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DOC: How to configure new transformers models (huggingface#1195)
I believe that new transformers architectures could be the most common case of users wanting to apply PEFT on a model that is not supported out of the box. Thus I added a section specifically to help users configure their configs for new transformers models. As I wanted to point users to a single file that contains all the existing transformers models, I added a new file `src/peft/utils/constants.py`, which contains all the mappings that previously lived in `src/peft/utils/other.py`. LMK if that makes sense. Notes To be absolutely backwards compatible, I re-imported the moved constants into `other.py`. This way, if there is code that imports them directly from there, it should continue to work. To avoid getting a linter error for unused imports, I added those constants to the `__all__` list in `other.py`. --------- Co-authored-by: Younes Belkada <[email protected]>
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| # coding=utf-8 | ||
| # Copyright 2023-present the HuggingFace Inc. team. | ||
| # | ||
| # 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 torch | ||
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| # needed for prefix-tuning of bloom model | ||
| def bloom_model_postprocess_past_key_value(past_key_values): | ||
| past_key_values = torch.cat(past_key_values) | ||
| total_layers, batch_size, num_attention_heads, num_virtual_tokens, head_dim = past_key_values.shape | ||
| keys = past_key_values[: total_layers // 2] | ||
| keys = keys.transpose(2, 3).reshape( | ||
| total_layers // 2, batch_size * num_attention_heads, head_dim, num_virtual_tokens | ||
| ) | ||
| values = past_key_values[total_layers // 2 :] | ||
| values = values.reshape(total_layers // 2, batch_size * num_attention_heads, num_virtual_tokens, head_dim) | ||
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| return tuple(zip(keys, values)) | ||
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| # needed for prefix-tuning of StarCoder models | ||
| def starcoder_model_postprocess_past_key_value(past_key_values): | ||
| result = [] | ||
| for k in past_key_values: | ||
| k = k[:, :, 0] | ||
| k = k.permute([1, 2, 0, 3]) | ||
| k = k.reshape(*k.shape[:-2], -1) | ||
| result.append(k) | ||
| return tuple(result) | ||
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| TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = { | ||
| "bloom": bloom_model_postprocess_past_key_value, | ||
| "gpt_bigcode": starcoder_model_postprocess_past_key_value, | ||
| } | ||
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| TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = { | ||
| "t5": ["q", "v"], | ||
| "mt5": ["q", "v"], | ||
| "bart": ["q_proj", "v_proj"], | ||
| "gpt2": ["c_attn"], | ||
| "bloom": ["query_key_value"], | ||
| "blip-2": ["q", "v", "q_proj", "v_proj"], | ||
| "opt": ["q_proj", "v_proj"], | ||
| "gptj": ["q_proj", "v_proj"], | ||
| "gpt_neox": ["query_key_value"], | ||
| "gpt_neo": ["q_proj", "v_proj"], | ||
| "bert": ["query", "value"], | ||
| "roberta": ["query", "value"], | ||
| "xlm-roberta": ["query", "value"], | ||
| "electra": ["query", "value"], | ||
| "deberta-v2": ["query_proj", "value_proj"], | ||
| "deberta": ["in_proj"], | ||
| "layoutlm": ["query", "value"], | ||
| "llama": ["q_proj", "v_proj"], | ||
| "chatglm": ["query_key_value"], | ||
| "gpt_bigcode": ["c_attn"], | ||
| "mpt": ["Wqkv"], | ||
| "RefinedWebModel": ["query_key_value"], | ||
| "RefinedWeb": ["query_key_value"], | ||
| "falcon": ["query_key_value"], | ||
| "btlm": ["c_proj", "c_attn"], | ||
| "codegen": ["qkv_proj"], | ||
| "mistral": ["q_proj", "v_proj"], | ||
| "stablelm": ["q_proj", "v_proj"], | ||
| "phi": ["Wqkv", "out_proj", "fc1", "fc2"], | ||
| } | ||
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| TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING = { | ||
| "t5": ["k", "v", "wo"], | ||
| "mt5": ["k", "v", "wi_1"], | ||
| "gpt2": ["c_attn", "mlp.c_proj"], | ||
| "bloom": ["query_key_value", "mlp.dense_4h_to_h"], | ||
| "roberta": ["key", "value", "output.dense"], | ||
| "opt": ["q_proj", "k_proj", "fc2"], | ||
| "gptj": ["q_proj", "v_proj", "fc_out"], | ||
| "gpt_neox": ["query_key_value", "dense_4h_to_h"], | ||
| "gpt_neo": ["q_proj", "v_proj", "c_proj"], | ||
| "bart": ["q_proj", "v_proj", "fc2"], | ||
| "gpt_bigcode": ["c_attn", "mlp.c_proj"], | ||
| "llama": ["k_proj", "v_proj", "down_proj"], | ||
| "bert": ["key", "value", "output.dense"], | ||
| "deberta-v2": ["key_proj", "value_proj", "output.dense"], | ||
| "deberta": ["in_proj", "output.dense"], | ||
| "RefinedWebModel": ["query_key_value", "dense_4h_to_h"], | ||
| "RefinedWeb": ["query_key_value", "dense_4h_to_h"], | ||
| "falcon": ["query_key_value", "dense_4h_to_h"], | ||
| } | ||
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| TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING = { | ||
| "t5": ["wo"], | ||
| "mt5": [], | ||
| "gpt2": ["mlp.c_proj"], | ||
| "bloom": ["mlp.dense_4h_to_h"], | ||
| "roberta": ["output.dense"], | ||
| "opt": ["fc2"], | ||
| "gptj": ["fc_out"], | ||
| "gpt_neox": ["dense_4h_to_h"], | ||
| "gpt_neo": ["c_proj"], | ||
| "bart": ["fc2"], | ||
| "gpt_bigcode": ["mlp.c_proj"], | ||
| "llama": ["down_proj"], | ||
| "bert": ["output.dense"], | ||
| "deberta-v2": ["output.dense"], | ||
| "deberta": ["output.dense"], | ||
| "RefinedWeb": ["dense_4h_to_h"], | ||
| "RefinedWebModel": ["dense_4h_to_h"], | ||
| "falcon": ["dense_4h_to_h"], | ||
| } | ||
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| TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = { | ||
| "t5": ["q", "k", "v", "o", "wi", "wo"], | ||
| "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"], | ||
| "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], | ||
| "gpt2": ["c_attn"], | ||
| "bloom": ["query_key_value"], | ||
| "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], | ||
| "gptj": ["q_proj", "v_proj"], | ||
| "gpt_neox": ["query_key_value"], | ||
| "gpt_neo": ["q_proj", "v_proj"], | ||
| "llama": ["q_proj", "v_proj"], | ||
| "bert": ["query", "value"], | ||
| "roberta": ["query", "key", "value", "dense"], | ||
| # "xlm-roberta": ["query", "value"], | ||
| # "electra": ["query", "value"], | ||
| "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"], | ||
| "gpt_bigcode": ["c_attn"], | ||
| "deberta": ["in_proj"], | ||
| # "layoutlm": ["query", "value"], | ||
| } | ||
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| COMMON_LAYERS_PATTERN = ["layers", "h", "block", "blocks", "layer"] | ||
| WEIGHTS_NAME = "adapter_model.bin" | ||
| SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors" | ||
| CONFIG_NAME = "adapter_config.json" | ||
| EMBEDDING_LAYER_NAMES = ["embed_tokens", "lm_head"] |
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