Update config and add decoding code (buggy)

It loads all weights, and does a forward pass, but logits have NaNs

Need to find the bug

config.json

@@ -23,8 +23,11 @@
     "relative_attention_max_distance": 128,
     "relative_attention_num_buckets": 32,
     "task_specific_params": {},
-    "tie_word_embeddings": false,
+    "tie_word_embeddings": true,
     "transformers_version": "4.23.1",
     "use_cache": true,
-    "vocab_size": 256512
+    "vocab_size": 256512,
+    "parallel_layers": true,
+    "has_relative_attention_bias": false,
+    "multi_query_attention": true
 }

decoder_only_t5/config.py

@@ -0,0 +1,11 @@
+from transformers.models.t5.configuration_t5 import T5Config
+
+
+class DecoderOnlyT5Config(T5Config):
+    is_decoder_only = True
+    # whether to call attention and mlp in parallel.
+    # https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L384
+    parallel_layers = True
+    has_relative_attention_bias = False
+    # https://arxiv.org/abs/1911.02150
+    multi_query_attention = True

decoder_only_t5/modeling.py

@@ -0,0 +1,620 @@
+import copy
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.models.t5 import modeling_t5
+from transformers.modeling_outputs import Seq2SeqLMOutput
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+from decoder_only_t5.config import DecoderOnlyT5Config
+
+
+logger = logging.get_logger(__name__)
+_CONFIG_FOR_DOC = "DecoderOnlyT5Config"
+
+
+class DecoderOnlyT5LayerFF(modeling_t5.T5LayerFF):
+    def __init__(self, config: DecoderOnlyT5Config):
+        super(modeling_t5.T5LayerFF, self).__init__()
+        if config.is_gated_act:
+            self.DenseReluDense = modeling_t5.T5DenseGatedActDense(config)
+        else:
+            self.DenseReluDense = modeling_t5.T5DenseActDense(config)
+
+        if not config.parallel_layers:
+            self.layer_norm = modeling_t5.T5LayerNorm(
+                config.d_model, eps=config.layer_norm_epsilon
+            )
+        else:
+            self.layer_norm = nn.Identity()
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+
+# https://github.com/huggingface/transformers/blob/7ee995fd9c692761c4601ddbffa2ac2ec9f27b0b/src/transformers/models/llama/modeling_llama.py#L263
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class DecoderOnlyT5Attention(modeling_t5.T5Attention):
+    """
+    Supports both multi-head and multi-query attention.
+    https://arxiv.org/abs/1911.02150
+    https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/components/attention/dense_attention.py#L292
+    """
+
+    def __init__(self, config: DecoderOnlyT5Config, has_relative_attention_bias=False):
+        super(modeling_t5.T5Attention, self).__init__()
+        self.is_decoder = config.is_decoder
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.relative_attention_num_buckets = config.relative_attention_num_buckets
+        self.relative_attention_max_distance = config.relative_attention_max_distance
+        self.d_model = config.d_model
+        self.key_value_proj_dim = config.d_kv
+        self.n_heads = config.num_heads
+        self.n_kv_heads = 1 if config.multi_query_attention else self.n_heads
+        self.n_kv_groups = self.n_heads // self.n_kv_heads
+        self.dropout = config.dropout_rate
+        self.inner_dim = self.n_heads * self.key_value_proj_dim
+        self.kv_inner_dim = self.n_kv_heads * self.key_value_proj_dim
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+
+        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.k = nn.Linear(self.d_model, self.kv_inner_dim, bias=False)
+        self.v = nn.Linear(self.d_model, self.kv_inner_dim, bias=False)
+        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(
+                self.relative_attention_num_buckets, self.n_heads
+            )
+        self.pruned_heads = set()
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        mask=None,
+        key_value_states=None,
+        position_bias=None,
+        past_key_value=None,
+        layer_head_mask=None,
+        query_length=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        """
+        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+        """
+        # Input is (batch_size, seq_length, dim)
+        # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
+        # past_key_value[0] is (batch_size, n_kv_heads, q_len - 1, dim_per_head)
+        batch_size, seq_length = hidden_states.shape[:2]
+
+        real_seq_length = seq_length
+
+        if past_key_value is not None:
+            if len(past_key_value) != 2:
+                raise ValueError(
+                    f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
+                )
+            real_seq_length += (
+                past_key_value[0].shape[2] if query_length is None else query_length
+            )
+
+        key_length = (
+            real_seq_length if key_value_states is None else key_value_states.shape[1]
+        )
+
+        def shape(states, n_heads):
+            """projection"""
+            return states.view(
+                batch_size, -1, n_heads, self.key_value_proj_dim
