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README.md

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+---
+tags:
+- transformers
+- xlm-roberta
+library_name: transformers
+license: cc-by-nc-4.0
+language:
+  - multilingual
+  - af
+  - am
+  - ar
+  - as
+  - az
+  - be
+  - bg
+  - bn
+  - br
+  - bs
+  - ca
+  - cs
+  - cy
+  - da
+  - de
+  - el
+  - en
+  - eo
+  - es
+  - et
+  - eu
+  - fa
+  - fi
+  - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - he
+  - hi
+  - hr
+  - hu
+  - hy
+  - id
+  - is
+  - it
+  - ja
+  - jv
+  - ka
+  - kk
+  - km
+  - kn
+  - ko
+  - ku
+  - ky
+  - la
+  - lo
+  - lt
+  - lv
+  - mg
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - my
+  - ne
+  - nl
+  - 'no'
+  - om
+  - or
+  - pa
+  - pl
+  - ps
+  - pt
+  - ro
+  - ru
+  - sa
+  - sd
+  - si
+  - sk
+  - sl
+  - so
+  - sq
+  - sr
+  - su
+  - sv
+  - sw
+  - ta
+  - te
+  - th
+  - tl
+  - tr
+  - ug
+  - uk
+  - ur
+  - uz
+  - vi
+  - xh
+  - yi
+  - zh
+---
+Core implementation of Jina XLM-RoBERTa
+
+This implementation is adapted from [XLM-Roberta](https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta). In contrast to the original implementation, this model uses Rotary positional encodings and supports flash-attention 2.
+
+### Models that use this implementation
+
+- [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3)
+- [jinaai/jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2)
+
+### Converting weights
+
+Weights from an [original XLMRoberta model](https://huggingface.co/FacebookAI/xlm-roberta-large) can be converted using the `convert_roberta_weights_to_flash.py` script in the model repository.

block.py

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+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/block.py
+# Commit id: abbc1311731867310635f9edc2a9ec18317c8c48
+
+# Copyright (c) 2024, Tri Dao.
+
+from functools import partial
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from .mha import MHA
+from .mlp import Mlp
+from .stochastic_depth import StochasticDepth
+
+try:
+    from flash_attn.ops.triton.layer_norm import RMSNorm, layer_norm_fn
+except ImportError:
+    layer_norm_fn, RMSNorm = None, None
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim,
+        mixer_cls=None,
+        mlp_cls=None,
+        norm_cls=nn.LayerNorm,
+        dropout_cls=nn.Dropout,
+        prenorm=True,
+        resid_dropout1=0.0,
+        resid_dropout2=0.0,
+        drop_path1=0.0,
+        drop_path2=0.0,
+        fused_dropout_add_ln=False,
+        return_residual=False,
+        residual_in_fp32=False,
+        sequence_parallel=False,
+        mark_shared_params=False,
+    ):
+        """
+        For prenorm=True, this Block has a slightly different structure compared to a regular
+        prenorm Transformer block.
+        The standard block is: LN -> MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add.
+        [Ref: https://arxiv.org/abs/2002.04745]
+        Here we have: Dropout -> Add -> LN -> MHA -> Dropout -> Add -> LN -> MLP, returning both
+        the hidden_states (output of the MLP) and the residual.
+        This is for performance reasons, as we can fuse the dropout, add and LayerNorm.
+        The residual needs to be provided (except for the very first block).
+
+        For prenorm=False, this Block has the same structure as a regular postnorm Transformer
+        block: MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add -> LN.
+
+        return_residual: whether each of the sub-layers (mixer and mlp) will return the residual.
+        This is for performance reason: for post-norm architecture, returning the input allows us
+        to fuse the backward of nn.Linear with the residual connection.
