Eoleconv

python/ctranslate2/converters/eole_ct2.py

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+import argparse
+
+from eole.config.run import PredictConfig
+from eole.constants import PositionEncodingType
+from eole.inputters.inputter import vocabs_to_dict
+from eole.models.model import BaseModel
+
+from ctranslate2.converters import utils
+from ctranslate2.converters.converter import Converter
+from ctranslate2.specs import common_spec, transformer_spec
+
+_SUPPORTED_ACTIVATIONS = {
+    "gelu": common_spec.Activation.GELU,
+    "fast_gelu": common_spec.Activation.GELUTanh,
+    "relu": common_spec.Activation.RELU,
+    "gated-silu": common_spec.Activation.SWISH,
+}
+
+
+def _get_model_spec_seq2seq(
+    config, variables, src_vocabs, tgt_vocabs, num_source_embeddings
+):
+    """Creates a model specification from the model config."""
+    with_relative_position = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Relative
+    )
+    with_rotary = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Rotary
+    )
+    if with_rotary:
+        raise ValueError(
+            "Rotary embeddings are not supported yet for encoder/decoder models"
+        )
+    with_alibi = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Alibi
+    )
+    if with_alibi:
+        raise ValueError("Alibi is not supported yet for encoder/decoder models")
+    activation_fn = getattr(config, "mlp_activation_fn", "relu")
+
+    # Return the first head of the last layer unless the model was trained with alignments.
+    if getattr(config.decoder, "lambda_align", 0) == 0:
+        alignment_layer = -1
+        alignment_heads = 1
+    else:
+        alignment_layer = config.decoder.alignment_layer
+        alignment_heads = config.decoder.alignment_heads
+
+    num_heads = getattr(config.decoder, "heads", 8)
+    # num_kv = getattr(config.decoder, "heads_kv", 0)
+    # if num_kv == num_heads or num_kv == 0:
+    #    num_kv = None
+    # rotary_dim = 0 if with_rotary else None
+    # rotary_interleave = getattr(config.rope_config, "rotary_interleave", True)
+    ffn_glu = activation_fn == "gated-silu"
+    sliding_window = getattr(config, "sliding_window", 0)
+    if sliding_window != 0:
+        raise ValueError(
+            "Sliding window is not suported yet for encoder/decoder models"
+        )
+
+    model_spec = transformer_spec.TransformerSpec.from_config(
+        (config.encoder.layers, config.decoder.layers),
+        num_heads,
+        with_relative_position=with_relative_position,
+        # alibi=with_alibi,
+        activation=_SUPPORTED_ACTIVATIONS[activation_fn],
+        ffn_glu=ffn_glu,
+        rms_norm=config.layer_norm == "rms",
+        # rotary_dim=rotary_dim,
+        # rotary_interleave=rotary_interleave,
+        # num_heads_kv=num_kv,
+        # sliding_window=sliding_window,
+        alignment_layer=alignment_layer,
+        alignment_heads=alignment_heads,
+        num_source_embeddings=num_source_embeddings,
+        # multi_query_attention=getattr(opt, "multiquery", False),
+    )
+
+    set_transformer_spec(model_spec, variables)
+    for src_vocab in src_vocabs:
+        model_spec.register_source_vocabulary(src_vocab)
+    for tgt_vocab in tgt_vocabs:
+        model_spec.register_target_vocabulary(tgt_vocab)
+
+    return model_spec
+
+
+def _get_model_spec_lm(
+    config, variables, src_vocabs, tgt_vocabs, num_source_embeddings
+):
+    """Creates a model specification from the model config."""
