models/unival/unify_transformer_layer.py

# Copyright 2022 The OFA-Sys Team. 
# All rights reserved.
# This source code is licensed under the Apache 2.0 license 
# found in the LICENSE file in the root directory.

from typing import Dict, List, Optional

import torch
import torch.nn as nn
from fairseq import utils
from fairseq.modules import LayerNorm
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
from torch import Tensor

from .unify_multihead_attention import MultiheadAttention


def drop_path(x, drop_prob: float = 0.0, training: bool = False):
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (1, x.shape[1], 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output

def init_bert_weights(module):
    """Initialize the weights."""
    if isinstance(module, (nn.Linear, nn.Embedding)):
        # std defaults to 0.02, this might need to be changed
        module.weight.data.normal_(mean=0.0, std=0.02)
    elif isinstance(module, nn.LayerNorm):
        module.bias.data.zero_()
        module.weight.data.fill_(1.0)
    if isinstance(module, nn.Linear) and module.bias is not None:
        module.bias.data.zero_()


class Adapter_Layer(torch.nn.Module):
    def __init__(self,
                 d_model=None,
                 down_size=None,
                 dropout=0.0,
                 init_option="bert",
                 adapter_scalar="1.0"):
        super().__init__()
        self.n_embd = d_model
        self.down_size = down_size


        if adapter_scalar == "learnable_scalar":
            self.scale = nn.Parameter(torch.ones(1))
        else:
            self.scale = float(adapter_scalar)

        self.down_proj = nn.Linear(self.n_embd, self.down_size)
        self.non_linear_func = nn.ReLU()
        self.up_proj = nn.Linear(self.down_size, self.n_embd)

        self.dropout = dropout
        if init_option == "bert":
            self.apply(init_bert_weights)
        elif init_option == "lora":
            with torch.no_grad():
                nn.init.kaiming_uniform_(self.down_proj.weight, a=math.sqrt(5))
                nn.init.zeros_(self.up_proj.weight)
                nn.init.zeros_(self.down_proj.bias)
                nn.init.zeros_(self.up_proj.bias)

    def forward(self, x, add_residual=True, residual=None):
        residual = x if residual is None else residual

        down = self.down_proj(x)
        down = self.non_linear_func(down)
        down = nn.functional.dropout(down, p=self.dropout, training=self.training)
        up = self.up_proj(down)
        up = up * self.scale
        if add_residual:
            output = up + residual
        else:
            output = up

        return output

class VLAdapter_Layer(torch.nn.Module):
    def __init__(self,
                 d_model=None,
                 down_size=None,
                 dropout=0.0,
                 init_option="bert",
                 adapter_scalar="1.0"):
        super().__init__()
        print("load VL adapter")
        self.v_adapter = Adapter_Layer(d_model=d_model,
                 down_size=down_size,
                 dropout=dropout,
                 init_option=init_option,
                 adapter_scalar=adapter_scalar)

        self.l_adapter = Adapter_Layer(d_model=d_model,
                 down_size=down_size,
                 dropout=dropout,
                 init_option=init_option,
                 adapter_scalar=adapter_scalar)


    def forward(self, x, add_residual=True, residual=None, num_image_tokens=None):
        
        if num_image_tokens is not None:
            v_x = x[:num_image_tokens, :,  :]
            l_x = x[num_image_tokens:, :, :]
        else:
            v_x = x
            l_x = x

        v_x = self.v_adapter(v_x, add_residual=add_residual, residual=residual)
        l_x = self.l_adapter(l_x, add_residual=add_residual, residual=residual)

        if num_image_tokens is not None:
            x = torch.cat((v_x, l_x), dim=0)
        else:
            x = v_x + l_x

        return x


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob=None):
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return "p={}".format(self.drop_prob)


class TransformerEncoderLayer(nn.Module):
    """Encoder layer block.

    In the original paper each operation (multi-head attention or FFN) is
    postprocessed with: `dropout -> add residual -> layernorm`. In the
    tensor2tensor code they suggest that learning is more robust when
    preprocessing each layer with layernorm and postprocessing with:
    `dropout -> add residual`. We default to the approach in the paper, but the
    tensor2tensor approach can be enabled by setting
    *args.encoder_normalize_before* to ``True``.

