Add-whisper-conversion

src/transformers/models/whisper/convert_openai_to_hf.py

@@ -0,0 +1,187 @@
+# Copyright 2022 The HuggingFace Inc. team and the OpenAI team. All rights reserved.
+#
+# 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 argparse
+import hashlib
+import os
+import urllib
+import warnings
+
+import torch
+from torch import nn
+from tqdm import tqdm
+
+from transformers import WhisperConfig, WhisperForConditionalGeneration
+
+
+_MODELS = {
+    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
+    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
+    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
+    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
+    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
+    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
+    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
+    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
+    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
+    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
+}
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["layers", "blocks"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+WHISPER_MAPPING = {
+    "blocks": "layers",
+    "mlp.0": "fc1",
+    "mlp.2": "fc2",
+    "mlp_ln": "final_layer_norm",
+    "blocks": "layers",
+    ".attn.query": ".self_attn.q_proj",
+    ".attn.key": ".self_attn.k_proj",
+    ".attn.value": ".self_attn.v_proj",
+    ".attn_ln": ".self_attn_layer_norm",
+    ".attn.out": ".self_attn.out_proj",
+    ".cross_attn.query": ".encoder_attn.q_proj",
+    ".cross_attn.key": ".encoder_attn.k_proj",
+    ".cross_attn.value": ".encoder_attn.v_proj",
+    ".cross_attn_ln": ".encoder_attn_layer_norm",
+    ".cross_attn.out": ".encoder_attn.out_proj",
+    "decoder.ln.": "decoder.layer_norm.",
+    "encoder.ln.": "encoder.layer_norm.",
+    "token_embedding": "embed_tokens",
+    "encoder.positional_embedding": "encoder.embed_positions.weight",
+    "decoder.positional_embedding": "decoder.embed_positions.weight",
+    "ln_post": "layer_norm",
+}
+
+
+def rename_keys(s_dict):
+    keys = list(s_dict.keys())
+    for key in keys:
+        new_key = key
+        for k, v in WHISPER_MAPPING.items():
+            if k in key:
+                new_key = new_key.replace(k, v)
+
+        print(f"{key} -> {new_key}")
+
+        s_dict[new_key] = s_dict.pop(key)
+    return s_dict
+
+
+def make_linear_from_emb(emb):
+    vocab_size, emb_size = emb.weight.shape
+    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
+    lin_layer.weight.data = emb.weight.data
+    return lin_layer
+
+
+def _download(url: str, root: str) -> bytes:
+    os.makedirs(root, exist_ok=True)
+    filename = os.path.basename(url)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, filename)
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        model_bytes = open(download_target, "rb").read()
+        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
+            return model_bytes
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(
+            total=int(source.info().get("Content-Length")), ncols=80, unit="iB", unit_scale=True, unit_divisor=1024
+        ) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    model_bytes = open(download_target, "rb").read()
+    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
+        raise RuntimeError(
+            "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
+        )
+
+    return model_bytes
+
+
+def convert_openai_whisper_to_tfms(checkpoint_path, pytorch_dump_folder_path):
+    if ".pt" not in checkpoint_path:
+        original_checkpoint = _download(_MODELS[checkpoint_path])
+    else:
+        original_checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    dimensions = original_checkpoint["dims"]
+    state_dict = original_checkpoint["model_state_dict"]
+    proj_out_weights = state_dict["decoder.token_embedding.weight"]
+    remove_ignore_keys_(state_dict)
+    rename_keys(state_dict)
+    tie_embeds = True
+    ffn_dim = state_dict["decoder.layers.0.fc1.weight"].shape[0]
+
+    config = WhisperConfig(
+        vocab_size=dimensions["n_vocab"],
+        encoder_ffn_dim=ffn_dim,
+        decoder_ffn_dim=ffn_dim,
+        num_mel_bins=dimensions["n_mels"],
+        d_model=dimensions["n_audio_state"],
+        max_target_positions=dimensions["n_text_ctx"],
+        encoder_layers=dimensions["n_audio_layer"],
+        encoder_attention_heads=dimensions["n_audio_head"],
+        decoder_layers=dimensions["n_text_layer"],
+        decoder_attention_heads=dimensions["n_text_state"],
+        max_source_positions=dimensions["n_audio_ctx"],
+    )
+
+    model = WhisperForConditionalGeneration(config)
+    missing, unexpected = model.model.load_state_dict(state_dict, strict=False)
+    if len(missing) > 0 and not set(missing) <= set(
+        [
+            "encoder.embed_positions.weights",
+            "decoder.embed_positions.weights",
+        ]
+    ):
+        raise ValueError(
+            "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
+            f" but all the following weights are missing {missing}"
+        )
+
+    if tie_embeds:
+        model.proj_out = make_linear_from_emb(model.model.decoder.embed_tokens)
+    else:
+        model.proj_out.weight.data = proj_out_weights
+
+    model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # # Required parameters
+    parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
+    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    args = parser.parse_args()
+
+    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)