+            ).transpose(1, 2)
+
+        def unshape(states):
+            """reshape"""
+            return (
+                states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
+            )
+
+        def project(hidden_states, proj_layer, key_value_states, past_key_value):
+            """projects hidden states correctly to key/query states"""
+            if key_value_states is None:
+                # self-attn
+                # (batch_size, n_kv_heads, seq_length, dim_per_head)
+                hidden_states = shape(proj_layer(hidden_states), self.n_kv_heads)
+            elif past_key_value is None:
+                # cross-attn
+                # (batch_size, n_kv_heads, seq_length, dim_per_head)
+                hidden_states = shape(proj_layer(key_value_states), self.n_kv_heads)
+
+            if past_key_value is not None:
+                if key_value_states is None:
+                    # self-attn
+                    # (batch_size, n_kv_heads, key_length, dim_per_head)
+                    hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
+                elif past_key_value.shape[2] != key_value_states.shape[1]:
+                    # checking that the `sequence_length` of the `past_key_value` is the same as
+                    # the provided `key_value_states` to support prefix tuning
+                    # cross-attn
+                    # (batch_size, n_kv_heads, seq_length, dim_per_head)
+                    hidden_states = shape(proj_layer(key_value_states), self.n_kv_heads)
+                else:
+                    # cross-attn
+                    hidden_states = past_key_value
+            return hidden_states
+
+        # get query states
+        query_states = shape(
+            self.q(hidden_states), self.n_heads
+        )  # (batch_size, n_heads, seq_length, dim_per_head)
+
+        # get key/value states
+        key_states = project(
+            hidden_states,
+            self.k,
+            key_value_states,
+            past_key_value[0] if past_key_value is not None else None,
+        )
+        value_states = project(
+            hidden_states,
+            self.v,
+            key_value_states,
+            past_key_value[1] if past_key_value is not None else None,
+        )
+
+        # compute scores
+        scores = torch.matmul(
+            query_states, repeat_kv(key_states, self.n_kv_groups).transpose(3, 2)
+        )  # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
+
+        if position_bias is None:
+            if not self.has_relative_attention_bias:
+                position_bias = torch.zeros(
+                    (1, self.n_heads, real_seq_length, key_length),
+                    device=scores.device,
+                    dtype=scores.dtype,
+                )
+                if self.gradient_checkpointing and self.training:
+                    position_bias.requires_grad = True
+            else:
+                position_bias = self.compute_bias(
+                    real_seq_length, key_length, device=scores.device
+                )
+
+            # if key and values are already calculated
+            # we want only the last query position bias
+            if past_key_value is not None:
+                position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
+
+            if mask is not None:
+                position_bias = (
+                    position_bias + mask
+                )  # (batch_size, n_heads, seq_length, key_length)
+
+        if self.pruned_heads:
+            mask = torch.ones(position_bias.shape[1])
+            mask[list(self.pruned_heads)] = 0
+            position_bias_masked = position_bias[:, mask.bool()]
+        else:
+            position_bias_masked = position_bias
+
+        scores += position_bias_masked
+        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
+            scores
+        )  # (batch_size, n_heads, seq_length, key_length)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )  # (batch_size, n_heads, seq_length, key_length)
+
+        # Mask heads if we want to
+        if layer_head_mask is not None:
+            attn_weights = attn_weights * layer_head_mask
+
+        attn_output = unshape(
+            torch.matmul(attn_weights, value_states)
+        )  # (batch_size, seq_length, dim)
+        attn_output = self.o(attn_output)
+
+        present_key_value_state = (
+            (key_states, value_states) if (self.is_decoder and use_cache) else None
+        )
+        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
+
+        if output_attentions:
+            outputs = outputs + (attn_weights,)
+        return outputs
+
+
+class DecoderOnlyT5LayerSelfAttention(modeling_t5.T5LayerSelfAttention):
+    def __init__(self, config, has_relative_attention_bias=False):
+        super(modeling_t5.T5LayerSelfAttention, self).__init__()
+        self.SelfAttention = DecoderOnlyT5Attention(
+            config, has_relative_attention_bias=has_relative_attention_bias
+        )
+        self.layer_norm = modeling_t5.T5LayerNorm(
+            config.d_model, eps=config.layer_norm_epsilon
+        )
