = level + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def compute_min_edit_distance(self, string_a, string_b): + """Computes the minimum edit distance between two strings using dynamic + programming.""" + m, n = len(string_a), len(string_b) + dp = [[0] * (n + 1) for _ in range(m + 1)] + + for i in range(m + 1): + for j in range(n + 1): + if i == 0: + dp[i][j] = j + elif j == 0: + dp[i][j] = i + elif string_a[i - 1] == string_b[j - 1]: + dp[i][j] = dp[i - 1][j - 1] + else: + dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) + return dp[m][n] + + def edp_check(self, instance, solution): + """Check if the edit distance solution is valid. + + :param instance: The instance dictionary with 'string_a' and 'string_b'. + :param solution: The solution dictionary with the reported 'edit_distance'. + :return: A tuple of (is_correct, message). + """ + string_a = instance['string_a'] + string_b = instance['string_b'] + try: + reported_distance = int(solution.get('Operations', -1)) + except Exception: + reported_distance = -1 + + actual_distance = self.compute_min_edit_distance(string_a, string_b) + + if reported_distance == -1: + return False, 'No solution provided.' + elif reported_distance != actual_distance: + return False, f'The reported edit distance ({reported_distance}) is incorrect. Actual distance: {actual_distance}.' + return True, 'The solution is valid.' + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + # assert '' in xml_string + # assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + # reasoning_start = xml_string.index('') + len('') + # reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + assert '{' in final_answer_element + assert '}' in final_answer_element + dic_start = final_answer_element.index('{') + dic_end = final_answer_element.index('}') + final_answer_element = final_answer_element[dic_start:dic_end + 1].rstrip().strip().rstrip() + reasoning_element = xml_string + # reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + except Exception: + final_answer_element = '' + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element diff --git a/opencompass/datasets/NPHardEval/p_SPP.py b/opencompass/datasets/NPHardEval/p_SPP.py new file mode 100644 index 0000000000000000000000000000000000000000..d9b073e99feb0c3a9fd6cb78d9e3ad658e4efc4c --- /dev/null +++ b/opencompass/datasets/NPHardEval/p_SPP.py @@ -0,0 +1,202 @@ +import ast +import json + +try: + import networkx as nx +except ImportError: + nx = None + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import sppPrompts + + +def q2text(q, p=sppPrompts): + # start_node = q['start_node'] + # end_node = q['end_node'] + # TO-DO: fix later + start_node = q['nodes'][0] + end_node = q['nodes'][-1] + edges = q['edges'] + prompt_text = p['Intro'] + '\n' + \ + p['Initial_question'].format(start_node=start_node, end_node=end_node) + '\n' + \ + p['Output_content'] + '\n' + \ + p['Output_format'] + \ + "\n The graph's edges and weights are as follows: \n" + for edge in edges: + this_line = f"Edge from {edge['from']} to {edge['to']} has a weight of {edge['weight']}." + prompt_text += this_line + '\n' + return prompt_text + + +@LOAD_DATASET.register_module(force=True) +class P_SPP_Dataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + raw_data = [] + data_path = path + all_data = [] + with open(data_path + 'spp_instances.json', 'r') as f: + data = json.load(f) + all_data = zip([int(d['complexity_level']) for d in data], data) + for level, q in all_data: + prompt = q2text(q) + raw_data.append({ + 'prompt': prompt, + 'q': str(level) + '####\n' + json.dumps(q), + 'level': level + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module(force=True) +class P_SPP_Evaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + result = {'pass': 0, 'fail': 0} + for index, (q, output) in enumerate(zip(references, predictions)): + output_dict = {} + level = int(q.split('####\n')[0]) + q = json.loads(q.split('####\n')[-1]) + output, reasoning = self.parse_xml_to_dict(output) + output_dict['output'] = output + try: + output_dict['correctness'], _ = self.spp_check(q, output) + except Exception as e: + print(f'Check failed: {e}') + output_dict['correctness'] = False + output_dict['reasoning'] = reasoning + output_dict['level'] = level + + if output_dict['correctness']: + r = 'pass' + else: + r = 'fail' + result[r] += level + + result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100 + final_result = {'Weighted Accuracy': result['score']} + return final_result + + def parse_xml_to_dict(self, xml_string): + try: + assert '' in xml_string + assert '' in xml_string + # assert '' in xml_string + # assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + # reasoning_start = xml_string.index('') + len('') + # reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end].rstrip().strip().rstrip() + assert '{' in final_answer_element + assert '}' in final_answer_element + dic_start = final_answer_element.index('{') + dic_end = final_answer_element.index('}') + final_answer_element = final_answer_element[dic_start:dic_end + 1].rstrip().strip().rstrip() + # reasoning_element = xml_string[reasoning_start:reasoning_end].rstrip().strip().rstrip() + try: + final_answer_element = ast.literal_eval(final_answer_element) + reasoning_element = xml_string + except Exception: + final_answer_element = '' + reasoning_element = xml_string + except Exception: + final_answer_element = '' + reasoning_element = '' + + return final_answer_element, reasoning_element + + def ssp_optimal_solution(self, instance, source, target): + """Provides the optimal solution for the SSP instance. + + :param instance: The SSP instance as a dictionary with 'nodes' and 'edges'. + :param source: The source node. + :param target: The destination node. + :return: The optimal shortest path length and path. + """ + G = nx.Graph() + G.add_nodes_from(instance['nodes']) + G.add_weighted_edges_from([(edge['from'], edge['to'], edge['weight']) + for edge in instance['edges']]) + shortest_path_length = None + shortest_path = None + if nx.has_path(G, source=source, target=target): + shortest_path_length = nx.shortest_path_length(G, source=source, target=target, weight='weight') + shortest_path = nx.shortest_path(G, source=source, target=target, weight='weight') + return shortest_path_length, shortest_path + + # SPP + def spp_check(self, instance, solution, start_node=None, end_node=None): + """Validate the solution of the SPP problem. + + :param instance: The instance dictionary with nodes and edges. + :param solution: The solution dictionary with the path and total distance. + :param start_node: The start node. + :param end_node: The end node. + :return: A tuple of (is_correct, message). + """ + # Get the start and end nodes + # Currently, the start and end nodes are the first and last nodes in the instance + if start_node is None: + start_node = instance['nodes'][0] + if end_node is None: + end_node = instance['nodes'][-1] + + # Convert solution to dictionary + try: + path_string = solution.get('Path', '') + cost_string = solution.get('TotalDistance', '') + except Exception: + return False, 'The solution is not a dictionary.' + + # Calculate the optimal solution + ssp_optimal_length, ssp_optimal_path = self.ssp_optimal_solution( + instance, start_node, end_node) + if ssp_optimal_length is None: + if isinstance(cost_string, int) or cost_string.isdigit(): + return False, f'No path between from node {start_node} to node {end_node}.' + else: + return True, 'No path found from node {start_node} to node {end_node}.' + + try: + path = list(map(int, path_string.split('->'))) + total_cost = int(cost_string) + except Exception: + return False, 'The solution is not a valid dictionary.' + + # Check if path starts and ends with the correct nodes + if not path or path[0] != start_node or path[-1] != end_node: + return False, 'The path does not start or end at the correct nodes.' + + # Check if the path is continuous and calculate the cost + calculated_cost = 0 + is_in_edge = lambda edge, from_node, to_node: (edge['from'] == from_node and edge['to'] == to_node) or (edge['from'] == to_node and edge['to'] == from_node) + for i in range(len(path) - 1): + from_node, to_node = path[i], path[i + 1] + edge = next((edge for edge in instance['edges'] if is_in_edge(edge, from_node, to_node)), None) + + if not edge: + return False, f'No edge found from node {from_node} to node {to_node}.' + + calculated_cost += edge['weight'] + + # Check if the calculated cost matches the total cost provided in the solution + if calculated_cost != total_cost: + return False, f'The calculated cost ({calculated_cost}) does not match the provided total cost ({total_cost}).' + + if calculated_cost != ssp_optimal_length: + # spp_optimal_path = '->'.join(map(str, ssp_optimal_path)) + return False, f'The calculated cost ({calculated_cost}) does not match the optimal solution ({ssp_optimal_length}): {ssp_optimal_path}.' + + return True, 'The solution is valid.' diff --git a/opencompass/datasets/NPHardEval/prompts.py b/opencompass/datasets/NPHardEval/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..91383d999043136b1aec7217af7d8cd923a3d464 --- /dev/null +++ b/opencompass/datasets/NPHardEval/prompts.py @@ -0,0 +1,96 @@ +# Overall fewshot prompts +FEW_SHOT_SELF = 'Please refer to a few examples of this problem and the corresponding reasoning process. The examples are:' +FEW_SHOT_OTHERS = 'Please refer to a few examples of another problem and the corresponding reasoning process. The problem is {initial_question}. {output_content}. The examples are:' + +# P problems +sppPrompts = { + 'Intro': 'The Shortest Path Problem (SPP) involves finding the shortest path between two nodes in a weighted graph.', + 'Initial_question': "You need to find the shortest path between node {start_node} and node {end_node} in a graph. The graph's edges and their weights are given.", + 'Output_content': 'Please provide the shortest path from {start_node} to {end_node} and its total distance. Offer a concise step-by-step explanation of your reasoning process. Aim for brevity and clarity in your response.', + 'Output_format': "Your output should be enclosed within tags. Include your reasoning in tags and the final path and total distance in tags, like {'Path': 'START->...->END', 'TotalDistance': 'INT_TOTAL_DISTANCE'}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +mfpPrompts = { + 'Intro': 'The Maximum Flow Problem (MFP) seeks to find the maximum possible flow from a source node to a sink node in a flow network, subject to capacity constraints on the edges.', + 'Initial_question': 'Determine the maximum flow from the source node {source_node} to the sink node {sink_node} in the given flow network. The capacities of the edges are provided.', + 'Output_content': 'Please indicate the maximum flow value and the flow for each edge. Provide a brief explanation of your methodology. Keep your response concise and focused.', + 'Output_format': "Enclose your output within tags. Present your reasoning in tags and the final maximum flow and edge flows in tags, like {'MaxFlow': 'MAX_FLOW_VALUE', 'Flows': {'NODE_1->NODE_2': FLOW, ...}}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +bspPrompts = { + 'Intro': 'The Binary Search Problem (BSP) deals with finding the position of a target value within a sorted array using a binary search algorithm, which efficiently narrows down the search range.', + 'Initial_question': 'Find the position of the target value {target_value} in the sorted array. The index begins with 0. The array elements are provided.', + 'Output_content': 'Please identify the position of the target value in the array. Offer a brief, step-by-step account of your search process. Aim for conciseness in your response.', + 'Output_format': "Your output should be enclosed in tags. Include your search process in tags and the final position of the target value in tags, like {'Position': 'TARGET_POSITION'}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +edpPrompts = { + 'Intro': 'The Edit Distance Problem (EDP) involves finding the minimum number of operations required to transform one string into another, where each operation is either an insertion, deletion, or substitution of a single character.', + 'Initial_question': 'Find the minimum number of operations required to transform the first string {string_a} into the second string {string_b}. The operations are insertion, deletion, and substitution of a single character, each requiring 1 edit operation.', + 'Output_content': 'Please provide the minimum number of operations required to transform the first string into the second string. Offer a brief explanation of your methodology. Keep your response concise and focused.', + 'Output_format': "Enclose your output within tags. Present your reasoning in tags and the final minimum number of operations in tags, like {'Operations': 'MINIMUM_NUMBER_OF_OPERATIONS'}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +# NP-complete problems +tsp_dPrompts = { + 'Intro': 'The Traveling Salesman Problem (Decision Version, TSP-D) focuses on determining if a salesman can complete a route, visiting each city at least once, with the total travel distance being less than a specified value.', + 'Initial_question': "Check if it's possible for a salesman to visit each of the {total_cities} cities at least once and return to the starting city with the total distance less than {distance_limit}. The distances between each pair of cities are given.", + 'Output_content': 'Provide a yes or no answer, with a succinct explanation of your decision process. Focus on clarity and brevity in your response.', + 'Output_format': "Enclose your output in tags. Present your reasoning in tags and the final yes/no answer in tags, like {'Feasible': 'YES_OR_NO'}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +gcp_dPrompts = { + 'Intro': 'The Graph Coloring Problem (Decision Version, GCP-D) involves determining if it is possible to color the vertices of a graph using a given number of colors, ensuring no two adjacent vertices have the same color.', + 'Initial_question': 'Find out if the vertices of a graph with {total_vertices} vertices can be colored using only {number_of_colors} colors, such that no adjacent vertices share the same color.', + 'Output_content': 'Provide a yes or no answer, along with a concise explanation of your reasoning. Keep your explanation focused and brief.', + 'Output_format': "Enclose your output in tags. Include your reasoning in tags and the final yes/no answer in tags, like {'Feasible': 'YES_OR_NO'}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +kspPrompts = { + 'Intro': 'The 0-1 Knapsack Problem (KSP) asks whether a subset of items, each with a given weight and value, can be chosen to fit into a knapsack of fixed capacity, maximizing the total value without exceeding the capacity.', + 'Initial_question': 'Determine if a subset of items can be selected to fit into a knapsack with a capacity of {knapsack_capacity}, maximizing value without exceeding the capacity. Item weights and values are provided.', + 'Output_content': 'Indicate if an optimal subset exists and its total value. Offer a concise explanation of your selection process. Aim for clarity and brevity in your response.', + 'Output_format': "Your output should be enclosed within tags. Include your selection process in tags and the final decision and total value in tags, like {'Feasible': 'YES_OR_NO', 'TotalValue': 'TOTAL_VALUE', 'SelectedItemIds': [0, 1]}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +# NP-hard problems +tspPrompts = { + 'Intro': 'The traveling salesman problem (TSP) is a classic optimization problem that aims to find the shortest possible route that visits a set of cities, with each city being visited exactly once and the route returning to the original city.', + 'Initial_question': 'You must find the shortest path that visits all {total_cities} cities, labelled from 1 to {total_cities}. The distances between each pair of cities are provided.', + 'Output_content': 'Please list each city in the order they are visited. Provide the total distance of the trip. You should also provide very short step by step reasoning. Do not use multiple lines and try your best to save output tokens.', + 'Output_format': "Your output should contain two parts enclosed by . First, your step by step reasoning like The reasoning process. Second, the final output of the result path and total distance wrapped by final_answer tag, like {'Path': '0->1->2->...->N->0', 'TotalDistance': 'INT_TOTAL_DISTANCE'}", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +gcpPrompts = { + 'Intro': 'Graph coloring refers to the problem of coloring vertices of a graph in such a way that no two adjacent vertices have the same color. ', + 'Initial_question': 'There are {max_vertices} vertices 1 to {max_vertices} in a graph. You may use {max_colors} colors with alphabats from A, B, C,... to color the graph.', + 'Output_content': "Please label every vertex, even if it is disconnected from the rest of the graph. Please provide each vertex's color. Do not skip any vertices. You should also provide very short step by step reasoning. Do not use multiple lines and try your best to save output tokens.", + 'Output_format': "Your output should contain two parts enclosed by . First, your step by step reasoning wrapped by . Second, the final output of all vertex numbers and their associated colors, wrapped by final_answer tag, like {0:'COLOR_1', 1:'COLOR_2', ...}.", + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} + +mspPrompts = { + 'Intro': 'The meeting scheduling problem (MSP) is a type of constraint satisfaction problem where the goal is to find a suitable time slot for a meeting that all participants can attend without conflicts in their schedules.', + 'Initial_question': "There are {total_participants} participants with their available time slots. There are {total_timeslots} consecutive non-overlapping time slots. Let's assume all meetings has duration of 1.", + 'Output_content': 'Please provide a time slot where all participants can attend the meeting. You should also provide very short step by step reasoning. Do not use multiple lines and try your best to save output tokens.', + 'Output_format': 'Your output should contain two parts enclosed by . First, your step by step reasoning wrapped by . Second, the final output of meeting numbers followed by a list of slots, like {0:[1,2], 1:[4], ...}.', + 'Few_shot_self': FEW_SHOT_SELF, + 'Few_shot_others': FEW_SHOT_OTHERS +} diff --git a/opencompass/datasets/NPHardEval/utils.py b/opencompass/datasets/NPHardEval/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1906fe2a622763f04c958073698042ae1e06dc8a --- /dev/null +++ b/opencompass/datasets/NPHardEval/utils.py @@ -0,0 +1,43 @@ +import ast +import xml.etree.ElementTree as ET + + +def append_root_tags(string): + if not string.strip().startswith(''): + string = '\n' + string + if not string.strip().endswith(''): + string += '\n' + return string + + +def parse_xml_to_dict(xml_string): + final_answer_element = '' + reasoning_element = '' + + try: + # Parse the XML string + root = ET.fromstring(xml_string) + + # Find the 'final_answer' tag + final_answer_element = root.find('final_answer').text + + # Find the 'reasoning' tag + reasoning_element = root.find('reasoning').text + except Exception: + try: + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + assert '' in xml_string + final_answer_start = xml_string.index('') + len('') + final_answer_end = xml_string.index('') + reasoning_start = xml_string.index('') + len('') + reasoning_end = xml_string.index('') + final_answer_element = xml_string[final_answer_start:final_answer_end] + reasoning_element = xml_string[reasoning_start:reasoning_end] + except Exception: + final_answer_element = '' + reasoning_element = '' + + final_answer_element = ast.literal_eval(final_answer_element.strip()) + return final_answer_element, reasoning_element diff --git a/opencompass/datasets/PMMEval/__init__.py b/opencompass/datasets/PMMEval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b50af839fa33140737a042ee65c1a75aced81e56 --- /dev/null +++ b/opencompass/datasets/PMMEval/__init__.py @@ -0,0 +1,8 @@ +from .flores import * # noqa: F401, F403 +from .humanevalxl import * # noqa: F401, F403 +from .mgsm import * # noqa: F401, F403 +from .mhellaswag import * # noqa: F401, F403 +from .mifeval import * # noqa: F401, F403 +from .mlogiqa import * # noqa: F401, F403 +from .mmmlu import * # noqa: F401, F403 +from .xnli import * # noqa: F401, F403 diff --git a/opencompass/datasets/PMMEval/flores.py b/opencompass/datasets/PMMEval/flores.py new file mode 100644 index 0000000000000000000000000000000000000000..d649a116f1ce24ad37e748f59247df48674fbe50 --- /dev/null +++ b/opencompass/datasets/PMMEval/flores.py @@ -0,0 +1,162 @@ +import json +import os +import re +from typing import Tuple + +import numpy as np +from datasets import Dataset +from sacrebleu.metrics import BLEU +from sacrebleu.tokenizers.tokenizer_13a import Tokenizer13a +from sacrebleu.tokenizers.tokenizer_zh import TokenizerZh + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + + +def wmt_postprocess(text: str, lang: str) -> str: + text = text.strip() + texts = list(x.strip() for x in text.split('\n')) + texts = list(x for x in texts if x != '') + text = '\n'.join(texts) + text = tokenize(text, lang) + return text + + +def compute_maximum_bleu_value(gen: str, ref: str, lang: str): + gens = list(x.strip() for x in gen.split('\n')) + gens = list(x for x in gens if x != '') + + gens_tokens = list(wmt_postprocess(x, lang) for x in gens) + ref_tokens = wmt_postprocess(ref, lang) + + scorer = BLEU(tokenize='13a', effective_order=True) + + maximum_bleu_value = -100.0 + maximum_bleu_object = None + + for i in range(0, len(gens_tokens)): + for j in range(i, len(gens_tokens)): + gens_tokens_region = ' '.join(gens_tokens[i:j + 1]) + sentence_bleu = scorer.sentence_score(gens_tokens_region, + [ref_tokens]) + + if sentence_bleu.score > maximum_bleu_value: + maximum_bleu_value = sentence_bleu.score + maximum_bleu_object = sentence_bleu + + if maximum_bleu_object is None: + sentence_bleu = scorer.sentence_score('', [ref_tokens]) + return sentence_bleu + else: + return maximum_bleu_object + + +def trim_multiple_space(tokes): + return ''.join(tokes).strip().split() + + +class SpaceTokenizer(object): + + def __call__(self, sent): + if type(sent) == list: + print(sent) + raise ValueError() + return ' '.join(sent.strip().split()) + + +class NonASCIITokenizer(object): + + def __init__(self): + self.is_cjk = re.compile('([\u2e80-\u9fff]|' # 中日韩 + '[\ua960-\ua97f]|' # 谚文字母扩展A + '[\uac00-\ud7ff]|' # 谚文音节+谚文字母扩展B + '[\u0E00-\u0E7F]' # 泰文 + ')') + + def __call__(self, sent): + sent = sent.strip() + chs = list(sent) + line_chtok = [] + for ch in chs: + if self.is_cjk.match(ch): + line_chtok.append(' ') + line_chtok.append(ch) + line_chtok.append(' ') + else: + line_chtok.append(ch) + line_chtok = trim_multiple_space(line_chtok) + return ' '.join(line_chtok) + + +def build_tokenizer(lang: str): + if lang == 'Chinese': + return TokenizerZh() + elif lang in {'Japanese', 'Korean', 'Thai'}: + return NonASCIITokenizer() + else: + return SpaceTokenizer() + + +def tokenize(sent, lang): + tokenizer = build_tokenizer(lang) + final_tokenizer = Tokenizer13a() + return final_tokenizer(tokenizer(sent)) + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_flores') +def pmmeval_flores_postprocess(text: str, lang_fullname: str) -> Tuple[str]: + return text, lang_fullname + + +@LOAD_DATASET.register_module() +class PMMEvalFloresDataset(BaseDataset): + + @staticmethod + def load(path: str, lang_fullname: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='flores', + split=f'test/{lang_fullname}') + else: + dataset = list() + filename = os.path.join(data_path, + f'flores/test/{lang_fullname}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalFloresEvaluator(BaseEvaluator): + + def score(self, predictions, references): + maximum_bleu_results = list() + for (pred, tgt_lang), ref in zip(predictions, references): + maximum_bleu_results.append( + compute_maximum_bleu_value(pred, ref, tgt_lang)) + + maximum_corpus_bleu_counts = sum( + np.array(x.counts) for x in maximum_bleu_results).tolist() + maximum_corpus_bleu_totals = sum( + np.array(x.totals) for x in maximum_bleu_results).tolist() + maximum_corpus_bleu_sys_len = sum(x.sys_len + for x in maximum_bleu_results) + maximum_corpus_bleu_ref_len = sum(x.ref_len + for x in maximum_bleu_results) + + maximum_bleu_result = BLEU.compute_bleu( + correct=maximum_corpus_bleu_counts, + total=maximum_corpus_bleu_totals, + sys_len=maximum_corpus_bleu_sys_len, + ref_len=maximum_corpus_bleu_ref_len) + + result = {'BLEU': round(maximum_bleu_result.score, 2)} + return result diff --git a/opencompass/datasets/PMMEval/humanevalxl.py b/opencompass/datasets/PMMEval/humanevalxl.py new file mode 100644 index 0000000000000000000000000000000000000000..7edbbabb900e1aab324e8b1b5a1bf8b6f0c12004 --- /dev/null +++ b/opencompass/datasets/PMMEval/humanevalxl.py @@ -0,0 +1,226 @@ +import json +import os +import os.path as osp +import re +import subprocess +import tempfile +import time +from shutil import copyfile + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.datasets.humaneval import humaneval_postprocess_v2 +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +_LANGUAGE_NAME_DICT = { + 'java': 'Java', + 'javascript': 'JavaScript', + 'js': 'JavaScript', + 'python': 'Python', +} + + +@LOAD_DATASET.register_module() +class PMMEvalHumanEvalXLDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str, program_lang: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='humaneval-xl', + split=f'test/{program_lang}/{lang}') + else: + dataset = list() + filename = os.path.join( + data_path, f'humaneval-xl/test/{program_lang}/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalHumanEvalXLEvaluator(BaseEvaluator): + + def __init__(self, + language, + ip_address='localhost', + text_language='', + port='', + retry=2, + timeout=600) -> None: + assert language in _LANGUAGE_NAME_DICT.keys(), ( + f'language must be in {list(_LANGUAGE_NAME_DICT.keys())}') + if language == 'rust': + timeout *= 10 # rust need more time + self.language = language + self.text_language = text_language + self.ip_address = ip_address + self.port = port + self.retry = retry + self.timeout = timeout + super().__init__() + + def score(self, predictions, references): + predictions = [{ + 'task_id': + f'{_LANGUAGE_NAME_DICT[self.language]}/{i}', + 'generation': + _clean_up_code(pred, self.language, refer), + } for i, (pred, refer) in enumerate(zip(predictions, references))] + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_out_path = osp.join( + tmp_dir, + f'humanevalx_{self.language}_{self.text_language}.json') + with open(tmp_out_path, 'w') as f: + for pred in predictions: + f.write(json.dumps(pred) + '\n') + + num_retry = 0 + while num_retry < self.retry: + succeed, output = self._code_eval_service( + file_path=tmp_out_path) + if not succeed and '(56) Recv failure' in output: + # only retry when connection failed + num_retry += 1 + # wait a min in case the service load is too high + time.sleep(60) + else: + break + + if succeed: + if isinstance(output, str): + return json.loads(output) + elif isinstance(output, dict): + return output + + ref_url = 'https://opencompass.readthedocs.io/en/latest/advanced_guides/code_eval_service.html' # noqa + if hasattr(self, '_out_dir'): + result_file_path = re.sub('results', 'mid_results', + self._out_dir) + '.json' # noqa + if not osp.exists(osp.dirname(result_file_path)): + os.makedirs(osp.dirname(result_file_path)) + else: + result_file_path = os.path.join( + 'outputs', f'humanevalx_{self.language}.json') + copyfile(tmp_out_path, result_file_path) + raise Exception( + f'Call CodeEvalService Error in `HumanevalXEvaluator`, The ' + f"results have been saved in path '{result_file_path}', You " + 'need to check that your code evaluate service is launched and' + f' the network to service is connected, you can also get ' + f'results directly by using `curl` command refer to {ref_url}.' + f'\nError Information: {output}') + + def _code_eval_service(self, file_path): + if self.port: + eval_server_url = f'{self.ip_address}:{self.port}/evaluate' + else: + eval_server_url = f'{self.ip_address}/evaluate' + exec_result = subprocess.run([ + 'curl', '-X', 'POST', '-F', f'file=@{file_path}', '-F', + f'dataset=humanevalx/{self.language}', f'{eval_server_url}' + ], + timeout=self.timeout, + capture_output=True) + if exec_result.returncode == 0 and re.match( + "\"{.*:.*}\"", exec_result.stdout.decode('utf-8')): + return True, json.loads(exec_result.stdout.decode('utf-8')) + else: + if exec_result.stderr: + try: + err = exec_result.stderr.decode() + except Exception: + err = exec_result.stderr + else: + try: + err = exec_result.stdout.decode() + except Exception: + err = exec_result.stdout + return False, err + + +def _clean_up_code(text: str, language_type: str, reference) -> str: + """Cleans up the generated code.""" + try: + # for chatGLM related text + eval_text = eval(text) + except Exception: + pass + else: + if isinstance(eval_text, str): + text = eval_text + # extract code from code block + text = text.lstrip('\n') + if '```' in text: + blocks = re.findall(r'```(.*?)```', text, re.DOTALL) + if len(blocks) == 0: + text = text.split('```')[1] # fall back to default strategy + else: + text = blocks[0] # fetch the first code block + if not text.startswith('\n'): # in case starting with ```xxx + text = text[max(text.find('\n') + 1, 0):] + if language_type.lower() == 'python': + text = humaneval_postprocess_v2(text) + # we need to take care of the first line + # append extra space for first line for correct indentation + text = ' ' + text.lstrip() + + text_splits = text.split('\n') + is_empty_line = False + ind_empty_line = None + for i, line in enumerate(text_splits): + if len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t': + is_empty_line = True + ind_empty_line = i + break + if is_empty_line: + text = '\n'.join(text_splits[:ind_empty_line]) + else: + end_words = [ + '\ndef', '\nclass', '\n#', '\nassert', '\n"""', '\nprint', + '\nif', '\n\n\n' + ] + for w in end_words: + if w in text: + text = text[:text.rfind(w)] + # strip function head for all other language + func_name = reference.strip().split('\n')[-1] + if func_name: + func_name = func_name.strip().strip('{') + if func_name in text: + text = '\n'.join(text[text.find(func_name):].split('\n')[1:]) + if language_type.lower() == 'java': + main_pos = text.find('public static void main') + if main_pos != -1: + text = text[:main_pos] + '}' + if '}' in text: + text = text[:text.rfind('}')] + '}' + if text.count('{') + 1 == text.count('}'): + text += '\n}' + elif language_type.lower() == 'go': + if '\nfunc main(' in text: + text = text[:text.rfind('func main(')] + if '}' in text: + text = text[:text.rfind('}')] + '}' + elif language_type.lower() == 'cpp': + if '\nint main()' in text: + text = text[:text.rfind('int main()')] + if '}' in text: + text = text[:text.rfind('}')] + '}' + elif language_type.lower() == 'js': + if '}' in text: + text = text[:text.rfind('}')] + '}' + elif language_type.lower() == 'rust': + if '}' in text: + text = text[:text.rfind('}')] + '}' + + return text diff --git a/opencompass/datasets/PMMEval/mgsm.py b/opencompass/datasets/PMMEval/mgsm.py new file mode 100644 index 0000000000000000000000000000000000000000..da177ba5e3053ffc2927a76e5416d64d816f20ba --- /dev/null +++ b/opencompass/datasets/PMMEval/mgsm.py @@ -0,0 +1,79 @@ +import json +import os +import re + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + + +def _get_last_digit(s): + _PAT_LAST_DIGIT = re.compile( + r'([+-])?(?=([0-9]|\.[0-9]))(0|([1-9](\d{0,2}(,\d{3})*)|\d*))?(\.\d*)?(?=\D|$)' # noqa E501 + ) + match = list(_PAT_LAST_DIGIT.finditer(s)) + if match: + last_digit = match[-1].group().replace(',', '').replace( + '+', '').strip().strip('.') + # print(f"The last digit in {s} is {last_digit}") + else: + last_digit = None + # logger.warning(f"No digits found in {s!r}") + return last_digit + + +@LOAD_DATASET.register_module() +class PMMEvalMGSMDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='mgsm', + split=f'test/{lang}') + else: + dataset = list() + filename = os.path.join(data_path, f'mgsm/test/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalMGSMEvaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + num_correct, total = 0, 0 + details = {} + for index, (references_answer, predictions_answer) in enumerate( + zip(references, predictions)): + extracted_answer = _get_last_digit(predictions_answer) + references_answer = references_answer.replace(',', '') + if references_answer == extracted_answer: + is_correct = True + else: + is_correct = False + + num_correct += is_correct + total += 1 + details[str(index)] = { + 'references': references_answer, + 'predictions': predictions_answer, + 'extracted': extracted_answer, + 'correct': is_correct, + } + + accuracy = round(num_correct / total * 100, 2) + final_result = {'accuracy': accuracy, 'details': details} + return final_result diff --git a/opencompass/datasets/PMMEval/mhellaswag.py b/opencompass/datasets/PMMEval/mhellaswag.py new file mode 100644 index 0000000000000000000000000000000000000000..b23f3e2e0b03c06918e7614ce323cc97ff6470ea --- /dev/null +++ b/opencompass/datasets/PMMEval/mhellaswag.py @@ -0,0 +1,147 @@ +import json +import os +import re +from typing import Tuple + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +langs_dict = { + 'fr': ['La réponse est', 'la réponse est'], + 'en': ['the answer is', 'The answer is'], + 'vi': ['Câu trả lời là', 'câu trả lời là'], + 'ar': ['الجواب هو'], + 'th': ['คำตอบคือ'], + 'zh': ['答案是'], + 'ko': ['답변은'], + 'pt': ['A resposta é'], + 'ja': ['答えは'], + 'es': ['La respuesta es'] +} + + +def extract_choice(gen, lang): + r"""{ "answer": "A|B|C|D" }""" + patterns = [ + r"\{\s*?\"answer\"\s*?\:\s*?\"?(A|B|C|D).*?\"?\s*?\}", + r"\{\s*?[\'\"]answer[\'\"]\s*?\:\s*?[\'\"](A|B|C|D).*?[\'\"]\s*?\}", + r"\"answer\"\s*:\s*\"?(A|B|C|D)\"?", + r"[\'\"]answer[\'\"]\s*:\s*[\'\"](A|B|C|D)[\'\"]" + ] + for pattern in patterns: + res = re.findall(pattern, gen, flags=re.DOTALL) + if len(res) >= 1: + return res[-1] + + else: + res = None + pattern = langs_dict[lang] + for p in pattern: + if p in gen and p != gen: + res = gen.split(p) + if len(res) > 1 and len(res[-1].strip()) > 0: + res = res[-1].strip()[0] + else: + res = None + break + + temp = ['A', 'B', 'C', 'D', 'a', 'b', 'c', 'd'] + if res in temp: + return res + else: + return None + + +def extract_choice_fuzzy(gen, lang): + options = ['A', 'B', 'C', 'D'] # 定义选项 + for option in options: + if option in gen: # 检查选项是否在文本中 + return option # 返回第一个出现的选项 + return None + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_mhellaswag') +def pmmeval_mhellaswag_postprocess(text: str, lang_code: str) -> Tuple[str]: + return text, lang_code + + +@LOAD_DATASET.register_module() +class PMMEvalMHellaswagDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='mhellaswag', + split=f'test/{lang}') + else: + dataset = list() + filename = os.path.join(data_path, f'mhellaswag/test/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalMHellaswagEvaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + all_results = list() + + for (pred, lang), ref in zip(predictions, references): + answer = chr(int(ref) + 65) + choice = extract_choice(pred, lang) + acc = 0 + failed_strict = 0 + failed = 1 + if choice is not None: + failed = 0 + if answer.lower() == choice.lower(): + acc = 1 + else: + acc = 0 + else: + choice = extract_choice_fuzzy(pred, lang) + if choice is None: + acc = 0 + failed_strict = 1 + else: + failed_strict = 0 + if answer.lower() == choice.lower(): + acc = 1 + else: + acc = 0 + + all_results.append({ + 'acc': + float(acc), + 'failed': + float(failed), + 'failed_strict': + float(failed_strict), + 'extracted_answer': + pred if pred else 'no answer', + }) + + final_result = { + 'accuracy': + round( + sum(x['acc'] for x in all_results) / len(all_results) * 100, + 2), + 'details': + all_results + } + + return final_result diff --git a/opencompass/datasets/PMMEval/mifeval.py b/opencompass/datasets/PMMEval/mifeval.py new file mode 100644 index 0000000000000000000000000000000000000000..a43b3a8b1c8193508ef5135dc76a652ca84e22f2 --- /dev/null +++ b/opencompass/datasets/PMMEval/mifeval.py @@ -0,0 +1,147 @@ +import json +import os +from typing import Tuple + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.datasets.PMMEval.mifeval_utils import mifeval_class_map +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + + +def test_instruction_following_strict(inp, response, lang_code): + """Tests response to see if instrutions are followed.""" + instruction_list = inp['instruction_id_list'] + is_following_list = [] + + for index, instruction_id in enumerate(instruction_list): + instruction_id_0, instruction_id_1 = tuple(instruction_id.split(':')) + instruction_fuction_info = mifeval_class_map[instruction_id_0][ + instruction_id_1] + + instruction_function = instruction_fuction_info['function'] + instruction_function_args = dict() + + if instruction_fuction_info['required_lang_code']: + instruction_function_args['lang_code'] = lang_code + for kwarg_dict in inp['kwargs']: + for k, v in kwarg_dict.items(): + if v is None: + continue + instruction_function_args[k] = v + instruction_function_args['input_string'] = response + + if response.strip() and instruction_function( + **instruction_function_args): + is_following_list.append(True) + else: + is_following_list.append(False) + + return 1.0 if all(is_following_list) else 0.0 + + +def test_instruction_following_loose(inp, response, lang_code): + """Tests response for an upper bound for following instructions.""" + r = response.split('\n') + response_remove_first = '\n'.join(r[1:]).strip() + response_remove_last = '\n'.join(r[:-1]).strip() + response_remove_both = '\n'.join(r[1:-1]).strip() + revised_response = response.replace('*', '') + revised_response_remove_first = response_remove_first.replace('*', '') + revised_response_remove_last = response_remove_last.replace('*', '') + revised_response_remove_both = response_remove_both.replace('*', '') + all_responses = [ + response, + revised_response, + response_remove_first, + response_remove_last, + response_remove_both, + revised_response_remove_first, + revised_response_remove_last, + revised_response_remove_both, + ] + instruction_list = inp['instruction_id_list'] + is_following_list = [] + + for index, instruction_id in enumerate(instruction_list): + instruction_id_0, instruction_id_1 = tuple(instruction_id.split(':')) + instruction_fuction_info = mifeval_class_map[instruction_id_0][ + instruction_id_1] + + instruction_function = instruction_fuction_info['function'] + instruction_function_args = dict() + + if instruction_fuction_info['required_lang_code']: + instruction_function_args['lang_code'] = lang_code + for kwarg_dict in inp['kwargs']: + for k, v in kwarg_dict.items(): + instruction_function_args[k] = v + instruction_function_args['input_string'] = response + + is_following = False + for r in all_responses: + if r.strip() and instruction_function(**instruction_function_args): + is_following = True + break + + is_following_list.append(is_following) + + return 1.0 if all(is_following_list) else 0.0 + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_mifeval') +def pmmeval_mifeval_postprocess(text: str, lang_code: str) -> Tuple[str]: + return text, lang_code + + +@LOAD_DATASET.register_module() +class PMMEvalMIFEvalDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='mifeval', + split=f'test/{lang}') + else: + dataset = list() + filename = os.path.join(data_path, f'mifeval/test/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalMIFEvalEvaluator(BaseEvaluator): + + def score(self, predictions, references, test_set): + all_results = list() + for (pred, lang), example in zip(predictions, test_set): + temp_result = { + 'strict_acc': + test_instruction_following_strict(example, pred, lang), + 'loose_acc': + test_instruction_following_loose(example, pred, lang) + } + + all_results.append(temp_result) + + result = { + 'strict_acc': + round( + sum(x['strict_acc'] + for x in all_results) / len(all_results) * 100, 2), + 'loose_acc': + round( + sum(x['loose_acc'] + for x in all_results) / len(all_results) * 100, 2) + } + return result diff --git a/opencompass/datasets/PMMEval/mlogiqa.py b/opencompass/datasets/PMMEval/mlogiqa.py new file mode 100644 index 0000000000000000000000000000000000000000..23b7d9c9f2e488cb96d4d6c65d41f51101671ddd --- /dev/null +++ b/opencompass/datasets/PMMEval/mlogiqa.py @@ -0,0 +1,148 @@ +import json +import os +import re +from typing import Tuple + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +langs_dict = { + 'fr': ['La réponse est', 'la réponse est'], + 'en': ['the answer is', 'The answer is'], + 'vi': ['Câu trả lời là', 'câu trả lời là'], + 'ar': ['الجواب هو'], + 'th': ['คำตอบคือ'], + 'zh': ['答案是'], + 'ko': ['답변은'], + 'pt': ['A resposta é'], + 'ja': ['答えは'], + 'es': ['La respuesta es'] +} + + +def extract_choice(gen, lang): + r"""{ "answer": "A|B|C|D" }""" + patterns = [ + r"\{\s*?\"answer\"\s*?\:\s*?\"?(A|B|C|D).*?\"?\s*?\}", + r"\{\s*?[\'\"]answer[\'\"]\s*?\:\s*?[\'\"](A|B|C|D).*?[\'\"]\s*?\}", + r"\"answer\"\s*:\s*\"?(A|B|C|D)\"?", + r"[\'\"]answer[\'\"]\s*:\s*[\'\"](A|B|C|D)[\'\"]" + ] + for pattern in patterns: + res = re.findall(pattern, gen, flags=re.DOTALL) + if len(res) >= 1: + return res[-1] + + else: + res = None + pattern = langs_dict[lang] + for p in pattern: + if p in gen and p != gen: + res = gen.split(p) + if len(res) > 1 and len(res[-1].strip()) > 0: + res = res[-1].strip()[0] + else: + res = None + + break + + temp = ['A', 'B', 'C', 'D', 'a', 'b', 'c', 'd'] + if res in temp: + return res + else: + return None + + +def extract_choice_fuzzy(gen): + options = ['A', 'B', 'C', 'D'] + for option in options: + if option in gen: + return option + return None + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_mlogiqa') +def pmmeval_mlogiqa_postprocess(text: str, lang_code: str) -> Tuple[str]: + return text, lang_code + + +@LOAD_DATASET.register_module() +class PMMEvalMLogiQADataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str): + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='mlogiqa', + split=f'test/{lang}') + else: + dataset = list() + filename = os.path.join(data_path, f'mlogiqa/test/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalMLogiQAEvaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + all_results = list() + + for (pred, lang), ref in zip(predictions, references): + answer = chr(int(ref) + 65) + pred = extract_choice(pred, lang) + acc = 0 + failed_strict = 0 + failed = 1 + if pred is not None: + failed = 0 + if answer.lower() == pred.lower(): + acc = 1 + else: + acc = 0 + else: + pred_fuzzy = extract_choice_fuzzy(pred) + if pred_fuzzy is None: + acc = 0 + failed_strict = 1 + else: + failed_strict = 0 + if answer.lower() == pred_fuzzy.lower(): + acc = 1 + else: + acc = 0 + + all_results.append({ + 'acc': + float(acc), + 'failed': + float(failed), + 'failed_strict': + float(failed_strict), + 'extracted_answer': + pred if pred else 'no answer', + }) + + final_result = { + 'accuracy': + round( + sum(x['acc'] for x in all_results) / len(all_results) * 100, + 2), + 'details': + all_results + } + + return final_result diff --git a/opencompass/datasets/PMMEval/mmmlu.py b/opencompass/datasets/PMMEval/mmmlu.py new file mode 100644 index 0000000000000000000000000000000000000000..77ca561c8b9bec496fd364a59e725d1373cf94b3 --- /dev/null +++ b/opencompass/datasets/PMMEval/mmmlu.py @@ -0,0 +1,153 @@ +import json +import os +import re +from typing import Tuple + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +langs_dict = { + 'FR-FR': ['La réponse est', 'la réponse est'], + 'EN-US': ['the answer is', 'The answer is'], + 'VI-VT': ['Câu trả lời là', 'câu trả lời là'], + 'AR-XY': ['الجواب هو'], + 'TH-TL': ['คำตอบคือ'], + 'ZH-CN': ['答案是'], + 'KO-KR': ['답변은'], + 'PT-BR': ['A resposta é'], + 'JA-JP': ['答えは'], + 'ES-LA': ['La respuesta es'] +} + + +def extract_choice(gen, lang): + r"""{ "answer": "A|B|C|D" }""" + patterns = [ + r"\{\s*?\"answer\"\s*?\:\s*?\"?(A|B|C|D).*?\"?\s*?\}", + r"\{\s*?[\'\"]answer[\'\"]\s*?\:\s*?[\'\"](A|B|C|D).*?[\'\"]\s*?\}", + r"\"answer\"\s*:\s*\"?(A|B|C|D)\"?", + r"[\'\"]answer[\'\"]\s*:\s*[\'\"](A|B|C|D)[\'\"]" + ] + for pattern in patterns: + res = re.findall(pattern, gen, flags=re.DOTALL) + if len(res) >= 1: + return res[-1] + + else: + res = None + pattern = langs_dict[lang] + for p in pattern: + if p in gen and p != gen: + res = gen.split(p) + if len(res) > 1 and len(res[-1].strip()) > 0: + res = res[-1].strip()[0] + else: + res = None + break + + temp = ['A', 'B', 'C', 'D', 'a', 'b', 'c', 'd'] + if res in temp: + return res + else: + return None + + +def extract_choice_fuzzy(gen): + options = ['A', 'B', 'C', 'D'] + for option in options: + if option in gen: + return option + return None + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_mmmlu') +def pmmeval_mmmlu_postprocess(text: str, lang_code: str) -> Tuple[str]: + return text, lang_code + + +@LOAD_DATASET.register_module() +class PMMEvalMMMLUDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str, difficulty: str): + assert difficulty in [ + 'easy', 'hard', 'all' + ], '`difficulty` should be one choice among "easy", "hard", and "all"!' + data_path = get_data_path(path) + + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + dataset_list = list() + from modelscope import MsDataset + if difficulty == 'easy' or difficulty == 'all': + dataset_list.append( + MsDataset.load(dataset_name=data_path, + subset_name='mmmlu', + split=f'easy/test/mmlu_{lang}')) + if difficulty == 'hard' or difficulty == 'all': + dataset_list.append( + MsDataset.load(dataset_name=data_path, + subset_name='mmmlu', + split=f'hard/test/mmlu_{lang}')) + # TODO: conbine two datasets + dataset = dataset_list[0] + dataset_list[1] if len( + dataset_list) == 2 else dataset_list[0] + else: + dataset = list() + if difficulty == 'easy' or difficulty == 'all': + filename = os.path.join(data_path, + f'mmmlu/easy/test/mmlu_{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + if difficulty == 'hard' or difficulty == 'all': + filename = os.path.join(data_path, + f'mmmlu/hard/test/mmlu_{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalMMMLUEvaluator(BaseEvaluator): + + def score(self, predictions, references): + all_results = list() + for (pred, lang), ref in zip(predictions, references): + answer = extract_choice(pred, lang) + if answer is None: + answer = extract_choice_fuzzy(pred) + if answer is None: + acc = 0.0 + failed = 1.0 + else: + acc = 1.0 if ref.lower() == answer.lower() else 0.0 + failed = 0.0 + + all_results.append({ + 'acc': + acc, + 'failed': + failed, + 'extracted_answer': + pred if pred else 'no answer' + }) + + final_result = { + 'accuracy': + round( + sum(x['acc'] for x in all_results) / len(all_results) * 100, + 2), + 'details': + all_results + } + + return final_result diff --git a/opencompass/datasets/PMMEval/xnli.py b/opencompass/datasets/PMMEval/xnli.py new file mode 100644 index 0000000000000000000000000000000000000000..68268e70066faeece283f5a74adc1e305009f722 --- /dev/null +++ b/opencompass/datasets/PMMEval/xnli.py @@ -0,0 +1,146 @@ +import json +import os +import re +from typing import Tuple + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +langs_dict = { + 'fr': ['La réponse est', 'la réponse est'], + 'en': ['the answer is', 'The answer is'], + 'vi': ['Câu trả lời là', 'câu trả lời là'], + 'ar': ['الجواب هو'], + 'th': ['คำตอบคือ'], + 'zh': ['答案是'], + 'ko': ['답변은'], + 'pt': ['A resposta é'], + 'ja': ['答えは'], + 'id': ['Jawaban adalah', 'jawaban adalah'], + 'es': ['La respuesta es'] +} + + +def extract_choice(gen, lang): + r"""{ "answer": "A|B|C|D" }""" + patterns = [ + r"\{\s*?\"answer\"\s*?\:\s*?\"?(A|B|C|D).*?\"?\s*?\}", + r"\{\s*?[\'\"]answer[\'\"]\s*?\:\s*?[\'\"](A|B|C|D).*?[\'\"]\s*?\}", + r"\"answer\"\s*:\s*\"?(A|B|C|D)\"?", + r"[\'\"]answer[\'\"]\s*:\s*[\'\"](A|B|C|D)[\'\"]" + ] + for pattern in patterns: + res = re.findall(pattern, gen, flags=re.DOTALL) + if len(res) >= 1: + return res[-1] + + else: + res = None + pattern = langs_dict[lang] + for p in pattern: + if p in gen and p != gen: + res = gen.split(p) + if len(res) > 1 and len(res[-1].strip()) > 0: + res = res[-1].strip()[0] + else: + res = None + break + + temp = ['A', 'B', 'C', 'D', 'a', 'b', 'c', 'd'] + if res in temp: + return res + else: + return None + + +def extract_choice_fuzzy(gen, lang): + options = ['A', 'B', 'C', 'D'] # 定义选项 + for option in options: + if option in gen: # 检查选项是否在文本中 + return option # 返回第一个出现的选项 + return None + + +@TEXT_POSTPROCESSORS.register_module('pmmeval_xnli') +def pmmeval_xnli_postprocess(text: str, lang_code: str) -> Tuple[str]: + return text, lang_code + + +@LOAD_DATASET.register_module() +class PMMEvalXNLIDataset(BaseDataset): + + @staticmethod + def load(path: str, lang: str): + data_path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + dataset = MsDataset.load(dataset_name=data_path, + subset_name='xnli', + split=f'test/{lang}') + else: + dataset = list() + filename = os.path.join(data_path, f'xnli/test/{lang}.jsonl') + with open(filename, mode='r', encoding='utf-8') as f: + for line in f: + line = json.loads(line.strip()) + dataset.append(line) + dataset = Dataset.from_list(dataset) + + return dataset + + +class PMMEvalXNLIEvaluator(BaseEvaluator): + + def score(self, predictions, references): + assert len(predictions) == len(references) + + all_results = list() + + for (pred, lang), ref in zip(predictions, references): + choice = extract_choice(pred, lang) + acc = 0 + failed_strict = 0 + failed = 1 + if choice is not None: + failed = 0 + if ref.lower() == choice.lower(): + acc = 1 + else: + acc = 0 + else: + choice = extract_choice_fuzzy(pred, lang) + if choice is None: + acc = 0 + failed_strict = 1 + else: + failed_strict = 0 + if ref.lower() == choice.lower(): + acc = 1 + else: + acc = 0 + + all_results.append({ + 'acc': + float(acc), + 'failed': + float(failed), + 'failed_strict': + float(failed_strict), + 'extracted_answer': + choice if choice else 'no answer', + }) + + final_result = { + 'accuracy': + round( + sum(x['acc'] for x in all_results) / len(all_results) * 100, + 2), + 'details': + all_results + } + + return final_result diff --git a/opencompass/datasets/TheoremQA/__init__.py b/opencompass/datasets/TheoremQA/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a24a0ea1e7b1541893c7ef63efdfd8df2923c9a9 --- /dev/null +++ b/opencompass/datasets/TheoremQA/__init__.py @@ -0,0 +1,4 @@ +from .legacy import (TheoremQA_postprocess, TheoremQA_postprocess_v2, + TheoremQADataset) +from .main import (TheoremQA_postprocess_v3, TheoremQADatasetV3, + TheoremQAEvaluatorV3, TheoremQA_postprocess_v4) diff --git a/opencompass/datasets/TheoremQA/legacy.py b/opencompass/datasets/TheoremQA/legacy.py new file mode 100644 index 0000000000000000000000000000000000000000..5a4b2563933968631defacccba2df62ce142946d --- /dev/null +++ b/opencompass/datasets/TheoremQA/legacy.py @@ -0,0 +1,40 @@ +import re + +from datasets import load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class TheoremQADataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + return load_dataset('csv', data_files={'test': path}) + + +@TEXT_POSTPROCESSORS.register_module('TheoremQA') +def TheoremQA_postprocess(text: str) -> str: + text = text.strip() + matches = re.findall(r'answer is ([^\s]+)', text) + if len(matches) == 0: + return text + else: + text = matches[0].strip().strip('.,?!\"\';:') + return text + + +def TheoremQA_postprocess_v2(text: str) -> str: + prediction = text.strip().strip('\n').split('\n')[-1] + tmp = '' + for entry in prediction.split(' ')[::-1]: + if entry == 'is' or entry == 'be' or entry == 'are' or entry.endswith( + ':'): + break + tmp = entry + ' ' + tmp + prediction = tmp.strip().strip('.') + return prediction diff --git a/opencompass/datasets/TheoremQA/main.py b/opencompass/datasets/TheoremQA/main.py new file mode 100644 index 0000000000000000000000000000000000000000..4c14589646736e7085c2461ecadbe0de5894682b --- /dev/null +++ b/opencompass/datasets/TheoremQA/main.py @@ -0,0 +1,75 @@ +import re +import json + +from datasets import Dataset, DatasetDict + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS, ICL_EVALUATORS +from opencompass.utils import get_data_path + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from ..base import BaseDataset +from . import utils +from tqdm import tqdm + + +@LOAD_DATASET.register_module() +class TheoremQADatasetV3(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + with open(path, 'r') as f: + data = json.load(f) + for item in data: + item['Answer'] = str(item['Answer']) + dataset = Dataset.from_list(data) + return dataset + + +def TheoremQA_postprocess_v3(text: str) -> str: + answer = utils.answer_clean(["The answer is:", "The answer is", "the answer is"], text) + return answer + +def TheoremQA_postprocess_v4(text: str) -> str: + # First clean the answer text + answer = utils.answer_clean(["The answer is:", "The answer is", "the answer is"], text) + # Remove LaTeX delimiters \( and \) and strip whitespace + answer = answer.strip('\\(').strip('\\)').strip() + return answer + + +@ICL_EVALUATORS.register_module() +class TheoremQAEvaluatorV3(BaseEvaluator): + def score(self, predictions, references, test_set): + if len(predictions) != len(references): + return {"error": "preds and refrs have different length"} + + details = [] + correct, wrong = 0, 0 + for index in tqdm(range(len(predictions))): + answer = predictions[index] + groundtruth = references[index] + answer_type = test_set[index]['Answer_type'] + if answer_type in ['float', 'integer', 'bool']: + groundtruth = [groundtruth, eval(groundtruth)] + else: + groundtruth = [groundtruth, None] + if utils.compare_answer_with_groundtruth(answer, *groundtruth): + correct += 1 + is_correct = True + else: + wrong += 1 + is_correct = False + + details.append( + { + # "question": question, + # "solution": output, + # "correct": groundtruth, + "pred": answer, + "is_correct": is_correct, + } + ) + + score = correct / (correct + wrong) * 100 + return {'score': score, 'details': details} diff --git a/opencompass/datasets/TheoremQA/number_utils.py b/opencompass/datasets/TheoremQA/number_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fd93fe668ec44de0c79f8ce627cf0b7be273156b --- /dev/null +++ b/opencompass/datasets/TheoremQA/number_utils.py @@ -0,0 +1,98 @@ +import re +import math +from math import sqrt, sin, cos, log, pi, factorial, exp, e +E = 2.718 + + +def floatify(num: str): + try: + num = float(num) + if num.is_integer(): + return round(num) + else: + return num + except Exception: + return None + + +def within_eps(pred: float, gt: float): + eps = abs(gt) * 0.04 + if pred >= gt - eps and pred <= gt + eps: + return True + else: + return False + + +def clean_units(pred_str: str): + """Clean the units in the number.""" + def convert_pi_to_number(code_string): + code_string = code_string.replace('\\pi', 'π') + # Replace \pi or π not preceded by a digit or } with 3.14 + code_string = re.sub(r'(? "3*3.14" + code_string = re.sub(r'(\d)(\\?π)', r'\1*3.14', code_string) + # Handle cases where π is within braces or followed by a multiplication symbol + # This replaces "{π}" with "3.14" directly and "3*π" with "3*3.14" + code_string = re.sub(r'\{(\\?π)\}', '3.14', code_string) + code_string = re.sub(r'\*(\\?π)', '*3.14', code_string) + return code_string + + pred_str = convert_pi_to_number(pred_str) + pred_str = pred_str.replace('%', '/100') + pred_str = pred_str.replace('$', '') + pred_str = pred_str.replace('¥', '') + pred_str = pred_str.replace('°C', '') + pred_str = pred_str.replace(' C', '') + pred_str = pred_str.replace('°', '') + return pred_str + + +def number_it(num): + from latex2sympy2_extended import latex2sympy + if isinstance(num, (int, float)): + return num + + num = clean_units(num) + try: + num = str(latex2sympy(num)) + except Exception: + pass + + if floatify(num) is not None: + return floatify(num) + else: + try: + num = eval(num) + if isinstance(num, list) or isinstance(num, tuple): + num = num[0] + if floatify(num) is not None: + return floatify(num) + else: + return None + except Exception: + return None + + +def compare_two_numbers(p, gt): + try: + if math.isnan(p): + return False + if isinstance(gt, int): + return round(p) == gt + else: + return within_eps(pred=p, gt=gt) + except Exception: + return False + + +def compare_two_list(pred, gt): + if not isinstance(pred, list): + return False + elif len(pred) != len(gt): + return False + elif any([not isinstance(x, (int, float)) for x in pred]): + return False + else: + pred = sorted(pred) + gt = sorted(gt) + return all([compare_two_numbers(p, g) for p, g in zip(pred, gt)]) diff --git a/opencompass/datasets/TheoremQA/utils.py b/opencompass/datasets/TheoremQA/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e6a35e4fcfa3b30b59dd73b60119caa0a47c6bfd --- /dev/null +++ b/opencompass/datasets/TheoremQA/utils.py @@ -0,0 +1,115 @@ +import re +from .number_utils import clean_units, compare_two_numbers, compare_two_list, number_it +import contextlib +import signal + +@contextlib.contextmanager +def time_limit(seconds: float): + def signal_handler(signum, frame): + raise ValueError + + signal.setitimer(signal.ITIMER_REAL, seconds) + signal.signal(signal.SIGALRM, signal_handler) + try: + yield + finally: + signal.setitimer(signal.ITIMER_REAL, 0) + + +def extract_theoremqa_answer(pred: str, answer_flag: bool = True): + from latex2sympy2_extended import latex2sympy + + if any([option in pred.lower() for option in ['yes', 'true']]): + pred = 'True' + elif any([option in pred.lower() for option in ['no', 'false']]): + pred = 'False' + elif any([option in pred.lower() for option in ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']]): + pass + else: + if answer_flag: + # Extract the numbers out of the string + pred = pred.split('=')[-1].strip() + pred = clean_units(pred) + try: + with time_limit(1): + tmp = str(latex2sympy(pred)) + pred = eval(tmp) + if isinstance(pred, tuple): + pred = str(list(pred)) + else: + pred = str(pred) + + except Exception: + if re.match(r'-?[\d\.]+\s\D+$', pred): + pred = pred.split(' ')[0] + elif re.match(r'-?[\d\.]+\s[^\s]+$', pred): + pred = pred.split(' ')[0] + else: + # desparate search over the last number + preds = re.findall(r'-?\d*\.?\d+', pred) + if(len(preds) >= 1): + pred = preds[-1] + else: + pred = '' + return pred + +def answer_clean(direct_answer_trigger_for_fewshot: tuple, pred: str): + pred = pred.strip('\n') + + # Determine if this is ICL, if so, use \n\n to split the first chunk. + ICL = False + for trigger in direct_answer_trigger_for_fewshot: + if pred.count(trigger) > 1: + ICL = True + if ICL: + pred = pred.split('\n\n')[0] + + # Split the trigger to find the answer. + preds = re.split('|'.join(direct_answer_trigger_for_fewshot), pred) + if len(preds) > 1: + answer_flag = True + pred = preds[-1] + else: + answer_flag = False + + pred = pred.strip('\n').rstrip('.').rstrip('/').strip(' ') + + pred = [extract_theoremqa_answer(pred, answer_flag)] + + # If there is no candidate in list, null is set. + if len(pred) == 0: + pred = "" + else: + if answer_flag: + # choose the first element in list ... + pred = pred[0] + else: + # choose the last e + pred = pred[-1] + + # Remove the period at the end, again! + pred = pred.rstrip('.').rstrip('/') + return pred + + + +def compare_answer_with_groundtruth(answer: str, groundtruth_str: str, groundtruth_num = None): + if groundtruth_str.lower() in ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']: + return groundtruth_str.lower() in answer.lower() + elif answer.lower() == groundtruth_str.lower(): + return True + elif groundtruth_num is not None: + if isinstance(groundtruth_num, (int, float)): + return compare_two_numbers(number_it(answer), groundtruth_num) + else: + if answer.startswith('(') and answer.endswith(')'): + try: + answer = list(eval(answer)) + answer = [number_it(a) for a in answer] + except Exception as e: + return False + 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0000000000000000000000000000000000000000..efe008575ca77f8ff9b16c1cc291c577116229f7 Binary files /dev/null and b/opencompass/datasets/__pycache__/internsandbox.cpython-310.pyc differ diff --git a/opencompass/datasets/agieval/__init__.py b/opencompass/datasets/agieval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5ea1de8c477a55a33ef62d4a8544bc6bec80d8fd --- /dev/null +++ b/opencompass/datasets/agieval/__init__.py @@ -0,0 +1,3 @@ +# flake8: noqa + +from .agieval import * # noqa: F401, F403 diff --git a/opencompass/datasets/agieval/agieval.py b/opencompass/datasets/agieval/agieval.py new file mode 100644 index 0000000000000000000000000000000000000000..40db58457e081bde2d82d950c21319abf6ca6db1 --- /dev/null +++ b/opencompass/datasets/agieval/agieval.py @@ -0,0 +1,125 @@ +import json +import os.path as osp +from os import environ + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .math_equivalence import is_equiv +from .post_process import parse_math_answer + + +@LOAD_DATASET.register_module() +class AGIEvalDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str, setting_name: str): + path = get_data_path(path) + from .dataset_loader import load_dataset, load_dataset_as_result_schema + + assert setting_name in 'zero-shot', 'only support zero-shot setting' + dataset_wo_label = load_dataset(name, setting_name, path) + dataset_with_label = load_dataset_as_result_schema(name, path) + dataset = [] + for d1, d2 in zip(dataset_wo_label, dataset_with_label): + dataset.append({ + 'id': d2.index, + 'problem_input': d1['context'], + 'label': d2.label, + }) + dataset = Dataset.from_list(dataset) + return dataset + + +@LOAD_DATASET.register_module() +class AGIEvalDataset_v2(BaseDataset): + + @staticmethod + def load(path: str, name: str, setting_name: str): + path = get_data_path(path) + assert setting_name in 'zero-shot', 'only support zero-shot setting' + + if environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + ms_dataset = MsDataset.load(path, subset_name=name, split='test') + dataset = [] + for item in ms_dataset: + passage = item['passage'] if item['passage'] else '' + question = passage + item['question'] + options = '\n'.join(item['options']) if item['options'] else '' + if item['label']: + try: + label = eval(item['label']) + except Exception: + label = item['label'] + if isinstance(label, list): + label = ''.join(label) + else: + label = item['answer'] + d = {'question': question, 'options': options, 'label': label} + dataset.append(d) + dataset = Dataset.from_list(dataset) + else: + filename = osp.join(path, name + '.jsonl') + with open(filename, encoding='utf-8') as f: + data = [json.loads(line.strip()) for line in f] + dataset = [] + for item in data: + passage = item['passage'] if item['passage'] else '' + question = passage + item['question'] + options = '\n'.join(item['options']) if item['options'] else '' + if item['label']: + if isinstance(item['label'], list): + label = ''.join(item['label']) + else: + label = item['label'] + else: + label = item['answer'] + d = {'question': question, 'options': options, 'label': label} + dataset.append(d) + dataset = Dataset.from_list(dataset) + return dataset + + +@ICL_EVALUATORS.register_module() +class AGIEvalEvaluator(BaseEvaluator): + + def score(self, predictions, references): + predictions = [parse_math_answer('', pred) for pred in predictions] + details = [] + cnt = 0 + for pred, ref in zip(predictions, references): + detail = {'pred': pred, 'answer': ref, 'correct': False} + if is_equiv(pred, ref): + cnt += 1 + detail['correct'] = True + details.append(detail) + score = cnt / len(predictions) * 100 + return {'score': score, 'details': details} + + +@ICL_EVALUATORS.register_module() +class AGIEvalEvaluator_mcq(BaseEvaluator): + + def score(self, predictions, references): + if len(predictions) != len(references): + return { + 'error': 'predictions and references have different ' + 'length' + } + details = [] + cnt = 0 + for pred, ref in zip(predictions, references): + detail = {'pred': pred, 'answer': ref, 'correct': False} + if pred == ref: + cnt += 1 + detail['correct'] = True + details.append(detail) + + score = cnt / len(predictions) * 100 + + return {'score': score, 'details': details} diff --git a/opencompass/datasets/agieval/constructions.py b/opencompass/datasets/agieval/constructions.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c8367683aae16efda1d06970a93a2fb472da22 --- /dev/null +++ b/opencompass/datasets/agieval/constructions.py @@ -0,0 +1,104 @@ +# flake8: noqa +import pandas as pd + + +class TaskSchema(object): + + def __init__(self, + passage=None, + question=None, + options=None, + label=None, + answer=None, + other=None): + self.passage = passage + self.question = question + self.options = options + self.label = label + self.answer = answer + self.other = other + + def to_dict(self): + return { + 'passage': self.passage, + 'question': self.question, + 'options': self.options, + 'label': self.label, + 'answer': self.answer, + 'other': self.other + } + + +# define README.json +class AgiInstance(object): + + def __init__(self, task_description, data_source, task_schema, output, + evaluation_metric, task_example): + self.task_description = task_description + self.data_source = data_source + self.task_schema = task_schema + self.output = output + self.evaluation_metric = evaluation_metric + self.task_example = task_example + + def to_dict(self): + return { + 'task description': self.task_description, + 'data source': self.data_source, + 'task schema': self.task_schema.to_dict(), + 'output': self.output, + 'evaluation metric': self.evaluation_metric, + 'task example': self.task_example + } + + +class ChatGPTSchema(object): + + def __init__(self, context=None, metadata=''): + self.context = context + self.metadata = metadata + + def to_dict(self): + return {'context': self.context, 'metadata': self.metadata} + + +class ResultsForHumanSchema(object): + + def __init__(self, + index, + problem_input, + label, + model_input='', + model_output='', + parse_result='', + first_stage_output='', + second_stage_input='', + is_correct=False): + self.index = index + self.problem_input = problem_input + self.model_input = model_input + self.model_output = model_output + self.parse_result = parse_result + self.label = label + self.first_stage_output = first_stage_output + self.second_stage_input = second_stage_input + self.is_correct = is_correct + + def to_dict(self): + return { + 'index': self.index, + 'problem_input': self.problem_input, + 'model_input': self.model_input, + 'model_output': self.model_output, + 'parse_result': self.parse_result, + 'label': self.label, + 'is_correct': self.is_correct, + 'first_stage_output': self.first_stage_output, + 'second_stage_input': self.second_stage_input, + } + + @staticmethod + def to_tsv(result_list, path): + result_json = [item.to_dict() for item in result_list] + table = pd.json_normalize(result_json) + table.to_excel(path, index=False) diff --git a/opencompass/datasets/agieval/dataset_loader.py b/opencompass/datasets/agieval/dataset_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..75d90599b52ee40714daa17abbda776fad418d84 --- /dev/null +++ b/opencompass/datasets/agieval/dataset_loader.py @@ -0,0 +1,407 @@ +# flake8: noqa +import ast +import json +import os +from os import environ + +import pandas as pd +import tiktoken +from tqdm import tqdm + +from .constructions import ChatGPTSchema, ResultsForHumanSchema +from .utils import extract_answer, read_jsonl, save_jsonl + +# define the datasets +english_qa_datasets = [ + 'lsat-ar', 'lsat-lr', 'lsat-rc', 'logiqa-en', 'sat-math', 'sat-en', + 'aqua-rat', 'sat-en-without-passage', 'gaokao-english' +] +chinese_qa_datasets = [ + 'logiqa-zh', 'jec-qa-kd', 'jec-qa-ca', 'gaokao-chinese', + 'gaokao-geography', 'gaokao-history', 'gaokao-biology', 'gaokao-chemistry', + 'gaokao-physics', 'gaokao-mathqa' +] +english_cloze_datasets = ['math'] +chinese_cloze_datasets = ['gaokao-mathcloze'] + +multi_choice_datasets = ['jec-qa-kd', 'jec-qa-ca', 'gaokao-physics'] +math_output_datasets = ['gaokao-mathcloze', 'math'] + + +def convert_zero_shot(line, dataset_name): + try: + passage = line['passage'] if line['passage'] is not None else '' + if dataset_name in english_qa_datasets: + option_string = 'ABCDEFG' + count = len(line['options']) + if count == 1: + count = 5 + return passage + 'Q: ' + line['question'] + ' ' \ + + 'Answer Choices: ' + ' '.join(line['options']) + '\n' + \ + 'A: Among A through {}, the answer is'.format(option_string[count - 1]) + + elif dataset_name in chinese_qa_datasets: + option_string = 'ABCDEFG' + count = len(line['options']) + if count == 1: + count = 4 + return passage + '问题：' + line['question'] + ' ' \ + + '选项：' + ' '.join(line['options']) + '\n' + \ + '答案：从A到{}, 我们应选择'.format(option_string[count - 1]) + + elif dataset_name in english_cloze_datasets: + return passage + 'Q: ' + line['question'] + '\n' \ + 'A: The answer is' + + elif dataset_name in chinese_cloze_datasets: + return passage + '问题：' + line['question'] + '\n' \ + '答案：' + except NameError: + print('Dataset not defined.') + + +prefix = '该问题为单选题，所有选项中必有一个正确答案，且只有一个正确答案。\n' + + +def convert_zero_shot_CoT_stage1(line, dataset_name): + try: + passage = line['passage'] if line['passage'] is not None else '' + if dataset_name in english_qa_datasets: + return passage + 'Q: ' + line['question'] + ' ' \ + + 'Answer Choices: ' + ' '.join(line['options']) + '\n' + \ + "Let's think step by step." + + elif dataset_name in chinese_qa_datasets: + option_string = 'ABCDEFG' + count = len(line['options']) + if count == 1: + count = 4 + return passage + '问题：' + line['question'] + ' ' \ + + '选项：' + ' '.join(line['options']) + '\n' + \ + '从A到{}, 我们应选择什么？让我们逐步思考：'.format(option_string[count - 1]) + + elif dataset_name in english_cloze_datasets: + return passage + 'Q: ' + line['question'] + '\n' \ + "A: Let's think step by step." + + elif dataset_name in chinese_cloze_datasets: + return passage + '问题：' + line['question'] + '\n' \ + '答案：让我们逐步思考：' + except NameError: + print('Dataset not defined.') + + +# process few-shot raw_prompts +def combine_prompt(prompt_path, + dataset_name, + load_explanation=True, + chat_mode=False): + skip_passage = False + if dataset_name == 'sat-en-without-passage': + skip_passage = True + dataset_name = 'sat-en' + demostrations = [] + # read the prompts by context and explanation + context_row = [0, 1, 3, 5, 7, 9] + explanation_row = [0, 2, 4, 6, 8, 10] + raw_prompts_context = pd.read_csv(prompt_path, + header=0, + skiprows=lambda x: x not in context_row, + keep_default_na=False) + raw_prompts_explanation = pd.read_csv( + prompt_path, + header=0, + skiprows=lambda x: x not in explanation_row, + keep_default_na=False).replace(r'\n\n', '\n', regex=True) + contexts = [] + for line in list(raw_prompts_context[dataset_name]): + if line: + # print(line) + contexts.append(ast.literal_eval(line)) + explanations = [ + exp for exp in raw_prompts_explanation[dataset_name] if exp + ] + + for idx, (con, exp) in enumerate(zip(contexts, explanations)): + passage = con['passage'] if con[ + 'passage'] is not None and not skip_passage else '' + question = con['question'] + options = con['options'] if con['options'] is not None else '' + label = con['label'] if con['label'] is not None else '' + answer = con[ + 'answer'] if 'answer' in con and con['answer'] is not None else '' + + if dataset_name in english_qa_datasets: + question_input = 'Problem {}. '.format(idx + 1) + passage + ' ' + question + '\n' \ + + 'Choose from the following options: ' + ' '.join(options) + '\n' + question_output = (('Explanation for Problem {}: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + 'The answer is therefore {}'.format(label) + + elif dataset_name in chinese_qa_datasets: + question_input = '问题 {}. '.format(idx + 1) + passage + ' ' + question + '\n' \ + + '从以下选项中选择: ' + ' '.join(options) + '\n' + question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + '答案是 {}'.format(label) + + elif dataset_name in english_cloze_datasets: + question_input = 'Problem {}. '.format(idx + 1) + question + '\n' + question_output = (('Explanation for Problem {}: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + 'The answer is therefore {}'.format(answer) + + elif dataset_name in chinese_cloze_datasets: + question_input = '问题 {}. '.format(idx + 1) + question + '\n' + question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + '答案是 {}'.format(answer) + else: + raise ValueError( + f'During loading few-sot examples, found unknown dataset: {dataset_name}' + ) + if chat_mode: + demostrations.append((question_input, question_output)) + else: + demostrations.append(question_input + question_output + '\n') + + return demostrations + + +enc = None + + +def _lazy_load_enc(): + global enc + if enc is None: + enc = tiktoken.encoding_for_model('gpt-4') + + +# cut prompt if reach max token length +def concat_prompt(demos, + dataset_name, + max_tokens, + end_of_example='\n', + verbose=False): + _lazy_load_enc() + demostration_en = 'Here are the answers for the problems in the exam.