Merge pull request #209 from tatsu-lab/weight_diff

add weight diff conversion.

weight_diff.py

@@ -0,0 +1,157 @@
+#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
+#
+#    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.
+
+from typing import Optional
+
+import fire
+import torch
+import tqdm
+import transformers
+from train import smart_tokenizer_and_embedding_resize
+
+
+@torch.inference_mode()
+def make_diff(
+    path_raw: str, path_tuned: str, path_diff: str, device="cpu",  # "cuda" or "cpu"
+):
+    """Make the weight diff.
+
+    This function is given to present full transparency of how the weight diff was created.
+
+    Run:
+        python weight_diff.py make_diff --path_raw <your_path_raw> --path_tuned <your_path_tuned> --path_diff <your_path_diff>
+    """
+    model_tuned: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
+        path_tuned,
+        device_map={"": torch.device(device)},
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+    model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
+        path_raw,
+        device_map={"": torch.device(device)},
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+
+    tokenizer_tuned: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
+        path_tuned
+    )
+    tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
+        path_raw
+    )
+    if tokenizer_raw.pad_token is None:
+        smart_tokenizer_and_embedding_resize(
+            special_tokens_dict=dict(pad_token="[PAD]"),
+            model=model_raw,
+            tokenizer=tokenizer_raw,
+        )
+
+    state_dict_tuned = model_tuned.state_dict()
+    state_dict_raw = model_raw.state_dict()
+    for key in tqdm.tqdm(state_dict_tuned):
+        state_dict_tuned[key].add_(-state_dict_raw[key])
+
+    model_tuned.save_pretrained(path_diff)
+    tokenizer_tuned.save_pretrained(path_diff)
+
+
+@torch.inference_mode()
+def recover(
+    path_raw,
+    path_diff,
+    path_tuned: Optional[str] = None,
+    device="cpu",
+    test_inference=True,
+    check_integrity_naively=True,
+):
+    """Recover the original weights from the released weight diff.
+
+    This function is given for you to run.
+
+    Things to do before running this:
+        1. Convert Meta's released weights into huggingface format. Follow this guide:
+            https://huggingface.co/docs/transformers/main/model_doc/llama
+        2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
+            https://huggingface.co/tatsu-lab/alpaca-7b/tree/main
+        3. Run this function with the correct paths. E.g.,
+            python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir>
+
+    Additional notes:
+        - If things run too slowly, and you have an 80G GPU lying around, let GPU go brrr by setting `--device "cuda"`.
+        - If you want to save the recovered weights, set `--path_tuned <your_path_tuned>`.
+            Next time you can load the recovered weights directly from `<your_path_tuned>`.
+    """
+    model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
+        path_raw,
+        device_map={"": torch.device(device)},
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+    model_recovered: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
+        path_diff,
+        device_map={"": torch.device(device)},
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+
+    tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
+        path_raw
+    )
+    if tokenizer_raw.pad_token is None:
+        smart_tokenizer_and_embedding_resize(
+            special_tokens_dict=dict(pad_token="[PAD]"),
+            model=model_raw,
+            tokenizer=tokenizer_raw,
+        )
+    tokenizer_recovered: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
+        path_diff
+    )
+
+    state_dict_recovered = model_recovered.state_dict()
+    state_dict_raw = model_raw.state_dict()
+    for key in tqdm.tqdm(state_dict_recovered):
+        state_dict_recovered[key].add_(state_dict_raw[key])
+
+    if check_integrity_naively:
+        # This is not a rigorous, cryptographically strong integrity check :)
+        allsum = sum(state_dict_recovered[key].sum() for key in state_dict_recovered)
+        assert torch.allclose(
+            allsum, torch.full_like(allsum, fill_value=50637.1836), atol=1e-2, rtol=0
+        ), "Naive integrity check failed. This could imply that some of the checkpoint files are corrupted."
+
+    if path_tuned is not None:
+        model_recovered.save_pretrained(path_tuned)
+
+    if test_inference:
+        input_text = (
+            "Below is an instruction that describes a task. "
+            "Write a response that appropriately completes the request.\r\n\r\n"
+            "### Instruction:\r\nList three technologies that make life easier.\r\n\r\n### Response:"
+        )
+        inputs = tokenizer_recovered(input_text, return_tensors="pt")
+        out = model_recovered.generate(inputs=inputs.input_ids, max_new_tokens=100)
+        output_text = tokenizer_recovered.batch_decode(out, skip_special_tokens=True)[0]
+        output_text = output_text[len(input_text) :]
+        print(f"Input: {input_text}\nCompletion: {output_text}")
+
+    return model_recovered, tokenizer_recovered
+
+
+def main(task, **kwargs):
+    globals()[task](**kwargs)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)