sockeye/train.py [857:906]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                                    strategy: str) -> List[str]:
    """
    Generate a fixed parameter list given a list of all parameter names and
    a strategy.
    """
    # Number of encoder/decoder layers in model.
    num_encoder_layers = config.config_encoder.num_layers
    num_decoder_layers = config.config_decoder.num_layers

    def is_fixed(name: str) -> bool:
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_DECODER:
            # Any decoder layer.
            return not name.startswith(C.DECODER_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_OUTER_LAYERS:
            # First and last encoder and decoder layers.
            first_encoder_prefix = f'{C.ENCODER_PREFIX}.layers.{0}'
            last_encoder_prefix = f'{C.ENCODER_PREFIX}.layers.{num_encoder_layers - 1}'
            first_decoder_prefix = f'{C.DECODER_PREFIX}.layers.{0}'
            last_decoder_prefix = f'{C.DECODER_PREFIX}.layers.{num_decoder_layers - 1}'
            return not (name.startswith(first_encoder_prefix) or
                        name.startswith(last_encoder_prefix) or
                        name.startswith(first_decoder_prefix) or
                        name.startswith(last_decoder_prefix))
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_EMBEDDINGS:
            # Any type of learned embedding.
            return not (name.startswith(C.SOURCE_EMBEDDING_PREFIX) or name.startswith(C.TARGET_EMBEDDING_PREFIX))
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_OUTPUT_PROJ:
            # Target output projection.
            return not name.startswith(C.DEFAULT_OUTPUT_LAYER_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_FEED_FORWARD:
            return not (name.endswith("ff.ff1.bias") or name.endswith("ff.ff1.weight") or
                        name.endswith("ff.ff2.bias") or name.endswith("ff.ff2.weight"))
        if strategy == C.FIXED_PARAM_STRATEGY_ENCODER_AND_SOURCE_EMBEDDINGS:
            return name.startswith(C.ENCODER_PREFIX) or name.startswith(C.SOURCE_EMBEDDING_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ENCODER_HALF_AND_SOURCE_EMBEDDINGS:
            if name.startswith(C.ENCODER_PREFIX):
                for i in range(num_encoder_layers // 2):
                    if name.startswith(f"{C.ENCODER_PREFIX}.layers.{i}"):
                        return True
            return name.startswith(C.SOURCE_EMBEDDING_PREFIX)
        raise ValueError("Unknown fixed parameter strategy: %s" % strategy)

    return [name for name in params if is_fixed(name)]


def main():
    params = arguments.ConfigArgumentParser(description='Train Sockeye sequence-to-sequence models.')
    arguments.add_train_cli_args(params)
    args = params.parse_args()
    train(args)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



sockeye/train_pt.py [799:848]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                                    strategy: str) -> List[str]:
    """
    Generate a fixed parameter list given a list of all parameter names and
    a strategy.
    """
    # Number of encoder/decoder layers in model.
    num_encoder_layers = config.config_encoder.num_layers
    num_decoder_layers = config.config_decoder.num_layers

    def is_fixed(name: str) -> bool:
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_DECODER:
            # Any decoder layer.
            return not name.startswith(C.DECODER_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_OUTER_LAYERS:
            # First and last encoder and decoder layers.
            first_encoder_prefix = f'{C.ENCODER_PREFIX}.layers.{0}'
            last_encoder_prefix = f'{C.ENCODER_PREFIX}.layers.{num_encoder_layers - 1}'
            first_decoder_prefix = f'{C.DECODER_PREFIX}.layers.{0}'
            last_decoder_prefix = f'{C.DECODER_PREFIX}.layers.{num_decoder_layers - 1}'
            return not (name.startswith(first_encoder_prefix) or
                        name.startswith(last_encoder_prefix) or
                        name.startswith(first_decoder_prefix) or
                        name.startswith(last_decoder_prefix))
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_EMBEDDINGS:
            # Any type of learned embedding.
            return not (name.startswith(C.SOURCE_EMBEDDING_PREFIX) or name.startswith(C.TARGET_EMBEDDING_PREFIX))
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_OUTPUT_PROJ:
            # Target output projection.
            return not name.startswith(C.DEFAULT_OUTPUT_LAYER_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ALL_EXCEPT_FEED_FORWARD:
            return not (name.endswith("ff.ff1.bias") or name.endswith("ff.ff1.weight") or
                        name.endswith("ff.ff2.bias") or name.endswith("ff.ff2.weight"))
        if strategy == C.FIXED_PARAM_STRATEGY_ENCODER_AND_SOURCE_EMBEDDINGS:
            return name.startswith(C.ENCODER_PREFIX) or name.startswith(C.SOURCE_EMBEDDING_PREFIX)
        if strategy == C.FIXED_PARAM_STRATEGY_ENCODER_HALF_AND_SOURCE_EMBEDDINGS:
            if name.startswith(C.ENCODER_PREFIX):
                for i in range(num_encoder_layers // 2):
                    if name.startswith(f"{C.ENCODER_PREFIX}.layers.{i}"):
                        return True
            return name.startswith(C.SOURCE_EMBEDDING_PREFIX)
        raise ValueError("Unknown fixed parameter strategy: %s" % strategy)

    return [name for name in params if is_fixed(name)]


def main():
    params = arguments.ConfigArgumentParser(description='Train Sockeye sequence-to-sequence models.')
    arguments.add_train_cli_args(params)
    args = params.parse_args()
    train(args)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -