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config.json

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+{
+    "architectures": [
+      "NorT5ForConditionalGeneration"
+    ],
+    "auto_map": {
+      "AutoConfig": "configuration_nort5.NorT5Config",
+      "AutoModel": "modeling_nort5.NorT5Model",
+      "AutoModelForSeq2SeqLM": "modeling_nort5.NorT5ForConditionalGeneration",
+      "AutoModelForConditionalGeneration": "modeling_nort5.NorT5ForConditionalGeneration"
+    },
+    "attention_probs_dropout_prob": 0.0,
+    "bos_token_id": 5,
+    "cls_token_id": 2,
+    "eos_token_id": 6,
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 2048,
+    "layer_norm_eps": 1e-07,
+    "max_position_embeddings": 512,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "output_all_encoded_layers": true,
+    "pad_token_id": 0,
+    "position_bucket_size": 32,
+    "sep_token_id": 3,
+    "torch_dtype": "float32",
+    "vocab_size": 50000,
+    "max_length": 512,
+    "max_new_tokens": 256,
+    "is_encoder_decoder": true
+  }
+  

configuration_nort5.py

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+from transformers.configuration_utils import PretrainedConfig
+
+
+class NorT5Config(PretrainedConfig):
+    """Configuration class to store the configuration of a `NorT5`.
+    """
+    def __init__(
+        self,
+        vocab_size=50000,
+        attention_probs_dropout_prob=0.0,
+        hidden_dropout_prob=0.0,
+        hidden_size=768,
+        intermediate_size=2048,
+        max_position_embeddings=512,
+        position_bucket_size=32,
+        num_attention_heads=12,
+        num_hidden_layers=12,
+        layer_norm_eps=1.0e-7,
+        output_all_encoded_layers=True,
+        pad_token_id=0,
+        cls_token_id=2,
+        sep_token_id=3,
+        bos_token_id=5,
+        eos_token_id=6,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.output_all_encoded_layers = output_all_encoded_layers
+        self.position_bucket_size = position_bucket_size
+        self.layer_norm_eps = layer_norm_eps
+        self.pad_token_id = pad_token_id
+        self.cls_token_id = cls_token_id
+        self.sep_token_id = sep_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+

generation_config.json

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+{
+    "_from_model_config": true,
+    "decoder_start_token_id": 5,
+    "eos_token_id": 6,
+    "pad_token_id": 0
+  }
+    

modeling_nort5.py

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+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.pytorch_utils import softmax_backward_data
+from torch.utils import checkpoint
+
+from .configuration_nort5 import NorT5Config
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import gelu_new
+from transformers.modeling_outputs import (
+    Seq2SeqModelOutput, Seq2SeqLMOutput, BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions
+)
+
+
+class Encoder(nn.Module):
+    def __init__(self, config, activation_checkpointing=False):
+        super().__init__()
+        self.main_input_name = "input_ids"
+
+        self.relative_embedding = RelativeEmbedding(config)
+        self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+        for i, layer in enumerate(self.layers):
+            layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+            layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+
+        self.activation_checkpointing = activation_checkpointing
+    
+    def forward(self, hidden_states, attention_mask):
+        relative_embedding = self.relative_embedding()
+        hidden_states, attention_probs = [hidden_states], []
+
+        for layer in self.layers:
+            if self.activation_checkpointing:
+                hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding)
+            else:
+                hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding)
+
+            hidden_states.append(hidden_state)
+            attention_probs.append(attention_p)
+
+        return hidden_states, attention_probs
+
+
+class Decoder(nn.Module):
+    def __init__(self, config, activation_checkpointing=False):
+        super().__init__()
+        self.self_relative_embedding = RelativeEmbedding(config)
+        self.cross_relative_embedding = RelativeEmbedding(config)
+        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+        for i, layer in enumerate(self.layers):
+            layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+            layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+
+        self.activation_checkpointing = activation_checkpointing
+
+    def forward(self, x, encoder_output, encoder_padding_mask, past_key_values=None):
+        self_relative_embedding = self.self_relative_embedding()
+        cross_relative_embedding = self.cross_relative_embedding()
+
+        if past_key_values is None:
+            autoreg_mask = torch.triu(
+                torch.full((x.size(0), x.size(0)), True, device=x.device),
+                diagonal=1
+            )
+        else:
+            autoreg_mask = None
+
+        # initialize past_key_values with `None` if past does not exist
+        if past_key_values is None:
+            past_key_values = [None] * len(self.layers)
+
+        hidden_states, self_attention_probs, cross_attention_probs, key_value_states = [x], [], [], []
+        for layer, past_key_value in zip(self.layers, past_key_values):
+            if self.activation_checkpointing:
+                hidden_state, self_attention_p, cross_attention_p, key_value_state = checkpoint.checkpoint(layer, hidden_states[-1], autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=None)
+            else:
+                hidden_state, self_attention_p, cross_attention_p, key_value_state = layer(hidden_states[-1], autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=past_key_value)
+
+            hidden_states.append(hidden_state)
+            self_attention_probs.append(self_attention_p)
+            cross_attention_probs.append(cross_attention_p)
+            key_value_states.append(key_value_state)
+
+        return hidden_states, self_attention_probs, cross_attention_probs, key_value_states
+
+
+class MaskClassifier(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.nonlinearity = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Dropout(config.hidden_dropout_prob),
+            nn.Linear(config.hidden_size, config.vocab_size)
+        )
+        self.initialize(config.hidden_size)
+
+    def initialize(self, hidden_size):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.nonlinearity[-1].bias.data.zero_()
+
+    def forward(self, x):
+        x = self.nonlinearity(x)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention = Attention(config, is_cross_attention=False)
+        self.mlp = FeedForward(config)
+
+    def forward(self, x, padding_mask, relative_embedding):
+        attention_output, attention_probs, _ = self.attention(x, x, padding_mask, relative_embedding)
+        x = x + attention_output
+        x = x + self.mlp(x)
+        return x, attention_probs
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self_attention = Attention(config, is_cross_attention=False)
+        self.cross_attention = Attention(config, is_cross_attention=True)
+        self.mlp = FeedForward(config)
+
+    def forward(self, x, autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=None):
+        query_offset = 0
+        if past_key_value is not None:
+            self_attn_past_key_value = past_key_value[:2]
+            cross_attn_past_key_value = past_key_value[2:]
+            query_offset = self_attn_past_key_value[0].size(2)
+        else:
+            self_attn_past_key_value, cross_attn_past_key_value = None, None
+
+        x_, self_attention_probs, self_key_value_state = self.self_attention(x, x, autoreg_mask, self_relative_embedding, past_key_value=self_attn_past_key_value, query_offset=query_offset)
+        x = x + x_
+        x_, cross_attention_probs, cross_key_value_state = self.cross_attention(x, encoder_output, encoder_padding_mask, cross_relative_embedding, past_key_value=cross_attn_past_key_value, query_offset=query_offset)
+        x = x + x_
+        x = x + self.mlp(x)
+
+        return x, self_attention_probs, cross_attention_probs, self_key_value_state + cross_key_value_state
+
+
+class GeGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        x = x * gelu_new(gate)
+        return x
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
+            GeGLU(),
+            nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
+            nn.Dropout(config.hidden_dropout_prob)
+        )
+        self.initialize(config.hidden_size)
+
+    def initialize(self, hidden_size):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class MaskedSoftmax(torch.autograd.Function):
+    @staticmethod
+    def forward(self, x, mask, dim):
+        self.dim = dim
+        if mask is not None:
+            x.masked_fill_(mask, float('-inf'))
+        x = torch.softmax(x, self.dim)
+        if mask is not None:
+            x.masked_fill_(mask, 0.0)
+        self.save_for_backward(x)
+        return x
+
+    @staticmethod
+    def backward(self, grad_output):
+        output, = self.saved_tensors
+        input_grad = softmax_backward_data(self, grad_output, output, self.dim, output)
+        return input_grad, None, None
+
+
+class Attention(nn.Module):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__()
+
+        self.config = config
+        self.is_cross_attention = is_cross_attention
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}")
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_size = config.hidden_size // config.num_attention_heads
+
+        self.in_proj_q = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+        self.in_proj_k = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+        self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+        self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
+        self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
+
+        position_indices = torch.arange(512, dtype=torch.long).unsqueeze(1) \
+            - torch.arange(512, dtype=torch.long).unsqueeze(0)
+        position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, 512)
+        position_indices = config.position_bucket_size - 1 + position_indices
+        self.register_buffer("position_indices", position_indices, persistent=False)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.scale = 1.0 / math.sqrt(3 * self.head_size)
+        self.initialize()
+
+    def make_log_bucket_position(self, relative_pos, bucket_size, max_position):
+        sign = torch.sign(relative_pos)
+        mid = bucket_size // 2
+        abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
+        log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
+        bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
+        return bucket_pos
+
+    def initialize(self):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.in_proj_q.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.in_proj_k.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.in_proj_q.bias.data.zero_()
+        self.in_proj_k.bias.data.zero_()
+        self.in_proj_v.bias.data.zero_()
+        self.out_proj.bias.data.zero_()
+
+    def forward(self, q, kv, attention_mask, relative_embedding, past_key_value=None, query_offset=0):
+        key_len, batch_size, _ = kv.size()
+        query_len, _, _ = q.size()
+
+        if not self.is_cross_attention or past_key_value is None or past_key_value[0].size(1) != kv.size(0):
+            kv = self.pre_layer_norm(kv)
+            key = self.in_proj_k(kv)  # shape: [T, B, D]
+            value = self.in_proj_v(kv)  # shape: [T, B, D]
+            key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)  # shape: [BxH, T, D]
+            value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)  # shape: [BxH, T, D]
+
+        if past_key_value is not None:
+            if not self.is_cross_attention:
+                key = torch.cat([past_key_value[0].flatten(0, 1), key], dim=1)
+                value = torch.cat([past_key_value[1].flatten(0, 1), value], dim=1)
+                key_len = key.size(1)
+            elif past_key_value[0].size(1) == kv.size(0):
+                key = past_key_value[0].flatten(0, 1)
+                value = past_key_value[1].flatten(0, 1)
+
+        if self.position_indices.size(0) < max(query_len, key_len):
+            position_indices = torch.arange(max(query_len, key_len), dtype=torch.long).unsqueeze(1) \
+                - torch.arange(max(query_len, key_len), dtype=torch.long).unsqueeze(0)
+            position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512)
+            position_indices = self.config.position_bucket_size - 1 + position_indices
+            self.register_buffer("position_indices", position_indices.to(q.device), persistent=False)
+
+        q = self.pre_layer_norm(q)
+        query = self.in_proj_q(q)  # shape: [T, B, D]
+        query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+
+        attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale)
+        
+        query_pos = self.in_proj_q(self.dropout(relative_embedding))  # shape: [2T-1, D]
+        query_pos = query_pos.view(-1, self.num_heads, self.head_size)  # shape: [2T-1, H, D]
+        key_pos = self.in_proj_k(self.dropout(relative_embedding))  # shape: [2T-1, D]
+        key_pos = key_pos.view(-1, self.num_heads, self.head_size)  # shape: [2T-1, H, D]
+
+        query_ = query.view(batch_size, self.num_heads, query_len, self.head_size)
+        key_ = key.view(batch_size, self.num_heads, key_len, self.head_size)
+        
+        attention_c_p = torch.einsum("bhqd,khd->bhqk", query_, key_pos.squeeze(1) * self.scale)
+        attention_p_c = torch.einsum("bhkd,qhd->bhqk", key_ * self.scale, query_pos.squeeze(1))
+        position_indices = self.position_indices[query_offset:query_offset+query_len, :key_len].expand(batch_size, self.num_heads, -1, -1)
+        attention_c_p = attention_c_p.gather(3, position_indices)
+        attention_p_c = attention_p_c.gather(2, position_indices)
+        
+        attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len)
+        attention_scores.add_(attention_c_p)
+        attention_scores.add_(attention_p_c)
+
+        attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
+
+        attention_probs = self.dropout(attention_probs)
+        context = torch.bmm(attention_probs.flatten(0, 1), value)  # shape: [B*H, Q, D]
+        context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size)  # shape: [Q, B, H*D]
+        context = self.out_proj(context)
+        context = self.post_layer_norm(context)
+        context = self.dropout(context)
+
+        key = key.detach().unflatten(0, (-1, self.num_heads))
+        value = value.detach().unflatten(0, (-1, self.num_heads))
+
+        return context, attention_probs.detach(), (key, value)
+
+
+class WordEmbedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.initialize()
+
+    def initialize(self):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, input_ids):
+        return self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
+
+
+class RelativeEmbedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
+        self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.initialize(config.hidden_size)
+
+    def initialize(self, hidden_size):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self):
+        return self.relative_layer_norm(self.relative_embedding)
+
+
+#
+# HuggingFace wrappers
+#
+
+class NorT5PreTrainedModel(PreTrainedModel):
+    config_class = NorT5Config
+    base_model_prefix = "norT5"
+    supports_gradient_checkpointing = True
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, Encoder):
+            module.activation_checkpointing = value
+
+    def _init_weights(self, module):
+        pass  # everything is already initialized
+
+
+class NorT5Model(NorT5PreTrainedModel):
+    def __init__(self, config, add_lm_layer=False, add_decoder=True):
+        super().__init__(config)
+        self.config = config
+
+        self.cls_token_id = config.cls_token_id
+        self.sep_token_id = config.sep_token_id
+        self.bos_token_id = config.bos_token_id
+        self.eos_token_id = config.eos_token_id
+        self.pad_token_id = config.pad_token_id
+
+        self.embedding = WordEmbedding(config)
+        self.encoder = Encoder(config, activation_checkpointing=False)
+        self.decoder = Decoder(config, activation_checkpointing=False) if add_decoder else None
+        self.classifier = MaskClassifier(config) if add_lm_layer else None
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def get_encoder(self):
+        class EncoderWrapper:
+            def __call__(cls, *args, **kwargs):
+                return cls.forward(*args, **kwargs)
+
+            def forward(
+                cls,
+                input_ids: Optional[torch.Tensor] = None,
+                attention_mask: Optional[torch.Tensor] = None,
+                output_hidden_states: Optional[bool] = None,
+                output_attentions: Optional[bool] = None,
+                return_dict: Optional[bool] = None,
+            ):
+                return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+                return self.get_encoder_output(
+                    input_ids, attention_mask, output_hidden_states, output_attentions, return_dict=return_dict
+                )
+        return EncoderWrapper()
+
+    def get_decoder(self):
+        return self.get_decoder_output
+
+    def set_decoder_special_tokens(self, target_id):
+        target_id.masked_fill_(target_id == self.cls_token_id, self.bos_token_id)
+        target_id.masked_fill_(target_id == self.sep_token_id, self.eos_token_id)
+        return target_id
+
+    def _shift_right(self, input_ids):
+        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+        shifted_input_ids[..., 0] = self.bos_token_id
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id)
+
+        return shifted_input_ids
+
+    def get_encoder_output(
+        self,
+        input_ids: torch.Tensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict = False
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            raise ValueError("You have to specify input_ids")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        if attention_mask is None:
+            attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
+        else:
+            attention_mask = ~attention_mask.bool()
+        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        static_embeddings = self.embedding(input_ids.t())
+        contextualized_embeddings, attention_probs = self.encoder(static_embeddings, attention_mask)
+        contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
+        last_layer = contextualized_embeddings[-1]
+        contextualized_embeddings = [contextualized_embeddings[0]] + [
+            contextualized_embeddings[i] - contextualized_embeddings[i - 1]
+            for i in range(1, len(contextualized_embeddings))
+        ]
+
+        if not return_dict:
+            return (
+                last_layer,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+        
+        return BaseModelOutput(
+            last_hidden_state=last_layer,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+    
+    def get_decoder_output(
+        self,
+        target_ids: torch.Tensor = None,
+        encoder_output: torch.Tensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict = False
+    ):
+        batch_size, seq_length, _ = encoder_output.shape
+        device = target_ids.device
+
+        if attention_mask is None:
+            attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
+        else:
+            attention_mask = ~attention_mask.bool()
+        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        hidden_states, self_attention_p, cross_attention_p, key_value_states = self.decoder(
+            self.embedding(target_ids.t()),
+            encoder_output.transpose(0, 1),
+            attention_mask,
+            past_key_values
+        )
+
+        hidden_states = [e.transpose(0, 1) for e in hidden_states]
+        last_layer = hidden_states[-1]
+        hidden_states = [hidden_states[0]] + [
+            hidden_states[i] - hidden_states[i - 1]
+            for i in range(1, len(hidden_states))
+        ]
+
+        if not return_dict:
+            return (
+                last_layer,
+                *([key_value_states] if use_cache else []),
+                *([hidden_states] if output_hidden_states else []),
+                *([self_attention_p] if output_attentions else []),
+                *([cross_attention_p] if output_attentions else []),
+            )
+        
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=last_layer,
+            past_key_values=key_value_states if use_cache else None,
+            hidden_states=hidden_states if output_hidden_states else None,
+            attentions=self_attention_p if output_attentions else None,
+            cross_attentions=cross_attention_p if output_attentions else None
+        )
+
+
+    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,
+        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None
+    ):
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        decoder_input_ids = self.set_decoder_special_tokens(decoder_input_ids)
+
+        if encoder_outputs is None:
+            encoder_outputs = self.get_encoder_output(
+                input_ids, attention_mask, output_hidden_states, output_attentions, return_dict
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+    
+        decoder_outputs = self.get_decoder_output(
+            decoder_input_ids, encoder_outputs[0], attention_mask, past_key_values, use_cache, output_hidden_states, output_attentions, return_dict
+        )
+
+        if not return_dict:
+            return decoder_outputs + encoder_outputs
+        
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+
+class NorT5ForConditionalGeneration(NorT5Model):
+
+    def __init__(self, config):
+        super().__init__(config, add_lm_layer=True)
+
+    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,
+        token_type_ids: Optional[torch.LongTensor] = None, # for compatibility
+    ):
+        use_cache = use_cache if use_cache is not None else getattr(self.config, "use_cache", False)
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if encoder_outputs is None:
+            encoder_outputs = self.get_encoder_output(
+                input_ids, attention_mask, output_hidden_states, output_attentions, return_dict
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        if labels is not None:
+            labels = self.set_decoder_special_tokens(labels)
+
+        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+            decoder_input_ids = self._shift_right(labels)
+        elif decoder_input_ids is not None:
+            decoder_input_ids = self.set_decoder_special_tokens(decoder_input_ids)
+
+        decoder_outputs = self.get_decoder_output(
+            decoder_input_ids, encoder_outputs[0], attention_mask, past_key_values, use_cache, output_hidden_states, output_attentions, return_dict
+        )
+        lm_logits = self.classifier(decoder_outputs[0])
+
+        loss = None
+        if labels is not None:
+            labels.masked_fill_(labels == self.pad_token_id, -100)
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            loss = loss_fct(lm_logits.flatten(0, 1), labels.flatten())
+
+        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,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        use_cache=None,
+        encoder_outputs=None,
+        **kwargs,
+    ):
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "decoder_input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "encoder_outputs": encoder_outputs,
+            "attention_mask": attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
+            "use_cache": use_cache,
+        }
+
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return self._shift_right(labels)
+
+    def _reorder_cache(self, past_key_values, beam_idx):
+        # if decoder past is not included in output
+        # speedy decoding is disabled and no need to reorder
+        if past_key_values is None:
+            print("You might want to consider setting `use_cache=True` to speed up decoding")
+            return past_key_values
+
+        reordered_decoder_past = ()
+        for layer_past_states in past_key_values:
+            # get the correct batch idx from layer past batch dim
+            # batch dim of `past` is at 2nd position
+            reordered_layer_past_states = ()
+            for layer_past_state in layer_past_states:
+                # need to set correct `past` for each of the four key / value states
+                layer_past_state = layer_past_state.index_select(0, beam_idx.to(layer_past_state.device))
+                reordered_layer_past_states = reordered_layer_past_states + (layer_past_state,)
+
+            assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
+            assert len(reordered_layer_past_states) == len(layer_past_states)
+
+            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
+        return reordered_decoder_past
+
+
+class NorT5Encoder(NorT5Model):
+    def __init__(self, config):
+        super().__init__(config, add_lm_layer=False, add_decoder=True)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        return self.get_encoder_output(
+            input_ids, attention_mask, output_hidden_states, output_attentions, return_dict=return_dict
+        )

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b736a358f3397fd94fe7115b821aa3474e71c80225bf06a2d33b6812cfded286
+size 1177063530

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "[BOS]", "eos_token": "[EOS]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

@@ -0,0 +1,4 @@
+{
+    "tokenizer_class": "PreTrainedTokenizerFast"
+}
+