+        """
+        super().__init__()
+        self.prenorm = prenorm
+        self.fused_dropout_add_ln = fused_dropout_add_ln
+        self.return_residual = return_residual
+        self.residual_in_fp32 = residual_in_fp32
+        if self.residual_in_fp32:
+            assert self.prenorm, "residual_in_fp32 is only compatible with prenorm=True"
+        if mixer_cls is None:
+            mixer_cls = partial(MHA, num_heads=dim // 64)
+        if mlp_cls is None:
+            mlp_cls = partial(Mlp, hidden_features=4 * dim)
+        self.mixer = mixer_cls(dim)
+        self.dropout1 = dropout_cls(resid_dropout1)
+        self.drop_path1 = StochasticDepth(drop_path1, mode="row")
+        self.norm1 = norm_cls(dim)
+        self.mlp = mlp_cls(dim)
+        if not isinstance(self.mlp, nn.Identity):
+            self.dropout2 = dropout_cls(resid_dropout2)
+            self.drop_path2 = StochasticDepth(drop_path2, mode="row")
+            self.norm2 = norm_cls(dim)
+
+        if self.fused_dropout_add_ln:
+            assert layer_norm_fn is not None, "Triton is not installed"
+            assert isinstance(self.norm1, (nn.LayerNorm, RMSNorm)) and isinstance(
+                self.dropout1, nn.Dropout
+            )
+
+        # TD [2023-01-07]: TODO: During training, if sequence_parallel is False and dropout != 0.0,
+        # then the input to each worker in the tensor parallel group will be different.
+        # This would produce wrong outputs? Somehow we'd need to sync the RNG state across workers.
+        # For now this is not an issue because we always use sequence_parallel=True during training
+        # and only use sequence_parallel=False during inference.
+
+        # Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
+        if sequence_parallel:
+            for p in self.norm1.parameters():
+                p._sequence_parallel = True
+            if hasattr(self, "norm2"):
+                for p in self.norm2.parameters():
+                    p._sequence_parallel = True
+        # Mark the norm parameters as "shared_params" so that we sync their values at init.
+        if mark_shared_params:
+            for p in self.norm1.parameters():
+                p._shared_params = True
+            if hasattr(self, "norm2"):
+                for p in self.norm2.parameters():
+                    p._shared_params = True
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
+        return self.mixer.allocate_inference_cache(
+            batch_size, max_seqlen, dtype=dtype, **kwargs
+        )
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        residual: Optional[Tensor] = None,
+        mixer_subset=None,
+        mixer_kwargs=None,
+    ):
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            hidden_states: the sequence to the encoder layer (required).
+            residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual))
+            mixer_subset: for cross-attention only. If not None, will take a subset of x
+                before applying the query projection. Useful for e.g., ViT where we only care
+                about the CLS token in the last layer.
+        """
+        if self.prenorm:
+            if not self.fused_dropout_add_ln:
+                dropped = self.drop_path1(self.dropout1(hidden_states))
+                residual = (dropped + residual) if residual is not None else dropped
+                hidden_states = self.norm1(residual.to(dtype=self.norm1.weight.dtype))
+                if self.residual_in_fp32:
+                    residual = residual.to(torch.float32)
+            else:
+                if self.drop_path1.p == 0 or not self.training:
+                    rowscale1 = None
+                else:
+                    rowscale1 = self.drop_path1(
+                        torch.ones(
+                            hidden_states.shape[:-1],
+                            device=hidden_states.device,
+                            dtype=hidden_states.dtype,
+                        )
+                    )
+                hidden_states, residual = layer_norm_fn(
+                    hidden_states,
+                    self.norm1.weight,
+                    self.norm1.bias,
+                    residual=residual,
+                    eps=self.norm1.eps,
+                    dropout_p=self.dropout1.p if self.training else 0.0,
+                    rowscale=rowscale1,
+                    prenorm=True,
+                    residual_in_fp32=self.residual_in_fp32,
+                    is_rms_norm=isinstance(self.norm1, RMSNorm),
+                )
+            if mixer_kwargs is None:
+                mixer_kwargs = {}
+            if mixer_subset is not None:
+                mixer_kwargs["mixer_subset"] = mixer_subset
+            hidden_states = self.mixer(hidden_states, **mixer_kwargs)
+            if mixer_subset is not None:
+                residual = residual[:, mixer_subset]
+            if not isinstance(self.mlp, nn.Identity):
+                if not self.fused_dropout_add_ln:
+                    dropped = self.drop_path2(self.dropout2(hidden_states))
+                    residual = (dropped + residual) if residual is not None else dropped
+                    hidden_states = self.norm2(