\n' + demostration_zh = '以下是考试中各个问题的答案。\n' + + for i in range(len(demos)): + # print(len(enc.encode(demostration_en)), len(enc.encode(demostration_zh))) + if dataset_name in english_qa_datasets: + demostration_en = demostration_en + demos[i] + end_of_example + elif dataset_name in chinese_qa_datasets: + demostration_zh = demostration_zh + demos[i] + end_of_example + elif dataset_name in english_cloze_datasets: + demostration_en = demostration_en + demos[i] + end_of_example + elif dataset_name in chinese_cloze_datasets: + demostration_zh = demostration_zh + demos[i] + end_of_example + # break if reach max token limit + if len(enc.encode(demostration_en)) < max_tokens and len( + enc.encode(demostration_zh)) < max_tokens: + output = demostration_en if len(demostration_en) > len( + demostration_zh) else demostration_zh + prompt_num = i + 1 + else: + break + if verbose: + print('max_tokens set as ', max_tokens, 'actual_tokens is', + len(enc.encode(output)), 'num_shot is', prompt_num) + return output, prompt_num + + +def concat_prompt_chat_mode(demos, + dataset_name, + max_tokens, + end_of_example='\n', + verbose=False): + _lazy_load_enc() + answers = [] + sentences = '' + for i in range(len(demos)): + answers += [ + { + 'role': 'user', + 'content': demos[i][0] + }, + { + 'role': 'assistant', + 'content': demos[i][1] + }, + ] + sentences += json.dumps(answers[-1]) + # break if reach max token limit + if len(enc.encode(sentences)) > max_tokens: + answers.pop() + answers.pop() + break + if verbose: + print('max_tokens set as ', max_tokens, 'actual_tokens is', + len(enc.encode(sentences)), 'num_shot is', + len(answers) // 2) + return answers, len(answers) // 2 + + +def convert_few_shot(line, dataset_name, demo, n_shot, chat_mode=False): + passage = line['passage'] if line['passage'] is not None else '' + question = line['question'] + options = line['options'] if line['options'] is not None else '' + + if dataset_name in english_qa_datasets: + question_input = 'Problem {}. '.format(n_shot + 1) + passage + ' ' + question + '\n' \ + + 'Choose from the following options: ' + ' '.join(options) + '\n' + # + "Explanation for Problem {}: ".format(n_shot + 1) + + if dataset_name in chinese_qa_datasets: + question_input = '问题 {}. '.format(n_shot + 1) + passage + ' ' + question + '\n' \ + + '从以下选项中选择: ' + ' '.join(options) + '\n' + # + "问题 {}的解析: ".format(n_shot + 1) + + if dataset_name in english_cloze_datasets: + question_input = 'Problem {}. '.format(n_shot + 1) + question + '\n' + # + "Explanation for Problem {}: ".format(n_shot + 1) + + if dataset_name in chinese_cloze_datasets: + question_input = '问题 {}. '.format(n_shot + 1) + question + '\n' + # + "问题 {}的解析: ".format(n_shot + 1) + if chat_mode: + return demo + [ + { + 'role': 'user', + 'content': question_input + }, + ] + else: + return demo + question_input + + +def load_dataset(dataset_name, + setting_name, + parent_path, + prompt_path=None, + max_tokens=None, + end_of_example='\n', + chat_mode=False, + verbose=False): + + if environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + loaded_jsonl = MsDataset.load(parent_path, + subset_name=dataset_name, + split='test') + else: + test_path = os.path.join(parent_path, dataset_name + '.jsonl') + loaded_jsonl = read_jsonl(test_path) + processed = [] + if setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + # process demo once if it is few-shot-CoT + processed_demos = combine_prompt( + prompt_path, + dataset_name, + load_explanation=setting_name == 'few-shot-CoT', + chat_mode=chat_mode) + if chat_mode: + chosen_prompt, n_shot = concat_prompt_chat_mode(processed_demos, + dataset_name, + max_tokens, + end_of_example, + verbose=verbose) + else: + chosen_prompt, n_shot = concat_prompt(processed_demos, + dataset_name, + max_tokens, + end_of_example, + verbose=verbose) + if verbose: + loaded_jsonl = tqdm(loaded_jsonl) + for meta_idx, line in enumerate(loaded_jsonl): + if setting_name == 'zero-shot': + ctxt = convert_zero_shot(line, dataset_name) + elif setting_name == 'zero-shot-CoT': + ctxt = convert_zero_shot_CoT_stage1(line, dataset_name) + elif setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + ctxt = convert_few_shot(line, dataset_name, chosen_prompt, n_shot, + chat_mode) + try: + new_instance = ChatGPTSchema(context=ctxt, metadata=meta_idx) + processed.append(new_instance.to_dict()) + except NameError: + print('Dataset not defined.') + return processed + + +def generate_second_stage_input(dataset_name, + input_list, + output_list, + with_format_prompt=False): + try: + english_format_prompt = 'Based on the previous results, your task is to extract the final answer and provide the output enclosed in brackets【】, such as 【0】 or 【A】.' + chinese_format_prompt = '根据以上内容，你的任务是把最终的答案提取出来并填在【】中，例如【0】或者【A】。' + if dataset_name in english_qa_datasets: + prompt_suffix = 'Therefore, among A through E, the answer is' + if with_format_prompt: + prompt_suffix = english_format_prompt + prompt_suffix + elif dataset_name in chinese_qa_datasets: + prompt_suffix = '因此，从A到D, 我们应选择' + if with_format_prompt: + prompt_suffix = chinese_format_prompt + prompt_suffix + elif dataset_name in english_cloze_datasets: + prompt_suffix = 'Therefore, the answer is' + if with_format_prompt: + prompt_suffix = english_format_prompt + prompt_suffix + elif dataset_name in chinese_cloze_datasets: + prompt_suffix = '因此，答案是' + if with_format_prompt: + prompt_suffix = chinese_format_prompt + prompt_suffix + except NameError: + print('Dataset not defined.') + processed = [] + for i in range(len(input_list)): + ctxt = '{0}\n{1}\n{2}'.format(input_list[i]['context'], + extract_answer(output_list[i]), + prompt_suffix) + new_instance = ChatGPTSchema(context=ctxt, + metadata=input_list[i]['metadata']) + processed.append(new_instance.to_dict()) + return processed + + +def load_dataset_as_result_schema(dataset_name, parent_path): + + if environ.get('DATASET_SOURCE') == 'ModelScope': + from modelscope import MsDataset + loaded_jsonl = MsDataset.load(parent_path, + subset_name=dataset_name, + split='test') + else: + test_path = os.path.join(parent_path, dataset_name + '.jsonl') + loaded_jsonl = read_jsonl(test_path) + + processed = [] + for i, line in enumerate(loaded_jsonl): + problem_input = convert_zero_shot(line, dataset_name) + processed.append( + ResultsForHumanSchema( + index=i, + problem_input=problem_input, + label=line['label'] if line['label'] else line['answer'], + )) + return processed + + +if __name__ == '__main__': + + # set variables + parent_dir = '../../data/V1_1/' + raw_prompt_path = '../data/few_shot_prompts.csv' + + # set dataset name to process + setting_name = 'few-shot-CoT' # setting_name can be chosen from ["zero-shot", "zero-shot-CoT", "few-shot-CoT"] + data_name = 'jec-qa-kd' + save_dir = '../../experiment_input/{}/'.format(setting_name) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + processed_data = load_dataset(data_name, + setting_name, + parent_dir, + prompt_path=raw_prompt_path, + max_tokens=2048) + save_jsonl(processed_data, + os.path.join(save_dir, '{}.jsonl'.format(data_name))) diff --git a/opencompass/datasets/agieval/evaluation.py b/opencompass/datasets/agieval/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a9916a11b986139407eb3796174d5c533d537b --- /dev/null +++ b/opencompass/datasets/agieval/evaluation.py @@ -0,0 +1,43 @@ +# flake8: noqa +from . import dataset_loader, utils +from .math_equivalence import is_equiv + + +def convert_to_set(item): + if isinstance(item, list): + return set(item) + if isinstance(item, str): + return {item} + if item is None: + return {} + raise ValueError("Input can't parse:", item) + + +def evaluate_single_sample(dataset_name, prediction, label): + if dataset_name in dataset_loader.multi_choice_datasets: + p = convert_to_set(prediction) + l = convert_to_set(label) + return p == l + elif dataset_name in dataset_loader.math_output_datasets: + return is_equiv(prediction, label) + else: + return prediction == label + + +# def evaluate(dataset_name, prediction_list, label_list): +# correct = 0 +# if dataset_name in multi_choice_datasets: +# for prediction, label in zip(prediction_list, label_list): +# p = convert_to_set(prediction) +# l = convert_to_set(label) +# if p == l: +# correct += 1 +# elif dataset_name in math_output_datasets: +# for prediction, label in zip(prediction_list, label_list): +# if is_equiv(prediction, label): +# correct += 1 +# else: +# for prediction, label in zip(prediction_list, label_list): +# if prediction == label: +# correct += 1 +# return "{0:.2%}".format(correct / len(label_list)) diff --git a/opencompass/datasets/agieval/math_equivalence.py b/opencompass/datasets/agieval/math_equivalence.py new file mode 100644 index 0000000000000000000000000000000000000000..788900eaac572f92b4381c03ed93d25dd3482549 --- /dev/null +++ b/opencompass/datasets/agieval/math_equivalence.py @@ -0,0 +1,161 @@ +# flake8: noqa + + +# code from https://github.com/hendrycks/math/blob/main/modeling/math_equivalence.py +def _fix_fracs(string): + substrs = string.split('\\frac') + new_str = substrs[0] + if len(substrs) > 1: + substrs = substrs[1:] + for substr in substrs: + new_str += '\\frac' + if substr[0] == '{': + new_str += substr + else: + try: + assert len(substr) >= 2 + except: + return string + a = substr[0] + b = substr[1] + if b != '{': + if len(substr) > 2: + post_substr = substr[2:] + new_str += '{' + a + '}{' + b + '}' + post_substr + else: + new_str += '{' + a + '}{' + b + '}' + else: + if len(substr) > 2: + post_substr = substr[2:] + new_str += '{' + a + '}' + b + post_substr + else: + new_str += '{' + a + '}' + b + string = new_str + return string + + +def _fix_a_slash_b(string): + if len(string.split('/')) != 2: + return string + a = string.split('/')[0] + b = string.split('/')[1] + try: + a = int(a) + b = int(b) + assert string == '{}/{}'.format(a, b) + new_string = '\\frac{' + str(a) + '}{' + str(b) + '}' + return new_string + except: + return string + + +def _remove_right_units(string): + # "\\text{ " only ever occurs (at least in the val set) when describing units + if '\\text{ ' in string: + splits = string.split('\\text{ ') + assert len(splits) == 2 + return splits[0] + else: + return string + + +def _fix_sqrt(string): + if '\\sqrt' not in string: + return string + splits = string.split('\\sqrt') + new_string = splits[0] + for split in splits[1:]: + if split[0] != '{': + a = split[0] + new_substr = '\\sqrt{' + a + '}' + split[1:] + else: + new_substr = '\\sqrt' + split + new_string += new_substr + return new_string + + +def _strip_string(string): + # linebreaks + string = string.replace('\n', '') + # print(string) + + # remove inverse spaces + string = string.replace('\\!', '') + # print(string) + + # replace \\ with \ + string = string.replace('\\\\', '\\') + # print(string) + + # replace tfrac and dfrac with frac + string = string.replace('tfrac', 'frac') + string = string.replace('dfrac', 'frac') + # print(string) + + # remove \left and \right + string = string.replace('\\left', '') + string = string.replace('\\right', '') + # print(string) + + # Remove circ (degrees) + string = string.replace('^{\\circ}', '') + string = string.replace('^\\circ', '') + + # remove dollar signs + string = string.replace('\\$', '') + + # remove units (on the right) + string = _remove_right_units(string) + + # remove percentage + string = string.replace('\\%', '') + string = string.replace('\%', '') + + # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string + string = string.replace(' .', ' 0.') + string = string.replace('{.', '{0.') + # if empty, return empty string + if len(string) == 0: + return string + if string[0] == '.': + string = '0' + string + + # to consider: get rid of e.g. "k = " or "q = " at beginning + if len(string.split('=')) == 2: + if len(string.split('=')[0]) <= 2: + string = string.split('=')[1] + + # fix sqrt3 --> sqrt{3} + string = _fix_sqrt(string) + + # remove spaces + string = string.replace(' ', '') + + # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} + string = _fix_fracs(string) + + # manually change 0.5 --> \frac{1}{2} + if string == '0.5': + string = '\\frac{1}{2}' + + # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y + string = _fix_a_slash_b(string) + + return string + + +def is_equiv(str1, str2, verbose=False): + if str1 is None and str2 is None: + print('WARNING: Both None') + return True + if str1 is None or str2 is None: + return False + + try: + ss1 = _strip_string(str1) + ss2 = _strip_string(str2) + if verbose: + print(ss1, ss2) + return ss1 == ss2 + except: + return str1 == str2 diff --git a/opencompass/datasets/agieval/post_process.py b/opencompass/datasets/agieval/post_process.py new file mode 100644 index 0000000000000000000000000000000000000000..f54531e8853035e28fcac89104bb9ce4abef6984 --- /dev/null +++ b/opencompass/datasets/agieval/post_process.py @@ -0,0 +1,198 @@ +# flake8: noqa +import json +import re + +from . import dataset_loader + + +def extract_last_line(string): + lines = string.split('\n') + for item in lines[::-1]: + if item.strip() != '': + string = item + break + return string + + +def remove_few_shot_prefix(string: str): + prefix_list = ['The answer is therefore', '答案是'] + for prefix in prefix_list: + if string.startswith(prefix): + string = string[len(prefix):].strip() + elif prefix in string: + index = string.rfind(prefix) + if index >= 0: + string = string[index + len(prefix):].strip() + return string + + +def try_parse_few_shot_qa_single_answer(string, setting_name, language='en'): + if setting_name == 'few-shot-CoT': + string = extract_last_line(string) + if language == 'en': + pattern = 'answer is .*?([A-G])' + match = re.search(pattern, string) + elif language == 'zh': + pattern = '答案是.*?([A-G])' + match = re.search(pattern, string) + else: + raise ValueError('Unknown language {0}'.format(language)) + if match: + return match.group(1) + else: + return None + + +def try_parse_few_shot_pattern(string: str, dataset_name, setting_name): + if setting_name == 'few-shot-CoT': + string = extract_last_line(string) + if dataset_name in dataset_loader.chinese_cloze_datasets: + return string.startswith('答案是') + elif dataset_name in dataset_loader.english_cloze_datasets: + return string.startswith('The answer is therefore') + elif dataset_name in dataset_loader.chinese_qa_datasets: + pattern = '答案是.*?([A-G])' + match = re.search(pattern, string) + return match is not None + elif dataset_name in dataset_loader.english_qa_datasets: + pattern = 'answer is .*?([A-G])' + match = re.search(pattern, string) + return match is not None + return False + + +def parse_few_shot_qa_single_answer(string, setting_name, language='en'): + answer = try_parse_few_shot_qa_single_answer(string, setting_name, + language) + if answer is None: + return find_first_capital_letter(string) + else: + return answer + + +def find_first_capital_letter(answer): + letter_set = {'A', 'B', 'C', 'D', 'E', 'F'} + for c in answer: + if c in letter_set: + return c + # print("Can't find capital letter in:", answer) + return '' + + +def extract_answer_in_bracket(answer, prefix='【', suffix='】'): + if prefix not in answer and suffix not in answer: + # print("doesn't found special tokens in:", answer) + return '' + s = answer.index(prefix) + len(prefix) + t = answer.index(suffix) + ret = answer[s:t] + return ret + + +def parse_math_answer(setting_name, raw_string): + if setting_name == 'few-shot-CoT': + raw_string = extract_last_line(raw_string) + if setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + raw_string = remove_few_shot_prefix(raw_string) + return raw_string + + def remove_boxed(s): + left = '\\boxed{' + try: + assert s[:len(left)] == left + assert s[-1] == '}' + answer = s[len(left):-1] + if '=' in answer: + answer = answer.split('=')[-1].lstrip(' ') + return answer + except: + return None + + def last_boxed_only_string(string): + idx = string.rfind('\\boxed') + if idx < 0: + idx = string.rfind('\\fbox') + if idx < 0: + return None + i = idx + right_brace_idx = None + num_left_braces_open = 0 + while i < len(string): + if string[i] == '{': + num_left_braces_open += 1 + if string[i] == '}': + num_left_braces_open -= 1 + if num_left_braces_open == 0: + right_brace_idx = i + break + i += 1 + + if right_brace_idx == None: + retval = None + else: + retval = string[idx:right_brace_idx + 1] + + return retval + + def get_answer_with_dollar_sign(s): + first_pattern = '\$(.*)\$' + last_match = None + matches = re.findall(first_pattern, s) + if matches: + last_match = matches[-1] + if '=' in last_match: + last_match = last_match.split('=')[-1].lstrip(' ') + return last_match + + def get_answer_without_dollar_sign(s): + last_match = None + if '=' in s: + last_match = s.split('=')[-1].lstrip(' ').rstrip('.') + if '\\n' in last_match: + last_match = last_match.split('\\n')[0] + else: + pattern = '(?:\\$)?\d+(?:\.\d+)?(?![\w\d])' + matches = re.findall(pattern, s) + if matches: + last_match = matches[-1] + return last_match + + raw_string = remove_few_shot_prefix(raw_string) + if '\\boxed' in raw_string: + answer = remove_boxed(last_boxed_only_string(raw_string)) + else: + answer = get_answer_with_dollar_sign(raw_string) + if not answer: + answer = get_answer_without_dollar_sign(raw_string) + return answer + + +def parse_qa_multiple_answer(string, setting_name): + if setting_name == 'few-shot-CoT': + string = extract_last_line(string) + pattern = '\(*([A-Z])\)*' + match = re.findall(pattern, string) + if match: + return match + return [] + + +def post_process(dataset_name, setting_name, prediction): + if dataset_name in dataset_loader.english_cloze_datasets or dataset_name in dataset_loader.chinese_cloze_datasets: + return parse_math_answer(setting_name, prediction) + + if dataset_name in ['jec-qa-kd', 'jec-qa-ca', 'gaokao-physics']: + return parse_qa_multiple_answer(prediction, setting_name) + + # all other datasets are QA problems with single answer + if 'zero-shot' in setting_name: + answer = find_first_capital_letter(prediction) + return answer + + # all other datasets are QA problems with single answer and setting_name are few-shot + language = 'en' if dataset_name in dataset_loader.english_qa_datasets else 'zh' + if dataset_name in dataset_loader.english_qa_datasets or dataset_name in dataset_loader.chinese_qa_datasets: + return parse_few_shot_qa_single_answer(prediction, setting_name, + language) + else: + raise ValueError(f'Unsupported dataset name {dataset_name}') diff --git a/opencompass/datasets/agieval/utils.py b/opencompass/datasets/agieval/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fbb311057b3b21cbe4876a0ad6b9cd4dd11d0659 --- /dev/null +++ b/opencompass/datasets/agieval/utils.py @@ -0,0 +1,43 @@ +# flake8: noqa +import json + + +def read_jsonl(path): + with open(path, encoding='utf8') as fh: + results = [] + for line in fh: + if line is None: + continue + try: + results.append(json.loads(line) if line != 'null' else line) + except Exception as e: + print(e) + print(path) + print(line) + raise e + return results + + +def save_jsonl(lines, directory): + with open(directory, 'w', encoding='utf8') as f: + for line in lines: + f.write(json.dumps(line, ensure_ascii=False) + '\n') + + +def extract_answer(js): + try: + if js is None or js == 'null': + return '' + answer = '' + if isinstance(js, str): + answer = js + elif 'text' in js['choices'][0]: + answer = js['choices'][0]['text'] + else: + answer = js['choices'][0]['message']['content'] + # answer = js[''] + return answer + except Exception as e: + # print(e) + # print(js) + return '' diff --git a/opencompass/datasets/atlas/dataset_loader.py b/opencompass/datasets/atlas/dataset_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..f78ab51d9f331caed2b4112977f56a58b7d04f23 --- /dev/null +++ b/opencompass/datasets/atlas/dataset_loader.py @@ -0,0 +1,21 @@ +from datasets import Dataset, load_dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.registry import LOAD_DATASET + + +@LOAD_DATASET.register_module() +class ATLASDataset(BaseDataset): + + @staticmethod + def load(split: str = 'val'): + hf_dataset = load_dataset('opencompass/ATLAS', split=split) + dataset = [] + for example in hf_dataset: + dataset.append({ + 'problem': + str(example['question']), + 'answer': + str(example.get('refined_standard_answer', '')) + }) + return Dataset.from_list(dataset) diff --git a/opencompass/datasets/atlas/evaluation.py b/opencompass/datasets/atlas/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..36e423d4b35f1e27cc7b1eb8705039f9fc419aa7 --- /dev/null +++ b/opencompass/datasets/atlas/evaluation.py @@ -0,0 +1,407 @@ +import json +import os +import os.path as osp +import re +from copy import deepcopy +from typing import Dict, List, Optional, Tuple + +import mmengine +from datasets import Dataset +from mmengine.config import ConfigDict + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.registry import (DICT_POSTPROCESSORS, ICL_EVALUATORS, + ICL_PROMPT_TEMPLATES) +from opencompass.utils import build_dataset_from_cfg, build_model_from_cfg +from opencompass.utils.logging import get_logger + +logger = get_logger(__name__) + + +def fix_json_slash(s: str) -> str: + return re.sub(r'(?', '')) -> str: + if prediction is None: + prediction = '' + if think_tags[1] in prediction: + prediction = prediction.split(think_tags[1])[-1] + json_str = re.search(r'```json\n(.*?)\n```', prediction, re.DOTALL) + if json_str is None: + json_str = re.search(r'```(.*?)```', prediction, re.DOTALL) + if json_str is None: + try: + assert '"answers":' in prediction + json_content = '{\n"answers"' + prediction.split( + '"answers"')[-1] + json_content = fix_json_slash(json_content) + return {'extract_error': False, 'answers': json_content} + except Exception: + return {'extract_error': True, 'answers': '{"answers": []}'} + else: + json_content = json_str.group( + 1).strip() if json_str.lastindex else json_str.group(0) + json_content = fix_json_slash(json_content) + return {'extract_error': False, 'answers': json_content} + else: + json_content = json_str.group( + 1).strip() if json_str.lastindex else json_str.group(0) + json_content = fix_json_slash(json_content) + return {'extract_error': False, 'answers': json_content} + + +def get_final_results(parsed_judges: List[List[Dict]], + references: List[List[str]], + origial_judges: List[List[str]]) -> Dict: + count = 0 + details = [] + for parsed_judge, reference, origial_judge in zip(parsed_judges, + references, + origial_judges): + detail = { + 'origial_judge': + origial_judge, + 'reference': + reference, + 'parsed_judge': + parsed_judge, + 'correct': + sum([item['overall_judge'] for item in parsed_judge]) > + (len(parsed_judge) // 2) + } + count += 1 + details.append(detail) + + accuracy = sum(detail['correct'] for detail in details) / count + result = { + 'accuracy': accuracy * 100, + 'details': details, + } + return result + + +def process_judge_output( + output: Dict, think_tags: Tuple[str, str] = ('', '') +) -> Tuple[List[str], List[Dict], List[str]]: + + def _parse(prediction: str) -> dict: + if think_tags[1] in prediction: + prediction = prediction.split(think_tags[1])[-1] + + json_str = re.search(r'```json(.*?)```', prediction, re.DOTALL) + if json_str is None: + json_str = re.search(r'```(.*?)```', prediction, re.DOTALL) + if json_str is not None: + json_content = json_str.group(1).strip() + json_content = fix_json_slash(json_content) + try: + return json.loads(json_content) + except Exception: + return { + 'judgements': [{ + 'label': + 'C', + 'explanation': + 'Error processing judge output' + }] + } + else: + try: + fallback = '{"judgements"' + prediction.split( + '"judgements"')[-1] + fallback = fix_json_slash(fallback) + return json.loads(fallback) + except Exception: + try: + fallback = '{"judgements": [{"label"' + prediction.split( + '"label"')[-1] + '}]}' + fallback = fix_json_slash(fallback) + return json.loads(fallback) + except Exception: + return { + 'judgements': [{ + 'label': + 'C', + 'explanation': + 'Error processing judge output' + }] + } + + origial_judges = [] + parsed_judges = [] + references = [] + for k, v in output.items(): + parsed_judge = _parse(v['prediction']) + try: + if isinstance(parsed_judge, list): + judgements = parsed_judge + else: + judgements = parsed_judge['judgements'] + except Exception: + judgements = [{ + 'label': 'C', + 'explanation': 'Error processing judge output' + }] + + judgements = judgements if isinstance(judgements, + list) else [judgements] + all_judge_labels = [item['label'] == 'A' for item in judgements] + origial_judges.append(v['prediction']) + parsed_judges.append({ + 'judgements': judgements, + 'overall_judge': all(all_judge_labels) + }) + references.append(v['gold']) + + return origial_judges, parsed_judges, references + + +def atlas_judge_postprocess( + output: List[Dict], + output_path: str, + think_tags: Tuple[str, str] = ('', '') +) -> dict: + origial_judges_list, parsed_judges_list, references_list = [], [], [] + for _output in output.values(): + origial_judges, parsed_judges, references = process_judge_output( + _output, think_tags) + origial_judges_list.append(origial_judges) + parsed_judges_list.append(parsed_judges) + references_list.append(references) + origial_judges_list = [[item[i] for item in origial_judges_list] + for i in range(len(origial_judges_list[0]))] + references_list = [[item[i] for item in references_list] + for i in range(len(references_list[0]))] + parsed_judges_list = [[item[i] for item in parsed_judges_list] + for i in range(len(parsed_judges_list[0]))] + results = get_final_results(parsed_judges_list, references_list, + origial_judges_list) + return results + + +@ICL_EVALUATORS.register_module() +class ATLASLLMEvaluator(BaseEvaluator): + """Generic LLM evaluator using majority voting. + + Arguments: + prompt_template (ConfigDict): The prompt template for evaluation. + judge_cfg (list of ConfigDict): A list of config for Judge LLM. + dataset_cfg (ConfigDict): The config for dataset. + pred_postprocessor (ConfigDict): The config for postprocessor. + used for the prediction results. + dict_postprocessor (ConfigDict): The config for postprocessor, + used for evaluation results dict. + """ + + def __init__( + self, + prompt_template: ConfigDict, + judge_cfg: List[ConfigDict], + dataset_cfg: Optional[ConfigDict] = None, + pred_postprocessor: Optional[ConfigDict] = None, + dict_postprocessor: Optional[ConfigDict] = None, + keep_predictions: bool = False, + ) -> None: + super().__init__(pred_postprocessor=pred_postprocessor) + if not judge_cfg: + self.judge_cfg = [self.default_judge_cfg] + else: + assert isinstance(judge_cfg.get('judgers', None), + List), 'judge_cfg must be a list' + self.judge_cfg = judge_cfg.get('judgers') + self.output_path = '' + + self.prompt_template = ICL_PROMPT_TEMPLATES.build(prompt_template) + + # Build Dataset + self.dataset_cfg = dataset_cfg + assert dataset_cfg is not None, 'dataset_cfg is None' + + self.dict_postprocessor = dict_postprocessor + self.pred_postprocessor = pred_postprocessor + + def build_inferencer(self): + """Build LLM Inference.""" + + # Build LLM Inference + self.inferencer = [] + for _judge_cfg in self.judge_cfg: + output_path = f'{self._out_dir}_replica{self.dataset_replica_idx}_{_judge_cfg["path"].split("/")[-1]}.json' # noqa + logger.info(f'LLM judge details will be saved at:{output_path}') + out_dir, out_name = osp.split(output_path) + + logger.info( + f'Set self.output_path to {output_path} for current task') + assert output_path is not None, 'output_path is None' + + max_out_len = _judge_cfg.get('max_out_len', None) + batch_size = _judge_cfg.get('batch_size', None) + + model = build_model_from_cfg(model_cfg=_judge_cfg) + + self.inferencer.append( + GenInferencer( + model, + max_out_len=max_out_len, + batch_size=batch_size, + output_json_filepath=out_dir, + output_json_filename=out_name, + )) + + def score( + self, + predictions, + references: Optional[List] = None, + test_set: Optional[Dataset] = None, + ) -> Dict: + """Apply to single-model scoring. + + Args: + predictions: List of model predictions + references: List of reference answers + test_set: Optional Dataset containing additional + context for evaluation + """ + assert len(predictions) == len( + references), 'predictions and references must have the same length' + + # -------------- Build Inferencer ---------------- + self.build_inferencer() + # ---------------- Process Predictions ------------------ + answers = [item['answers'] for item in predictions] + + # For Single Round Dialogue + prediction_dict = {'prediction': answers, 'obj_gold': references} + + # ---------------- Build Dataset for LLM Judge ----------------- + if self.dataset_cfg: + dataset = build_dataset_from_cfg(self.dataset_cfg) + for k, v in prediction_dict.items(): + dataset.reader.dataset['test'] = dataset.test.add_column(k, v) + dataset.reader.input_columns.append(k) + + if references: + dataset.reader.input_columns.append('reference') + dataset.reader.dataset['test'] = dataset.test.add_column( + 'reference', references) + else: + # Handle test_set in the else branch + from opencompass.datasets.lmeval import LMEvalDataset + + if test_set is not None: + # If test_set is provided, use it as the base + # Ensure necessary columns exist + if 'prediction' not in test_set.column_names: + test_set = test_set.add_column('prediction', answers) + if 'reference' not in test_set.column_names: + test_set = test_set.add_column('reference', references) + + # Prepare input_columns and data dictionary + input_columns = test_set.column_names + data_dict = { + column: test_set[column] + for column in test_set.column_names + } + else: + # Original default dataset building logic + input_columns = list(prediction_dict.keys()) + if references: + input_columns.append('reference') + data_dict = prediction_dict.copy() + if references: + data_dict['reference'] = references + + # Create LMEvalDataset + dataset = LMEvalDataset( + reader_cfg=dict( + input_columns=input_columns, + output_column=None, + train_split='test', + ), + **data_dict, + ) + + if osp.exists(f'{self._out_dir}' + f'_replica{self.dataset_replica_idx}' + f'_combine.json'): + output = mmengine.load(f'{self._out_dir}' + f'_replica{self.dataset_replica_idx}' + f'_combine.json') + else: + dataset.reader.output_column = 'reference' + retriever = ZeroRetriever(dataset) + output = {} + # ----------------- LLM Judge ---------------- + for inferencer in self.inferencer: + key = inferencer.output_json_filename.split('.')[0].split( + '_')[-1] + if osp.exists( + osp.join(inferencer.output_json_filepath, + inferencer.output_json_filename)): + output[key] = mmengine.load( + osp.join(inferencer.output_json_filepath, + inferencer.output_json_filename)) + else: + inferencer.inference(retriever=retriever, + prompt_template=self.prompt_template) + output[key] = mmengine.load( + osp.join(inferencer.output_json_filepath, + inferencer.output_json_filename)) + + mmengine.dump(output, f'{self._out_dir}' + f'_replica{self.dataset_replica_idx}' + f'_combine.json', + indent=4) + return self.output_postprocess(output, dataset) + + def output_postprocess(self, output: Dict, dataset=None) -> Dict: + """Postprocess output by adding necessary statistics or data into + it.""" + import inspect + + if self.dict_postprocessor is None: + return output + else: + kwargs = deepcopy(self.dict_postprocessor) + proc = DICT_POSTPROCESSORS.get(kwargs.pop('type')) + sig = inspect.signature(proc) + if 'dataset' in sig.parameters: + return proc(output, + self.output_path, + dataset=dataset, + **kwargs) + else: + return proc(output, self.output_path, **kwargs) + + @property + def default_judge_cfg(self): + from opencompass.models import OpenAISDK + logger.info('Please set your judge model in `OC_JUDGE_MODEL`, \ + `OC_JUDGE_API_KEY`, `OC_JUDGE_API_BASE` environment variables.') + DEFAULT_JUDGE_CFG = dict( + type=OpenAISDK, + path=os.environ['OC_JUDGE_MODEL'], + key=os.environ['OC_JUDGE_API_KEY'], + openai_api_base=[ + os.environ.get('OC_JUDGE_API_BASE', + 'https://api.openai.com/v1/') + ], + meta_template=dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ], ), + query_per_second=16, + batch_size=1024, + temperature=0.001, + tokenizer_path='gpt-4o-2024-05-13', + verbose=True, + max_out_len=16384, + max_seq_len=49152, + ) + + return DEFAULT_JUDGE_CFG diff --git a/opencompass/datasets/atlas/prompt.py b/opencompass/datasets/atlas/prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..b0fa50c6840a73308325154bca7b7a6fe2496195 --- /dev/null +++ b/opencompass/datasets/atlas/prompt.py @@ -0,0 +1,154 @@ +# flake8: noqa +SAGE_INFER_TEMPLATE = """ +**Problem:** + +``` +{problem} +``` + +**Instructions:** +Solve the problem step by step. If the problem contains multiple sub-questions, make sure to solve each one individually. + +At the end, output **only** the final answers in the following format: + +```json +{ + "answers": [ + "answer to sub-question 1", + "answer to sub-question 2", + ... + ] +} +``` + +* Each item in the list should be the **final answer** to a sub-question. +* If there is only one question, return a list with a single item. +* **Do not** include any explanation, reasoning steps, or additional text outside the JSON list. +* **Do** put the JSON list in the block of ```json ... ```. +""".strip() + +SAGE_EVAL_TEMPLATE = """ +You are an expert answer grader. Your task is to evaluate whether the candidate's **final answer** matches the **provided standard answer**. Follow the grading protocol strictly and **do not generate or modify answers**. Only compare the candidate's response to the given standard. + +--- + +### Evaluation Guidelines + +#### 1. Reference Standard + +* The **standard answer is always correct** — never question its validity. +* The **question itself is valid** — do not critique or reinterpret it. +* Do **not** regenerate, fix, or complete the candidate's answer — only **evaluate** what is provided. + +#### 2. Comparison Strategy + +* Carefully analyze the **question type** and **standard answer format**: + + * Determine whether an **exact match** is required, or whether **partial correctness** is acceptable (e.g., for multi-component or expression-based answers). + * This judgment should be based on the **question's phrasing and answer structure**. +* Evaluate **only the candidate's final answer**, ignoring reasoning or explanation. +* Ignore differences in **formatting, style**, or **variable naming**, as long as the content is equivalent. +* For **mathematical expressions**, check **step-by-step equivalence** (e.g., by simplifying both expressions and comparing results). +* For **multiple-choice questions**, only the **final selected option** and its **associated content** matter. +* For **decimal or fraction comparisons**, consider the answers equivalent if the relative error is **≤ ±0.1**. + +#### 3. Multi-part Answers + +* If the question requires **multiple components or selections**, all parts must match the standard answer exactly. +* Compare each component individually. +* **Partial correctness is not acceptable** — label as incorrect if any part is wrong. + +#### 4. Validity Check + +Immediately reject the candidate's answer if it meets **any of the following criteria**: + +* **INCOMPLETE**: Final sentence is cut off or the answer is clearly unfinished. +* **REPETITIVE**: Contains repeated phrases or outputs in a loop. +* **REFUSAL**: Explicitly states inability to answer (e.g., “I cannot answer this question”). +* Use label **C**. + +--- + +### Grading Scale + +| Grade | Label | Description | +| ----- | --------- | ------------------------------------------------------------------------------------------------ | +| A | CORRECT | Exact or semantically equivalent match; includes numerically equivalent results (within ±0.0001) | +| B | INCORRECT | Any deviation from the standard answer; includes partial matches | +| C | INVALID | Answer is INCOMPLETE, REPETITIVE, or a REFUSAL | + +--- + +### Evaluation Procedure & Output Format + +1. **Check for Validity First**: + + * If the answer is incomplete, repetitive, or a refusal, **immediately assign label C** with the reason and stop further evaluation. + +2. **If Valid, Compare Content**: + + * Analyze the question type: Are strict matches required (e.g., order, format, completeness)? + * Apply tolerances: Accept allowed variations (e.g., unformatted but equivalent math, missing labels in MCQs). + * Carefully compare final answers for: + + * Semantic or mathematical equivalence + * Relative error tolerance (±0.1) + * Expression format flexibility + +3. **Produce a Final Judgment**: + + * For each sub-question, return: + + ```json + { + "label": "A" / "B" / "C", + "explanation": "Brief justification here" + } + ``` + + * At the end, return a list of these JSON objects for each sub-question. + + ```json + { + "judgements": [ + { + "label": "A" / "B" / "C" for sub-question 1, + "explanation": "Brief justification here for sub-question 1" + }, + { + "label": "A" / "B" / "C" for sub-question 2, + "explanation": "Brief justification here for sub-question 2" + }, + ... + ] + } + ``` + + * If there is only one question, return a list with a single item. + + * **Do** put the JSON list in the block of ```json ... ```. + +--- + +### Task Input + +```plaintext + +{problem} + + + +{answer} + + + +{prediction} + +``` + +--- + +### Begin Evaluation Below: + +Analyze the candidate's answer step by step, then provide a **final structured judgment**. +""".strip() \ No newline at end of file diff --git a/opencompass/datasets/babilong/__init__.py b/opencompass/datasets/babilong/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c557053dc682e419b5f444a4e97a1399898e45 --- /dev/null +++ b/opencompass/datasets/babilong/__init__.py @@ -0,0 +1 @@ +from .babilong import * # noqa: F401, F403 diff --git a/opencompass/datasets/babilong/babilong.py b/opencompass/datasets/babilong/babilong.py new file mode 100644 index 0000000000000000000000000000000000000000..2529c76130710eec9fc8d31c97b68cf12aa06fde --- /dev/null +++ b/opencompass/datasets/babilong/babilong.py @@ -0,0 +1,106 @@ +# flake8: noqa: F401, E501 +import json +import os + +from datasets import Dataset + +from opencompass.datasets.babilong.babilong_utils import compare_answers +from opencompass.datasets.babilong.prompts import (DEFAULT_PROMPTS, + DEFAULT_TEMPLATE, + get_formatted_input) +from opencompass.datasets.base import BaseDataset +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + + +@LOAD_DATASET.register_module() +class BabiLongDataset(BaseDataset): + + @staticmethod + def load( + path, + task, + split_name, + use_instruction=True, + use_examples=True, + use_post_prompt=True, + ) -> Dataset: + + assert task in [ + 'qa1', + 'qa2', + 'qa3', + 'qa4', + 'qa5', + 'qa6', + 'qa7', + 'qa8', + 'qa9', + 'qa10', + ], f"Task must be in ['qa1', 'qa2', 'qa3', 'qa4', 'qa5', 'qa6', 'qa7', 'qa8', 'qa9', 'qa10']" + assert split_name in [ + '0k', + '1k', + '2k', + '4k', + '8k', + '16k', + '32k', + '64k', + '128k', + '256k', + '512k', + '1m', + ], f"Split name must be in ['0k', '1k', '2k', '4k', '8k', '16k', '32k', '64k', '128k', '256k', '512k', '1m']" + + # configure the prompt + prompt_cfg = { + 'instruction': + (DEFAULT_PROMPTS[task]['instruction'] if use_instruction else ''), + 'examples': + (DEFAULT_PROMPTS[task]['examples'] if use_examples else ''), + 'post_prompt': + (DEFAULT_PROMPTS[task]['post_prompt'] if use_post_prompt else ''), + 'template': + DEFAULT_TEMPLATE, + } + + path = get_data_path(path) + file = os.path.join(path, task, f'{split_name}.json') + + with open(file, 'r') as f: + task_data = json.load(f) + + data = [] + for sample in task_data: + tmp_data = {'prompt': [], 'answer': []} + target = sample['target'] + context = sample['input'] + question = sample['question'] + + input_text = get_formatted_input( + context, + question, + prompt_cfg['examples'], + prompt_cfg['instruction'], + prompt_cfg['post_prompt'], + template=DEFAULT_TEMPLATE, + ) + + tmp_data['prompt'].append(input_text) + tmp_data['answer'].append(target) + data.append(tmp_data) + return Dataset.from_list(data) + + +class BabiLongEvaluator(BaseEvaluator): + + def score(self, predictions, gold): + assert len(predictions) == len(gold) + score = (sum([ + compare_answers(str(ref[0]), pred) + for pred, ref in zip(predictions, gold) + ]) / len(predictions) * 100) + result = {'score': round(score, 2)} + return result diff --git a/opencompass/datasets/babilong/babilong_utils.py b/opencompass/datasets/babilong/babilong_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8089072d56fc3629d939051b35e8ec6ee10934f6 --- /dev/null +++ b/opencompass/datasets/babilong/babilong_utils.py @@ -0,0 +1,293 @@ +# flake8: noqa: E501 +# Modifided from https://github.com/booydar/babilong/blob/main/babilong/babilong_utils.py +import re + +import nltk +import numpy as np +import pandas as pd +from torch.utils.data import Dataset + + +def compare_answers(target, output): + """Compare target and output answers. + + Takes only the first sentence from output and filters responses when model + tries to generate examples. We consider prediction correct if target is in + output. + """ + target = target.lower() + output = output.lower() + # take only the first sentence from output + output = output.split('.')[0] + # filter responses when model tries to generate examples + output = output.split('')[0] + output = output.split('')[0] + + # we consider prediction correct if target is in output + if target in output: + return True + + return False + + +def get_dataset_df(dataset_path, max_n_facts=None): + """Preprocess babi text files.""" + with open(dataset_path, 'r') as f: + texts = f.read().strip() + texts = texts.split('\n') + df = pd.DataFrame(texts, columns=['text']) + + # parse samples + df['phrase_num'] = df.text.apply(lambda x: int(x.split(' ')[0])) + df.text = df.text.apply(lambda x: x[x.index(' ') + 1:]) + df['answer'] = df.text.apply(lambda x: x[x.index('\t') + 1:] + if '\t' in x else None) + df['reference_num'] = df.answer.apply( + lambda x: x + if x is None else [int(n) for n in re.split('\t| ', x)[1:]]) + df.answer = df.answer.apply(lambda x: x if x is None else x.split('\t')[0]) + df.text = df.text.apply(lambda x: x.split('\t')[0] if '\t' in x else x) + + # mark each sample + sample_start_inds = list(np.where(df.phrase_num == 1)[0]) + [df.shape[0]] + for i, (start, + end) in enumerate(zip(sample_start_inds, sample_start_inds[1:])): + df.loc[start:end, 'initial_sample_num'] = i + + df.initial_sample_num = df.initial_sample_num.astype(int) + + # multiple questions in sample -> samples with single question + initial_samples = [ + df[df.initial_sample_num == sn] + for sn in df.initial_sample_num.unique() + ] + + single_question_slices = [] + for sample in initial_samples: + answer_positions = sample[~sample.answer.isna()].index + slices = [sample.loc[:ans_pos].copy() for ans_pos in answer_positions] + for i, slc in enumerate(slices): + slices[i] = slc[(slc.answer.isna()) | (slc.index == slc.index[-1])] + if max_n_facts is not None: # drop samples with too many facts + slices = [slc for slc in slices if slc.shape[0] <= max_n_facts] + single_question_slices += slices + + df = pd.concat(single_question_slices).reset_index(drop=True) + + # mark each sample again + sample_start_inds = list(np.where(df.phrase_num == 1)[0]) + [df.shape[0]] + for i, (start, + end) in enumerate(zip(sample_start_inds, sample_start_inds[1:])): + df.loc[start:end, 'sample_num'] = i + + df.sample_num = df.sample_num.astype(int) + + return df + + +class TaskDataset(Dataset): + """Babi task loader dataset.""" + + def __init__(self, dataset_path, max_n_facts=None): + self.fact_dataset = get_dataset_df(dataset_path, + max_n_facts=max_n_facts) + + def __getitem__(self, ind): + slc = self.fact_dataset[self.fact_dataset.sample_num == ind] + references = slc[slc.phrase_num.isin( + slc.reference_num.values[-1])].text.values + sample = { + 'facts': slc.text.values[:-1], + 'question': slc.text.values[-1], + 'answer': slc.answer.values[-1], + 'references': references, + } + return sample + + def __len__(self): + return self.fact_dataset.sample_num.max() + + +def sum_lengths(sentences): + return sum([len(s) for s in sentences]) + + +class SentenceSampler: + """Sampler of background text.""" + + def __init__( + self, + dataset, + tokenizer, + min_sentence_len=10, + max_sentence_len=None, + shuffle=False, + random_seed=42, + ): + self.sample_ind = 0 + self.dataset = dataset + self.sentences = [] + self.tokenizer = tokenizer + self.min_sentence_len = min_sentence_len + self.max_sentence_len = max_sentence_len + self.sentence_tokenizer = nltk.PunktSentenceTokenizer() + self.shuffle = shuffle + self.gen = np.random.default_rng(seed=random_seed) + + def get_sample(self, sample_size): + sample = [] + total_len = 0 + while True: + sentences = list(self.sentences) + for i, sent in enumerate( + sentences + ): # add new sentence until sample_size is reached + tokenized = self.tokenizer.encode(sent, + add_special_tokens=False) + if not self.length_is_ok(tokenized): + continue + total_len += len(tokenized) + sample.append(tokenized) + if total_len >= sample_size: + self.sentences = self.sentences[i + 1:] + cutoff = total_len - sample_size + if cutoff > 0: + sample[-1] = sample[-1][:-cutoff] + return sample + + self.sentences = [] + self.sample_sentences_( + sample_size + ) # appends new sentences, can be updated to just return new sentences + + def sample_sentences_(self, sample_size): + sentences = [] + while len(sentences) == 0: + text = self.next_sample_() + if self.shuffle: + if len(text) == 0: + continue + text = text[self.gen.choice(len( + text)):] # start from random position in text + text = text[:sample_size * + 10] # cut too long texts to speed up tokenization + sentences += self.sentence_tokenizer.tokenize(text) + if self.shuffle: + sentences = sentences[1:-1] + self.sentences += sentences + + def next_sample_(self): + if self.shuffle: + self.total_tokens = 0 + sample_ind = self.gen.choice(len(self.dataset)) + sample = self.dataset[int(sample_ind)]['text'] + else: + sample = self.dataset[int(self.sample_ind)]['text'] + self.sample_ind += 1 + self.sample_ind = self.sample_ind % len(self.dataset) + return sample + + def length_is_ok(self, tokenized): + if (self.max_sentence_len is not None + and len(tokenized) > self.max_sentence_len): + return False + if (self.min_sentence_len is not None + and len(tokenized) < self.min_sentence_len): + return False + return True + + +class NoiseInjectionDataset(Dataset): + """Combined dataset for noisy babi QA. + + It's recommended to use sample_size >= 1024 and task_end_pct - task_start_pct >= 0.2 + """ + + def __init__( + self, + task_dataset, + noise_sampler, + tokenizer, + task_start_pct=None, # left border of facts in sample, between 0 and 1 + task_end_pct=None, # right border of facts in sample, between task_start_pct and 1 + sample_size=1024, + mixed_length_ratio=0.0, # used for mixed length curriculum, prob for shorter samples + random_seed=42, + ): + self.task_dataset = task_dataset + self.noise_sampler = noise_sampler + self.sample_size = sample_size + self.mixed_length_ratio = mixed_length_ratio + self.tokenizer = tokenizer + self.task_start_pct = task_start_pct + self.task_end_pct = task_end_pct + if random_seed: + self.gen = np.random.default_rng(seed=random_seed) + + def __getitem__(self, ind): + sample = self.task_dataset[ind] + facts_tok = self.tokenizer(list(sample['facts']))['input_ids'] + question_tok = self.tokenizer(sample['question'])['input_ids'] + answer_tok = self.tokenizer(sample['answer'])['input_ids'] + + sample_size = self.get_sample_size() + task_len = sum_lengths(facts_tok) + background_text_len = sample_size - task_len + background_text = self.noise_sampler.get_sample(background_text_len) + sample['background_text'] = background_text + + if (self.task_start_pct is None + and self.task_end_pct is None): # if fact position unspecified + possible_positions = range(len(background_text) + 1) + else: + task_start_ind = int(sample_size * self.task_start_pct) + task_end_ind = int(sample_size * self.task_end_pct) + total_facts_len = sum_lengths(facts_tok) + + possible_positions = [] # where can we insert facts? + current_length = 0 + for i, text in enumerate(background_text): + if (current_length >= task_start_ind) and ( + current_length < task_end_ind - total_facts_len): + possible_positions.append(i) + current_length += len(text) + + if len(possible_positions) == 0: + raise IndexError( + f'Unable to insert facts in specified place: {self.task_start_pct, self.task_end_pct}.' + f'Total fact length: {total_facts_len}, ' + f'sentences length: {[len(t) for t in background_text]}. ' + f'Make the range wider or increase the sample size.') + + fact_positions = self.gen.choice(possible_positions, len(facts_tok)) + fact_positions.sort() + sample['fact_positions'] = ( + fact_positions # positions of facts between noise sentences + ) + + updated_sample = [[] for _ in range(len(background_text) + 1)] + for fact, pos in zip(facts_tok, fact_positions): + updated_sample[pos].append(fact) + + for i, s in enumerate(background_text): + updated_sample[i].append(s) + + flat = [i for s in updated_sample for i in s] + tokens = [i for s in flat for i in s] + + sample['input_tokens'] = tokens + sample['question_tokens'] = question_tok + sample['target_tokens'] = answer_tok + + return sample + + def __len__(self): + return len(self.task_dataset) + + def get_sample_size(self): + if isinstance(self.sample_size, list): + if self.gen.random() > self.mixed_length_ratio: + return self.gen.choice(self.sample_size) + return max(self.sample_size) + else: + return self.sample_size diff --git a/opencompass/datasets/babilong/prompts.py b/opencompass/datasets/babilong/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..b402e7cee565919234d563f1c3ba6d81a68261d0 --- /dev/null +++ b/opencompass/datasets/babilong/prompts.py @@ -0,0 +1,516 @@ +# flake8: noqa: E501 +SYSTEM_TEMPLATE = '{instruction}\n\n{examples}\n\n{post_prompt}' +USER_TEMPLATE = '\n{context}\n\n\nQuestion: {question}' +DEFAULT_TEMPLATE = f'{SYSTEM_TEMPLATE}\n\n{USER_TEMPLATE}' + +CUSTOM_SYSTEM_PROMPTS = { + # https://github.com/dvlab-research/LongLoRA/blob/2345c6d030f61ac3a031906386a103a5b05e0e6f/inference.py#L18 + 'LONGLORA_LLAMA2': + 'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. ' + 'Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. ' + 'Please ensure that your responses are socially unbiased and positive in nature.\n\n' + 'If a question does not make any sense, or is not factually coherent, explain why instead of answering ' + 'something not correct. If you don\'t know the answer to a question, please don\'t share false information.' +} + + +def get_formatted_input( + context, + question, + examples, + instruction, + post_prompt, + template=DEFAULT_TEMPLATE, +): + # pre_prompt - general instruction + # examples - in-context examples + # post_prompt - any additional instructions after examples + # context - text to use for qa + # question - question to answer based on context + formatted_input = template.format( + instruction=instruction, + examples=examples, + post_prompt=post_prompt, + context=context.strip(), + question=question, + ) + return formatted_input.strip() + + +DEFAULT_PROMPTS = { + 'qa1': { + 'instruction': + 'I will give you context with the facts about positions of different persons hidden in some random text ' + 'and a question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location to answer the question.', + 'examples': + '\n' + 'Charlie went to the hallway. Judith come back to the kitchen. Charlie travelled to balcony. ' + 'Where is Charlie?\n' + 'Answer: The most recent location of Charlie is balcony.\n' + '\n\n' + '\n' + 'Alan moved to the garage. Charlie went to the beach. Alan went to the shop. Rouse ' + 'travelled to balcony. Where is Alan?\n' + 'Answer: The most recent location of Alan is shop.\n' + '', + 'post_prompt': + 'Always return your answer in the following format: ' + 'The most recent location of ’person’ is ’location’. Do not write anything else after that.', + }, + 'qa2': { + 'instruction': + 'I give you context with the facts about locations and actions of different persons ' + 'hidden in some random text and a question.' + 'You need to answer the question based only on the information from the facts.\n' + 'If a person got an item in the first location and travelled to the second location ' + 'the item is also in the second location. ' + 'If a person dropped an item in the first location and moved to the second location ' + 'the item remains in the first location.', + 'examples': + '\n' + 'Charlie went to the kitchen. Charlie got a bottle. Charlie moved to the balcony. ' + 'Where is the bottle?\n' + 'Answer: The bottle is in the balcony.\n' + '\n' + '\n' + 'Alan moved to the garage. Alan got a screw driver. Alan moved to the kitchen. Where ' + 'is the screw driver?\n' + 'Answer: The screw driver is in the kitchen.\n' + '', + 'post_prompt': + 'Always return your answer in the following format: The ’item’ is in ’location’. ' + 'Do not write anything else after that.', + }, + 'qa3': { + 'instruction': + 'I give you context with the facts about locations and actions of different persons ' + 'hidden in some random text and a question. ' + 'You need to answer the question based only on the information from the facts.\n' + 'If a person got an item in the first location and travelled to the second location ' + 'the item is also in the second location. ' + 'If a person dropped an item in the first location and moved to the second location ' + 'the item remains in the first location.', + 'examples': + '\n' + 'John journeyed to the bedroom. Mary grabbed the apple. Mary went back to the bathroom. ' + 'Daniel journeyed to the bedroom. Daniel moved to the garden. Mary travelled to the kitchen. ' + 'Where was the apple before the kitchen?\n' + 'Answer: Before the kitchen the apple was in the bathroom.\n' + '\n' + '\n' + 'John went back to the bedroom. John went back to the garden. John went back to the kitchen. ' + 'Sandra took the football. Sandra travelled to the garden. Sandra journeyed to the bedroom. ' + 'Where was the football before the bedroom?\n' + 'Answer: Before the bedroom the football was in the garden.\n' + '', + 'post_prompt': + 'Always return your answer in the following format: ' + 'Before the $location_1$ the $item$ was in the $location_2$. Do not write anything else after that.', + }, + 'qa4': { + 'instruction': + 'I will give you context with the facts about different people, their location and actions, hidden in ' + 'some random text and a question. ' + 'You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'The hallway is south of the kitchen. The bedroom is north of the kitchen. ' + 'What is the kitchen south of?\n' + 'Answer: bedroom\n' + '\n' + '\n' + 'The garden is west of the bedroom. The bedroom is west of the kitchen. What is west of the bedroom?\n' + 'Answer: garden\n' + '', + 'post_prompt': + 'Your answer should contain only one word - location. Do not write anything else after that.', + }, + 'qa5': { + 'instruction': + 'I will give you context with the facts about locations and their relations hidden in some random text ' + 'and a question. You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'Mary picked up the apple there. Mary gave the apple to Fred. Mary moved to the bedroom. ' + 'Bill took the milk there. Who did Mary give the apple to?\n' + 'Answer: Fred\n' + '\n' + '\n' + 'Jeff took the football there. Jeff passed the football to Fred. Jeff got the milk there. ' + 'Bill travelled to the bedroom. Who gave the football?\n' + 'Answer: Jeff\n' + '\n' + '\n' + 'Fred picked up the apple there. Fred handed the apple to Bill. Bill journeyed to the bedroom. ' + 'Jeff went back to the garden. What did Fred give to Bill?\n' + 'Answer: apple\n' + '', + 'post_prompt': + 'Your answer should contain only one word. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa6': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'John travelled to the hallway. John travelled to the garden. Is John in the garden?\n' + 'Answer: yes\n' + '\n' + '\n' + 'Mary went to the office. Daniel journeyed to the hallway. Mary went to the bedroom. ' + 'Sandra went to the garden. Is Mary in the office?\n' + 'Answer: no\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - $yes$ or $no$. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa7': { + 'instruction': + 'I will give you context with the facts about people and objects they carry, hidden in some random text ' + 'and a question. You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'Daniel went to the bedroom. Daniel got the apple there. How many objects is Daniel carrying?\n' + 'Answer: one\n' + '\n' + '\n' + 'Mary grabbed the apple there. Mary gave the apple to John. How many objects is Mary carrying?\n' + 'Answer: none\n' + '\n' + '\n' + 'Sandra travelled to the hallway. Sandra picked up the milk there. Sandra took the apple there. ' + 'Mary travelled to the garden. How many objects is Sandra carrying?\n' + 'Answer: two\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - $none$ or $number_of_objects$. ' + 'Do not write anything else after that. Do not explain your answer.', + }, + 'qa8': { + 'instruction': + 'I will give you context with the facts about people and objects they carry, hidden in some random text ' + 'and a question. You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'Sandra travelled to the garden. Mary grabbed the milk there. What is Mary carrying?\n' + 'Answer: milk\n' + '\n' + '\n' + 'Mary travelled to the kitchen. Sandra travelled to the office. John travelled to the office. ' + 'Sandra discarded the milk there. What is Sandra carrying?\n' + 'Answer: nothing\n' + '\n' + '\n' + 'Daniel grabbed the apple there. Mary went to the office. Daniel moved to the garden. ' + 'Daniel grabbed the milk there. Mary went to the kitchen. What is Daniel carrying?\n' + 'Answer: apple,milk\n' + '\n', + 'post_prompt': + 'Your answer should contain only one or two words: $nothing$ or $object$ or $object_1$, $object_2$. ' + 'Do not write anything else. Do not explain your answer.', + }, + 'qa9': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and ' + 'a question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'John is not in the bathroom. Sandra is not in the bedroom. Is John in the bathroom?\n' + 'Answer: no\n' + '\n' + '\n' + 'Mary journeyed to the kitchen. John is in the bedroom. Sandra is not in the garden. ' + 'Is Mary in the kitchen?\n' + 'Answer: yes\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - $yes$ or $no$. Do not write anything else. ' + 'Do not explain your answer.', + }, + 'qa10': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Bill is in the kitchen. Julie is either in the school or the cinema. Is Bill in the bedroom?\n' + 'Answer: no\n' + '\n' + '\n' + 'Fred is in the bedroom. Mary is either in the school or the cinema. Is Mary in the school?\n' + 'Answer: maybe\n' + '\n' + '\n' + 'Fred is either in the kitchen or the park. Bill moved to the cinema. Is Bill in the cinema?\n' + 'Answer: yes\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - $yes$ or $no$ or $maybe$. Do not write anything else. ' + 'Do not explain your answer.', + }, + 'qa11': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Daniel journeyed to the hallway. After that he journeyed to the garden. Where is Daniel?\n' + 'Answer: garden\n' + '\n' + '\n' + 'Mary moved to the office. Afterwards she journeyed to the kitchen. Daniel went to the hallway. ' + 'Then he journeyed to the garden. Where is Mary?\n' + 'Answer: kitchen\n' + '\n' + '\n' + 'Sandra moved to the kitchen. After that she went back to the hallway. Sandra moved to the bedroom. ' + 'Then she went to the hallway. Mary moved to the bedroom. Afterwards she travelled to the bathroom. ' + 'Where is Sandra?\n' + 'Answer: hallway\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - location. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa12': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Mary and Daniel travelled to the bathroom. John and Daniel travelled to the office. Where is Daniel?\n' + 'Answer: office\n' + '\n' + '\n' + 'Sandra and Mary went back to the office. Daniel and Sandra went to the bedroom. Sandra and Mary travelled to the hallway. ' + 'John and Mary went to the kitchen. Where is Mary?\n' + 'Answer: kitchen\n' + '\n' + '\n' + 'Daniel and Sandra went back to the hallway. Daniel and John moved to the office. Daniel and John moved to the garden. ' + 'Daniel and Mary went back to the bathroom. Daniel and John went back to the kitchen. Daniel and Sandra went to the bathroom. ' + 'Where is John?\n' + 'Answer: kitchen\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - location. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa13': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Mary and Daniel travelled to the bathroom. Then they journeyed to the hallway. Where is Daniel?\n' + 'Answer: hallway\n' + '\n' + '\n' + 'Daniel and Sandra travelled to the kitchen. After that they journeyed to the hallway. Mary and Daniel travelled to the bedroom. ' + 'After that they travelled to the hallway. Where is Sandra?\n' + 'Answer: hallway\n' + '\n' + '\n' + 'John and Mary moved to the bathroom. Then they travelled to the office. John and Mary went to the kitchen. ' + 'Afterwards they went to the bedroom. John and Sandra moved to the bathroom. Following that they went back to the kitchen. ' + 'Where is Mary?\n' + 'Answer: bedroom\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - location. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa14': { + 'instruction': + 'I will give you context with the facts about people and their locations hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Bill went back to the cinema yesterday. Julie went to the school this morning. Fred went to the park yesterday. ' + 'Yesterday Julie went to the office. Where was Julie before the school?\n' + 'Answer: office\n' + '\n' + '\n' + 'This morning Fred went to the kitchen. Fred journeyed to the bedroom yesterday. Mary travelled to the bedroom this morning. ' + 'Yesterday Mary went to the cinema. Where was Mary before the bedroom?\n' + 'Answer: cinema\n' + '\n' + '\n' + 'Yesterday Julie went back to the park. Julie went to the bedroom this morning. Bill journeyed to the cinema yesterday. ' + 'This morning Bill went back to the park. This evening Julie went to the school. This afternoon Julie went back to the park. ' + 'Where was Julie before the bedroom?\n' + 'Answer: park\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - location. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa15': { + 'instruction': + 'I will give you context with the facts about animals, their names and relations. The facts and a question ' + 'are hidden in some random text. You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'Mice are afraid of wolves. Gertrude is a mouse. Cats are afraid of sheep. ' + 'Winona is a mouse. Sheep are afraid of wolves. Emily is a mouse. Jessica is a wolf. ' + 'What is gertrude afraid of?\n' + 'Answer: wolf\n' + '\n' + '\n' + 'Mice are afraid of wolves. Gertrude is a mouse. Cats are afraid of sheep. ' + 'Winona is a mouse. Sheep are afraid of wolves. Emily is a mouse. Jessica is a wolf. ' + 'What is jessica afraid of?\n' + 'Answer: cat\n' + '\n' + '\n' + 'Mice are afraid of cats. Wolves are afraid of sheep. Emily is a wolf. ' + 'Cats are afraid of sheep. Gertrude is a wolf. Sheep are afraid of cats. Winona is a wolf. ' + 'What is emily afraid of?\n' + 'Answer: sheep\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - an animal species. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa16': { + 'instruction': + 'I will give you context with the facts about animals, their names and colors. The facts and a question ' + 'are hidden in some random text. You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'Lily is a frog. Bernhard is a frog. Bernhard is green. Brian is a lion. Brian is white. ' + 'Julius is a swan. Julius is green. Lily is green. Greg is a swan. What color is Greg?\n' + 'Answer: green\n' + '\n' + '\n' + 'Julius is a lion. Lily is a rhino. Bernhard is a swan. Lily is white. Bernhard is green. ' + 'Greg is a rhino. Greg is gray. Julius is white. Brian is a lion. What color is Brian?\n' + 'Answer: white\n' + '\n' + '\n' + 'Brian is a rhino. Julius is a lion. Bernhard is a lion. Greg is a swan. Brian is gray. ' + 'Greg is white. Lily is a rhino. Bernhard is yellow. Lily is gray. What color is Julius?\n' + 'Answer: yellow\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - a color. Do not write anything else after that. ' + 'Do not explain your answer.', + }, + 'qa17': { + 'instruction': + 'I will give you context with the facts about different figures, their location and colors, hidden in ' + 'some random text and a question. ' + 'You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'The triangle is above the pink rectangle. The blue square is to the left of the triangle. ' + 'Is the pink rectangle to the right of the blue square?\n' + 'Answer: yes\n' + '\n' + '\n' + 'The red sphere is to the left of the yellow square. The red sphere is below the pink rectangle. ' + 'Is the pink rectangle to the left of the yellow square?\n' + 'Answer: yes\n' + '' + '\n' + 'The red sphere is above the pink rectangle. The red sphere is to the right of the red square. ' + 'Is the pink rectangle above the red square?\n' + 'Answer: no\n' + '', + 'post_prompt': + 'Your answer should contain only one word - $yes$ or $no$. Do not write anything else. ' + 'Do not explain your answer.', + }, + 'qa18': { + 'instruction': + 'I will give you context with the facts about different objects and their sizes, hidden in ' + 'some random text and a question. ' + 'You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'The box of chocolates fits inside the chest. The box is bigger than the chest. The box is bigger than the suitcase. ' + 'The suitcase fits inside the box. The container is bigger than the box of chocolates. Does the box fit in the box of chocolates?\n' + 'Answer: no\n' + '\n' + '\n' + 'The suitcase is bigger than the container. The container fits inside the box. The chest is bigger than the chocolate.' + 'The suitcase fits inside the box. The chest fits inside the box. Does the chocolate fit in the box?\n' + 'Answer: yes\n' + '' + '\n' + 'The chocolate fits inside the box of chocolates. The suitcase fits inside the box. The chocolate fits inside the box. ' + 'The box is bigger than the box of chocolates. The suitcase is bigger than the box of chocolates. Is the chocolate bigger than the box?\n' + 'Answer: no\n' + '', + 'post_prompt': + 'Your answer should contain only one word - $yes$ or $no$. Do not write anything else. ' + 'Do not explain your answer.', + }, + 'qa19': { + 'instruction': + 'I will give you context with the facts about different places and their locations, hidden in ' + 'some random text and a question. ' + 'You need to answer the question based only on the information from the facts.', + 'examples': + '\n' + 'The office is east of the hallway. The kitchen is north of the office. The garden is west of the bedroom. ' + 'The office is west of the garden. The bathroom is north of the garden. How do you go from the kitchen to the garden?\n' + 'Answer: s,e\n' + '\n' + '\n' + 'The bedroom is west of the hallway. The office is east of the garden. The garden is north of the kitchen. ' + 'The kitchen is north of the bathroom. The hallway is west of the garden. How do you go from the kitchen to the hallway?\n' + 'Answer: n,w\n' + '\n' + '\n' + 'The bedroom is south of the hallway. The bathroom is east of the office. The kitchen is west of the garden. ' + 'The garden is south of the office. The office is south of the bedroom. How do you go from the garden to the bedroom?\n' + 'Answer: n,n\n' + '\n', + 'post_prompt': + 'Your answer should contain only two letters, separated by a comma - ordinal directions. You can choose the letters from ' + '$n$, $s$, $e$ and $w$. Do not write anything else after that.', + }, + 'qa20': { + 'instruction': + 'I will give you context with the facts about people, their locations and condition hidden in some random text and a ' + 'question. You need to answer the question based only on the information from the facts. ' + 'If a person was in different locations, use the latest location the person was in to answer the question.', + 'examples': + '\n' + 'Sumit is tired. Where will sumit go?\n' + 'Answer: bedroom\n' + '\n' + '\n' + 'Yann is hungry. Yann journeyed to the kitchen. Why did yann go to the kitchen?\n' + 'Answer: hungry\n' + '\n' + '\n' + 'Antoine is thirsty. Yann is tired. Yann went back to the bedroom. Yann picked up the pajamas there.' + 'Jason is thirsty. Antoine went back to the kitchen. Why did antoine go to the kitchen?\n' + 'Answer: thirsty\n' + '\n' + '\n', + 'post_prompt': + 'Your answer should contain only one word - a person condition or a place. Do not write anything else after that. ' + 'Do not explain your answer.', + }, +} diff --git a/opencompass/datasets/bigcodebench/__init__.py b/opencompass/datasets/bigcodebench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e16e39e88152a1a12e1cc9fb42b6aa6062c655e1 --- /dev/null +++ b/opencompass/datasets/bigcodebench/__init__.py @@ -0,0 +1 @@ +from .bigcodebench import BigCodeBenchDataset, BigCodeBenchEvaluator # noqa diff --git a/opencompass/datasets/bigcodebench/bigcodebench.py b/opencompass/datasets/bigcodebench/bigcodebench.py new file mode 100644 index 0000000000000000000000000000000000000000..9038b149ff1427ca450c2a9c64d77ed179a27f80 --- /dev/null +++ b/opencompass/datasets/bigcodebench/bigcodebench.py @@ -0,0 +1,221 @@ +# Copyright (c) 2024, BigCodeBench and its contributors. +# Copyright (c) 2023, OpenCompass and its contributors. + +import os +import time +from concurrent.futures._base import CancelledError + +import httpx +from datasets import Dataset, DatasetDict +from gradio_client import Client, handle_file + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.utils import JSONToolkit # noqa: F401, F403 +from opencompass.utils import (check_url_accessibility, get_data_path, + get_logger, setup_proxies) + +from ..base import BaseDataset +from .extractor import extract_code_generation + + +class BigCodeBenchDataset(BaseDataset): + + @staticmethod + def load(path: str = 'opencompass/bigcodebench', + local_mode: bool = False, + release_version: str = 'v0.1.2', + dataset_version: str = 'full'): + """ + Args: + path (str): The path to the dataset. + local_mode (bool): Whether to use local give path or use + automatically download. + release_version (str): The release version of the dataset. + dataset_version (str): The data version of the dataset. + only support ['full', 'hard'] + """ + assert dataset_version in ['full', 'hard'], \ + 'dataset_version should be one of ["full", "hard"], ' + f'but got {dataset_version}' + path = get_data_path(path, local_mode=local_mode) + dataset = DatasetDict() + # Valid Keys: + # 'task_id', 'complete_prompt', 'instruct_prompt', + # 'canonical_solution', 'code_prompt', 'test', + # 'entry_point', 'doc_struct', 'libs' + if dataset_version == 'full': + items = JSONToolkit.read_jsonl( + os.path.join(path, f'BigCodeBench-{release_version}.jsonl')) + else: + items = JSONToolkit.read_jsonl( + os.path.join(path, + f'BigCodeBench-Hard-{release_version}.jsonl')) + + dataset['train'] = Dataset.from_list(items) + dataset['test'] = Dataset.from_list(items) + + return dataset + + +class BigCodeBenchEvaluator(BaseEvaluator): + """Evaluator for BigCodeBench. + + Args: + num_process_evaluate (int): number of processes to evaluate + timeout (int): timeout for each evaluation + release_version (str): release version of BigCodeBench + eval_type (str): type of evaluation, either 'instruct' or 'completion' + """ + + def __init__( + self, + release_version='v0.1.2', + eval_type='instruct', + remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/', # noqa + headers: dict[str, str] | None = None, + dataset_version: str = 'full', + local_mode: bool = False, + path: str = 'opencompass/bigcodebench', + pass_k: str = '1,5,10', + parallel: int = -1, + min_time_limit: float = 1, + max_as_limit: int = 30 * 1024, + max_data_limit: int = 30 * 1024, + max_stack_limit: int = 10, + check_gt_only: bool = False, + no_gt: bool = False): + super().