+    with_relative_position = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Relative
+    )
+    with_rotary = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Rotary
+    )
+    with_alibi = (
+        getattr(config.embeddings, "position_encoding_type", None)
+        == PositionEncodingType.Alibi
+    )
+    activation_fn = getattr(config, "mlp_activation_fn", "relu")
+    num_heads = getattr(config.decoder, "heads", 8)
+    num_kv = getattr(config.decoder, "heads_kv", 0)
+    if num_kv == num_heads or num_kv == 0:
+        num_kv = None
+    rotary_dim = 0 if with_rotary else None
+    rotary_interleave = getattr(config.rope_config, "rotary_interleave", True)
+    ffn_glu = activation_fn == "gated-silu"
+    sliding_window = getattr(config, "sliding_window", 0)
+
+    model_spec = transformer_spec.TransformerDecoderModelSpec.from_config(
+        config.decoder.layers,
+        num_heads,
+        activation=_SUPPORTED_ACTIVATIONS[activation_fn],
+        ffn_glu=ffn_glu,
+        with_relative_position=with_relative_position,
+        alibi=with_alibi,
+        rms_norm=config.layer_norm == "rms",
+        rotary_dim=rotary_dim,
+        rotary_interleave=rotary_interleave,
+        num_heads_kv=num_kv,
+        sliding_window=sliding_window,
+        # multi_query_attention=getattr(opt, "multiquery", False),
+    )
+
+    set_transformer_decoder(
+        model_spec.decoder,
+        variables,
+        with_encoder_attention=False,
+    )
+
+    for tgt_vocab in tgt_vocabs:
+        model_spec.register_vocabulary(tgt_vocab)
+
+    return model_spec
+
+
+def get_vocabs(vocab):
+    src_vocabs = [vocab["src"]]
+    tgt_vocabs = [vocab["tgt"]]
+    return src_vocabs, tgt_vocabs
+
+
+class EoleConverter(Converter):
+    """Converts models generated by OpenNMT-py."""
+
+    def __init__(self, model_path: str):
+        """Initializes the OpenNMT-py converter.
+
+        Arguments:
+          model_path: Path to the OpenNMT-py PyTorch model (.pt file).
+        """
+        self._model_path = model_path
+
+    def _load(self):
+        import torch
+
+        config = PredictConfig(model_path=self._model_path, src="dummy")
+
+        vocabs, model, model_config = BaseModel.load_test_model(config)
+        vocabs_dict = vocabs_to_dict(vocabs)
+
+        config.model = model_config
+        src_vocabs, tgt_vocabs = get_vocabs(vocabs_dict)
+
+        if config.model.decoder.decoder_type == "transformer_lm":
+            spec = _get_model_spec_lm(
+                config.model,
+                model.state_dict(),
+                src_vocabs,
+                tgt_vocabs,
+                num_source_embeddings=len(src_vocabs),
+            )
+        else:
+            spec = _get_model_spec_seq2seq(
+                config.model,
+                model.state_dict(),
+                src_vocabs,
+                tgt_vocabs,
+                num_source_embeddings=len(src_vocabs),
+            )
+            spec.config.decoder_start_token = vocabs["decoder_start_token"]
+
+        spec.config.bos_token = vocabs["specials"]["bos_token"]
+        spec.config.eos_token = vocabs["specials"]["eos_token"]
+        spec.config.unk_token = vocabs["specials"]["unk_token"]
+        spec.config.layer_norm_epsilon = getattr(config, "norm_eps", 1e-6)
+
+        return spec
+
+
+def set_transformer_spec(spec, variables):
+    set_transformer_encoder(spec.encoder, variables)
+    set_transformer_decoder(spec.decoder, variables)
+
+
+def set_transformer_encoder(spec, variables):
+    set_input_layers(spec, variables, "src_emb")
+    set_layer_norm(spec.layer_norm, variables, "encoder.layer_norm")
+    for i, layer in enumerate(spec.layer):
+        set_transformer_encoder_layer(
+            layer, variables, "encoder.transformer_layers.%d" % i
+        )
+
+
+def set_transformer_decoder(spec, variables, with_encoder_attention=True):
+    set_input_layers(spec, variables, "tgt_emb")
+    set_layer_norm(spec.layer_norm, variables, "decoder.layer_norm")
+    for i, layer in enumerate(spec.layer):
+        set_transformer_decoder_layer(
+            layer,
+            variables,
+            "decoder.transformer_layers.%d" % i,
+            with_encoder_attention=with_encoder_attention,
+        )
+
+    set_linear(spec.projection, variables, "generator")
+
+
+def set_input_layers(spec, variables, scope):
+    if hasattr(spec, "position_encodings"):
