Upload 13 files

config.json

@@ -4,18 +4,13 @@
     "FlashT5ForConditionalGeneration"
   ],
   "attention_dropout_rate": 0.0,
-  "attention_scale": 1.0,
-  "attention_type": "ref",
   "auto_map": {
     "AutoConfig": "configuration_flash_t5.FlashT5Config",
     "AutoModel": "modeling_flash_t5.FlashT5ForConditionalGeneration",
-    "AutoModelForQuestionAnswering": "custom_heads_flash_t5.FlashT5ForQuestionAnswering",
-    "AutoModelForSeq2SeqLM": "modeling_flash_t5.FlashT5ForConditionalGeneration",
-    "AutoModelForSequenceClassification": "custom_heads_flash_t5.FlashT5ForSequenceClassification",
-    "AutoModelForTokenClassification": "custom_heads_flash_t5.FlashT5ForTokenClassification"
+    "AutoModelForSeq2SeqLM": "modeling_flash_t5.FlashT5ForConditionalGeneration"
   },
   "classifier_dropout": 0.0,
-  "d_ff": 1024,
+  "d_ff": 2048,
   "d_kv": 64,
   "d_model": 512,
   "decoder_start_token_id": 0,
@@ -23,17 +18,16 @@
   "dropout_rate": 0.0,
   "eos_token_id": 1,
   "feed_forward_proj": "relu",
-  "fire_mlp_width": 32,
   "initializer_factor": 1.0,
   "is_encoder_decoder": false,
   "is_gated_act": false,
-  "label_smoothing": 0.1,
+  "label_smoothing": 0.0,
   "layer_norm_epsilon": 1e-06,
   "max_sequence_length": 1024,
   "model_type": "flash_t5",
-  "num_decoder_layers": 8,
-  "num_heads": 6,
-  "num_layers": 8,
+  "num_decoder_layers": 12,
+  "num_heads": 8,
+  "num_layers": 12,
   "pad_token_id": 0,
   "position_encoding_type": "t5",
   "relative_attention_max_distance": 128,
@@ -44,17 +38,16 @@
   "rotary_scale_base": null,
   "tie_word_embeddings": false,
   "torch_dtype": "float32",
-  "transformers_version": "4.39.3",
+  "transformers_version": "4.37.2",
   "use_cache": true,
-  "use_flash_attention": "triton",
+  "use_flash_attention": "ref",
   "use_full_bias_size": false,
   "use_gelu_act": true,
   "use_glu_mlp": true,
-  "use_masking": false,
   "use_randomized_position_encoding": false,
-  "use_triton_crossentropy": false,
+  "use_triton_crossentropy": true,
   "use_triton_gated_mlp": false,
   "use_triton_layernorm": false,
-  "vocab_size": 32128,
+  "vocab_size": 32768,
   "z_loss": 0.0001
 }

custom_heads_flash_t5(1).py

@@ -0,0 +1,404 @@
+import torch
+import torch.nn as nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+import copy
+from typing import Optional, Union, Tuple, List
+from transformers.modeling_outputs import (
+    Seq2SeqQuestionAnsweringModelOutput,
+    QuestionAnsweringModelOutput,
+    TokenClassifierOutput,
+    BaseModelOutput,
+    Seq2SeqSequenceClassifierOutput,
+    SequenceClassifierOutput
+)
+
+from .modeling_flash_t5 import FlashT5PreTrainedModel, FlashT5Stack, FlashT5Model, FlashT5EncoderModel
+from .configuration_flash_t5 import FlashT5Config
+
+
+################## Encoder only head ##################
+class FlashT5ForTokenClassification(FlashT5PreTrainedModel):
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        self.encoder = FlashT5Stack(config, self.shared)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Initialize classifier
+        self.classifier.weight.data.normal_(mean=0.0, std=config.initializer_factor * 1.0)
+        self.classifier.bias.data.zero_()
+
+        self.model_parallel = False
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        Returns:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits, outputs[2:-1])
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FlashT5ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__()
+        self.dense = nn.Linear(config.d_model, config.d_model)
+        self.dropout = nn.Dropout(p=config.classifier_dropout)
+        self.out_proj = nn.Linear(config.d_model, config.num_labels)
+
+        # initialize weights
+        factor = config.initializer_factor
+        self.dense.weight.data.normal_(mean=0.0, std=factor * ((config.d_model) ** -0.5))
+        if hasattr(self.dense, "bias") and self.dense.bias is not None:
+            self.dense.bias.data.zero_()
+        self.out_proj.weight.data.normal_(mean=0.0, std=factor * ((config.d_model) ** -0.5))
+        if hasattr(self.out_proj, "bias") and self.out_proj.bias is not None:
+            self.out_proj.bias.data.zero_()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = torch.tanh(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+class FlashT5ForSequenceClassification(FlashT5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.model_dim = config.d_model
+        self.config.problem_type = None
+        self.config.is_encoder_decoder = False
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.is_encoder_decoder = False
+        encoder_config.use_cache = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