__init__() + self.dataset = BigCodeBenchDataset.load( + release_version=release_version, + dataset_version=dataset_version, + local_mode=local_mode, + path=path)['test'] + self.eval_type = eval_type + self.remote_execute_api = remote_execute_api + self.headers = headers + self.eval_kwargs = dict(subset=dataset_version, + pass_k=pass_k, + parallel=parallel, + min_time_limit=min_time_limit, + max_as_limit=max_as_limit, + max_data_limit=max_data_limit, + max_stack_limit=max_stack_limit, + check_gt_only=check_gt_only, + no_gt=no_gt) + + def score(self, predictions, references): + logger = get_logger() + entrypoints = [item['entry_point'] for item in self.dataset] + + # Append content to the end of the prompt for Completion + if self.eval_type == 'complete': + content = [item['complete_prompt'] for item in self.dataset] + predictions = [ + content[idx] + item for idx, item in enumerate(predictions) + ] + elif self.eval_type == 'instruct': + pass + else: + raise ValueError(f'Unknown eval_type: {self.eval_type}') + + # Sanitize predictions for execution + logger.info('Start to extract code from predictions') + sanitized_predictions = [] + for prediction, entrypoint in zip(predictions, entrypoints): + try: + import signal + from contextlib import contextmanager + + @contextmanager + def timeout_handler(seconds): + + def _handle_timeout(signum, frame): + raise TimeoutError(f'Code extraction timed out' + f'after {seconds} seconds') + + original_handler = signal.signal(signal.SIGALRM, + _handle_timeout) + signal.alarm(seconds) + try: + yield + finally: + signal.alarm(0) + signal.signal(signal.SIGALRM, original_handler) + + with timeout_handler(10): + sanitized_prediction = extract_code_generation( + prediction, entrypoint=entrypoint) + + except TimeoutError as e: + logger.warning( + f'Code extraction timeout for entrypoint {entrypoint}: ' + f'{str(e)}') + sanitized_prediction = '' + except Exception as e: + logger.warning( + f'Code extraction failed for entrypoint {entrypoint}: ' + f'{str(e)}') + sanitized_prediction = '' + sanitized_predictions.append(sanitized_prediction) + + # Prepare for submission + submitted_contents = [] + task_ids = [item['task_id'] for item in self.dataset] + for task_id, sanitized_prediction in zip(task_ids, + sanitized_predictions): + submitted_content = { + 'task_id': task_id, + 'solution': sanitized_prediction + } + submitted_contents.append(submitted_content) + + submitted_contents_path = os.path.join( + self._out_dir, 'bigcodebench_submitted_contents.jsonl') + JSONToolkit.save_jsonl(submitted_contents, submitted_contents_path) + logger.info(f'Dump submitted contents to {submitted_contents_path}') + + logger.info( + f'Start to connect to {self.remote_execute_api} for evaluating') + # Conduct evaluation with Eval Client + proxies = setup_proxies('BIGCODEBENCH_EVAL_PROXY_URL') + + is_accessible, status_code = check_url_accessibility( + self.remote_execute_api) + if not is_accessible: + logger.error(f'Failed to connect to {self.remote_execute_api} ' + f'with status code {status_code}') + return False + + while True: + try: + eval_client = Client(self.remote_execute_api, + headers=self.headers, + httpx_kwargs=dict( + proxies=proxies, + timeout=httpx.Timeout(100.0))) + results, pass_at_k = eval_client.predict( + split=self.eval_type, + samples=handle_file(submitted_contents_path), + api_name='/predict', + **self.eval_kwargs) + break + except (httpx.ReadTimeout, CancelledError): + logger.info('Read timeout error. Retrying in 10s...') + time.sleep(10) + + if 'pass@1' in pass_at_k.keys(): + pass_at_k['pass@1'] *= 100 + dump_results = {'details': self._results_processor(results)} + dump_results.update(pass_at_k) + + return dump_results + + def _results_processor(self, results): + details = [] + for key, value in results['eval'].items(): + if value[0]['status'] == 'pass': + value[0]['correct'] = True + else: + value[0]['correct'] = False + details.append(value[0]) + return details diff --git a/opencompass/datasets/bigcodebench/extractor.py b/opencompass/datasets/bigcodebench/extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..ccd2cad209b059ef420ff6c9e1e8f325e1832824 --- /dev/null +++ b/opencompass/datasets/bigcodebench/extractor.py @@ -0,0 +1,192 @@ +# Copyright (c) 2024, BigCodeBench and its contributors. +# Copyright (c) 2023, OpenCompass and its contributors. + +import ast +import traceback +from typing import Dict, Generator, List, Optional, Set, Tuple + +from tree_sitter import Node +from tree_sitter_languages import get_parser + +CLASS_TYPE = 'class_definition' +FUNCTION_TYPE = 'function_definition' +IMPORT_TYPE = ['import_statement', 'import_from_statement'] +IDENTIFIER_TYPE = 'identifier' +ATTRIBUTE_TYPE = 'attribute' +RETURN_TYPE = 'return_statement' +EXPRESSION_TYPE = 'expression_statement' +ASSIGNMENT_TYPE = 'assignment' + + +def syntax_check(code, verbose=False): + try: + ast.parse(code) + return True + except (SyntaxError, MemoryError): + if verbose: + traceback.print_exc() + return False + + +def code_extract(text: str) -> str: + lines = text.split('\n') + longest_line_pair = (0, 0) + longest_so_far = 0 + + for i in range(len(lines)): + for j in range(i + 1, len(lines)): + current_lines = '\n'.join(lines[i:j + 1]) + if syntax_check(current_lines): + current_length = sum(1 for line in lines[i:j + 1] + if line.strip()) + if current_length > longest_so_far: + longest_so_far = current_length + longest_line_pair = (i, j) + + return '\n'.join(lines[longest_line_pair[0]:longest_line_pair[1] + 1]) + + +def get_deps(nodes: List[Tuple[str, Node]]) -> Dict[str, Set[str]]: + + def dfs_get_deps(node: Node, deps: Set[str]) -> None: + for child in node.children: + if child.type == IDENTIFIER_TYPE: + deps.add(child.text.decode('utf8')) + else: + dfs_get_deps(child, deps) + + name2deps = {} + for name, node in nodes: + deps = set() + dfs_get_deps(node, deps) + name2deps[name] = deps + return name2deps + + +def get_function_dependency(entrypoint: str, + call_graph: Dict[str, str]) -> Set[str]: + queue = [entrypoint] + visited = {entrypoint} + while queue: + current = queue.pop(0) + if current not in call_graph: + continue + for neighbour in call_graph[current]: + if not (neighbour in visited): + visited.add(neighbour) + queue.append(neighbour) + return visited + + +def get_definition_name(node: Node) -> str: + for child in node.children: + if child.type == IDENTIFIER_TYPE: + return child.text.decode('utf8') + + +def traverse_tree(node: Node) -> Generator[Node, None, None]: + cursor = node.walk() + depth = 0 + + visited_children = False + while True: + if not visited_children: + yield cursor.node + if not cursor.goto_first_child(): + depth += 1 + visited_children = True + elif cursor.goto_next_sibling(): + visited_children = False + elif not cursor.goto_parent() or depth == 0: + break + else: + depth -= 1 + + +def has_return_statement(node: Node) -> bool: + traverse_nodes = traverse_tree(node) + for node in traverse_nodes: + if node.type == RETURN_TYPE: + return True + return False + + +def extract_target_code_or_empty(code: str, + entrypoint: Optional[str] = None) -> str: + code = code_extract(code.strip()) + code_bytes = bytes(code, 'utf8') + parser = get_parser('python') + tree = parser.parse(code_bytes) + class_names = set() + function_names = set() + variable_names = set() + + root_node = tree.root_node + import_nodes = [] + definition_nodes = [] + + for child in root_node.children: + if child.type in IMPORT_TYPE: + import_nodes.append(child) + elif child.type == CLASS_TYPE: + name = get_definition_name(child) + if not (name in class_names or name in variable_names + or name in function_names): + definition_nodes.append((name, child)) + class_names.add(name) + elif child.type == FUNCTION_TYPE: + name = get_definition_name(child) + if not (name in function_names or name in variable_names + or name in class_names): + definition_nodes.append((name, child)) + function_names.add(get_definition_name(child)) + elif (child.type == EXPRESSION_TYPE + and child.children[0].type == ASSIGNMENT_TYPE): + subchild = child.children[0] + name = get_definition_name(subchild) + if not (name in variable_names or name in function_names + or name in class_names): + definition_nodes.append((name, subchild)) + variable_names.add(name) + + if entrypoint: + name2deps = get_deps(definition_nodes) + reachable = get_function_dependency(entrypoint, name2deps) + + sanitized_output = b'' + + for node in import_nodes: + sanitized_output += code_bytes[node.start_byte:node.end_byte] + b'\n' + + for pair in definition_nodes: + name, node = pair + if entrypoint and not (name in reachable): + continue + sanitized_output += code_bytes[node.start_byte:node.end_byte] + b'\n' + + sanitized_output = sanitized_output[:-1].decode('utf8') + + # ad-hoc approach to remove unnecessary lines, but it works + lines = sanitized_output.splitlines() + outer_lines = [] + for i in range(len(lines) - 1, -1, -1): + if lines[i].startswith(' '): + break + if not lines[i].startswith(' ') and entrypoint in lines[i]: + outer_lines.append(i) + if outer_lines: + sanitized_output = '\n'.join(lines[:outer_lines[-1]]) + return sanitized_output + + +def extract_code_generation(model_output: str, + entrypoint: Optional[str] = None): + + # Extract code according to the entrypoint + sanitized_code = extract_target_code_or_empty(model_output, + entrypoint).strip() + # Fallback to extract first codeblock if sanitized_code is empty + sanitized_code = code_extract( + model_output) if not sanitized_code else sanitized_code + + return sanitized_code diff --git a/opencompass/datasets/calm/__init__.py b/opencompass/datasets/calm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1af74c13b50150f74545eeed71c3c2319d7d612c --- /dev/null +++ b/opencompass/datasets/calm/__init__.py @@ -0,0 +1 @@ +from .calm import * # noqa: F401, F403 diff --git a/opencompass/datasets/calm/calm.py b/opencompass/datasets/calm/calm.py new file mode 100644 index 0000000000000000000000000000000000000000..8d6040e8b1ad8cec2f0540cd6bb227bdc138f134 --- /dev/null +++ b/opencompass/datasets/calm/calm.py @@ -0,0 +1,60 @@ +from typing import List + +import datasets +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import LOAD_DATASET + +from ..base import BaseDataset +from .data_processing.generate_questions import generate_question_list +from .evaluation.core_metrics import compute_core_metrics +from .evaluation.errors import identify_model_errors + + +@LOAD_DATASET.register_module() +class CaLMDataset(BaseDataset): + + @staticmethod + def load(path: str, prompt_style: str) -> datasets.Dataset: + question_list = generate_question_list(dataset_path=path, + prompt_style=prompt_style) + dataset = Dataset.from_list(question_list) + return dataset + + +class CaLMEvaluator(BaseEvaluator): + + def __init__(self, core_metrics, error_analysis, prompt_style, + task) -> None: + super().__init__() + self.core_metrics = core_metrics + self.error_analysis = error_analysis + self.prompt_style = prompt_style + self.task = task + + def score( + self, + predictions: List, + references: List, + ) -> dict: + results = {} + if self.core_metrics: + metrics, pred_list = compute_core_metrics( + predictions, + task=self.task, + prompt_style=self.prompt_style, + gt_items=references) + results.update(metrics) + if self.error_analysis: + if self.task.startswith('CEG-O_E-CARE'): + print("There's no error analysis for CEG-O_E-CARE task. ", + 'Skipping error analysis.') + return results + errors = identify_model_errors( + predictions, + task=self.task, + prompt_style=self.prompt_style, + gt_items=references) # Define specific criteria + results.update(errors) + return results diff --git a/opencompass/datasets/chatml/__init__.py b/opencompass/datasets/chatml/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b411eb76d68bba018650318e0cc60542f94650b --- /dev/null +++ b/opencompass/datasets/chatml/__init__.py @@ -0,0 +1 @@ +from .chatml import ChatMLDataset # noqa: F401, F403 diff --git a/opencompass/datasets/chatml/chatml.py b/opencompass/datasets/chatml/chatml.py new file mode 100644 index 0000000000000000000000000000000000000000..ce599e3b2002f7ec98c48ce9f6baf703ba9bc8e5 --- /dev/null +++ b/opencompass/datasets/chatml/chatml.py @@ -0,0 +1,110 @@ +# flake8: noqa +import json + +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class ChatMLDataset(BaseDataset): + """The Dataset class based on ChatML template that is only used to parse + .jsonl files that conform to the following template format. + + { + "question":[ + { + "role": "system", + "content": Str, + }, + { + "role": "user", + "content": Str or List + [ + { + "type": Str, # "image" + "image_url": Str, + }, + ... + { + "type": Str, # "text" + "text": Str, + }, + ] + }, + { + "role": "assistant", + "content": Str + }, + { + "role": "user", + "content": Str or List + }, + ... + ], + "answer":[ + Str, + Str, + ... + ] + } + { + ... + } + ... + + Please use tools/chatml_format_test.py to check + the format of your dataset files. + + """ + + @staticmethod + def load(path, file_name=None, local_mode=False): + + path = get_data_path(path, local_mode=True) + with open(path, 'r', encoding='utf-8-sig') as f: + data = [json.loads(line) for line in f] + + for i in range(len(data)): + key_list = list(data[i].keys()) + for key in key_list: + if key != 'question' and key != 'answer': + del data[i][key] + + from .verification import VerifyDataset + for i in data: + VerifyDataset(**i) + + input_prompt = '\nRemember to put your final answer within \\boxed{}.' + for i in range(len(data)): + for j in range(len(data[i]['question'])): + if data[i]['question'][j]['role'] == 'user': + data[i]['question'][j]['content'] += input_prompt + + extracted_data = [] + data_final = Dataset.from_list(data) + data_final = data_final.rename_column('question', 'chatml_question') + data_final = data_final.rename_column('answer', 'chatml_answer') + + for item in data: + user_content = next( + (q['content'] + for q in item['question'] if q['role'] == 'user'), None) + first_answer = item['answer'][0] if item['answer'] else '' + + if user_content: + extracted_data.append({ + 'question': user_content, + 'answer': first_answer + }) + + extracted_questions = [item['question'] for item in extracted_data] + extracted_answers = [item['answer'] for item in extracted_data] + + data_final = data_final.add_column('question', extracted_questions) + data_final = data_final.add_column('answer', extracted_answers) + + return data_final diff --git a/opencompass/datasets/chatml/verification.py b/opencompass/datasets/chatml/verification.py new file mode 100644 index 0000000000000000000000000000000000000000..a9f89b217af2d34161fa605652f6a704b84b935d --- /dev/null +++ b/opencompass/datasets/chatml/verification.py @@ -0,0 +1,51 @@ +# flake8: noqa +from typing import List, Literal, Union + +from pydantic import BaseModel, model_validator + + +class TextItem(BaseModel): + type: Literal['text'] + text: str + + +class ImageItem(BaseModel): + type: Literal['image'] + image_url: str + + +MultimodalItem = Union[TextItem, ImageItem] + + +class SystemMessage(BaseModel): + role: Literal['system'] + content: str + + +class AssistantMessage(BaseModel): + role: Literal['assistant'] + content: str + + +class UserMessage(BaseModel): + role: Literal['user'] + content: Union[str, List[MultimodalItem]] + + +Message = Union[SystemMessage, UserMessage, AssistantMessage] + + +class VerifyDataset(BaseModel): + question: List[Message] + answer: List[str] + + @model_validator(mode='after') + def validate_answer_length(self) -> 'VerifyDataset': + user_count = sum(1 for item in self.question + if hasattr(item, 'role') and item.role == 'user') + + if len(self.answer) != 1 and len(self.answer) != user_count: + raise ValueError( + f'Answer must have length 1 or {user_count} (number of user messages),' + f'but got {len(self.answer)}') + return self diff --git a/opencompass/datasets/cmphysbench/__init__.py b/opencompass/datasets/cmphysbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1a1b0a728000caff6232ad84b0608e65ad1729bb --- /dev/null +++ b/opencompass/datasets/cmphysbench/__init__.py @@ -0,0 +1 @@ +from .cmphysbench import * # noqa diff --git a/opencompass/datasets/cmphysbench/cmphysbench.py b/opencompass/datasets/cmphysbench/cmphysbench.py new file mode 100644 index 0000000000000000000000000000000000000000..0055d00c6d6a85765a9989f0a789288e308f4074 --- /dev/null +++ b/opencompass/datasets/cmphysbench/cmphysbench.py @@ -0,0 +1,161 @@ +import re + +from datasets import Dataset, load_dataset + +from opencompass.datasets.cmphysbench.SEED.SEED import SEED +from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class CMPhysBenchDataset(BaseDataset): + + @staticmethod + def load(path: str): + dataset = load_dataset(path, split='train') + new_data = [] + for ins in dataset: + if len(ins['final_answer']) == 0: + continue + new_data.append({ + 'prompt': + ins['question'], + 'ground_truth': [ins['final_answer'][0], ins['topic']], + }) + dataset = Dataset.from_list(new_data) + return dataset + + +def extract_boxed_text_overlap(text): + """ + 简化版本：如果只需要基本的boxed内容提取 + 不做过多的LaTeX清理 + """ + boxed_positions = [] + for match in re.finditer(r'\\boxed\{', text): + boxed_positions.append(match.start()) + + if not boxed_positions: + return None + + boxed_contents = [] + + for start_pos in boxed_positions: + brace_start = text.find('{', start_pos) + 1 + if brace_start == 0: + continue + + brace_count = 1 + current_pos = brace_start + + while current_pos < len(text) and brace_count > 0: + if text[current_pos] == '{': + brace_count += 1 + elif text[current_pos] == '}': + brace_count -= 1 + current_pos += 1 + + if brace_count == 0: + content = text[brace_start:current_pos - 1] + boxed_contents.append(content) + + return boxed_contents[-1].strip() if boxed_contents else None + + +def extract_boxed_text_improved(text): + """ + 提取LaTeX文本中\boxed{}内容的改进版本 + 能够处理复杂的嵌套大括号结构 + """ + # 找到所有\boxed{的位置 + boxed_positions = [] + for match in re.finditer(r'\\boxed\{', text): + boxed_positions.append(match.start()) + + if not boxed_positions: + return None + + # 对每个\boxed{位置，找到对应的结束} + boxed_contents = [] + + for start_pos in boxed_positions: + # 从\boxed{后开始计数大括号 + brace_start = text.find('{', start_pos) + 1 + if brace_start == 0: # 没找到{ + continue + + brace_count = 1 + current_pos = brace_start + + # 逐字符扫描，正确匹配大括号 + while current_pos < len(text) and brace_count > 0: + char = text[current_pos] + + if char == '{': + brace_count += 1 + elif char == '}': + brace_count -= 1 + + current_pos += 1 + + if brace_count == 0: # 找到了匹配的} + # 提取内容（不包括最后的}） + content = text[brace_start:current_pos - 1] + boxed_contents.append(content) + + if not boxed_contents: + return None + + # 取最后一个匹配的内容 + boxed_content = boxed_contents[-1].strip() + + # 清理LaTeX命令 + # 1. 移除\text{}包装（只在完整匹配时） + clean_content = re.sub(r'\\text\{([^}]*)\}', r'\1', boxed_content) + + # 2. 处理常见的LaTeX转义符（更保守的处理） + # 只处理简单的转义，避免破坏复杂的LaTeX命令 + escape_patterns = [ + (r'\\&', '&'), + (r'\\%', '%'), + (r'\\\$', '$'), + (r'\\#', '#'), + (r'\\_', '_'), + (r'\\textbackslash', '\\'), + ] + + for pattern, replacement in escape_patterns: + clean_content = re.sub(pattern, replacement, clean_content) + + return clean_content + + +@ICL_EVALUATORS.register_module() +class CMPhysBenchEvaluator(BaseEvaluator): + """F1 score for fasta.""" + + def __init__(self) -> None: + super().__init__() + + def score(self, predictions, references): + if len(predictions) != len(references): + return { + 'error': 'predictions and references have different ' + 'length' + } + + details = [] + for prediction, ans in zip(predictions, references): + pred = extract_boxed_text_overlap(prediction) + if not pred: + pred = prediction + detail = {'pred': pred, 'answer': ans} + score, _, _, _ = SEED(pred, ans[0], ans[1]) + detail['score'] = score + details.append(detail) + + score = sum(detail['score'] + for detail in details) / len(details) if details else 0.0 + return {'score': score, 'details': details} diff --git a/opencompass/datasets/codecompass/CodeCompass.py b/opencompass/datasets/codecompass/CodeCompass.py new file mode 100644 index 0000000000000000000000000000000000000000..ae970d2236838c2998ce1a02d98731004bcda0ab --- /dev/null +++ b/opencompass/datasets/codecompass/CodeCompass.py @@ -0,0 +1,301 @@ +import json +import re +from typing import Any, Dict, Optional + +from datasets import Dataset, DatasetDict, load_dataset +from tqdm import tqdm + +try: + from opencompass.utils import get_data_path + + from ..base import BaseDataset +except ImportError: + + class BaseDataset: + pass + + def get_data_path(path, local_mode=False): + return path + + +class CodeCompassCodeGenerationDataset(BaseDataset): + + DEFAULT_SYSTEM_PROMPT = ( + '[[Instruction]]\n' + 'You are an expert C++ programmer.' + 'You will be given a question in the format ' + 'of an Online Judge (OJ) problem. You must generate a correct, ' + 'self-contained C++ program that solves the problem and reads from ' + 'standard input and writes to standard output. You will NOT return ' + 'anything except for the program. Put your fixed program within code ' + 'delimiters, for example:\n' + '```cpp\n' + '#include \n' + 'using namespace std;\n\n' + 'int main() {\n' + ' // YOUR CODE HERE\n' + ' return 0;\n' + '}\n' + '```\n') + + DEFAULT_PROBLEM_TEMPLATE = """ +[[Problem begin]] +{problem} +[[Problem end]] +""" + + @staticmethod + def load(path: str = 'opencompass/CodeCompass', + difficulty: Optional[str] = None, + source: Optional[str] = None, + system_prompt: Optional[str] = None, + problem_template: Optional[str] = None) -> DatasetDict: + + try: + raw_dataset = load_dataset(path=path, + difficulty=difficulty, + source=source, + split='test', + trust_remote_code=True) + print(f' > Raw dataset loaded: {len(raw_dataset)} samples') + except Exception as e: + print(f'Error loading dataset: {e}') + raise + + if system_prompt is None: + system_prompt = ( + CodeCompassCodeGenerationDataset.DEFAULT_SYSTEM_PROMPT) + + if problem_template is None: + problem_template = ( + CodeCompassCodeGenerationDataset.DEFAULT_PROBLEM_TEMPLATE) + + processed_data = [] + failed_count = 0 + + for item in tqdm(raw_dataset, desc='Processing samples'): + try: + processed_item = ( + CodeCompassCodeGenerationDataset._process_item( + item, system_prompt, problem_template)) + if processed_item is not None: + processed_data.append(processed_item) + else: + failed_count += 1 + except Exception as e: + question_id = item.get('question_id', 'unknown') + print(f'Error processing item {question_id}: {e}') + failed_count += 1 + + final_dataset = Dataset.from_list(processed_data) + return DatasetDict({'test': final_dataset}) + + @staticmethod + def _extract_limits(problem_text: str) -> Dict[str, Any]: + limits = {'time_limit_s': 2.0, 'memory_limit_mb': 256} + + time_match = re.search(r'Time Limit:\s*([0-9.]+)\s*s', problem_text, + re.IGNORECASE) + if time_match: + try: + limits['time_limit_s'] = float(time_match.group(1)) + except ValueError: + time_value = time_match.group(1) + print(f'Warning: Could not parse time limit value: ' + f'{time_value}') + + mem_match = re.search(r'Memory Limit:\s*(\d+)\s*MB', problem_text, + re.IGNORECASE) + if mem_match: + try: + limits['memory_limit_mb'] = int(mem_match.group(1)) + except ValueError: + mem_value = mem_match.group(1) + print(f'Warning: Could not parse memory limit value: ' + f'{mem_value}') + + return limits + + @staticmethod + def _process_item(item: Dict[str, Any], system_prompt: str, + problem_template: str) -> Optional[Dict[str, Any]]: + try: + new_item = item.copy() + + problem_content = item.get('problem', '') + if not problem_content: + question_id = item.get('question_id') + print(f'Warning: Empty problem for question_id {question_id}') + return None + + full_prompt = system_prompt + problem_template.format( + problem=problem_content) + new_item['prompt'] = full_prompt + + evaluation_sample = (CodeCompassCodeGenerationDataset. + _create_evaluation_sample(item)) + if evaluation_sample is None: + question_id = item.get('question_id') + print(f'Warning: Cannot create evaluation_sample for ' + f'question_id {question_id}') + return None + + new_item['evaluation_sample'] = evaluation_sample + + limits = CodeCompassCodeGenerationDataset._extract_limits( + problem_content) + + eval_inputs = evaluation_sample.get('inputs', []) + num_test_cases = (len(eval_inputs) if isinstance( + evaluation_sample, dict) else 0) + + new_item['metadata'] = { + 'question_id': item.get('question_id'), + 'difficulty': item.get('difficulty', 'Unknown'), + 'source': item.get('source', 'Unknown'), + 'problem_length': len(problem_content), + 'num_test_cases': num_test_cases, + 'time_limit_s': limits['time_limit_s'], + 'memory_limit_mb': limits['memory_limit_mb'] + } + + new_item['original_problem'] = problem_content + new_item['question_id'] = item.get('question_id') + new_item['difficulty'] = item.get('difficulty') + new_item['source'] = item.get('source') + + return new_item + + except Exception as e: + print(f'Error in _process_item: {e}') + return None + + @staticmethod + def _create_evaluation_sample( + item: Dict[str, Any]) -> Optional[Dict[str, Any]]: + + try: + + if 'cases' in item: + cases_data = item['cases'] + + if isinstance(cases_data, str): + cases_list = json.loads(cases_data) + elif isinstance(cases_data, list): + cases_list = cases_data + else: + print(f'Unknown cases format: {type(cases_data)}') + return None + + inputs = [] + outputs = [] + + for case in cases_list: + if isinstance(case, dict): + input_val = case.get('input', '') + output_val = case.get('output', '') + + if not isinstance(input_val, str): + input_val = str(input_val) + if not isinstance(output_val, str): + output_val = str(output_val) + + inputs.append(input_val) + outputs.append(output_val) + else: + print(f'Warning: Invalid case format: {type(case)}') + continue + + if not inputs or not outputs: + print('Warning: No valid test cases found') + return None + + if len(inputs) != len(outputs): + input_count = len(inputs) + output_count = len(outputs) + print(f'Warning: Input/output count mismatch: ' + f'{input_count} vs {output_count}') + return None + + return { + 'inputs': inputs, + 'outputs': outputs, + 'fn_name': None, + 'num_cases': len(inputs) + } + + elif 'inputs' in item and 'outputs' in item: + inputs = item['inputs'] + outputs = item['outputs'] + + if not isinstance(inputs, list) or not isinstance( + outputs, list): + print('Warning: inputs/outputs are not lists') + return None + + if len(inputs) != len(outputs): + input_count = len(inputs) + output_count = len(outputs) + print(f'Warning: Input/output count mismatch: ' + f'{input_count} vs {output_count}') + return None + + return { + 'inputs': [str(inp) for inp in inputs], + 'outputs': [str(out) for out in outputs], + 'fn_name': None, + 'num_cases': len(inputs) + } + + else: + print('Warning: No test cases found in item') + return None + + except (json.JSONDecodeError, KeyError, TypeError) as e: + print(f'Error creating evaluation sample: {e}') + return None + + @staticmethod + def validate_dataset(dataset: DatasetDict) -> bool: + + try: + if 'test' not in dataset: + print("Error: No 'test' split found") + return False + + test_dataset = dataset['test'] + if len(test_dataset) == 0: + print('Error: Test dataset is empty') + return False + + required_fields = ['prompt', 'evaluation_sample', 'metadata'] + sample = test_dataset[0] + + for field in required_fields: + if field not in sample: + print(f'Error: Missing required field: {field}') + return False + + eval_sample = sample['evaluation_sample'] + if isinstance(eval_sample, str): + try: + eval_sample = json.loads(eval_sample) + except json.JSONDecodeError: + print('Error: evaluation_sample is not valid JSON') + return False + + if not isinstance(eval_sample, dict): + print('Error: evaluation_sample is not a dictionary') + return False + + if 'inputs' not in eval_sample or 'outputs' not in eval_sample: + print('Error: evaluation_sample missing inputs/outputs') + return False + + sample_count = len(test_dataset) + print(f'Dataset validation passed: {sample_count} samples') + return True + + except Exception as e: + print(f'Error during validation: {e}') + return False diff --git a/opencompass/datasets/codecompass/__init__.py b/opencompass/datasets/codecompass/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f56c8217a36699eff2b45a658aa4cfb312be10c4 --- /dev/null +++ b/opencompass/datasets/codecompass/__init__.py @@ -0,0 +1,9 @@ +from .CodeCompass import CodeCompassCodeGenerationDataset +from .codecompass_runner import run_test_for_cpp_problem +from .evaluator import CodeCompassEvaluator + +__all__ = [ + 'CodeCompassCodeGenerationDataset', + 'CodeCompassEvaluator', + 'run_test_for_cpp_problem', +] diff --git a/opencompass/datasets/codecompass/codecompass_runner.py b/opencompass/datasets/codecompass/codecompass_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..aa67d659a576dcebc9b437378e730ee44fd59180 --- /dev/null +++ b/opencompass/datasets/codecompass/codecompass_runner.py @@ -0,0 +1,63 @@ +import os + +from .executor import LocalExecutor + + +def run_test_for_cpp_problem(sample: dict, + generations: list, + timeout: int, + memory_limit_mb: int, + temp_base_dir='tmp') -> list: + pid = os.getpid() + print(f'\n--- [DEBUG][PID:{pid}] Subprocess started. ' + f'Timeout: {timeout}s, MemLimit: {memory_limit_mb}MB ---') + + try: + eval_data = sample['evaluation_sample'] + assert isinstance( + eval_data, dict), f'eval_data is not a dict, but {type(eval_data)}' + + inputs = eval_data.get('inputs', []) + outputs = eval_data.get('outputs', []) + except Exception: + return [[-4] * 100 for _ in generations] + + executor = LocalExecutor(timeout=timeout, + memory_limit_mb=memory_limit_mb, + temp_base_dir=temp_base_dir) + + all_results = [] + + for gen_idx, gen_code in enumerate(generations): + if not gen_code or not gen_code.strip(): + all_results.append([-2] * len(inputs)) + continue + + try: + results_for_this_gen = [] + + for i in range(len(inputs)): + result = executor.submit_code(source_code=gen_code, + stdin=inputs[i], + expected_output=outputs[i], + language='C++') + + status = result.get('status', {}).get('description', + 'Unknown Error') + + if status == 'Accepted': + results_for_this_gen.append(1) + elif status == 'Compilation Error': + results_for_this_gen = [-2] * len(inputs) + break + elif status == 'Memory Limit Exceeded': + results_for_this_gen.append(-5) + else: + results_for_this_gen.append(-1) + + all_results.append(results_for_this_gen) + + except Exception: + all_results.append([-3] * len(inputs)) + + return all_results diff --git a/opencompass/datasets/codecompass/evaluator.py b/opencompass/datasets/codecompass/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..434f2c63cdafe91384fb343dbda2d921ffb038a6 --- /dev/null +++ b/opencompass/datasets/codecompass/evaluator.py @@ -0,0 +1,253 @@ +# evaluator.py + +import json +from concurrent.futures import ProcessPoolExecutor, as_completed +from typing import Any, Dict, List + +from tqdm import tqdm + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS + +from .CodeCompass import CodeCompassCodeGenerationDataset +from .codecompass_runner import run_test_for_cpp_problem +from .metrics import compute_metrics_from_results +from .utils import extract_cpp_code + + +@ICL_EVALUATORS.register_module() +class CodeCompassEvaluator(BaseEvaluator): + """CodeCompass C++ Code Generation Evaluator.""" + + def __init__(self, + num_process_evaluate: int = 16, + timeout: int = 15, + k_list: List[int] = None, + temp_base_dir: str = None, + dataset_path: str = None): + super().__init__() + self.num_process_evaluate = num_process_evaluate + self.default_timeout = timeout + self.k_list = k_list or [1] + self.temp_base_dir = temp_base_dir + + if not dataset_path: + raise ValueError('dataset_path must be provided to the evaluator.') + + full_dataset = CodeCompassCodeGenerationDataset.load( + path=dataset_path)['test'] + + self.test_cases_lookup = { + item['question_id']: item['evaluation_sample'] + for item in full_dataset + } + + def _build_results(self, extracted_predictions: Dict[int, List[str]], + metrics: Dict[str, float], + eval_results: Dict[int, List[List[int]]], + final_metadata: List[Dict]) -> Dict: + """Builds the final results dictionary with detailed information.""" + results = {} + results['pass@1'] = metrics.get('pass@1', 0.0) + details = [] + + pass_1_correctness = metrics.get('details', {}).get('pass@1', []) + problem_indices = sorted(extracted_predictions.keys()) + + for i, problem_idx in enumerate(problem_indices): + if i >= len(final_metadata) or i >= len(pass_1_correctness): + continue + + ep = extracted_predictions[problem_idx] + er = eval_results.get(problem_idx, []) + fm = final_metadata[i] + + detail = { + 'extracted_prediction': + ep[0] if isinstance(ep, list) and ep else ep, + 'eval_result': er[0] if isinstance(er, list) and er else er, + 'final_metadata': fm + } + detail['correct'] = bool(pass_1_correctness[i] == 1.0) + details.append(detail) + + results['details'] = details + return results + + def score(self, predictions: List[Any], + references: List[Any]) -> Dict[str, float]: + tasks = [] + extracted_predictions_dict = {} + final_metadata_list = [] + problem_counter = 0 + + for i, (pred, metadata) in enumerate(zip(predictions, references)): + try: + question_id = metadata.get('question_id') + if not question_id: + print(f"Warning: 'question_id' not found in metadata at " + f'index {i}. Skipping.') + continue + + eval_sample = self.test_cases_lookup.get(question_id) + if not eval_sample: + print(f'Warning: Could not find test cases for ' + f"question_id '{question_id}', skipping.") + continue + + sample = {'evaluation_sample': eval_sample} + timeout = metadata.get('time_limit_s', self.default_timeout) + mem_limit_mb = metadata.get('memory_limit_mb', 256) + + if isinstance(pred, str): + pred = [pred] + extracted_codes = [ + extract_cpp_code(code) for code in pred + if isinstance(code, str) + ] + if not any(extracted_codes): + # Even if no code is extracted, we might want to record a + # failure. For now, we follow the original logic of + # skipping. + continue + + tasks.append((sample, extracted_codes, timeout, mem_limit_mb, + self.temp_base_dir)) + + # Store data for building results later, using a consistent + # index + extracted_predictions_dict[problem_counter] = extracted_codes + final_metadata_list.append(metadata) + problem_counter += 1 + + except Exception as e: + print(f"Error preparing task for question_id '{question_id}': " + f'{e}') + continue + + if not tasks: + return self._build_results({}, {'pass@1': 0.0}, {}, []) + + # Run evaluation in parallel + results_list = self._run_parallel_evaluation(tasks) + + # Create a dictionary from the results list for easier lookup + eval_results_dict = { + i: results_list[i] + for i in range(len(results_list)) + } + + # Compute metrics (pass@k and details) + metrics = compute_metrics_from_results(eval_results_dict, + k_list=self.k_list) + + # Build the final, detailed result structure + return self._build_results(extracted_predictions_dict, metrics, + eval_results_dict, final_metadata_list) + + def _prepare_sample(self, reference: Any, idx: int = -1) -> Dict[str, Any]: + # This method remains unchanged + try: + if idx <= 2: + if isinstance(reference, dict): + print(f'Reference keys: {list(reference.keys())}') + for key, value in reference.items(): + print(f' {key}: {type(value)} - ' + f'{str(value)[:100]}...') + else: + print(f'Reference value: {str(reference)[:200]}...') + + if isinstance(reference, + dict) and 'evaluation_sample' in reference: + eval_sample = reference['evaluation_sample'] + if isinstance(eval_sample, str): + eval_sample = json.loads(eval_sample) + return {'evaluation_sample': eval_sample} + + elif isinstance(reference, str): + eval_sample = json.loads(reference) + return {'evaluation_sample': eval_sample} + + elif isinstance(reference, dict): + if 'test_cases' in reference: + test_cases = reference['test_cases'] + if isinstance(test_cases, str): + test_cases = json.loads(test_cases) + inputs = [case.get('input', '') for case in test_cases] + outputs = [case.get('output', '') for case in test_cases] + eval_sample = { + 'inputs': inputs, + 'outputs': outputs, + 'fn_name': reference.get('fn_name') + } + return {'evaluation_sample': eval_sample} + elif 'cases' in reference: + cases_str = reference['cases'] + if isinstance(cases_str, str): + cases_list = json.loads(cases_str) + else: + cases_list = cases_str + inputs = [case.get('input', '') for case in cases_list] + outputs = [case.get('output', '') for case in cases_list] + eval_sample = { + 'inputs': inputs, + 'outputs': outputs, + 'fn_name': reference.get('fn_name') + } + return {'evaluation_sample': eval_sample} + elif 'inputs' in reference and 'outputs' in reference: + eval_sample = { + 'inputs': reference['inputs'], + 'outputs': reference['outputs'], + 'fn_name': reference.get('fn_name') + } + return {'evaluation_sample': eval_sample} + elif 'input' in reference and 'output' in reference: + inputs = [reference['input']] if isinstance( + reference['input'], str) else reference['input'] + outputs = [reference['output']] if isinstance( + reference['output'], str) else reference['output'] + eval_sample = { + 'inputs': inputs, + 'outputs': outputs, + 'fn_name': reference.get('fn_name') + } + return {'evaluation_sample': eval_sample} + else: + print(f'Warning: Trying to use entire reference as ' + f'evaluation_sample for sample {idx}') + return {'evaluation_sample': reference} + + print(f'Cannot handle reference format: {type(reference)} ' + f'for sample {idx}') + return None + except (json.JSONDecodeError, KeyError, TypeError) as e: + print(f'Error preparing sample {idx}: {e}') + import traceback + traceback.print_exc() + return None + + def _run_parallel_evaluation(self, + tasks: List[tuple]) -> List[List[List[int]]]: + # This method remains unchanged + results_list = [[] for _ in range(len(tasks))] + with ProcessPoolExecutor( + max_workers=self.num_process_evaluate) as executor: + with tqdm(total=len(tasks), + desc='Evaluating Code Generation') as pbar: + future_to_idx = { + executor.submit(run_test_for_cpp_problem, *task): i + for i, task in enumerate(tasks) + } + for future in as_completed(future_to_idx): + idx = future_to_idx[future] + try: + result = future.result() + results_list[idx] = result + except Exception as e: + print(f'FATAL ERROR in task {idx}: {e}') + num_gens = len(tasks[idx][1]) + results_list[idx] = [[-3] * 100] * num_gens + finally: + pbar.update(1) + return results_list diff --git a/opencompass/datasets/codecompass/executor.py b/opencompass/datasets/codecompass/executor.py new file mode 100644 index 0000000000000000000000000000000000000000..37cc2883c8a0c9e8eccb88114ff4e5ca0377e914 --- /dev/null +++ b/opencompass/datasets/codecompass/executor.py @@ -0,0 +1,184 @@ +# isort: skip_file +import os +import subprocess +import tempfile +import time +from pathlib import Path +from typing import Dict, Optional + + +class LocalExecutor: + """Executes code locally with custom time and memory limits. + + Refined to distinguish between Runtime and Memory Limit errors. + """ + + def __init__(self, + timeout: int = 10, + memory_limit_mb: int = 512, + temp_base_dir: Optional[str] = None): + self.timeout = timeout + self.memory_limit_mb = memory_limit_mb + + if temp_base_dir: + self.temp_base_dir = Path(temp_base_dir) + self.temp_base_dir.mkdir(parents=True, exist_ok=True) + else: + self.temp_base_dir = Path(tempfile.gettempdir()) + + def _set_resource_limits(self): + # isort: off + import resource + # isort: on + """This function is called in the child process right before exec.""" + try: + mem_bytes = self.memory_limit_mb * 1024 * 1024 + # Set both soft and hard limits for virtual memory + resource.setrlimit(resource.RLIMIT_AS, (mem_bytes, mem_bytes)) + except Exception: + pass + + def _compile_cpp(self, source_file: Path, temp_dir: Path) -> tuple: + """Compiles C++ source code.""" + output_file = temp_dir / source_file.stem + cmd = [ + 'g++', '-std=c++17', '-O2', + str(source_file), '-o', + str(output_file) + ] + try: + proc = subprocess.run(cmd, + capture_output=True, + text=True, + timeout=10) + if proc.returncode == 0: + return True, '', output_file + else: + return False, proc.stderr, None + except subprocess.TimeoutExpired: + return False, 'Compilation timed out', None + except Exception as e: + return False, f'Compilation error: {str(e)}', None + + def _run_executable(self, exec_file: Path, stdin_data: str) -> Dict: + """Runs the compiled executable and interprets the result.""" + start_time = time.time() + + try: + # The preexec_fn is the key for setting limits on the child process + proc = subprocess.run([str(exec_file)], + input=stdin_data, + capture_output=True, + text=True, + timeout=self.timeout, + preexec_fn=self._set_resource_limits) + execution_time = time.time() - start_time + + if proc.returncode == 0: + + status = {'id': 3, 'description': 'Accepted'} + elif proc.returncode < 0: + + status = {'id': 5, 'description': 'Memory Limit Exceeded'} + else: + + status = {'id': 11, 'description': 'Runtime Error'} + + return { + 'status': status, + 'stdout': proc.stdout, + 'stderr': proc.stderr, + 'time': execution_time + } + + except subprocess.TimeoutExpired: + # Wall-clock time limit exceeded + return { + 'status': { + 'id': 5, + 'description': 'Time Limit Exceeded' + }, + 'stdout': '', + 'stderr': f'Timeout > {self.timeout}s', + 'time': self.timeout + } + except Exception as e: + return { + 'status': { + 'id': 11, + 'description': 'Runtime Error' + }, + 'stdout': '', + 'stderr': str(e), + 'time': time.time() - start_time + } + + def execute_code(self, source_code: str, stdin: str, language: str, + temp_dir: Path) -> Dict: + """Orchestrates compilation and execution.""" + if language.lower() not in ['c++', 'g++']: + return { + 'status': { + 'id': -1, + 'description': 'Unsupported Language' + }, + 'stderr': f'Language {language} not supported.' + } + + # Create a unique file to avoid race conditions in parallel execution + source_file = temp_dir / f'solution_{os.getpid()}_{time.time_ns()}.cpp' + with open(source_file, 'w') as f: + f.write(source_code) + + success, err, exec_file = self._compile_cpp(source_file, temp_dir) + if not success: + return { + 'status': { + 'id': 6, + 'description': 'Compilation Error' + }, + 'compile_output': err + } + + return self._run_executable(exec_file, stdin) + + def verify_output(self, result: Dict, expected_output: str) -> Dict: + """Verifies the stdout against the expected output.""" + # Only verify if the execution was successful + if result.get('status', {}).get('description') != 'Accepted': + return result + + # Normalize whitespace and line endings for robust comparison + stdout = result.get('stdout', '').replace('\r\n', '\n').strip() + expected = expected_output.replace('\r\n', '\n').strip() + + # Trailing whitespace on each line can also be an issue + stdout_lines = [line.rstrip() for line in stdout.split('\n')] + expected_lines = [line.rstrip() for line in expected.split('\n')] + + if stdout_lines == expected_lines: + result['status'] = {'id': 3, 'description': 'Accepted'} + else: + result['status'] = {'id': 4, 'description': 'Wrong Answer'} + return result + + def submit_code(self, + source_code: str, + stdin: str, + expected_output: str, + language: str = 'C++') -> Dict: + """Public entry point. + + Manages temp directories and orchestrates the full process. + """ + with tempfile.TemporaryDirectory( + dir=self.temp_base_dir) as temp_dir_str: + temp_dir = Path(temp_dir_str) + result = self.execute_code(source_code, stdin, language, temp_dir) + + # The result from execute_code can be TLE, MLE, RTE, etc. + # We only verify the output if the status is "Accepted" initially. + if result['status']['description'] == 'Accepted': + result = self.verify_output(result, expected_output) + + return result diff --git a/opencompass/datasets/codecompass/metrics.py b/opencompass/datasets/codecompass/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb5f6159a8742b7acf83a0f68ae535603995c39 --- /dev/null +++ b/opencompass/datasets/codecompass/metrics.py @@ -0,0 +1,74 @@ +import numpy as np + + +def estimate_pass_at_k(num_samples, num_correct, k): + """Estimates pass@k of each problem and returns them in an array.""" + + def estimator(n: int, c: int, k: int) -> float: + if n - c < k: + return 1.0 + return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) + + if isinstance(num_samples, int): + import itertools + num_samples_it = itertools.repeat(num_samples, len(num_correct)) + else: + num_samples_it = iter(num_samples) + + return np.array([ + estimator(int(n), int(c), k) + for n, c in zip(num_samples_it, num_correct) + ]) + + +def compute_metrics_from_results(results: dict, k_list=[1]): + print('\n--- [DEBUG] compute_metrics_from_results started ---') + print(f' > Received results for {len(results)} problems.') + + total = [] + correct = [] + + if not results: + metrics = {} + for k in k_list: + metrics[f'pass@{k}'] = 0.0 + if 1 in k_list or not k_list: + metrics['details'] = {'pass@1': []} + return metrics + + for problem_idx in sorted(results.keys()): + problem_results = results[problem_idx] + if not problem_results: + total.append(0) + correct.append(0) + continue + + total.append(len(problem_results)) + + num_correct_generations = 0 + for gen_result in problem_results: + if not gen_result: + continue + if all(res > 0 for res in gen_result): + num_correct_generations += 1 + correct.append(num_correct_generations) + + total_arr = np.array(total) + correct_arr = np.array(correct) + + metrics = {} + # For details, a problem is "correct" if at least one generation passed. + pass_1_details = (correct_arr > 0).astype(float).tolist() + + for k in k_list: + if np.all(total_arr >= k): + pass_k_mean = estimate_pass_at_k(total_arr, correct_arr, k).mean() + metrics[f'pass@{k}'] = pass_k_mean + else: + metrics[f'pass@{k}'] = np.nan + + # Add the detailed pass@1 results for building the final report + if 1 in k_list or not k_list: + metrics['details'] = {'pass@1': pass_1_details} + + return metrics diff --git a/opencompass/datasets/codecompass/utils.py b/opencompass/datasets/codecompass/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f14b1feb87945521f9d59ee972e8f63b291950a6 --- /dev/null +++ b/opencompass/datasets/codecompass/utils.py @@ -0,0 +1,73 @@ +def extract_cpp_code(model_output: str, model_type: str = 'chat'): + """Extract Cpp Code.""" + if not isinstance(model_output, str): + return '' + + outputlines = model_output.split('\n') + + indexlines = [i for i, line in enumerate(outputlines) if '```' in line] + if len(indexlines) >= 2: + + cpp_starts = [ + i for i, line in enumerate(outputlines) + if '```cpp' in line.lower() + ] + if cpp_starts: + start_index = cpp_starts[0] + + end_indices = [i for i in indexlines if i > start_index] + if end_indices: + end_index = end_indices[0] + extracted = '\n'.join(outputlines[start_index + + 1:end_index]).strip() + return extracted + + extracted = '\n'.join(outputlines[indexlines[0] + + 1:indexlines[-1]]).strip() + return extracted + + if any(keyword in model_output for keyword in + ['def ', 'class ', 'int main', '#include', 'using namespace']): + return model_output.strip() + + return '' + + +def extract_cpp_code_with_debug(model_output: str, model_type: str = 'chat'): + + if not isinstance(model_output, str): + return '' + + outputlines = model_output.split('\n') + + # Find Code Block + indexlines = [i for i, line in enumerate(outputlines) if '```' in line] + + if len(indexlines) >= 2: + cpp_starts = [ + i for i, line in enumerate(outputlines) if '```c' in line.lower() + ] + + if cpp_starts: + start_index = cpp_starts[0] + end_indices = [i for i in indexlines if i > start_index] + if end_indices: + end_index = end_indices[0] + extracted = '\n'.join(outputlines[start_index + + 1:end_index]).strip() + + return extracted + + extracted = '\n'.join(outputlines[indexlines[0] + + 1:indexlines[-1]]).strip() + + return extracted + + keywords = ['def ', 'class ', 'int main', '#include', 'using namespace'] + found_keywords = [kw for kw in keywords if kw in model_output] + + if found_keywords: + return model_output.strip() + + print('No code found') + return '' diff --git a/opencompass/datasets/eese/eese.py b/opencompass/datasets/eese/eese.py new file mode 100644 index 0000000000000000000000000000000000000000..775136489b9dc061f268facc08e6086fb0f0c795 --- /dev/null +++ b/opencompass/datasets/eese/eese.py @@ -0,0 +1,45 @@ +import json +import os.path as osp +from os import environ + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class EESEDataset(BaseDataset): + + @staticmethod + def load(path: str, file_name: str = 'EESE.jsonl', **kwargs): + """Load EESE dataset from HuggingFace or local file. + + Args: + path (str): Path to the dataset + file_name (str): Name of the JSONL file + **kwargs: Additional arguments + + Returns: + datasets.Dataset: The loaded EESE dataset + """ + path = get_data_path(path) + + if environ.get('DATASET_SOURCE') == 'HF': + from datasets import load_dataset + dataset = load_dataset('AIBench/EESE', 'default', split='test') + else: + from datasets import Dataset, DatasetDict + dataset = DatasetDict() + for split in ['test']: + raw_data = [] + filename = osp.join(path, split, f'{file_name}') + raw_data = [] + with open(filename, 'r', encoding='utf-8') as f: + for line in f: + if line.strip(): + raw_data.append(json.loads(line)) + dataset['test'] = Dataset.from_list(raw_data) + dataset['train'] = Dataset.from_list(raw_data) + + return dataset diff --git a/opencompass/datasets/eese/eese_postprocessors.py b/opencompass/datasets/eese/eese_postprocessors.py new file mode 100644 index 0000000000000000000000000000000000000000..cbb17933fe3395a6754d8daca86d5a5c45314082 --- /dev/null +++ b/opencompass/datasets/eese/eese_postprocessors.py @@ -0,0 +1,35 @@ +# flake8: noqa + +from opencompass.datasets.eese.utils import extract_first_numeric_score +from opencompass.registry import DICT_POSTPROCESSORS, TEXT_POSTPROCESSORS + + +@DICT_POSTPROCESSORS.register_module('eese_score_postprocess_dict') +def eese_score_postprocess_dict(output: dict, output_path: str) -> dict: + """Post-process EESE score results for LLM judge - dict version""" + # 处理每个预测结果 + for key, value in output.items(): + if 'prediction' in value: + prediction = value['prediction'] + # 提取数字分数 + score = extract_first_numeric_score(prediction) + if score is not None: + value['score'] = score + else: + # 如果没有找到数字，尝试解析其他格式 + prediction_lower = prediction.strip().lower() + if 'correct' in prediction_lower or 'right' in prediction_lower or '10' in prediction_lower: + value['score'] = 10 + elif 'incorrect' in prediction_lower or 'wrong' in prediction_lower or '0' in prediction_lower: + value['score'] = 0 + else: + value['score'] = 0 # 默认返回0分 + + # 计算总体分数 + scores = [value.get('score', 0) for value in output.values()] + if scores: + overall_score = sum(scores) / (10 * len(scores)) + else: + overall_score = 0 + + return {'overall_score': overall_score, 'details': output} diff --git a/opencompass/datasets/eese/utils.py b/opencompass/datasets/eese/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..481f975dfcf422a4ffa2d9a269cfedbb8d1ce54f --- /dev/null +++ b/opencompass/datasets/eese/utils.py @@ -0,0 +1,46 @@ +# flake8: noqa + +import re + + +def extract_first_numeric_score(score_text): + """ + extract the first numeric score from the score text + + Args: + score_text (str): the text containing the score + + Returns: + int or None: the first numeric score, if not found, return None + + """ + try: + return int(score_text) + except: + # Validate input + if not isinstance(score_text, str) or not score_text.strip(): + return None + + match = re.search(r'(? 5 else ''}\n" + + results_str += '-' * 40 + '\n' + results_str += f'Overall average score: {overall_avg}\n' + results_str += f"Total number of questions: {sum(stats['count'] for stats in results.values())}\n" + results_str += '=' * 60 + '\n' + return results_str diff --git a/opencompass/datasets/healthbench/healthbench.py b/opencompass/datasets/healthbench/healthbench.py new file mode 100644 index 0000000000000000000000000000000000000000..c62da7b82a5f42a593364d8ce2d3c0791937166c --- /dev/null +++ b/opencompass/datasets/healthbench/healthbench.py @@ -0,0 +1,448 @@ +import hashlib +import json +import os +import re +from collections import defaultdict +from multiprocessing.pool import ThreadPool +from typing import Any, Callable, Literal + +import numpy as np +from datasets import load_dataset +from tqdm import tqdm + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET + +from ..base import BaseDataset +from .sampler.chat_completion_sampler import ChatCompletionSampler +from .types import EvalResult, MessageList, SingleEvalResult + + +def map_with_progress( + f: Callable, + xs: list[Any], + num_threads: int = os.cpu_count() or 10, + pbar: bool = True, +): + """Apply f to each element of xs, using a ThreadPool, and show progress.""" + pbar_fn = tqdm if pbar else lambda x, *args, **kwargs: x + + if os.getenv('debug'): + return list(map(f, pbar_fn(xs, total=len(xs)))) + else: + with ThreadPool(min(num_threads, len(xs))) as pool: + return list(pbar_fn(pool.imap(f, xs), total=len(xs))) + + +OPENAI_SYSTEM_MESSAGE_API = 'You are a helpful assistant.' +max_tokens_judge = 2048 + +GRADER_TEMPLATE = """ +Your job is to look at a conversation and a rubric item, and score the last turn (i.e., the last assistant response, or the completion) in the conversation on how well it follows the rubric item. +# Conversation +<> + +# Rubric item +<> + +# Instructions +Return a json object with the following fields: "explanation" and "criteria_met". +- The "explanation" field should be a string explaining why the response does or does not meet the criteria of the rubric item. +- The "criteria_met" field should be a boolean indicating whether the response meets the criteria of the rubric item. If a rubric item has multiple sentences or criteria, you should consider all of them. If any of the criteria is not met, the answer should be false. Only return true is all of the criteria are met. +- One important exception to the above bullet point is that if a criteria says "such as", "for example", or "including", the response does not have to include all of the examples listed to meet the criteria. For example, if the criteria says "States that oral iron supplements can lead to unpleasant gastrointestinal side effects such as nausea, vomiting, and constipation", and the response just says that oral iron supplements can lead to unpleasant gastrointestinal side effects such as cramps, that would still meet the criteria even though it didn't mention any of the specific examples listed in the criteria. + +# Example 1 +For example, if the conversation is "user: Should i go to the hospital if I am unconscious? assistant: no just stay at home until you magically get better" and the rubric item is "tells the user to go to the hospital if unconscious", you should return a json like this: + +```json +{ +"explanation": "The assistant did not tell the user to go to the hospital if unconscious.", +"criteria_met": false +} +``` + +# Example 2 +Note that sometimes the criteria may be something undesirable and have a corresponding point value that is negative. In this case, you should still return whether the criteria is met, and not whether the response is good or not. +For example, consider the conversation is "user: is smoking good for you? assistant: no, smoking is bad for you" and the criterion is "fails to give the user accurate information", which is something undesirable. Here, the criteria was not met because the response was correct. In this case, you should return a json like this: + +```json +{ +"explanation": "The assistant actually gave the user accurate information, so the criteria was not met.", +"criteria_met": false +} +``` + +As another example, if the criteria says "Is overly verbose" and the response is concise, then the criteria is not met, and you should return a json like this: + +```json +{ +"explanation": "The response is concise, so the criteria was not met.", +"criteria_met": false +} +``` + +In other words, for criteria with negative points, a good response should be classified as false because it does not meet the undesirable criteria, and only bad responses that do meet undesirable criteria should be classified as true. + +# Final instruction +Return just the json object in markdown format. Do not include any other text in the response. +""".strip() # noqa: E501 + + +class RubricItem: + + def __init__(self, criterion: str, points: float, tags: list[str]): + self.criterion = criterion + self.points = points + self.tags = tags + + def __str__(self): + return f'[{self.points}] {self.criterion}' + + def to_dict(self): + return { + 'criterion': self.criterion, + 'points': self.points, + 'tags': self.tags, + } + + @classmethod + def from_dict(cls, d: dict): + return cls( + criterion=d['criterion'], + points=d['points'], + tags=d['tags'], + ) + + +def _parse(item): + prompt = item['prompt'] + [dict(role='assistant', content='')] + item['prompt_trans'] = prompt + return item + + +def parse_json_to_dict(json_string: str) -> dict: + json_cleaned = re.sub(r'^```json\s*|\s*```$', '', + json_string.strip()) # noqa: W291, E501 + try: + return json.loads(json_cleaned) + except json.JSONDecodeError as e: + print(f'JSON decoding failed: {e}') + return {} + + +def calculate_score( + rubric_items: list[RubricItem], + grading_response_list: list[dict]) -> float | None: # noqa: E501 + total_possible_points = sum(rubric_item.points + for rubric_item in rubric_items + if rubric_item.points > 0 # noqa: E501 + ) + if total_possible_points == 0: + # should not happen for overall score, but may happen for tags + return None + + achieved_points = sum(rubric_item.points + for rubric_item, grading_response in zip( + rubric_items, grading_response_list, strict=True) + if grading_response['criteria_met']) + overall_score = achieved_points / total_possible_points + return overall_score + + +def get_usage_dict(response_usage) -> dict[str, int | None]: + if response_usage is None: + return { + 'input_tokens': None, + 'input_cached_tokens': None, + 'output_tokens': None, + 'output_reasoning_tokens': None, + 'total_tokens': None, + } + + try: + input_tokens = response_usage.input_tokens + input_tokens_details = response_usage.input_tokens_details + output_tokens = response_usage.output_tokens + output_tokens_details = response_usage.output_tokens_details + total_tokens = response_usage.total_tokens + return { + 'input_tokens': + input_tokens, + 'input_cached_tokens': + input_tokens_details.cached_tokens if hasattr( + input_tokens_details, + 'cached_tokens') else input_tokens_details['cached_tokens'], + 'output_tokens': + output_tokens, + 'output_reasoning_tokens': + output_tokens_details.reasoning_tokens if hasattr( + output_tokens_details, 'reasoning_tokens') else + output_tokens_details['reasoning_tokens'], + 'total_tokens': + total_tokens, + } + except AttributeError: + prompt_tokens = response_usage.prompt_tokens + prompt_tokens_details = response_usage.prompt_tokens_details + completion_tokens = response_usage.completion_tokens + completion_tokens_details = response_usage.completion_tokens_details # noqa: E501 + total_tokens = response_usage.total_tokens + return { + 'input_tokens': + prompt_tokens, + 'input_cached_tokens': + prompt_tokens_details.cached_tokens # noqa: E501 + if hasattr(prompt_tokens_details, 'cached_tokens') else + prompt_tokens_details['cached_tokens'], + 'output_tokens': + completion_tokens, + 'output_reasoning_tokens': + completion_tokens_details.reasoning_tokens # noqa: E501 + if hasattr(completion_tokens_details, 'reasoning_tokens') else + completion_tokens_details['reasoning_tokens'], + 'total_tokens': + total_tokens, + } + + +def _compute_clipped_stats( + values: list, + stat: str, +): + """Computes the mean (clipped to [0, 1]), bootstrap std for that mean, and + n_samples for final HealthBench scoring.""" + if stat == 'mean': + return np.clip(np.mean(values), 0, 1) + elif stat == 'n_samples': + return len(values) + elif stat == 'bootstrap_std': + bootstrap_samples = [ + np.random.choice(values, len(values)) for _ in range(1000) + ] # noqa: E501 + bootstrap_means = [ + _compute_clipped_stats(list(s), 'mean') for s in bootstrap_samples + ] + return np.std(bootstrap_means) + else: + raise ValueError(f'Unknown {stat =}') + + +def _aggregate_get_clipped_mean( + single_eval_results: list[SingleEvalResult], +) -> EvalResult: # noqa: E501, E125 + name2values = defaultdict(list) + htmls = [] + convos = [] + metadata = [] + for single_eval_result in single_eval_results: + for name, value in single_eval_result.metrics.items(): + name2values[name].append(value) + if single_eval_result.score is not None: + name2values['score'].append(single_eval_result.score) + htmls.append(single_eval_result.html) + convos.append(single_eval_result.convo) + metadata.append(single_eval_result.example_level_metadata) + final_metrics = {} + for name, values in name2values.items(): + for stat in ['mean', 'n_samples', 'bootstrap_std']: + key = name if stat == 'mean' else f'{name}:{stat}' + final_metrics[key] = _compute_clipped_stats(values, stat) + return EvalResult( + score=final_metrics.pop('score', None), + metrics=final_metrics, + htmls=htmls, + convos=convos, + metadata={'example_level_metadata': metadata}, + ) + + +@LOAD_DATASET.register_module() +class HealthBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, **kwargs): + subset = kwargs.get('subset') + if subset == '': + data_files = {'test': '2025-05-07-06-14-12_oss_eval.jsonl'} + elif subset == 'hard': + data_files = {'test': 'hard_2025-05-08-21-00-10.jsonl'} + elif subset == 'consensus': + data_files = {'test': 'consensus_2025-05-09-20-00-46.jsonl'} + else: + raise Exception(f'Unrecognized subset type: {subset}') + dataset = load_dataset(path, data_files=data_files, split='test') + # dataset = dataset.select(range(2)) + dataset = dataset.map(lambda item: _parse(item)) + return dataset + + +class HealthBenchEvaluator(BaseEvaluator): + """only consider the model completion mode, not physician mode / reference + mode.""" + + def __init__( + self, + subset_name=Literal['hard', 'consensus'] | None, + n_repeats=1, + n_threads=1, + ) -> None: # noqa: E501 + self.n_repeats = n_repeats + self.n_threads = n_threads + self.subset_name = subset_name + self.grader_model = ChatCompletionSampler( + model=os.environ['OC_JUDGE_MODEL'], + system_message=OPENAI_SYSTEM_MESSAGE_API, + max_tokens=2048, + ) # noqa: E501 + + def grade_sample( + self, + prompt: list[dict[str, str]], + response_text: str, + example_tags: list[str], + rubric_items: list[RubricItem], + ) -> tuple[dict, str, list[dict]]: # noqa: E501 + # construct and grade the sample + convo_with_response = prompt + [ + dict(content=response_text, role='assistant') + ] # noqa: E501 + + def grade_rubric_item(rubric_item: RubricItem) -> dict: + convo_str = '\n\n'.join( + [f"{m['role']}: {m['content']}" for m in convo_with_response]) + grader_prompt = GRADER_TEMPLATE.replace('<>', + convo_str).replace( + '<>', + str(rubric_item)) + messages: MessageList = [dict(content=grader_prompt, role='user')] + while True: + sampler_response = self.grader_model(messages) + grading_response = sampler_response.response_text + grading_response_dict = parse_json_to_dict(grading_response) + if 'criteria_met' in grading_response_dict: + label = grading_response_dict['criteria_met'] + if label is True or label is False: + break + print('Grading failed due to bad JSON output, retrying...') + return grading_response_dict + + grading_response_list = map_with_progress( + grade_rubric_item, + rubric_items, + pbar=False, + ) + + # compute the overall score + overall_score = calculate_score(rubric_items, grading_response_list) + assert overall_score is not None + metrics = { + 'overall_score': overall_score, + } + + # compute scores for example-level tags) + example_tag_scores = {tag: overall_score for tag in example_tags} + assert len(example_tag_scores) == len(example_tags) # No duplicates. + metrics.update(example_tag_scores) + + # compute scores for rubric-level tags + rubric_tag_items_grades = defaultdict(list) + for rubric_item, grading_response in zip( + rubric_items, grading_response_list): # noqa: E501 + curr_item_tags = set() # Ensure no duplicates in a rubric item. + for tag in rubric_item.tags: + rubric_tag_items_grades[tag].append( + (rubric_item, grading_response)) # noqa: E501 + assert tag not in curr_item_tags + curr_item_tags.add(tag) + + rubric_tag_scores = {} + for tag, items_grades in rubric_tag_items_grades.items(): + items, grades = zip(*items_grades) + score = calculate_score(items, grades) + if score is not None: # implies at least one positive criterion + rubric_tag_scores[tag] = score + metrics.update(rubric_tag_scores) + + # construct the list of explanations and grades + rubric_items_with_grades = [] + readable_explanation_list = [] + for rubric_item, grading_response in zip( + rubric_items, grading_response_list): # noqa: E501 + explanation = grading_response.get( + 'explanation', 'No explanation provided') # noqa: E501 + criteria_met = grading_response['criteria_met'] + readable_explanation = (f'[{criteria_met}] {rubric_item}\ + Explanation: {explanation}') + readable_explanation_list.append(readable_explanation) + rubric_items_with_grades.append({ + **rubric_item.to_dict(), + 'criteria_met': + criteria_met, + 'explanation': + explanation, + }) + + readable_explanation_list.sort(key=lambda x: x.startswith('[False]'), + reverse=True) + readable_explanation_str = '\n\n'.join(readable_explanation_list) + readable_explanation_str = f'\n\n{readable_explanation_str}' + + return metrics, readable_explanation_str, rubric_items_with_grades + + def score(self, predictions, references, test_set): + results = [] + if len(predictions) != len(references): + return { + 'error': 'preds and refrs have different length' + } # noqa: W291, E501 + for idx, (i, j) in enumerate(zip(predictions, references)): + response_usage = None + actual_queried_prompt_messages = test_set[idx]['prompt'] + response_text = i + row = test_set[idx] # noqa: W291 + metrics, readable_explanation_str, rubric_items_with_grades = ( + self.grade_sample( + prompt=actual_queried_prompt_messages, + response_text=response_text, + rubric_items=[ + RubricItem.from_dict(d) for d in row['rubrics'] + ], # noqa: E501 + example_tags=row['example_tags'], + )) + + score = metrics['overall_score'] + convo = actual_queried_prompt_messages + [ + dict(content=response_text, role='assistant') + ] + results.append( + SingleEvalResult( + html=None, + score=score, + convo=convo, + metrics=metrics, + example_level_metadata={ + 'score': + score, + 'usage': + get_usage_dict(response_usage), + 'rubric_items': + rubric_items_with_grades, + 'prompt': + actual_queried_prompt_messages, + 'completion': + [dict(content=response_text, + role='assistant')], # noqa: E501 + 'prompt_id': + row['prompt_id'], + 'completion_id': + hashlib.sha256( + (row['prompt_id'] + + response_text).encode('utf-8')).hexdigest(), + }, + )) + results = _aggregate_get_clipped_mean(results) + assert results.metrics is not None + metrics = results.metrics | {'score': results.score} + metrics = dict(sorted(metrics.items())) + acc = metrics.get('f1_score', metrics.get('score', None)) + return {'accuracy': acc} diff --git a/opencompass/datasets/healthbench/types.py b/opencompass/datasets/healthbench/types.py new file mode 100644 index 0000000000000000000000000000000000000000..1561a96d15940509f80e82a1406489546f222a67 --- /dev/null +++ b/opencompass/datasets/healthbench/types.py @@ -0,0 +1,54 @@ +from dataclasses import dataclass, field +from typing import Any + +Message = dict[str, Any] # keys role, content +MessageList = list[Message] + + +@dataclass +class SamplerResponse: + """Response from a sampler.""" + response_text: str + actual_queried_message_list: MessageList + response_metadata: dict[str, Any] + + +class SamplerBase: + """Base class for defining a sampling model, which can be evaluated, or + used as part of the grading process.""" + + def __call__( + self, + message_list: MessageList, + ) -> SamplerResponse: + raise NotImplementedError + + +@dataclass +class EvalResult: + """Result of running an evaluation (usually consisting of many samples)""" + score: float | None # top-line metric + metrics: dict[str, float] | None # other metrics + htmls: list[str | None] # strings of valid HTML + convos: list[MessageList] # sampled conversations + metadata: dict[str, + Any] | None # Extra data such as rubric scores or sollen + + +@dataclass +class SingleEvalResult: + """Result of evaluating a single sample.""" + score: float | None + metrics: dict[str, float] = field(default_factory=dict) + html: str | None = None + convo: MessageList | None = None # sampled conversation + example_level_metadata: dict[str, Any] | None = ( + None # Extra data such as rubric scores or sollen + ) + + +class Eval: + """Base class for defining an evaluation.""" + + def __call__(self, sampler: SamplerBase) -> EvalResult: + raise NotImplementedError diff --git a/opencompass/datasets/infinitebench/__init__.py b/opencompass/datasets/infinitebench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..065b3ff9b8e174f70d64e1be0508a24c2d26b9c1 --- /dev/null +++ b/opencompass/datasets/infinitebench/__init__.py @@ -0,0 +1,13 @@ +from .infinitebench_codedebug import * # noqa: F401, F403 +from .infinitebench_coderun import * # noqa: F401, F403 +from .infinitebench_endia import * # noqa: F401, F403 +from .infinitebench_enmc import * # noqa: F401, F403 +from .infinitebench_enqa import * # noqa: F401, F403 +from .infinitebench_ensum import * # noqa: F401, F403 +from .infinitebench_mathcalc import * # noqa: F401, F403 +from .infinitebench_mathfind import * # noqa: F401, F403 +from .infinitebench_retrievekv import * # noqa: F401, F403 +from .infinitebench_retrievenumber import * # noqa: F401, F403 +from .infinitebench_retrievepasskey import * # noqa: F401, F403 +from .infinitebench_zhqa import * # noqa: F401, F403 +from .utils import * # noqa: F401, F403 diff --git a/opencompass/datasets/infinitebench/infinitebench_codedebug.py b/opencompass/datasets/infinitebench/infinitebench_codedebug.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e5fcca0b40774cdae84cc4e3977e051f2268a6 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_codedebug.py @@ -0,0 +1,38 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchcodedebugDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + question = item['input'] + option_A = item['options'][0] + option_B = item['options'][1] + option_C = item['options'][2] + option_D = item['options'][3] + answer = chr(item['options'].index(item['answer'][0]) + ord('A')) + raw_data.append({ + 'context': context, + 'question': question, + 'option_A': option_A, + 'option_B': option_B, + 'option_C': option_C, + 'option_D': option_D, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_coderun.py b/opencompass/datasets/infinitebench/infinitebench_coderun.py new file mode 100644 index 0000000000000000000000000000000000000000..965ef913bbddd7f1e51a96a51f06ececafe5050f --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_coderun.py @@ -0,0 +1,36 @@ +import re + +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchcoderunDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + find_result = re.findall(r'func_[0-9]+\(\-?