Add new meta w2v2-conformer BERT-like model

src/transformers/models/seamless_m4t/modeling_seamless_m4t.py

@@ -583,7 +583,6 @@ def __init__(self, config, use_position_embeddings=True):
             self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
             self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
 
-    # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerSelfAttention.forward
     def forward(
         self,
         hidden_states: torch.Tensor,

src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py

@@ -184,16 +184,29 @@ class Wav2Vec2ConformerConfig(PretrainedConfig):
             Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
             if `add_adapter is True`.
         position_embeddings_type (`str`, *optional*, defaults to `"relative"`):
-            Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left
-            `None` no relative position embedding is applied.
+            Can be specified to `relative`, `relative_key` or `rotary` for relative, relative as defined by Shaw, or rotary position embeddings respectively. If left
+            `None` no relative position embedding is applied. For more information on `"relative_key"`, please refer to [Self-Attention
+            with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
         rotary_embedding_base (`int`, *optional*, defaults to 10000):
             If `"rotary"` position embeddings are used, defines the size of the embedding base.
         max_source_positions (`int`, *optional*, defaults to 5000):
             if `"relative"` position embeddings are used, defines the maximum source input positions.
+        left_max_position_embeddings (`int`, *optional*, defaults to 64):
+            if `"_key"` position embeddings are used, defines the left clipping value for relative positions.
+        right_max_position_embeddings (`int`, *optional*, defaults to 8):
+            if `"_key"` position embeddings are used, defines the right clipping value for relative positions.
         conv_depthwise_kernel_size (`int`, defaults to 31):
             Kernel size of convolutional depthwise 1D layer in Conformer blocks.
         conformer_conv_dropout (`float`, defaults to 0.1):
             The dropout probability for all convolutional layers in Conformer blocks.
+        non_causal_depth_wise_conv (`bool`, defaults to `True`):
+            If `False`, use causal convolutional depthwise layers in Conformer blocks.
+        skip_feature_encoder (`bool`, defaults to `False`):
+            Whether to skip the feature encoder layers. Only relevant when using a feature extractor that computes spectrogram-like inputs instead of raw waveforms.
+        skip_encoder_layer_norm (`bool`, defaults to `False`):
+            Whether to skip the input layer norm of the encoder.
+        skip_pos_conv_embed (`bool`, defaults to `False`):
+            Whether to skip the positional layer convolutional embedding layer of the encoder.
 
     Example:
 
@@ -270,8 +283,14 @@ def __init__(
         position_embeddings_type="relative",
         rotary_embedding_base=10000,
         max_source_positions=5000,
+        left_max_position_embeddings=64,
+        right_max_position_embeddings=8,
         conv_depthwise_kernel_size=31,
         conformer_conv_dropout=0.1,
+        non_causal_depth_wise_conv=True,
+        skip_feature_encoder=False,
+        skip_encoder_layer_norm=False,
+        skip_pos_conv_embed=False,
         **kwargs,
     ):
         super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
@@ -302,6 +321,8 @@ def __init__(
         self.max_source_positions = max_source_positions
         self.position_embeddings_type = position_embeddings_type
         self.rotary_embedding_base = rotary_embedding_base
+        self.left_max_position_embeddings = left_max_position_embeddings
+        self.right_max_position_embeddings = right_max_position_embeddings
 
         if (
             (len(self.conv_stride) != self.num_feat_extract_layers)
@@ -318,6 +339,7 @@ def __init__(
         # Conformer-block related
         self.conv_depthwise_kernel_size = conv_depthwise_kernel_size
         self.conformer_conv_dropout = conformer_conv_dropout
+        self.non_causal_depth_wise_conv = non_causal_depth_wise_conv
 
         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         self.apply_spec_augment = apply_spec_augment
@@ -358,6 +380,10 @@ def __init__(
         self.tdnn_dilation = list(tdnn_dilation)
         self.xvector_output_dim = xvector_output_dim
 
+        self.skip_feature_encoder = skip_feature_encoder
+        self.skip_encoder_layer_norm = skip_encoder_layer_norm
+        self.skip_pos_conv_embed = skip_pos_conv_embed
+
     @property
     def inputs_to_logits_ratio(self):
         return functools.reduce(operator.mul, self.conv_stride, 1)

src/transformers/models/wav2vec2_conformer/convert_wav2vec2_seamless_checkpoint.py