+        set_position_encodings(
+            spec.position_encodings,
+            variables,
+            "%s.pe" % scope,
+        )
+    else:
+        spec.scale_embeddings = False
+
+    embeddings_specs = spec.embeddings
+    # encoder embeddings are stored in a list(onmt/ct2 legacy with features)
+    if isinstance(embeddings_specs, list):
+        embeddings_specs = embeddings_specs[0]
+    set_embeddings(embeddings_specs, variables, "%s.embeddings" % scope)
+
+
+def set_transformer_encoder_layer(spec, variables, scope):
+    set_multi_head_attention(
+        spec.self_attention,
+        variables,
+        "%s.self_attn" % scope,
+        self_attention=True,
+    )
+    set_layer_norm(
+        spec.self_attention.layer_norm, variables, "%s.input_layernorm" % scope
+    )
+    set_layer_norm(
+        spec.ffn.layer_norm, variables, "%s.post_attention_layernorm" % scope
+    )
+    set_ffn(spec.ffn, variables, "%s.mlp" % scope)
+
+
+def set_transformer_decoder_layer(spec, variables, scope, with_encoder_attention=True):
+    set_multi_head_attention(
+        spec.self_attention,
+        variables,
+        "%s.self_attn" % scope,
+        self_attention=True,
+    )
+    set_layer_norm(
+        spec.self_attention.layer_norm, variables, "%s.input_layernorm" % scope
+    )
+    if with_encoder_attention:
+        set_multi_head_attention(spec.attention, variables, "%s.context_attn" % scope)
+        set_layer_norm(
+            spec.attention.layer_norm, variables, "%s.precontext_layernorm" % scope
+        )
+    set_layer_norm(
+        spec.ffn.layer_norm, variables, "%s.post_attention_layernorm" % scope
+    )
+    set_ffn(spec.ffn, variables, "%s.mlp" % scope)
+
+
+def set_ffn(spec, variables, scope):
+    set_linear(spec.linear_0, variables, "%s.gate_up_proj" % scope)
+    set_linear(spec.linear_1, variables, "%s.down_proj" % scope)
+    if hasattr(spec, "linear_0_noact"):
+        set_linear(spec.linear_0_noact, variables, "%s.up_proj" % scope)
+
+
+def set_multi_head_attention(spec, variables, scope, self_attention=False):
+    if self_attention:
+        split_layers = [common_spec.LinearSpec() for _ in range(3)]
+        set_linear(split_layers[0], variables, "%s.linear_query" % scope)
+        set_linear(split_layers[1], variables, "%s.linear_keys" % scope)
+        set_linear(split_layers[2], variables, "%s.linear_values" % scope)
+        utils.fuse_linear(spec.linear[0], split_layers)
+    else:
+        set_linear(spec.linear[0], variables, "%s.linear_query" % scope)
+        split_layers = [common_spec.LinearSpec() for _ in range(2)]
+        set_linear(split_layers[0], variables, "%s.linear_keys" % scope)
+        set_linear(split_layers[1], variables, "%s.linear_values" % scope)
+        utils.fuse_linear(spec.linear[1], split_layers)
+    set_linear(spec.linear[-1], variables, "%s.final_linear" % scope)
+    if hasattr(spec, "relative_position_keys"):
+        spec.relative_position_keys = _get_variable(
+            variables, "%s.relative_positions_embeddings.weight" % scope
+        )
+        spec.relative_position_values = spec.relative_position_keys
+
+
+def set_layer_norm(spec, variables, scope):
+    try:
+        spec.gamma = _get_variable(variables, "%s.weight" % scope)
+    except KeyError:
+        # Compatibility with older models using a custom LayerNorm module.
+        spec.gamma = _get_variable(variables, "%s.a_2" % scope)
+        spec.beta = _get_variable(variables, "%s.b_2" % scope)
+    try:
+        spec.beta = _get_variable(variables, "%s.bias" % scope)
+    except KeyError:
+        pass
+
+
+def set_linear(spec, variables, scope):
+    spec.weight = _get_variable(variables, "%s.weight" % scope)
+    bias = variables.get("%s.bias" % scope)
+    if bias is not None:
+        spec.bias = bias
+
+
+def set_embeddings(spec, variables, scope):
+    spec.weight = _get_variable(variables, "%s.weight" % scope)
+
+
+def set_position_encodings(spec, variables, scope):
+    spec.encodings = _get_variable(variables, "%s.pe" % scope).squeeze()
+
+
+def _get_variable(variables, name):
+    return variables[name]
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("--model_path", required=True, help="Model path.")
+    Converter.declare_arguments(parser)
+    args = parser.parse_args()
+    EoleConverter(args.model_path).convert_from_args(args)
+
+
+if __name__ == "__main__":
+    main()