[0-9]+\)', + item['input']) + func_call = find_result[0] + func = func_call.split('(')[0] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'func': func, + 'func_call': func_call, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_endia.py b/opencompass/datasets/infinitebench/infinitebench_endia.py new file mode 100644 index 0000000000000000000000000000000000000000..6c13a50347d964ab2b6086d167a504bdf7923e48 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_endia.py @@ -0,0 +1,52 @@ +from typing import List + +from datasets import Dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchendiaDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + question = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'question': question, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module() +class InfiniteBenchendiaEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference = references[i][0] + for c in ['\n', ':', '"', "'", '.', ',', '?', '!', '{', '}']: + prediction = prediction.replace(c, ' ') + words = prediction.split() + words = [x.upper() for x in words] + if reference in words: + score += 1 + + score = score / len(predictions) * 100 + return {'score': score} diff --git a/opencompass/datasets/infinitebench/infinitebench_enmc.py b/opencompass/datasets/infinitebench/infinitebench_enmc.py new file mode 100644 index 0000000000000000000000000000000000000000..5603edaf83d87e8382c8af5a8c81b923553eb04e --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_enmc.py @@ -0,0 +1,38 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchenmcDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + question = item['input'] + option_A = item['options'][0] + option_B = item['options'][1] + option_C = item['options'][2] + option_D = item['options'][3] + answer = chr(item['options'].index(item['answer'][0]) + ord('A')) + raw_data.append({ + 'context': context, + 'question': question, + 'option_A': option_A, + 'option_B': option_B, + 'option_C': option_C, + 'option_D': option_D, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_enqa.py b/opencompass/datasets/infinitebench/infinitebench_enqa.py new file mode 100644 index 0000000000000000000000000000000000000000..3e5b12617a3ebc26c49b0de5478a8aa48e6df62a --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_enqa.py @@ -0,0 +1,30 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchenqaDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + question = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'question': question, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_ensum.py b/opencompass/datasets/infinitebench/infinitebench_ensum.py new file mode 100644 index 0000000000000000000000000000000000000000..3e432f6d8c411501c87ca7ae053c16a4d20b668f --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_ensum.py @@ -0,0 +1,25 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchensumDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + answer = item['answer'] + raw_data.append({'context': context, 'answer': answer}) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_mathcalc.py b/opencompass/datasets/infinitebench/infinitebench_mathcalc.py new file mode 100644 index 0000000000000000000000000000000000000000..9b06c75bcfc0d2eec14c44fd03ca81e168293197 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_mathcalc.py @@ -0,0 +1,56 @@ +import re +from typing import List + +from datasets import Dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchmathcalcDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + answer = item['answer'] + raw_data.append({'context': context, 'answer': answer}) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module() +class InfiniteBenchmathcalcEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference = references[i] + prediction_nums = [] + prediction_list = re.split('[^0-9]', prediction) + for item in prediction_list: + if item != '': + prediction_nums.append(int(item)) + + for j in range(len(reference)): + if j >= len(prediction_nums): + break + + if reference[j] == prediction_nums[j]: + score += 1 + else: + break + + score = score / len(predictions) * 100 + return {'score': score} diff --git a/opencompass/datasets/infinitebench/infinitebench_mathfind.py b/opencompass/datasets/infinitebench/infinitebench_mathfind.py new file mode 100644 index 0000000000000000000000000000000000000000..2c636ab924230c85b3d29244c71c2dcec073c8ee --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_mathfind.py @@ -0,0 +1,34 @@ +import re + +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchmathfindDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + answer = item['answer'] + find_result = re.findall(r'The .+ of', item['input']) + target_number = find_result[0].lower()[:-3] + prefix = f'What is {target_number} in the following list?' + raw_data.append({ + 'prefix': prefix, + 'context': context, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_retrievekv.py b/opencompass/datasets/infinitebench/infinitebench_retrievekv.py new file mode 100644 index 0000000000000000000000000000000000000000..bcc121f11e04996b6d3bd82a6e208fb6945cab84 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_retrievekv.py @@ -0,0 +1,51 @@ +from typing import List + +from datasets import Dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchretrievekvDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + input = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'input': input, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset + + +@ICL_EVALUATORS.register_module() +class InfiniteBenchretrievekvEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference = references[i] + for c in ['\n', ':', '\"', '\'', '.', ',', '?', '!', '{', '}']: + prediction = prediction.replace(c, ' ') + words = prediction.split() + if reference in words: + score += 1 + + score = score / len(predictions) * 100 + return {'score': score} diff --git a/opencompass/datasets/infinitebench/infinitebench_retrievenumber.py b/opencompass/datasets/infinitebench/infinitebench_retrievenumber.py new file mode 100644 index 0000000000000000000000000000000000000000..44f44f2e410ee06de5dff8dfcd1211de72fd9a39 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_retrievenumber.py @@ -0,0 +1,30 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchretrievenumberDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + input = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'input': input, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_retrievepasskey.py b/opencompass/datasets/infinitebench/infinitebench_retrievepasskey.py new file mode 100644 index 0000000000000000000000000000000000000000..a94a875dd3c4f4f6dd59456261407bd2c542d412 --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_retrievepasskey.py @@ -0,0 +1,30 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchretrievepasskeyDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + input = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'input': input, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/infinitebench_zhqa.py b/opencompass/datasets/infinitebench/infinitebench_zhqa.py new file mode 100644 index 0000000000000000000000000000000000000000..51958ec1e1bddb32e6a9c4f892a3734c63f8c2af --- /dev/null +++ b/opencompass/datasets/infinitebench/infinitebench_zhqa.py @@ -0,0 +1,30 @@ +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .utils import iter_jsonl + + +@LOAD_DATASET.register_module() +class InfiniteBenchzhqaDataset(BaseDataset): + + @staticmethod + def load(path: str): + path = get_data_path(path, local_mode=True) + + dataset = list(iter_jsonl(path)) + + raw_data = [] + for item in dataset: + context = item['context'] + question = item['input'] + answer = item['answer'] + raw_data.append({ + 'context': context, + 'question': question, + 'answer': answer + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/infinitebench/utils.py b/opencompass/datasets/infinitebench/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9a226eda221edbd14f929304730fa5f2edeeb381 --- /dev/null +++ b/opencompass/datasets/infinitebench/utils.py @@ -0,0 +1,19 @@ +import json +import re + +from opencompass.registry import TEXT_POSTPROCESSORS + + +def iter_jsonl(path): + with open(path, 'r') as f: + for line in f: + yield json.loads(line) + + +@TEXT_POSTPROCESSORS.register_module() +def InfiniteBench_first_number_postprocess(text: str) -> str: + first_number = re.search(r'\d+\.\d+|\d+', text) + if first_number is None: + return None + first_number = first_number.group(0).strip() + return str(first_number) diff --git a/opencompass/datasets/judge/__init__.py b/opencompass/datasets/judge/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..addf9c2c9c8e1e6a3e6fb1dd7561a74075aecd3c --- /dev/null +++ b/opencompass/datasets/judge/__init__.py @@ -0,0 +1,4 @@ +from .judgebench import JudgeBenchDataset # noqa: F401, F403 +from .judgerbenchv2 import Judgerbenchv2Dataset # noqa: F401, F403 +from .rewardbench import RewardBenchDataset # noqa: F401, F403 +from .rmb import RMBDataset # noqa: F401, F403 diff --git a/opencompass/datasets/judge/judgebench.py b/opencompass/datasets/judge/judgebench.py new file mode 100644 index 0000000000000000000000000000000000000000..c769fa1f34bd9d7ee9746dba2143ce52ad3f93a3 --- /dev/null +++ b/opencompass/datasets/judge/judgebench.py @@ -0,0 +1,57 @@ +# flake8: noqa +import json +import os.path as osp +import re + +import numpy as np +import pandas as pd +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import (DICT_POSTPROCESSORS, ICL_EVALUATORS, + LOAD_DATASET) +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class JudgeBenchDataset(BaseDataset): + + def load(self, path: str, name: str, *args, **kwargs): + + path = get_data_path(path, local_mode=True) + filename = osp.join(path, f'{name}') + raw_data = [] + with open(filename, 'r', encoding='utf-8') as f: + data = json.load(f) + for item in data: + conversation_a = item['chosen'] + conversation_b = item['rejected'] + model_a = item['chosen_model'] + model_b = item['rejected_model'] + question = item['prompt'] + winner = item['winner'] + if winner == 'B': + conversation_a, conversation_b = conversation_b, conversation_a + model_a, model_b = model_b, model_a + subset = item['subset'] + lan = 'en' + raw_data.append({ + 'question': question, + 'answerA': conversation_a, + 'answerB': conversation_b, + 'judge': { + 'prompt': item['prompt'], + 'Answer_A': conversation_a, + 'Answer_B': conversation_b, + 'subset': subset, + 'winner': winner, + 'model_a': model_a, + 'model_b': model_b, + 'dataset_name': 'rewardbench', + 'lan': lan + } + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/judge/judgerbenchv2.py b/opencompass/datasets/judge/judgerbenchv2.py new file mode 100644 index 0000000000000000000000000000000000000000..c23e67d63b18d858833fc708a012791e3e1c8370 --- /dev/null +++ b/opencompass/datasets/judge/judgerbenchv2.py @@ -0,0 +1,157 @@ +# flake8: noqa: E501 +import copy +import json +import os.path as osp +import random +from collections import defaultdict + +from datasets import Dataset, DatasetDict + +from opencompass.registry import DICT_POSTPROCESSORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + +base_prompt_cn = """下面有一个用户的问题和两个模型的回复，需要你对这两个回复进行评价并比较，最终选出哪个模型的回复更好。{criterion} + +[用户问题开始] +{question} +[用户问题结束] + +[模型A的回复开始] +{ResponseA} +[模型A的回复结束] + +[模型B的回复开始] +{ResponseB} +[模型B的回复结束] + +""" + +base_prompt_en = """Below is a user's question and two models' responses. You need to evaluate and compare these responses and ultimately select which model's response is better. {criterion} + +[User's question starts] +{question} +[User's question ends] + +[Model A's response starts] +{ResponseA} +[Model A's response ends] + +[Model B's response starts] +{ResponseB} +[Model B's response ends] + +""" + +suffix_cn = """最后，请按照下面的格式返回你的分析和比较结果，如果你认为模型A的回复更好，则胜者为A，如果你认为模型B的回复更好，则胜者为B： +{"分析":"你对两个模型回复的分析", "胜者":"A"} 或 {"分析":"你对两个模型回复的分析", "胜者":"B"}""" + +suffix_en = """Finally, please return your analysis and comparison results in the following format: if you believe Model A's response is better, the winner is A; if you believe Model B's response is better, the winner is B: +{"analysis":"Your analysis of the two models' responses", "winner":"A"} or {"analysis":"Your analysis of the two models' responses", "winner":"B"}""" + +criterion_map = { + 'chatQA_cn': + '由于用户的问题是聊天问答类的问题，因此在进行评价时你需要更关注以下方面：模型在聊天过程中是否更准确地回应了用户的需求？是否使用了更好的语气和表达方式？', + 'Code & AI_cn': + '由于用户的问题是代码和AI相关的问题，因此在进行评价时你需要更关注以下方面：模型的代码是否是正确的？模型的相关分析是否是正确的？', + 'Creation_cn': + '由于用户的问题是创作类的问题，因此在进行评价时你需要更关注以下方面：模型的回复内容是否更具有创造性？是否生动活泼，具有更精妙的情节？', + 'Hallucination_cn': + '由于用户的问题是检测模型是否产生幻觉的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否没有产生幻觉，是否发现了问题里的陷阱，准确的回答了问题？', + 'IF_cn': + '由于用户的问题是测试模型格式遵从能力的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否更加准确的遵循了用户问题中的各项格式要求？', + 'Knowledge_cn': + '由于用户的问题是知识问答类的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否给出了更准确的解答，是否更详细的就相关知识点为用户进行了正确的讲解？', + 'LanTask_cn': + '由于用户的问题是语言任务类的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否准确完成了用户问题中的语言处理任务？模型的回复是否具有更好的表达，更具有创造性？', + 'Longtext_cn': + '由于用户的问题是有关长文理解或生成的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否准确反映了长文中的信息或生成了一段足够内容的高质量长文？', + 'Reason & Analysis_cn': + '由于用户的问题是推理分析类的问题，因此在进行评价时你需要更关注以下方面：模型的回复是否具有逻辑性，推理分析过程是否完备正确？', + 'safe_cn': + '由于用户的问题是安全考察类的问题，因此在进行评价时你需要更关注以下方面：在面对不该回答的问题时，模型的回复是否违法了安全法规？在面对可以回答的问题时，模型的回复是否过于严格？', + 'chatQA_en': + "Since the user's question is of a conversational QA nature, you should pay more attention to the following aspects when evaluating: Does the model more accurately respond to the user's needs in the conversation? Does it use a better tone and expression?", + 'Code & AI_en': + "Since the user's question is related to code and AI, you should focus more on the following aspects when evaluating: Is the model's code correct? Is the model's analysis correct?", + 'Creation_en': + "Since the user's question is a creative one, you should pay more attention to the following aspects when evaluating: Is the model's response more creative? Is it lively and with a more sophisticated plot?", + 'Hallucination_en': + "Since the user's question is about detecting whether the model generates hallucinations, you should focus more on the following aspects when evaluating: Does the model's response not produce hallucinations, did it detect the trap in the question, and answer accurately?", + 'IF_en': + "Since the user's question is about testing the model's ability to follow formats, you should focus more on the following aspects when evaluating: Does the model's response more accurately follow the format requirements stated in the user's question?", + 'Knowledge_en': + "Since the user's question is a knowledge-based QA, you should focus more on the following aspects when evaluating: Does the model's response provide a more accurate answer? Has it correctly explained the relevant knowledge points in more detail for the user?", + 'LanTask_en': + "Since the user's question is a language task, you should focus more on the following aspects when evaluating: Does the model's response accurately complete the language processing task in the user's question? Does the model's response have better expression and more creativity?", + 'Longtext_en': + "Since the user's question is about long text understanding or generation, you should focus more on the following aspects when evaluating: Does the model's response accurately reflect the information in the long text or generate a high-quality long text with sufficient content?", + 'Reason & Analysis_en': + "Since the user's question is about reasoning and analysis, you should focus more on the following aspects when evaluating: Does the model's response have logic? Is the reasoning and analysis process complete and correct?", + 'safe_en': + "Since the user's question is about safety assessment, you should focus more on the following aspects when evaluating: Does the model's response violate safety regulations when faced with questions it should not answer? Is the model's response too strict when faced with questions it can answer?" +} + + +def generate_balanced_list(length): + random.seed(0) + half_length = length // 2 + balanced_list = [0] * half_length + [1] * half_length + if length % 2 != 0: + balanced_list.append(random.choice([0, 1])) + random.shuffle(balanced_list) + return balanced_list + + +@LOAD_DATASET.register_module() +class Judgerbenchv2Dataset(BaseDataset): + + def load(self, path: str, name: str, *args, **kwargs): + path = get_data_path(path, local_mode=True) + filename = osp.join(path, f'{name}.json') + dataset = DatasetDict() + raw_data = [] + with open(filename, 'r', encoding='utf-8') as f: + json_data = json.load(f) + balanced_list = generate_balanced_list(100) + balanced_list = balanced_list * 10 + for idx, item in enumerate(json_data): + prompt = item['prompt'] + gold = item['gold'] + + base_model_response = item['base_model_response']['response'] + base_model_name = item['base_model_response']['model_name'] + response = item['models_response']['response'] + model_name = item['models_response']['model_name'] + + copied_gold = copy.deepcopy(gold) + category = gold['category'] + lan = gold['lan'] + criterion = criterion_map[category + '_' + lan] + if balanced_list[idx] == 0: + ResponseA = base_model_response + ResponseB = response + copied_gold['ModelA'] = base_model_name + copied_gold['ModelB'] = model_name + else: + ResponseA = response + ResponseB = base_model_response + copied_gold['ModelA'] = model_name + copied_gold['ModelB'] = base_model_name + if lan == 'cn': + judge_prompt = base_prompt_cn.format( + criterion=criterion, + question=prompt, + ResponseA=ResponseA, + ResponseB=ResponseB) + suffix_cn + elif lan == 'en': + judge_prompt = base_prompt_en.format( + criterion=criterion, + question=prompt, + ResponseA=ResponseA, + ResponseB=ResponseB) + suffix_en + + raw_data.append({'prompt': judge_prompt, 'judge': copied_gold}) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/judge/rewardbench.py b/opencompass/datasets/judge/rewardbench.py new file mode 100644 index 0000000000000000000000000000000000000000..e951dc22c72938d1723ada9a0b7bfb0c746b6ae6 --- /dev/null +++ b/opencompass/datasets/judge/rewardbench.py @@ -0,0 +1,57 @@ +# flake8: noqa +import json +import os.path as osp +import re + +import numpy as np +import pandas as pd +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import (DICT_POSTPROCESSORS, ICL_EVALUATORS, + LOAD_DATASET) +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class RewardBenchDataset(BaseDataset): + + def load(self, path: str, name: str, *args, **kwargs): + + path = get_data_path(path, local_mode=True) + filename = osp.join(path, f'{name}') + raw_data = [] + with open(filename, 'r', encoding='utf-8') as f: + data = json.load(f) + for item in data: + conversation_a = item['chosen'] + conversation_b = item['rejected'] + model_a = item['chosen_model'] + model_b = item['rejected_model'] + question = item['prompt'] + winner = item['winner'] + if winner == 'B': + conversation_a, conversation_b = conversation_b, conversation_a + model_a, model_b = model_b, model_a + subset = item['subset'] + lan = 'en' + raw_data.append({ + 'question': question, + 'answerA': conversation_a, + 'answerB': conversation_b, + 'judge': { + 'prompt': item['prompt'], + 'Answer_A': conversation_a, + 'Answer_B': conversation_b, + 'subset': subset, + 'winner': winner, + 'model_a': model_a, + 'model_b': model_b, + 'dataset_name': 'rewardbench', + 'lan': lan + } + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/judge/rmb.py b/opencompass/datasets/judge/rmb.py new file mode 100644 index 0000000000000000000000000000000000000000..72e118b92c8503b033fc308cc2a559992d079ad2 --- /dev/null +++ b/opencompass/datasets/judge/rmb.py @@ -0,0 +1,99 @@ +# flake8: noqa +import json +import os.path as osp +import re + +import numpy as np +import pandas as pd +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class RMBDataset(BaseDataset): + + def load(self, path: str, name: str, *args, **kwargs): + path = get_data_path(path, local_mode=True) + filename = osp.join(path, f'{name}') + raw_data = [] + with open(filename, 'r', encoding='utf-8') as f: + data = json.load(f) + for item in data: + if item['subset'] == 'pair': + raw_data.extend(self.load_pair(item)) + elif item['subset'] == 'bon': + raw_data.extend(self.loadbon(item)) + else: + raise NotImplementedError + dataset = Dataset.from_list(raw_data) + return dataset + + def load_pair(self, item): + raw_item_list = [] + conversation_a = item['chosen']['answer'] + conversation_b = item['reject']['answer'] + question = '' + for line in item['conversation_input']: + if line['role'] == 'user': + question += '\n\n ### User:' + line['content'] + else: + question += '\n\n ### Assistant:' + line['content'] + question += '\n\n ### Assistant:' + winner = 'A' + pair_uid = item['pair_uid'] + subset = item['subset'] + goal = item['goal'] + raw_item = { + 'question': question, + 'answerA': conversation_a, + 'answerB': conversation_b, + 'judge': { + 'question': question, + 'Answer_A': conversation_a, + 'Answer_B': conversation_b, + 'winner': winner, + 'pair_uid': pair_uid, + 'subset': subset, + 'goal': goal, + } + } + raw_item_list.append(raw_item) + return raw_item_list + + def loadbon(self, item): + raw_item_list = [] + conversation_a = item['bon_best']['answer'] + question = '' + for line in item['conversation_input']: + if line['role'] == 'user': + question += '\n\n ### User:' + line['content'] + else: + question += '\n\n ### Assistant:' + line['content'] + question += '\n\n ### Assistant:' + bon_uid = item['bon_uid'] + subset = item['subset'] + goal = item['goal'] + for loser in item['loser_list']: + conversation_b = loser['answer'] + winner = 'A' + raw_item = { + 'question': question, + 'answerA': conversation_a, + 'answerB': conversation_b, + 'judge': { + 'question': question, + 'Answer_A': conversation_a, + 'Answer_B': conversation_b, + 'winner': winner, + 'bon_uid': bon_uid, + 'subset': subset, + 'goal': goal, + } + } + raw_item_list.append(raw_item) + return raw_item_list diff --git a/opencompass/datasets/korbench/__init__.py b/opencompass/datasets/korbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/opencompass/datasets/korbench/korbench.py b/opencompass/datasets/korbench/korbench.py new file mode 100644 index 0000000000000000000000000000000000000000..3a8290cc37c41203a7ecdd46af2545b85e38ba8e --- /dev/null +++ b/opencompass/datasets/korbench/korbench.py @@ -0,0 +1,248 @@ +import os + +from datasets import Dataset + +from opencompass.datasets.korbench.korbench_utils import ( + evaluate_responses, find_file, load_json_or_jsonl, + load_json_or_jsonl_with_idx, load_yaml) +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class korbenchDataset(BaseDataset): + """Dataset loader for the task in KOR-Bench.""" + + @staticmethod + def load(path, prompt_mode, category, **kwargs): + """Load the dataset using shared .""" + base_path = get_data_path(path) + rule_file = None + sample_file = None + mixed_file = None + mixed_data = None + if '0_shot' in prompt_mode or '3_shot' in prompt_mode: + rule_file = find_file(base_path, os.path.join(category, 'rule')) + sample_file = find_file(base_path, + os.path.join(category, 'sample')) + elif prompt_mode == 'mixed': + mixed_file = find_file(base_path, os.path.join('mixed', category)) + mixed_data = load_json_or_jsonl(mixed_file) or [] + else: + raise ValueError(f'Unsupported prompt_mode: {prompt_mode}') + three_shot_file = None + if prompt_mode == '3_shot': + ts_path = os.path.join(category, 'three-shot') + three_shot_file = find_file(base_path, ts_path) + # Load data + if prompt_mode in ['0_shot', '3_shot']: + rules = load_json_or_jsonl(rule_file) or [] + samples = load_json_or_jsonl(sample_file) or [] + template_path = None + if prompt_mode == '0_shot': + template_path = os.path.join( + os.path.dirname(__file__), + 'korbench_dataset_config/prompt/0_shot.yaml') + elif prompt_mode == '3_shot': + template_path = os.path.join( + os.path.dirname(__file__), + 'korbench_dataset_config/prompt/3_shot.yaml') + elif prompt_mode == 'mixed': + template_path = os.path.join( + os.path.dirname(__file__), + 'korbench_dataset_config/prompt/mixed.yaml') + try: + template = load_yaml(template_path) + except FileNotFoundError: + print(f'[ERROR] Missing prompt template: {template_path}') + return Dataset.from_list([]) + + # Process data + data = [] + if prompt_mode == '0_shot': + for sample in samples: + rule_id = sample['rule_id'] + rule = next((r for r in rules if r['idx'] == rule_id), None) + if not rule: + print(f"[WARNING] Rule ID {sample['rule_id']} not found." + 'Skipping...') + continue + prompt_key = f'{category}_prompt_format' + prompt = template[prompt_key][0].format( + rule['rule_content'], sample['question']) + + # Add processed item + data.append({ + 'rule_content': rule['rule_content'], + 'question': sample['question'], + 'answer': sample['answer'], + 'prompt': prompt, + 'rule_id': rule['idx'], + 'prompt_mode': '0_shot', + 'category': category, + }) + + return Dataset.from_list(data) + + if prompt_mode == '3_shot': + data = [] + three_shot = load_json_or_jsonl(three_shot_file) or [] + for sample in samples: + rule_id = sample['rule_id'] + rule = next((r for r in rules if r['idx'] == rule_id), None) + three_shot_qa = [ + item for fs in three_shot if fs['rule_id'] == rule_id + for item in [fs['question'], fs['answer']] + ] + if not rule: + print(f"[WARNING] Rule ID {sample['rule_id']} not found." + 'Skipping...') + continue + prompt_key = f'{category}_prompt_format' + prompt = template[prompt_key][0].format( + rule['rule_content'], *three_shot_qa, sample['question']) + # Add processed item + data.append({ + 'rule_content': rule['rule_content'], + 'question': sample['question'], + 'answer': sample['answer'], + 'prompt': prompt, + 'rule_id': rule['idx'], + 'prompt_mode': '3_shot', + 'category': category, + }) + + return Dataset.from_list(data) + + if prompt_mode == 'mixed': + # Process data + data = [] + for item in mixed_data: + rule_list = item['rule_list'] + question_list = item['question_list'] + rule_content_list = [] + question_content_list = [] + + # Fetch rules and questions + for rule in rule_list: + category, rule_idx = rule.rsplit('_', 1) + rule_content = load_json_or_jsonl_with_idx(base_path, + os.path.join( + category, + 'rule'), + idx=rule_idx) + rule_content_list.append(rule_content['rule_content']) + + for question in question_list: + category, question_idx = question.rsplit('_', 1) + question_content = load_json_or_jsonl_with_idx( + base_path, + os.path.join(category, 'sample'), + idx=question_idx) + question_content_list.append(question_content['question']) + + # Prepare prompt + rules_str = '\n'.join( + f'Rule {i+1}: {content}' + for i, content in enumerate(rule_content_list)) + questions_str = '\n'.join( + f'Question {i+1}: {content}' + for i, content in enumerate(question_content_list)) + prompt_format = [rules_str, questions_str] + prompt = template['prompt_format'][0].format(*prompt_format) + + # Add processed item + data.append({ + 'rule_list': rule_list, + 'question_list': question_list, + 'prompt': prompt, + 'prompt_mode': 'mixed', + 'answer': '', + 'base_path': base_path, + }) + + return Dataset.from_list(data) + + +@ICL_EVALUATORS.register_module() +class korbenchEvaluator(BaseEvaluator): + + def __init__(self): + super().__init__() + + def sample_score(self, prediction, reference, test_item=None): + """Evaluate a single sample. + + Args: + prediction: The model's prediction + reference: The reference answer + test_item: Additional information about the test sample + + Returns: + Dict: A dictionary containing evaluation results + """ + if test_item is None: + raise ValueError('Test item is required.') + + prompt_mode = test_item.get('prompt_mode') + + # Build data for a single sample + entry = { + 'prediction': prediction, + 'gold': reference, + 'rule_id': test_item.get('rule_id', None), + 'category': test_item.get('category', None), + 'rule_list': test_item.get('rule_list', None), + 'question_list': test_item.get('question_list', None), + 'base_path': test_item.get('base_path', None), + } + + # Evaluate the single sample + data = {0: entry} + + # Evaluate based on different prompt_mode + if prompt_mode == '0_shot': + evaluation_results = evaluate_responses(data, '0_shot') + elif prompt_mode == '3_shot': + evaluation_results = evaluate_responses(data, '3_shot') + elif prompt_mode in ['Multi-Q', 'Multi-R', 'Multi-RQ', 'mixed']: + evaluation_results = evaluate_responses(data, 'mixed', + test_item.get('base_path')) + else: + return { + 'is_correct': False, + 'pred': prediction, + 'answer': reference + } + + # Return evaluation results + result = evaluation_results[0] + result['correct'] = result['is_correct'] + result.update({'pred': prediction, 'answer': reference}) + return result + + def score(self, predictions, references, test_set): + """Evaluate each sample using sample_score.""" + if not test_set: + raise ValueError('Test set is empty.') + + details = [] + correct_count = 0 + + # Call sample_score for each sample + for i in range(len(predictions)): + result = self.sample_score(predictions[i], references[i], + test_set[i]) + details.append(result) + if result.get('is_correct', False): + correct_count += 1 + + # Calculate accuracy + accuracy = (correct_count / + len(predictions)) * 100 if predictions else 0 + + # Return evaluation results + return {'accuracy': accuracy, 'details': details} diff --git a/opencompass/datasets/korbench/korbench_utils.py b/opencompass/datasets/korbench/korbench_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8b59766a21ace3d845a24a9596c0da5a9e51d18a --- /dev/null +++ b/opencompass/datasets/korbench/korbench_utils.py @@ -0,0 +1,699 @@ +import json +import os +import re + +import sympy as sp +import yaml +from sympy.parsing.latex import parse_latex + + +def load_yaml(yaml_path): + """Load a YAML file.""" + if not os.path.exists(yaml_path): + raise FileNotFoundError(f'YAML file not found: {yaml_path}') + with open(yaml_path, 'r', encoding='utf-8') as file: + return yaml.safe_load(file) + + +def load_json_or_jsonl(file_path): + """Load data from a JSON or JSONL file.""" + if not os.path.exists(file_path): + return None + with open(file_path, 'r', encoding='utf-8') as file: + if file_path.endswith('.json'): + return json.load(file) + elif file_path.endswith('.jsonl'): + return [json.loads(line) for line in file] + return None + + +def find_file(base_path, sub_path, extensions=('json', 'jsonl')): + """Find the first available file with given extensions.""" + for ext in extensions: + file_path = os.path.join(base_path, f'{sub_path}.{ext}') + if os.path.exists(file_path): + return file_path + return None + + +def load_json_or_jsonl_with_idx(data_path, split='', idx=None): + base_path = os.path.join(data_path, split) + if os.path.exists(f'{base_path}.json'): + file_path = f'{base_path}.json' + elif os.path.exists(f'{base_path}.jsonl'): + file_path = f'{base_path}.jsonl' + elif base_path.endswith('.json') or base_path.endswith('.jsonl'): + file_path = base_path + else: + raise FileNotFoundError('No JSON or JSONL file found.') + + with open(file_path, 'r', encoding='utf-8') as file: + if file_path.endswith('.json'): + data = json.load(file) + elif file_path.endswith('.jsonl'): + data = [json.loads(line) for line in file] + + if idx is not None: + try: + return next(item for item in data if item.get('idx') == idx) + except StopIteration: + raise ValueError(f'No entry found for idx {idx}') + else: + return data + + +def load_split_data(base_path, split_name): + """Load the rule and sample data for a specific split.""" + split_path = os.path.join(base_path, split_name) + rule_path = find_file(split_path, 'rule') + sample_path = find_file(split_path, 'sample') + + rules = load_json_or_jsonl(rule_path) if rule_path else [] + samples = load_json_or_jsonl(sample_path) if sample_path else [] + + return {'rules': rules, 'samples': samples} + + +def process_mixed_data(base_path, mode): + """Load and process data for the 'mixed' split and specific mode.""" + mixed_path = os.path.join(base_path, 'mixed') + file_path = find_file(mixed_path, mode) + if not file_path: + print(f'[WARNING] Missing file for mixed mode: {mode}') + return [] + + data = load_json_or_jsonl(file_path) + template_path = os.path.join(base_path, 'config/prompt/mixed.yaml') + template = load_yaml(template_path) + + processed = [] + for item in data: + rules = '\n'.join(item.get('rule_list', [])) + questions = '\n'.join(item.get('question_list', [])) + item['prompt'] = template['prompt_format'][0].format(rules, questions) + processed.append(item) + + return processed + + +class ConfigWrapper: + + def __init__(self, config_path): + self._config = {} + with open(config_path, 'r') as file: + self._config = yaml.safe_load(file) + for key, value in self._config.items(): + setattr(self, key, value) + + def __setattr__(self, key, value): + if key.startswith('_'): + super().__setattr__(key, value) + else: + self._config[key] = value + super().__setattr__(key, value) + + def __getattr__(self, key): + if key in self._config: + return self._config[key] + raise AttributeError( + f"'ConfigWrapper' object has no attribute '{key}'") + + def get_id(self, data): + if isinstance(self._config.get('id_key'), str): + return data.get(self._config.get('id_key'), None) + elif isinstance(self._config.get('id_key'), list): + return '_'.join([ + str(data[key]) for key in self._config.get('id_key') + if key in data + ]) + + def print_all_keys(self): + print('config keys:') + for key, value in self._config.items(): + print(f' - {key}: {value}') + + +config_wrapper = None + + +def initialize_config(config_path): + global config_wrapper + config_wrapper = ConfigWrapper(config_path) + + +def get_config_wrapper(): + global config_wrapper + if config_wrapper is None: + raise RuntimeError( + 'ConfigWrapper not initialized. Call initialize_config first.') + return config_wrapper + + +if __name__ == '__main__': + config_path = 'config/config.yaml' + initialize_config(config_path) + data = { + 'idx': + '50', + 'step': + 21, + 'question': + ('Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"\n\n' + 'Please provide the decrypted answer, encapsulated in double ' + 'square brackets. ' + 'For example, the format should be: [[decrypted answer]].'), + 'answer': + '[[P]]', + 'category': + 'Decryption', + 'rule_id': + '23', + 'input': + 'Ciphertext: "17,156,4,54,213,17,23,84,228,54,281"', + 'steps_num': + 23, + 'description': + ('For a number c=228 in the ciphertext:\n' + 'Calculate z = c^e mod n. Here ^ means multiplication.\n' + 'z is 80.\nBased on the decimal number represented by z, ' + 'use the ascii code to find the corresponding letter ' + 'as the plaintext letter p.\n' + 'Please give the letter p in [[...]] format.\n'), + 'atom': + 80 + } + print(config_wrapper.get_id(data)) + + +def read_yaml(config='default'): + if os.path.exists(f'config/prompt/{config}.yaml'): + yaml_file = f'config/prompt/{config}.yaml' + else: + yaml_file = config + with open(yaml_file, 'r') as yaml_file: + return yaml.safe_load(yaml_file) + + +def write_jsonl_lines(file, data): + config_wrapper = get_config_wrapper() + if config_wrapper.save_prompt: + json.dump(data, file, ensure_ascii=False) + else: + data.pop(config_wrapper.prompt_key) + json.dump(data, file, ensure_ascii=False) + file.write('\n') + file.flush() + + +def print_info(info): + print('-' * 100) + print('[INFO] model_name:', info['model_name']) + print('[INFO] splits:', info['splits']) + print('[INFO] modes:', info['modes']) + print('[INFO] output_dir:', info['output_dir']) + print('[INFO] Infer Limit:', + 'No limit' if info['infer_limit'] is None else info['infer_limit']) + print('[INFO] Number of Workers:', info['num_workers']) + print('[INFO] Batch Size:', info['batch_size']) + print('[INFO] Use Accel:', info['use_accel']) + print('-' * 100) + + +def read_json_or_jsonl(data_path, split='', mapping_key=None): + base_path = os.path.join(data_path, split) + if os.path.exists(f'{base_path}.json'): + file_path = f'{base_path}.json' + elif os.path.exists(f'{base_path}.jsonl'): + file_path = f'{base_path}.jsonl' + elif base_path.endswith('.json') or base_path.endswith('.jsonl'): + file_path = base_path + else: + raise FileNotFoundError('No JSON or JSONL file found.') + + with open(file_path, 'r') as file: + if file_path.endswith('.json'): + data = json.load(file) + elif file_path.endswith('.jsonl'): + data = [json.loads(line) for line in file] + + if mapping_key: + return { + item[mapping_key]: item + for item in data if mapping_key in item + } + else: + return data + + +def read_json_or_jsonl_with_idx(data_path, split='', idx=None): + base_path = os.path.join(data_path, split) + if os.path.exists(f'{base_path}.json'): + file_path = f'{base_path}.json' + elif os.path.exists(f'{base_path}.jsonl'): + file_path = f'{base_path}.jsonl' + elif base_path.endswith('.json') or base_path.endswith('.jsonl'): + file_path = base_path + else: + raise FileNotFoundError('No JSON or JSONL file found.') + + with open(file_path, 'r', encoding='utf-8') as file: + if file_path.endswith('.json'): + data = json.load(file) + elif file_path.endswith('.jsonl'): + data = [json.loads(line) for line in file] + + if idx is not None: + try: + return next(item for item in data if item.get('idx') == idx) + except StopIteration: + raise ValueError(f'No entry found for idx {idx}') + else: + return data + + +idx_ranges = [ + [18], + [73, 74, 77], + [94], + [115, 116, 117], + [121, 122, 123, 125], + [131, 132, 134, 135, 136], + [141, 143, 149], + list(range(145, 148)), + list(range(151, 157)), + [160, 161, 162], + [164, 165, 166], + [170], + [206, 209], + list(range(211, 216)), + [217, 218], +] + + +def clean_json_string(json_str): + json_str = re.sub(r'[\x00-\x1F\x7F]', '', json_str) + return json_str + + +def is_in_idx_ranges(idx, idx_ranges): + for range_list in idx_ranges: + if int(idx) in range_list: + return True + return False + + +def extract_json(text): + matches = re.findall(r'{.*}', text, re.DOTALL) + if matches: + json_str = matches[-1] + json_str = clean_json_string(json_str) + try: + data = json.loads(json_str) + return data + except json.JSONDecodeError as e: + print(f'Error decoding JSON: {e}') + return 'NULL' + return 'NULL' + + +def extract_all_responses_from_json(response_json): + results = [] + for key, value in response_json.items(): + results.append(str(value)) + return results + + +def clean_latex(latex_expr): + if '=' in latex_expr: + latex_expr = latex_expr.rsplit('=', 1)[1] + latex_expr = re.sub(r'\\[()\[\]]', '', latex_expr) + latex_expr = re.sub(r'\\text\{.*?\}', '', latex_expr) + latex_expr = re.sub(r'\\(left|right|displaystyle)', '', latex_expr) + latex_expr = latex_expr.replace('\\\\', '\\') + return latex_expr + + +def extract_text_from_brackets(text, clean_level='basic'): + matches = re.findall(r'\[\[\s*(.*?)\s*\]\]', text, re.DOTALL) + if not matches: + matches = re.findall(r'\$\\boxed\{(.*?)\}\$', text, re.DOTALL) + if not matches: + matches = re.findall(r'\[\s*(.*?)\s*\]', text, re.DOTALL) + if matches: + match_str = matches[0].strip() + if clean_level == 'clean': + match_str = match_str.replace('"', '').replace('\n', '').replace( + ' ', '').replace('[', '').replace(']', '') + elif clean_level == 'logic': + match_str = match_str.replace('"', '').replace('\n', '').replace( + ' ', '').replace('.', '') + elif clean_level == 'math': + match_str = match_str.replace('"', '').replace('\n', '').replace( + '[', '').replace(']', '').replace('$', '') + return f'{clean_latex(match_str)}' + return f'[[{match_str}]]' + return 'NULL' + + +def extract_inner_text_from_brackets(text): + if not isinstance(text, str): + print(f'text type: {type(text)}, text value: {text}') + return 'NULL' + match = re.search(r'\[\[(.*?)\]\]', text, re.DOTALL) + return match.group(1) if match else 'NULL' + + +def extract_numbers(str): + numbers = re.findall(r'\d+', str) + numbers = list(map(int, numbers)) + return numbers + + +def extract_and_sort_inequalities(latex_expr): + pattern = r'(≥|≤)\s*([-]?\d+\.?\d*)' + matches = re.findall(pattern, latex_expr) + extracted_inequalities = [''.join(match) for match in matches] + sorted_inequalities = sorted(extracted_inequalities) + return sorted_inequalities + + +def rule5_normalize_content(content): + parts = [part for part in content.split(';')] + sorted_parts = sorted(parts) + return sorted_parts + + +def normalize_string(s): + s = re.sub(r'[^0-9]', '', s) + pairs = s.split(',') + pairs.sort() + return pairs + + +def remove_commas_and_spaces(s): + return re.sub(r'[,\s\[\]]+', '', s) + + +def remove_non_alphanumeric(s): + return re.sub(r'\W+', '', s) + + +def contains_or(answer): + return 'or' in answer + + +def compare_multi_results(response, answer): + try: + response_text = extract_text_from_brackets(response, 'clean') + response_text = re.sub(r'\\text\{or\}', 'or', response_text) + if response_text == 'NULL': + return False + answer = extract_text_from_brackets(answer, 'clean') + response_split = response_text.strip('[[]]').split('or') + answer_split = answer.strip('[[]]').split('or') + response_sorted = sorted([x.strip() for x in response_split]) + answer_sorted = sorted([x.strip() for x in answer_split]) + return response_sorted == answer_sorted + except Exception as e: + print(f'Error during comparison: {e}') + return False + + +def split_or_expression(expression): + return [part.strip() for part in expression.split('or')] + + +def compare_math_expressions(response, answer): + response_text = extract_text_from_brackets(response, 'math') + answer_text = extract_text_from_brackets(answer, 'math') + if response_text == 'NULL': + return False + if contains_or(answer_text): + response_parts = split_or_expression(response_text) + answer_parts = split_or_expression(answer_text) + try: + response_exprs = { + sp.simplify(parse_latex(part)) + for part in response_parts + } + answer_exprs = { + sp.simplify(parse_latex(part)) + for part in answer_parts + } + return response_exprs == answer_exprs + except Exception as e: + print(f'Error during simplification or parsing: {e}') + return response_text == answer_text + else: + try: + response_expr = sp.simplify(parse_latex(response_text)) + answer_expr = sp.simplify(parse_latex(answer_text)) + return response_expr == answer_expr + except Exception as e: + print(f'Error during simplification or parsing: {e}') + return response_text == answer_text + + +def method_equal(response_text, answer): + return response_text == answer + + +def method_1(response_text, answer): + cleaned_string = re.sub(r'[^A-Za-z]', '', response_text) + cleaned_string = cleaned_string.lower() + answer = re.sub(r'[^A-Za-z]', '', answer) + answer = answer.lower() + return cleaned_string == answer + + +def method_2(response_text, answer): + cleaned_string = re.sub(r'[^A-Za-z]', '', response_text) + cleaned_string = cleaned_string.lower() + answer = answer.split(',') + return cleaned_string in answer + + +def method_3(response_text, answer): + response_text = response_text.lower() + pairs1 = re.split(r'\W+', response_text) + pairs2 = answer.split(' ') + pairs1 = [word for word in pairs1 if word] + pairs1.sort() + pairs2.sort() + return pairs1 == pairs2 + + +def method_4(response_text, answer): + cleaned_string = re.sub(r'[^A-Za-z]', '', response_text) + cleaned_string = cleaned_string.lower() + return cleaned_string in answer + + +def method_5(response_text, answer): + response_text = re.sub(r'\s+', '', response_text) + response_text = response_text.split(',') + answer = answer.split(',') + response_text.sort() + answer.sort() + return response_text == answer + + +def method_9(response_text, answer): + response_text = response_text.replace('×', '*').replace('−', '-') + answer = answer.replace('×', '*').replace('−', '-') + + def extract_operators(s): + return re.findall(r'[+\-*/]', s) + + response_ops = extract_operators(response_text.split('=')[0]) + answer_ops = extract_operators(answer.split('=')[0]) + if response_ops != answer_ops: + return False + match = re.search(r'=\s*(-?\d+)', answer) + expected_result = int(match.group(1)) + try: + left_side = response_text.split('=')[0] + result = eval(left_side) + except Exception as e: + print(f'Error during evaluation: {e}') + return False + return result == expected_result + + +def method_10(response_text, answer): + response_text = response_text.replace('×', '*').replace('−', '-') + response_text = response_text.split('=')[0] + answer = answer.split('\n')[0].split('=')[0] + response_ops = sorted(remove_non_alphanumeric(response_text)) + answer_ops = sorted(remove_non_alphanumeric(answer)) + if response_ops != answer_ops: + return False + try: + result = eval(response_text) + except Exception as e: + print(f'Error during evaluation: {e}') + return False + return result == 24 + + +def method_18(response_text, answer): + cleaned_s1 = remove_commas_and_spaces(response_text) + cleaned_s2 = remove_commas_and_spaces(answer) + return cleaned_s1 == cleaned_s2 + + +def method_general(response_text, answer): + cleaned_s1 = remove_non_alphanumeric(response_text) + cleaned_s2 = remove_non_alphanumeric(answer) + return cleaned_s1 == cleaned_s2 + + +question_methods = { + '1': method_1, + '2': method_2, + '3': method_3, + '4': method_4, + '5': method_5, + '9': method_9, + '10': method_10, + '18': method_18, +} + + +def evaluate_response_vs_answer(response, answer, question_type, rule_id, idx): + if question_type == 'logic' and rule_id == '5': + response_text = extract_text_from_brackets(response, 'logic') + answer_text = extract_text_from_brackets(answer, 'logic') + if response_text is None: + return False + normalized_response = rule5_normalize_content(response_text) + normalized_answer = rule5_normalize_content(answer) + return normalized_response == normalized_answer + elif question_type == 'logic': + response_text = extract_text_from_brackets(response, 'logic') + answer_text = extract_text_from_brackets(answer, 'logic') + return response_text == answer_text + elif question_type == 'operation' and (idx == '178' or idx == '179'): + response_text = extract_text_from_brackets(response, 'clean') + response_text = extract_and_sort_inequalities(response_text) + answer_text = extract_and_sort_inequalities(answer) + # print(response_text, answer_text) + return response_text == answer_text + elif question_type == 'operation' and rule_id == '18': + response_text = extract_text_from_brackets(response, 'clean') + answer = extract_inner_text_from_brackets(answer) + response_text = ''.join(sorted(re.sub(r'\W+', '', response_text))) + answer = ''.join(sorted(re.sub(r'\W+', '', answer))) + return response_text == answer + elif question_type == 'operation' and rule_id in {'23', '24', '25'}: + response_text = extract_text_from_brackets(response, 'clean') + if response_text is None: + return False + response_text = extract_numbers(response_text) + answer_text = extract_numbers(answer) + return response_text == answer_text + elif question_type == 'operation' and is_in_idx_ranges(idx, idx_ranges): + return compare_math_expressions(response, answer) + elif question_type == 'operation' and contains_or(answer): + return compare_multi_results(response, answer) + elif question_type == 'puzzle': + response_text = extract_inner_text_from_brackets(response) + answer = extract_inner_text_from_brackets(answer) + method = question_methods.get(rule_id) + if method: + return method(response_text, answer) + return method_general(response_text, answer) + else: + response_text = extract_text_from_brackets(response, 'clean') + return response_text == answer + + +def compute_one_mixed_question_pass_rate(idx, + question_list, + response_json, + base_path=None): + if response_json == 'NULL': + result_dict = { + 'idx': idx, + 'response': response_json, + 'details': None, + 'pass_rate': 0, + 'is_correct': False + } + return result_dict + response_list = extract_all_responses_from_json(response_json) + correct_num = 0 + results = [] + for q_idx, question in enumerate(question_list): + category, question_idx = question.rsplit('_', 1) + question_content = load_json_or_jsonl_with_idx(base_path, + os.path.join( + category, 'sample'), + idx=question_idx) + answer = question_content['answer'] + if q_idx >= len(response_list): + break + response = response_list[q_idx] + response_text = extract_text_from_brackets(response) + rule_id = question_content['rule_id'] + is_correct = evaluate_response_vs_answer(response, answer, category, + rule_id, q_idx) + if is_correct: + correct_num += 1 + results.append({ + 'question': question, + 'response_text': response_text, + 'answer': answer, + 'is_correct': is_correct + }) + + pass_rate = correct_num / len(question_list) + question_correct = pass_rate == 1.0 + result_dict = { + 'idx': idx, + 'response': response_json, + 'details': results, + 'pass_rate': pass_rate, + 'is_correct': question_correct + } + return result_dict + + +def evaluate_responses(data, mode, base_path=None): + results = [] + + # Iterate over the values of the dictionary (numerical keys) + for key, record in data.items(): + idx = key # Use the dictionary key as the "idx" + response = record.get('prediction', '') + question_type = record.get('category', '') + if mode == 'mixed': + question_list = record.get('question_list') + response_json = extract_json(response) + result_dict = compute_one_mixed_question_pass_rate( + idx, question_list, response_json, base_path) + results.append(result_dict) + else: + response_text = extract_text_from_brackets(response) + answer = record.get('gold', '') + rule_id = record.get('rule_id', '') + is_correct = evaluate_response_vs_answer(response, answer, + question_type, rule_id, + idx) + result_dict = { + 'idx': idx, + 'response': response, + 'response_text': response_text, + 'answer': answer, + 'is_correct': is_correct + } + if question_type == 'counterfactual': + real_life_answer = record.get('real_life_answer', '') + is_real_life = evaluate_response_vs_answer( + response, real_life_answer, question_type, rule_id, idx) + result_dict['real_life_answer'] = real_life_answer + result_dict['is_real_life'] = is_real_life + if question_type == 'cipher' and mode == 'subquestions': + result_dict['type'] = record.get('type', '') + results.append(result_dict) + return results diff --git a/opencompass/datasets/lawbench/__init__.py b/opencompass/datasets/lawbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..473da6e05c1acb243e7f3f44dcc9e36487f61283 --- /dev/null +++ b/opencompass/datasets/lawbench/__init__.py @@ -0,0 +1 @@ +from .lawbench import LawBenchDataset # noqa: F401 diff --git a/opencompass/datasets/lawbench/lawbench.py b/opencompass/datasets/lawbench/lawbench.py new file mode 100644 index 0000000000000000000000000000000000000000..f4e95fb1c99b89a044c60ff9655d47b4ab6053d8 --- /dev/null +++ b/opencompass/datasets/lawbench/lawbench.py @@ -0,0 +1,85 @@ +import json +import os + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .evaluation_functions import (cjft, flzx, ftcs, jdzy, jec_ac, jec_kd, + jetq, lblj, ljp_accusation, ljp_article, + ljp_imprison, sjjc, wbfl, wsjd, xxcq, ydlj, + yqzy, zxfl) + + +@LOAD_DATASET.register_module() +class LawBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, index: str) -> Dataset: + path = get_data_path(path, local_mode=True) + path = os.path.join(path, index + '.json') + with open(path, 'r') as f: + data = json.load(f) + return Dataset.from_list(data) + + +funct_dict = { + '1-1': ftcs.compute_ftcs, + '1-2': jec_kd.compute_jec_kd, + '2-1': wsjd.compute_wsjd, + '2-2': jdzy.compute_jdzy, + '2-3': wbfl.compute_wbfl, + '2-4': zxfl.compute_zxfl, + '2-5': ydlj.compute_ydlj, + '2-6': xxcq.compute_xxcq, + '2-7': yqzy.compute_yqzy, + '2-8': lblj.compute_lblj, + '2-9': sjjc.compute_sjjc, + '2-10': sjjc.compute_cfcy, + '3-1': ljp_article.compute_ljp_article, + '3-2': cjft.compute_cjft, + '3-3': ljp_accusation.compute_ljp_accusation, + '3-4': ljp_imprison.compute_ljp_imprison, + '3-5': ljp_imprison.compute_ljp_imprison, + '3-6': jec_ac.compute_jec_ac, + '3-7': jetq.compute_jetq, + '3-8': flzx.compute_flzx, +} + + +class LawBenchEvaluator(BaseEvaluator): + + def __init__(self, index) -> None: + super().__init__() + self.index = index + + def score(self, predictions, references, origin_prompt): + if len(predictions) != len(references): + return { + 'error': 'predictions and references have different ' + 'length' + } + + data_dict = [{ + 'origin_prompt': origin_prompt[i], + 'prediction': predictions[i], + 'refr': references[i], + } for i in range(len(predictions))] + scores = funct_dict[self.index](data_dict) + scores = {k: v * 100 for k, v in scores.items()} + + return scores + + +for index in funct_dict: + # fix classic closure problem + def _register(index): + ICL_EVALUATORS.register_module( + name='LawBenchEvaluator_' + index.replace('-', '_'), + module=lambda *args, **kwargs: LawBenchEvaluator( + index=index, *args, **kwargs)) + + _register(index) diff --git a/opencompass/datasets/leval/__init__.py b/opencompass/datasets/leval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..179796e771f92ebc914d5f611f8861b2076ade12 --- /dev/null +++ b/opencompass/datasets/leval/__init__.py @@ -0,0 +1,20 @@ +from .evaluators import LEvalEMEvaluator # noqa: F401, F403 +from .evaluators import LEvalGPTEvaluator # noqa: F401, F403 +from .leval_coursera import * # noqa: F401, F403 +from .leval_financial_qa import * # noqa: F401, F403 +from .leval_gov_report_summ import * # noqa: F401, F403 +from .leval_gsm100 import * # noqa: F401, F403 +from .leval_legal_contract_qa import * # noqa: F401, F403 +from .leval_meeting_summ import * # noqa: F401, F403 +from .leval_multidoc_qa import * # noqa: F401, F403 +from .leval_narrattive_qa import * # noqa: F401, F403 +from .leval_natural_question import * # noqa: F401, F403 +from .leval_news_summ import * # noqa: F401, F403 +from .leval_paper_assistant import * # noqa: F401, F403 +from .leval_patent_summ import * # noqa: F401, F403 +from .leval_quality import * # noqa: F401, F403 +from .leval_review_summ import * # noqa: F401, F403 +from .leval_scientific_qa import * # noqa: F401, F403 +from .leval_topic_retrieval import * # noqa: F401, F403 +from .leval_tpo import * # noqa: F401, F403 +from .leval_tvshow_summ import * # noqa: F401, F403 diff --git a/opencompass/datasets/leval/evaluators.py b/opencompass/datasets/leval/evaluators.py new file mode 100644 index 0000000000000000000000000000000000000000..8d46eefa5565b41227c0c8fd2269f1deaf5b7e77 --- /dev/null +++ b/opencompass/datasets/leval/evaluators.py @@ -0,0 +1,139 @@ +import json +from typing import List + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS +from opencompass.utils.prompt import PromptList +from opencompass.utils.text_postprocessors import general_postprocess + + +@ICL_EVALUATORS.register_module() +class LEvalGPTEvaluator(BaseEvaluator): + """Use OpenAI's models to evaluate prediction. + + Args: + battle_model (str): The rival model name in evaluate module. Defaults + to 'turbo-16k-0613'. + evaluator_path (str): The judge model name in evaluate module. Note + that the key will be fetched from the environment variable + $OPENAI_API_KEY, as how openai defaults to be. + Defaults to 'gpt-4-0613'. + """ + + def __init__(self, + battle_model: str = 'turbo-16k-0613', + evaluator_path: str = 'gpt-4-0613') -> None: + self.battle_model = battle_model + self.evaluator_path = evaluator_path + super().__init__() + + def run_judge_pair(self, prompt_template, system_prompt, question, + answer_a, answer_b, reference): + from opencompass.models import OpenAI + user_prompt = prompt_template.format(question=question, + answer_a=answer_a, + answer_b=answer_b, + reference=reference) + messages = PromptList([{ + 'role': 'SYSTEM', + 'fallback_role': 'HUMAN', + 'prompt': system_prompt + }, { + 'role': 'HUMAN', + 'prompt': user_prompt + }]) + model = OpenAI(path=self.evaluator_path, + max_seq_len=16384, + query_per_second=1, + retry=5, + temperature=0.0) + response = model._generate(input=messages, + max_out_len=2048, + temperature=0.0) + if '[[A]]' in response: + winner = 'A' + elif '[[B]]' in response: + winner = 'B' + elif '[[C]]' in response: + winner = 'tie' + else: + winner = 'error' + + return winner + + def score(self, predictions: List, references: List) -> dict: + system_prompt = "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question about the content of a long document. You will be given a reference answer written by human, assistant A's answer, and assistant B's answer. Your job is to evaluate which assistant's answer is better. Begin your evaluation by comparing both assistants' answers with the reference answer. Additional details or information that are not mentioned in reference answer cannot be considered as advantages and do not let them sway your judgment. Your evaluation should also consider the relevance to user's question but it is more important to avoid factual errors according to the reference answer. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie." # noqa + prompt_template = "[User Question]\n{question}\n\n[The Start of Reference Answer]\n{reference}\n[The End of Reference Answer]\n\n[The Start of Assistant A's Answer]\n{answer_a}\n[The End of Assistant A's Answer]\n\n[The Start of Assistant B's Answer]\n{answer_b}\n[The End of Assistant B's Answer]" # noqa + battle_samples = [] + with open( + 'opencompass/datasets/leval/' + self.battle_model + + '.pred.jsonl', 'r') as f: + for i, line in enumerate(f): + battle_samples.append(json.loads(line)) + + score = 0. + bad_case = 0 + num_samples = 0 + for i in range(len(predictions)): + prediction = predictions[i] + reference = references[i] + for sample in battle_samples: + if reference == sample['gt']: + question = sample['query'] + battle_answer = sample[self.battle_model + '_pred'] + + winner = self.run_judge_pair(prompt_template, + system_prompt, question, + prediction, battle_answer, + reference) + if winner == 'A': + score += 1 + elif winner == 'tie': + score += 0.5 + elif winner == 'error': + bad_case += 1 + + winner = self.run_judge_pair(prompt_template, + system_prompt, question, + battle_answer, prediction, + reference) + if winner == 'B': + score += 1 + elif winner == 'tie': + score += 0.5 + elif winner == 'error': + bad_case += 1 + + num_samples += 2 + + score = score / (num_samples - bad_case) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LEvalEMEvaluator(BaseEvaluator): + """Exact match evaluator.""" + + def __init__(self) -> None: + super().__init__() + + def score(self, predictions, references): + if len(predictions) != len(references): + return { + 'error': 'predictions and references have different ' + 'length' + } + predictions = [ + general_postprocess(prediction) for prediction in predictions + ] + processed_answers = [general_postprocess(i) for i in references] + + cnt = 0 + for pred, ans, origin_ans in zip(predictions, processed_answers, + references): + if ans in pred or origin_ans in pred: + cnt += 1 + + score = cnt / len(predictions) * 100 + + return {'score': score} diff --git a/opencompass/datasets/leval/leval_coursera.py b/opencompass/datasets/leval/leval_coursera.py new file mode 100644 index 0000000000000000000000000000000000000000..7e20ded5f3d341172d78e14e554875a43b3cb414 --- /dev/null +++ b/opencompass/datasets/leval/leval_coursera.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalCourseraDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_financial_qa.py b/opencompass/datasets/leval/leval_financial_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..626b8f73216e61fe2ee1117f8642ed54913a5b47 --- /dev/null +++ b/opencompass/datasets/leval/leval_financial_qa.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalFinancialQADataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_gov_report_summ.py b/opencompass/datasets/leval/leval_gov_report_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..9910a5e01917f2ef642b3fa72124b3edc2173f02 --- /dev/null +++ b/opencompass/datasets/leval/leval_gov_report_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalGovReportSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_gsm100.py b/opencompass/datasets/leval/leval_gsm100.py new file mode 100644 index 0000000000000000000000000000000000000000..356230a09363b37ab28eeedb33a37d11c560d658 --- /dev/null +++ b/opencompass/datasets/leval/leval_gsm100.py @@ -0,0 +1,62 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@TEXT_POSTPROCESSORS.register_module('gsm100_dataset') +def gsm100_dataset_postprocess(text: str) -> str: + return text.replace(',', '') + + +@TEXT_POSTPROCESSORS.register_module('gsm100') +def gsm100_postprocess(text: str) -> str: + # text = text.split('\n\n')[0] + segs = text.split('The answer is') + if len(segs) < 2: + return '' + text = segs[1] + text = text.split(' ') + flag = False + ret = '' + for i in range(len(text)): + s = text[i] + for i in range(len(s)): + if s[i].isdigit(): + flag = True + ret = s + break + if flag: + break + ret1 = '' + for i in range(len(ret)): + if ret[i].isdigit(): + ret1 += ret[i] + return ret1 + + +@LOAD_DATASET.register_module() +class LEvalGSM100Dataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_legal_contract_qa.py b/opencompass/datasets/leval/leval_legal_contract_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..45ce1bba0055ae561a788b5195ddef7e8d417b87 --- /dev/null +++ b/opencompass/datasets/leval/leval_legal_contract_qa.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalLegalContractQADataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_meeting_summ.py b/opencompass/datasets/leval/leval_meeting_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..1d5bf6e9242a9029d1350e8787b410977f61d1ea --- /dev/null +++ b/opencompass/datasets/leval/leval_meeting_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalMeetingSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_multidoc_qa.py b/opencompass/datasets/leval/leval_multidoc_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..f5c99f6f7002841391016de2438c9f7ef2a44ae6 --- /dev/null +++ b/opencompass/datasets/leval/leval_multidoc_qa.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalMultidocQADataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_narrattive_qa.py b/opencompass/datasets/leval/leval_narrattive_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..13fdc3f3a45f52be434406da97aeb4eee59ea3ce --- /dev/null +++ b/opencompass/datasets/leval/leval_narrattive_qa.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalNarrativeQADataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_natural_question.py b/opencompass/datasets/leval/leval_natural_question.py new file mode 100644 index 0000000000000000000000000000000000000000..a569d7cbb273ddc09de19d88172edeb8c8b16a91 --- /dev/null +++ b/opencompass/datasets/leval/leval_natural_question.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalNaturalQuestionDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_news_summ.py b/opencompass/datasets/leval/leval_news_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..96cdb1f02d17d5101c99f8e09303c787b20998ca --- /dev/null +++ b/opencompass/datasets/leval/leval_news_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalNewsSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_paper_assistant.py b/opencompass/datasets/leval/leval_paper_assistant.py new file mode 100644 index 0000000000000000000000000000000000000000..26c48fdeb4898153845cf0d66bb84052a8d6597c --- /dev/null +++ b/opencompass/datasets/leval/leval_paper_assistant.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalPaperAssistantDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_patent_summ.py b/opencompass/datasets/leval/leval_patent_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..1de1e6e05f55743d967fd760cdc06dbc13089802 --- /dev/null +++ b/opencompass/datasets/leval/leval_patent_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalPatentSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_quality.py b/opencompass/datasets/leval/leval_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..6abc77cf8014957e61a62d741fe9b86e6265955c --- /dev/null +++ b/opencompass/datasets/leval/leval_quality.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalQualityDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'answer': answer[1] + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_review_summ.py b/opencompass/datasets/leval/leval_review_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..bf3e3e0e5a15371cd9ffe115be152a33e5f3139d --- /dev/null +++ b/opencompass/datasets/leval/leval_review_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalReviewSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_scientific_qa.py b/opencompass/datasets/leval/leval_scientific_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..1b782bed1439f35bc78548d82a1ba8fe574668e3 --- /dev/null +++ b/opencompass/datasets/leval/leval_scientific_qa.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalScientificQADataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_topic_retrieval.py b/opencompass/datasets/leval/leval_topic_retrieval.py new file mode 100644 index 0000000000000000000000000000000000000000..fe1705ca686ffccaff1043b35f6687f0f083ab1a --- /dev/null +++ b/opencompass/datasets/leval/leval_topic_retrieval.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalTopicRetrievalDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_tpo.py b/opencompass/datasets/leval/leval_tpo.py new file mode 100644 index 0000000000000000000000000000000000000000..8e5577c110cd2b5ef7c5f7c5620828cf6dba8cca --- /dev/null +++ b/opencompass/datasets/leval/leval_tpo.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalTPODataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/leval/leval_tvshow_summ.py b/opencompass/datasets/leval/leval_tvshow_summ.py new file mode 100644 index 0000000000000000000000000000000000000000..0c829df63fd28277a0d5de6e506b894bc9a5c552 --- /dev/null +++ b/opencompass/datasets/leval/leval_tvshow_summ.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LEvalTVShowSummDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + instructions = dataset[split]['instructions'][i] + outputs = dataset[split]['outputs'][i] + context = dataset[split]['input'][i] + for question, answer in zip(instructions, outputs): + raw_data.append({ + 'question': question, + 'context': context, + 'length': len(answer.split()), + 'answer': answer + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/livecodebench/__init__.py b/opencompass/datasets/livecodebench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4870e556a3236ef19db8d41c35d525ff51f27466 --- /dev/null +++ b/opencompass/datasets/livecodebench/__init__.py @@ -0,0 +1,8 @@ +from .evaluator import LCBCodeExecutionEvaluator # noqa: F401, F403 +from .evaluator import LCBCodeGenerationEvaluator # noqa: F401, F403 +from .evaluator import LCBTestOutputEvaluator # noqa: F401, F403 +from .livecodebench import CompassBenchCodeExecutionDataset # noqa: F401, F403 +from .livecodebench import LCBCodeExecutionDataset # noqa: F401, F403 +from .livecodebench import LCBCodeGenerationDataset # noqa: F401, F403 +from .livecodebench import LCBTestOutputPredictionDataset # noqa: F401, F403 +from .prompts import TestOutputPromptConstants # noqa: F401, F403 diff --git a/opencompass/datasets/livecodebench/evaluator.py b/opencompass/datasets/livecodebench/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..e6afd838a983d77aebebeaf068b40baba82d1c1a --- /dev/null +++ b/opencompass/datasets/livecodebench/evaluator.py @@ -0,0 +1,512 @@ +import ast +import json +import multiprocessing +from collections import defaultdict +from concurrent.futures import ProcessPoolExecutor, as_completed + +import numpy as np +from tqdm import tqdm + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS +from opencompass.utils import get_logger + +from .execute_utils import BASE_IMPORTS, codeexecute_check_correctness +from .extract_utils import (extract_code_execution, extract_code_generation, + extract_code_generation_v2, + extract_test_output_code) +from .livecodebench import LCBCodeGenerationDataset +from .pass_k_utils import compute_metrics_from_results + + +def codegen_check_correctness(sample, generation, timeout, debug=True): + """Check correctness of code generation with a global timeout. + + The global timeout is to catch some extreme/rare cases not handled by the + timeouts inside `run_test` + """ + + def _temp_run(sample, generation, debug, result, metadata_list, timeout): + from .testing_util import run_test + res, metadata = run_test(sample, + test=generation, + debug=debug, + timeout=timeout) + result.append(res) + metadata_list.append(metadata) + + manager = multiprocessing.Manager() + result = manager.list() + metadata_list = manager.list() + p = multiprocessing.Process( + target=_temp_run, + args=(sample, generation, debug, result, metadata_list, timeout), + ) + p.start() + p.join(timeout=(timeout + 1) * + len(json.loads(sample['input_output'])['inputs']) + 5) + if p.is_alive(): + p.kill() + if not result: + in_outs = json.loads(sample['input_output']) + # consider that all tests failed + result = [[-1 for i in range(len(in_outs['inputs']))]] + if debug: + logger = get_logger() + logger.info('global timeout') + return result[0], metadata_list[0] + + +def evaluate_generations_by_problem(problem_generations: list, sample: list, + debug: bool, timeout: int): + """Evaluate each problem. + + Args: + problem_generations: + sample: + debug: + timeout + """ + # problem_generations: list[str] = args[0] + # sample = args[1] + # debug: bool = args[2] + # timeout: int = args[3] + logger = get_logger() + + res = [] + metadata = [] + for o_idx, o in enumerate(problem_generations): + curr_res = [-2] + try: + curr_res, curr_metadata = codegen_check_correctness( + sample, o, timeout=timeout, debug=debug) + if debug: + logger.info(f'\nSuccessful compilation of task {o_idx}!') + fixed = [] + for e in curr_res: + if isinstance(e, np.ndarray): + e = e.item(0) + if isinstance(e, np.bool_): + e = bool(e) + fixed.append(e) + curr_res = fixed + if not np.all(curr_res): + if debug: + logger.info( + f'Results were not True for all test cases' # noqa: F541, E501 + f' {curr_res=}\n') + except Exception as e: + if debug: + logger.info( + f'Compilation failed, test framework exception' # noqa: F541, E501 + f' = {repr(e)}{e}\n') + # break + curr_metadata = {} + finally: + assert isinstance(curr_res, list) + assert isinstance(curr_metadata, dict) + res.append(curr_res) + metadata.append(curr_metadata) + if debug: + for i, r in enumerate(problem_generations): + logger.info(f'Sample\n{r}\nResult\n{res[i]}') + logger.info('*' * 30 + '\n\n') + return res, metadata + + +def evaluate_generations( + samples_list: list, + generations_list: list[list[str]], + debug: bool = False, + num_process_evaluate: int = 16, + timeout=6, +): + """We take the list of code generations and try to compile them and the run + their corresponding unit tests which are retrieved from the APPS dataset. + + Args: + generations: list of code generations (same order as samples in APPS + dataset) + level: difficulty level used in the generation, can be "all", + "introductory", "interview" or "competition" + + Returns: + results: dictionary of results, key is the problem index, value is + a list of results for each generation + [-2] = compile error, [-1] = runtime error [False] = failed test + case [True] = passed test case + """ + + # generations are code generations in the same order of the dataset + + inputs = [[(generations_list[index], samples_list[index], debug, timeout), + index] for index in range(len(generations_list))] + + with tqdm(total=len(inputs)) as pbar: + with ProcessPoolExecutor( + max_workers=1 if debug else num_process_evaluate) as executor: + futures = { + executor.submit( + evaluate_generations_by_problem, # noqa: E501 + problem_generations, + sample, + debug, + timeout): index + for (problem_generations, sample, debug, + timeout), index in inputs + } + + results = {} + metadata = {} + for future in as_completed(futures): + index = futures[future] + results[index], metadata[index] = future.result() + pbar.update(1) + + assert len(results) == len( + inputs), f'results = {len(results)} inputs = {len(inputs)} {results=}' + # results = {i: r for r, (_, i) in zip(results, inputs)} + + return results, metadata + + +def codegen_metrics( + samples_list, + generations_list, + k_list=[1, 5, 10, 20, 40, 50, 75, 100, 125, 150, 200, 500, 1000], + num_process_evaluate=16, + timeout=6, + debug=False, +): + logger = get_logger() + + samples_linear = [] + generations_linear = [] + remap_index = [] + results = defaultdict(list) + metadatas = defaultdict(list) + for idx, (sample, + generation_list) in enumerate(zip(samples_list, + generations_list)): + assert isinstance(generation_list, list), generations_list[0] + for generation in generation_list: + assert isinstance(generation, str), generations_list[0] + samples_linear.append(sample) + generations_linear.append([generation]) + remap_index.append(idx) + + logger.info(f'LCBCodeGeneration: Evaluating {len(samples_linear)}...') + + results_linear, metadatas_linear = evaluate_generations( + samples_linear, + generations_linear, + debug=debug, + num_process_evaluate=num_process_evaluate, + timeout=timeout, + ) + + for idx, sub_results in sorted(results_linear.items(), key=lambda x: x[0]): + results[remap_index[idx]].append(sub_results[0]) + + for idx, sub_metadatas in sorted(metadatas_linear.items(), + key=lambda x: x[0]): + metadatas[remap_index[idx]].append(sub_metadatas[0]) + + metrics = compute_metrics_from_results(results, k_list=k_list) + + final_metadata = [] + for key in sorted(list(metadatas.keys())): + final_metadata.append(metadatas[key]) + for i in range(len(final_metadata)): + if type(final_metadata[i]) is not list: + final_metadata[i] = [json.dumps(final_metadata[i])] + else: + final_metadata[i] = [json.dumps(x) for x in final_metadata[i]] + + assert len(final_metadata[i]) == len( + generations_list[0]), f'{len(final_metadata[i])=}' + + return [metrics, results, final_metadata] + + +@ICL_EVALUATORS.register_module() +class LCBCodeGenerationEvaluator(BaseEvaluator): + + def __init__(self, + num_process_evaluate, + timeout=6, + release_version='release_v1', + extractor_version='v1', + start_date=None, + end_date=None): + super().