def convert_model()

in src/accelerate/utils/transformer_engine.py [0:0]

• 52 lines of code
• 23 McCabe index (conditional complexity)

def convert_model(model, to_transformer_engine=True, _convert_linear=True, _convert_ln=True):
    """
    Recursively converts the linear and layernorm layers of a model to their `transformers_engine` counterpart.
    """
    if not is_transformer_engine_available():
        raise ImportError("Using `convert_model` requires transformer_engine to be installed.")

    if is_hpu_available():
        import intel_transformer_engine as te

        if not hasattr(te, "LayerNorm"):
            # HPU does not have a LayerNorm implementation in TE
            te.LayerNorm = nn.LayerNorm
    else:
        import transformer_engine.pytorch as te

    for name, module in model.named_children():
        if isinstance(module, nn.Linear) and to_transformer_engine and _convert_linear:
            has_bias = module.bias is not None
            params_to_gather = [module.weight]
            if has_bias:
                params_to_gather.append(module.bias)

            with GatheredParameters(params_to_gather, modifier_rank=0):
                if any(p % 16 != 0 for p in module.weight.shape):
                    return
                te_module = te.Linear(
                    module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
                )
                te_module.weight.copy_(module.weight)
                if has_bias:
                    te_module.bias.copy_(module.bias)

                setattr(model, name, te_module)
        # Note: @xrsrke (Phuc) found that te.LayerNorm doesn't have any real memory savings or speedups over nn.LayerNorm
        elif isinstance(module, nn.LayerNorm) and to_transformer_engine and _convert_ln:
            with GatheredParameters([module.weight, module.bias], modifier_rank=0):
                te_module = te.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
                te_module.weight.copy_(module.weight)
                te_module.bias.copy_(module.bias)

            setattr(model, name, te_module)
        elif isinstance(module, te.Linear) and not to_transformer_engine and _convert_linear:
            has_bias = module.bias is not None
            new_module = nn.Linear(
                module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype
            )
            new_module.weight.copy_(module.weight)
            if has_bias:
                new_module.bias.copy_(module.bias)

            setattr(model, name, new_module)
        elif isinstance(module, te.LayerNorm) and not to_transformer_engine and _convert_ln:
            new_module = nn.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype)
            new_module.weight.copy_(module.weight)
            new_module.bias.copy_(module.bias)

            setattr(model, name, new_module)
        else:
            convert_model(
                module,
                to_transformer_engine=to_transformer_engine,
                _convert_linear=_convert_linear,
                _convert_ln=_convert_ln,
            )