+                        residual.to(dtype=self.norm2.weight.dtype)
+                    )
+                    if self.residual_in_fp32:
+                        residual = residual.to(torch.float32)
+                else:
+                    if self.drop_path2.p == 0 or not self.training:
+                        rowscale2 = None
+                    else:
+                        rowscale2 = self.drop_path2(
+                            torch.ones(
+                                hidden_states.shape[:-1],
+                                device=hidden_states.device,
+                                dtype=hidden_states.dtype,
+                            )
+                        )
+                    hidden_states, residual = layer_norm_fn(
+                        hidden_states,
+                        self.norm2.weight,
+                        self.norm2.bias,
+                        residual=residual,
+                        eps=self.norm2.eps,
+                        dropout_p=self.dropout2.p if self.training else 0.0,
+                        rowscale=rowscale2,
+                        prenorm=True,
+                        residual_in_fp32=self.residual_in_fp32,
+                        is_rms_norm=isinstance(self.norm2, RMSNorm),
+                    )
+                hidden_states = self.mlp(hidden_states)
+            return hidden_states, residual
+        else:
+            assert residual is None
+            mixer_out = self.mixer(
+                hidden_states, **(mixer_kwargs if mixer_kwargs is not None else {})
+            )
+            if self.return_residual:  # mixer out is actually a pair here
+                mixer_out, hidden_states = mixer_out
+            if not self.fused_dropout_add_ln:
+                hidden_states = self.norm1(
+                    (self.drop_path1(self.dropout1(mixer_out)) + hidden_states).to(
+                        dtype=self.norm1.weight.dtype
+                    )
+                )
+            else:
+                if self.drop_path1.p == 0 or not self.training:
+                    rowscale1 = None
+                else:
+                    rowscale1 = self.drop_path1(
+                        torch.ones(
+                            mixer_out.shape[:-1],
+                            device=mixer_out.device,
+                            dtype=mixer_out.dtype,
+                        )
+                    )
+                hidden_states = layer_norm_fn(
+                    mixer_out,
+                    self.norm1.weight,
+                    self.norm1.bias,
+                    residual=hidden_states,
+                    eps=self.norm1.eps,
+                    dropout_p=self.dropout1.p if self.training else 0.0,
+                    rowscale=rowscale1,
+                    prenorm=False,
+                    is_rms_norm=isinstance(self.norm1, RMSNorm),
+                )
+            if not isinstance(self.mlp, nn.Identity):
+                mlp_out = self.mlp(
+                    hidden_states, adapter_mask=mixer_kwargs.get("adapter_mask")
+                )
+                if self.return_residual:  # mlp out is actually a pair here
+                    mlp_out, hidden_states = mlp_out
+                if not self.fused_dropout_add_ln:
+                    hidden_states = self.norm2(
+                        (self.drop_path2(self.dropout2(mlp_out)) + hidden_states).to(
+                            dtype=self.norm2.weight.dtype
+                        )
+                    )
+                else:
+                    if self.drop_path2.p == 0 or not self.training:
+                        rowscale2 = None
+                    else:
+                        rowscale2 = self.drop_path2(
+                            torch.ones(
+                                mlp_out.shape[:-1],
+                                device=mlp_out.device,
+                                dtype=mlp_out.dtype,
+                            )
+                        )
+                    hidden_states = layer_norm_fn(
+                        mlp_out,
+                        self.norm2.weight,
+                        self.norm2.bias,
+                        residual=hidden_states,
+                        eps=self.norm2.eps,
+                        dropout_p=self.dropout2.p if self.training else 0.0,
+                        rowscale=rowscale2,
+                        prenorm=False,
+                        is_rms_norm=isinstance(self.norm2, RMSNorm),
+                    )
+            return hidden_states
+
+
+class ParallelBlock(nn.Module):
+    """The attention (mixer) and MLP blocks are done in parallel, similar to GPT-J, GPT-NeoX,
+    and PaLM.
+    """
+
+    def __init__(
+        self,
+        dim,
+        mixer_cls=None,
+        mlp_cls=None,
+        norm_cls=nn.LayerNorm,
+        dropout_cls=nn.Dropout,
+        resid_dropout1=0.0,
+        resid_dropout2=0.0,
+        tied_norm=False,
+        fused_dropout_add_ln=False,
+        residual_in_fp32=False,
+        sequence_parallel=False,
+        mark_shared_params=False,
+    ):
+        """
+        This Block has a slightly different structure compared to a regular
+        prenorm Transformer block.