+        self.classification_head = FlashT5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.model_parallel = False
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[List[torch.FloatTensor]] = None,
+        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, Seq2SeqSequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        Returns:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        if input_ids is None and inputs_embeds is not None:
+            raise NotImplementedError(
+                f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+            )
+
+
+        outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+
+        eos_mask = input_ids.eq(self.config.eos_token_id).to(sequence_output.device)
+
+        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+            raise ValueError("All examples must have the same number of <eos> tokens.")
+        batch_size, _, hidden_size = sequence_output.shape
+        sentence_representation = sequence_output[eos_mask, :].view(batch_size, -1, hidden_size)[:, -1, :]
+        logits = self.classification_head(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.config.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions
+        )
+
+
+class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.transformer = FlashT5EncoderModel(config)
+
+        self.num_labels = config.num_labels
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
+            are not taken into account for computing the loss.
+
+        Returns:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = encoder_outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + encoder_outputs[1:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+
+class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.qa_outputs.weight.data.normal_(mean=0.0, std=config.initializer_factor * 1.0)
+        self.qa_outputs.bias.data.zero_()
+
+        self.model_parallel = False
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MTxEncoderForQuestionAnswering
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("MTx-small")
+        >>> model = MTxEncoderForQuestionAnswering.from_pretrained("MTx-small")
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> outputs = model(input_ids=input_ids)
+        >>> start_logits = outputs.start_logits
+        >>> end_logits = outputs.end_logits
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.encoder(
+            input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[1:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

generation_config.json

@@ -3,5 +3,5 @@
   "decoder_start_token_id": 0,
   "eos_token_id": 1,
   "pad_token_id": 0,
-  "transformers_version": "4.39.3"
+  "transformers_version": "4.37.2"
 }

modeling_flash_t5(1).py

@@ -0,0 +1,840 @@
+# From: https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
+
+from dataclasses import dataclass
+
+import copy
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.modeling_utils import ModuleUtilsMixin
+from transformers.modeling_outputs import ModelOutput, Seq2SeqModelOutput, BaseModelOutput
+from transformers import PreTrainedModel
+
+try:
+    from .rms_norm import fast_rms_layernorm
+except ImportError:
+    fast_rms_layernorm = None
+
+try:
+    from .cross_entropy_loss import fast_cross_entropy_loss
+except ImportError:
+    fast_cross_entropy_loss = None
+
+try:
+    from .flash_attention_v2_bias import attention as flash_attention_triton
+except ImportError:
+    fast_cross_entropy_loss = None
+
+try:
+    from .gated_mlp import gated_mlp
+except ImportError:
+    gated_mlp = None
+
+try:
+    #from flash_attn import flash_attn_kvpacked_func, flash_attn_func
+    from .fa2_compilable import flash_attn_kvpacked_func, flash_attn_func
+except ImportError:
+    flash_attn_kvpacked_func, flash_attn_func = None, None
+
+from .attn_ref import attn_ref
+
+from .configuration_flash_t5 import FlashT5Config
+from .positional_encoding import ALiBiPositionalEncoding, RelativePositionalEncoding, RotaryPositionalEncoding
+
+@dataclass
+class EncoderOutput(ModelOutput):
+    hidden_states: torch.FloatTensor = None
+    attention_mask: torch.FloatTensor = None
+    
+@dataclass
+class Seq2SeqLMOutput(ModelOutput):
+    loss: torch.FloatTensor = None
+    logits: torch.FloatTensor = None
+    encoder_outputs: EncoderOutput = None
+
+    
+class FlashT5CrossEntropyLoss(nn.Module):
+    def __init__(self, z_loss_factor=0.0, label_smoothing=0.0, use_triton_crossentropy=False):
+
+        super().__init__()
+
+        if use_triton_crossentropy and fast_cross_entropy_loss is None:
+            raise ImportError("fast_cross_entropy_loss is not available")
+