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.parallel_layers = config.parallel_layers
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_bias=None,
+        layer_head_mask=None,
+        past_key_value=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        if not self.parallel_layers:
+            x = self.layer_norm(hidden_states)
+        else:
+            x = hidden_states
+        attention_output = self.SelfAttention(
+            x,
+            mask=attention_mask,
+            position_bias=position_bias,
+            layer_head_mask=layer_head_mask,
+            past_key_value=past_key_value,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        if not self.parallel_layers:
+            # When parallel_layers is True, the residual connection is applied
+            # in the decoder block instead of here.
+            hidden_states = hidden_states + self.dropout(attention_output[0])
+        else:
+            hidden_states = attention_output[0]
+        outputs = (hidden_states,) + attention_output[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+class DecoderOnlyT5Block(modeling_t5.T5Block):
+    def __init__(self, config, has_relative_attention_bias=False):
+        super(modeling_t5.T5Block, self).__init__()
+        self.is_decoder = config.is_decoder
+        self.is_decoder_only = config.is_decoder_only
+        self.layer = nn.ModuleList()
+        self.layer.append(
+            DecoderOnlyT5LayerSelfAttention(
+                config, has_relative_attention_bias=has_relative_attention_bias
+            )
+        )
+        if self.is_decoder:
+            if config.is_decoder_only:
+                self.layer.append(nn.Identity())
+            else:
+                self.layer.append(modeling_t5.T5LayerCrossAttention(config))
+        self.parallel_layers = config.parallel_layers
+        self.layer.append(DecoderOnlyT5LayerFF(config))
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_bias=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        encoder_decoder_position_bias=None,
+        layer_head_mask=None,
+        cross_attn_layer_head_mask=None,
+        past_key_value=None,
+        use_cache=False,
+        output_attentions=False,
+        return_dict=True,
+    ):
+        if past_key_value is not None:
+            if not self.is_decoder:
+                logger.warning(
+                    "`past_key_values` is passed to the encoder. Please make sure this is intended."
+                )
+            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
+
+            if len(past_key_value) != expected_num_past_key_values:
+                raise ValueError(
+                    f"There should be {expected_num_past_key_values} past states. "
+                    f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
+                    f"Got {len(past_key_value)} past key / value states"
+                )
+
+            self_attn_past_key_value = past_key_value[:2]
+            cross_attn_past_key_value = past_key_value[2:]
+        else:
+            self_attn_past_key_value, cross_attn_past_key_value = None, None
+
+        ff_layer = self.layer[-1]
+        if self.parallel_layers:
+            x = self.layer[0].layer_norm(hidden_states)
+            ff_output = ff_layer(hidden_states)
+        else:
+            x = hidden_states
+
+        self_attention_outputs = self.layer[0](
+            x,
+            attention_mask=attention_mask,
+            position_bias=position_bias,
+            layer_head_mask=layer_head_mask,
+            past_key_value=self_attn_past_key_value,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        x, present_key_value_state = self_attention_outputs[:2]
+        attention_outputs = self_attention_outputs[
+            2:
+        ]  # Keep self-attention outputs and relative position weights
+
+        # clamp inf values to enable fp16 training
+        if x.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(x).any(),
+                torch.finfo(x.dtype).max - 1000,
+                torch.finfo(x.dtype).max,
+            )
+            x = torch.clamp(x, min=-clamp_value, max=clamp_value)
+
+        do_cross_attention = (
+            self.is_decoder
+            and not self.is_decoder_only
+            and encoder_hidden_states is not None
+        )
+        if do_cross_attention:
+            # the actual query length is unknown for cross attention
+            # if using past key value states. Need to inject it here
+            if present_key_value_state is not None:
+                query_length = present_key_value_state[0].shape[2]
+            else:
+                query_length = None
+
+            cross_attention_outputs = self.layer[1](
+                x,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                position_bias=encoder_decoder_position_bias,
+                layer_head_mask=cross_attn_layer_head_mask,
+                past_key_value=cross_attn_past_key_value,
+                query_length=query_length,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+            )