    Args:
        args (argparse.Namespace): parsed command-line arguments
    """

    def __init__(self, args, drop_path_rate=0.0, use_adapter=False, adapter_dim=200, adapter_type='UM'):
        super().__init__()
        self.args = args
        self.use_adapter = use_adapter
        self.embed_dim = args.encoder_embed_dim
        self.adapter_type = adapter_type
        if self.use_adapter:
            if adapter_type == 'VL':
                self.adapter = VLAdapter_Layer(d_model=self.embed_dim, down_size=adapter_dim)
            else:
                self.adapter = Adapter_Layer(d_model=self.embed_dim, down_size=adapter_dim)
        self.quant_noise = getattr(args, 'quant_noise_pq', 0)
        self.quant_noise_block_size = getattr(args, 'quant_noise_pq_block_size', 8) or 8
        self.self_attn = self.build_self_attention(self.embed_dim, args)
        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
        self.dropout_module = FairseqDropout(
            args.dropout, module_name=self.__class__.__name__
        )
        self.activation_fn = utils.get_activation_fn(
            activation=getattr(args, 'activation_fn', 'relu') or "relu"
        )
        activation_dropout_p = getattr(args, "activation_dropout", 0) or 0
        if activation_dropout_p == 0:
            # for backwards compatibility with models that use args.relu_dropout
            activation_dropout_p = getattr(args, "relu_dropout", 0) or 0
        self.activation_dropout_module = FairseqDropout(
            float(activation_dropout_p), module_name=self.__class__.__name__
        )
        self.normalize_before = args.encoder_normalize_before
        self.fc1 = self.build_fc1(
            self.embed_dim,
            args.encoder_ffn_embed_dim,
            self.quant_noise,
            self.quant_noise_block_size,
        )
        self.fc2 = self.build_fc2(
            args.encoder_ffn_embed_dim,
            self.embed_dim,
            self.quant_noise,
            self.quant_noise_block_size,
        )

        self.attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
        self.nh = self.self_attn.num_heads
        self.head_dim = self.self_attn.head_dim

        self.ffn_layernorm = LayerNorm(args.encoder_ffn_embed_dim) if getattr(args, 'scale_fc', False) else None
        self.w_resid = nn.Parameter(torch.ones(self.embed_dim, ), requires_grad=True) if getattr(args, 'scale_resids', False) else None

        self.final_layer_norm = LayerNorm(self.embed_dim)

        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()

    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
        return quant_noise(
            nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
        )

    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
        return quant_noise(
            nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
        )

    def build_self_attention(self, embed_dim, args):
        return MultiheadAttention(
            embed_dim,
            args.encoder_attention_heads,
            dropout=args.attention_dropout,
            self_attention=True,
            q_noise=self.quant_noise,
            qn_block_size=self.quant_noise_block_size,
            scale_factor=args.attn_scale_factor,
            scale_heads=getattr(args, 'scale_heads', False),
            qk_norm=getattr(args, 'qk_norm', False),
        )

    def residual_connection(self, x, residual):
        return residual + self.drop_path(x)

    def upgrade_state_dict_named(self, state_dict, name):
        """
        Rename layer norm states from `...layer_norms.0.weight` to
        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
        `...final_layer_norm.weight`
        """
        layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"}
        for old, new in layer_norm_map.items():
            for m in ("weight", "bias"):
                k = "{}.layer_norms.{}.{}".format(name, old, m)
                if k in state_dict:
                    state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k]
                    del state_dict[k]
                if "{}.{}.{}".format(name, new, m) not in state_dict and "{}.{}".format(new, m) in self.state_dict():
                    state_dict[
                        "{}.{}.{}".format(name, new, m)
                    ] = self.state_dict()["{}.{}".format(new, m)]

        prefix = name + "." if name != "" else ""
        for param_name, param_tensor in self.state_dict().items():
            if (prefix + param_name) not in state_dict:
                state_dict[prefix + param_name] = self.state_dict()[param_name]
 
    def forward(
        self,
        x,
        encoder_padding_mask: Optional[Tensor],
        attn_mask: Optional[Tensor] = None,
        self_attn_bias: Optional[Tensor] = None,
        prompt_kv: Optional[Tensor] = None,
        num_image_tokens = None,
    ):
        """
        Args:
            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_padding_mask (ByteTensor): binary ByteTensor of shape
                `(batch, seq_len)` where padding elements are indicated by ``1``.
            attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`,
                where `tgt_len` is the length of output and `src_len` is the
                length of input, though here both are equal to `seq_len`.
                `attn_mask[tgt_i, src_j] = 1` means that when calculating the
                embedding for `tgt_i`, we exclude (mask out) `src_j`. This is
                useful for strided self-attention.

        Returns:
            encoded output of shape `(seq_len, batch, embed_dim)`
        """
        # anything in original attn_mask = 1, becomes -1e8
        # anything in original attn_mask = 0, becomes 0
        # Note that we cannot use -inf here, because at some edge cases,
        # the attention weight (before softmax) for some padded element in query
        # will become -inf, which results in NaN in model parameters
        if attn_mask is not None:
            attn_mask = attn_mask.masked_fill(
                attn_mask.to(torch.bool),
                -1e8 if x.dtype == torch.float32 else -1e4
            )

        residual = x
        if self.normalize_before:
            x = self.self_attn_layer_norm(x)
        x, _ = self.self_attn(
            query=x,
            key=x,
            value=x,
            key_padding_mask=encoder_padding_mask,
            need_weights=False,
            attn_mask=attn_mask,
            attn_bias=self_attn_bias,
            prompt_kv=prompt_kv
        )
        if self.attn_ln is not None:
            x = self.attn_ln(x)
        x = self.dropout_module(x)
        x = self.residual_connection(x, residual)
        if not self.normalize_before:
            x = self.self_attn_layer_norm(x)

        residual = x
        if self.normalize_before:
            x = self.final_layer_norm(x)
        x = self.activation_fn(self.fc1(x))
        x = self.activation_dropout_module(x)
        if self.ffn_layernorm is not None:
            x = self.ffn_layernorm(x)
        x = self.fc2(x)
        x = self.dropout_module(x)
        if self.use_adapter:
            if self.adapter_type == 'VL':
                x = self.adapter(x, num_image_tokens=num_image_tokens)
            else:
                x = self.adapter(x)
        if self.w_resid is not None:
            residual = torch.mul(self.w_resid, residual)
        x = self.residual_connection(x, residual)
        if not self.normalize_before:
            x = self.final_layer_norm(x)
        return x


class TransformerDecoderLayer(nn.Module):
    """Decoder layer block.

    In the original paper each operation (multi-head attention, encoder
    attention or FFN) is postprocessed with: `dropout -> add residual ->
    layernorm`. In the tensor2tensor code they suggest that learning is more
    robust when preprocessing each layer with layernorm and postprocessing with:
    `dropout -> add residual`. We default to the approach in the paper, but the
    tensor2tensor approach can be enabled by setting
    *args.decoder_normalize_before* to ``True``.

    Args:
        args (argparse.Namespace): parsed command-line arguments
        no_encoder_attn (bool, optional): whether to attend to encoder outputs
            (default: False).
    """

    def __init__(
        self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False, \
            drop_path_rate=0.0, use_adapter=False, adapter_dim=200):
        super().__init__()
        self.embed_dim = args.decoder_embed_dim
        self.use_adapter = use_adapter
        if use_adapter == True:
            self.adapter = Adapter_Layer(d_model=self.embed_dim, down_size=adapter_dim)

        self.dropout_module = FairseqDropout(
            args.dropout, module_name=self.__class__.__name__
        )
        self.quant_noise = getattr(args, "quant_noise_pq", 0)
        self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8)

        self.cross_self_attention = getattr(args, "cross_self_attention", False)

        self.self_attn = self.build_self_attention(
            self.embed_dim,
            args,
            add_bias_kv=add_bias_kv,
            add_zero_attn=add_zero_attn,
        )
        self.self_attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
        self.cross_attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
        self.nh = self.self_attn.num_heads
        self.head_dim = self.self_attn.head_dim

        self.activation_fn = utils.get_activation_fn(
            activation=str(args.activation_fn)
            if getattr(args, "activation_fn", None) is not None
            else "relu"
        )
        activation_dropout_p = getattr(args, "activation_dropout", 0) or 0
        if activation_dropout_p == 0:
            # for backwards compatibility with models that use args.relu_dropout
            activation_dropout_p = getattr(args, "relu_dropout", 0) or 0
        self.activation_dropout_module = FairseqDropout(
            float(activation_dropout_p), module_name=self.__class__.__name__
        )
        self.normalize_before = args.decoder_normalize_before