+        self.use_triton_crossentropy = use_triton_crossentropy
+        self.z_loss_factor = z_loss_factor
+
+        self.cross_entropy_loss = nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+
+    def compute_zloss(self, logits: torch.Tensor, z_loss: float):
+        logits_sum = torch.logsumexp(logits, dim=-1, keepdim=True)
+        log_z = torch.squeeze(logits_sum, axis=-1)
+        total_z_loss = z_loss * torch.square(log_z)
+        return total_z_loss.mean()
+
+    def forward(self, logits, labels):
+
+        if self.use_triton_crossentropy:
+            return fast_cross_entropy_loss(logits, labels, z_loss_factor=self.z_loss_factor)
+
+        # use standard method
+        batch, seq_len, d = logits.shape
+        logits_flatten = logits.float().view(batch*seq_len, d) # Must cast to float32 for numerical stability
+        labels_flatten = labels.view(-1)
+        loss = self.cross_entropy_loss(logits_flatten, labels_flatten)
+        z_loss = 0.0
+        if self.z_loss_factor != 0.0:
+            z_loss = self.compute_zloss(logits_flatten[labels_flatten != -100],
+                                   z_loss=self.z_loss_factor)
+        return loss, z_loss
+
+class FlashT5LayerNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6, use_triton_layernorm=False):
+        """
+        Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
+        """
+        super().__init__()
+
+        if use_triton_layernorm and fast_rms_layernorm is None:
+            raise ImportError("fast_rms_layernorm is not available")
+
+        self.use_triton_layernorm = use_triton_layernorm
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+
+        if self.use_triton_layernorm:
+            return fast_rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
+
+        # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
+        # half-precision inputs is done in fp32
+
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        # convert into half-precision if necessary
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
+
+class FlashT5DenseAct(nn.Module):
+    def __init__(self, config: FlashT5Config):
+        super().__init__()
+        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = torch.nn.GELU(approximate='tanh') if config.use_gelu_act else torch.nn.ReLU()
+
+    def forward(self, hidden_states):
+        hidden_states = self.wi(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        if (
+            isinstance(self.wo.weight, torch.Tensor)
+            and hidden_states.dtype != self.wo.weight.dtype
+            and self.wo.weight.dtype != torch.int8
+        ):
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
+
+        return hidden_states
+
+class FlashT5DenseGatedAct(nn.Module):
+    def __init__(self, config: FlashT5Config):
+        super().__init__()
+        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = torch.nn.GELU(approximate='tanh') if config.use_gelu_act else torch.nn.ReLU()
+
+        self.use_triton_gated_mlp = config.use_triton_gated_mlp
+        if self.use_triton_gated_mlp and gated_mlp is None:
+            raise ImportError("gated_mlp is not available")
+        self.use_gelu_act = config.use_gelu_act
+
+    def forward(self, hidden_states):
+
+        if self.use_triton_gated_mlp:
+            return gated_mlp(hidden_states, self.wi_0.weight, self.wi_1.weight, self.use_gelu_act)
+
+        hidden_act = self.act(self.wi_0(hidden_states))
+        hidden_linear = self.wi_1(hidden_states)
+        hidden_states = hidden_act * hidden_linear
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+class FlashT5LayerFF(nn.Module):
+    def __init__(self, config: FlashT5Config):
+        super().__init__()
+        if config.use_glu_mlp:
+            self.act = FlashT5DenseGatedAct(config)
+        else:
+            self.act = FlashT5DenseAct(config)
+
+        self.layer_norm = FlashT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon, use_triton_layernorm=config.use_triton_layernorm)
+        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, hidden_states):
+        forwarded_states = self.layer_norm(hidden_states).type_as(hidden_states)
+        forwarded_states = self.act(forwarded_states)
+        forwarded_states = self.wo(forwarded_states)
+        hidden_states = hidden_states + self.dropout(forwarded_states)
+        return hidden_states
+
+
+class FlashT5Attention(nn.Module, ModuleUtilsMixin):
+    def __init__(self, config: FlashT5Config, has_positional_encoding=False, is_causal=False):
+        super().__init__()
+        self.is_decoder = config.is_decoder
+        self.has_positional_encoding = has_positional_encoding
+        self.is_causal = is_causal