+            x = cross_attention_outputs[0]
+
+            # clamp inf values to enable fp16 training
+            if x.dtype == torch.float16:
+                clamp_value = torch.where(
+                    torch.isinf(x).any(),
+                    torch.finfo(x.dtype).max - 1000,
+                    torch.finfo(x.dtype).max,
+                )
+                x = torch.clamp(x, min=-clamp_value, max=clamp_value)
+
+            # Combine self attn and cross attn key value states
+            if present_key_value_state is not None:
+                present_key_value_state = (
+                    present_key_value_state + cross_attention_outputs[1]
+                )
+
+            # Keep cross-attention outputs and relative position weights
+            attention_outputs = attention_outputs + cross_attention_outputs[2:]
+
+        if self.parallel_layers:
+            # https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L295
+            hidden_states = x + ff_output
+            hidden_states *= 2**-0.5
+            hidden_states = hidden_states + self.layer[0].dropout(hidden_states)
+        else:
+            hidden_states = ff_layer(x)
+
+        # clamp inf values to enable fp16 training
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(hidden_states).any(),
+                torch.finfo(hidden_states.dtype).max - 1000,
+                torch.finfo(hidden_states.dtype).max,
+            )
+            hidden_states = torch.clamp(
+                hidden_states, min=-clamp_value, max=clamp_value
+            )
+
+        outputs = (hidden_states,)
+
+        if use_cache:
+            outputs = outputs + (present_key_value_state,) + attention_outputs
+        else:
+            outputs = outputs + attention_outputs
+
+        return outputs  # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
+
+
+class DecoderOnlyT5Stack(modeling_t5.T5Stack):
+    def __init__(self, config, embed_tokens=None):
+        super(modeling_t5.T5Stack, self).__init__(config)
+
+        self.embed_tokens = embed_tokens
+        self.is_decoder = config.is_decoder
+
+        self.block = nn.ModuleList(
+            [
+                DecoderOnlyT5Block(
+                    config,
+                    has_relative_attention_bias=(
+                        config.has_relative_attention_bias and bool(i == 0)
+                    ),
+                )
+                for i in range(config.num_layers)
+            ]
+        )
+        if not config.parallel_layers:
+            self.final_layer_norm = modeling_t5.T5LayerNorm(
+                config.d_model, eps=config.layer_norm_epsilon
+            )
+        else:
+            self.final_layer_norm = nn.Identity()
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.gradient_checkpointing = False
+
+
+class DecoderOnlyT5Model(modeling_t5.T5ForConditionalGeneration):
+    def __init__(self, config: DecoderOnlyT5Config):
+        super(modeling_t5.T5ForConditionalGeneration, self).__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+        assert (
+            self.config.num_layers == 0
+        ), "Decoder only model cannot have encoder layers"
+        self.encoder = None
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = DecoderOnlyT5Stack(decoder_config, self.shared)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+    @add_start_docstrings_to_model_forward(modeling_t5.T5_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        _input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        _inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
+            labels in `[0, ..., config.vocab_size]`
+
+        Returns:
+
+        Examples:
+
+        ```"""
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+
+        if (
+            labels is not None
+            and decoder_input_ids is None
+            and decoder_inputs_embeds is None
+        ):
+            # get decoder inputs from shifting lm labels to the right
+            decoder_input_ids = self._shift_right(labels)
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+            if decoder_input_ids is not None:
+                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(self.decoder.first_device)
+            if decoder_attention_mask is not None:
+                decoder_attention_mask = decoder_attention_mask.to(
+                    self.decoder.first_device
+                )
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            # encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+            self.lm_head = self.lm_head.to(self.decoder.first_device)
+            sequence_output = sequence_output.to(self.lm_head.weight.device)
+
+        if self.config.tie_word_embeddings:
+            # Rescale output before projecting on vocab
+            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            sequence_output = sequence_output * (self.model_dim**-0.5)
+
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            # move labels to correct device to enable PP
+            labels = labels.to(lm_logits.device)
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+        )