@@ -0,0 +1,161 @@
+# coding=utf-8
+# Copyright 2022 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.
+"""Convert Wav2Vec2Conformer BERT checkpoint."""
+
+
+import argparse
+
+import torch
+from seamless_communication.models.conformer_shaw import load_conformer_shaw_model
+
+from transformers import (
+    AutoProcessor,
+    Wav2Vec2ConformerConfig,
+    Wav2Vec2ConformerForPreTraining,
+    logging,
+)
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+wav2vec_convert_list = [
+    ("encoder", "wav2vec2_conformer.encoder"),
+    ("encoder_frontend.model_dim_proj", "feature_projection.projection"),
+    ("encoder_frontend.post_extract_layer_norm", "feature_projection.layer_norm"),
+    ("encoder_frontend.pos_encoder.conv", "encoder.pos_conv_embed.conv"),
+    ("encoder.inner.layers", "encoder.layers"),
+    ("encoder.inner_layer_norm", "encoder.layer_norm"),
+    ("encoder.adaptor_layers", "adapter.layers"),
+    ("inner_proj", "intermediate_dense"),
+    ("self_attn.output_proj", "self_attn.linear_out"),
+    ("output_proj", "output_dense"),
+    ("self_attn.k_proj", "self_attn.linear_k"),
+    ("self_attn.v_proj", "self_attn.linear_v"),
+    ("self_attn.q_proj", "self_attn.linear_q"),
+    ("self_attn.sdpa.u_bias", "self_attn.pos_bias_u"),
+    ("self_attn.sdpa.v_bias", "self_attn.pos_bias_v"),
+    ("self_attn.sdpa.rel_k_embed", "self_attn.distance_embedding"),
+    ("self_attn.sdpa.r_proj", "self_attn.linear_pos"),
+    ("conv.pointwise_conv1", "conv_module.pointwise_conv1"),
+    ("conv.pointwise_conv2", "conv_module.pointwise_conv2"),
+    ("conv.depthwise_conv", "conv_module.depthwise_conv"),
+    ("conv.layer_norm", "conv_module.depthwise_layer_norm"),
+    ("conv_layer_norm", "conv_module.layer_norm"),
+    ("encoder.proj1", "intermediate_ffn.intermediate_dense"),
+    ("encoder.proj2", "intermediate_ffn.output_dense"),
+    ("encoder.layer_norm", "inner_layer_norm"),
+    ("quantizer.entry_proj", "quantizer.weight_proj"),
+    ("final_proj", "project_hid"),
+    ("final_target_proj", "project_q"),
+    ("masker.temporal_mask_embed", "wav2vec2_conformer.masked_spec_embed"),
+    ("quantizer.entries", "quantizer.codevectors"),
+]
+
+
+def param_count(model):
+    return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0])
+
+
+def _convert_model(
+    original_model,
+    hf_model,
+    convert_list,
+):
+    state_dict = original_model.state_dict()
+
+    for k, v in list(state_dict.items()):
+        new_key = k
+        for old_layer_name, new_layer_name in convert_list:
+            if old_layer_name in new_key:
+                new_key = new_key.replace(old_layer_name, new_layer_name)
+
+        # must do it by hand
+        if ".layer_norm" in new_key and new_key.split(".layer_norm")[0][-1].isnumeric():
+            new_key = new_key.replace("layer_norm", "final_layer_norm")
+
+        state_dict[new_key] = state_dict.pop(k)
+
+    extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys())
+    extra_keys = set({k for k in extra_keys if "num_updates" not in k})  # filter unecessary param
+    missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys())
+    if len(extra_keys) != 0:
+        raise ValueError(f"extra keys found: {extra_keys}")
+    if len(missing_keys) != 0:
+        raise ValueError(f"missing keys: {missing_keys}")
+    hf_model.load_state_dict(state_dict, strict=False)
+    n_params = param_count(hf_model)
+
+    logger.info(f"model loaded: {round(n_params/1e6,1)}M params")
+
+    hf_model.eval()
+    del state_dict
+
+    return hf_model
+
+
+@torch.no_grad()
+def convert_wav2vec2_conformer_checkpoint(
+    checkpoint_path,
+    pytorch_dump_folder_path,
+    config_path=None,
+    repo_id=None,
+    process_path=None,
+):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    if config_path is not None:
+        config = Wav2Vec2ConformerConfig.from_pretrained(config_path, hidden_act="swish")
+    else:
+        config = Wav2Vec2ConformerConfig(apply_spec_augment=False)
+
+    hf_wav2vec = Wav2Vec2ConformerForPreTraining(config)
+
+    model = load_conformer_shaw_model(checkpoint_path, dtype=torch.float32)
+    model.eval()
+
+    hf_wav2vec = _convert_model(model, hf_wav2vec, wav2vec_convert_list)
+
+    hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
+
+    if repo_id:
+        hf_wav2vec.push_to_hub(repo_id)
+
+    if process_path:
+        processor = AutoProcessor.from_pretrained(process_path)
+
+        processor.feature_extractor.padding_value = 1
+        processor.save_pretrained(pytorch_dump_folder_path)
+
+        if repo_id:
+            processor.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    parser.add_argument(
+        "--checkpoint_path", default="conformer_shaw", type=str, help="Path to seamless communication checkpoint"
+    )
+    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+    parser.add_argument("--repo_id", default=None, type=str, help="Push to this repo id if precised.")
+    parser.add_argument("--process_path", default=None, type=str, help="Push to this repo id if precised.")
+
+    args = parser.parse_args()
+    convert_wav2vec2_conformer_checkpoint(
+        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.repo_id, args.process_path
+    )