+        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.p_dropout = config.attention_dropout_rate
+        self.inner_dim = self.n_heads * self.key_value_proj_dim
+        self.use_flash_attention = config.use_flash_attention
+        self.position_encoding_type = config.position_encoding_type
+        self.max_sequence_length = config.max_sequence_length
+        self.softmax_scale = 1.0/math.sqrt(self.n_heads)
+        self.use_full_bias_size = config.use_full_bias_size
+
+        if self.use_flash_attention == "triton" and flash_attention_triton is None:
+            raise ImportError("flash_attention_triton is not available")
+        elif self.use_flash_attention == "fa2" and flash_attn_func is None:
+            raise ImportError("Flash Attention 2 is not available")
+
+        assert (self.p_dropout == 0.0) or (self.use_flash_attention != "triton"), "Triton attention does not support dropout"
+
+        self.pe_encoding = None
+        if self.position_encoding_type == "ALiBi" and has_positional_encoding:
+            # build alibi matrix with an upper bound on seq length
+            self.pe_encoding = ALiBiPositionalEncoding(self.max_sequence_length, self.n_heads, config.alibi_mode, config.use_randomized_position_encoding)
+        elif self.position_encoding_type == "t5" and has_positional_encoding:
+            self.pe_encoding = RelativePositionalEncoding(self.relative_attention_num_buckets, self.relative_attention_max_distance, self.n_heads, self.max_sequence_length, config.use_randomized_position_encoding)
+        elif self.position_encoding_type == "RoPE":
+            self.pe_encoding = RotaryPositionalEncoding(int(self.key_value_proj_dim * config.rotary_emb_fraction), self.max_sequence_length, config.rotary_base, config.rotary_interleaved, config.rotary_scale_base, config.use_randomized_position_encoding)
+
+        self.Wq = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.Wk = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.Wv = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+    def forward(
+        self,
+        hidden_states,
+        mask=None,
+        key_value_states=None,
+        position_bias=None,
+    ):
+        """
+        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)
+        batch_size, seq_length = hidden_states.shape[:2]
+        key_length = seq_length if key_value_states is None else key_value_states.shape[1]
+        q = self.Wq(hidden_states)
+        if key_value_states is None:
+            k = self.Wk(hidden_states)
+            v = self.Wv(hidden_states)
+        else:
+            k = self.Wk(key_value_states)
+            v = self.Wv(key_value_states)
+
+        q = q.view(batch_size, seq_length, self.n_heads, self.key_value_proj_dim)
+        k = k.view(batch_size, key_length, self.n_heads, self.key_value_proj_dim)
+        v = v.view(batch_size, key_length, self.n_heads, self.key_value_proj_dim)
+
+        if position_bias is None and self.pe_encoding is not None:
+            q, k, v, position_bias = self.pe_encoding(q, k, v)
+
+        if position_bias is not None and self.use_full_bias_size and (self.use_flash_attention == "fa2" or self.use_flash_attention == "triton"):
+            position_bias = position_bias.expand(q.shape[0], q.shape[2], q.shape[1], k.shape[1]).contiguous()
+
+        if self.use_flash_attention == "fa2":
+            output = flash_attn_func(q, k, v, dropout_p=self.p_dropout, softmax_scale=self.softmax_scale, attn_bias=position_bias, causal=self.is_causal)
+        elif self.use_flash_attention == "triton":
+            q = q.permute(0, 2, 1, 3)
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            output = flash_attention_triton(q, k, v, position_bias, self.is_causal, self.softmax_scale)
+            output = output.permute(0, 2, 1, 3)
+        else: # use flash attention
+            q = q.permute(0, 2, 1, 3)
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            output = attn_ref(q, k, v, position_bias, dropout_p=self.p_dropout, sm_scale=self.softmax_scale, causal=self.is_causal)
+            output = output.permute(0, 2, 1, 3)
+
+        output = self.o(output.reshape(output.shape[0], output.shape[1], self.inner_dim))
+        return (output, position_bias)
+
+
+class FlashT5LayerSelfAttention(nn.Module):
+    def __init__(self, config, has_positional_encoding=False):
+        super().__init__()
+        self.self_attention = FlashT5Attention(config, has_positional_encoding=has_positional_encoding, is_causal=config.is_decoder)
+        self.layer_norm = FlashT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon, use_triton_layernorm=config.use_triton_layernorm)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_bias=None,
