Merge pull request #43 from jbarrow/main

BERT implementation

bert/README.md

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+# BERT
+
+An implementation of BERT [(Devlin, et al., 2019)](https://aclanthology.org/N19-1423/) within MLX.
+
+## Downloading and Converting Weights
+
+The `convert.py` script relies on `transformers` to download the weights, and exports them as a single `.npz` file.
+
+```
+python convert.py \
+    --bert-model bert-base-uncased
+    --mlx-model weights/bert-base-uncased.npz
+```
+
+## Usage
+
+To use the `Bert` model in your own code, you can load it with:
+
+```python
+from model import Bert, load_model
+
+model, tokenizer = load_model(
+    "bert-base-uncased",
+    "weights/bert-base-uncased.npz")
+
+batch = ["This is an example of BERT working on MLX."]
+tokens = tokenizer(batch, return_tensors="np", padding=True)
+tokens = {key: mx.array(v) for key, v in tokens.items()}
+
+output, pooled = model(**tokens)
+```
+
+The `output` contains a `Batch x Tokens x Dims` tensor, representing a vector for every input token.
+If you want to train anything at a **token-level**, you'll want to use this.
+
+The `pooled` contains a `Batch x Dims` tensor, which is the pooled representation for each input.
+If you want to train a **classification** model, you'll want to use this.
+
+## Comparison with 🤗 `transformers` Implementation
+
+In order to run the model, and have it forward inference on a batch of examples:
+
+```sh
+python model.py \
+  --bert-model bert-base-uncased \
+  --mlx-model weights/bert-base-uncased.npz
+```
+
+Which will show the following outputs:
+```
+MLX BERT:
+[[[-0.52508914 -0.1993871  -0.28210318 ... -0.61125606  0.19114694
+    0.8227601 ]
+  [-0.8783862  -0.37107834 -0.52238125 ... -0.5067165   1.0847603
+    0.31066895]
+  [-0.70010054 -0.5424497  -0.26593682 ... -0.2688697   0.38338926
+    0.6557663 ]
+  ...
+```
+
+They can be compared against the 🤗 implementation with:
+
+```sh
+python hf_model.py \
+  --bert-model bert-base-uncased
+```
+
+Which will show:
+```
+ HF BERT:
+[[[-0.52508944 -0.1993877  -0.28210333 ... -0.6112575   0.19114678
+    0.8227603 ]
+  [-0.878387   -0.371079   -0.522381   ... -0.50671494  1.0847601
+    0.31066933]
+  [-0.7001008  -0.5424504  -0.26593733 ... -0.26887015  0.38339025
+    0.65576553]
+  ...
+```

bert/convert.py

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+from transformers import BertModel
+
+import argparse
+import numpy
+
+
+def replace_key(key: str) -> str:
+    key = key.replace(".layer.", ".layers.")
+    key = key.replace(".self.key.", ".key_proj.")
+    key = key.replace(".self.query.", ".query_proj.")
+    key = key.replace(".self.value.", ".value_proj.")
+    key = key.replace(".attention.output.dense.", ".attention.out_proj.")
+    key = key.replace(".attention.output.LayerNorm.", ".ln1.")
+    key = key.replace(".output.LayerNorm.", ".ln2.")
+    key = key.replace(".intermediate.dense.", ".linear1.")
+    key = key.replace(".output.dense.", ".linear2.")
+    key = key.replace(".LayerNorm.", ".norm.")
+    key = key.replace("pooler.dense.", "pooler.")
+    return key
+
+
+def convert(bert_model: str, mlx_model: str) -> None:
+    model = BertModel.from_pretrained(bert_model)
+    # save the tensors
+    tensors = {
+        replace_key(key): tensor.numpy() for key, tensor in model.state_dict().items()
+    }
+    numpy.savez(mlx_model, **tensors)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert BERT weights to MLX.")
+    parser.add_argument(
+        "--bert-model",
+        choices=["bert-base-uncased", "bert-base-cased", "bert-large-uncased", "bert-large-cased"],
+        default="bert-base-uncased",
+        help="The huggingface name of the BERT model to save.",
+    )
+    parser.add_argument(
+        "--mlx-model",
+        type=str,
+        default="weights/bert-base-uncased.npz",
+        help="The output path for the MLX BERT weights.",
+    )
+    args = parser.parse_args()
+
+    convert(args.bert_model, args.mlx_model)

bert/hf_model.py

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+from transformers import AutoModel, AutoTokenizer
+
+import argparse
+
+
+def run(bert_model: str):
+    batch = [
+        "This is an example of BERT working on MLX.",
+        "A second string",
+        "This is another string.",
+    ]
+
+    tokenizer = AutoTokenizer.from_pretrained(bert_model)
+    torch_model = AutoModel.from_pretrained(bert_model)
+    torch_tokens = tokenizer(batch, return_tensors="pt", padding=True)
+    torch_forward = torch_model(**torch_tokens)
+    torch_output = torch_forward.last_hidden_state.detach().numpy()
+    torch_pooled = torch_forward.pooler_output.detach().numpy()
+
+    print("\n HF BERT:")
+    print(torch_output)
+    print("\n\n HF Pooled:")
+    print(torch_pooled[0, :20])
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the BERT model using HuggingFace Transformers.")
+    parser.add_argument(
+        "--bert-model",
+        choices=["bert-base-uncased", "bert-base-cased", "bert-large-uncased", "bert-large-cased"],
+        default="bert-base-uncased",
+        help="The huggingface name of the BERT model to save.",
+    )
+    args = parser.parse_args()
+
+    run(args.bert_model)