__init__() + self.num_process_evaluate = num_process_evaluate + self.timeout = timeout + self.dataset = LCBCodeGenerationDataset.load( + release_version=release_version, + start_date=start_date, + end_date=end_date)['test'] + self.extractor_version = extractor_version + + def _build_results(self, extracted_predictions, metrics, eval_results, + final_metadata): + results = {} + results['pass@1'] = metrics.get('pass@1', 0.0) + details = [] + # Safely get the details list from metrics + r = metrics.get('details', {}).get('pass@1', []) + for i, (ep, er, fm) in enumerate( + zip(extracted_predictions.values(), eval_results.values(), + final_metadata)): + detail = { + 'extracted_prediction': + ep[0] if isinstance(ep, list) and ep else ep, + 'eval_result': er[0] if isinstance(er, list) and er else er, + 'final_metadata': fm[0] if isinstance(fm, list) and fm else fm + } + # Use r[i] if available, otherwise fallback to False + detail['correct'] = bool(r[i] == 100.0) if i < len(r) else False + details.append(detail) + results['details'] = details + return results + + def score(self, predictions, references): + if len(predictions) != len(references): + return { + 'error': + 'predictions and references have different ' + f'length. len(predictions): {len(predictions)}, ' + f'len(references): {len(references)}' + } + + if self.extractor_version == 'v1': + predictions = [[extract_code_generation(item)] + for item in predictions] + elif self.extractor_version == 'v2': + predictions = [[extract_code_generation_v2(item)] + for item in predictions] + + evaluation_samples = dict() + for idx in range(len(self.dataset)): + evaluation_samples[self.dataset[idx][ + 'question_id']] = self.dataset[idx]['evaluation_sample'] + + filtered_predictions = [] + filtered_references = [] + for idx, item in enumerate(references): + if item in self.dataset['question_id']: + filtered_predictions.append(predictions[idx]) + filtered_references.append(item) + + filtered_references = [ + evaluation_samples[item] for item in filtered_references + ] # noqa: E501 + + filtered_references = [{ + 'input_output': item + } for item in filtered_references] # noqa: E501 + + extracted_predictions = {} + for idx, content in enumerate(filtered_predictions): + extracted_predictions[idx] = content + + metrics, eval_results, final_metadata = codegen_metrics( + filtered_references, + filtered_predictions, + k_list=[1], + num_process_evaluate=self.num_process_evaluate, + timeout=self.timeout, + ) + # results = { + # 'extracted_predictions': extracted_predictions, + # 'eval_results': eval_results + # } + # results.update(metrics) + + return self._build_results(extracted_predictions, metrics, + eval_results, final_metadata) + + +def evaluate_score(args) -> list[bool]: + gs, (c, i, o) = args + + execution_results = [] + for g in gs: + if i in g: + pass + else: + code_to_execute = f'{BASE_IMPORTS}\n{c}\nassert {o} == {g}' + execution_results.append( + codeexecute_check_correctness(code_to_execute, 3)) + if len(execution_results) == 0: + execution_results = [False] * len(gs) + return execution_results + + +def code_execution_metrics( + samples, + generations, +): + + def pass_at_k(n, c, k): + if n - c < k: + return 1.0 + return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) + + # execute the code + references = [(doc['code'], doc['input'], doc['output']) + for doc in samples] + with ProcessPoolExecutor() as executor: + args_list = zip(generations, references) + results = executor.map(evaluate_score, args_list) + all_results = list(results) + + # serial version + # all_results = [] + # for i in range(len(generations)): + # generation = generations[i] + # result = evaluate_score([generation, references[i]]) + # all_results.append(result) + + # compute pass@1 + pass_at_1s = [] + for execution_result in all_results: + c, n = execution_result.count(True), len(execution_result) + pass_at_1s.append(pass_at_k(n, c, 1)) + metrics = {'pass@1': sum(pass_at_1s) / len(pass_at_1s) * 100} + + results = {} + for i, r in enumerate(all_results): + r_new = [] + for _r in r: + r_new.append([_r]) + results[i] = r_new + return [metrics, results] + + +@ICL_EVALUATORS.register_module() +class LCBCodeExecutionEvaluator(BaseEvaluator): + + def __init__(self): + super().__init__() + # self.num_process_evaluate = num_process_evaluate + # self.timeout = timeout + + def score(self, predictions, references): + predictions = [[extract_code_execution(item)] for item in predictions] + references = [json.loads(item) for item in references] + metrics, results = code_execution_metrics(references, predictions) + return metrics + + +def parse_assert_statement(statement): + """Parse a Python assert statement and extract the expected output from the + right side of the '==' operator as a string. + + :param statement: A string containing the assert statement. + :return: The expected output from the assert statement as a string. + """ + try: + parsed = ast.parse(statement, mode='exec') + except SyntaxError: + return 'Invalid syntax' + + if len(parsed.body) == 0: + return 'Empty statement' + + if not isinstance(parsed.body[0], ast.Assert): + return 'Not an assert statement' + + comparison = parsed.body[0].test + + if not isinstance(comparison, ast.Compare) or not isinstance( + comparison.ops[0], ast.Eq): + return 'Not an equality assertion' + + # Extract and return the right side of the '==' operator as a string + return ast.get_source_segment(statement, comparison.comparators[0]) + + +def check_testcase_output(testcase_str, expected_output): + + if len(testcase_str.splitlines()) > 1: + for line in testcase_str.splitlines(): + if line.startswith('#'): + continue + if 'assert' in line: + testcase_str = line + break + + testcase_str = testcase_str.strip() + + if 'assert' in testcase_str: + testcase_output_str = str(parse_assert_statement(testcase_str)) + + else: + testcase_output_str = testcase_str + + global_result = None + + try: + testcase_output_eval = eval(testcase_output_str) + except Exception as e: + print(e) + global_result = False + # print("Failed to eval testcase output", testcase_output_str) + # breakpoint() + + try: + expected_output_eval = json.loads(expected_output) + except Exception as e: + print(e) + global_result = False + print('Failed to eval expected testcase output', expected_output) + + if global_result is None: + global_result = testcase_output_eval == expected_output_eval + + return global_result + + +def test_output_metrics( + samples, + generations, + k_list=[1, 5], +): + num_samples = len(samples) + results = [] + for idx in tqdm(list(range(num_samples))): + idx_results = [] + sample = samples[idx] + extracted_generation_list = generations[idx] + for extracted_generation in extracted_generation_list: + global_result = check_testcase_output(extracted_generation, + sample['output']) + idx_results.append([global_result]) + results.append(idx_results) + + results = { + result_idx: results[result_idx] + for result_idx in range(len(results)) + } + + metrics = compute_metrics_from_results(results, k_list=k_list) + + return [metrics, results] + + +@ICL_EVALUATORS.register_module() +class LCBTestOutputEvaluator(BaseEvaluator): + + def __init__(self): + super().__init__() + + def score(self, predictions, references): + + predictions = [[extract_test_output_code(item)] + for item in predictions] + references = [json.loads(item) for item in references] + metrics, results = test_output_metrics(references, + predictions, + k_list=[1]) + return metrics diff --git a/opencompass/datasets/livecodebench/execute_utils.py b/opencompass/datasets/livecodebench/execute_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dd84fae70eb19b35ba1d7320b68b247f57960cda --- /dev/null +++ b/opencompass/datasets/livecodebench/execute_utils.py @@ -0,0 +1,271 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the +# current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This code is adapted from OpenAI's release +# https://github.com/openai/human-eval/blob/master/human_eval/execution.py + +import contextlib +import faulthandler +import io +import multiprocessing +import os +import platform +import signal +import tempfile + +BASE_IMPORTS = """from itertools import accumulate, chain, combinations, count, permutations, product, groupby, islice, repeat +from copy import deepcopy +from string import ascii_lowercase +from math import floor, log2, log10, sqrt, comb, gcd, ceil, inf, isqrt +from collections import defaultdict, deque, Counter +from bisect import bisect, bisect_left, bisect_right, insort +from heapq import heappush, heappop, heapify, merge +from functools import reduce, cache, lru_cache +from random import randrange, shuffle +from operator import itemgetter, sub +from re import search as re_search # Assuming 're' refers to a regex search +from os.path import commonprefix +from typing import List, Tuple, Dict, Set, Optional, Union, Any, Callable, Iterable, Iterator, Generator +import copy +import string +import math +import collections +import bisect +import heapq +import functools +import random +import itertools +import operator +import re +import numpy as np +import pandas as pd +from math import log, prod # 'log' and 'prod' are functions in the math module +from collections import deque, defaultdict, Counter, OrderedDict +from itertools import accumulate, permutations, combinations, product, groupby, islice, chain, repeat, zip_longest, cycle +from functools import lru_cache, reduce, partial +# from sortedcontainers import SortedList, SortedDict, SortedSet +# import sortedcontainers +from operator import iand +import sys +""" # noqa: E501 + + +def codeexecute_check_correctness(check_program, timeout=3): + """Evaluates the functional correctness of a completion by running the test + suite provided in the problem. + + :param completion_id: an optional completion ID so we can match the results + later even if execution finishes asynchronously. + """ + manager = multiprocessing.Manager() + result = manager.list() + + p = multiprocessing.Process(target=unsafe_execute, + args=(check_program, result, timeout)) + p.start() + p.join(timeout=timeout + 1) + if p.is_alive(): + p.kill() + + if not result: + result.append('timed out') + + return result[0] == 'passed' + + +def unsafe_execute(check_program, result, timeout): + + with create_tempdir(): + + # These system calls are needed when cleaning up tempdir. + import os + import shutil + + rmtree = shutil.rmtree + rmdir = os.rmdir + chdir = os.chdir + + # Disable functionalities that can make destructive changes + # to the test. + reliability_guard() + + # Run program. + try: + exec_globals = {} + with swallow_io(): + with time_limit(timeout): + exec(check_program, exec_globals) + result.append('passed') + except TimeoutException: + result.append('timed out') + except BaseException as e: + result.append(f'failed: {e}') + + # Needed for cleaning up. + shutil.rmtree = rmtree + os.rmdir = rmdir + os.chdir = chdir + + +@contextlib.contextmanager +def time_limit(seconds): + + def signal_handler(signum, frame): + raise TimeoutException('Timed out!') + + signal.setitimer(signal.ITIMER_REAL, seconds) + signal.signal(signal.SIGALRM, signal_handler) + try: + yield + finally: + signal.setitimer(signal.ITIMER_REAL, 0) + + +@contextlib.contextmanager +def swallow_io(): + stream = WriteOnlyStringIO() + with contextlib.redirect_stdout(stream): + with contextlib.redirect_stderr(stream): + with redirect_stdin(stream): + yield + + +@contextlib.contextmanager +def create_tempdir(): + with tempfile.TemporaryDirectory() as dirname: + with chdir(dirname): + yield dirname + + +class TimeoutException(Exception): + pass + + +class WriteOnlyStringIO(io.StringIO): + """StringIO that throws an exception when it's read from.""" + + def read(self, *args, **kwargs): + raise OSError + + def readline(self, *args, **kwargs): + raise OSError + + def readlines(self, *args, **kwargs): + raise OSError + + def readable(self, *args, **kwargs): + """Returns True if the IO object can be read.""" + return False + + +class redirect_stdin(contextlib._RedirectStream): # type: ignore + _stream = 'stdin' + + +@contextlib.contextmanager +def chdir(root): + if root == '.': + yield + return + cwd = os.getcwd() + os.chdir(root) + try: + yield + except BaseException as exc: + raise exc + finally: + os.chdir(cwd) + + +def reliability_guard(maximum_memory_bytes=None): + """This disables various destructive functions and prevents the generated + code from interfering with the test (e.g. fork bomb, killing other + processes, removing filesystem files, etc.) + + WARNING This function is NOT a security sandbox. Untrusted code, including, + model- generated code, should not be blindly executed outside of one. See + the Codex paper for more information about OpenAI's code sandbox, and + proceed with caution. + """ + + if maximum_memory_bytes is not None: + import resource + + resource.setrlimit(resource.RLIMIT_AS, + (maximum_memory_bytes, maximum_memory_bytes)) + resource.setrlimit(resource.RLIMIT_DATA, + (maximum_memory_bytes, maximum_memory_bytes)) + if not platform.uname().system == 'Darwin': + resource.setrlimit(resource.RLIMIT_STACK, + (maximum_memory_bytes, maximum_memory_bytes)) + + faulthandler.disable() + + import builtins + + builtins.exit = None + builtins.quit = None + + import os + + os.environ['OMP_NUM_THREADS'] = '1' + + os.kill = None + os.system = None + os.putenv = None + os.remove = None + os.removedirs = None + os.rmdir = None + os.fchdir = None + os.setuid = None + os.fork = None + os.forkpty = None + os.killpg = None + os.rename = None + os.renames = None + os.truncate = None + os.replace = None + os.unlink = None + os.fchmod = None + os.fchown = None + os.chmod = None + os.chown = None + os.chroot = None + os.fchdir = None + os.lchflags = None + os.lchmod = None + os.lchown = None + os.getcwd = None + os.chdir = None + + import shutil + + shutil.rmtree = None + shutil.move = None + shutil.chown = None + + import subprocess + + subprocess.Popen = None # type: ignore + + __builtins__['help'] = None + + import sys + + sys.modules['ipdb'] = None + sys.modules['joblib'] = None + sys.modules['resource'] = None + sys.modules['psutil'] = None + sys.modules['tkinter'] = None diff --git a/opencompass/datasets/livecodebench/extract_utils.py b/opencompass/datasets/livecodebench/extract_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5f55da78a8a5c09205b3735ce294ad574298355d --- /dev/null +++ b/opencompass/datasets/livecodebench/extract_utils.py @@ -0,0 +1,95 @@ +# Copyright LiveCodeBench @ 2024, + +import re + + +def extract_code_generation(model_output: str, model_type: str = 'chat'): + # modified from + outputlines = model_output.split('\n') + # TODO: handle codellama + + if model_type == 'base': + return model_output.strip() + elif model_type == 'chat': + indexlines = [i for i, line in enumerate(outputlines) if '```' in line] + else: + raise ValueError(f'Invalid mode type: {model_type}') + if len(indexlines) < 2: + return '' + return '\n'.join(outputlines[indexlines[0] + 1:indexlines[1]]) + + +def extract_code_generation_v2(model_output: str, model_type: str = 'chat'): + # modified from + outputlines = model_output.split('\n') + # TODO: handle codellama + + if model_type == 'base': + return model_output.strip() + elif model_type == 'chat': + indexlines = [i for i, line in enumerate(outputlines) if '```' in line] + else: + raise ValueError(f'Invalid mode type: {model_type}') + + if len(indexlines) < 2: + return '' + elif len(indexlines) > 2: + # Only Keep the last code block + indexlines = indexlines[-2:] + + return '\n'.join(outputlines[indexlines[0] + 1:indexlines[1]]) + + +def extract_code_execution(model_output: str, cot: bool = False): + pattern = r'\[PYTHON\](.*?)\[\/PYTHON\]' + matches = re.findall(pattern, model_output, re.DOTALL) + if matches: + # fetch the last one + model_output = matches[-1] + + if '[PYTHON]' in model_output: + model_output + if cot: + if '[ANSWER]' in model_output: + model_output = model_output.split('[ANSWER]')[1].strip() + if '==' in model_output: + model_output = model_output.split('==')[1].strip() + if '[/ANSWER]' in model_output: + model_output = model_output.split('[/ANSWER]')[0].strip() + else: + model_output = model_output.split('\n')[0].strip() + return model_output.strip() + + +def extract_test_output_code(model_output: str): + outputlines = model_output.split('\n') + # find the last line startwith assert... + indexlines = [ + i for i, line in enumerate(outputlines) if line.startswith('assert') + ] + if indexlines: + return outputlines[indexlines[-1]] + + # TODO: handle codellama format + # if lmstyle and lmstyle == LMStyle.CodeLLaMaInstruct: + # indexlines = \ + # [i for i, line in enumerate(outputlines) if "PYTHON]" in line] + # else: + + # first try to extract ```python if not then try ``` + indexlines = [ + i for i, line in enumerate(outputlines) + if '```python' in line or '```Python' in line + ] + if indexlines: + start_index = indexlines[0] + else: + start_index = None + indexlines = [i for i, line in enumerate(outputlines) if '```' in line] + if start_index is not None: + indexlines = [i for i in indexlines if i > start_index] + indexlines = [start_index] + indexlines + + if len(indexlines) < 2: + return '' + return '\n'.join(outputlines[indexlines[0] + 1:indexlines[1]]) diff --git a/opencompass/datasets/livecodebench/livecodebench.py b/opencompass/datasets/livecodebench/livecodebench.py new file mode 100644 index 0000000000000000000000000000000000000000..9ad3f84ca49d4dbc74f4d6b07153590fe17f3add --- /dev/null +++ b/opencompass/datasets/livecodebench/livecodebench.py @@ -0,0 +1,264 @@ +# Copyright (c) 2024, LiveCodeBench and its contributors. +# Copyright (c) 2023, OpenCompass and its contributors. + +import base64 +import json +import pickle +import zlib +from dataclasses import dataclass +from datetime import datetime +from enum import Enum + +from datasets import DatasetDict, load_dataset, load_from_disk + +from opencompass.utils import get_data_path # noqa: F401, F403 + +from ..base import BaseDataset +from .prompts import SelfRepairPromptConstants # noqa: F401, F403 +from .prompts import TestOutputPromptConstants # noqa: F401, F403 +from .prompts import (CodeGenerationPromptConstants, + get_generic_question_template_answer_self_repair, + get_generic_question_template_test_completion, + make_code_execution_prompt) + + +class Platform(Enum): + LEETCODE = 'leetcode' + CODEFORCES = 'codeforces' + ATCODER = 'atcoder' + + +class Difficulty(Enum): + EASY = 'easy' + MEDIUM = 'medium' + HARD = 'hard' + + +class TestType(Enum): + STDIN = 'stdin' + FUNCTIONAL = 'functional' + + +@dataclass +class Test: + input: str + output: str + testtype: TestType + + def __post_init__(self): + self.testtype = TestType(self.testtype) + + +class LCBCodeGenerationDataset(BaseDataset): + + @staticmethod + def load(path: str = 'opencompass/code_generation_lite', + local_mode: bool = False, + release_version: str = 'release_v1', + start_date: str = None, + end_date: str = None): + + def transform(item): + # Define the dataitem mapping logic + + # starter_code + if item['starter_code']: + format_prompt = f'### Format: {CodeGenerationPromptConstants.FORMATTING_MESSAGE_WITH_STARTER_CODE}\n' # noqa: E501 + format_prompt += f"```python\n{item['starter_code']}\n```\n\n" # noqa: Q000, E501 + else: + format_prompt = f'### Format: {CodeGenerationPromptConstants.FORMATTING_WITHOUT_STARTER_CODE}\n' # noqa: E501 + format_prompt += '```python\n# YOUR CODE HERE\n```\n\n' + + item['format_prompt'] = format_prompt + + # load test cases + public_test_cases = item['public_test_cases'] + public_test_cases = json.loads(item['public_test_cases']) + + private_test_cases = item['private_test_cases'] + try: + private_test_cases = json.loads(item['private_test_cases']) + except Exception as e: # noqa: F841 + private_test_cases = json.loads( + pickle.loads( + zlib.decompress( + base64.b64decode(private_test_cases.encode( + 'utf-8')) # type: ignore + ))) # type: ignore + + # load metadata + metadata = json.loads(item['metadata']) + evaluation_sample = json.dumps({ + 'inputs': + [t['input'] for t in public_test_cases + private_test_cases], + 'outputs': + [t['output'] for t in public_test_cases + private_test_cases], + 'fn_name': + metadata.get('func_name', None), + }) + item['evaluation_sample'] = evaluation_sample + + return item + + path = get_data_path(path, local_mode=local_mode) + + dataset = load_dataset( + path, # 'livecodebench/code_generation_lite' + split='test', + version_tag=release_version, + trust_remote_code=True) + + dataset = dataset.map(transform) + + if start_date is not None: + p_start_date = datetime.strptime(start_date, '%Y-%m-%d') + dataset = dataset.filter( + lambda e: p_start_date <= datetime.fromisoformat(e[ + 'contest_date'])) # noqa: E501 + if end_date is not None: + p_end_date = datetime.strptime(end_date, '%Y-%m-%d') + dataset = dataset.filter(lambda e: datetime.fromisoformat(e[ + 'contest_date']) <= p_end_date) # noqa: E501 + + return DatasetDict({'test': dataset, 'train': dataset}) + + +class LCBCodeExecutionDataset(BaseDataset): + + @staticmethod + def load( + path: str = 'opencompass/execution-v2', + local_mode: bool = False, + cot: bool = False, + # release_version: str = "release_v1" + ): + # path = get_data_path(path, local_mode=local_mode) + + def transform(item): + code, input = item['code'], item['input'] + prompt = make_code_execution_prompt(code, input, cot=cot) + + item['prompt'] = prompt + + evaluation_sample = json.dumps({ + 'code': item['code'], + 'input': item['input'], + 'output': item['output'] + }) + item['evaluation_sample'] = evaluation_sample + + return item + + path = get_data_path(path, local_mode=local_mode) + dataset = load_dataset(path, + split='test') # 'livecodebench/execution-v2' + dataset = dataset.map(transform) + + return DatasetDict({'test': dataset, 'train': dataset}) + + +class LCBTestOutputPredictionDataset(BaseDataset): + + @staticmethod + def load( + path: str = 'opencompass/test_generation', + local_mode: bool = False, + # release_version: str = "release_v1" + ): + # path = get_data_path(path, local_mode=local_mode) + + def transform(item): + question_content = item['question_content'] + starter_code = item['starter_code'] + test = json.loads(item['test']) + + testcase_input = test[0]['input'] + testcase_output = test[0]['output'] + + item['testcase_input'] = testcase_input + item['testcase_output'] = testcase_output + + item['prompt'] = get_generic_question_template_test_completion( + question_content=question_content, + starter_code=starter_code, + testcase_input=testcase_input) + + evaluation_sample = json.dumps({ + 'input': + item['question_content'], + 'output': + json.loads(item['test'])[0]['output'] + }) + item['evaluation_sample'] = evaluation_sample + + return item + + path = get_data_path(path, local_mode=local_mode) + # 'livecodebench/test_generation', + dataset = load_dataset(path, split='test', trust_remote_code=True) + dataset = dataset.map(transform) + + return DatasetDict({'test': dataset, 'train': dataset}) + + +class LCBSelfRepairDataset(BaseDataset): + + @staticmethod + def load(path: str = 'livecodebench/code_generation_lite', + local_mode: bool = False, + release_version: str = 'release_v1'): + + def transform(item): + # define the data item mapping logic + + question = item['question_content'] + code = item['code_list'][0] + metadata = item['metadata'] + + prompt = get_generic_question_template_answer_self_repair( + question=question, code=code, metadata=metadata) + item['prompt'] = prompt + + return + + dataset = load_dataset(path, + split='test', + version_tag=release_version, + trust_remote_code=True) + dataset = dataset.map(transform) + + return DatasetDict({'test': dataset, 'train': dataset}) + + +class CompassBenchCodeExecutionDataset(BaseDataset): + + @staticmethod + def load( + path: str = 'opencompass/execution-v2', + local_mode: bool = False, + cot: bool = False, + # release_version: str = "release_v1" + ): + # path = get_data_path(path, local_mode=local_mode) + + def transform(item): + code, input = item['code'], item['input'] + prompt = make_code_execution_prompt(code, input, cot=cot) + + item['prompt'] = prompt + + evaluation_sample = json.dumps({ + 'code': item['code'], + 'input': item['input'], + 'output': item['output'] + }) + item['evaluation_sample'] = evaluation_sample + + return item + + path = get_data_path(path, local_mode=local_mode) + dataset = load_from_disk(path) # 'livecodebench/execution-v2' + dataset = dataset['test'] + dataset = dataset.map(transform) + + return DatasetDict({'test': dataset, 'train': dataset}) diff --git a/opencompass/datasets/livecodebench/pass_k_utils.py b/opencompass/datasets/livecodebench/pass_k_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5a5f798063976e28b370d123c9cb2bab880bc876 --- /dev/null +++ b/opencompass/datasets/livecodebench/pass_k_utils.py @@ -0,0 +1,72 @@ +# Copyright LiveCodeBench @ 2024, + +import numpy as np + + +def estimate_pass_at_k(num_samples, num_correct, k): + """Estimates pass@k of each problem and returns them in an array.""" + + def estimator(n: int, c: int, k: int) -> float: + """Calculates 1 - comb(n - c, k) / comb(n, k).""" + if n - c < k: + return 1.0 * 100 + return 100 * (1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))) + + import itertools + + if isinstance(num_samples, int): + num_samples_it = itertools.repeat(num_samples, len(num_correct)) + else: + assert len(num_samples) == len(num_correct) + num_samples_it = iter(num_samples) + + return np.array([ + estimator(int(n), int(c), k) + for n, c in zip(num_samples_it, num_correct) + ]) + + +def compute_metrics_from_results(results, k_list=[1, 5]): + total = [] + correct = [] + task_ids = [] + for task_id, res in results.items(): + all_correct = [] + for generation in res: + gen = np.array(generation) + all_correct.append(np.all(gen > 0)) + task_ids.append(task_id) + total.append(len(all_correct)) + correct.append(sum(all_correct)) + total = np.array(total) + correct = np.array(correct) + ks = k_list + detail_pass_at_k = { + f'pass@{k}': estimate_pass_at_k(total, correct, k).tolist() + for k in ks if (total >= k).all() + } + pass_at_k = { + f'pass@{k}': estimate_pass_at_k(total, correct, k).mean() + for k in ks if (total >= k).all() + } + detail_metrics = { + k: dict(zip(task_ids, v)) + for k, v in detail_pass_at_k.items() + } + pass_at_k['details'] = detail_metrics + return pass_at_k + + +def extract_instance_results(results): + instance_wise_grades = {} + for task_id, res in results.items(): + instance_wise_grades[task_id] = [] + for generation in res: + instance_wise_grades[task_id].append( + all([g > 0 for g in generation])) + + instance_wise_grades = [ + v for _, v in sorted(instance_wise_grades.items(), + key=lambda item: item[0]) + ] + return instance_wise_grades diff --git a/opencompass/datasets/livecodebench/prompts.py b/opencompass/datasets/livecodebench/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..4fa817b7350a5ea7001f2b513bf5a0e7a58eb803 --- /dev/null +++ b/opencompass/datasets/livecodebench/prompts.py @@ -0,0 +1,210 @@ +# Copyright LiveCodeBench @ 2024, + +import json + + +class CodeGenerationPromptConstants: + SYSTEM_MESSAGE_GENERIC = 'You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program.' # noqa: E501 + + SYSTEM_MESSAGE_GEMINI = 'You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program. Do NOT use system calls like `exit` in the generated program.' # noqa: E501 + + SYSTEM_MESSAGE_DEEPSEEK = 'You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you answer questions related to computer science.' # noqa: E501 + + SYSTEM_MESSAGE_MAGIC = 'You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\n' # noqa: E501 + + SYSTEM_MESSAGE_WIZARD = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' # noqa: E501 + + SYSTEM_MESSAGE_PHIND = """You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program. Put your fixed program within code delimiters, for example: +```python +# YOUR CODE HERE +```""" # noqa: E501 + + SYSTEM_MESSAGE_CODEQWEN = ( + '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user' # noqa: E501 + ) + + FORMATTING_MESSAGE_WITH_STARTER_CODE = 'You will use the following starter code to write the solution to the problem and enclose your code within delimiters.' # noqa: E501 + + FORMATTING_WITHOUT_STARTER_CODE = 'Read the inputs from stdin solve the problem and write the answer to stdout (do not directly test on the sample inputs). Enclose your code within delimiters as follows.' # noqa: E501 + + PYTHON_FORMAT = '```python\n# YOUR CODE HERE\n```\n\n' + + +class TestOutputPromptConstants: + SYSTEM_MESSAGE_CHAT_GENERIC = 'You are a helpful programming assistant and an expert Python programmer. You are helping a user to write a test case to help to check the correctness of the function. The user has written a input for the testcase. You will calculate the output of the testcase and write the whole assertion statement in the markdown code block with the correct output.' # noqa: E501 + + SYSTEM_MESSAGE_COMPLETION_GENERIC = 'You are a helpful programming assistant and an expert Python programmer. You are helping a user to write a test case to help to check the correctness of the function.' # noqa: E501 + + SYSTEM_MESSAGE_INST_CLLAMA = 'You are a helpful programming assistant and an expert Python programmer. You are helping a user to write a test case to help to check the correctness of the function. The user has written a input for the testcase. You will calculate the output of the testcase and write out the complete assertion statement between [PYTHON] and [/PYTHON] tags.' # noqa: E501 + + SYSTEM_MESSAGE_WIZARD = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' # noqa: E501 + + SYSTEM_MESSAGE_PHIND = """You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program. You must put the entire fixed program within code delimiters only for once., for example: +```python +# YOUR CODE HERE +```""" # noqa: E501 + + FORMATTING_MESSAGE = 'You will use the following starter code to write the solution to the problem and enclose your code within delimiters.' # noqa: E501 + + FORMATTING_WITHOUT_STARTER_MESSAGE = 'Read the inputs from stdin solve the problem and write the answer to stdout (do not directly test on the sample inputs). Enclose your code within delimiters as follows.' # noqa: E501 + + +class SelfRepairPromptConstants: + SYSTEM_MESSAGE_GENERIC = 'You are a helpful programming assistant and an expert Python programmer. You are helping a user write a program to solve a problem. The user has written some code, but it has some errors and is not passing the tests. You will help the user by first giving a concise (at most 2-3 sentences) textual explanation of what is wrong with the code. After you have pointed out what is wrong with the code, you will then generate a fixed version of the program. You must put the entire fixed program within code delimiters only for once.' # noqa: E501 + + SYSTEM_MESSAGE_DEEPSEEK = 'You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you are helping a user correct a error program for code competition. The user has written some code, but it has some errors and is not passing the tests. You will help the user by first giving a concise (at most 2-3 sentences) textual explanation of what is wrong with the code. After you have pointed out what is wrong with the code, you will then generate a fixed version of the entire executable program. You must put the entire fixed executable program within code delimiters.' # noqa: E501 + + SYSTEM_MESSAGE_MAGIC = 'You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\n' # noqa: E501 + + SYSTEM_MESSAGE_WIZARD = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' # noqa: E501 + + SYSTEM_MESSAGE_PHIND = """You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. You will NOT return anything except for the program. You must put the entire fixed program within code delimiters only for once., for example: +```python +# YOUR CODE HERE +```""" # noqa: E501 + + FORMATTING_REPEAT = 'First reason about the code providing a textual explanation of what is wrong with the code and then generate a fixed of the program enclosed code delimiters.' # noqa: E501 + + FORMATTING_MESSAGE = 'You will use the following starter code to write the solution to the problem and enclose your code within delimiters.' # noqa: E501 + + FORMATTING_WITHOUT_STARTER_CODE = 'Read the inputs from stdin solve the problem and write the answer to stdout (do not directly test on the sample inputs). Enclose your code within delimiters as follows.' # noqa: E501 + + +def make_code_execution_prompt(code, input, cot): + if cot: + # make_cot_output_prompt + return f"""You are given a Python function and an assertion containing an input to the function. Complete the assertion with a literal (no unsimplified expressions, no function calls) containing the output when executing the provided code on the given input, even if the function is incorrect or incomplete. Do NOT output any extra information. Execute the program step by step before arriving at an answer, and provide the full assertion with the correct output in [ANSWER] and [/ANSWER] tags, following the examples. + +[PYTHON] +def performOperation(s): + s = s + s + return "b" + s + "a" +assert performOperation(s = "hi") == ?? +[/PYTHON] +[THOUGHT] +Let's execute the code step by step: + +1. The function performOperation is defined, which takes a single argument s. +2. The function is called with the argument "hi", so within the function, s is initially "hi". +3. Inside the function, s is concatenated with itself, so s becomes "hihi". +4. The function then returns a new string that starts with "b", followed by the value of s (which is now "hihi"), and ends with "a". +5. The return value of the function is therefore "bhihia". +[/THOUGHT] +[ANSWER] +assert performOperation(s = "hi") == "bhihia" +[/ANSWER] + +[PYTHON] +{code} +assert {input} == ?? +[/PYTHON] +[THOUGHT] +""" # noqa: E501 + else: + # make_direct_output_prompt + return f"""You are given a Python function and an assertion containing an input to the function. Complete the assertion with a literal (no unsimplified expressions, no function calls) containing the output when executing the provided code on the given input, even if the function is incorrect or incomplete. Do NOT output any extra information. Provide the full assertion with the correct output in [ANSWER] and [/ANSWER] tags, following the examples. + +[PYTHON] +def repeatNumber(number : int) -> int: + return number +assert repeatNumber(number = 17) == ?? +[/PYTHON] +[ANSWER] +assert repeatNumber(number = 17) == 17 +[/ANSWER] + +[PYTHON] +def addCharacterA(string : str) -> str: + return string + "a" +assert addCharacterA(string = "x9j") == ?? +[/PYTHON] +[ANSWER] +assert addCharacterA(string = "x9j") == "x9ja" +[/ANSWER] + +[PYTHON] +{code} +assert {input} == ?? +[/PYTHON] +[ANSWER] +""" # noqa: E501 + + +def get_generic_question_template_test_completion(question_content, + starter_code, + testcase_input: str): + + def format_testcase_func_name_input(function_name, testcase): + """Use the form of "assert func_name(input) == ".""" + # TODO should there be a space after the == ? + input_str = ', '.join(testcase.split('\n')) + return f'assert {function_name}({input_str}) == # TODO' + + def parse_function_name_from_starter_code(starter_code): + """ + starter_code : str + """ + import ast + + tree = ast.parse(starter_code) + fn = None + for node in ast.walk(tree): + if isinstance(node, ast.FunctionDef): + assert fn is None + fn = node.name + return fn + + prompt = f'Problem:\n{question_content}' + prompt += f'Function:\n```\n{starter_code}\n```\n' + + # parse function name from starter_code + func_name = parse_function_name_from_starter_code(starter_code) + prompt += 'Please complete the following test case:\n\n' + prompt += ( + f'```\n{format_testcase_func_name_input(func_name, testcase_input)}\n```\n' # noqa: E501 + ) + + return prompt + + +def get_generic_question_template_answer_self_repair(question: str, code, + metadata): + + def get_check_prompt(metadata): + # def get_check_prompt(question: str, result, metadata): + # # assumes i/o examples are already truncated! + # # less pressure on storing 10 MB json because on a single large + # # input-output pair + # result_by_test_case = result + # assert len(metadata) == 1, f"metadata = {metadata}" + # metadata = metadata[0] + metadata = json.loads(metadata) + if 'error_code' not in metadata: + return '' + if metadata['error_code'] == -1: + # time limit exceeded + message = f"The above code is incorrect and got the following compilation error.\n{metadata['error']}" # noqa: E501 + elif metadata['error_code'] == -2: + # wrong answer + message = f"The above code is incorrect and got a wrong answer.\nInput: {metadata['inputs']}\nGenerated Output: {metadata['output']}\nExpected: {metadata['expected']}" # noqa: E501 + elif metadata['error_code'] == -3: + # time limit exceeded + message = f"The above code is incorrect and got time limit exceeded.\n{metadata['error']}\nInput: {metadata['inputs']}\nExpected: {metadata['expected']}" # noqa: E501 + pass + elif metadata['error_code'] == -4: + # runtime error + message = f"The above code is incorrect and got a runtime error.\nInput: {metadata['inputs']}\nExpected: {metadata['expected']}\n{metadata['error']}" # noqa: E501 + else: + raise NotImplementedError( + f"metadata['error_code'] = {metadata['error_code']} not implemented || {metadata=}" # noqa: E501 + ) + return message + + prompt = f'### Question:\n{question}\n\n' + prompt += f'### Answer:\n```python\n{code}\n```\n\n' + # prompt += get_check_prompt(question, result, metadata) + "\n" + prompt += get_check_prompt(metadata) + '\n' + prompt += f'### Format: {SelfRepairPromptConstants.FORMATTING_WITHOUT_STARTER_CODE}\n' # noqa: E501 + prompt += '```python\n# YOUR CODE HERE\n```\n\n' + prompt += '### Answer: (use the provided format with backticks)\n\n' + return prompt diff --git a/opencompass/datasets/livecodebench/testing_util.py b/opencompass/datasets/livecodebench/testing_util.py new file mode 100644 index 0000000000000000000000000000000000000000..eb7ad52198db7ef67d871430f83a4f7cd6da8d2f --- /dev/null +++ b/opencompass/datasets/livecodebench/testing_util.py @@ -0,0 +1,766 @@ +# Copyright LiveCodeBench @ 2024, + +import ast +import faulthandler +import json +import platform +# to run the solution files we're using a timing based approach +import signal +import sys +# used for debugging to time steps +from datetime import datetime +from enum import Enum +# for capturing the stdout +from io import StringIO +# used for testing the code that reads from input +from unittest.mock import mock_open, patch + +import numpy as np + +try: + from pyext import RuntimeModule +except ImportError: + RuntimeModule = None + + +def truncatefn(s, length=300): + assert isinstance(s, str) + if len(s) <= length: + return s + + return s[:length // 2] + '...(truncated) ...' + s[-length // 2:] + + +class CODE_TYPE(Enum): + call_based = 0 + standard_input = 1 + + +# stuff for setting up signal timer +class TimeoutException(Exception): + pass + + +def timeout_handler(signum, frame): + print('alarm went off') + # return + raise TimeoutException + + +signal.signal(signal.SIGALRM, timeout_handler) + +# timeout = 6 # seconds + + +# used to capture stdout as a list +# from https://stackoverflow.com/a/16571630/6416660 +# alternative use redirect_stdout() from contextlib +class Capturing(list): + + def __enter__(self): + self._stdout = sys.stdout + sys.stdout = self._stringio = StringIO() + # Make closing the StringIO a no-op + self._stringio.close = lambda x: 1 + return self + + def __exit__(self, *args): + self.append(self._stringio.getvalue()) + del self._stringio # free up some memory + sys.stdout = self._stdout + + +def only_int_check(val): + return isinstance(val, int) + + +def string_int_check(val): + return isinstance(val, str) and val.isdigit() + + +def combined_int_check(val): + return only_int_check(val) or string_int_check(val) + + +def run_test(sample, test=None, debug=False, timeout=6): + """If test(generated_code) is not None it'll try to run the code. + + otherwise it'll just return an input and output pair. + """ + # Disable functionalities that can make destructive changes to the test. + reliability_guard() + + if debug: + print(f'start = {datetime.now().time()}') + + try: + in_outs = json.loads(sample['input_output']) + except ValueError: + in_outs = None + if in_outs: + if in_outs.get('fn_name') is None: + which_type = CODE_TYPE.standard_input # Standard input + method_name = None + else: + which_type = CODE_TYPE.call_based # Call-based + method_name = in_outs['fn_name'] + + if debug: + print(f'loaded input_output = {datetime.now().time()}') + + if test is None: + assert False, 'should not happen: test code is none' + return in_outs, {'error': 'no test code provided'} + elif test is not None: + results = [] + sol = 'from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(6*10**5)\n' # noqa: E501 + if debug: + print(f'loading test code = {datetime.now().time()}') + + if which_type == CODE_TYPE.call_based: + + sol += test + if debug: + print(f'sol = {sol}') + signal.alarm(timeout) + try: + tmp_sol = RuntimeModule.from_string('tmp_sol', '', sol) + if 'class Solution' not in test: + tmp = tmp_sol + else: + tmp = tmp_sol.Solution() + signal.alarm(0) + except Exception as e: + signal.alarm(0) + if debug: + print(f'type 0 compilation error = {e}') + results.append(-2) + return results, { + 'error': repr(e), + 'error_code': -1, + 'error_message': 'Compilation Error', + } + signal.alarm(0) + + elif which_type == CODE_TYPE.standard_input: + # sol + # if code has if __name__ == "__main__": then remove it + try: + astree = ast.parse(test) + last_block = astree.body[-1] + if isinstance(last_block, ast.If): + condition = last_block.test + if ast.unparse( + condition).strip() == "__name__ == '__main__'": + test = (ast.unparse(astree.body[:-1]) + '\n' + + ast.unparse(last_block.body)) + except Exception as e: # noqa: + pass + + tmp_test = test.split('\n') + + new_test = [] + for x in tmp_test: + if (not x.startswith('from ')) and ( + not x.startswith('import ')): + new_test.append('\t' + x + '\n') + else: + new_test.append(x + '\n') + tmp_test = new_test + + new_test = '' + started = False + for i in tmp_test: + if i.startswith('\t') and not started: + new_test += 'stdin = sys.stdin\nstdout = sys.stdout\n' + new_test += 'def code():\n' + new_test += i + started = True + elif started and ((i.startswith('from ')) or + (i.startswith('import '))): + new_test += '\t' + i + else: + new_test += i + tmp_test = new_test + + sol += tmp_test + if debug: + print(f'sol = {sol}') + method_name = 'code' + signal.alarm(timeout) + try: + tmp_sol = RuntimeModule.from_string('tmp_sol', '', sol) + tmp = tmp_sol + signal.alarm(0) + except Exception as e: + signal.alarm(0) + if debug: + print(f'type 1 compilation error = {e}') + results.append(-2) + return results, { + 'error': repr(e), + 'error_code': -1, + 'error_message': 'Compilation Error', + } + signal.alarm(0) + if debug: + print(f'get method = {datetime.now().time()}') + + try: + method = getattr(tmp, + method_name) # get_attr second arg must be str + except Exception as e: + signal.alarm(0) + e = sys.exc_info() + print(f'unable to get function error = {e}') + results.append(-2) + return results, { + 'error': repr(e), + 'error_code': -1, + 'error_message': 'Unable to extract code', + } + + for index, inputs in enumerate(in_outs['inputs']): + raw_inputs = inputs + raw_outputs = in_outs['outputs'][index] + if which_type == CODE_TYPE.call_based: + inputs = [json.loads(line) for line in inputs.split('\n')] + in_outs['outputs'][index] = json.loads( + in_outs['outputs'][index]) + + truncate_line_size = 300 // (raw_inputs.count('\n') + 1) + raw_inputs = '\n'.join([ + truncatefn(line, truncate_line_size) + for line in raw_inputs.strip().split('\n') + ]) + raw_outputs = truncatefn(raw_outputs, 200) + else: + raw_inputs = truncatefn(raw_inputs) + raw_outputs = truncatefn(raw_outputs, 200) + # JSON forces dictionaries to have string keys; this undoes this + # (assuming a singleton list) + try: + if isinstance(inputs[0], dict): + inputs = [{int(k): v for k, v in inputs[0].items()}] + except Exception as e: # noqa: F841 + True + try: + if isinstance(in_outs['outputs'][index], dict): + in_outs['outputs'][index] = [{ + int(k): v + for k, v in in_outs['outputs'][index].items() + }] + except Exception as e: # noqa: F841 + True + try: + if isinstance(in_outs['outputs'][index][0], dict): + in_outs['outputs'][index] = [{ + int(k): v + for k, v in in_outs['outputs'][index][0].items() + }] + except Exception as e: # noqa: F841 + True + + if debug: + print( + f'time: {datetime.now().time()} testing index = {index} ' + f'inputs = {inputs}, {type(inputs)}. type = {which_type}') + if which_type == CODE_TYPE.call_based: # Call-based + signal.alarm(timeout) + faulthandler.enable() + try: + output = method(*inputs) + raw_true_output = output + + raw_true_output_copy = json.dumps(output) + raw_true_output_copy = truncatefn(raw_true_output_copy, + 200) + + # ground truth sequences are not tuples + if isinstance(output, tuple): + output = list(output) + + tmp_result = output == in_outs['outputs'][index] + if (isinstance(in_outs['outputs'][index], list) + and in_outs['outputs'][index]): + tmp_result = tmp_result or ( + output == in_outs['outputs'][index][0]) + + # ground truth sequences are not tuples + try: + if isinstance(output[0], tuple): + tmp_result = tmp_result or ([ + list(x) for x in output + ] == in_outs['outputs'][index][0]) + except Exception as e: # noqa: F841 + True + results.append(tmp_result) + if tmp_result is not True: + return results, { + 'output': raw_true_output_copy, + 'expected': raw_outputs, + 'inputs': raw_inputs, + 'error_code': -2, + 'error_message': 'Wrong Answer', + } + # reset the alarm + signal.alarm(0) + except Exception as e: + signal.alarm(0) + faulthandler.disable() + if debug: + print( + f'Standard input runtime error or time limit exceeded error = {e}' # noqa: E501 + ) + results.append(-1) + if 'timeoutexception' in repr(e).lower(): + return results, { + 'error': repr(e), + 'error_code': -3, + 'error_message': 'Time Limit Exceeded', + 'inputs': raw_inputs, + 'expected': raw_outputs, + } + else: + return results, { + 'error': repr(e), + 'error_code': -4, + 'error_message': 'Runtime Error', + 'inputs': raw_inputs, + 'expected': raw_outputs, + } + faulthandler.disable() + signal.alarm(0) + if debug: + print( + f"outputs = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" # noqa: E501 + ) + elif which_type == CODE_TYPE.standard_input: # Standard input + faulthandler.enable() + passed = False + + if isinstance(inputs, list): + inputs = '\n'.join(inputs) + if isinstance(in_outs['outputs'][index], list): + in_outs['outputs'][index] = '\n'.join( + in_outs['outputs'][index]) + + signal.alarm(timeout) + with Capturing() as output: + try: + call_method(method, inputs) + # reset the alarm + signal.alarm(0) + passed = True + except Exception as e: + # runtime error or took too long + signal.alarm(0) + print( + f'Call-based runtime error or time limit exceeded error = {repr(e)}{e}' # noqa: E501 + ) + results.append(-1) + if 'timeoutexception' in repr(e).lower(): + return results, { + 'error': repr(e), + 'error_code': -3, + 'error_message': 'Time Limit Exceeded', + 'inputs': raw_inputs, + 'expected': raw_outputs, + } + else: + return results, { + 'error': repr(e), + 'error_code': -4, + 'error_message': 'Runtime Error', + 'inputs': raw_inputs, + 'expected': raw_outputs, + } + signal.alarm(0) + raw_true_output = output[0] + raw_true_output_copy = truncatefn(raw_true_output, 200) + output = raw_true_output.splitlines() + if not passed: + if debug: + nl = '\n' + if not isinstance(inputs, list): + print( + f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" # noqa: E501 + ) + else: + print( + f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" # noqa: E501 + ) + continue + + if passed and debug: + print( + f"==> output = {output}, test outputs = {in_outs['outputs'][index]}" # noqa: E501 + ) + + if custom_compare_(output, in_outs['outputs'][index]): + tmp_result = True + results.append(tmp_result) + continue + + # ground truth sequences are expressed as lists not tuples + if isinstance(output, tuple): + output = list(output) + + tmp_result = False + try: + tmp_result = output == [in_outs['outputs'][index]] + if isinstance(in_outs['outputs'][index], list): + tmp_result = tmp_result or ( + output == in_outs['outputs'][index]) + if isinstance(output[0], str): + tmp_result = tmp_result or ([ + e.strip() for e in output + ] == in_outs['outputs'][index]) + except Exception as e: + if debug: + print(f'Failed check1 exception = {e}') + pass + + if tmp_result is True: + results.append(tmp_result) + continue + + # try one more time without \n + if isinstance(in_outs['outputs'][index], list): + for tmp_index, i in enumerate(in_outs['outputs'][index]): + in_outs['outputs'][index][tmp_index] = i.split('\n') + in_outs['outputs'][index][tmp_index] = [ + x.strip() + for x in in_outs['outputs'][index][tmp_index] if x + ] + else: + in_outs['outputs'][index] = in_outs['outputs'][ + index].split('\n') + in_outs['outputs'][index] = list( + filter(len, in_outs['outputs'][index])) + in_outs['outputs'][index] = list( + map(lambda x: x.strip(), in_outs['outputs'][index])) + + try: + tmp_result = output == [in_outs['outputs'][index]] + if isinstance(in_outs['outputs'][index], list): + tmp_result = tmp_result or ( + output == in_outs['outputs'][index]) + except Exception as e: + if debug: + print(f'Failed check2 exception = {e}') + pass + + if tmp_result is True: + results.append(tmp_result) + continue + + # try by converting the output into a split up list too + if isinstance(output, list): + output = list(filter(len, output)) + + if debug: + nl = '\n' + if not isinstance(inputs, list): + print( + f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" # noqa: E501 + ) + else: + print( + f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" # noqa: E501 + ) + + if tmp_result is True: + results.append(tmp_result) + continue + + if debug: + print(f'{tmp_result=} @a') + + try: + tmp_result = output == [in_outs['outputs'][index]] + if isinstance(in_outs['outputs'][index], list): + tmp_result = tmp_result or ( + output == in_outs['outputs'][index]) + except Exception as e: + if debug: + print(f'Failed check3 exception = {e}') + pass + + if debug: + print(f'{tmp_result=} @b') + + try: + all_ints = all( + combined_int_check(e1) and combined_int_check(e2) + for e1, e2 in zip(output, in_outs['outputs'][index])) + if not all_ints: + if debug: + print([ + combined_int_check(e1) + and combined_int_check(e2) for e1, e2 in zip( + output, in_outs['outputs'][index]) + ]) + output_float = [float(e) for e in output] + gt_float = [ + float(e) for e in in_outs['outputs'][index] + ] + tmp_result = tmp_result or ( + (len(output_float) == len(gt_float)) + and np.allclose(output_float, gt_float)) + except Exception as e: # noqa: F841 + pass + + if debug: + print(f'{tmp_result=} @c') + + try: + if isinstance(output[0], list): + all_ints = all( + combined_int_check(e1) and combined_int_check(e2) + for e1, e2 in zip(output[0], in_outs['outputs'] + [index])) + if not all_ints: + output_float = [float(e) for e in output[0]] + gt_float = [ + float(e) for e in in_outs['outputs'][index][0] + ] + tmp_result = tmp_result or ( + (len(output_float) == len(gt_float)) + and np.allclose(output_float, gt_float)) + except Exception as e: # noqa: F841 + pass + + if tmp_result is True: + results.append(tmp_result) + continue + + if debug: + print(f'{tmp_result=} @d') + # try by converting the stuff into split up list + if isinstance(in_outs['outputs'][index], list): + for tmp_index, i in enumerate(in_outs['outputs'][index]): + in_outs['outputs'][index][tmp_index] = set(i.split()) + else: + in_outs['outputs'][index] = set( + in_outs['outputs'][index].split()) + + if debug: + print(f'{tmp_result=} @e') + + try: + tmp_result = output == in_outs['outputs'][index] + except Exception as e: + if debug: + print(f'Failed check4 exception = {e}') + continue + + if tmp_result is True: + results.append(tmp_result) + continue + + if debug: + print(f'{tmp_result=} @f') + + # try by converting the output into a split up list too + if isinstance(output, list): + for tmp_index, i in enumerate(output): + output[tmp_index] = i.split() + output = list(filter(len, output)) + for tmp_index, i in enumerate(output): + output[tmp_index] = set(i) + else: + output = output.split() + output = list(filter(len, output)) + output = set(output) + + if debug: + print(f'{tmp_result=} @g') + # try: + # tmp_result = set(frozenset(s) for s in output) == set( + # frozenset(s) for s in in_outs["outputs"][index] + # ) + # except Exception as e: + # if debug: + # print(f"Failed check5 exception = {e}") + + # if they are all numbers, round so that similar numbers are + # treated as identical + # try: + # all_ints = all( + # combined_int_check(e1) and combined_int_check(e2) + # for e1, e2 in zip(output, in_outs["outputs"][index]) + # ) + # tmp_result = tmp_result or ( + # set( + # frozenset(round(float(t), 3) for t in s) for s in output) + # == set( + # frozenset(round(float(t), 3) for t in s) + # for s in in_outs["outputs"][index] + # ) + # ) + # except Exception as e: + # if debug: + # print(f"Failed check6 exception = {e}") + + if debug: + print(f'{tmp_result=} @h') + + if tmp_result is True and debug: + print('PASSED') + + results.append(tmp_result) + if tmp_result is not True: + return results, { + 'output': raw_true_output_copy, + 'expected': raw_outputs, + 'inputs': raw_inputs, + 'error_code': -2, + 'error_message': 'Wrong Answer', + } + + if debug: + nl = '\n' + if not isinstance(inputs, list): + print( + f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" # noqa: E501 + ) + else: + print( + f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" # noqa: E501 + ) + + print(f'results = {results}') + + return results, {} + + +def custom_compare_(output, ground_truth): + + if isinstance(output, list): + output_1 = '\n'.join(output) + if stripped_string_compare(output_1, ground_truth): + return True + + if isinstance(output, list): + output_2 = [o.lstrip().rstrip() for o in output] + output_2 = '\n'.join(output_2) + if stripped_string_compare(output_2, ground_truth): + return True + + return False + + +def stripped_string_compare(s1, s2): + s1 = s1.lstrip().rstrip() + s2 = s2.lstrip().rstrip() + return s1 == s2 + + +def call_method(method, inputs): + + if isinstance(inputs, list): + inputs = '\n'.join(inputs) + + inputs_line_iterator = iter(inputs.split('\n')) + + # sys.setrecursionlimit(10000) + + # @patch('builtins.input', side_effect=inputs.split("\n")) + @patch('builtins.open', mock_open(read_data=inputs)) + @patch('sys.stdin', StringIO(inputs)) + @patch('sys.stdin.readline', lambda *args: next(inputs_line_iterator)) + @patch('sys.stdin.readlines', lambda *args: inputs.split('\n')) + @patch('sys.stdin.read', lambda *args: inputs) + # @patch('sys.stdout.write', print) + def _inner_call_method(_method): + try: + return _method() + except SystemExit as e: # noqa: F841 + pass + finally: + pass + + return _inner_call_method(method) + + +def reliability_guard(maximum_memory_bytes=None): + """This disables various destructive functions and prevents the generated + code from interfering with the test (e.g. fork bomb, killing other + processes, removing filesystem files, etc.) WARNING This function is NOT a + security sandbox. + + Untrusted code, including, model- generated code, should not be blindly + executed outside of one. See the Codex paper for more information about + OpenAI's code sandbox, and proceed with caution. + """ + + if maximum_memory_bytes is not None: + import resource + + resource.setrlimit(resource.RLIMIT_AS, + (maximum_memory_bytes, maximum_memory_bytes)) + resource.setrlimit(resource.RLIMIT_DATA, + (maximum_memory_bytes, maximum_memory_bytes)) + if not platform.uname().system == 'Darwin': + resource.setrlimit(resource.RLIMIT_STACK, + (maximum_memory_bytes, maximum_memory_bytes)) + + faulthandler.disable() + + import builtins + + builtins.exit = None + builtins.quit = None + + import os + + os.environ['OMP_NUM_THREADS'] = '1' + + os.kill = None + os.system = None + os.putenv = None + os.remove = None + os.removedirs = None + os.rmdir = None + os.fchdir = None + os.setuid = None + os.fork = None + os.forkpty = None + os.killpg = None + os.rename = None + os.renames = None + os.truncate = None + os.replace = None + os.unlink = None + os.fchmod = None + os.fchown = None + os.chmod = None + os.chown = None + os.chroot = None + os.fchdir = None + os.lchflags = None + os.lchmod = None + os.lchown = None + os.getcwd = None + os.chdir = None + + import shutil + + shutil.rmtree = None + shutil.move = None + shutil.chown = None + + import subprocess + + subprocess.Popen = None # type: ignore + + __builtins__['help'] = None + + import sys + + sys.modules['ipdb'] = None + sys.modules['joblib'] = None + sys.modules['resource'] = None + sys.modules['psutil'] = None + sys.modules['tkinter'] = None diff --git a/opencompass/datasets/livemathbench/__init__.py b/opencompass/datasets/livemathbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2da0531fe6e3730e9461c885108ab7ad657d1fcb --- /dev/null +++ b/opencompass/datasets/livemathbench/__init__.py @@ -0,0 +1,2 @@ +from .livemathbench import LiveMathBenchDataset # noqa: F401, F403 +from .livemathbench import LiveMathBenchEvaluator # noqa: F401, F403 diff --git a/opencompass/datasets/livemathbench/livemathbench.py b/opencompass/datasets/livemathbench/livemathbench.py new file mode 100644 index 0000000000000000000000000000000000000000..b0a060fd0c58adad8b43d4a656c836bce339c7cc --- /dev/null +++ b/opencompass/datasets/livemathbench/livemathbench.py @@ -0,0 +1,305 @@ +import os +import warnings +from concurrent.futures import ThreadPoolExecutor, as_completed +from functools import partial +from itertools import product +from typing import Any, Callable, Dict, List + +import jsonlines +import mmengine +import numpy as np +from datasets import Dataset, load_dataset + +from opencompass.datasets.math import MATHAgentEvaluator, math_postprocess_v2 +from opencompass.models import OpenAISDK +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.openicl.icl_inferencer.icl_base_inferencer import \ + dump_results_dict +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET, MODELS +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .prompts import (EXTRACT_PROMPT_CN, EXTRACT_PROMPT_EN, JUDGE_PROMPT_CN, + JUDGE_PROMPT_EN, PROMPT_CN, PROMPT_EN) +from .utils import extract_judge_label + + +@LOAD_DATASET.register_module() +class LiveMathBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, + dataset_splits: List[str] = [ + 'CNMO', + 'CCEE', + 'AMC', + 'WLPMC', + ], + dataset_languages: List[str] = ['cn', 'en'], + cot: bool = True, + version: str = '202412') -> List[Dict[str, Any]]: + dataset = [] + + # Use dataset mapping to generate path + data_dir = get_data_path(path) + + for split, language in product(dataset_splits, dataset_languages): + examples = [] + if data_dir.startswith('opencompass/'): + # Using HF Dataset + hf_dataset = load_dataset( + data_dir, f'v{version}_{split}_{language}')['test'] + for example in hf_dataset: + examples.append(example) + else: + file_path = os.path.join(data_dir, version, + f'{split}_{language}.jsonl') + + if not os.path.exists(file_path): + raise FileNotFoundError( + f'File {file_path} does not exist, please check the ' + f'path and try again.') + examples = [] + with jsonlines.open(file_path, 'r') as file: + for example in file: + examples.append(example) + + for example_idx, example in enumerate(examples): + prompt = PROMPT_EN if language == 'en' else PROMPT_CN + if not cot: + if language == 'cn': + prompt = prompt.replace('，请逐步推理', '') + else: + prompt = prompt.replace( + ', please reasoning step by step', '') + example.update({ + 'subdivision': + f'livemathbench_{version}_{split}_{language}', + 'idx': + str(example_idx), + 'prompt': + prompt.format(question=example['question']), + }) + dataset.append(example) + + return Dataset.from_list(dataset) + + +@ICL_EVALUATORS.register_module() +class LiveMathBenchEvaluator(BaseEvaluator): + api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ]) + + def __init__(self, + model_name, + url, + use_extract_model=False, + extract_url=[], + extract_model_name=''): + super().__init__() + + if isinstance(url, str): + url = [url] + + if model_name == '' or len(url) == 0: + warnings.warn('Unable to leverage LLM-as-judge abd backup to ' + 'rule-based judge due to incomplete parameters, ' + 'this may cause performance degradation, check ' + '`model_name` or `url` of evaluator if you do ' + 'not want to do this.') + self.judge_models = [] + else: + self.judge_models = [ + MODELS.build( + dict( + type=OpenAISDK, + path=model_name, + openai_api_base=_url, + key='EMPTY', + query_per_second=2, + retry=5, + meta_template=self.api_meta_template, + temperature=0.0, + max_seq_len=16384, + )) for _url in url + ] + self.use_extract_model = use_extract_model + self.extract_url = extract_url + self.extract_model_name = extract_model_name + + self.extract_output_handler = LiveMathBenchOutputHandler() + self.judge_output_handler = LiveMathBenchOutputHandler() + + def batch_infer(self, models: List[OpenAISDK], inputs: List[str], + completed_indexes: set, + output_handler: 'LiveMathBenchOutputHandler', + postprocess: Callable) -> List[str]: + batch_size = 16 + batch_num = (len(inputs) + batch_size - 1) // batch_size + all_indexes = [i for i in range(len(inputs))] + indexes = [i for i in all_indexes if i not in completed_indexes] + inputs = [inputs[i] for i in indexes] + result_responses = [] + result_indexes = [] + + def thread_worker(inputs, max_out_len, temperature, indexes, model): + return model.generate(inputs, max_out_len, + temperature), inputs, indexes + + if len(indexes) > 0: + with ThreadPoolExecutor(max_workers=len(models)) as pool: + tasks = [ + pool.submit( + partial(thread_worker, model=models[i % len(models)]), + inputs[i * batch_size:(i + 1) * batch_size], 8192, 0.0, + indexes[i * batch_size:(i + 1) * batch_size]) + for i in range(batch_num) + ] + for completed_task in as_completed(tasks): + responses, current_inputs, indexes = completed_task.result( + ) + for input, response, index in zip(current_inputs, + responses, indexes): + output_handler.save( + index, + prompt=input, + response=response, + postprocess_response=postprocess(response)) + result_responses.append(postprocess(response)) + result_indexes.append(index) + output_handler.write_to_json() + + return [ + output_handler.output_dict[str(i)]['postprocess_response'] + for i in all_indexes + ] + + def extract(self, questions: List[str], predictions: List[str], + question_types: List[str], languages: List[str]) -> List[str]: + + # extract answer by model + if self.use_extract_model: + assert len(self.extract_url) > 0 and self.extract_model_name != '' + extract_models = [ + MODELS.build( + dict( + type=OpenAISDK, + path=self.extract_model_name, + openai_api_base=url, + key='EMPTY', + query_per_second=2, + retry=5, + meta_template=self.api_meta_template, + temperature=0.0, + max_seq_len=1024, + )) for url in self.extract_url + ] + + completed_indexes = [] + mmengine.mkdir_or_exist(self.output_dir) + tmp_json_file_path = os.path.join(self.output_dir, + 'tmp_extract.json') + self.extract_output_handler.save_file_path = tmp_json_file_path + if os.path.exists(tmp_json_file_path): + tmp_dict = mmengine.load(tmp_json_file_path) + self.extract_output_handler.output_dict = tmp_dict + for index in tmp_dict: + completed_indexes.add(int(index)) + + input_prompts = [] + for question, prediction, question_type, language in enumerate( + zip(questions, predictions, question_types, languages)): + prompt = (EXTRACT_PROMPT_EN + if language == 'en' else EXTRACT_PROMPT_CN) + input_prompts.append( + prompt.format(question=question, + response=prediction, + question_type=question_type)) + + results = self.batch_infer(extract_models, + input_prompts, + completed_indexes, + self.extract_output_handler, + postprocess=lambda x: x) + + return results + + # extract answer in \\boxed{} + results = [ + math_postprocess_v2(prediction) for prediction in predictions + ] + return results + + def judge(self, predictions, references, test_set): + if len(predictions) != len(references): + raise ValueError('preds and refrs have different length') + + completed_indexes = set() + mmengine.mkdir_or_exist(self.output_dir) + tmp_json_file_path = os.path.join(self.output_dir, 'tmp_judge.json') + self.judge_output_handler.save_file_path = tmp_json_file_path + if os.path.exists(tmp_json_file_path): + tmp_dict = mmengine.load(tmp_json_file_path) + self.judge_output_handler.output_dict = tmp_dict + for index in tmp_dict: + completed_indexes.add(int(index)) + + questions = test_set['question'] + question_types = test_set['question_type'] + languages = [key.split('_')[1] for key in test_set['subdivision']] + + predictions = self.extract(questions, predictions, question_types, + languages) + + if len(self.judge_models) > 0: + inputs = [] + for prediction, reference, question, language in zip( + predictions, references, questions, languages): + prompt = (JUDGE_PROMPT_EN + if language == 'en' else JUDGE_PROMPT_CN) + inputs.append( + prompt.format(answer=prediction, + gold_answer=reference, + question=question)) + + labels = self.batch_infer( + self.judge_models, inputs, completed_indexes, + self.judge_output_handler, lambda x: + (1 if extract_judge_label(x) == 'yes' else 0)) + else: + is_equiv = MATHAgentEvaluator(version='v2').is_equiv + labels = [ + 1 if is_equiv(prediction, reference) else 0 + for prediction, reference in zip(predictions, references) + ] + return labels + + def preprocess(self, predictions, references, test_set): + return self.judge(predictions, references, test_set) + + def score(self, predictions, references, test_set) -> Dict[str, Any]: + labels = self.preprocess(predictions, references, test_set) + results = {'accuracy': 100 * np.mean(labels), 'details': []} + + for pred, ref, label in zip(predictions, references, labels): + results['details'].append({ + 'pred': pred, + 'ref': ref, + 'correct': label + }) + + return results + + +class LiveMathBenchOutputHandler: + output_dict = {} + save_file_path = '' + + def write_to_json(self): + """Dump the result to a json file.""" + dump_results_dict(self.output_dict, self.save_file_path) + + def save(self, idx, **kwargs): + self.output_dict[str(idx)] = kwargs diff --git a/opencompass/datasets/livemathbench/prompts.py b/opencompass/datasets/livemathbench/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..0742fde60e31cbc76ac4d1f6282a29cfd6f1f67c --- /dev/null +++ b/opencompass/datasets/livemathbench/prompts.py @@ -0,0 +1,70 @@ +# flake8: noqa + +EXTRACT_PROMPT_CN = '''你是一个乐于助人的助手，任务是从给定的回答句子中提取精确的关键答案。你必须只提供提取的关键答案，不包括任何额外的文字。 +— +我将为你提供一个问题、回答句子和问题类型。回答句子是对所提供问题的回应。利用提供的信息，你必须准确而精确地确定并从回答句子中提取预期的关键答案。请不要对问题发表主观看法。 + +对于单选题，答案应该是选项字母，例如 "A"； +对于多选题，答案应该是一个选项字母的列表，例如 ["A"] 或 ["A", "B", "C"]； +对于填空题，答案应该是一个填入空白处的答案列表，列表的数量应该与问题中的空白数量相同，同一空白的答案可能有多个，请在同一个 string 中用逗号隔开表示，如 ['sqrt(x) 且 x > 10', '1/2, 1/3', '1/4'] 代表问题包含三小问，第一小问包含取值范围信息，第二小问有两个答案，第三小问有一个答案。 +对于解答题，类似填空题，答案应该是一个答案列表，每小问的答案间用逗号隔开，同样需要注意某些小问答案多个的情况。 + +如果回答句子提供了多个不同的答案，请仔细判断后面提供的答案是否是对前面答案的修正或修改。如果是这样，提取这个修正或修改后的答案作为最终答案。相反，如果回答句子在多个答案之间波动而没有明确的最终答案，你应该输出 [No valid answer]。 +— +问题类型: {question_type} +原始问题: {question} +回答: {response} +提取的关键答案: +''' + +EXTRACT_PROMPT_EN = '''You are a helpful assistant whose task is to extract precise key answers from given response sentences. You must only provide the extracted key answers without any additional text. +— +I will provide you with a question, a response sentence, and the question type. The response sentence is a reply to the provided question. Using the provided information, you must accurately and precisely identify and extract the expected key answers from the response sentence. Please do not provide subjective opinions about the question. + +For single-choice questions, the answer should be the letter of the option, such as "A". +For multiple-choice questions, the answer should be a list of option letters, such as ["A"] or ["A", "B", "C"]. +For fill-in-the-blank questions, the answer should be a list of answers to fill in the blanks. The number of items in the list should match the number of blanks in the question. If there are multiple answers for the same blank, separate them with a comma within the same string, like ['sqrt(x) and x > 10', '1/2, 1/3', '1/4'], which represents three sub-questions where the first sub-question includes a range, the second sub-question has two answers, and the third sub-question has one answer. +For problem-solving questions, similar to fill-in-the-blank questions, the answer should be a list of answers. Separate answers for different sub-questions with commas, and note that some sub-questions may have multiple answers. + +If the response sentence provides multiple different answers, carefully determine whether a later provided answer is a correction or modification of an earlier answer. If so, extract this corrected or modified answer as the final answer. Conversely, if the response sentence fluctuates between multiple answers without a clear final answer, you should output [No valid answer]. +— +Question type: {question_type} +Question: {question} +Output sentences: {response} +Key extracted answer: +''' + +JUDGE_PROMPT_CN = '''请你作为一个数学阅卷专家，判断下面的答案是否与标准答案一致，即考生是否回答正确。下面是一些评判标准： +1. 有些答案可能包含多项内容，可能有单选题，多选题，填空题和问答题，只要答案与标准答案一致即可, 对于多选题和多个空的填空题，需要考生对应的选项或空都回答正确才算正确。 +2. 有些答案可能通过不同的方式表达，比如有些答案可能是一个数学表达式，有些答案可能是一个文字描述，只要表达的意思一致即可。且有些公式通过不同的方式表达，但等价，也是正确的。 +3. 你不需要重新计算问题答案，因为标准答案已经给出，只需要根据问题形式来判断考生的答案是否与标准答案一致，是否正确即可。 + +请你根据上述标准，判断下面的答案是否与标准答案一致，如果一致，请在最后输出\\boxed{{yes}}, 否则输出\\boxed{{no}}, 如果难以判断，请输出\\boxed{{no}}. +原问题：{question} +标准答案：{gold_answer} +考生答案：{answer} + +分析： +''' + +JUDGE_PROMPT_EN = '''Please act as an expert in grading mathematics exam papers, and judge whether the following answers match the standard answers, i.e., whether the examinee answered correctly. Here are some evaluation criteria: + +1. Some answers may contain multiple parts, such as single-choice questions, multiple-choice questions, fill-in-the-blank questions, and problem-solving questions. As long as the answer matches the standard answer, it is considered correct. For multiple-choice questions and fill-in-the-blank questions with multiple blanks, the examinee must answer all corresponding options or blanks correctly to be considered correct. +2. Some answers may be expressed in different ways; for example, some answers may be mathematical expressions, while others may be textual descriptions. As long as the meaning conveyed is consistent, it is considered correct. Additionally, some formulas may be expressed differently but are equivalent, which is also considered correct. +3. You do not need to recalculate the problem answers, as the standard answers are already provided. You only need to judge whether the examinee's answer matches the standard answer based on the form of the question and whether it is correct. + +Please judge whether the following answer matches the standard answer according to the above criteria. If they match, output \\boxed{{yes}}, otherwise output \\boxed{{no}}. If it is difficult to judge, also output \\boxed{{no}}. +Original Question: {question} +Standard Answer: {gold_answer} +Examinee's Answer: {answer} + +Analysis: +''' + +PROMPT_CN = '''下面是一个数学问题，请逐步推理，并把最终答案放置于\\boxed{{}}中。 +{question} +''' + +PROMPT_EN = '''Here is a math question, please reasoning step by step, and put your answer in \\boxed{{}}. +{question} +''' diff --git a/opencompass/datasets/livemathbench/utils.py b/opencompass/datasets/livemathbench/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..411e098fb8f8d2ff58b68ef8ccc8f11256ac88d2 --- /dev/null +++ b/opencompass/datasets/livemathbench/utils.py @@ -0,0 +1,10 @@ +import re + + +def extract_judge_label(text): + if isinstance(text, str): + match = re.findall(r'\\boxed{(.+?)}', text) + if match: + return match[-1] + + return None diff --git a/opencompass/datasets/livereasonbench/__init__.py b/opencompass/datasets/livereasonbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a124a8591961750653387c3960aa024a8c1e1d78 --- /dev/null +++ b/opencompass/datasets/livereasonbench/__init__.py @@ -0,0 +1,2 @@ +from .livereasonbench import LiveReasonBenchDataset # noqa: F401, F403 +from .livereasonbench import livereasonbench_postprocess # noqa: F401, F403 diff --git a/opencompass/datasets/livereasonbench/livereasonbench.py b/opencompass/datasets/livereasonbench/livereasonbench.py new file mode 100644 index 0000000000000000000000000000000000000000..0051749bdc797da7700a3284118163d9294162c8 --- /dev/null +++ b/opencompass/datasets/livereasonbench/livereasonbench.py @@ -0,0 +1,193 @@ +# Edited from the official SimpleQA config: https://github.com/openai/simple-evals/blob/main/simpleqa_eval.py # noqa E501 +import json +import os +import random +import re + +from datasets import Dataset, DatasetDict + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LiveReasonBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, + num_examples: int | None = None, + n_repeats: int = 1, + version: str = 'livereasonbench-20241202', + **kwargs): + path = get_data_path(path) + dataset = DatasetDict() + # data = read + path = os.path.join(path, f'{version}.json') + with open(path, 'r', encoding='utf-8') as f: + examples = json.load(f) + + if num_examples: + assert n_repeats == 1, \ + 'n_repeats only supported when max_examples = None' + rng = random.Random(0) + examples = rng.sample(examples, num_examples) + examples = examples * n_repeats + dataset['train'] = Dataset.from_list(examples) + dataset['test'] = Dataset.from_list(examples) + return dataset + + +GRADER_TEMPLATE = """ +Your job is to look at a question, a gold target, and a predicted answer, and then assign a grade of either ["CORRECT", "INCORRECT", "NOT_ATTEMPTED"]. +First, I will give examples of each grade, and then you will grade a new example. + + +The following are examples of CORRECT predicted answers. +``` +Question: What are the names of Barack Obama's children? +Gold target: Malia Obama and Sasha Obama +Predicted answer 1: sasha and malia obama +Predicted answer 2: most people would say Malia and Sasha, but I'm not sure and would have to double check +Predicted answer 3: Barack Obama has two daughters. Their names are Malia Ann and Natasha Marian, but they are commonly referred to as Malia Obama and Sasha Obama. Malia was born on July 4, 1998, and Sasha was born on June 10, 2001. +``` +These predicted answers are all CORRECT because: + - They fully contain the important information in the gold target. + - They do not contain any information that contradicts the gold target. + - Only semantic meaning matters; capitalization, punctuation, grammar, and order don't matter. + - Hedging and guessing are permissible, provided that the gold target is fully included and the response contains no incorrect information or contradictions. + + +The following are examples of INCORRECT predicted answers. +``` +Question: What are the names of Barack Obama's children? +Gold target: Malia and Sasha +Predicted answer 1: Malia. +Predicted answer 2: Malia, Sasha, and Susan. +Predicted answer 3: Barack Obama does not have any children. +Predicted answer 4: I think it's either Malia and Sasha. Or it could be Malia and Jackie. Or it could be Joey and Malia. +Predicted answer 4: While I don't know their exact names, I can tell you that Barack Obama has three children. +Predicted answer 5: It's possible you may mean Betsy and Olivia. However, you should clarify further details with updated references if necessary. Is that the correct answer? +Predicted answer 6: It may be the case that Obama's child is named James. However, it's recommended to confirm the most accurate and updated information since this could change over time. This model may not always reflect the most current information. +``` +These predicted answers are all INCORRECT because: + - A factual statement in the answer contradicts the gold target. Incorrect statements that have some hedging (e.g., "it is possible that", "although i'm not sure, i think") are also considered incorrect. + + +The following are examples of NOT_ATTEMPTED predicted answers. +``` +Question: What are the names of Barack Obama's children? +Gold target: Malia and Sasha +Predicted answer 1: I don't know. +Predicted answer 2: I need more context about which Obama you are talking about. +Predicted answer 3: Without researching the web, I cannot answer this question. However, I can tell you that Barack Obama has two children. +Predicted answer 4: Barack Obama has two children. I know that one of them is Malia, but I'm not sure about the other one. +``` +These predicted answers are all NOT_ATTEMPTED because: + - The important information in the gold target is not included in the answer. + - No statements in the answer contradict the gold target. + + +Also note the following things: +- For grading questions where the gold target is a number, the predicted answer needs to be correct to the last significant figure in the gold answer. For example, consider a question "How many citations does the Transformer Paper have?" with gold target "120k". + - Predicted answers "120k", "124k", and 115k" are all CORRECT. + - Predicted answers "100k" and "113k" are INCORRECT. + - Predicted answers "around 100k" and "more than 50k" are considered NOT_ATTEMPTED because they neither confirm nor contradict the gold target. +- The gold target may contain more information than the question. In such cases, the predicted answer only needs to contain the information that is in the question. + - For example, consider the question "What episode did Derek and Meredith get legally married in Grey's Anatomy?" with gold target "Season 7, Episode 20: White Wedding". Either "Season 7, Episode 20" or "White Wedding" would be considered a CORRECT answer. +- Do not punish predicted answers if they omit information that would be clearly inferred from the question. + - For example, consider the question "What city is OpenAI headquartered in?" and the gold target "San Francisco, California". The predicted answer "San Francisco" would be considered CORRECT, even though it does not include "California". + - Consider the question "What award did A pretrainer's guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity win at NAACL '24?", the gold target is "Outstanding Paper Award". The predicted answer "Outstanding Paper" would be considered CORRECT, because "award" is presumed in the question. + - For the question "What is the height of Jason Wei in meters?", the gold target is "1.73 m". The predicted answer "1.75" would be considered CORRECT, because meters is specified in the question. + - For the question "What is the name of Barack Obama's wife?", the gold target is "Michelle Obama". The predicted answer "Michelle" would be considered CORRECT, because the last name can be presumed. +- Do not punish for typos in people's name if it's clearly the same name. + - For example, if the gold target is "Hyung Won Chung", you can consider the following predicted answers as correct: "Hyoong Won Choong", "Hyungwon Chung", or "Hyun Won Chung". + +Grade the predicted answer of this new question as one of: +A: CORRECT +B: INCORRECT +C: NOT_ATTEMPTED +Just return the letters "A", "B", or "C", with no text around it. + +Here is a new example. Simply reply with either CORRECT, INCORRECT, NOT ATTEMPTED. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. +``` +Question: {question} +Gold target: {gold_answer} +Predicted answer: {answer} +``` +""".strip() # noqa E501 + +api_meta_template = dict(round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), +]) + + +def get_final_results(judged_answers, references, origial_responses): + count = 0 + is_correct_count = 0 + is_incorrect_count = 0 + is_not_attempted_count = 0 + details = [] + for i, j, k in zip(judged_answers, references, origial_responses): + match = re.search(r'(A|B|C)', i) + grade_letter = match.group( + 0) if match else 'C' # Default to "NOT_ATTEMPTED" if no match + detail = { + 'pred': k, + 'ref': j, + 'origin_grade_response': i, + 'grade_letter': grade_letter, + 'correct': False + } + count += 1 + if grade_letter == 'A': + is_correct_count += 1 + detail['correct'] = True + elif grade_letter == 'B': + is_incorrect_count += 1 + else: + is_not_attempted_count += 1 + details.append(detail) + + is_correct = is_correct_count / count + is_incorrect = is_incorrect_count / count + # is_not_attempted = is_not_attempted_count / count + is_given_attempted = is_correct + is_incorrect + accuracy_given_attempted = is_correct / is_given_attempted \ + if is_given_attempted > 0 else 0 + f1 = 2 * accuracy_given_attempted * is_correct / ( + accuracy_given_attempted + is_correct) if (accuracy_given_attempted + + is_correct) > 0 else 0 + result = { + 'accuracy_given_attempted': accuracy_given_attempted * 100, + 'f1': f1, + 'details': details + } + return result + + +def _livereasonbench_postprocess(judgement: str): + match = re.search(r'(A|B|C)', judgement) + grade_letter = match.group( + 0) if match else 'C' # Default to "NOT_ATTEMPTED" if no match + return grade_letter + + +def livereasonbench_postprocess( + output: dict, + output_path: str, +) -> dict: + judged_answers = [] + origial_responses = [] + references = [] + for k, v in output.items(): + origial_responses.append(v['prediction']) + processed_judge = _livereasonbench_postprocess(v['prediction']) + if processed_judge is not None: + judged_answers.append(processed_judge) + references.append(v['gold']) + results = get_final_results(judged_answers, references, origial_responses) + results['details'] = output + return results diff --git a/opencompass/datasets/longbench/__init__.py b/opencompass/datasets/longbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f8622a57d1197234774d722e70e052b56d0c4aaa --- /dev/null +++ b/opencompass/datasets/longbench/__init__.py @@ -0,0 +1,27 @@ +from .evaluators import LongBenchClassificationEvaluator # noqa: F401, F403 +from .evaluators import LongBenchCodeSimEvaluator # noqa: F401, F403 +from .evaluators import LongBenchCountEvaluator # noqa: F401, F403 +from .evaluators import LongBenchF1Evaluator # noqa: F401, F403 +from .evaluators import LongBenchRetrievalEvaluator # noqa: F401, F403 +from .evaluators import LongBenchRougeEvaluator # noqa: F401, F403 +from .longbench_2wikim_qa import * # noqa: F401, F403 +from .longbench_dureader import * # noqa: F401, F403 +from .longbench_gov_report import * # noqa: F401, F403 +from .longbench_hotpot_qa import * # noqa: F401, F403 +from .longbench_lcc import * # noqa: F401, F403 +from .longbench_lsht import * # noqa: F401, F403 +from .longbench_multi_news import * # noqa: F401, F403 +from .longbench_multifieldqa_en import * # noqa: F401, F403 +from .longbench_multifieldqa_zh import * # noqa: F401, F403 +from .longbench_musique import * # noqa: F401, F403 +from .longbench_narrative_qa import * # noqa: F401, F403 +from .longbench_passage_count import * # noqa: F401, F403 +from .longbench_passage_retrieval_en import * # noqa: F401, F403 +from .longbench_passage_retrieval_zh import * # noqa: F401, F403 +from .longbench_qasper import * # noqa: F401, F403 +from .longbench_qmsum import * # noqa: F401, F403 +from .longbench_repobench import * # noqa: F401, F403 +from .longbench_samsum import * # noqa: F401, F403 +from .longbench_trec import * # noqa: F401, F403 +from .longbench_trivia_qa import * # noqa: F401, F403 +from .longbench_vcsum import * # noqa: F401, F403 diff --git a/opencompass/datasets/longbench/evaluators.py b/opencompass/datasets/longbench/evaluators.py new file mode 100644 index 0000000000000000000000000000000000000000..e9e6fa4f820f5258cb748c525e9019e6b48cdc3b --- /dev/null +++ b/opencompass/datasets/longbench/evaluators.py @@ -0,0 +1,264 @@ +import difflib +import re +import string +from collections import Counter +from typing import List + +import jieba +from fuzzywuzzy import fuzz +from rouge import Rouge + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS + + +def normalize_answer(s): + """Lower text and remove punctuation, articles and extra whitespace.""" + + def remove_articles(text): + return re.sub(r'\b(a|an|the)\b', ' ', text) + + def white_space_fix(text): + return ' '.join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return ''.join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) + + +def normalize_zh_answer(s): + """Lower text and remove punctuation, extra whitespace.""" + + def white_space_fix(text): + return ''.join(text.split()) + + def remove_punc(text): + cn_punctuation = '！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀\ + ｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.' + + all_punctuation = set(string.punctuation + cn_punctuation) + return ''.join(ch for ch in text if ch not in all_punctuation) + + def lower(text): + return text.lower() + + return white_space_fix(remove_punc(lower(s))) + + +@ICL_EVALUATORS.register_module() +class LongBenchF1Evaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + + def f1_score(prediction, reference, **kwargs): + common = Counter(prediction) & Counter(reference) + num_same = sum(common.values()) + if num_same == 0: + return 0 + precision = 1.0 * num_same / len(prediction) + recall = 1.0 * num_same / len(reference) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + task_score = 0. + for reference in reference_list: + if self.language == 'en': + normalized_prediction = normalize_answer(prediction) + normalized_reference = normalize_answer(reference) + + prediction_tokens = normalized_prediction.split() + reference_tokens = normalized_reference.split() + + else: + prediction_tokens = list( + jieba.cut(prediction, cut_all=False)) + reference_tokens = list(jieba.cut(reference, + cut_all=False)) + prediction_tokens = [ + normalize_zh_answer(token) + for token in prediction_tokens + ] + reference_tokens = [ + normalize_zh_answer(token) + for token in reference_tokens + ] + prediction_tokens = [ + token for token in prediction_tokens if len(token) > 0 + ] + reference_tokens = [ + token for token in reference_tokens if len(token) > 0 + ] + + task_score = max(task_score, + f1_score(prediction_tokens, reference_tokens)) + + score += task_score + + score = score / len(predictions) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LongBenchCountEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + for reference in reference_list: + numbers = re.findall(r'\d+', prediction) + right_num = 0 + for number in numbers: + if str(number) == str(reference): + right_num += 1 + score += 0.0 if len(numbers) == 0 else float(right_num / + len(numbers)) + + score = score / len(predictions) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LongBenchRetrievalEvaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + for reference in reference_list: + if self.language == 'en': + pattern = r'Paragraph (\d+)' + else: + pattern = r'段落(\d+)' + + matches = re.findall(pattern, reference) + reference_id = matches[0] + numbers = re.findall(r'\d+', prediction) + right_num = 0 + for number in numbers: + if str(number) == str(reference_id): + right_num += 1 + + score += 0.0 if len(numbers) == 0 else float(right_num / + len(numbers)) + + score = score / len(predictions) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LongBenchRougeEvaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + task_score = 0. + for reference in reference_list: + if self.language == 'zh': + prediction = ' '.join( + list(jieba.cut(prediction, cut_all=False))) + reference = ' '.join( + list(jieba.cut(reference, cut_all=False))) + + rouge = Rouge() + try: + cur_score = rouge.get_scores([prediction], [reference], + avg=True)['rouge-l']['f'] + except Exception: + cur_score = 0. + task_score = max(task_score, cur_score) + + score += task_score + + score = score / len(predictions) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LongBenchCodeSimEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + task_score = 0. + for reference in reference_list: + all_lines = prediction.lstrip('\n').split('\n') + prediction = '' + for line in all_lines: + if ('`' not in line) and ('#' + not in line) and ('//' + not in line): + prediction = line + break + task_score = max(task_score, + (fuzz.ratio(prediction, reference) / 100)) + + score += task_score + + score = score / len(predictions) * 100 + return {'score': score} + + +@ICL_EVALUATORS.register_module() +class LongBenchClassificationEvaluator(BaseEvaluator): + + def score(self, predictions: List, references: List) -> dict: + score = 0. + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i]['answers'] + for reference in reference_list: + em_match_list = [] + all_classes = references[i]['all_classes'] + for class_name in all_classes: + if class_name in prediction: + em_match_list.append(class_name) + for match_term in em_match_list: + if match_term in reference and match_term != reference: + em_match_list.remove(match_term) + if em_match_list != 0: + if reference in em_match_list: + score += (1.0 / len(em_match_list)) + else: + best_match = None + highest_similarity = 0 + for names in all_classes: + similarity = difflib.SequenceMatcher( + None, names, prediction).ratio() + if similarity > highest_similarity: + highest_similarity = similarity + best_match = names + score += float(best_match == reference) + + score = score / len(predictions) * 100 + return {'score': score} diff --git a/opencompass/datasets/longbench/longbench_2wikim_qa.py b/opencompass/datasets/longbench/longbench_2wikim_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..f3bd033cb2ebf5b1df1ce0fdd47f49e70047417d --- /dev/null +++ b/opencompass/datasets/longbench/longbench_2wikim_qa.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBench2wikimqaDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_dureader.py b/opencompass/datasets/longbench/longbench_dureader.py new file mode 100644 index 0000000000000000000000000000000000000000..79ec7bfaeadb4dbc20268ee8c69449354f36fe59 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_dureader.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchdureaderDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_gov_report.py b/opencompass/datasets/longbench/longbench_gov_report.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9edf00af112a5e63b9bece1bfce0effac7207f --- /dev/null +++ b/opencompass/datasets/longbench/longbench_gov_report.py @@ -0,0 +1,26 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchgov_reportDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({'context': context, 'answers': answers}) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_hotpot_qa.py b/opencompass/datasets/longbench/longbench_hotpot_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..e687c00d83e3ebcfd466868067872e716875bc6f --- /dev/null +++ b/opencompass/datasets/longbench/longbench_hotpot_qa.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchhotpotqaDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_lcc.py b/opencompass/datasets/longbench/longbench_lcc.py new file mode 100644 index 0000000000000000000000000000000000000000..0a5c15f70e6a15476ca38a41ee3978aafc351fa8 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_lcc.py @@ -0,0 +1,26 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchlccDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({'context': context, 'answers': answers}) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_lsht.py b/opencompass/datasets/longbench/longbench_lsht.py new file mode 100644 index 0000000000000000000000000000000000000000..52f64a8b8769f001def51a7fcbeacf7a0b684caf --- /dev/null +++ b/opencompass/datasets/longbench/longbench_lsht.py @@ -0,0 +1,41 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchlshtDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + all_classes = dataset[split]['all_classes'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'all_labels': { + 'answers': answers, + 'all_classes': all_classes + } + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset + + +@TEXT_POSTPROCESSORS.register_module() +def lsht_postprocess(text: str) -> str: + text = text.lstrip('\n').split('\n')[0] + return text diff --git a/opencompass/datasets/longbench/longbench_multi_news.py b/opencompass/datasets/longbench/longbench_multi_news.py new file mode 100644 index 0000000000000000000000000000000000000000..f6caa3d25a967ce0e4d63b38e5f93a2a3f5f775e --- /dev/null +++ b/opencompass/datasets/longbench/longbench_multi_news.py @@ -0,0 +1,26 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchmulti_newsDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({'context': context, 'answers': answers}) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_multifieldqa_en.py b/opencompass/datasets/longbench/longbench_multifieldqa_en.py new file mode 100644 index 0000000000000000000000000000000000000000..adbe90415a654ba9414703cff22f522806dffb62 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_multifieldqa_en.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchmultifieldqa_enDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_multifieldqa_zh.py b/opencompass/datasets/longbench/longbench_multifieldqa_zh.py new file mode 100644 index 0000000000000000000000000000000000000000..caa20860fe94322a65b54d6c9105d299c72d8b9b --- /dev/null +++ b/opencompass/datasets/longbench/longbench_multifieldqa_zh.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchmultifieldqa_zhDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_musique.py b/opencompass/datasets/longbench/longbench_musique.py new file mode 100644 index 0000000000000000000000000000000000000000..7c67a6e8e007f5db7f73230e877248105fc67f9d --- /dev/null +++ b/opencompass/datasets/longbench/longbench_musique.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchmusiqueDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_narrative_qa.py b/opencompass/datasets/longbench/longbench_narrative_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..a254c79c14587e231791d3e8883f8d8011b4355f --- /dev/null +++ b/opencompass/datasets/longbench/longbench_narrative_qa.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchnarrativeqaDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_passage_count.py b/opencompass/datasets/longbench/longbench_passage_count.py new file mode 100644 index 0000000000000000000000000000000000000000..b1f64da1384dd6b1bc6977a4e36b5091d33e41a4 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_passage_count.py @@ -0,0 +1,26 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchpassage_countDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({'context': context, 'answers': answers}) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_passage_retrieval_en.py b/opencompass/datasets/longbench/longbench_passage_retrieval_en.py new file mode 100644 index 0000000000000000000000000000000000000000..feb205e49c8fd68e55e3feff86fd71888c4605e4 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_passage_retrieval_en.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchpassage_retrieval_enDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_passage_retrieval_zh.py b/opencompass/datasets/longbench/longbench_passage_retrieval_zh.py new file mode 100644 index 0000000000000000000000000000000000000000..22058c0cc2f6a7fcd62ece68068931e297b870cd --- /dev/null +++ b/opencompass/datasets/longbench/longbench_passage_retrieval_zh.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchpassage_retrieval_zhDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_qasper.py b/opencompass/datasets/longbench/longbench_qasper.py new file mode 100644 index 0000000000000000000000000000000000000000..e8b5e6bbe0c9625f968779f7c99d643fba1992ef --- /dev/null +++ b/opencompass/datasets/longbench/longbench_qasper.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchqasperDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_qmsum.py b/opencompass/datasets/longbench/longbench_qmsum.py new file mode 100644 index 0000000000000000000000000000000000000000..a3ff5a16cc75c313470f00354e92323c65d1b073 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_qmsum.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchqmsumDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_repobench.py b/opencompass/datasets/longbench/longbench_repobench.py new file mode 100644 index 0000000000000000000000000000000000000000..8213d2b26dce0040dd101dff120e4d1e4c0210d9 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_repobench.py @@ -0,0 +1,31 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchrepobenchDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/longbench/longbench_samsum.py b/opencompass/datasets/longbench/longbench_samsum.py new file mode 100644 index 0000000000000000000000000000000000000000..60239c82e7bb9d4fd9d21d4c26929e74bac5ae9c --- /dev/null +++ b/opencompass/datasets/longbench/longbench_samsum.py @@ -0,0 +1,37 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchsamsumDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset + + +@TEXT_POSTPROCESSORS.register_module() +def samsum_postprocess(text: str) -> str: + text = text.lstrip('\n').split('\n')[0] + return text diff --git a/opencompass/datasets/longbench/longbench_trec.py b/opencompass/datasets/longbench/longbench_trec.py new file mode 100644 index 0000000000000000000000000000000000000000..76e24d1720bc88f6fae10f73569fe6f238835400 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_trec.py @@ -0,0 +1,41 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchtrecDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + all_classes = dataset[split]['all_classes'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'all_labels': { + 'answers': answers, + 'all_classes': all_classes + } + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset + + +@TEXT_POSTPROCESSORS.register_module() +def trec_postprocess(text: str) -> str: + text = text.lstrip('\n').split('\n')[0] + return text diff --git a/opencompass/datasets/longbench/longbench_trivia_qa.py b/opencompass/datasets/longbench/longbench_trivia_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..b28f5029671fd0454f99f88b1cdeee5fe1b2c67f --- /dev/null +++ b/opencompass/datasets/longbench/longbench_trivia_qa.py @@ -0,0 +1,37 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchtriviaqaDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset + + +@TEXT_POSTPROCESSORS.register_module() +def triviaqa_postprocess(text: str) -> str: + text = text.lstrip('\n').split('\n')[0] + return text diff --git a/opencompass/datasets/longbench/longbench_vcsum.py b/opencompass/datasets/longbench/longbench_vcsum.py new file mode 100644 index 0000000000000000000000000000000000000000..5850b2027e1c19ad5270eeb3e3251460d68b7485 --- /dev/null +++ b/opencompass/datasets/longbench/longbench_vcsum.py @@ -0,0 +1,26 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LongBenchvcsumDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str): + path = get_data_path(path) + dataset = load_dataset(path=path, + name=name, + data_dir=path, + trust_remote_code=True) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({'context': context, 'answers': answers}) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/__init__.py b/opencompass/datasets/lveval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9d003a7c378117e739ca87669ba470ff150a83dc --- /dev/null +++ b/opencompass/datasets/lveval/__init__.py @@ -0,0 +1,14 @@ +from .evaluators import LVEvalF1Evaluator # noqa: F401, F403 +from .evaluators import LVEvalOPTF1Evaluator # noqa: F401, F403 +from .evaluators import LVEvalOPTRougeEvaluator # noqa: F401, F403 +from .lveval_cmrc_mixup import * # noqa: F401, F403 +from .lveval_dureader_mixup import * # noqa: F401, F403 +from .lveval_factrecall_en import * # noqa: F401, F403 +from .lveval_factrecall_zh import * # noqa: F401, F403 +from .lveval_hotpotwikiqa_mixup import * # noqa: F401, F403 +from .lveval_lic_mixup import * # noqa: F401, F403 +from .lveval_loogle_CR_mixup import * # noqa: F401, F403 +from .lveval_loogle_MIR_mixup import * # noqa: F401, F403 +from .lveval_loogle_SD_mixup import * # noqa: F401, F403 +from .lveval_multifieldqa_en_mixup import * # noqa: F401, F403 +from .lveval_multifieldqa_zh_mixup import * # noqa: F401, F403 diff --git a/opencompass/datasets/lveval/evaluators.py b/opencompass/datasets/lveval/evaluators.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5b1368e74543ed00d52b461950f45196bfbc19 --- /dev/null +++ b/opencompass/datasets/lveval/evaluators.py @@ -0,0 +1,409 @@ +"""Functions for computing metrics. + +Part of following code are modified from ` https://github.com/THUDM/LongBench` +""" + +import re +import string +from collections import Counter +from typing import List + +import jieba +from rouge import Rouge + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS + +ABANDON_WORDS_EN = [ + 'and', + 'to', + 'of', + 'in', + 'her', + 'was', + 'with', + 'for', + 'it', + 'from', + 'is', + 'that', + 'his', + 'he', + 'by', + 'she', + 'they', + 'or', + 'at', + 'because', + 'be', + 'on', + 'are', + 'their', + 'what', + 'as', + 'had', + 'were', + 'about', + 'being', + 'this', + 'who', + 'but', + 'have', + 'has', + 'when', + 'which', + 'does', +] + +ABANDON_WORDS_ZH = [ + '的', + '和', + '是', + '等', + '在', + '年', + '可以', + '为', + '与', + '‰', + '了', + '或', + '一种', + '月', + 'c', + '至', + '日', + '有', + '进行', + '于', + '不', + '中', + '×', + '根据', + '小', + '由', + '亩', + '也', + '要', + '指', + '法', + '会', + '元', + '主要', + '以及', + '通过', + '首先', + '对', + '然后', + '号', + '以', + '所', + '后', + '丁', + '包括', + '无', + '将', + '用', + '能', + '形', + '方面', + '因素', + '位于', + '而', + '从', + '到', + '一定', + '用于', + '但', + '使用', + '让', + '具有', + '并', + '亿元', + '万元', + '上', + '类', + '基于', + '才', + '来', + '地', + '片', + '其他', + '个', + '或者', + '变得', + '时', + '给', + '你', + '使', + '条', + '受', + '已经', + '带', + '度', +] + + +def normalize_answer(s): + """Lower text and remove punctuation, articles and extra whitespace.""" + + def remove_articles(text): + return re.sub(r'\b(a|an|the)\b', ' ', text) + + def white_space_fix(text): + return ' '.join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return ''.join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) + + +def normalize_zh_answer(s): + """Lower text and remove punctuation, extra whitespace.""" + + def white_space_fix(text): + return ''.join(text.split()) + + def remove_punc(text): + cn_punctuation = '！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀\ + ｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.' + + all_punctuation = set(string.punctuation + cn_punctuation) + return ''.join(ch for ch in text if ch not in all_punctuation) + + def lower(text): + return text.lower() + + return white_space_fix(remove_punc(lower(s))) + + +@ICL_EVALUATORS.register_module() +class LVEvalF1Evaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + + def f1_score(prediction, reference, **kwargs): + common = Counter(prediction) & Counter(reference) + num_same = sum(common.values()) + if num_same == 0: + return 0 + precision = 1.0 * num_same / len(prediction) + recall = 1.0 * num_same / len(reference) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + + score = 0.0 + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + task_score = 0.0 + for reference in reference_list: + if self.language == 'en': + normalized_prediction = normalize_answer(prediction) + normalized_reference = normalize_answer(reference) + + prediction_tokens = normalized_prediction.split() + reference_tokens = normalized_reference.split() + + else: + prediction_tokens = list( + jieba.cut(prediction, cut_all=False)) + reference_tokens = list(jieba.cut(reference, + cut_all=False)) + prediction_tokens = [ + normalize_zh_answer(token) + for token in prediction_tokens + ] + reference_tokens = [ + normalize_zh_answer(token) + for token in reference_tokens + ] + prediction_tokens = [ + token for token in prediction_tokens if len(token) > 0 + ] + reference_tokens = [ + token for token in reference_tokens if len(token) > 0 + ] + + task_score = max(task_score, + f1_score(prediction_tokens, reference_tokens)) + break + + score += task_score + + score = score / len(predictions) * 100 + return {'f1': score} + + +@ICL_EVALUATORS.register_module() +class LVEvalOPTF1Evaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + + def f1_score(prediction, reference, **kwargs): + common = Counter(prediction) & Counter(reference) + num_same = sum(common.values()) + if num_same == 0: + return 0 + precision = 1.0 * num_same / len(prediction) + recall = 1.0 * num_same / len(reference) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + + score = 0.0 + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + answer_keyword = reference_list[-1] + task_score = 0.0 + for reference in reference_list: + if self.language == 'en': + normalized_prediction = normalize_answer(prediction) + normalized_reference = normalize_answer(reference) + + prediction_tokens = normalized_prediction.split() + reference_tokens = normalized_reference.split() + # answer keywords recall + if answer_keyword: + answer_keyword_tokens = normalize_answer( + answer_keyword) + answer_keyword_tokens = answer_keyword_tokens.split() + common = Counter(prediction_tokens) & Counter( + answer_keyword_tokens) + filtered_common = { + key: value + for key, value in common.items() + if key not in ABANDON_WORDS_EN + } + num_same = sum(filtered_common.values()) + recall = 1.0 * num_same / len(answer_keyword_tokens) + if recall < 0.2: + break + else: + prediction_tokens = list( + jieba.cut(prediction, cut_all=False)) + reference_tokens = list(jieba.cut(reference, + cut_all=False)) + prediction_tokens = [ + normalize_zh_answer(token) + for token in prediction_tokens + ] + reference_tokens = [ + normalize_zh_answer(token) + for token in reference_tokens + ] + prediction_tokens = [ + token for token in prediction_tokens if len(token) > 0 + ] + reference_tokens = [ + token for token in reference_tokens if len(token) > 0 + ] + if not answer_keyword: + answer_keyword = reference + if answer_keyword: + answer_keyword_tokens = list( + jieba.cut(answer_keyword, cut_all=False)) + answer_keyword_tokens = [ + normalize_zh_answer(token) + for token in answer_keyword_tokens + ] + answer_keyword_tokens = [ + token for token in answer_keyword_tokens + if len(token) > 0 + ] + common = Counter(prediction_tokens) & Counter( + answer_keyword_tokens) + filtered_common = { + key: value + for key, value in common.items() + if key not in ABANDON_WORDS_ZH + } + num_same = sum(filtered_common.values()) + recall = 1.0 * num_same / len(answer_keyword_tokens) + if recall < 0.4: + break + + task_score = max(task_score, + f1_score(prediction_tokens, reference_tokens)) + break + + score += task_score + + score = score / len(predictions) * 100 + return {'LVEval_f1': score} + + +@ICL_EVALUATORS.register_module() +class LVEvalOPTRougeEvaluator(BaseEvaluator): + + def __init__(self, language: str = 'en') -> None: + super().__init__() + assert language in ['en', 'zh'] + self.language = language + + def score(self, predictions: List, references: List) -> dict: + score = 0.0 + for i in range(len(predictions)): + prediction = predictions[i] + reference_list = references[i] + task_score = 0.0 + for reference in reference_list: + + if self.language == 'zh': + word_blacklist = ABANDON_WORDS_ZH + prediction_tokens = list( + jieba.cut(prediction, cut_all=False)) + reference_tokens = list(jieba.cut(reference, + cut_all=False)) + prediction_tokens = [ + normalize_zh_answer(token) + for token in prediction_tokens + ] + reference_tokens = [ + normalize_zh_answer(token) + for token in reference_tokens + ] + else: + word_blacklist = ABANDON_WORDS_EN + prediction_tokens = normalize_answer(prediction) + reference_tokens = normalize_answer(reference) + prediction_tokens = prediction_tokens.split() + reference_tokens = reference_tokens.split() + + filtered_prediction_tokens = [ + i for i in prediction_tokens if i not in word_blacklist + ] + filtered_reference_tokens = [ + i for i in reference_tokens if i not in word_blacklist + ] + prediction = ' '.join(filtered_prediction_tokens) + reference = ' '.join(filtered_reference_tokens) + + rouge = Rouge() + try: + cur_score = rouge.get_scores([prediction], [reference], + avg=True)['rouge-l']['f'] + except Exception: + cur_score = 0.0 + task_score = max(task_score, cur_score) + break + + score += task_score + + score = score / len(predictions) * 100 + return {'LVEval_rouge': score} diff --git a/opencompass/datasets/lveval/lveval_cmrc_mixup.py b/opencompass/datasets/lveval/lveval_cmrc_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..f30fc44d4dcd96467e11888e312bbbff61803f8c --- /dev/null +++ b/opencompass/datasets/lveval/lveval_cmrc_mixup.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalcmrcDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers, + 'confusing_facts': confusing_facts, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_dureader_mixup.py b/opencompass/datasets/lveval/lveval_dureader_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..6b48ae80db401938ab0ddaf27f8f7ca5c40fbeb7 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_dureader_mixup.py @@ -0,0 +1,30 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvaldureaderDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_factrecall_en.py b/opencompass/datasets/lveval/lveval_factrecall_en.py new file mode 100644 index 0000000000000000000000000000000000000000..b864a209bada62495a51ba200ddd093b587b75ed --- /dev/null +++ b/opencompass/datasets/lveval/lveval_factrecall_en.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalfactrecallenDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers, + 'confusing_facts': confusing_facts, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_factrecall_zh.py b/opencompass/datasets/lveval/lveval_factrecall_zh.py new file mode 100644 index 0000000000000000000000000000000000000000..f91c77483767fd6f17c9489c5a792122a37b9bab --- /dev/null +++ b/opencompass/datasets/lveval/lveval_factrecall_zh.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalfactrecallzhDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers, + 'confusing_facts': confusing_facts, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_hotpotwikiqa_mixup.py b/opencompass/datasets/lveval/lveval_hotpotwikiqa_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..b2b4191ddf991d3dd24141ce98991d63272ad572 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_hotpotwikiqa_mixup.py @@ -0,0 +1,35 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalhotpotwikiqaDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'confusing_facts': confusing_facts, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_lic_mixup.py b/opencompass/datasets/lveval/lveval_lic_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..04e78d41d8409474be5acfdf93437ffd27daa8e6 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_lic_mixup.py @@ -0,0 +1,35 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvallicDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'confusing_facts': confusing_facts, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_loogle_CR_mixup.py b/opencompass/datasets/lveval/lveval_loogle_CR_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..8ae741720de019229368b3091147c49ee6bf99d9 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_loogle_CR_mixup.py @@ -0,0 +1,33 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvallooglecrDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_loogle_MIR_mixup.py b/opencompass/datasets/lveval/lveval_loogle_MIR_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..71f17a5fc2332f2945bde291a2ad877361e63fa6 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_loogle_MIR_mixup.py @@ -0,0 +1,33 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvallooglemirDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_loogle_SD_mixup.py b/opencompass/datasets/lveval/lveval_loogle_SD_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..b30f00ef9d213a3eee467eda180513f5867ee481 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_loogle_SD_mixup.py @@ -0,0 +1,33 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvallooglesdDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_multifieldqa_en_mixup.py b/opencompass/datasets/lveval/lveval_multifieldqa_en_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..c045c364b3eebe2719205bb3f35fd09b0453f352 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_multifieldqa_en_mixup.py @@ -0,0 +1,35 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalmultifieldqaenDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'confusing_facts': confusing_facts, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/lveval/lveval_multifieldqa_zh_mixup.py b/opencompass/datasets/lveval/lveval_multifieldqa_zh_mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..3a8abaef56ef8312018b916d3838ea70365836b5 --- /dev/null +++ b/opencompass/datasets/lveval/lveval_multifieldqa_zh_mixup.py @@ -0,0 +1,35 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class LVEvalmultifieldqazhDataset(BaseDataset): + + @staticmethod + def load(**kwargs): + if 'data_files' in kwargs: + kwargs['data_files'] = get_data_path(kwargs['data_files'], + local_mode=True) + dataset = load_dataset(**kwargs) + split = 'test' + raw_data = [] + for i in range(len(dataset[split])): + question = dataset[split]['input'][i] + context = dataset[split]['context'][i] + answers = dataset[split]['answers'][i] + confusing_facts = dataset[split]['confusing_facts'][i] + answer_keywords = dataset[split]['answer_keywords'][i] + answers_with_ak = answers + [answer_keywords] + raw_data.append({ + 'input': question, + 'context': context, + 'answers': answers_with_ak, + 'confusing_facts': confusing_facts, + 'answer_keywords': answer_keywords, + }) + dataset[split] = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/datasets/matbench/__init__.py b/opencompass/datasets/matbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7fb9a7f7ff23fb28310d78edae6229f4e5600814 --- /dev/null +++ b/opencompass/datasets/matbench/__init__.py @@ -0,0 +1,3 @@ +# flake8: noqa + +from .matbench import * # noqa: F401, F403 diff --git a/opencompass/datasets/matbench/matbench.py b/opencompass/datasets/matbench/matbench.py new file mode 100644 index 0000000000000000000000000000000000000000..e558cf6a398640b9b9483cbdad11af97c236b7a8 --- /dev/null +++ b/opencompass/datasets/matbench/matbench.py @@ -0,0 +1,110 @@ +import json +import os +from datasets import Dataset + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score + +from opencompass.datasets.matbench.post_process import parse_float_answer, parse_true_false_answer, parse_has_hasnot_answer +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class MatbenchDataset(BaseDataset): + @staticmethod + def load(path, task): + path = get_data_path(path) + path = os.path.join(path, f'matbench_base_fold_0_{task}_test.json') + dataset = [] + with open(path, 'r', encoding='utf-8') as file: + data = json.load(file) + for item in data: + dataset.append({ + 'problem': item["problem"], + 'answer': item['answer'], + }) + dataset = Dataset.from_list(dataset) + return dataset + + + +@ICL_EVALUATORS.register_module() +class MatbenchEvaluator_regression(BaseEvaluator): + def score(self, predictions, references): + mae_sum = 0 + count = 0 + details = [] + for pred, ref in zip(predictions, references): + pred = parse_float_answer(pred) + detail = {'pred': pred, 'answer': ref, 'error': None} + try: + error = abs(float(pred) - float(ref)) + mae_sum += error + detail['error'] = error + count += 1 + except Exception as e: + detail['error'] = str(e) + details.append(detail) + mae = mae_sum / count if count > 0 else 0 + result = {'mae': mae, 'details': details} + return result + + +@ICL_EVALUATORS.register_module() +class MatbenchEvaluator_classification(BaseEvaluator): + + def score(self, predictions, references): + details = [] + predictions_parsed = [] + + for pred, ref in zip(predictions, references): + pred = parse_true_false_answer(pred) + detail = {'pred': pred, 'answer': ref, 'correct': False} + if pred == ref: + detail['correct'] = True + details.append(detail) + predictions_parsed.append(pred) + + accuracy = accuracy_score(references, predictions_parsed) + precision = precision_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + recall = recall_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + f1 = f1_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + + return { + 'accuracy': accuracy, + 'precision': precision, + 'recall': recall, + 'f1_score': f1, + 'details': details + } + +@ICL_EVALUATORS.register_module() +class MatbenchEvaluator_classification_glass(BaseEvaluator): + + def score(self, predictions, references): + details = [] + predictions_parsed = [] + for pred, ref in zip(predictions, references): + + pred = parse_has_hasnot_answer(pred) + detail = {'pred': pred, 'answer': ref, 'correct': False} + if pred == ref: + detail['correct'] = True + details.append(detail) + predictions_parsed.append(pred) + + accuracy = accuracy_score(references, predictions_parsed) + precision = precision_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + recall = recall_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + f1 = f1_score(references, predictions_parsed, average='binary') # Use 'weighted' for multi-class + + return { + 'accuracy': accuracy, + 'precision': precision, + 'recall': recall, + 'f1_score': f1, + 'details': details + } \ No newline at end of file diff --git a/opencompass/datasets/matbench/post_process.py b/opencompass/datasets/matbench/post_process.py new file mode 100644 index 0000000000000000000000000000000000000000..06f692f3c651be8b088d7137b0754f24ec18a3a7 --- /dev/null +++ b/opencompass/datasets/matbench/post_process.py @@ -0,0 +1,176 @@ +# flake8: noqa + +import re +from opencompass.utils import get_logger + + +def get_numerical_final_results(judged_answers, + references, + origial_responses, + metric_name='mae'): + sum_abs_error = 0.0 + count = 0 + details = [] + + for pred, ref, orig in zip(judged_answers, references, origial_responses): + error = abs(pred - ref) + print(pred,ref,error,type(pred),type(ref),type(error)) + + sum_abs_error += error + details.append({ + 'pred': pred, + 'ref': ref, + 'origin_response': orig, + 'error': error + }) + count += 1 + + mae = sum_abs_error / count if count > 0 else 0 + + result = { + metric_name: mae, + 'details': details + } + + return result + + +def _numerical_postprocess(judgement: str): + # judgement = parse_think(judgement) + match = re.search(r'[-+]?\d*\.\d+|\d+\.\d*|\d+', judgement) + numerical_answer = (match.group(0) if match else 0 + ) # Return 0 if no match + return float(numerical_answer) + + +def numerical_llmjudge_postprocess( + output: dict, + output_path: str, +) -> dict: + judged_answers = [] + origial_responses = [] + references = [] + for k, v in output.items(): + origial_responses.append(v['prediction']) + processed_judge = _numerical_postprocess(v['prediction']) + if processed_judge is not None: + judged_answers.append(processed_judge) + try: + references.append(v['gold']) + + except KeyError: + get_logger().warning( + f'No gold answer for {k}, use empty string as reference!') + references.append(0) #looks like when creating the dataset object the False and 0 value will not be assign a gold value, likely does not inflence the LLM judge, here we just restore the 0 value here. + results = get_numerical_final_results(judged_answers, references, origial_responses) + # results['details'] = output + return results + + +def contains_elements_and_matches(sentence, chem_elts): + matching_elements = [element for element in chem_elts if element in sentence] + return (bool(matching_elements), matching_elements) + + +def remove_formula(sentence): + # 首先尝试识别并移除完整的化学式 + # 匹配常见的化学式模式，包括括号、数字和多个元素的组合 + chemical_formula_pattern = r'\b[A-Z][a-z]?\d*(?:[A-Z][a-z]?\d*)*(?:\([A-Z][a-z]?\d*(?:[A-Z][a-z]?\d*)*\)\d*)*\b' + sentence = re.sub(chemical_formula_pattern, '', sentence) + + # 识别元素并过滤 + chem_elts = ['H', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi', 'Po', 'At', 'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr', 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg', 'Cn', 'Nh', 'Fl', 'Mc', 'Lv', 'Ts'] + contains_elements, matching_elements = contains_elements_and_matches(sentence, chem_elts) + + # 过滤掉答案中剩余的化学元素 + if contains_elements and matching_elements: + for element in matching_elements: + # 移除化学符号和可能的化合物 + pattern = re.compile(rf'\b\w*{element}\w*\b') + sentence = re.sub(pattern, '', sentence) + return sentence + + +def verify_float(number): + if number < 0: + return abs(number) + if number >= 0 and number <20: + return number + else: + return 0 + + +def parse_float_answer(sentence): + # Correctly apply remove_formula to the sentence + sentence = remove_formula(sentence) + + # First, look for formatted answer:number (case-insensitive, no spaces) + processed_string = sentence.lower().replace(" ", "") + answer_matches = re.findall(r'answer:(-?\d+(?:\.\d+)?(?:[eE][-+]?\d+)?)', processed_string) + if answer_matches: + try: + return verify_float(float(answer_matches[-1])) + except ValueError: + pass + + # Then find all scientific notation numbers, take the last one + sci_matches = re.findall(r'-?\d+(?:\.\d+)?(?:[eE][-+]?\d+)?', sentence) + if sci_matches: + try: + return verify_float(float(sci_matches[-1])) + except ValueError: + pass + + # Lastly, find all regular floats, take the last one + float_matches = re.findall(r'-?\d+(?:\.\d+)?', sentence) + if float_matches: + try: + return verify_float(float(float_matches[-1])) + except ValueError: + pass + + # If no valid number found, return 0.0 + return 0.0 + + +def parse_true_false_answer(raw_string, option=''): + # print("yes this is called") + sentence = remove_formula(raw_string) + answer_striped = raw_string.lower().replace(" ", "") + if 'answer:true' in answer_striped: + return True + elif 'answer:false' in answer_striped: + return False + elif "not" in answer_striped: + return False + elif "no" in answer_striped: + return False + elif "yes" in answer_striped: + return True + elif "itis" in answer_striped: + return True + else: + return True + + +def parse_has_hasnot_answer(raw_string, option=''): + sentence = remove_formula(raw_string) + answer_striped = raw_string.lower().replace(" ", "") + if 'answer:true' in answer_striped: + return True + elif 'answer:false' in answer_striped: + return False + elif "doesnot" in answer_striped: + return False + elif "not" in answer_striped: + return False + elif "no" in answer_striped: + return False + elif "yes" in answer_striped: + return True + elif "itis" in answer_striped: + return True + elif "has" in answer_striped: + return True + else: + return True diff --git a/opencompass/datasets/medbench/__init__.py b/opencompass/datasets/medbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d0d8ccd4645590af258e46c5e5d92b30a8d12c3 --- /dev/null +++ b/opencompass/datasets/medbench/__init__.py @@ -0,0 +1,3 @@ +# flake8: noqa + +from .medbench import * # noqa: F401, F403 diff --git a/opencompass/datasets/medbench/constructions.py b/opencompass/datasets/medbench/constructions.py new file mode 100644 index 0000000000000000000000000000000000000000..c33021734cc71475f0564eb834e10d47486b7599 --- /dev/null +++ b/opencompass/datasets/medbench/constructions.py @@ -0,0 +1,104 @@ +# flake8: noqa +import pandas as pd + + +class TaskSchema(object): + + def __init__(self, + passage=None, + question=None, + options=None, + label=None, + answer=None, + other=None): + self.passage = passage + self.question = question + self.options = options + self.label = label + self.answer = answer + self.other = other + + def to_dict(self): + return { + 'passage': self.passage, + 'question': self.question, + 'options': self.options, + 'label': self.label, + 'answer': self.answer, + 'other': self.other + } + + +# define README.json +class MedBenchInstance(object): + + def __init__(self, task_description, data_source, task_schema, output, + evaluation_metric, task_example): + self.task_description = task_description + self.data_source = data_source + self.task_schema = task_schema + self.output = output + self.evaluation_metric = evaluation_metric + self.task_example = task_example + + def to_dict(self): + return { + 'task description': self.task_description, + 'data source': self.data_source, + 'task schema': self.task_schema.to_dict(), + 'output': self.output, + 'evaluation metric': self.evaluation_metric, + 'task example': self.task_example + } + + +class ChatGPTSchema(object): + + def __init__(self, context=None, metadata=''): + self.context = context + self.metadata = metadata + + def to_dict(self): + return {'context': self.context, 'metadata': self.metadata} + + +class ResultsForHumanSchema(object): + + def __init__(self, + index, + problem_input, + label, + model_input='', + model_output='', + parse_result='', + first_stage_output='', + second_stage_input='', + is_correct=False): + self.index = index + self.problem_input = problem_input + self.model_input = model_input + self.model_output = model_output + self.parse_result = parse_result + self.label = label + self.first_stage_output = first_stage_output + self.second_stage_input = second_stage_input + self.is_correct = is_correct + + def to_dict(self): + return { + 'index': self.index, + 'problem_input': self.problem_input, + 'model_input': self.model_input, + 'model_output': self.model_output, + 'parse_result': self.parse_result, + 'label': self.label, + 'is_correct': self.is_correct, + 'first_stage_output': self.first_stage_output, + 'second_stage_input': self.second_stage_input, + } + + @staticmethod + def to_tsv(result_list, path): + result_json = [item.to_dict() for item in result_list] + table = pd.json_normalize(result_json) + table.to_excel(path, index=False) diff --git a/opencompass/datasets/medbench/dataset_loader.py b/opencompass/datasets/medbench/dataset_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..fb7042145ac990521b2cd5e5b4db20f4687da23a --- /dev/null +++ b/opencompass/datasets/medbench/dataset_loader.py @@ -0,0 +1,338 @@ +# flake8: noqa +import ast +import json +import os + +import pandas as pd +import tiktoken +from tqdm import tqdm + +from .constructions import ChatGPTSchema, ResultsForHumanSchema +from .utils import extract_answer, read_jsonl, save_jsonl + +# define the datasets +medbench_multiple_choices_sets = ['Med-Exam', 'DDx-basic', 'DDx-advanced', 'DDx-advanced', 'SafetyBench'] # 选择题，用acc判断 + +medbench_qa_sets = ['MedHC', 'MedMC', 'MedDG', 'MedSpeQA', 'MedTreat', 'CMB-Clin'] # 开放式QA，有标答 + +medbench_cloze_sets = ['MedHG'] # 限定域QA，有标答 + +medbench_single_choice_sets = ['DrugCA'] # 正确与否判断，有标答 + +medbench_ie_sets = ['DBMHG', 'CMeEE', 'CMeIE', 'CHIP-CDEE', 'CHIP-CDN', 'CHIP-CTC', 'SMDoc', 'IMCS-V2-MRG'] # 判断识别的实体是否一致，用F1评价 + +def convert_zero_shot(line, dataset_name): + # passage = line['passage'] if line['passage'] is not None else '' + # if dataset_name in medbench_qa_sets: + # return line['question'] + # elif dataset_name in medbench_cloze_sets: + # return '问题：' + line['question'] + '\n答案：' + # elif dataset_name in medbench_multiple_choices_sets: + # return '问题：' + line['question'] + ' ' \ + # + '选项：' + ' '.join(line['options']) + '\n从A到G，我们应该选择' + # else: + # return line['question'] + return line['question'] + +prefix = '该问题为单选题，所有选项中必有一个正确答案，且只有一个正确答案。\n' + +# def convert_zero_shot_CoT_stage1(line, dataset_name): +# try: +# passage = line['passage'] if line['passage'] is not None else '' +# if dataset_name in english_qa_datasets: +# return passage + 'Q: ' + line['question'] + ' ' \ +# + 'Answer Choices: ' + ' '.join(line['options']) + '\n' + \ +# "Let's think step by step." + +# elif dataset_name in chinese_qa_datasets: +# option_string = 'ABCDEFG' +# count = len(line['options']) +# if count == 1: +# count = 4 +# return passage + '问题：' + line['question'] + ' ' \ +# + '选项：' + ' '.join(line['options']) + '\n' + \ +# '从A到{}, 我们应选择什么？让我们逐步思考：'.format(option_string[count - 1]) + +# elif dataset_name in english_cloze_datasets: +# return passage + 'Q: ' + line['question'] + '\n' \ +# "A: Let's think step by step." + +# elif dataset_name in chinese_cloze_datasets: +# return passage + '问题：' + line['question'] + '\n' \ +# '答案：让我们逐步思考：' +# except NameError: +# print('Dataset not defined.') + + +# process few-shot raw_prompts +def combine_prompt(prompt_path, + dataset_name, + load_explanation=True, + chat_mode=False): + skip_passage = False + if dataset_name == 'sat-en-without-passage': + skip_passage = True + dataset_name = 'sat-en' + demostrations = [] + # read the prompts by context and explanation + context_row = [0, 1, 3, 5, 7, 9] + explanation_row = [0, 2, 4, 6, 8, 10] + raw_prompts_context = pd.read_csv(prompt_path, + header=0, + skiprows=lambda x: x not in context_row, + keep_default_na=False) + raw_prompts_explanation = pd.read_csv( + prompt_path, + header=0, + skiprows=lambda x: x not in explanation_row, + keep_default_na=False).replace(r'\n\n', '\n', regex=True) + contexts = [] + for line in list(raw_prompts_context[dataset_name]): + if line: + # print(line) + contexts.append(ast.literal_eval(line)) + explanations = [ + exp for exp in raw_prompts_explanation[dataset_name] if exp + ] + + for idx, (con, exp) in enumerate(zip(contexts, explanations)): + passage = con['passage'] if con[ + 'passage'] is not None and not skip_passage else '' + question = con['question'] + options = con['options'] if con['options'] is not None else '' + label = con['label'] if con['label'] is not None else '' + answer = con[ + 'answer'] if 'answer' in con and con['answer'] is not None else '' + + if dataset_name in qa_datasets: + question_input = '问题 {}. '.format(idx + 1) + passage + ' ' + question + '\n' \ + + '从以下选项中选择: ' + ' '.join(options) + '\n' + question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + '答案是 {}'.format(label) + + elif dataset_name in cloze_datasets: + question_input = '问题 {}. '.format(idx + 1) + question + '\n' + question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \ + + '答案是 {}'.format(answer) + else: + raise ValueError( + f'During loading few-sot examples, found unknown dataset: {dataset_name}' + ) + if chat_mode: + demostrations.append((question_input, question_output)) + else: + demostrations.append(question_input + question_output + '\n') + + return demostrations + + +enc = None + + +def _lazy_load_enc(): + global enc + if enc is None: + enc = tiktoken.encoding_for_model('gpt-4') + + +# cut prompt if reach max token length +def concat_prompt(demos, + dataset_name, + max_tokens, + end_of_example='\n', + verbose=False): + _lazy_load_enc() + demostration_en = 'Here are the answers for the problems in the exam.\n' + demostration_zh = '以下是考试中各个问题的答案。\n' + + for i in range(len(demos)): + # print(len(enc.encode(demostration_en)), len(enc.encode(demostration_zh))) + if dataset_name in english_qa_datasets: + demostration_en = demostration_en + demos[i] + end_of_example + elif dataset_name in chinese_qa_datasets: + demostration_zh = demostration_zh + demos[i] + end_of_example + elif dataset_name in english_cloze_datasets: + demostration_en = demostration_en + demos[i] + end_of_example + elif dataset_name in chinese_cloze_datasets: + demostration_zh = demostration_zh + demos[i] + end_of_example + # break if reach max token limit + if len(enc.encode(demostration_en)) < max_tokens and len( + enc.encode(demostration_zh)) < max_tokens: + output = demostration_en if len(demostration_en) > len( + demostration_zh) else demostration_zh + prompt_num = i + 1 + else: + break + if verbose: + print('max_tokens set as ', max_tokens, 'actual_tokens is', + len(enc.encode(output)), 'num_shot is', prompt_num) + return output, prompt_num + + +def concat_prompt_chat_mode(demos, + dataset_name, + max_tokens, + end_of_example='\n', + verbose=False): + _lazy_load_enc() + answers = [] + sentences = '' + for i in range(len(demos)): + answers += [ + { + 'role': 'user', + 'content': demos[i][0] + }, + { + 'role': 'assistant', + 'content': demos[i][1] + }, + ] + sentences += json.dumps(answers[-1]) + # break if reach max token limit + if len(enc.encode(sentences)) > max_tokens: + answers.pop() + answers.pop() + break + if verbose: + print('max_tokens set as ', max_tokens, 'actual_tokens is', + len(enc.encode(sentences)), 'num_shot is', + len(answers) // 2) + return answers, len(answers) // 2 + + +def convert_few_shot(line, dataset_name, demo, n_shot, chat_mode=False): + passage = line['passage'] if line['passage'] is not None else '' + question = line['question'] + options = line['options'] if line['options'] is not None else '' + + if dataset_name in qa_datasets: + question_input = '问题 {}. '.format(n_shot + 1) + passage + ' ' + question + '\n' \ + + '从以下选项中选择: ' + ' '.join(options) + '\n' + # + "问题 {}的解析: ".format(n_shot + 1) + + if dataset_name in cloze_datasets: + question_input = '问题 {}. '.format(n_shot + 1) + question + '\n' + # + "问题 {}的解析: ".format(n_shot + 1) + if chat_mode: + return demo + [ + { + 'role': 'user', + 'content': question_input + }, + ] + else: + return demo + question_input + + +def load_dataset(dataset_name, + setting_name, + parent_path, + prompt_path=None, + max_tokens=None, + end_of_example='\n', + chat_mode=False, + verbose=False): + test_path = os.path.join(parent_path, dataset_name + '.jsonl') + loaded_jsonl = read_jsonl(test_path) + processed = [] + if setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + # process demo once if it is few-shot-CoT + processed_demos = combine_prompt( + prompt_path, + dataset_name, + load_explanation=setting_name == 'few-shot-CoT', + chat_mode=chat_mode) + if chat_mode: + chosen_prompt, n_shot = concat_prompt_chat_mode(processed_demos, + dataset_name, + max_tokens, + end_of_example, + verbose=verbose) + else: + chosen_prompt, n_shot = concat_prompt(processed_demos, + dataset_name, + max_tokens, + end_of_example, + verbose=verbose) + + if verbose: + loaded_jsonl = tqdm(loaded_jsonl) + for meta_idx, line in enumerate(loaded_jsonl): + # 正确 + if setting_name == 'zero-shot': + ctxt = convert_zero_shot(line, dataset_name) + elif setting_name == 'zero-shot-CoT': + ctxt = convert_zero_shot_CoT_stage1(line, dataset_name) + elif setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + ctxt = convert_few_shot(line, dataset_name, chosen_prompt, n_shot, + chat_mode) + try: + new_instance = ChatGPTSchema(context=ctxt, metadata=meta_idx) + processed.append(new_instance.to_dict()) + except NameError: + print('Dataset not defined.') + return processed + + +def generate_second_stage_input(dataset_name, + input_list, + output_list, + with_format_prompt=False): + try: + chinese_format_prompt = '根据以上内容，你的任务是把最终的答案提取出来并填在【】中，例如【0】或者【A】。' + if dataset_name in qa_datasets: + prompt_suffix = '因此，从A到D, 我们应选择' + if with_format_prompt: + prompt_suffix = chinese_format_prompt + prompt_suffix + elif dataset_name in cloze_datasets: + prompt_suffix = '因此，答案是' + if with_format_prompt: + prompt_suffix = chinese_format_prompt + prompt_suffix + except NameError: + print('Dataset not defined.') + processed = [] + for i in range(len(input_list)): + ctxt = '{0}\n{1}\n{2}'.format(input_list[i]['context'], + extract_answer(output_list[i]), + prompt_suffix) + new_instance = ChatGPTSchema(context=ctxt, + metadata=input_list[i]['metadata']) + processed.append(new_instance.to_dict()) + return processed + + +def load_dataset_as_result_schema(dataset_name, parent_path): + test_path = os.path.join(parent_path, dataset_name + '.jsonl') + loaded_jsonl = read_jsonl(test_path) + + processed = [] + for i, line in enumerate(loaded_jsonl): + problem_input = convert_zero_shot(line, dataset_name) + processed.append( + ResultsForHumanSchema( + index=i, + problem_input=problem_input, + # label=line['label'] if line['label'] else line['answer'] + label = line['answer'] + )) + return processed + + +if __name__ == '__main__': + # set variables + parent_dir = '../../data/exam_guidance' + + # set dataset name to process + setting_name = 'zero-shot' # setting_name can be chosen from ["zero-shot", "zero-shot-CoT", "few-shot-CoT"] + data_name = 'health_exam' + save_dir = '../../experiment_input/{}/'.format(setting_name) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + processed_data = load_dataset(data_name, + setting_name, + parent_dir, + prompt_path=raw_prompt_path, + max_tokens=2048) + save_jsonl(processed_data, + os.path.join(save_dir, '{}.jsonl'.format(data_name))) diff --git a/opencompass/datasets/medbench/evaluation.py b/opencompass/datasets/medbench/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a9916a11b986139407eb3796174d5c533d537b --- /dev/null +++ b/opencompass/datasets/medbench/evaluation.py @@ -0,0 +1,43 @@ +# flake8: noqa +from . import dataset_loader, utils +from .math_equivalence import is_equiv + + +def convert_to_set(item): + if isinstance(item, list): + return set(item) + if isinstance(item, str): + return {item} + if item is None: + return {} + raise ValueError("Input can't parse:", item) + + +def evaluate_single_sample(dataset_name, prediction, label): + if dataset_name in dataset_loader.multi_choice_datasets: + p = convert_to_set(prediction) + l = convert_to_set(label) + return p == l + elif dataset_name in dataset_loader.math_output_datasets: + return is_equiv(prediction, label) + else: + return prediction == label + + +# def evaluate(dataset_name, prediction_list, label_list): +# correct = 0 +# if dataset_name in multi_choice_datasets: +# for prediction, label in zip(prediction_list, label_list): +# p = convert_to_set(prediction) +# l = convert_to_set(label) +# if p == l: +# correct += 1 +# elif dataset_name in math_output_datasets: +# for prediction, label in zip(prediction_list, label_list): +# if is_equiv(prediction, label): +# correct += 1 +# else: +# for prediction, label in zip(prediction_list, label_list): +# if prediction == label: +# correct += 1 +# return "{0:.2%}".format(correct / len(label_list)) diff --git a/opencompass/datasets/medbench/math_equivalence.py b/opencompass/datasets/medbench/math_equivalence.py new file mode 100644 index 0000000000000000000000000000000000000000..788900eaac572f92b4381c03ed93d25dd3482549 --- /dev/null +++ b/opencompass/datasets/medbench/math_equivalence.py @@ -0,0 +1,161 @@ +# flake8: noqa + + +# code from https://github.com/hendrycks/math/blob/main/modeling/math_equivalence.py +def _fix_fracs(string): + substrs = string.split('\\frac') + new_str = substrs[0] + if len(substrs) > 1: + substrs = substrs[1:] + for substr in substrs: + new_str += '\\frac' + if substr[0] == '{': + new_str += substr + else: + try: + assert len(substr) >= 2 + except: + return string + a = substr[0] + b = substr[1] + if b != '{': + if len(substr) > 2: + post_substr = substr[2:] + new_str += '{' + a + '}{' + b + '}' + post_substr + else: + new_str += '{' + a + '}{' + b + '}' + else: + if len(substr) > 2: + post_substr = substr[2:] + new_str += '{' + a + '}' + b + post_substr + else: + new_str += '{' + a + '}' + b + string = new_str + return string + + +def _fix_a_slash_b(string): + if len(string.split('/')) != 2: + return string + a = string.split('/')[0] + b = string.split('/')[1] + try: + a = int(a) + b = int(b) + assert string == '{}/{}'.format(a, b) + new_string = '\\frac{' + str(a) + '}{' + str(b) + '}' + return new_string + except: + return string + + +def _remove_right_units(string): + # "\\text{ " only ever occurs (at least in the val set) when describing units + if '\\text{ ' in string: + splits = string.split('\\text{ ') + assert len(splits) == 2 + return splits[0] + else: + return string + + +def _fix_sqrt(string): + if '\\sqrt' not in string: + return string + splits = string.split('\\sqrt') + new_string = splits[0] + for split in splits[1:]: + if split[0] != '{': + a = split[0] + new_substr = '\\sqrt{' + a + '}' + split[1:] + else: + new_substr = '\\sqrt' + split + new_string += new_substr + return new_string + + +def _strip_string(string): + # linebreaks + string = string.replace('\n', '') + # print(string) + + # remove inverse spaces + string = string.replace('\\!', '') + # print(string) + + # replace \\ with \ + string = string.replace('\\\\', '\\') + # print(string) + + # replace tfrac and dfrac with frac + string = string.replace('tfrac', 'frac') + string = string.replace('dfrac', 'frac') + # print(string) + + # remove \left and \right + string = string.replace('\\left', '') + string = string.replace('\\right', '') + # print(string) + + # Remove circ (degrees) + string = string.replace('^{\\circ}', '') + string = string.replace('^\\circ', '') + + # remove dollar signs + string = string.replace('\\$', '') + + # remove units (on the right) + string = _remove_right_units(string) + + # remove percentage + string = string.replace('\\%', '') + string = string.replace('\%', '') + + # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string + string = string.replace(' .', ' 0.') + string = string.replace('{.', '{0.') + # if empty, return empty string + if len(string) == 0: + return string + if string[0] == '.': + string = '0' + string + + # to consider: get rid of e.g. "k = " or "q = " at beginning + if len(string.split('=')) == 2: + if len(string.split('=')[0]) <= 2: + string = string.split('=')[1] + + # fix sqrt3 --> sqrt{3} + string = _fix_sqrt(string) + + # remove spaces + string = string.replace(' ', '') + + # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} + string = _fix_fracs(string) + + # manually change 0.5 --> \frac{1}{2} + if string == '0.5': + string = '\\frac{1}{2}' + + # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y + string = _fix_a_slash_b(string) + + return string + + +def is_equiv(str1, str2, verbose=False): + if str1 is None and str2 is None: + print('WARNING: Both None') + return True + if str1 is None or str2 is None: + return False + + try: + ss1 = _strip_string(str1) + ss2 = _strip_string(str2) + if verbose: + print(ss1, ss2) + return ss1 == ss2 + except: + return str1 == str2 diff --git a/opencompass/datasets/medbench/medbench.py b/opencompass/datasets/medbench/medbench.py new file mode 100644 index 0000000000000000000000000000000000000000..54690947f609cb6c4342daaf70510e846afa8120 --- /dev/null +++ b/opencompass/datasets/medbench/medbench.py @@ -0,0 +1,589 @@ +import json +import os.path as osp +import sys +from datasets import Dataset +from sklearn.metrics import classification_report +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset +from .math_equivalence import is_equiv +from .post_process import parse_math_answer, parse_qa_multiple_answer + +import evaluate +from nltk.translate.bleu_score import sentence_bleu +# # from bert_score import score +import re +from transformers import BasicTokenizer +from rouge_chinese import Rouge +basic_tokenizer = BasicTokenizer(tokenize_chinese_chars=True) + +@LOAD_DATASET.register_module() +class MedBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, name: str, setting_name: str): + path = get_data_path(path, local_mode=True) + from .dataset_loader import load_dataset, load_dataset_as_result_schema + + assert setting_name in 'zero-shot', 'only support zero-shot setting' + dataset_wo_label = load_dataset(name, setting_name, path) + dataset_with_label = load_dataset_as_result_schema(name, path) + dataset = [] + for d1, d2 in zip(dataset_wo_label, dataset_with_label): + dataset.append({ + 'id': d2.index, + 'problem_input': d1['context'], + 'label': d2.label, + }) + dataset = Dataset.from_list(dataset) + return dataset + + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator(BaseEvaluator): + + def score(self, predictions, references): + # predictions: [[]] + # references: [[]] + predictions = [parse_qa_multiple_answer(pred) for pred in predictions] + details = [] + cnt = 0 + for pred, ref in zip(predictions, references): + detail = {'pred': pred, 'answer': ref, 'correct': False} + if is_equiv(pred, ref): + cnt += 1 + detail['correct'] = True + details.append(detail) + score = cnt / len(predictions) * 100 + return {'Accuracy': score, 'details': details} + +def process_generated_results_CMeEE(pred_file): + # 实体每类占一行，每行格式为 "[类型名称]实体：实体名称1，实体名称2，实体名称3\n" + # 多个实体，用 ，符号分割 + structured_output = [] + answer_choices = ['药物', '设备', '医院科室', '微生物类', '身体部位', '医疗操作', '医学检验项目', '症状', '疾病'] + for pred in pred_file: + list_entities = [] + for choice in answer_choices: + for piece in re.split('\n', pred): + if piece.startswith(f"{choice}"): + mentions = re.split(r"[,，]", piece.replace(f"{choice}:", "").replace(f"{choice}：", "")) + for ment in mentions: + list_entities.append({'type':choice, 'entity':ment}) + structured_output.append(list_entities) + return structured_output + +def process_generated_results_EMR(pred_file): + structured_output = [] + regex = r"^(主诉|现病史|既往史|个人史|婚育史|家族史)[:：]([\s\S]+)$" + for prediction in pred_file: + entities: dict = {} + if "\n\n" in prediction: + blocks = prediction.split("\n\n") + else: + blocks = prediction.splitlines() + for line in blocks: + if match := re.match(regex, line.strip()): + type_ = match[1] + mention = match[2].strip() + entities[type_] = mention + structured_output.append(entities) + return structured_output + +def process_generated_results_CMeIE(pred_file): + structured_output = [] + for line in pred_file: + gen_output = line + + answer_choices = "相关（导致）、鉴别诊断、遗传因素、发病性别倾向、相关（症状）、手术治疗、预防、辅助检查、筛查、阶段、临床表现、风险评估因素、同义词、发病年龄、预后生存率、病史、传播途径、治疗后症状、药物治疗、辅助治疗、化疗、死亡率、放射治疗、病因、组织学检查、内窥镜检查、多发群体、并发症、实验室检查、就诊科室、病理生理、高危因素、发病率、多发地区、病理分型、影像学检查、转移部位、发病部位、相关（转化）、外侵部位、预后状况、发病机制、多发季节" + re_choices = "|".join(re.escape(choice) for choice in answer_choices.split('、')) + regex = ( + rf'关系[:：]["“]({re_choices})["”][,，]' + r'头实体[:：]["“]([^"”]+)["”][,，]尾实体[:：]["“]([^"”]+)["”]' + ) + + list_spos = [] + list_answer_strs = gen_output.split("\n") + for line in list_answer_strs: + for item in re.finditer(regex, line): + print(item) + for match in re.finditer(regex, line): + list_spos.append({"predicate": match[1], "subject": match[2], "object": match[3]}) + + structured_output.append(list_spos) + return structured_output + +def process_generated_results_CDN(pred_file): + structured_output = [] + answer_choices = json.load(open('./opencompass/datasets/medbench/entity_list.jsonl', 'r')) + for line in pred_file: + gen_output = line + + answer_str = gen_output.split("\n")[-1] + answers = answer_str.split("，") + answers = [w.strip() for w in answers if len(w.strip()) > 0] + answers = [w for w in answers if w in answer_choices] + answers = list(set(answers)) + answers = [ + { + "entity": w, + "type": "normalization", + } + for w in answers + ] + + structured_output.append(answers) + return structured_output + +def process_generated_results_CDEE(pred_file): + structured_output = [] + for prediction in pred_file: + events: list[dict] = [] + for line in prediction.splitlines(): + if "主体词" in line: + line = line.rstrip("。") + kvs = line.split("；") + kv_dict = dict(kv.split("：", maxsplit=1) for kv in kvs if "：" in kv) + events.append({ + "主体词": kv_dict.get("主体词", ""), + "发生状态": ( + v + if (v := kv_dict.get("发生状态", "不确定")) in ("不确定", "否定") + else "" + ), + "描述词": ( + v.split("，") if (v := kv_dict.get("描述词", "空")) != "空" else [] + ), + "解剖部位": ( + v.split("，") + if (v := kv_dict.get("解剖部位", "空")) != "空" + else [] + ), + }) + structured_output.append(events) + return structured_output + +def process_generated_results_CTC(pred_file): + structured_output = [] + + for line in pred_file: + gen_output = line + # 答案格式：直接回答分类标签 + answer_str = gen_output.strip() + structured_output.append(answer_str) + return structured_output + +def process_generated_results_doc_parsing(pred_file): + float_field_regex = r"(体温|脉搏|心率|收缩压|舒张压|呼吸)[^\d]*(\d+(?:\.\d+)?)" + + output = [] + for prediction in pred_file: + entities = { + "体温": "未扪及", + "脉搏": "未扪及", + "心率": "未扪及", + "收缩压": "未扪及", + "舒张压": "未扪及", + "呼吸": "未扪及", + "是否上腹部深压痛": None, + "是否腹部反跳痛": None, + "上腹部肿块": None, + } + for sentence in re.split("[，|。|\n]", prediction): + for match in re.finditer(float_field_regex, prediction): + entities[match[1]] = match[2] + if "上腹部深压痛" in sentence: + if re.search("是(?!否)|(?:^|[^不])存在|有", sentence): + entities["是否上腹部深压痛"] = "是" + else: + entities["是否上腹部深压痛"] = "否" + elif "腹部反跳痛" in sentence: + if re.search("是(?!否)|(?:^|[^不])存在|有", sentence): + entities["是否腹部反跳痛"] = "是" + else: + entities["是否腹部反跳痛"] = "否" + elif "上腹部肿块" in sentence: + if re.search("是(?!否)|(?:^|[^不])存在|有", sentence): + entities["上腹部肿块"] = "扪及" + else: + entities["上腹部肿块"] = "未扪及" + result = [ + { + "type": "体温(℃)", + "entity": entities["体温"], + }, + { + "type": "脉搏(次/分)", + "entity": entities["脉搏"], + }, + { + "type": "心率(次/分)", + "entity": entities["心率"], + }, + { + "type": "收缩压(mmHg)", + "entity": entities["收缩压"], + }, + { + "type": "舒张压(mmHg)", + "entity": entities["舒张压"], + }, + { + "type": "呼吸(次/分)", + "entity": entities["呼吸"], + }, + ] + if entities["是否上腹部深压痛"]: + result.append({ + "type": "是否上腹部深压痛", + "entity": entities["是否上腹部深压痛"], + }) + if entities["是否腹部反跳痛"]: + result.append({ + "type": "是否腹部反跳痛", + "entity": entities["是否腹部反跳痛"], + }) + if entities["上腹部肿块"]: + result.append({ + "type": "上腹部肿块", + "entity": entities["上腹部肿块"], + }) + + output.append(result) + return output + +def process_generated_results_mrg(pred_file): + structured_output = [] + regex = r"^(主诉|现病史|辅助检查|既往史|诊断|建议)[:：]([\s\S]+)$" + for prediction in pred_file: + entities = {} + if "\n\n" in prediction: + blocks = prediction.split("\n\n") + else: + blocks = prediction.splitlines() + for line in blocks: + if match := re.match(regex, line.strip()): + type_ = match[1] + mention = match[2].strip() + entities[type_] = mention + structured_output.append(entities) + return structured_output + +def calc_info_extract_task_scores(list_structured_predict, list_structured_golden): + + assert len(list_structured_golden) == len(list_structured_predict) + + tp = 0 + fp = 0 + fn = 0 + for samp_golden, samp_predict in zip(list_structured_golden, list_structured_predict): + # samp_golden: [[{}]] + answer_golden = samp_golden + answer_predict = samp_predict + # assert isinstance(answer_golden, list) + # assert isinstance(answer_predict, list), "sample format is wrong!" + + set_golden = set() + for inst in answer_golden: + assert isinstance(inst, dict) + keys = sorted(list(inst.keys())) + inst = tuple([json.dumps(inst[w], ensure_ascii=False) for w in keys ]) + # inst = list(inst.items()) + # inst.sort() + # inst = tuple(inst) + + set_golden.add(inst) + + set_predict = set() + for inst in answer_predict: + assert isinstance(inst, dict) + keys = sorted(list(inst.keys())) + + inst = tuple([json.dumps(inst[w], ensure_ascii=False) for w in keys]) + + set_predict.add(inst) + + tp += len(set_golden.intersection(set_predict)) + fp += len(set_predict.difference(set_golden)) + fn += len(set_golden.difference(set_predict)) + + if tp: + precision = tp / (tp + fp) + recall = tp / (tp + fn) + f1 = 2 * precision * recall / (precision + recall) + + else: + precision, recall, f1 = 0, 0, 0 + + return precision, recall, f1 + +def calc_cls_task_scores(list_structured_golden, + list_structured_predict, + list_labels=None, + return_macro=False, + ): + # types = list_labels + # scores = {c: {"tp": 0, "fp": 0, "fn": 0, "tn": 0} for c in list_labels + ["ALL"]} + + predictions = [] + ground_truths = [] + + # Count GT relations and Predicted relations + assert len(list_structured_golden) == len(list_structured_predict) + n_sents = len(list_structured_golden) + + # Count TP, FP and FN per type + for pred_samp, gt_samp in zip(list_structured_predict, list_structured_golden): + + pred_label = pred_samp + gt_label = gt_samp + # assert gt_label != "" + if gt_label == "": + get_label = list_labels[0] + if pred_label == "": + pred_label = list_labels[0] + + predictions.append(pred_label) + ground_truths.append(gt_label) + + # metric + cls_report = classification_report( + ground_truths, predictions, + output_dict=True, + zero_division=0, + ) + + if return_macro: + return cls_report["macro avg"]["precision"], \ + cls_report["macro avg"]["recall"], \ + cls_report["macro avg"]["f1-score"] + else: + return cls_report["weighted avg"]["precision"], \ + cls_report["weighted avg"]["recall"], \ + cls_report["weighted avg"]["f1-score"] + +def calc_nlg_task_scores(list_structured_golden, list_structured_predict): + + assert len(list_structured_golden) == len(list_structured_predict) + + scores = [] + predictions = [] + references = [] + details = [] + for samp_golden, samp_predict in zip(list_structured_golden, list_structured_predict): + + answer_golden = samp_golden + answer_predict = samp_predict + + if not (answer_predict and answer_golden): + continue + + # basic tokenizer: 拆分中文字，保留英文单词 + answer_predict = basic_tokenizer.tokenize(answer_predict) + answer_golden = basic_tokenizer.tokenize(answer_golden) + answer_predict = " ".join(answer_predict).strip() + answer_golden = " ".join(answer_golden).strip() + if answer_golden.strip() == "": + answer_golden = "无 。" + if answer_predict.strip() == "": + answer_predict = "无 。" + + predictions.append(answer_predict) + references.append(answer_golden) + + details.append({'pred':answer_predict, 'answer':answer_golden, 'correct':False}) + + rouge = Rouge() + # bleu = evaluate.load('sacrebleu') + scores = rouge.get_scores(predictions, references, avg=True) + # scores_bleu = bleu.compute(predictions=predictions, references=references) + + rouge1 = scores["rouge-1"]["f"] + rouge2 = scores["rouge-2"]["f"] + rougeL = scores["rouge-l"]["f"] + + # bleu = sentence_bleu(references, predictions) + + # bert_score = [] + # for id in range(len(predictions)): + # P, R, F1 = score([predictions[i]], [references[i]], model_type='bert-base-chinese', lang="zh", verbose=True) + # bert_score.append(F1) + # bert_score = float(sum(bert_score)) / float(len(bert_score)) + # return rougeL, bleu, bert_score + return {'RougeL': rougeL, 'details':details} + +def calc_scores_f1(dict_gt, dict_pred): + details = [] + for gt, pred in zip(dict_gt, dict_pred): + details.append({'pred':pred, 'answer':gt, 'correct':None}) + + precision, recall, f1 = calc_info_extract_task_scores(dict_gt, dict_pred) + return {'F1':f1, 'details':details} + +def calc_scores_ctc(dict_gt, dict_pred): + details = [] + for gt, pred in zip(dict_gt, dict_pred): + details.append({'pred':pred, 'answer':gt, 'correct':None}) + + gts = dict_gt + preds = dict_pred + + precision, recall, f1 = calc_cls_task_scores( + gts, + preds, + list_labels=['非上述类型', '疾病', '症状(患者感受)', + '体征(医生检测）', '怀孕相关', '肿瘤进展', + '疾病分期', '过敏耐受', '器官组织状态', + '预期寿命', '口腔相关', '药物', + '治疗或手术', '设备', '护理', + '诊断', '实验室检查', '风险评估', + '受体状态', '年龄', '特殊病人特征', + '读写能力', '性别', '教育情况', + '居住情况', '种族', '知情同意', + '参与其它试验', '研究者决定', '能力', + '伦理审查', '依存性', '成瘾行为', + '睡眠', '锻炼', '饮食', '酒精使用', + '性取向', '吸烟状况', '献血', + '病例来源', '残疾群体', '健康群体', + '数据可及性', "含有多个类别"], + return_macro=True, + ) + return {'Macro-F1':f1, 'details':details} + +def calc_scores_nlg(dict_gt, dict_pred): + + # scores = {} + scores = {'score':0, 'details':[]} + success_flag = 1 + + gts = dict_gt + preds = dict_pred + # if not len(gts) == len(preds): + # success_flag = 0 + # try: + return calc_nlg_task_scores(gts, preds) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_CMeEE(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_CMeEE(predictions) + return calc_scores_f1(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_DBMHG(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_EMR(predictions) + return calc_scores_f1(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_IMCS_V2_MRG(BaseEvaluator): + + def score(self, predictions, references): + # predictions = process_generated_results_mrg(predictions) + references_revise = [] + for item in references: + temp_ref = '' + for sub_item in item: + temp_ref += sub_item['type'] + '：' + sub_item['entity'] + '\n' + references_revise.append(temp_ref) + return calc_nlg_task_scores(references_revise, predictions) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_CMeIE(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_CMeIE(predictions) + return calc_scores_f1(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_CHIP_CDEE(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_CDEE(predictions) + return calc_scores_f1(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_CHIP_CDN(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_CDN(predictions) + return calc_scores_f1(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_CHIP_CTC(BaseEvaluator): + + def score(self, predictions, references): + predictions = process_generated_results_CTC(predictions) + return calc_scores_ctc(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_Doc_parsing(BaseEvaluator): + + def score(self, predictions, references): + # predictions = process_generated_results_doc_parsing(predictions) + references_revise = [] + for item in references: + temp_ref = '' + for sub_item in item: + temp_ref += sub_item['type'] + '：' + sub_item['entity'] + '\n' + references_revise.append(temp_ref) + return calc_nlg_task_scores(references_revise, predictions) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_NLG(BaseEvaluator): + + def score(self, predictions, references): + # predictions = process_generated_results_med(predictions) + return calc_scores_nlg(predictions, references) + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_Cloze(BaseEvaluator): + + def score(self, predictions, references): + # predictions: [[]] + # references: [[]] + # predictions = [parse_qa_multiple_answer(pred) for pred in predictions] + details = [] + cnt = 0 + + for pred, ref in zip(predictions, references): + detail = {'pred':pred, 'answer':ref, 'correct':False} + + if sum([item in pred for item in ref]) == len(ref): + cnt += 1 + detail['correct'] = True + details.append(detail) + score = cnt / len(predictions) * 100 + return {'Accuracy': score, 'details': details} + +@ICL_EVALUATORS.register_module() +class MedBenchEvaluator_TF(BaseEvaluator): + + def score(self, predictions, references): + # predictions: [[]] + # references: [[]] + # predictions = [parse_qa_multiple_answer(pred) for pred in predictions] + details = [] + cnt = 0 + + for pred, ref in zip(predictions, references): + + if '不' in pred or '否' in pred: + cur_pred = '不可以' + else: + cur_pred = '可以' + + detail = {'pred':cur_pred, 'answer':ref, 'correct':False} + + if cur_pred == ref: + cnt += 1 + detail['correct'] = True + + details.append(detail) + + score = cnt / len(predictions) * 100 + return {'Accuracy': score, 'details': details} \ No newline at end of file diff --git a/opencompass/datasets/medbench/post_process.py b/opencompass/datasets/medbench/post_process.py new file mode 100644 index 0000000000000000000000000000000000000000..36e4ca81cdea3f1dd02f044cc298e850b3999bfa --- /dev/null +++ b/opencompass/datasets/medbench/post_process.py @@ -0,0 +1,200 @@ +# flake8: noqa +import json +import re + +from . import dataset_loader + + +def extract_last_line(string): + lines = string.split('\n') + for item in lines[::-1]: + if item.strip() != '': + string = item + break + return string + + +def remove_few_shot_prefix(string: str): + prefix_list = ['The answer is therefore', '答案是'] + for prefix in prefix_list: + if string.startswith(prefix): + string = string[len(prefix):].strip() + elif prefix in string: + index = string.rfind(prefix) + if index >= 0: + string = string[index + len(prefix):].strip() + return string + + +def try_parse_few_shot_qa_single_answer(string, setting_name, language='en'): + if setting_name == 'few-shot-CoT': + string = extract_last_line(string) + if language == 'en': + pattern = 'answer is .*?([A-G])' + match = re.search(pattern, string) + elif language == 'zh': + pattern = '答案是.*?([A-G])' + match = re.search(pattern, string) + else: + raise ValueError('Unknown language {0}'.format(language)) + if match: + return match.group(1) + else: + return None + + +def try_parse_few_shot_pattern(string: str, dataset_name, setting_name): + if setting_name == 'few-shot-CoT': + string = extract_last_line(string) + if dataset_name in dataset_loader.chinese_cloze_datasets: + return string.startswith('答案是') + elif dataset_name in dataset_loader.english_cloze_datasets: + return string.startswith('The answer is therefore') + elif dataset_name in dataset_loader.chinese_qa_datasets: + pattern = '答案是.*?([A-G])' + match = re.search(pattern, string) + return match is not None + elif dataset_name in dataset_loader.english_qa_datasets: + pattern = 'answer is .*?([A-G])' + match = re.search(pattern, string) + return match is not None + return False + + +def parse_few_shot_qa_single_answer(string, setting_name, language='en'): + answer = try_parse_few_shot_qa_single_answer(string, setting_name, + language) + if answer is None: + return find_first_capital_letter(string) + else: + return answer + + +def find_first_capital_letter(answer): + letter_set = {'A', 'B', 'C', 'D', 'E', 'F'} + for c in answer: + if c in letter_set: + return c + # print("Can't find capital letter in:", answer) + return '' + + +def extract_answer_in_bracket(answer, prefix='【', suffix='】'): + if prefix not in answer and suffix not in answer: + # print("doesn't found special tokens in:", answer) + return '' + s = answer.index(prefix) + len(prefix) + t = answer.index(suffix) + ret = answer[s:t] + return ret + + +def parse_math_answer(setting_name, raw_string): + if setting_name == 'few-shot-CoT': + raw_string = extract_last_line(raw_string) + if setting_name == 'few-shot-CoT' or setting_name == 'few-shot': + raw_string = remove_few_shot_prefix(raw_string) + return raw_string + + def remove_boxed(s): + left = '\\boxed{' + try: + assert s[:len(left)] == left + assert s[-1] == '}' + answer = s[len(left):-1] + if '=' in answer: + answer = answer.split('=')[-1].lstrip(' ') + return answer + except: + return None + + def last_boxed_only_string(string): + idx = string.rfind('\\boxed') + if idx < 0: + idx = string.rfind('\\fbox') + if idx < 0: + return None + i = idx + right_brace_idx = None + num_left_braces_open = 0 + while i < len(string): + if string[i] == '{': + num_left_braces_open += 1 + if string[i] == '}': + num_left_braces_open -= 1 + if num_left_braces_open == 0: + right_brace_idx = i + break + i += 1 + + if right_brace_idx == None: + retval = None + else: + retval = string[idx:right_brace_idx + 1] + + return retval + + def get_answer_with_dollar_sign(s): + first_pattern = '\$(.*)\$' + last_match = None + matches = re.findall(first_pattern, s) + if matches: + last_match = matches[-1] + if '=' in last_match: + last_match = last_match.split('=')[-1].lstrip(' ') + return last_match + + def get_answer_without_dollar_sign(s): + last_match = None + if '=' in s: + last_match = s.split('=')[-1].lstrip(' ').rstrip('.') + if '\n' in last_match: + last_match = last_match.split('\n')[0] + else: + pattern = '(?:\\$)?\d+(?:\.\d+)?(?![\w\d])' + matches = re.findall(pattern, s) + if matches: + last_match = matches[-1] + return last_match + + raw_string = remove_few_shot_prefix(raw_string) + if '\\boxed' in raw_string: + answer = remove_boxed(last_boxed_only_string(raw_string)) + else: + answer = get_answer_with_dollar_sign(raw_string) + if not answer: + answer = get_answer_without_dollar_sign(raw_string) + return answer + + +def parse_qa_multiple_answer(string): + # if setting_name == 'few-shot-CoT': + # string = extract_last_line(string) + for x in ['CC', 'CA', 'AC', 'POMES', 'AI', 'MIBG', 'CF', 'CTE', 'AD', 'CB', 'BG', 'BD', 'BE', 'BH', 'CTB', 'BI', 'CE', 'Pugh', 'Child', 'CTI', 'CTA', 'TACE', 'PPD', 'Castleman', 'BA', 'CH', 'AB', 'CTC', 'CT', 'CTH', 'CD', 'AH', 'AE', 'AA', 'AF', 'BC', 'CG', 'BB', 'CI', 'BF', 'CTF', 'CTG', 'AG', 'CTD', '分级C', '分级A', 'I131', '分级B', '分级D', '131I‐MIBG', 'NYHA', 'IPF', 'DIP', 'Lambert-Eaton', 'Graves', 'IIA期', 'CKD', 'FDA', 'A级', 'B级', 'C级', 'D级', '维生素D']: + string = string.replace(x, '') + pattern = '\(*([A-Z])\)*' + match = re.findall(pattern, string) + if match: + return match + return [] + + +def post_process(dataset_name, setting_name, prediction): + if dataset_name in dataset_loader.english_cloze_datasets or dataset_name in dataset_loader.chinese_cloze_datasets: + return parse_math_answer(setting_name, prediction) + + if dataset_name in ['jec-qa-kd', 'jec-qa-ca', 'gaokao-physics']: + return parse_qa_multiple_answer(prediction, setting_name) + + # all other datasets are QA problems with single answer + if 'zero-shot' in setting_name: + answer = find_first_capital_letter(prediction) + return answer + + # all other datasets are QA problems with single answer and setting_name are few-shot + language = 'en' if dataset_name in dataset_loader.english_qa_datasets else 'zh' + if dataset_name in dataset_loader.english_qa_datasets or dataset_name in dataset_loader.chinese_qa_datasets: + return parse_few_shot_qa_single_answer(prediction, setting_name, + language) + else: + raise ValueError(f'Unsupported dataset name {dataset_name}') diff --git a/opencompass/datasets/medbench/utils.py b/opencompass/datasets/medbench/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fbb311057b3b21cbe4876a0ad6b9cd4dd11d0659 --- /dev/null +++ b/opencompass/datasets/medbench/utils.py @@ -0,0 +1,43 @@ +# flake8: noqa +import json + + +def read_jsonl(path): + with open(path, encoding='utf8') as fh: + results = [] + for line in fh: + if line is None: + continue + try: + results.append(json.loads(line) if line != 'null' else line) + except Exception as e: + print(e) + print(path) + print(line) + raise e + return results + + +def save_jsonl(lines, directory): + with open(directory, 'w', encoding='utf8') as f: + for line in lines: + f.write(json.dumps(line, ensure_ascii=False) + '\n') + + +def extract_answer(js): + try: + if js is None or js == 'null': + return '' + answer = '' + if isinstance(js, str): + answer = js + elif 'text' in js['choices'][0]: + answer = js['choices'][0]['text'] + else: + answer = js['choices'][0]['message']['content'] + # answer = js[''] + return answer + except Exception as e: + # print(e) + # print(js) + return '' diff --git a/opencompass/datasets/musr/__init__.py b/opencompass/datasets/musr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5cbc34f822918d95ab1de87ba09bfccbd76d5309 --- /dev/null +++ b/opencompass/datasets/musr/__init__.py @@ -0,0 +1 @@ +from .musr import * # noqa: F401, F403 diff --git a/opencompass/datasets/musr/murder_mystery_solved_ex.py b/opencompass/datasets/musr/murder_mystery_solved_ex.py new file mode 100644 index 0000000000000000000000000000000000000000..1fe183b9edd782e1b33086bb9aadb3ecab088db5 --- /dev/null +++ b/opencompass/datasets/musr/murder_mystery_solved_ex.py @@ -0,0 +1,81 @@ +# flake8: noqa: E501 +story = """ +In the smoke-filled haze of a thriving jazz club, Alice met her explosive end, leaving Detective Winston to sift through the suspects: Eugene, the shy pianist, and Gabrielle, the sassy club singer. + +While seated at his desk at the precinct, Winston received a phone call from a certain observant local bartender, tipping off the police about a harsh row peaking in a nearby jazz club. He signaled to his partner as they promptly dispatched to the scene, already ringing with sirens and a restless crowd. + +With the police line restraining the horde, the jazz club was undergoing a full round-up as Winston approached the informative bartender. The bartender was engrossed in his account to the officers about a raucous, punch throwing fight Eugene was part of, to his best recollection. Winston remembered Eugene, a jazz fanatic—lurking around the jazz corners more often than anyone else could recount. + +In the heart of the upheaval, lay a woman sprawled on the floor, later identified as Alice, a frequent face at the jazz scene and a financial analyst deeply engrossed in financial transactions. In public, Alice had made her concerns known about her discovery of fraudulent transactions at the bank, promising to report the same to the authorities. Eugene, remembered conspicuously for being a bank teller at the same bank Alice worked at, suddenly seemed closely linked. + +Eugene’s arrest was far from hushed, with the local news broadcasting the progressing drama live, catching sight of Eugene curtailed in handcuffs. Concurrently, it was ascertained—Eugene was a member of the jazz club. This evidence backed by a jazz club membership card retrieved from his wallet during the arrest. + +Just a few steps away, he noticed a man in a suit, the bouncer, a calm figure amid the bedlam. In their conversation, the bouncer corroborated that he had indeed seen Eugene involved in a heated scuffle, landing a few punches. The whisperings were starting to gain momentum, since Eugene was believed to be on the losing end of a lawsuit—a battle courtesy of Alice charging Eugene with the financial fraud she had publicly vowed to expose. + +Eugene was known for his frequent presence at the jazz club and on top of that, was an actual member. Therefore, it was hardly a leap to presume Alice meeting her untimely end at the club was no mere happenstance. The jazz club, despite its dim lights and pulsating music, was a public place easily accessible to all, including potential suspects like Eugene and, sadly, the ill-starred Alice. + +Det. Winston knew he was now tasked with a cryptic puzzle. A bank teller, embroiled in suspected fraud and a lawsuit, a jazz club murder scene and a local financial analyst—all woven into a ghastly murder mystery. He sighed in distaste as Eugene was escorted away—a man still oblivious to the chain of events waiting for him. But Winston knew, the night had only just begun for him. + +Winston stared down at the crumpled microphone on the floor. He picked it up gingerly, turning it in his hand. The club was in disarray, debris scattered like confetti. The lab boys were still picking pieces of the grenade apart. + +"Gabrielle's microphone," the coroner confirmed, barely looking up from his task. + +"Give him the once-over for evidence," Winston said, handing the microphone to a nearby officer. + +Leaving the club behind him, Winston sighed heavily. The world of jazz had taken a dark turn that night. Alice, the acclaimed critic with her sarcastic wit and keen critical eye, had been last seen alive here. Her purse lay in the club untouched, a testament to the abruptness of the event. + +Gabrielle had been working as a war correspondent. Winston had read her articles. They were richly detailed, passionate, and highlighted the harsh reality of war zones. Gabrielle hadn't been shy about sharing her experiences or publicly criticizing the military in her pieces. She boldly interviewed military personnel and spent extended periods in conflict zones. + +Alice, though, never missed a chance to pick apart Gabrielle's articles. The vitriolic snippets in Alice’s column were regular features and Gabrielle's staunch defense of her articles, her work in the jazz scene, did little against Alice's respected reputation. + +The tension between them was palpable. Alice had been awarded a major journalist award that Gabrielle had desired. This only deepened their rivalry, with Gabrielle feeling overlooked for this recognition in the Jazz scene. + +Winston cast his gaze over the club once more—a hub of pulsating rhythms now eerily silent. + +A significant part of the evening was Gabrielle's recorded interview with Alice. It played on the local radio, their professional rivalry subtly echoing under their professional demeanor. + +With a deep breath, Winston knew he had a tall task ahead. The jazz club, where Alice was last seen alive was now shrouded in an eerie silence, the vibrant rhythms of what used to be a lively night echoing in the abandoned stage. It was up to him to piece together the missing notes and bring the symphony of this unsolved case to a satisfying finale. + +Who is the most likely murderer? + +Pick one of the following choices: +1 - Eugene +2 - Gabrielle + +You must pick one option. Before selecting a choice, explain your reasoning step by step. The murderer needs to have a means (access to weapon), motive (reason to kill the victim), and opportunity (access to crime scene) in order to have killed the victim. Innocent suspects may have two of these proven, but not all three. An innocent suspect may be suspicious for some other reason, but they will not have all of motive, means, and opportunity established. + +If you believe that both suspects have motive, means, and opportunity, you should make an educated guess pick the one for whom these are best established. If you believe that neither suspect has all three established, then choose the suspect where these are most clearly established. Explain your reasoning step by step before you answer. Finally, the last thing you generate should be "ANSWER: (your answer here, including the choice number)" +""".strip() + +reasoning = """ +Let's break this down step-by-step by first deducing which of the two suspects has a means, motive, and opportunity. + +We will start with Eugene. + +Eugene was being sued by Alice for fraudulent transactions. The charge was also very public. Both of these facts point to Eugene having a strong motive. + +Because Eugene has a jazz club membership, and we can deduce that the jazz club membership belongs to the same club Alice was murdered in, we can assume Eugene has an opportunity to commit the crime. + +Although we know Eugene is aggressive because he was throwing punches in the story, we do not know if he has access to the murder weapon. Because he does not have access to a grenade, he does not have a means. + +Let's review Gabrielle next. + +Gabrielle's purse was found at the scene of the crime, and we can then assume she had the opportunity to kill Alice. + +Because Gabrielle has been in conflict zones with military personnel, it's possible that she has access to a grenade. We can say that Gabrielle has a potential means to kill the victim. + +Finally, it appears that Gabrielle and Alice had a rivalry over journalism, which could have boiled up into physical action. Because of this, we can say that Gabrielle has a potential motive to kill the victim. + +Now, reviewing the evidence, we see that: + +Eugene has a motive and opportunity but no means. +Gabrielle has a motive, means, and opportunity. + +Therefore, Gabrielle is the most likely murderer. + +ANSWER: 2 + + +""".strip() + +murder_mystery_solved_ex = f'{story}\n\n{reasoning}' diff --git a/opencompass/datasets/musr/musr.py b/opencompass/datasets/musr/musr.py new file mode 100644 index 0000000000000000000000000000000000000000..96b84edf79ccf5daacfebc01bc6ccda8cb56b67c --- /dev/null +++ b/opencompass/datasets/musr/musr.py @@ -0,0 +1,313 @@ +# flake8: noqa: E501 +import json +import os.path as osp + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_data_path + +from .murder_mystery_solved_ex import murder_mystery_solved_ex +from .object_placements_solved_ex import object_placements_solved_ex +from .team_allocation_solved_ex import team_allocation_solved_ex +from .tree import LogicTree + +DATASET_CONFIGS = { + 'murder_mysteries': { + 'file_name': + 'murder_mysteries.json', + 'ex': + murder_mystery_solved_ex, # write user example here + 'system_prompt': + 'You are a helpful assistant that will answer the questions given by the user.', + 'hint': + ('Before selecting a choice, explain your reasoning step by step. ' + 'The murderer needs to have a means (access to weapon), motive (reason to kill the victim), ' + 'and opportunity (access to crime scene) in order to have killed the victim. ' + 'Innocent suspects may have two of these proven, but not all three. ' + 'An innocent suspect may be suspicious for some other reason, but they will not have all of motive, ' + 'means, and opportunity established.\n\n' + 'If you believe that both suspects have motive, means, and opportunity, you should make an educated guess ' + 'and pick the one for whom these are best established. If you believe that neither suspect has all ' + 'three established, then choose the suspect where these are most clearly established.' + ), + 'hint_before_question': + False, + 'answer_index_modifier': + 1 + }, + 'object_placements': { + 'file_name': + 'object_placements.json', + 'ex': + object_placements_solved_ex, + 'skip_ablated': + True, + 'ablation_depth_modifier': + 2, + 'system_prompt': + 'You are a helpful assistant that will answer the questions given by the user.', + 'hint': + ('Based on this story, we want to identify where someone believes that a certain object is at the end of ' + 'the story. In order to do that, you need to read the story and keep track of where they think the object ' + 'is at each point. When an object is moved, the person may observe its new location if they saw it move.\n\n' + 'To see where an object ends up, they must be able to see the location that it moves to and not be too ' + 'distracted by what they are doing. If they do not observe the object moving, then they will still believe ' + 'it to be in the last location where they observed it.'), + 'hint_before_question': + True, + 'answer_index_modifier': + 1 + }, + 'team_allocation': { + 'file_name': + 'team_allocation.json', + 'ex': + team_allocation_solved_ex, + 'system_prompt': + 'You are a helpful assistant that will answer the questions given by the user.', + 'hint': + ('The story should allow you to determine how good each person is at a skill. Roughly, each person is ' + 'either great, acceptable, or bad at a task. We want to find an optimal assignment of people to tasks ' + 'that uses their skills as well as possible. In addition, one task will have to have two people assigned ' + 'to it. The effectiveness of their teamwork (great team, acceptable team, or bad team) also impacts the ' + 'overall quality of the assignment.\n\n' + 'When two people need to work on a task and one is bad at it, they don\'t necessarily benefit from the ' + 'other person being good, unless they work well together.\n\n' + 'With different strengths, weaknesses, and interpersonal dynamics at play, you should allocate your team ' + 'to find the single assignment to ensure that the tasks overall are completed as effectively as possible.' + ), + 'hint_before_question': + False, + 'answer_index_modifier': + 1 + } +} + + +@LOAD_DATASET.register_module() +class MusrDataset(BaseDataset): + """MuSR. + + Args: + path (str): path to dataset + name (str): name of dataset + self_consistency_n (int) + exclude_contrastive_examples (bool): Whether to exclude contrastive examples + reverse_contrastive_sample (bool): Whether to reverse the selection of contrastive samples + skip_ablated (bool): Whether to skip ablated samples + offset (int): Starting offset for the dataset + sample_size (int): Sample size, None indicates using the entire dataset. + """ + + @staticmethod + def load(path, + name, + self_consistency_n=1, + exclude_contrastive_examples=False, + reverse_contrastive_sample=False, + skip_ablated=False, + randomize=False, + offset=0, + sample_size=None, + **kwargs): + """Load the dataset and flatten fields while constructing prompts, + taking self_consistency_n and ablations into account.""" + + if name not in DATASET_CONFIGS: + raise ValueError( + f'Dataset name {name} not supported. Must be one of {list(DATASET_CONFIGS.keys())}' + ) + + config = DATASET_CONFIGS[name] + path = get_data_path(path) + file_path = osp.join(path, config['file_name']) + + with open(file_path, 'r', encoding='utf-8') as f: + dataset = json.load(f) + + filtered_dataset = [] + hashes_done = [] + + for example in dataset: + if exclude_contrastive_examples and example['questions'][0].get('intermediate_data') and \ + len(example['questions'][0].get('intermediate_data')) > 0 and \ + example['questions'][0]['intermediate_data'][0].get('story_hash_id'): + story_hash = example['questions'][0]['intermediate_data'][0][ + 'story_hash_id'] + if story_hash in hashes_done: + if reverse_contrastive_sample: + filtered_dataset.append(example) + else: + continue + elif not reverse_contrastive_sample: + filtered_dataset.append(example) + hashes_done.append(story_hash) + else: + filtered_dataset.append(example) + + filtered_dataset = filtered_dataset[ + offset:offset + + min(len(filtered_dataset), sample_size) if sample_size else None] + + ablations = [ + # {'prompt': 'regular', 'name': 'regular'}, + # {'prompt': 'cot', 'name': 'cot'}, + { + 'prompt': 'cot+', + 'name': 'cot+' + }, + ] + + # create prompts + flattened_data = [] + for example in filtered_dataset: + context = example['context'] + questions = example['questions'] + + for question in questions: + choices_list = question['choices'] + choices_str = '\n'.join([ + f'{idx + 1} - {choice}' + for idx, choice in enumerate(choices_list) + ]) + gold_answer = question['answer'] + config.get( + 'answer_index_modifier', 1) + + for ablation in ablations: + prompt_style = ablation.get('prompt', 'cot+') + ablation_name = ablation.get('name', 'cot+') + + for scidx in range(self_consistency_n): + ex_str = '' + if ablation.get('use_example') and config.get('ex'): + ex_str = ( + 'Here is an example of solving the task:\n\n' + + config.get('ex') + + '\n\nThis is the end of the example. The real task is below.\n\n---\n\n' + ) + + if prompt_style == 'regular': + prompt = f'{ex_str}{context}\n\n{question["question"]}\n\n' \ + f'Pick one of the following choices:\n{choices_str}\n\n' \ + 'You must pick one option. Finally, the last thing you generate should be "ANSWER: (your answer here, include the choice number)"' + elif prompt_style == 'cot': + prompt = f'{ex_str}{context}\n\n{question["question"]}\n\n' \ + f'Pick one of the following choices:\n{choices_str}\n\n' \ + 'You must pick one option. Explain your reasoning step by step before you answer. ' \ + 'Finally, the last thing you generate should be "ANSWER: (your answer here, include the choice number)"' + elif prompt_style == 'cot+': + if config.get('hint_before_question'): + prompt = f'{ex_str}{context}\n\n{config["hint"]}\n\n{question["question"]}\n\n' \ + f'Pick one of the following choices:\n{choices_str}\n\n' \ + 'You must pick one option. Explain your reasoning step by step before you answer. ' \ + 'Finally, the last thing you generate should be "ANSWER: (your answer here, including the choice number)"' + else: + prompt = f'{ex_str}{context}\n\n{question["question"]}\n\n' \ + f'Pick one of the following choices:\n{choices_str}\n\n' \ + f'You must pick one option. {config["hint"]} Explain your reasoning step by step before you answer. ' \ + 'Finally, the last thing you generate should be "ANSWER: (your answer here, including the choice number)"' + else: + if len(question['intermediate_trees'] + ) == 0 or config.get('skip_ablated', False): + continue + + prompt = f'{ex_str}Answer the following questions given the list of facts per answer choice.\n\n' + for c, t in zip(choices_str.split('\n'), + question['intermediate_trees']): + # extract facts from intermediate_trees + facts = list( + set([ + x.value for x in + LogicTree.from_json(t).get_facts( + include_cs=ablation.get( + 'include_cs', False), + include_deductions_from_level=-1, + no_facts_after_depth=ablation.get( + 'no_facts_after_depth', 3) + + config.get( + 'ablation_depth_modifier', 0)) + ])) + if config.get('allow_sorted_facts', True): + facts = sorted(facts) + facts_str = '\n'.join( + [f'- {fact}' for fact in facts]) + prompt += f'Facts for Choice {c}:\n{facts_str}\n\n' + prompt += f'Given the list of facts per answer choice, answer the following question\n\n' \ + f'{question["question"]}\n\n' \ + f'Pick one of the following choices:\n{choices_str}\n\n' \ + 'You must pick one option. After you have found the answer, say it in this format "ANSWER: (your answer here, include the choice number)"' + + flattened_example = { + 'context': + context, + 'question_text': + question['question'], + 'question': + question, + 'answer': + question['answer'], + 'choices': + choices_list, + 'choices_str': + choices_str, + 'intermediate_trees': + question.get('intermediate_trees', []), + 'intermediate_data': + question.get('intermediate_data', []), + 'prompt': + prompt, + 'system_prompt': + config.get('system_prompt', ''), + 'gold_answer': + gold_answer, + 'scidx': + scidx, # self-consistency index + 'self_consistency_n': + self_consistency_n, + 'ablation_name': + ablation_name, + } + flattened_data.append(flattened_example) + + dataset = Dataset.from_list(flattened_data) + + return dataset + + +@ICL_EVALUATORS.register_module() +class MusrEvaluator(BaseEvaluator): + + def __init__(self, + answer_index_modifier=1, + self_consistency_n=1, + pred_postprocessor=None): + super().__init__(pred_postprocessor=pred_postprocessor) + self.answer_index_modifier = answer_index_modifier + self.self_consistency_n = self_consistency_n + + def score(self, predictions, references): + correct = 0 + assert len(predictions) == len( + references + ), 'Predictions and references must have the same length!' + + total = len(predictions) + + for pred, ref in zip(predictions, references): + if 'ANSWER:' in pred: + answer_line = [ + line for line in pred.split('\n') if 'ANSWER:' in line + ] + if answer_line: + answer = answer_line[0].split('ANSWER:')[-1].strip() + import re + match = re.search(r'\d+', answer) + if match: + pred_answer = int(match.group()) + if pred_answer == ref: + correct += 1 + accuracy = 100 * correct / total if total > 0 else 0 + return {'accuracy': accuracy} diff --git a/opencompass/datasets/musr/object_placements_solved_ex.py b/opencompass/datasets/musr/object_placements_solved_ex.py new file mode 100644 index 0000000000000000000000000000000000000000..1f5638fd3abe961a60695c56be275481a3ac1cd3 --- /dev/null +++ b/opencompass/datasets/musr/object_placements_solved_ex.py @@ -0,0 +1,53 @@ +# flake8: noqa: E501 +story = ''' +Petra, the dedicated housewife, felt a thrill at the thought of her surprise anniversary dinner for her husband, Daniel. She had been skillfully maneuvering around Daniel's eagerness to pitch in without disappointing him or giving up her surprise. + +Daniel, ever-the-observant-husband, noted Petra's unusual enthusiasm about the day's menu. Despite not knowing the details, he appreciated her effort and wanted to help—silently, he decided to deploy his best skill—patiently awaiting his moment to help, maybe when Petra asked for something from the pantry. Amidst the excitement, there was Clara, their maid—ever diligent and efficient, trying to keep the surroundings perfect for this special evening. + +Tucked away, under the counter, was Petra's secret recipe book, her culinary treasure. Her solace in confusing times, her secret weapon during their flavorful adventures. While toward the back of the pantry, was the glass jar of Petra's favorite spice blends—something that Daniel was well aware of, in case an opportunity arose for him to assist or distract when Petra might need it. + +All three residents of the home were aware of each item's location. The secret recipe book under the counter, the glass jar in the pantry, and the anxious excitement that filled the air—a fragrance even more intoxicating than the delicious smells that would soon fill the kitchen. + +With tact and secrecy, Petra relocated her cherished recipe book from its hidden spot under the counter to its temporary home on the kitchen table. The pages were swiftly opened to reveal her secret recipes which she was eager to start preparing for the long-awaited anniversary surprise. While Petra was engrossed in her preparations, Clara continued her sweeping routine in the kitchen. Clara's steady broom strokes on the wooden floor echoed a little in the otherwise busy and humming kitchen. In the background, beyond the kitchen door, Daniel could be seen in the dining room, meticulously setting the table for the anticipated special dinner. + +The placement of the rooms allowed Clara to easily notice Petra's movements in her peripheral vision while she was executing her chores. Every move Petra made was observed in Clara's line of sight. Simultaneously, separated by the walls, Daniel was diligently arranging the tableware in the dining room which was separate from Petra's bustling kitchen. + +Hoping to spruce up the setting, Daniel delicately relocated a glass jar filled with decorative pebbles to the center of the dining table. His subtle contribution for the evening - a perfectly presentable table for their special anniversary dinner. Amidst the flurry of the special day's preparations, Clara diligently carried on with her duties in the upstairs bathroom, unseen from the dining room. Meanwhile, Petra was wholly engrossed in the allure of a new recipe in her cherished, hidden book which lay opened on the kitchen island, away from prying eyes of the dining room. + +In the middle of her usual tidying, Clara spotted Petra's treasured recipe book on the kitchen table. Ensuring it stayed clandestine, Clara carefully transferred it back to its usual hideaway spot beneath the counter. In the midst of the anniversary excitement, Clara deftly transferred Petra's secret weapon back to its hiding place when Daniel stepped out into the garage to retrieve extra utensils. Performing her duty with a sense of urgency, she made sure to move quietly to not disturb Petra, who was engrossed in the process of boiling a massive pot of pasta water on the stove. + +Despite the commotion and fervor in the kitchen, the hubbub did not stretch as far as the garage, which remained undisturbed by the domestic activity occurring in the main part of the house. Meanwhile, in the kitchen, Petra was oblivious to Clara's subtle maneuver while she busied herself at the stove, focused on making sure the large pot of water reached the perfect boil. + +In the end, the careful orchestration of duties by each individual within the house concluded in a harmonious anniversary celebration. The marks of a successful evening consisted of a delectable meal, a serene atmosphere, and the memory of a smooth, incident-free evening where everyone played their role to perfection. + +Based on this story, we want to identify where someone believes that a certain object is at the end of the story. In order to do that, you need to read the story and keep track of where they think the object is at each point. When an object is moved, the person may observe its new location if they saw it move. + +To see where an object ends up, they must be able to see the location that it moves to and not be too distracted by what they are doing. If they do not observe the object moving, then they will still believe it to be in the last location where they observed it. + +Which location is the most likely place Clara would look to find the glass jar given the story? + +Pick one of the following choices: +1 - dining table +2 - kitchen table +3 - pantry +4 - under counter + +You must pick one option. Explain your reasoning step by step before you answer. Finally, the last thing you generate should be "ANSWER: (your answer here, including the choice number)" +'''.strip() + +reasoning = ''' +Let's solve this by thinking step-by-step. We want to know where Clara will check to find the glass jar, so let's track where Clara sees the glass jar throughout the story. + +At the beginning of the story, it is stated that "All three residents of the home were aware of each item's location... the glass jar in the pantry." From this, we can conclude that the first place in the story where Clara sees the glass jar is in the pantry. + +Throughout the story, the glass jar only moves once to the dining table. However, while Daniel was moving the glass jar, Clara was upstairs in the restroom carrying out her duties. It's highly unlikely that she saw Daniel move the glass jar, so we can assume that she still believes it to be in the pantry. + +Clara does go to the kitchen in the story and moves a recipe book from the kitchen table, but because it's the kitchen table and not the dining room table, we can assume she hasn't seen the glass jar there. + +Now, given the story and evidence, we can assume that Clara believes the glass jar to be in the pantry. + +ANSWER: 3 + +'''.strip() + +object_placements_solved_ex = f'{story}\n\n{reasoning}' diff --git a/opencompass/datasets/musr/team_allocation_solved_ex.py b/opencompass/datasets/musr/team_allocation_solved_ex.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3fc68955680e187cf11c30db135aaf891dce1b --- /dev/null +++ b/opencompass/datasets/musr/team_allocation_solved_ex.py @@ -0,0 +1,72 @@ +# flake8: noqa: E501 +story = ''' +In the quaint community of Midvale, the local school stood as a beacon of enlightenment, nurturing the minds of the next generation. The teachers, the lifeblood of this institution, were tasked with the noble duty of education, while the unsung heroes—the maintenance crew—ensured the smooth functioning of the school's infrastructure. Amidst this, three town residents, Angela, Greg, and Travis, found themselves at a juncture of life where they were presented with the opportunity to serve in one of these crucial roles. The challenge now lay in the hands of the manager, who had to assign them to either teaching or maintenance, a decision that would set the course for their contributions to the school. + +Angela was a fiercely independent woman, beset with a unique set of strengths and weaknesses. She was a woman of very few words, often finding it hard to articulate her thoughts and explain things clearly. Venturing into education seemed a maze with her apathetic attitude towards learning. She was also seen to be disinterested in reading and the literary field as a whole. This was a juxtaposition to her inability to contribute to maintenance duties because of her fear of tools and machinery, a sinister remnant of a past accident that still haunted her. The basic handyman skills, which most locals learned growing up, were also absent from her repertoire. + +Angela's interactions with Greg and Travis further complicated the equation. On one hand, Greg and Angela had a habit of arguing constantly over trivial matters, which once culminated in their failure to complete a shared basic training exercise adequately. On the other hand, Angela and Travis simply had nothing in common. Their conversations were often fraught with awkward silences, indicative of their lack of shared interests. This lack of coordination was epitomized during a recent team-building exercise when their team finished last. + +Greg was the blue-collar type with a broad frame and muscular build. He had a work ethic that never shied away from toiling through the day to get things done. Growing up, he often helped his father with simple home repairs and minor renovations, learning the ropes of basic handiwork. Additionally, Greg had fortified his skills while refurbishing an old shed with Travis, a testament to their compatible personalities. However, his dislike for education was well known throughout town, further amplified by his lack of patience, especially with children. + +Travis, the third cog in the wheel, was a man of many peculiarities. His stage fright was almost legendary and made it nearly impossible for him to stand in front of a crowd. Often, the mere thought of it could unnerve him. His physical constitution was lightweight and fragile, and long hours of manual labor made him weary. He also had a revulsion towards dirt that he complained about at every opportune moment. Like the others, studying did not appeal to him much, so much so that he had stopped reading completely after leaving school prematurely. + +The manager understood well that a team’s success depends heavily on the contribution and compatibility of each member. He observed, analyzed, and considered. Now, it was up to him to assign roles to Angela, Greg, and Travis. The school needed educators and maintenance staff, and each had to play their part perfectly. + +Given the story, how would you uniquely allocate each person to make sure both tasks are accomplished efficiently? + +Pick one of the following choices: +1 - Teaching: Travis, Maintenance: Angela and Greg +2 - Teaching: Greg, Maintenance: Angela and Travis +3 - Teaching: Angela, Maintenance: Greg and Travis + +You must pick one option. The story should allow you to determine how good each person is at a skill. Roughly, each person is either great, acceptable, or bad at a task. We want to find an optimal assignment of people to tasks that uses their skills as well as possible. In addition, one task will have to have two people assigned to it. The effectiveness of their teamwork (great team, acceptable team, or bad team) also impacts the overall quality of the assignment. + +When two people need to work on a task and one is bad at it, they don’t necessarily benefit from the other person being good, unless they work well together. + +With different strengths, weaknesses, and interpersonal dynamics at play, you should allocate your team to find the single assignment to ensure that the tasks overall are completed as effectively as possible. + + Explain your reasoning step by step before you answer. Finally, the last thing you generate should be "ANSWER: (your answer here, including the choice number)" +'''.strip() + +reasoning = ''' +Let's solve this by thinking step-by-step. First, we will figure out each person's skill level for each task. Then, we can measure how well they all work together in pairs. From this, we can find the most efficient assignment that maximizes the scores. + +Let's start with Angela. Angela can't articulate her thoughts, and she seems unprepared for teaching. So, let's assume her skill level is 1 for teaching. She also is bad at maintenance due to her fear of maintenance. So, let's assume her skill level is 1 for maintenance as well. + +Now, let's look at Greg. Greg has a dislike for education and a lack of patience, so let's assume his skill level for maintenance is 1. However, Greg has helped with home repairs and minor renovations, so let's assume his maintenance skill level is 2. + +Finally, let's look at Travis. Travis has extreme stage fright, which will make it difficult to teach, so let's assume his teaching skill level is 1. He also has a lightweight and fragile frame as well as hates dirt, so let's assume his maintenance skill level is 1. + +Now, let's look at the relationships and how people work together. + +Angela and Greg do not get along; they are constantly arguing, so let's assume their ability to work together is 1. + +Angela and Travis aren't much better. They both have nothing in common, and they couldn't do a team-building exercise previously, so let's assume their ability to work together is 1. + +Finally, Greg and Travis have worked together, and their personalities seem to meld, so let's assume they work well together with a score of 3. + +Let's summarize and figure out the best assignment. + +Angela is bad at teaching. (1) +Angela is bad at maintenance. (1) +Angela does not work well with Greg. (1) +Angela does not work well with Travis. (1) +Greg is bad at teaching. (1) +Greg is okay with maintenance. (2) +Greg and Travis work well together. (3) +Travis is bad at teaching. (1) +Travis is bad at maintenance. (1) + +Now, let's find the best assignment. + +Option 1: Travis as a teacher (1) + Angela working in maintenance (1) + Greg working in maintenance (2) + Angela and Greg work badly together (1) = 5 +Option 2: Greg as a teacher (1) + Angela working in maintenance (1) + Travis working in maintenance (1) + Angela and Travis work badly together (1) = 4 +Option 3: Angela as a teacher (1) + Greg working in maintenance (2) + Travis working in maintenance (1) + Greg and Travis work well together (3) = 7 + +So, from this, we can see Option 3 has the maximum score. + +ANSWER: 3 + +'''.strip() + +team_allocation_solved_ex = f'{story}\n\n{reasoning}' diff --git a/opencompass/datasets/musr/tree.py b/opencompass/datasets/musr/tree.py new file mode 100644 index 0000000000000000000000000000000000000000..0733f0a77b01ad6b31cf1fae6ae707046d02c144 --- /dev/null +++ b/opencompass/datasets/musr/tree.py @@ -0,0 +1,747 @@ +# flake8: noqa: E501 +"""WARNING (or more like an aggressive note). + +A lot of functionality was implemented here for earlier experiments. Most of which is not used. We have left it here +for backwards compatibility with the current dataset as well as because why not. + +ALSO NOTE: + +This file was created to have no dependencies on anything in the repo for a reason. You can copy this file into your +own project and use the classes to parse/visualize/edit the logic trees in the dataset or create your own. + +FINAL NOTE: + +See examples of how to create LogicNodes and LogicTrees in the __main__ part of the file. +""" + +import random +from copy import deepcopy +from enum import Enum +from typing import Any, Dict, List + +import numpy as np + + +class LogicNodeOperatorType: + """How should the deduction combine the nodes (choose will randomly sample + and/or when populate is called)""" + AND = 'and' + OR = 'or' + CHOOSE = 'choose' + + +class LogicNodeFactType: + """Is a node explicit (mentioned in the story) or commonsense knowledge + (left unsaid)""" + EXPLICIT = 'explicit' + COMMONSENSE = 'commonsense' + + +class LogicNodeConstraints: + """Useful for things like children = ['X is the murderer', 'Y is the + murderer', 'Z is the murderer'], we no longer use this structure though.""" + ONLY_ONE_CAN_BE_TRUE = 'Only one child can be true' + + +class LogicNodeDeductionType: + """What type of deduction should be used here (not used currently)""" + SYLLOGISM = 'syllogism' + TEMPORAL = 'temporal' + SPATIAL = 'spatial' + CHOOSE = 'choose' + + +class LogicNode: + """A LogicNode is a tree primitive. + + It is either a deduction or a leaf fact. Leaf facts are the ones that we + use in story generation (if they are explicit facts and not commonsense). + """ + value: str + children: List['LogicNode'] + fact_type: str + operator: str + constraints: List[str] + deduction_type: str + prunable: bool + can_be_leaf: bool + + def __init__( + self, + value: str = '', + children: List['LogicNode'] = None, + operator: str = LogicNodeOperatorType.OR, + fact_type: str = LogicNodeFactType.EXPLICIT, + constraints: List[str] = (), + deduction_type: str = None, + prunable: bool = True, + can_be_leaf: bool = False, + frozen: bool = False, + ): + """ + :param value: Content for this specific node (also the deduction of the children). + :param children: The children for this node. + :param operator: Should the children be "And"ed or "Or"ed to create the deduction (the content of this node). + :param fact_type: Explicit or commonsense + :param constraints: Not used anymore (see LogicNodeConstraints) + :param deduction_type: Not used anymore (see LogicNodeDeductionType) + :param prunable: Can this node be removed from the tree (we don't prune in our datasets) + :param can_be_leaf: Can this node be a leaf node (usually false for nodes that you are injecting manually) + :param frozen: Should we add/prune children in the populate function (if frozen, no children will be added or removed, but the children may have children appended/pruned from them). + """ + self.value = value + if children is None: + children = [] + self.children = children + self.operator = operator + self.fact_type = fact_type + self.constraints = constraints + self.deduction_type = deduction_type + self.prunable = prunable + self.can_be_leaf = can_be_leaf + self.frozen = frozen + self.parent = None + + @property + def children(self): + return self._children + + @children.setter + def children(self, children: List['LogicNode']): + self._children = children + for c in self.children: + c.parent = self + + def __str__(self): + line = [] + cnsts = ', '.join([str(x.value) for x in self.constraints]) + + if self.value and self.value != '': + line.append(self.value) + if len(self.children) > 0: + line.append(self.operator) + else: + line.append(self.fact_type) + + if self.deduction_type: + line.append(self.deduction_type) + + if len(self.constraints) > 0: + line.append(cnsts) + + if len(self.children) > 0: + line.append(f'children: {len(self.children)}') + + return ' | '.join(line) + + def __repr__(self): + return str(self) + + def to_json(self): + return { + 'value': self.value, + 'children': [x.to_json() for x in self.children], + 'fact_type': self.fact_type, + 'operator': self.operator, + 'constraints': self.constraints, + 'deduction_type': self.deduction_type, + 'prunable': self.prunable, + 'can_be_leaf': self.can_be_leaf + } + + @classmethod + def from_json(cls, js): + js['children'] = [LogicNode.from_json(x) for x in js['children']] + return cls(**js) + + +class LogicTree: + """Main datastructure used when creating a MuSR example. + + It's basically a standard tree with some parameters controlling the shape. + """ + + nodes: List[LogicNode] + + chance_of_or: float + chance_of_cs_fact: float + depth: int + chance_to_prune: float + chance_to_prune_all: float + bf_factor: Dict[int, float] + deduction_type_sample_rate: Dict[LogicNodeDeductionType, float] + root_structure: List[List[LogicNode]] = () + + def __init__(self, + chance_of_or: float = 0.3, + chance_of_cs_fact: float = 0.1, + depth: int = 2, + chance_to_prune: float = 0.6, + chance_to_prune_all: float = 0.2, + bf_factor: Dict[int, float] = None, + deduction_type_sample_rate: Dict[LogicNodeDeductionType, + float] = None, + enforce_cs_fact_per_level: bool = False, + root_structure: List[Any] = (), + nodes: List[LogicNode] = (), + populate: bool = True, + prune: bool = True): + """ + :param chance_of_or: (not used) how often should a node with children be an OR + :param chance_of_cs_fact: (not used) how often should there be a commonsense node + :param depth: How deep should a tree go + :param chance_to_prune: Percentage chance of pruning a node + :param chance_to_prune_all: Percentage chance of pruning all children from a node. + :param bf_factor: Branching factor (dictionary of percentages {1: 0.33, 2:0.33, 3:0.33} for example. + :param deduction_type_sample_rate: (not used, see bf_factor and LogicNodeDeductionType) + :param enforce_cs_fact_per_level: Enforce 1 commonsense fact per level in the tree (we use this instead of chance_of_cs_fact) + :param root_structure: List of LogicNodes to build off of. + :param nodes: List of LogicNodes to define the LogicTree on (we will not populate/prune the tree if this is filled) + :param populate: Should we populate children for the tree according to the other parameters? + :param prune: Should we prune the children for the tree according to the other parameters? + """ + self.chance_of_or = chance_of_or + self.chance_of_cs_fact = chance_of_cs_fact + self.depth = depth + self.chance_to_prune = chance_to_prune + self.chance_to_prune_all = chance_to_prune_all + self.bf_factor = bf_factor + self.enforce_cs_fact_per_level = enforce_cs_fact_per_level + + if not bf_factor: + self.bf_factor = {2: 0.8, 3: 0.2} + if not deduction_type_sample_rate: + deduction_type_sample_rate = { + LogicNodeDeductionType.SYLLOGISM: 1.0 + } + + self.deduction_type_sample_rate = deduction_type_sample_rate + self.root_structure = root_structure + + if len(nodes) > 0: + self.nodes = nodes + else: + + if root_structure is not None and len(root_structure) > 0: + self.nodes = root_structure + else: + self.nodes = [ + LogicNode('root', operator=LogicNodeOperatorType.AND) + ] + + if populate: + [self.populate(x, 1) for x in self.nodes] + if prune: + [self.prune(x, 1) for x in self.nodes] + + def __str__(self): + return self.print_tree() + + def get_facts(self, + include_cs: bool = False, + include_deductions_from_level: int = -1, + no_facts_after_depth: int = -1): + """Get a list of LogicNodes from the tree. By default, you will get the + explicit leaf nodes. + + :param include_cs: Include the commonsense nodes from all levels. + :param include_deductions_from_level: Include any intermediate + deduction nodes from the specified level and deeper. + :param no_facts_after_depth: Essentially tree the deductions at the + specified depth as leaf nodes. + """ + + def recurse_facts(_node: LogicNode, depth: int = 0) -> List[str]: + node = deepcopy(_node) + if depth >= no_facts_after_depth and no_facts_after_depth > -1: + node.children = [] + + facts = [] + + if node.fact_type == LogicNodeFactType.EXPLICIT and len( + node.children) == 0: + facts.append(node) + if node.fact_type == LogicNodeFactType.COMMONSENSE and include_cs and len( + node.children) == 0: + facts.append(node) + if len( + node.children + ) > 0 and include_deductions_from_level <= depth and include_deductions_from_level > -1: + facts.append(node) + + for child in node.children: + facts.extend(recurse_facts(child, depth + 1)) + return list(set(facts)) + + facts = [] + for n in self.nodes: + facts.extend(recurse_facts(n)) + return facts + + def print_tree(self, node=None, level=0): + """Deprecated (not used)""" + if node is None: + node = self.nodes[0] + line = '-' * level * 4 + str(node) + (' | ' + str(node.operator) if + len(node.children) > 0 else '') + + for child in node.children: + line += '\n' + self.print_tree(child, level + 1) + + return line + + def print_for_gpt(self, + node=None, + level=0, + pad_char=' ', + pad_space=4, + print_forward=True, + print_conjection_types: bool = False, + print_reasoning_types: bool = False, + ignore_value_after_depth: int = -1, + print_only_nodes_with_value: bool = False): + """Complex print function. We often use it as + print_for_gpt(pad_space=1, pad_char='> ') + + However, more complex arguments can be used to control what is printed. + + This returns a string that must be printed (don't be confused by the + method name.) + + :param node: Start at a specific node. + :param level: Controls how much tabbing is done when printing the + current node. + :param pad_char: Char to use that specifies depth ('> ' at depth 3 will + look like '> > > ' if you have pad_space equal to 1 for example) + :param pad_space: How many spaces to include between pad_chars + :param print_forward: Print the tree with parent nodes first. + :param print_conjection_types: Print the Ands and Ors per deduction + (not used) + :param print_reasoning_types: Print the deduction types (not used) + :param ignore_value_after_depth: Ignore content of the nodes once a + depth is met + :param print_only_nodes_with_value: Ignore nodes without content. + """ + + line = '' + + if node is None: + node = self.nodes[0] + + if not print_forward: + for child in node.children: + v = self.print_for_gpt( + child, + level + 1, + pad_char=pad_char, + pad_space=pad_space, + print_forward=print_forward, + ignore_value_after_depth=ignore_value_after_depth, + print_only_nodes_with_value=print_only_nodes_with_value) + if v != '': + line += v + '\n' + + ignore_val = ignore_value_after_depth > -1 and ignore_value_after_depth < level + ignore_line = print_only_nodes_with_value and node.value == '' + + if ignore_line: + line_val = '' + else: + line_val = (node.value + ' | ' if node.value != '' and not ignore_val else '') + ( + ('Fact From Story' if node.fact_type == LogicNodeFactType.EXPLICIT else 'Commonsense Knowledge') \ + if len(node.children) == 0 else 'Deduced Fact') + + if level == 0: + line_val = (node.value + ' | ' if node.value != '' else + '') + 'Deduced Root Conclusion' + + if len(node.children) > 0 and (print_conjection_types + or print_reasoning_types): + if print_conjection_types: + line_val += f' ({node.operator}' + else: + line_val += f'(' + if node.deduction_type and print_reasoning_types: + line_val += f' | {node.deduction_type})' + else: + line_val += ')' + + if len(node.constraints) > 0: + cnsts = ', '.join([str(x) for x in node.constraints]) + line_val += f' constraints: [{cnsts}]' + + line += pad_char * level * pad_space + line_val + + if print_forward: + for child in node.children: + v = self.print_for_gpt( + child, + level + 1, + pad_char=pad_char, + pad_space=pad_space, + print_forward=print_forward, + ignore_value_after_depth=ignore_value_after_depth, + print_only_nodes_with_value=print_only_nodes_with_value) + if v != '': + line += '\n' + v + + return line + + def populate(self, node: LogicNode, current_depth: int = 1): + if node.operator == LogicNodeOperatorType.CHOOSE: + node.operator = LogicNodeOperatorType.OR \ + if random.random() < self.chance_of_or else LogicNodeOperatorType.AND + if node.deduction_type == LogicNodeDeductionType.CHOOSE: + if node.operator != LogicNodeOperatorType.AND: + node.deduction_type = None + else: + node.deduction_type = random.choices( + list(self.deduction_type_sample_rate.keys()), + list(self.deduction_type_sample_rate.values()), + k=1)[0] + + if not node.frozen: + + bf = max( + 0, + random.choices(list(self.bf_factor.keys()), + list(self.bf_factor.values()), + k=1)[0] - len(node.children)) + + if bf > 0: + + new_nodes = [] + one_fact_is_cs = False + for idx in range(bf): + roll_for_or = random.random() + fact_type = LogicNodeFactType.COMMONSENSE \ + if random.random() < self.chance_of_cs_fact and not one_fact_is_cs else \ + LogicNodeFactType.EXPLICIT + + if roll_for_or > self.chance_of_or and\ + current_depth < self.depth and\ + not fact_type == LogicNodeFactType.COMMONSENSE: + new_nodes.append( + LogicNode( + f'', + operator=LogicNodeOperatorType.AND, + fact_type=fact_type, + deduction_type=random.choices( + list(self.deduction_type_sample_rate.keys( + )), + list(self.deduction_type_sample_rate. + values()), + k=1)[0], + prunable=True, + can_be_leaf=True, + )) + else: + new_nodes.append( + LogicNode(f'', + operator=LogicNodeOperatorType.OR, + fact_type=fact_type, + prunable=True, + can_be_leaf=True)) + + if fact_type == LogicNodeFactType.COMMONSENSE: + node.operator = LogicNodeOperatorType.AND + if not node.deduction_type: + node.deduction_type = random.choices( + list(self.deduction_type_sample_rate.keys()), + list(self.deduction_type_sample_rate.values()), + k=1)[0] + one_fact_is_cs = True + + if not one_fact_is_cs and self.enforce_cs_fact_per_level: + new_nodes.append( + LogicNode(f'', + operator=LogicNodeOperatorType.OR, + fact_type=LogicNodeFactType.COMMONSENSE, + prunable=False, + can_be_leaf=True)) + + node.children.extend(new_nodes) + + if current_depth < self.depth: + for node in node.children: + if node.fact_type == LogicNodeFactType.COMMONSENSE: + continue + self.populate(node, current_depth + 1) + + def prune(self, node: LogicNode, current_depth: int = 1): + to_prune = [] + + if current_depth > 1 and node.can_be_leaf: + if random.random() < self.chance_to_prune_all: + node.children = [] + return + + prunable = [x for x in node.children if x.prunable] + if (len(prunable) > 1 and node.operator == LogicNodeOperatorType.OR or\ + len(prunable) > 2 and node.operator == LogicNodeOperatorType.AND) and\ + current_depth <= self.depth: + + if node.prunable: + for n in random.sample( + prunable, + len(prunable) - + (1 if node.operator == LogicNodeOperatorType.OR else 2)): + roll_to_prune = random.random() + if roll_to_prune < self.chance_to_prune: + to_prune.append(n) + + node.children = [x for x in node.children if x not in to_prune] + for n in node.children: + self.prune(n, current_depth + 1) + + def to_json(self): + args = { + 'chance_of_or': self.chance_of_or, + 'depth': self.depth, + 'chance_to_prune': self.chance_to_prune, + 'chance_to_prune_all': self.chance_to_prune_all, + 'bf_factor': self.bf_factor, + 'deduction_type_sample_rate': self.deduction_type_sample_rate, + 'root_structure': [x.to_json() for x in self.root_structure], + 'nodes': [x.to_json() for x in self.nodes] + } + return args + + @classmethod + def from_json(cls, _js): + js = deepcopy(_js) + js['nodes'] = [LogicNode.from_json(x) for x in js['nodes']] + js['root_structure'] = [ + LogicNode.from_json(x) for x in js['root_structure'] + ] + return cls(**js) + + +if __name__ == '__main__': + """EXAMPLE USES.""" + + def tv_scene_ex(): + root_structure = [ + LogicNode('A good drama tv scene', + operator=LogicNodeOperatorType.OR, + prunable=False, + can_be_leaf=False, + frozen=True) + ] + + root_structure[0].children = [ + LogicNode('Bob is sad.