+    ):
+        normed_hidden_states = self.layer_norm(hidden_states).type_as(hidden_states)
+        attention_output = self.self_attention(
+            normed_hidden_states,
+            mask=attention_mask,
+            position_bias=position_bias,
+        )
+        hidden_states = hidden_states + self.dropout(attention_output[0])
+        outputs = (hidden_states,) + attention_output[1:]
+        return outputs
+
+
+class FlashT5LayerCrossAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.cross_attention = FlashT5Attention(config, has_positional_encoding=False)
+        self.layer_norm = FlashT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon, use_triton_layernorm=config.use_triton_layernorm)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        hidden_states,
+        key_value_states,
+        attention_mask=None,
+        position_bias=None,
+    ):
+        normed_hidden_states = self.layer_norm(hidden_states)
+        attention_output = self.cross_attention(
+            normed_hidden_states,
+            mask=attention_mask,
+            key_value_states=key_value_states,
+            position_bias=position_bias,
+        )
+        layer_output = hidden_states + self.dropout(attention_output[0])
+        outputs = (layer_output,) + attention_output[1:]
+        return outputs
+
+
+class FlashT5Block(nn.Module):
+    def __init__(self, config, has_positional_encoding=False):
+        super().__init__()
+        self.is_decoder = config.is_decoder
+
+        self.self_attention_layer = FlashT5LayerSelfAttention(config, has_positional_encoding=has_positional_encoding)
+
+        if self.is_decoder:
+            self.cross_attention_layer = FlashT5LayerCrossAttention(config)
+
+        self.ff_layer = FlashT5LayerFF(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,
+    ):
+        self_attention_outputs = self.self_attention_layer(
+            hidden_states,
+            attention_mask=attention_mask,
+            position_bias=position_bias,
+        )
+        hidden_states = self_attention_outputs[0]
+        attention_outputs = self_attention_outputs[1:]  # Relative position weights
+
+        if self.is_decoder and encoder_hidden_states is not None:
+            cross_attention_outputs = self.cross_attention_layer(
+                hidden_states,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                position_bias=encoder_decoder_position_bias,
+            )
+            hidden_states = cross_attention_outputs[0]
+
+            # Keep relative position weights
+            attention_outputs = attention_outputs + cross_attention_outputs[1:]
+
+        # Apply Feed Forward layer
+        hidden_states = self.ff_layer(hidden_states)
+
+        outputs = (hidden_states,) + attention_outputs
+        return outputs  # hidden-states, (self-attention position bias), (cross-attention position bias)
+
+class FlashT5Stack(nn.Module, ModuleUtilsMixin):
+    def __init__(self, config, embed_tokens):
+        super().__init__()
+        assert embed_tokens is not None
+
+        self.config = config
+        self.embed_tokens = embed_tokens
+        self.is_decoder = config.is_decoder
+        self.use_flash_attention = config.use_flash_attention
+
+        self.block = nn.ModuleList(
+            [FlashT5Block(config, has_positional_encoding=bool(i == 0)) for i in range(config.num_layers)]
+        )
+
+        self.final_layer_norm = FlashT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon, use_triton_layernorm=config.use_triton_layernorm)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        inputs_embeds=None,
+        head_mask=None,
+        cross_attn_head_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None) -> BaseModelOutput:
+        input_shape = input_ids.size()
+        batch_size, seq_length = input_shape
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if torch.is_autocast_enabled() and input_ids.device.type == 'cuda':
+            inputs_embeds = inputs_embeds.to(torch.get_autocast_gpu_dtype())
+
+        # Masking
+        if attention_mask is None:
+            attention_mask = torch.ones(batch_size, seq_length, device=inputs_embeds.device, dtype=torch.bool)
+
+        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
+            encoder_seq_length = encoder_hidden_states.shape[1]
+            encoder_attention_mask = torch.ones(
+                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.bool
+            )
+
+        position_bias = None
+        encoder_decoder_position_bias = None
+
+        hidden_states = self.dropout(inputs_embeds)