        # use layerNorm rather than FusedLayerNorm for exporting.
        # char_inputs can be used to determint this.
        # TODO  remove this once we update apex with the fix
        export = getattr(args, "char_inputs", False)
        self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)

        if no_encoder_attn:
            self.encoder_attn = None
            self.encoder_attn_layer_norm = None
        else:
            self.encoder_attn = self.build_encoder_attention(self.embed_dim, args)
            self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)

        self.ffn_layernorm = LayerNorm(args.decoder_ffn_embed_dim) if getattr(args, 'scale_fc', False) else None
        self.w_resid = nn.Parameter(torch.ones(self.embed_dim, ), requires_grad=True) if getattr(args, 'scale_resids', False) else None

        self.fc1 = self.build_fc1(
            self.embed_dim,
            args.decoder_ffn_embed_dim,
            self.quant_noise,
            self.quant_noise_block_size,
        )
        self.fc2 = self.build_fc2(
            args.decoder_ffn_embed_dim,
            self.embed_dim,
            self.quant_noise,
            self.quant_noise_block_size,
        )

        self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
        self.need_attn = True

        self.onnx_trace = False

        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()

    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
        return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)

    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
        return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)

    def build_self_attention(
        self, embed_dim, args, add_bias_kv=False, add_zero_attn=False
    ):
        return MultiheadAttention(
            embed_dim,
            args.decoder_attention_heads,
            dropout=args.attention_dropout,
            add_bias_kv=add_bias_kv,
            add_zero_attn=add_zero_attn,
            self_attention=not getattr(args, "cross_self_attention", False),
            q_noise=self.quant_noise,
            qn_block_size=self.quant_noise_block_size,
            scale_factor=args.attn_scale_factor,
            scale_heads=getattr(args, 'scale_heads', False),
            qk_norm=getattr(args, 'qk_norm', False),
        )

    def build_encoder_attention(self, embed_dim, args):
        return MultiheadAttention(
            embed_dim,
            args.decoder_attention_heads,
            kdim=getattr(args, "encoder_embed_dim", None),
            vdim=getattr(args, "encoder_embed_dim", None),
            dropout=args.attention_dropout,
            encoder_decoder_attention=True,
            q_noise=self.quant_noise,
            qn_block_size=self.quant_noise_block_size,
            scale_factor=args.attn_scale_factor,
            scale_heads=getattr(args, 'scale_heads', False),
            qk_norm=getattr(args, 'qk_norm', False),
        )

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def residual_connection(self, x, residual):
        return residual + self.drop_path(x)

    def forward(
        self,
        x,
        encoder_out: Optional[torch.Tensor] = None,
        encoder_padding_mask: Optional[torch.Tensor] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        prev_self_attn_state: Optional[List[torch.Tensor]] = None,
        prev_attn_state: Optional[List[torch.Tensor]] = None,
        self_attn_mask: Optional[torch.Tensor] = None,
        self_attn_padding_mask: Optional[torch.Tensor] = None,
        need_attn: bool = False,
        need_head_weights: bool = False,
        self_attn_bias: Optional[Tensor] = None,
        cross_attn_bias: Optional[Tensor] = None,
        prompt_kv: Optional[Tensor] = None,
    ):
        """
        Args:
            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_padding_mask (ByteTensor, optional): binary
                ByteTensor of shape `(batch, src_len)` where padding
                elements are indicated by ``1``.
            need_attn (bool, optional): return attention weights
            need_head_weights (bool, optional): return attention weights
                for each head (default: return average over heads).

        Returns:
            encoded output of shape `(seq_len, batch, embed_dim)`
        """
        if need_head_weights:
            need_attn = True

        residual = x
        if self.normalize_before:
            x = self.self_attn_layer_norm(x)
        if prev_self_attn_state is not None:
            prev_key, prev_value = prev_self_attn_state[:2]
            saved_state: Dict[str, Optional[Tensor]] = {
                "prev_key": prev_key,
                "prev_value": prev_value,
            }
            if len(prev_self_attn_state) >= 3:
                saved_state["prev_key_padding_mask"] = prev_self_attn_state[2]
            assert incremental_state is not None
            self.self_attn._set_input_buffer(incremental_state, saved_state)
        _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state)
        if self.cross_self_attention and not (
            incremental_state is not None
            and _self_attn_input_buffer is not None
            and "prev_key" in _self_attn_input_buffer
        ):
            if self_attn_mask is not None:
                assert encoder_out is not None
                self_attn_mask = torch.cat(
                    (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1
                )
            if self_attn_padding_mask is not None:
                if encoder_padding_mask is None:
                    assert encoder_out is not None
                    encoder_padding_mask = self_attn_padding_mask.new_zeros(
                        encoder_out.size(1), encoder_out.size(0)
                    )
                self_attn_padding_mask = torch.cat(
                    (encoder_padding_mask, self_attn_padding_mask), dim=1
                )
            assert encoder_out is not None
            y = torch.cat((encoder_out, x), dim=0)
        else:
            y = x