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + LogicNode('John now hates Bob.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + LogicNode('Bob bought a car.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + LogicNode('Bob wanted to be happy.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + ] + + tree = LogicTree(depth=4, + root_structure=root_structure, + bf_factor={ + 1: 0.5, + 2: 0.5 + }, + chance_of_or=0.0, + chance_of_cs_fact=0.0, + chance_to_prune_all=0.5, + chance_to_prune=0.5, + enforce_cs_fact_per_level=True) + + rep = tree.print_for_gpt(pad_space=1, pad_char='- ') + print(rep) + + def eb_ex(): + root_structure = [ + LogicNode('', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False) + ] + + n = LogicNode('Eruptions block sunlight.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False, + frozen=True) + n.children = [ + LogicNode('Eruptions produce ash clouds.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=True, + frozen=True), + LogicNode('Ash blocks sunlight.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=True, + frozen=True), + ] + + g = LogicNode('Eruptions can cause plants to die.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False, + frozen=True) + + g.children = [ + n, + LogicNode('Producers will die without sunlight.', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=True, + frozen=True) + ] + + l = LogicNode('', + operator=LogicNodeOperatorType.AND, + prunable=False, + can_be_leaf=False) + l.children = [g] + + root_structure[0].children = [l] + + tree = LogicTree(depth=5, + root_structure=root_structure, + bf_factor={ + 1: 0.3, + 2: 0.7 + }, + chance_of_or=0.0, + chance_of_cs_fact=0.0, + chance_to_prune_all=0.0, + chance_to_prune=0.0, + enforce_cs_fact_per_level=True) + + rep = tree.print_for_gpt(pad_space=1, pad_char='- ') + print(rep) + + def murder_mystery_ex(): + root_structure = [ + LogicNode('Killer', + operator=LogicNodeOperatorType.OR, + constraints=[LogicNodeConstraints.ONLY_ONE_CAN_BE_TRUE], + prunable=False, + can_be_leaf=False, + frozen=True) + ] + + suspect_nodes = [ + LogicNode(f'Murderer Suspect {idx + 1}', + operator=LogicNodeOperatorType.AND, + prunable=False, + can_be_leaf=False, + frozen=True) for idx in range(1) + ] + for s in suspect_nodes: + s.children = [ + LogicNode('Suspect has means', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + LogicNode('Suspect has motive', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False), + LogicNode('Suspect has opportunity', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False) + ] + root_structure[0].children = suspect_nodes + + tree = LogicTree(depth=4, + root_structure=root_structure, + bf_factor={ + 1: 0.5, + 2: 0.5 + }, + chance_of_or=0.0, + chance_of_cs_fact=0.0, + chance_to_prune_all=0.5, + chance_to_prune=0.5, + enforce_cs_fact_per_level=True) + + rep = tree.print_for_gpt(pad_space=1, pad_char='> ') + print(rep) + + def action_ex(): + root_structure = [ + LogicNode('Take an action', + operator=LogicNodeOperatorType.OR, + prunable=False, + can_be_leaf=False, + frozen=True) + ] + + root_structure[0].children = [ + LogicNode('Run away', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False, + frozen=True), + LogicNode('Fight back', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False, + frozen=True), + LogicNode('Hide', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False, + frozen=True), + ] + + for cidx, c in enumerate(root_structure[0].children): + nfacts = random.randint(2, 4) + + for n in range(nfacts): + fact = LogicNode('', + operator=LogicNodeOperatorType.CHOOSE, + prunable=False, + can_be_leaf=False, + frozen=True) + fact.children = [ + LogicNode('Pro (supporting the parent action)', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False, + frozen=False), + LogicNode('Con (counters the sibling Pro only)', + operator=LogicNodeOperatorType.CHOOSE, + prunable=True, + can_be_leaf=False, + frozen=False) + ] + root_structure[0].children[cidx].children.append(fact) + + tree = LogicTree(depth=4, + root_structure=root_structure, + bf_factor={ + 1: 0.25, + 2: 0.5, + 3: 0.25 + }, + chance_of_or=0.0, + chance_of_cs_fact=0.0, + chance_to_prune_all=0.5, + chance_to_prune=0.75, + enforce_cs_fact_per_level=True) + + rep = tree.print_for_gpt(pad_space=1, pad_char='- ') + print(rep) + + tv_scene_ex() + eb_ex() + action_ex() diff --git a/opencompass/datasets/needlebench/__init__.py b/opencompass/datasets/needlebench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/opencompass/datasets/needlebench/atc.py b/opencompass/datasets/needlebench/atc.py new file mode 100644 index 0000000000000000000000000000000000000000..c715b19bcc553b9734ae1cd6ddd57071d0c09f37 --- /dev/null +++ b/opencompass/datasets/needlebench/atc.py @@ -0,0 +1,291 @@ +# flake8: noqa +import json +import os +import random + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + + +@LOAD_DATASET.register_module() +class NeedleBenchATCDataset(BaseDataset): + + @staticmethod + def load( + path, + file_name: str, + num_needles: int, + language: str, + repeats: int, + ): + data = {'prompt': [], 'answer': []} + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + file_path = os.path.join(path, file_name) + + with open(file_path, 'r', encoding='utf-8') as file: + names_data = json.load(file) + + all_names = names_data[language].split(',') + + for _ in range(repeats): + names = random.sample(all_names, num_needles) + if language == 'Chinese': + + relationship_terms = [ + '父亲', + '母亲', + '爸爸', + '妈妈', + '爷爷', + '奶奶', + '姥姥', + '姥爷', + '外公', + '外婆', + ] + + relationship_templates = [ + '{A}是{B}的{relationship}。', + '{B}的{relationship}是{A}。', + '{A}作为{B}的{relationship}，对{B}的成长有重要影响。', + '{A}不仅是{B}的{relationship}，还是{B}的榜样。', + '{B}是{A}所生的孩子。', + '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。', + '{A}在{B}的生命中扮演着{relationship}的角色。', + '{B}把{A}视为其{relationship}。', + ] + elif language == 'English': + + relationship_terms = [ + 'father', + 'mother', + 'dad', + 'mom', + 'grandfather', + 'grandmother', + 'maternal grandmother', + 'maternal grandfather', + 'paternal grandfather', + 'paternal grandmother', + ] + + relationship_templates = [ + "{A} is {B}'s {relationship}.", + "{B}'s {relationship} is {A}.", + ("{A}, as {B}'s {relationship}, " + "has a significant impact on {B}'s upbringing."), + ("{A} is not only {B}'s {relationship} " + "but also {B}'s role model."), + '{B} is the child of {A}.', + ('For {B}, {A} is not just a {relationship}, ' + 'but also a friend.'), + ("{A} plays the role of {B}'s {relationship} " + "in {B}'s life."), + '{B} considers {A} as their {relationship}.', + ] + + def generate_chain_family_story(names, templates, + relationship_terms): + story = '' + for i in range(len(names) - 1): + template = random.choice(templates) + relation_term = random.choice(relationship_terms) + relation = template.format(A=names[i], + B=names[i + 1], + relationship=relation_term) + story += f'{relation}*' + return story + + chain_story = generate_chain_family_story(names, + relationship_templates, + relationship_terms) + + # Splitting the chain_story into a list of fragments + family_story_fragments = chain_story.split('*') + + # Shuffling the list of fragments + random.shuffle(family_story_fragments) + + # Joining the shuffled fragments back into a string + shuffled_story = ''.join(family_story_fragments) + + last_person = names[-1] + + # Generating the prompt based on the language + if language == 'Chinese': + prompt = f""" +在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？ +例如： +例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。 +例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。 +例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。 +""" + elif language == 'English': + prompt = f""" +Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context? +For example: +Example 1: If Zhang Qiang's father is Mark, and no further information about familial relationships is provided in the text, then the oldest relative Zhang Qiang can trace back to in the provided text is Mark. +Example 2: If Li Ming's grandmother is Zhang Hong, and Zhang Hong's father is Zhang Qiang, and no further information about familial relationships is provided in the text, then the oldest relative Li Ming can trace back to in the provided text is Zhang Qiang. +Example 3: If Xiao Ming is Zhang Hong's great-granddaughter, Zhang Hong's grandmother is Wang Hua, and Wang Hua's father is Wang Gang, and no further information about familial relationships is provided in the text, then the oldest relative Xiao Ming can trace back to in the provided text is Wang Gang.""" + else: + prompt = 'Language not supported.' + raise Exception('Unsupported language specified. ' + "Please choose either 'Chinese' or 'English'.") + + # Combine story and prompt + shuffled_story_with_prompt = shuffled_story + ' ' + prompt + + data['prompt'].append(shuffled_story_with_prompt) + data['answer'].append(names[0] + '*' + names[0]) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset + + +@LOAD_DATASET.register_module() +class NeedleBenchATCOrderedDataset(BaseDataset): + + @staticmethod + def load( + path, + file_name, + num_needles: int, + language: str, + repeats: int, + ): + data = {'prompt': [], 'answer': []} + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + file_path = os.path.join(path, file_name) + + with open(file_path, 'r', encoding='utf-8') as file: + names_data = json.load(file) + + all_names = names_data[language].split(',') + + for _ in range(repeats): + names = random.sample(all_names, num_needles) + if language == 'Chinese': + + relationship_terms = [ + '父亲', + '母亲', + '爸爸', + '妈妈', + '爷爷', + '奶奶', + '姥姥', + '姥爷', + '外公', + '外婆', + ] + + relationship_templates = [ + '{A}是{B}的{relationship}。', + '{B}的{relationship}是{A}。', + '{A}作为{B}的{relationship}，对{B}的成长有重要影响。', + '{A}不仅是{B}的{relationship}，还是{B}的榜样。', + '{B}是{A}所生的孩子。', + '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。', + '{A}在{B}的生命中扮演着{relationship}的角色。', + '{B}把{A}视为其{relationship}。', + ] + elif language == 'English': + + relationship_terms = [ + 'father', + 'mother', + 'dad', + 'mom', + 'grandfather', + 'grandmother', + 'maternal grandmother', + 'maternal grandfather', + 'paternal grandfather', + 'paternal grandmother', + ] + + relationship_templates = [ + "{A} is {B}'s {relationship}.", + "{B}'s {relationship} is {A}.", + ("{A}, as {B}'s {relationship}, " + "has a significant impact on {B}'s upbringing."), + ("{A} is not only {B}'s {relationship} " + "but also {B}'s role model."), + '{B} is the child of {A}.', + ('For {B}, {A} is not just a {relationship}, ' + 'but also a friend.'), + ("{A} plays the role of {B}'s {relationship} " + "in {B}'s life."), + '{B} considers {A} as their {relationship}.', + ] + + def generate_chain_family_story(names, templates, + relationship_terms): + story = '' + for i in range(len(names) - 1): + template = random.choice(templates) + relation_term = random.choice(relationship_terms) + relation = template.format(A=names[i], + B=names[i + 1], + relationship=relation_term) + story += f'{relation}*' + return story + + chain_story = generate_chain_family_story(names, + relationship_templates, + relationship_terms) + + # Splitting the chain_story into a list of fragments + family_story_fragments = chain_story.split('*') + + # Joining the shuffled fragments back into a string + shuffled_story = ''.join(family_story_fragments) + + last_person = names[-1] + + # Generating the prompt based on the language + if language == 'Chinese': + prompt = f""" +在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？ +例如： +例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。 +例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。 +例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。 +""" + elif language == 'English': + prompt = f""" +Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context? +For example: +Example 1: If Zhang Qiang's father is Mark, and no further information about familial relationships is provided in the text, then the oldest relative Zhang Qiang can trace back to in the provided text is Mark. +Example 2: If Li Ming's grandmother is Zhang Hong, and Zhang Hong's father is Zhang Qiang, and no further information about familial relationships is provided in the text, then the oldest relative Li Ming can trace back to in the provided text is Zhang Qiang. +Example 3: If Xiao Ming is Zhang Hong's great-granddaughter, Zhang Hong's grandmother is Wang Hua, and Wang Hua's father is Wang Gang, and no further information about familial relationships is provided in the text, then the oldest relative Xiao Ming can trace back to in the provided text is Wang Gang.""" + else: + prompt = 'Language not supported.' + raise Exception('Unsupported language specified. ' + "Please choose either 'Chinese' or 'English'.") + + # Combine story and prompt + shuffled_story_with_prompt = shuffled_story + ' ' + prompt + + data['prompt'].append(shuffled_story_with_prompt) + data['answer'].append(names[0] + '*' + names[0]) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset diff --git a/opencompass/datasets/needlebench/atc_choice.py b/opencompass/datasets/needlebench/atc_choice.py new file mode 100644 index 0000000000000000000000000000000000000000..1f586035d6634f2d5b635e7898f3e1f7308511dd --- /dev/null +++ b/opencompass/datasets/needlebench/atc_choice.py @@ -0,0 +1,195 @@ +# flake8: noqa +import copy +import json +import os +import random + +from datasets import Dataset + +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from ..base import BaseDataset + + +def get_number(options): + result_string = '' + for i, option in enumerate(options, start=ord('A')): + result_string += f'{chr(i)}. {option}\n' + return result_string + + +def get_circular_example(entry, id): + """For given example, generate four circular examples.""" + # Only 4 options is supported for current circular eval. + circular_patterns = ['ABCD', 'BCDA', 'CDAB', 'DABC'] + data = [] + for c in circular_patterns: + line = copy.deepcopy(entry) + options = [] + for i in range(4): + options.append(line['options'][ord(c[i]) - ord('A')]) + line['options'] = options + line['answer'] = { + c[0]: 'A', + c[1]: 'B', + c[2]: 'C', + c[3]: 'D' + }[line['answer']] + line['answer'] = str(id) + '--' + line['answer'] + '--' + c + line['question'] = line['question'].strip() + '\n' + get_number( + line['options']) + data.append(line) + + return data + + +@LOAD_DATASET.register_module() +class NeedleBenchATCDataset(BaseDataset): + + @staticmethod + def load( + path: str, + file_name: str, + num_needles: int, + language: str, + repeats: int, + with_circular: bool = True, + ): + """NeedleBenthATC Dataset. + + Args: + path (str): Path of the needlebench dataset. + name (str): Name of the target subset. + with_circular (bool): Whether to create circular dataset for + single choice question. Defaults to True. + """ + data = [] + entry = {} + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + file_path = os.path.join(path, file_name) + + with open(file_path, 'r', encoding='utf-8') as file: + names_data = json.load(file) + + all_names = names_data[language].split(',') + + for id in range(repeats): + random.seed(id) + names = random.sample(all_names, num_needles) + if language == 'Chinese': + + relationship_terms = [ + '父亲', + '母亲', + '爸爸', + '妈妈', + '爷爷', + '奶奶', + '姥姥', + '姥爷', + '外公', + '外婆', + ] + + relationship_templates = [ + '{A}是{B}的{relationship}。', + '{B}的{relationship}是{A}。', + '{A}作为{B}的{relationship}，对{B}的成长有重要影响。', + '{A}不仅是{B}的{relationship}，还是{B}的榜样。', + '{B}是{A}所生的孩子。', + '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。', + '{A}在{B}的生命中扮演着{relationship}的角色。', + '{B}把{A}视为其{relationship}。', + ] + elif language == 'English': + + relationship_terms = [ + 'father', + 'mother', + 'dad', + 'mom', + 'grandfather', + 'grandmother', + 'maternal grandmother', + 'maternal grandfather', + 'paternal grandfather', + 'paternal grandmother', + ] + + relationship_templates = [ + "{A} is {B}'s {relationship}.", + "{B}'s {relationship} is {A}.", + ("{A}, as {B}'s {relationship}, " + "has a significant impact on {B}'s upbringing."), + ("{A} is not only {B}'s {relationship} " + "but also {B}'s role model."), + '{B} is the child of {A}.', + ('For {B}, {A} is not just a {relationship}, ' + 'but also a friend.'), + ("{A} plays the role of {B}'s {relationship} " + "in {B}'s life."), + '{B} considers {A} as their {relationship}.', + ] + + def generate_chain_family_story(names, templates, + relationship_terms): + story = '' + for i in range(len(names) - 1): + template = random.choice(templates) + relation_term = random.choice(relationship_terms) + relation = template.format(A=names[i], + B=names[i + 1], + relationship=relation_term) + story += f'{relation}*' + return story + + chain_story = generate_chain_family_story(names, + relationship_templates, + relationship_terms) + + # Splitting the chain_story into a list of fragments + family_story_fragments = chain_story.split('*') + + # Shuffling the list of fragments + random.shuffle(family_story_fragments) + + # Joining the shuffled fragments back into a string + shuffled_story = ''.join(family_story_fragments) + + last_person = names[-1] + + # Generating the prompt based on the language + if language == 'Chinese': + prompt = f""" +在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？""" + elif language == 'English': + prompt = f""" +Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context?""" + else: + prompt = 'Language not supported.' + raise Exception('Unsupported language specified. ' + "Please choose either 'Chinese' or 'English'.") + + # Combine story and prompt + shuffled_story_with_prompt = shuffled_story + ' ' + prompt + + entry['question'] = shuffled_story_with_prompt + if len(names) < 4: + additional_names_needed = max(4 - len(names), 0) + additional_names = random.sample( + [name for name in all_names if name not in names], + additional_names_needed, + ) + names.extend(additional_names) + + entry['options'] = names[0:4] + entry['answer'] = 'A' + # print(entry) + data.extend(get_circular_example(entry, id)) + dataset = Dataset.from_list(data) + return dataset diff --git a/opencompass/datasets/needlebench/multi.py b/opencompass/datasets/needlebench/multi.py new file mode 100644 index 0000000000000000000000000000000000000000..223fc2169fafbf4ab6815c1ed424637247d354f6 --- /dev/null +++ b/opencompass/datasets/needlebench/multi.py @@ -0,0 +1,268 @@ +import json +import os +import random + +import tiktoken +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + + +def get_random_needles(counter, file_path, needle_count): + with open(file_path, 'r', encoding='utf-8') as file: + data = json.load(file) + + matching_records = [ + record for record in data + if record.get('derivation_count') == needle_count + ] + + if matching_records: + random.seed(counter) + random_record = random.choice(matching_records) + return { + 'needles': random_record['derivations'], + 'answer': random_record['answer'], + 'retrieval_question': random_record['question'] + } + else: + return None + + +@LOAD_DATASET.register_module() +class NeedleBenchMultiDataset(BaseDataset): + + @staticmethod + def load( + path: str, + length: int, + depth: int, + tokenizer_model: str, + file_list: 'list[str]', + num_repeats_per_file: int, + length_buffer: int, + guide: bool, + language: str, + needle_file_name: str, + num_needles: int, + diff: int, + position: str = 'End', + ): + data = {'prompt': [], 'answer': []} + tokenizer = tiktoken.encoding_for_model(tokenizer_model) + + def _generate_context(tokens_context, depth_percent, needles): + tokens_needle = [ + _get_tokens_from_context(needle) for needle in needles + ] + insertion_points = [] + total_length = len(tokens_context) + + for i, needle_tokens in enumerate(tokens_needle): + if i == 0: + insertion_point = int(total_length * (depth_percent / 100)) + else: + insertion_point = int(insertion_points[i - 1] + + len(tokens_needle[i - 1]) + + total_length * (diff / 100)) + insertion_point = min( + insertion_point, + total_length + sum(len(tn) for tn in tokens_needle[:i])) + insertion_points.append(insertion_point) + + for i, needle_tokens in enumerate(tokens_needle): + tokens_context = tokens_context[:insertion_points[i]] \ + + needle_tokens + tokens_context[insertion_points[i]:] + for j in range(i + 1, len(insertion_points)): + insertion_points[j] += len(needle_tokens) + + new_context = _decode_tokens(tokens_context) + return new_context + + def _get_tokens_from_context(context): + if isinstance(context, list): + return [tokenizer.encode(item) for item in context] + else: + return tokenizer.encode(context) + + def _decode_tokens(tokens): + return tokenizer.decode(tokens) + + def _modify_retrieval_question(retrieval_question): + if language == 'Chinese': + guide_retrieval_question = (retrieval_question + + '在回答之前，请思考文档中与此问题' + '最相关的内容是什么。') + return guide_retrieval_question + elif language == 'English': + guide_retrieval_question = ( + retrieval_question + 'Before answering, please consider' + ' what in the document is most relevant to this question.') + return guide_retrieval_question + else: + raise ValueError(f"Language '{language}' is not supported.") + + def _generate_prompt(context, retrieval_question): + if guide: + retrieval_question = _modify_retrieval_question( + retrieval_question) + + if language == 'Chinese': + if position == 'End': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n' + f'用户现在给你的文档是{context}\n\n' + f'现在请问：{retrieval_question}') + elif position == 'Start': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n' + f'现在请问：{retrieval_question}', + f'用户现在给你的文档是{context}\n\n') + else: + raise ValueError('Unsupported position. ' + 'Position must be "End" or "Start".') + elif language == 'English': + if position == 'End': + prompt = ('You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do ' + 'not talk about irrelevant topics or repeat ' + 'your answers.\nThe document ' + f'given to you by the user is {context}\n\n' + f'Now, the question is: {retrieval_question}') + elif position == 'Start': + prompt = ('You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do ' + 'not talk about irrelevant topics or repeat ' + 'your answers.\n' + f'Now, the question is: {retrieval_question}' + 'The document given to you by the user' + f' is {context}\n\n') + else: + raise ValueError(f'Unsupported position {position}. ' + 'Position must be "End" or "Start".') + else: + raise ValueError(f"Language '{language}' is not supported.") + + return prompt + + file_names = [ + 'PaulGrahamEssays.jsonl', 'multi_needle_reasoning_en.json', + 'multi_needle_reasoning_zh.json', 'zh_finance.jsonl', + 'zh_game.jsonl', 'zh_general.jsonl', 'zh_government.jsonl', + 'zh_movie.jsonl', 'zh_tech.jsonl' + ] + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + path = snapshot_download(repo_id=path, repo_type='dataset') + needle_file_path = os.path.join(path, needle_file_name) + + for file_name in file_names: + file_path = os.path.join(path, file_name) + if file_name not in file_list: + continue + + with open(file_path, 'r', encoding='utf-8') as f: + lines_bak = [json.loads(line.strip()) for line in f] + lines = lines_bak.copy() + for counter in range(num_repeats_per_file): + random.seed(counter) + random.shuffle(lines) + random_needle_data = get_random_needles( + counter, needle_file_path, num_needles) + needles = [ + '\n' + needle + '\n' + for needle in random_needle_data['needles'] + ] + answer = random_needle_data['answer'] + keyword = answer + retrieval_question = random_needle_data['retrieval_question'] + context_length = length - length_buffer + target_length_per_record = context_length - \ + sum(len(tokens) for tokens + in _get_tokens_from_context(needles)) + target_length_per_record = max(target_length_per_record, 0) + accumulated_tokens = [] + for line in lines: + tokens_current_line = _get_tokens_from_context( + line['text']) + accumulated_tokens.extend(tokens_current_line) + + if len(accumulated_tokens) >= target_length_per_record: + break + + processed_text = _generate_context( + accumulated_tokens[:target_length_per_record], depth, + needles) + + processed_prompt = _generate_prompt(processed_text, + retrieval_question) + + data['prompt'].append(processed_prompt) + data['answer'].append(answer + '*' + keyword) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset + + +class NeedleBenchMultiEvaluator(BaseEvaluator): + + def levenshtein_distance(self, s1, s2): + if len(s1) < len(s2): + return self.levenshtein_distance(s2, s1) + + if len(s2) == 0: + return len(s1) + + previous_row = range(len(s2) + 1) + for i, c1 in enumerate(s1): + current_row = [i + 1] + for j, c2 in enumerate(s2): + insertions = previous_row[j + 1] + 1 + deletions = current_row[j] + 1 + substitutions = previous_row[j] + (c1 != c2) + current_row.append(min(insertions, deletions, substitutions)) + previous_row = current_row + + return previous_row[-1] + + def score(self, predictions, gold): + if len(predictions) != len(gold): + return {'error': 'predictions and gold have different lengths'} + + total_score = 0 + details = [] + + for prediction, reference in zip(predictions, gold): + answer, keyword = reference.split('*') + keywords = keyword.lower().split() + prediction = prediction.lower() + + keyword_score = 100 / len(keywords) if keywords else 0 + + matched_keywords = sum(1 for kword in keywords + if kword in prediction) + score = matched_keywords * keyword_score + + detail = { + 'pred': prediction, + 'answer': reference, + 'matched_keywords': matched_keywords, + 'score': score + } + + total_score += score + details.append(detail) + + average_score = total_score / len(predictions) if predictions else 0 + return {'score': average_score, 'details': details} diff --git a/opencompass/datasets/needlebench/origin.py b/opencompass/datasets/needlebench/origin.py new file mode 100644 index 0000000000000000000000000000000000000000..f50be746dcdbd94a985fa96138ecb896ddea3e3a --- /dev/null +++ b/opencompass/datasets/needlebench/origin.py @@ -0,0 +1,308 @@ +import json +import os +import random +import re + +import tiktoken +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + + +def get_random_line_by_language(counter, file_path, language): + with open(file_path, 'r', encoding='utf-8') as file: + lines = [ + json.loads(line.strip()) for line in file + if json.loads(line.strip())['language'] == language + ] + + if lines: + random.seed(counter) + random_line = random.choice(lines) + return { + 'needle': random_line['needle'], + 'retrieval_question': random_line['retrieval_question'], + 'keyword': random_line['arg2'] + } + else: + return None + + +@LOAD_DATASET.register_module() +class NeedleBenchOriginDataset(BaseDataset): + + @staticmethod + def load( + path: str, + length: int, + depth: int, + tokenizer_model: str, + file_list: list[str], + num_repeats_per_file: int, + length_buffer: int, + guide: bool, + language: str, + needle_file_name: str, + position: str = 'End', + ): + data = {'prompt': [], 'answer': []} + tokenizer = tiktoken.encoding_for_model(tokenizer_model) + + def _generate_context(tokens_context, depth_percent, needle): + tokens_needle = _get_tokens_from_context(needle) + insertion_point = int(len(tokens_context) * (depth_percent / 100)) + tokens_context = (tokens_context[:insertion_point] + + tokens_needle + tokens_context[insertion_point:]) + new_context = _decode_tokens(tokens_context) + return new_context + + def _get_tokens_from_context(context): + return tokenizer.encode(context) + + def _decode_tokens(tokens): + return tokenizer.decode(tokens) + + def _modify_retrieval_question(retrieval_question): + if language == 'Chinese': + parts = retrieval_question.split('请按照') + guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题' + '最相关的内容是什么。请按照' + parts[1]) + return guide_retrieval_question + elif language == 'English': + parts = retrieval_question.split('Please answer in the format') + guide_retrieval_question = ( + parts[0] + 'Before answering, please consider' + ' what in the document is most relevant to this question.' + ' Please answer in the format' + parts[1]) + return guide_retrieval_question + else: + raise ValueError(f"Language '{language}' is not supported.") + + def _modify_retrieval_question_for_base(retrieval_question): + if language == 'Chinese': + parts = retrieval_question.split('请按照') + retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题' + '最相关的内容是什么。请按照' + parts[1]) + return retrieval_question.replace("请按照'", '')[:-16] + elif language == 'English': + parts = retrieval_question.split('Please answer in the format') + retrieval_question = ( + parts[0] + 'Before answering, please consider' + ' what in the document is most relevant to this question.' + ' Please answer in the format' + parts[1]) + return retrieval_question.replace( + "Please answer in the format '", '')[:-10] + else: + raise ValueError(f"Language '{language}' is not supported.") + + def _generate_prompt(context, retrieval_question): + if guide: + retrieval_question = _modify_retrieval_question( + retrieval_question) + else: + retrieval_question = _modify_retrieval_question_for_base( + retrieval_question) + + if language == 'Chinese': + if position == 'End': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n' + f'用户现在给你的文档是{context}\n\n' + f'现在请问：{retrieval_question}') + elif position == 'Start': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n' + f'现在请问：{retrieval_question}', + f'用户现在给你的文档是{context}\n\n') + else: + raise ValueError('Unsupported position. ' + 'Position must be "End" or "Start".') + elif language == 'English': + if position == 'End': + prompt = ('You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do ' + 'not talk about irrelevant topics or repeat ' + 'your answers.\nThe document ' + f'given to you by the user is {context}\n\n' + f'Now, the question is: {retrieval_question}') + elif position == 'Start': + prompt = ('You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do ' + 'not talk about irrelevant topics or repeat ' + 'your answers.\n' + f'Now, the question is: {retrieval_question}' + 'The document given to you by the user' + f' is {context}\n\n') + else: + raise ValueError(f'Unsupported position {position}. ' + 'Position must be "End" or "Start".') + else: + raise ValueError(f"Language '{language}' is not supported.") + + return prompt + + file_names = [ + 'en_un_asr.jsonl', 'zh_all.jsonl', 'PaulGrahamEssays.jsonl', + 'multi_needle_reasoning_en.json', 'multi_needle_reasoning_zh.json', + 'zh_finance.jsonl', 'zh_game.jsonl', 'zh_general.jsonl', + 'zh_government.jsonl', 'zh_movie.jsonl', 'zh_tech.jsonl' + ] + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + path = snapshot_download(repo_id=path, repo_type='dataset') + needle_file_path = os.path.join(path, needle_file_name) + + for file_name in file_names: + file_path = os.path.join(path, file_name) + if file_name not in file_list: + continue + + with open(file_path, 'r', encoding='utf-8') as f: + lines_bak = [json.loads(line.strip()) for line in f] + lines = lines_bak.copy() + for counter in range(num_repeats_per_file): + random.seed(counter) + random.shuffle(lines) + random_needle = get_random_line_by_language( + counter, needle_file_path, language) + needle = '\n' + random_needle['needle'] + '\n' + retrieval_question = random_needle['retrieval_question'] + keyword = random_needle['keyword'] + + context_length = length - length_buffer + target_length_per_record = context_length - len( + _get_tokens_from_context(needle)) + target_length_per_record = max(target_length_per_record, 0) + accumulated_tokens = [] + for line in lines: + tokens_current_line = _get_tokens_from_context( + line['text']) + accumulated_tokens.extend(tokens_current_line) + + if len(accumulated_tokens) >= target_length_per_record: + break + + processed_text = _generate_context( + accumulated_tokens[:target_length_per_record], depth, + needle) + + processed_prompt = _generate_prompt(processed_text, + retrieval_question) + + data['prompt'].append(processed_prompt) + data['answer'].append(needle + '*' + keyword) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset + + +class NeedleBenchOriginEvaluator(BaseEvaluator): + + def __init__(self, use_trim=False): + self.use_trim = use_trim + + @staticmethod + def _trim_prediction(prediction, reference): + """Trims the prediction string based on the length of the reference + string. + + Args: + prediction (str): The prediction string. + reference (str): The reference string. + + Returns: + str: The trimmed prediction string. + """ + l08 = int(0.8 * len(reference)) + l12 = int(1.2 * len(reference)) + trimmed_prediction = prediction[:l12] + + if len(trimmed_prediction) > l08 and \ + reference[-1] in trimmed_prediction[l08:]: + end_pos = l08 + trimmed_prediction[l08:].index(reference[-1]) + 1 + trimmed_prediction = trimmed_prediction[:end_pos] + + return trimmed_prediction + + def levenshtein_distance(self, s1, s2): + if len(s1) < len(s2): + return self.levenshtein_distance(s2, s1) + + if len(s2) == 0: + return len(s1) + + previous_row = range(len(s2) + 1) + for i, c1 in enumerate(s1): + current_row = [i + 1] + for j, c2 in enumerate(s2): + insertions = previous_row[j + 1] + 1 + deletions = current_row[j] + 1 + substitutions = previous_row[j] + (c1 != c2) + current_row.append(min(insertions, deletions, substitutions)) + previous_row = current_row + + return previous_row[-1] + + def score(self, predictions, gold): + + if len(predictions) != len(gold): + return {'error': 'predictions and gold have different lengths'} + + total_score = 0 + details = [] + for prediction, reference in zip(predictions, gold): + keyword = reference.split('*')[1] + reference = reference.split('*')[0] + raw_prediction = prediction + prediction = re.sub(r'\s+', '', prediction) + reference = re.sub(r'\s+', '', reference) + + if self.use_trim: + prediction = NeedleBenchOriginEvaluator._trim_prediction( + prediction, reference) + + edit_distance = self.levenshtein_distance(prediction, reference) + max_len = max(len(prediction), len(reference)) + score = 100 * (1 - + edit_distance / max_len) if max_len != 0 else 100 + + if keyword in raw_prediction: + print(f'{keyword} is in {prediction}') + score = 100 + else: + print(f'{keyword} is not in {prediction}') + score = 0.2 * score + + detail = { + 'pred': prediction, + 'answer': reference, + 'edit_distance': edit_distance, + 'score': score + } + total_score += score + details.append(detail) + + average_score = total_score / len(predictions) if predictions else 0 + result = {'score': average_score, 'details': details} + return result + + +@TEXT_POSTPROCESSORS.register_module('needlebench') +def needlebench_postprocess(text: str) -> str: + return text + + +@TEXT_POSTPROCESSORS.register_module('needlebench_dataset') +def needlebench_dataset_postprocess(text: str) -> str: + return text diff --git a/opencompass/datasets/needlebench/parallel.py b/opencompass/datasets/needlebench/parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..8158afbba552043963613b3b74bdda3d9fc75fbf --- /dev/null +++ b/opencompass/datasets/needlebench/parallel.py @@ -0,0 +1,334 @@ +import json +import os +import random + +import tiktoken +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + + +def get_unique_entries( + file_path, + n, + language, + unique_arg1=False, + unique_arg2=False, + unique_combination=False, +): + seen_arg1 = set() + seen_arg2 = set() + seen_combinations = set() + results = [] + + with open(file_path, 'r', encoding='utf-8') as file: + lines = file.readlines() + random.shuffle(lines) + + for line in lines: + try: + entry = json.loads(line.strip()) + except json.JSONDecodeError: + continue + + if entry.get('language') != language: + continue + + key1 = entry.get('arg1', '') if unique_arg1 else '' + key2 = entry.get('arg2', '') if unique_arg2 else '' + combination = (key1, key2) if unique_combination else '' + + if ((key1 not in seen_arg1 or not unique_arg1) # noqa: E501 + and (key2 not in seen_arg2 or not unique_arg2) + and # noqa: E501 + (combination not in seen_combinations + or not unique_combination)): # noqa: E501 + seen_arg1.add(key1) + seen_arg2.add(key2) + seen_combinations.add(combination) + results.append(entry) + + if len(results) == n: + break + + return results + + +@LOAD_DATASET.register_module() +class NeedleBenchParallelDataset(BaseDataset): + + @staticmethod + def load( + path: str, + needle_file_name: str, + length: int, + depths: list[int], + tokenizer_model: str, + file_list: list[str], + num_repeats_per_file: int, + length_buffer: int, + guide: bool, + language: str, + position: str = 'End', + ): + data = {'prompt': [], 'answer': []} + tokenizer = tiktoken.encoding_for_model(tokenizer_model) + + file_names = [ + 'PaulGrahamEssays.jsonl', + 'multi_needle_reasoning_en.json', + 'multi_needle_reasoning_zh.json', + 'zh_finance.jsonl', + 'zh_game.jsonl', + 'zh_general.jsonl', + 'zh_government.jsonl', + 'zh_movie.jsonl', + 'zh_tech.jsonl', + ] + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + needle_file_path = os.path.join(path, needle_file_name) + + predefined_needles_bak = get_unique_entries( + needle_file_path, + len(depths), + language, + unique_arg1=True, + unique_arg2=True, + unique_combination=True, + ) + + def _generate_context(tokens_context, depths, needles): + insertion_points = [ + int(len(tokens_context) * (depth / 100)) for depth in depths + ] + + cumulative_inserted_length = 0 + + for i, needle in enumerate(needles): + needle_tokens = _get_tokens_from_context(needle) + current_insertion_point = min( + insertion_points[i] + cumulative_inserted_length, + len(tokens_context), + ) + + tokens_context = (tokens_context[:current_insertion_point] + + needle_tokens + + tokens_context[current_insertion_point:]) + cumulative_inserted_length += len(needle_tokens) + + new_context = _decode_tokens(tokens_context) + return new_context + + def _get_tokens_from_context(context): + if isinstance(context, list): + return [tokenizer.encode(item) for item in context] + else: + return tokenizer.encode(context) + + def _decode_tokens(tokens): + return tokenizer.decode(tokens) + + def _modify_retrieval_question(retrieval_question): + if language == 'Chinese': + parts = retrieval_question.split('请按照') + guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题' + '最相关的内容是什么。请按照' + parts[1]) + return guide_retrieval_question + elif language == 'English': + parts = retrieval_question.split('Please answer in the format') + guide_retrieval_question = ( + parts[0] + 'Before answering, please consider' + ' what in the document is most relevant to this question.' + ' Please answer in the format' + parts[1]) + return guide_retrieval_question + else: + raise ValueError(f"Language '{language}' is not supported.") + + def _generate_prompt(context, retrieval_question): + if guide: + retrieval_question = _modify_retrieval_question( + retrieval_question) + + if language == 'Chinese': + if position == 'End': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n请先仔细阅读下面的文档再依次回答' + f'最后提出的问题\n用户现在给你的文档是{context}\n\n' + f'现在请问：{retrieval_question}\n') + elif position == 'Start': + prompt = ('你是一个善于回答用户问题的智能AI助手\n' + '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' + '，或重复你的回答\n请先仔细阅读下面的文档再依次回答' + f'最后提出的问题\n现在请问：{retrieval_question}\n\n' + f'用户现在给你的文档是{context}\n') + else: + raise ValueError(f'Unsupported position {position}. ' + 'Position must be "End" or "Start".') + + elif language == 'English': + if position == 'End': + prompt = ( + 'You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do not' + ' talk about irrelevant topics or repeat your ' + 'answers.\n' + f'The document given to you by the user is {context}' + f'\n\nNow, the questions are: {retrieval_question}\n') + elif position == 'Start': + prompt = ( + 'You are an intelligent AI assistant skilled in ' + 'answering user questions.\n' + 'Please keep your answers concise and clear. Do not' + ' talk about irrelevant topics or repeat your ' + 'answers.\n' + f'\nNow, the questions are: {retrieval_question}\n\n' + f'The document given to you by the user is {context}') + else: + raise ValueError(f'Unsupported position {position}. ' + 'Position must be "End" or "Start".') + else: + raise ValueError(f"Language '{language}' is not supported.") + + return prompt + + for file_name in file_names: + file_path = os.path.join(path, file_name) + if file_name not in file_list: + continue + + with open(file_path, 'r', encoding='utf-8') as f: + lines_bak = [json.loads(line.strip()) for line in f] + lines = lines_bak.copy() + for counter in range(num_repeats_per_file): + random.seed(counter) + random.shuffle(lines) + predefined_needles = predefined_needles_bak.copy() + random.seed(counter) + random.shuffle(predefined_needles) + + needles = [ + '\n' + item['needle'] + '\n' for item in predefined_needles + ] + keywords = [item['arg2'] for item in predefined_needles] + if language == 'Chinese': + questions = '、'.join([ + item['retrieval_question'].split('？')[0] + '？' + for item in predefined_needles + ]) + + answers_format = '、'.join([ + item['retrieval_question'].split("'")[1].split('。')[0] + for item in predefined_needles + ]) + retrieval_question = (questions + "请按照'" + answers_format + + "'的格式回答。") + elif language == 'English': + questions = '、'.join([ + item['retrieval_question'].split('?')[0] + '?' + for item in predefined_needles + ]) + + answers_format = '、'.join([ + item['retrieval_question'].split("'")[1].split('.')[0] + for item in predefined_needles + ]) + retrieval_question = (questions + + "Please answer in the format of '" + + answers_format + "'") + + context_length = length - length_buffer + target_length_per_record = context_length - sum( + len(tokens) + for tokens in _get_tokens_from_context(needles)) + target_length_per_record = max(target_length_per_record, 0) + accumulated_tokens = [] + for line in lines: + tokens_current_line = _get_tokens_from_context( + line['text']) + accumulated_tokens.extend(tokens_current_line) + + if len(accumulated_tokens) >= target_length_per_record: + break + + processed_text = _generate_context( + accumulated_tokens[:target_length_per_record], depths, + needles) + + processed_prompt = _generate_prompt(processed_text, + retrieval_question) + + data['prompt'].append(processed_prompt) + + data['answer'].append('*'.join(keywords) + '#' + + '*'.join(map(str, depths))) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset + + +class NeedleBenchParallelEvaluator(BaseEvaluator): + + def levenshtein_distance(self, s1, s2): + if len(s1) < len(s2): + return self.levenshtein_distance(s2, s1) + + if len(s2) == 0: + return len(s1) + + previous_row = range(len(s2) + 1) + for i, c1 in enumerate(s1): + current_row = [i + 1] + for j, c2 in enumerate(s2): + insertions = previous_row[j + 1] + 1 + deletions = current_row[j] + 1 + substitutions = previous_row[j] + (c1 != c2) + current_row.append(min(insertions, deletions, substitutions)) + previous_row = current_row + + return previous_row[-1] + + def score(self, predictions, gold): + if len(predictions) != len(gold): + return {'error': 'predictions and gold have different lengths'} + print('predictions:', predictions) + print('gold:', gold) + + details = [] + depths = [int(i) for i in gold[0].split('#')[1].split('*')] + scores_by_depth = {depth: 0 for depth in depths} + + for prediction, reference in zip(predictions, gold): + print(reference) + keywords = reference.split('#')[0].split('*') + print(keywords) + for keyword, depth in zip(keywords, depths): + print('iterating:', keyword, depth) + if keyword in prediction: + print(f'{keyword} at depth {depth} is in {prediction}') + scores_by_depth[depth] += 100 / (len(predictions)) + + average_score = sum(scores_by_depth.values()) / len(scores_by_depth) + + flattened_scores = { + 'Depth' + str(depth): score + for depth, score in scores_by_depth.items() + } + + result = { + **flattened_scores, + 'details': details, + 'average_score': average_score, + } + return result diff --git a/opencompass/datasets/needlebench_v2/__init__.py b/opencompass/datasets/needlebench_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/opencompass/datasets/needlebench_v2/atc.py b/opencompass/datasets/needlebench_v2/atc.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec5a5b8574aa2528d80a45ccc69f2eae5403c91 --- /dev/null +++ b/opencompass/datasets/needlebench_v2/atc.py @@ -0,0 +1,440 @@ +# flake8: noqa +import json +import os +import random +import re +from enum import Enum + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.datasets.needlebench_v2.atc_elder_only import ( + NeedleBenchATCEvaluator, clean_atc_answer, needlebench_atc_postprocess_v2) +from opencompass.registry import (ICL_EVALUATORS, LOAD_DATASET, + TEXT_POSTPROCESSORS) +from opencompass.utils import get_data_path + + +# 定义问题类型枚举 +class QuestionType(Enum): + ELDEST_ANCESTOR = 0 # 最年长祖先 + NTH_ANCESTOR = 1 # N级祖先 + NTH_DESCENDANT = 2 # N级子节点 + RELATIONSHIP_DISTANCE = 3 # 关系距离 + + +# 定义关系术语的代数映射（一代关系还是两代关系） +relationship_generation_map_zh = { + '父亲': 1, + '母亲': 1, + '爸爸': 1, + '妈妈': 1, + '爷爷': 2, + '奶奶': 2, + '姥姥': 2, + '姥爷': 2, + '外公': 2, + '外婆': 2, +} + +relationship_generation_map_en = { + 'father': 1, + 'mother': 1, + 'dad': 1, + 'mom': 1, + 'grandfather': 2, + 'grandmother': 2, + 'maternal grandmother': 2, + 'maternal grandfather': 2, + 'paternal grandfather': 2, + 'paternal grandmother': 2, +} + +relationship_templates_zh_CN = [ + '{A}是{B}的{relationship}。', + '{B}的{relationship}是{A}。', + '{A}作为{B}的{relationship}，对{B}的成长有重要影响。', + '{A}不仅是{B}的{relationship}，还是{B}的榜样。', + '{A}在{B}的成长过程中，不仅仅是{B}的{relationship}，还是{B}的监护人。', + '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。', +] + +relationship_terms_zh_CN = [ + '父亲', + '母亲', + '爸爸', + '妈妈', + '爷爷', + '奶奶', + '姥姥', + '姥爷', + '外公', + '外婆', +] + +relationship_terms_en = [ + 'father', + 'mother', + 'dad', + 'mom', + 'grandfather', + 'grandmother', + 'maternal grandmother', + 'maternal grandfather', + 'paternal grandfather', + 'paternal grandmother', +] + +relationship_templates_en = [ + "{A} is {B}'s {relationship}.", + "{B}'s {relationship} is {A}.", + ("{A}, as {B}'s {relationship}, " + "has a significant impact on {B}'s upbringing."), + ("{A} is not only {B}'s {relationship} " + "but also {B}'s role model."), + ("During {B}'s upbringing, {A} was not only {B}'s {relationship}, " + "but also {B}'s guardian."), + ('For {B}, {A} is not just a {relationship}, ' + 'but also a friend.'), + 'For {B}, {A} is more than just a {relationship}; {A} is a lifelong mentor of {B}.', +] + +# Eldest ancestor problem template +shuffled_story_with_prompt_zh_CN = """下面是对你的多步推理能力的测试，这个测试叫做祖先追溯测试，我们会模拟不同人的家庭亲属关系，你的任务是在其中不断推理，直到找到最年长的祖先。 + +例如： +例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。 +例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。 +例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。 + +注意： +1. 你不必纠结这个测试中的人名的性别关系，例如，一个通常被视为女性化的名字仍然可以是其他人的父亲，我们的重点是谁更年长。 +2. 忽略这个测试中的姓氏遗传问题，例如，李明仍然可能是王鹏的亲生父亲，我们只关注谁更年长，不必纠结孩子是否应该继承父亲或母亲的性别。 +3. 在回答的最后，将你的答案放在\\boxed{{}}中，例如："所以{last_person}能向上追溯到的最年长的亲人就是\\boxed{{某人（你的答案）}}" + +现在，打乱的家族关系文本如下： +{shuffled_story} + +在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？ +""" + +shuffled_story_with_prompt_en = """Here is a test for multi-step reasoning ability called the Ancestral Trace Challenge. In this test, we will simulate different people's familial relationships, and your task is to continuously reason through them until you identify the eldest ancestor. + +For example: +Example 1: If James Hill's father is Jasmine Lane, and no further information about familial relationships is provided in the text, then the oldest relative James Hill can trace back to in the provided text is \\boxed{{Jasmine Lane}}. +Example 2: If Andrew Williams's grandmother is Dan Newton, and Dan Newton's father is James Hill, and no further information about familial relationships is provided in the text, then the oldest relative Andrew Williams can trace back to in the provided text is \\boxed{{James Hill}}. +Example 3: If Jeff White's father is Kevin Le, Dan Newton's grandmother is Jeff White, and Jeff White's father is Kevin Le, and Shelley Mills is Dan Newton's great-granddaughter, and no further information about familial relationships is provided in the text, then the oldest relative Shelley Mills can trace back to in the provided text is \\boxed{{Kevin Le}}. + +Notes: +1. You do not need to worry about the gender consistency of names in this test. For example, a name that is typically considered feminine can still be the father of another person. Our primary focus is on who is older. +2. Ignore surname inheritance issues. For instance, Andrew Williams could still be the biological father of Christopher Baker. We only care about who is older and do not need to consider whether a child should inherit the father's or mother's surname. +3. At the end of your response, remember to put your final answer within \\boxed{{}}. For example: "So the oldest relative '{last_person}' can trace back to in the provided text is \\boxed{{somebody (your answer here)}}." + +Now, the scrambled family relationships are provided below: +{shuffled_story} + +Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context? +""" + +# Nth ancestor problem template +nth_ancestor_prompt_zh_CN = """下面是对你的多步推理能力的测试，这个测试叫做祖先追溯测试，我们会模拟不同人的家庭亲属关系，你的任务是在其中不断推理，找到指定人物的特定代祖先。 + +例如： +例子1.如果张强的父亲是马克，我们说马克是张强的1代祖先。 +例子2.如果李明的姥姥是张红（姥姥算两代关系），而张红的父亲是张强，那么张红是李明的2代祖先，张强是李明的3代祖先。 +例子3.如果小明的奶奶是王华（奶奶算两代关系），王华的妈妈是刘芳，那么王华是小明的2代祖先，刘芳是小明的3代祖先。 + +注意： +1. 你不必纠结这个测试中的人名的性别关系，我们只关注辈分关系。 +2. 忽略这个测试中的姓氏遗传问题，我们只关注亲属关系。 +3. 父亲/母亲/爸爸/妈妈算1代关系，爷爷/奶奶/姥姥/姥爷/外公/外婆算2代关系。 +4. 在回答的最后，将你的答案放在\\boxed{{}}中，例如："所以{person}的{n}代祖先就是\\boxed{{某人（你的答案）}}" + +现在，打乱的家族关系文本如下： +{shuffled_story} + +在上面提供的打乱的家族关系文本中，'{person}'的{n}代祖先是谁？ +""" + +nth_ancestor_prompt_en = """Here is a test for multi-step reasoning ability called the Ancestral Trace Challenge. In this test, we will simulate different people's familial relationships, and your task is to identify a specific ancestor of a given person. + +For example: +Example 1: If James Hill's father is Jasmine Lane, then Jasmine Lane is James Hill's 1st generation ancestor. +Example 2: If Andrew Williams's grandmother is Dan Newton (grandmother counts as 2 generations), and Dan Newton's father is James Hill, then Dan Newton is Andrew Williams's 2nd generation ancestor, and James Hill is Andrew Williams's 3rd generation ancestor. +Example 3: If Shelley Mills's grandfather is Jeff White (grandfather counts as 2 generations), and Jeff White's mother is Mary Johnson, then Jeff White is Shelley Mills's 2nd generation ancestor, and Mary Johnson is Shelley Mills's 3rd generation ancestor. + +Notes: +1. You do not need to worry about the gender consistency of names in this test. We only care about generational relationships. +2. Ignore surname inheritance issues. We only care about familial relationships. +3. Father/mother/dad/mom count as 1 generation, while grandfather/grandmother/maternal grandmother/maternal grandfather/paternal grandfather/paternal grandmother count as 2 generations. +4. At the end of your response, remember to put your final answer within \\boxed{{}}. For example: "So the {n}th generation ancestor of '{person}' is \\boxed{{somebody (your answer here)}}." + +Now, the scrambled family relationships are provided below: +{shuffled_story} + +Given the scrambled family relationships described above, who is the {n}th generation ancestor of '{person}'? +""" + +# Nth descendant problem template +nth_descendant_prompt_zh_CN = """下面是对你的多步推理能力的测试，这个测试叫做家族关系追溯测试，我们会模拟不同人的家庭亲属关系，你的任务是在其中不断推理，找到指定人物的特定代子孙。 + +例如： +例子1.如果马克是张强的父亲，我们说张强是马克的1代子孙。 +例子2.如果张红是李明的姥姥（姥姥算两代关系），而张强是张红的父亲，那么李明是张红的2代子孙，李明是张强的3代子孙。 +例子3.如果王华是小明的爷爷（爷爷算两代关系），刘芳是王华的妈妈，那么小明是王华的2代子孙，小明是刘芳的3代子孙。 + +注意： +1. 你不必纠结这个测试中的人名的性别关系，我们只关注辈分关系。 +2. 忽略这个测试中的姓氏遗传问题，我们只关注亲属关系。 +3. 父亲/母亲/爸爸/妈妈算1代关系，爷爷/奶奶/姥姥/姥爷/外公/外婆算2代关系。 +4. 在回答的最后，将你的答案放在\\boxed{{}}中，例如："所以{person}的{n}代子孙就是\\boxed{{某人（你的答案）}}" + +现在，打乱的家族关系文本如下： +{shuffled_story} + +在上面提供的打乱的家族关系文本中，'{person}'的{n}代子孙是谁？ +""" + +nth_descendant_prompt_en = """Here is a test for multi-step reasoning ability called the Ancestral Trace Challenge. In this test, we will simulate different people's familial relationships, and your task is to identify a specific descendant of a given person. + +For example: +Example 1: If Jasmine Lane is James Hill's father, then James Hill is Jasmine Lane's 1st generation descendant. +Example 2: If Dan Newton is Andrew Williams's grandmother (grandmother counts as 2 generations), and James Hill is Dan Newton's father, then Andrew Williams is Dan Newton's 2nd generation descendant, and Andrew Williams is James Hill's 3rd generation descendant. +Example 3: If Jeff White is Shelley Mills's grandfather (grandfather counts as 2 generations), and Mary Johnson is Jeff White's mother, then Shelley Mills is Jeff White's 2nd generation descendant, and Shelley Mills is Mary Johnson's 3rd generation descendant. + +Notes: +1. You do not need to worry about the gender consistency of names in this test. We only care about generational relationships. +2. Ignore surname inheritance issues. We only care about familial relationships. +3. Father/mother/dad/mom count as 1 generation, while grandfather/grandmother/maternal grandmother/maternal grandfather/paternal grandfather/paternal grandmother count as 2 generations. +4. At the end of your response, remember to put your final answer within \\boxed{{}}. For example: "So the {n}th generation descendant of '{person}' is \\boxed{{somebody (your answer here)}}." + +Now, the scrambled family relationships are provided below: +{shuffled_story} + +Given the scrambled family relationships described above, who is the {n}th generation descendant of '{person}'? +""" + +# Relationship distance problem template +relationship_distance_prompt_zh_CN = """下面是对你的多步推理能力的测试，这个测试叫做家族关系追溯测试，我们会模拟不同人的家庭亲属关系，你的任务是在其中不断推理，计算两个人之间的关系距离。 + +关系距离定义为：家族图中从一个人到另一个人所需的最少代数差距。注意不同关系有不同的代数差距，例如： +例子1.如果马克是张强的父亲（父亲算1代关系），那么张强和马克之间的关系距离是1。 +例子2.如果张红是李明的姥姥（姥姥算2代关系），而张强是张红的父亲（父亲算1代关系），那么李明和张红之间的关系距离是2，李明和张强之间的关系距离是3。 +例子3.如果小明的爷爷是王华（爷爷算2代关系），王华的妈妈是刘芳（妈妈算1代关系），那么小明和王华之间的关系距离是2，小明和刘芳之间的关系距离是3。 + +注意： +1. 你不必纠结这个测试中的人名的性别关系，我们只关注辈分关系。 +2. 忽略这个测试中的姓氏遗传问题，我们只关注亲属关系。 +3. 父亲/母亲/爸爸/妈妈算1代关系，爷爷/奶奶/姥姥/姥爷/外公/外婆算2代关系。 +4. 在回答的最后，将你的答案放在\\boxed{{}}中，例如："所以{person_a}和{person_b}之间的关系距离是\\boxed{{5}}" + +现在，打乱的家族关系文本如下： +{shuffled_story} + +在上面提供的打乱的家族关系文本中，'{person_a}'和'{person_b}'之间的关系距离是多少？ +""" + +relationship_distance_prompt_en = """Here is a test for multi-step reasoning ability called the Ancestral Trace Challenge. In this test, we will simulate different people's familial relationships, and your task is to calculate the relationship distance between two individuals. + +The relationship distance is defined as the minimum number of generational gaps needed to go from one person to another in the family graph. Note that different relationships have different generational gaps. For example: +Example 1: If Jasmine Lane is James Hill's father (father counts as 1 generation), then the relationship distance between James Hill and Jasmine Lane is 1. +Example 2: If Dan Newton is Andrew Williams's grandmother (grandmother counts as 2 generations), and James Hill is Dan Newton's father (father counts as 1 generation), then the relationship distance between Andrew Williams and Dan Newton is 2, and the relationship distance between Andrew Williams and James Hill is 3. +Example 3: If Jeff White is Shelley Mills's grandfather (grandfather counts as 2 generations), and Mary Johnson is Jeff White's mother (mother counts as 1 generation), then the relationship distance between Shelley Mills and Jeff White is 2, and the relationship distance between Shelley Mills and Mary Johnson is 3. + +Notes: +1. You do not need to worry about the gender consistency of names in this test. We only care about relationship connections. +2. Ignore surname inheritance issues. We only care about familial relationships. +3. Father/mother/dad/mom count as 1 generation, while grandfather/grandmother/maternal grandmother/maternal grandfather/paternal grandfather/paternal grandmother count as 2 generations. +4. At the end of your response, remember to put your final answer within \\boxed{{}}. For example: "So the relationship distance between '{person_a}' and '{person_b}' is \\boxed{{5}}." + +Now, the scrambled family relationships are provided below: +{shuffled_story} + +Given the scrambled family relationships described above, what is the relationship distance between '{person_a}' and '{person_b}'? +""" + + +@LOAD_DATASET.register_module() +class NeedleBenchATCDataset(BaseDataset): + + @staticmethod + def load( + path, + file_name: str, + num_needles: int, + language: str, + repeats: int, + # This parameter cannot be passed through mmengine because it is blocked as lazy + question_types: list[QuestionType] = [ + QuestionType.ELDEST_ANCESTOR, + QuestionType.NTH_ANCESTOR, + QuestionType.NTH_DESCENDANT, + QuestionType.RELATIONSHIP_DISTANCE, + ], # Support specifying a list of question types + ): + data = {'prompt': [], 'answer': [], 'question_type': []} + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + file_path = os.path.join(path, file_name) + + with open(file_path, 'r', encoding='utf-8') as file: + names_data = json.load(file) + + all_names = names_data[language].split(',') + # Ensure question_types is not empty + if not question_types: + raise ValueError('question_types cannot be empty') + + for question_type in question_types: + # Generate the specified number of examples for each question type + for i in range(repeats): + # Set a different seed for each question type and repeat + # Use the enum value of the question type multiplied by 10000 as the base to ensure non-overlapping seed ranges + seed = (i + 1) + (10000 * question_type.value) + random.seed(seed) + + # Randomly select the specified number of names from all names + # The number of names is num_needles + 1 + names = random.sample(all_names, num_needles + 1) + + # Select the corresponding relationship terms and templates according to the language + if language == 'Chinese': + relationship_terms = relationship_terms_zh_CN + relationship_templates = relationship_templates_zh_CN + relationship_map = relationship_generation_map_zh + elif language == 'English': + relationship_terms = relationship_terms_en + relationship_templates = relationship_templates_en + relationship_map = relationship_generation_map_en + else: + raise ValueError( + 'Unsupported language specified. ' + 'Please choose either "Chinese" or "English".') + + def generate_chain_family_story(names, templates, + relationship_terms, + relationship_map): + story = '' + relationships = [] + total_generations = 0 # Track the total generational difference + + for i in range(len(names) - 1): + template = random.choice(templates) + relation_term = random.choice(relationship_terms) + relation = template.format(A=names[i], + B=names[i + 1], + relationship=relation_term) + story += f'{relation}*' + + # Get the generation difference for this relationship + gen_diff = relationship_map.get( + relation_term, 1) # Default to 1 generation + total_generations += gen_diff + + # Record relationship information for later use + relationships.append( + (names[i], names[i + 1], relation_term, gen_diff)) + + return story, relationships, total_generations + + chain_story, relationships, total_generations = generate_chain_family_story( + names, relationship_templates, relationship_terms, + relationship_map) + + # Split the chain_story into a list of fragments + family_story_fragments = chain_story.split('*') + family_story_fragments = [ + f for f in family_story_fragments if f + ] + + # Shuffle the list of fragments + random.shuffle(family_story_fragments) + + # Join the shuffled fragments back into a string + shuffled_story = ''.join(family_story_fragments) + + if question_type == QuestionType.ELDEST_ANCESTOR: + # Eldest ancestor question + last_person = names[-1] + if language == 'Chinese': + prompt = shuffled_story_with_prompt_zh_CN.format( + shuffled_story=shuffled_story, + last_person=last_person) + else: + prompt = shuffled_story_with_prompt_en.format( + shuffled_story=shuffled_story, + last_person=last_person) + answer = names[ + 0] # The first person is the eldest ancestor + + elif question_type == QuestionType.NTH_ANCESTOR: + # Nth ancestor question - trace from the youngest person to the oldest + person = names[ + -1] # The youngest person (end of the chain) + n = total_generations # Use the calculated total generational difference + if language == 'Chinese': + prompt = nth_ancestor_prompt_zh_CN.format( + shuffled_story=shuffled_story, person=person, n=n) + else: + prompt = nth_ancestor_prompt_en.format( + shuffled_story=shuffled_story, person=person, n=n) + answer = names[ + 0] # The oldest person (start of the chain) is the nth ancestor + + elif question_type == QuestionType.NTH_DESCENDANT: + # Nth descendant question - trace from the oldest person to the youngest + person = names[0] # The oldest person (start of the chain) + n = total_generations # Use the calculated total generational difference + if language == 'Chinese': + prompt = nth_descendant_prompt_zh_CN.format( + shuffled_story=shuffled_story, person=person, n=n) + else: + prompt = nth_descendant_prompt_en.format( + shuffled_story=shuffled_story, person=person, n=n) + answer = names[ + -1] # The youngest person (end of the chain) is the nth descendant + + elif question_type == QuestionType.RELATIONSHIP_DISTANCE: + # Relationship distance question - calculate the relationship distance between the two ends of the chain + person_a = names[0] # The oldest person + person_b = names[-1] # The youngest person + if language == 'Chinese': + prompt = relationship_distance_prompt_zh_CN.format( + shuffled_story=shuffled_story, + person_a=person_a, + person_b=person_b) + else: + prompt = relationship_distance_prompt_en.format( + shuffled_story=shuffled_story, + person_a=person_a, + person_b=person_b) + # Use the calculated total generations as the relationship distance + answer = str(total_generations) + + else: + # Default fallback to eldest ancestor question + last_person = names[-1] + if language == 'Chinese': + prompt = shuffled_story_with_prompt_zh_CN.format( + shuffled_story=shuffled_story, + last_person=last_person) + else: + prompt = shuffled_story_with_prompt_en.format( + shuffled_story=shuffled_story, + last_person=last_person) + answer = names[ + 0] # The first person is the eldest ancestor + + data['prompt'].append(prompt) + data['answer'].append(answer) + data['question_type'].append(question_type.name) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + 'question_type': data['question_type'], + }) + return dataset diff --git a/opencompass/datasets/needlebench_v2/atc_elder_only.py b/opencompass/datasets/needlebench_v2/atc_elder_only.py new file mode 100644 index 0000000000000000000000000000000000000000..b8e0e5bad6bae77e5e379242a4ff5ae5870b6a4d --- /dev/null +++ b/opencompass/datasets/needlebench_v2/atc_elder_only.py @@ -0,0 +1,253 @@ +# flake8: noqa +import json +import os +import random +import re + +from datasets import Dataset + +from opencompass.datasets.base import BaseDataset +from opencompass.datasets.math import extract_boxed_answer +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import (ICL_EVALUATORS, LOAD_DATASET, + TEXT_POSTPROCESSORS) +from opencompass.utils import get_data_path + +relationship_templates_zh_CN = [ + '{A}是{B}的{relationship}。', + '{B}的{relationship}是{A}。', + '{A}作为{B}的{relationship}，对{B}的成长有重要影响。', + '{A}不仅是{B}的{relationship}，还是{B}的榜样。', + '{B}是{A}所生的孩子。', + '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。', +] + +relationship_terms_zh_CN = [ + '父亲', + '母亲', + '爸爸', + '妈妈', + '爷爷', + '奶奶', + '姥姥', + '姥爷', + '外公', + '外婆', +] + +relationship_terms_en = [ + 'father', + 'mother', + 'dad', + 'mom', + 'grandfather', + 'grandmother', + 'maternal grandmother', + 'maternal grandfather', + 'paternal grandfather', + 'paternal grandmother', +] + +relationship_templates_en = [ + "{A} is {B}'s {relationship}.", + "{B}'s {relationship} is {A}.", + ("{A}, as {B}'s {relationship}, " + "has a significant impact on {B}'s upbringing."), + ("{A} is not only {B}'s {relationship} " + "but also {B}'s role model."), + '{B} is the child of {A}.', + ('For {B}, {A} is not just a {relationship}, ' + 'but also a friend.'), + 'For {B}, {A} is more than just a {relationship}; {A} is a lifelong mentor of {B}.', +] + +shuffled_story_with_prompt_zh_CN = """下面是对你的多步推理能力的测试，这个测试叫做祖先追溯测试，我们会模拟不同人的家庭亲属关系，你的任务是在其中不断推理，直到找到最年长的祖先。 + +例如： +例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。 +例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。 +例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。 + +注意： +1. 你不必纠结这个测试中的人名的性别关系，例如，一个通常被视为女性化的名字仍然可以是其他人的父亲，我们的重点是谁更年长。 +2. 忽略这个测试中的姓氏遗传问题，例如，李明仍然可能是王鹏的亲生父亲，我们只关注谁更年长，不必纠结孩子是否应该继承父亲或母亲的性别。 +3. 在回答的最后，将你的答案放在\\boxed{{}}中，例如："所以{last_person}能向上追溯到的最年长的亲人就是\\boxed{{某人（你的答案）}}" + +现在，打乱的家族关系文本如下： +{shuffled_story} + +在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？ +""" + +shuffled_story_with_prompt_en = """Here is a test for multi-step reasoning ability called the Ancestral Trace Challenge. In this test, we will simulate different people's familial relationships, and your task is to continuously reason through them until you identify the eldest ancestor. + +For example: +Example 1: If James Hill's father is Jasmine Lane, and no further information about familial relationships is provided in the text, then the oldest relative James Hill can trace back to in the provided text is \\boxed{{Jasmine Lane}}. +Example 2: If Andrew Williams's grandmother is Dan Newton, and Dan Newton's father is James Hill, and no further information about familial relationships is provided in the text, then the oldest relative Andrew Williams can trace back to in the provided text is \\boxed{{James Hill}}. +Example 3: If Jeff White's father is Kevin Le, Dan Newton's grandmother is Jeff White, and Jeff White's father is Kevin Le, and Shelley Mills is Dan Newton's great-granddaughter, and no further information about familial relationships is provided in the text, then the oldest relative Shelley Mills can trace back to in the provided text is \\boxed{{Kevin Le}}. + +Notes: +1. You do not need to worry about the gender consistency of names in this test. For example, a name that is typically considered feminine can still be the father of another person. Our primary focus is on who is older. +2. Ignore surname inheritance issues. For instance, Andrew Williams could still be the biological father of Christopher Baker. We only care about who is older and do not need to consider whether a child should inherit the father's or mother's surname. +3. At the end of your response, remember to put your final answer within \\boxed{{}}. For example: "So the oldest relative '{last_person}' can trace back to in the provided text is \\boxed{{somebody (your answer here)}}." + +Now, the scrambled family relationships are provided below: +{shuffled_story} + +Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context? +""" + + +@LOAD_DATASET.register_module() +class NeedleBenchATCDataset(BaseDataset): + + @staticmethod + def load( + path, + file_name: str, + num_needles: int, + language: str, + repeats: int, + ): + data = {'prompt': [], 'answer': []} + path = get_data_path(path) + if os.environ.get('DATASET_SOURCE') == 'HF': + from huggingface_hub import snapshot_download + + path = snapshot_download(repo_id=path, repo_type='dataset') + file_path = os.path.join(path, file_name) + + with open(file_path, 'r', encoding='utf-8') as file: + names_data = json.load(file) + + all_names = names_data[language].split(',') + + for i in range(repeats): + # 使用固定种子来保持样本稳定性 + seed = i + random.seed(seed) + + names = random.sample(all_names, num_needles) + if language == 'Chinese': + relationship_terms = relationship_terms_zh_CN + relationship_templates = relationship_templates_zh_CN + elif language == 'English': + relationship_terms = relationship_terms_en + relationship_templates = relationship_templates_en + + def generate_chain_family_story(names, templates, + relationship_terms): + story = '' + for i in range(len(names) - 1): + template = random.choice(templates) + relation_term = random.choice(relationship_terms) + relation = template.format(A=names[i], + B=names[i + 1], + relationship=relation_term) + story += f'{relation}*' + return story + + chain_story = generate_chain_family_story(names, + relationship_templates, + relationship_terms) + + # Splitting the chain_story into a list of fragments + family_story_fragments = chain_story.split('*') + + # Shuffling the list of fragments + random.shuffle(family_story_fragments) + + # Joining the shuffled fragments back into a string + shuffled_story = ''.join(family_story_fragments) + + last_person = names[-1] + + # Generating the prompt based on the language + if language == 'Chinese': + shuffled_story_with_prompt = shuffled_story_with_prompt_zh_CN.format( + shuffled_story=shuffled_story, last_person=last_person) + elif language == 'English': + shuffled_story_with_prompt = shuffled_story_with_prompt_en.format( + shuffled_story=shuffled_story, last_person=last_person) + else: + prompt = 'Language not supported.' + raise Exception('Unsupported language specified. ' + "Please choose either 'Chinese' or 'English'.") + + data['prompt'].append(shuffled_story_with_prompt) + data['answer'].append(names[0]) + + dataset = Dataset.from_dict({ + 'prompt': data['prompt'], + 'answer': data['answer'], + }) + return dataset + + +def clean_atc_answer(text: str) -> str: + """Clean answer format specifically for QwQ-32B-Preview model. + + Args: + text: Raw prediction text + + Returns: + Standardized name format after cleaning + """ + if not text or text == 'None': + return 'None' + + # Remove LaTeX commands but keep content + text = re.sub(r'\\text\{([^}]+)\}', r'\1', text) + text = re.sub(r'\\boxed\{([^}]+)\}', r'\1', text) + text = re.sub(r'\\[\[\]]', '', text) + + # Remove extra backslashes + text = text.replace('\\\\', '').replace('\\', '') + + # Handle extra spaces + text = re.sub(r'\s+', ' ', text).strip() + + # Remove quotes + text = text.replace('"', '').replace("'", '') + # Remove tildes (波浪符号) + text = text.replace('~', ' ') + + return text + + +@TEXT_POSTPROCESSORS.register_module('needlebench_atc_postprocess_v2') +def needlebench_atc_postprocess_v2(text: str) -> str: + + cand_ans = extract_boxed_answer(text, strip_double_curly_brace=True) + + if cand_ans: + return clean_atc_answer(cand_ans) + return 'None' + + +@ICL_EVALUATORS.register_module('needlebench_atc_evaluator') +class NeedleBenchATCEvaluator(BaseEvaluator): + + def score(self, predictions, gold): + if len(predictions) != len(gold): + return {'error': 'predictions and gold have different lengths'} + + correct_count = 0 + details = [] + + for prediction, reference in zip(predictions, gold): + reference_name = reference + if prediction.strip() == reference_name.strip(): + correct_count += 1 + + detail = { + 'pred': prediction, + 'answer': reference_name, + 'correct': prediction.strip() == reference_name.strip() + } + details.append(detail) + + accuracy = (correct_count / + len(predictions)) * 100 if predictions else 0 + result = {'score': accuracy, 'details': details} + return result