+
+        for _, layer_module in enumerate(self.block):
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_bias=position_bias,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                encoder_decoder_position_bias=encoder_decoder_position_bias,
+            )
+
+            # We share the position biases between the layers - the first layer store them
+            position_bias = layer_outputs[1]
+            if self.is_decoder and encoder_hidden_states is not None:
+                encoder_decoder_position_bias = layer_outputs[2]
+
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.final_layer_norm(hidden_states).type_as(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return BaseModelOutput(
+            last_hidden_state=hidden_states
+        )
+
+
+class FlashT5PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = FlashT5Config
+    base_model_prefix = "transformer"
+    is_parallelizable = False
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["FlashT5Block"]
+    _keep_in_fp32_modules = []
+
+    def _init_weights(self, module):
+        factor = self.config.initializer_factor  # Used for testing weights initialization
+        if isinstance(module, FlashT5LayerNorm):
+            module.weight.data.fill_(factor * 1.0)
+        elif isinstance(module, (FlashT5ForConditionalGeneration)):
+            module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
+            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
+                module.lm_head.weight.data.normal_(mean=0.0, std=factor * self.config.d_model ** -0.5)
+        elif isinstance(module, FlashT5DenseGatedAct):
+            d_ff, d_model = module.wi_0.weight.data.size()
+            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
+            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
+        elif isinstance(module, FlashT5LayerFF):
+            d_ff, d_model = module.wo.weight.data.size()
+            module.wo.weight.data.normal_(mean=0.0, std=factor * ((d_ff) ** -0.5))
+        elif isinstance(module, FlashT5Attention):
+            d_model = self.config.d_model
+            key_value_proj_dim = self.config.d_kv
+            n_heads = self.config.num_heads
+            module.Wq.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
+            module.Wk.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+            module.Wv.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
+            if module.has_positional_encoding:
+                if hasattr(module.pe_encoding, "relative_attention_bias"):
+                    module.pe_encoding.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
+
+    def _shift_right(self, input_ids):
+        decoder_start_token_id = self.config.decoder_start_token_id
+        pad_token_id = self.config.pad_token_id
+
+        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+        shifted_input_ids[..., 0] = decoder_start_token_id
+
+        # replace possible -100 values in labels by `pad_token_id`
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+        return shifted_input_ids
+
+
+class FlashT5Model(FlashT5PreTrainedModel):
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
+
+        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 = FlashT5Stack(decoder_config, self.shared)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    def get_encoder(self):
+        return self.encoder
+
+    def get_decoder(self):
+        return self.decoder
+
+    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.FloatTensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        decoder_inputs_embeds: Optional[torch.Tensor] = 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], Seq2SeqModelOutput]:
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask
+        )
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+        )
+
+class FlashT5ForConditionalGeneration(FlashT5PreTrainedModel):
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        config.is_encoder_decoder = False
+        assert not config.tie_word_embeddings
+
+        self.config = config
+        self.model_dim = config.d_model
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = FlashT5Stack(decoder_config, self.shared)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        self.loss_fct = FlashT5CrossEntropyLoss(z_loss_factor=config.z_loss,
+                                                label_smoothing=config.label_smoothing,
+                                                use_triton_crossentropy=config.use_triton_crossentropy)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # do nothing
+        model_inputs = {"input_ids": input_ids, "attention_mask": attention_mask}