        x, attn = self.self_attn(
            query=x,
            key=y,
            value=y,
            key_padding_mask=self_attn_padding_mask,
            incremental_state=incremental_state,
            need_weights=False,
            attn_mask=self_attn_mask,
            attn_bias=self_attn_bias,
            prompt_kv=prompt_kv
        )
        if self.self_attn_ln is not None:
            x = self.self_attn_ln(x)
        x = self.dropout_module(x)
        x = self.residual_connection(x, residual)
        if not self.normalize_before:
            x = self.self_attn_layer_norm(x)

        if self.encoder_attn is not None and encoder_out is not None:
            residual = x
            if self.normalize_before:
                x = self.encoder_attn_layer_norm(x)
            if prev_attn_state is not None:
                prev_key, prev_value = prev_attn_state[:2]
                saved_state: Dict[str, Optional[Tensor]] = {
                    "prev_key": prev_key,
                    "prev_value": prev_value,
                }
                if len(prev_attn_state) >= 3:
                    saved_state["prev_key_padding_mask"] = prev_attn_state[2]
                assert incremental_state is not None
                self.encoder_attn._set_input_buffer(incremental_state, saved_state)

            x, attn = self.encoder_attn(
                query=x,
                key=encoder_out,
                value=encoder_out,
                key_padding_mask=encoder_padding_mask,
                incremental_state=incremental_state,
                static_kv=True,
                need_weights=need_attn or (not self.training and self.need_attn),
                need_head_weights=need_head_weights,
                attn_bias=cross_attn_bias
            )
            if self.cross_attn_ln is not None:
                x = self.cross_attn_ln(x)
            x = self.dropout_module(x)
            x = self.residual_connection(x, residual)
            if not self.normalize_before:
                x = self.encoder_attn_layer_norm(x)

        residual = x
        if self.normalize_before:
            x = self.final_layer_norm(x)

        x = self.activation_fn(self.fc1(x))
        x = self.activation_dropout_module(x)
        if self.ffn_layernorm is not None:
            x = self.ffn_layernorm(x)
        x = self.fc2(x)
        x = self.dropout_module(x)
        if self.use_adapter == True:
            x = self.adapter(x)

        if self.w_resid is not None:
            residual = torch.mul(self.w_resid, residual)
        x = self.residual_connection(x, residual)
        if not self.normalize_before:
            x = self.final_layer_norm(x)
        if self.onnx_trace and incremental_state is not None:
            saved_state = self.self_attn._get_input_buffer(incremental_state)
            assert saved_state is not None
            if self_attn_padding_mask is not None:
                self_attn_state = [
                    saved_state["prev_key"],
                    saved_state["prev_value"],
                    saved_state["prev_key_padding_mask"],
                ]
            else:
                self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]]
            return x, attn, self_attn_state
        return x, attn, None

    def make_generation_fast_(self, need_attn: bool = False, **kwargs):
        self.need_attn = need_attn

    def upgrade_state_dict_named(self, state_dict, name):
        """
        Rename layer norm states from `...layer_norms.0.weight` to
        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
        `...final_layer_norm.weight`
        """
        # update layer norms
        layer_norm_map = {
            "0": "self_attn_layer_norm",
            "1": "encoder_attn_layer_norm",
            "2": "final_layer_norm",
        }
        for old, new in layer_norm_map.items():
            for m in ("weight", "bias"):
                k = "{}.layer_norms.{}.{}".format(name, old, m)
                if k in state_dict:
                    state_dict[
                        "{}.{}.{}".format(name, new, m)
                    ] = state_dict[k]
                    del state_dict[k]
                if "{}.{}.{}".format(name, new, m) not in state_dict and "{}.{}".format(new, m) in self.state_dict():
                    state_dict[
                        "{}.{}.{}".format(name, new, m)
                    ] = self.state_dict()["{}.{}".format(new, m)]

        prefix = name + "." if name != "" else ""
        for param_name, param_tensor in self.state_dict().items():
            if (prefix + param_name) not in state_dict:
                state_dict[prefix + param_name] = self.state_dict()[param_name]