+
+        return model_inputs
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, value):
+        self.shared = value
+
+    def generate(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        max_length = 32,
+        **kwargs,
+    ) -> torch.LongTensor:
+        """
+            input_ids: B x L_encoder, int64
+            attention_mask: B x L_encoder, int64
+                1 for tokens to attend to, 0 for tokens to ignore
+
+            Generation:
+                Starts with 0, ends with 1, padding is 0
+
+            # For 20 input/outputs, the diff between my implementation and HF is 9.8s vs 11.4s
+        """
+        B, _ = input_ids.size()
+        labels = torch.zeros(B, 1, dtype=torch.long, device=input_ids.device)
+        encoder_outputs = None
+
+        for _ in range(max_length):
+            out = self.forward(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                decoder_input_ids=labels,
+                encoder_outputs=encoder_outputs,
+            )
+            encoder_outputs = out.encoder_outputs
+            top_labels = out.logits[:, -1].argmax(-1).unsqueeze(-1)
+            labels = torch.cat([labels, top_labels], dim=-1)
+
+            if (labels == 1).sum(-1).clamp(min=0, max=1).sum().item() == B:
+                break
+
+        labels[:, -1] = 1
+
+        # Mask out the padding, i.e., all positions after the first 1 with 0
+        B, L = labels.size()
+        mask = torch.arange(L, device=labels.device).unsqueeze(0) <= (labels == 1).long().argmax(-1).unsqueeze(-1)
+        labels = labels.masked_fill(~mask, 0)
+
+        return labels
+
+    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,
+        labels: Optional[torch.LongTensor] = None,
+        encoder_outputs = None,
+    ) -> Seq2SeqLMOutput:
+        """
+            input_ids: B x L_encoder, int64
+            attention_mask: B x L_encoder, int64
+                1 for tokens to attend to, 0 for tokens to ignore
+            labels: B x L_decoder, int64
+        """
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+            )
+
+        hidden_states = encoder_outputs.hidden_states
+
+        if labels is not None and decoder_input_ids is None:
+            decoder_input_ids = self._shift_right(labels)
+
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+        )
+
+        sequence_output = decoder_outputs[0]
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss, z_loss = self.loss_fct(lm_logits, labels)
+            loss += z_loss
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            encoder_outputs=encoder_outputs,
+        )
+
+
+
+class FlashT5EncoderModel(FlashT5PreTrainedModel):
+    _tied_weights_keys = ["encoder.embed_tokens.weight"]
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+    
+    def parallelize(self, device_map=None):
+        warnings.warn(
+            "`T5EncoderModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
+            " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
+            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
+            " 'block.1': 1, ...}",
+            FutureWarning,
+        )
+        self.device_map = (
+            get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
+            if device_map is None
+            else device_map
+        )
+        assert_device_map(self.device_map, len(self.encoder.block))
+        self.encoder.parallelize(self.device_map)
+        self.model_parallel = True
+
+    def deparallelize(self):
+        warnings.warn(
+            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
+            FutureWarning,
+        )
+        self.encoder.deparallelize()
+        self.encoder = self.encoder.to("cpu")
+        self.model_parallel = False
+        self.device_map = None
+        torch.cuda.empty_cache()
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+
+    def get_encoder(self):
+        return self.encoder
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]:
+        r"""
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, T5EncoderModel
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
+        >>> model = T5EncoderModel.from_pretrained("t5-small")
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> outputs = model(input_ids=input_ids)
+        >>> last_hidden_states = outputs.last_hidden_state
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        return encoder_outputs

special_tokens_map.json

@@ -0,0 +1,110 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "cls_token": "<cls>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "<sep>",
+  "unk_token": "<unk>"
+}