+        The standard block is: LN -> MHA / MLP -> Dropout -> Add.
+        [Ref: https://arxiv.org/abs/2002.04745]
+        Here we have: Dropout -> Add -> LN -> MHA / MLP, returning both
+        the hidden_states (output1 of the MHA / MLP) and the residual.
+        This is for performance reasons, as we can fuse the dropout, add and LayerNorm.
+        The residual needs to be provided (except for the very first block).
+        """
+        super().__init__()
+        self.tied_norm = tied_norm
+        self.fused_dropout_add_ln = fused_dropout_add_ln
+        self.residual_in_fp32 = residual_in_fp32
+        if mixer_cls is None:
+            mixer_cls = partial(MHA, num_heads=dim // 64)
+        if mlp_cls is None:
+            mlp_cls = partial(Mlp, hidden_features=4 * dim)
+        self.mixer = mixer_cls(dim)
+        self.dropout1 = dropout_cls(resid_dropout1)
+        self.norm1 = norm_cls(dim)
+        self.mlp = mlp_cls(dim)
+        self.dropout2 = dropout_cls(resid_dropout2)
+        if not self.tied_norm:
+            self.norm2 = norm_cls(dim)
+
+        if self.fused_dropout_add_ln:
+            assert layer_norm_fn is not None, "Triton is not installed"
+            assert isinstance(self.norm1, (nn.LayerNorm, RMSNorm)) and isinstance(
+                self.dropout1, nn.Dropout
+            )
+
+        # TD [2023-01-07]: TODO: During training, if sequence_parallel is False and dropout != 0.0,
+        # then the input to each worker in the tensor parallel group will be different.
+        # This would produce wrong outputs? Somehow we'd need to sync the RNG state across workers.
+        # For now this is not an issue because we always use sequence_parallel=True during training
+        # and only use sequence_parallel=False during inference.
+
+        # Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
+        if sequence_parallel:
+            for p in self.norm1.parameters():
+                p._sequence_parallel = True
+            if hasattr(self, "norm2"):
+                for p in self.norm2.parameters():
+                    p._sequence_parallel = True
+        # Mark the norm parameters as "shared_params" so that we sync their values at init.
+        if mark_shared_params:
+            for p in self.norm1.parameters():
+                p._shared_params = True
+            if hasattr(self, "norm2"):
+                for p in self.norm2.parameters():
+                    p._shared_params = True
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
+        return self.mixer.allocate_inference_cache(
+            batch_size, max_seqlen, dtype=dtype, **kwargs
+        )
+
+    def forward(
+        self,
+        hidden_states1: Tensor,
+        hidden_states2: Optional[Tensor] = None,
+        residual: Optional[Tensor] = None,
+        mixer_kwargs=None,
+    ):
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            hidden_states1: the output of the previous attention (mixer) or embedding layer.
+            hidden_states2: the output of the previous MLP layer (if None, will use hidden_states1).
+            residual.
+        """
+        # TODO: Ideally we should only do the allgather / allreduce once for
+        # the Linear to MLP & Attention
+        if not self.fused_dropout_add_ln:
+            dropped1 = self.dropout1(hidden_states1)
+            # For the very 1st block, we only want 1 dropout, not two different dropouts
+            if hidden_states2 is not None:
+                dropped2 = self.dropout2(hidden_states2)
+                residual = (
+                    (residual + dropped1 + dropped2)
+                    if residual is not None
+                    else dropped1 + dropped2
+                )
+            else:
+                residual = (residual + dropped1) if residual is not None else dropped1
+            hidden_states1 = self.norm1(residual.to(dtype=self.norm1.weight.dtype))
+            hidden_states2 = (
+                self.norm2(residual.to(dtype=self.norm2.weight.dtype))
+                if not self.tied_norm
+                else hidden_states1
+            )
+            if self.residual_in_fp32:
+                residual = residual.to(torch.float32)
+        else:
+            weight2, bias2 = (
+                (self.norm2.weight, self.norm2.bias)
+                if not self.tied_norm
+                else (None, None)
+            )
+            hidden_states1, *rest, residual = layer_norm_fn(
+                hidden_states1,
+                self.norm1.weight,
+                self.norm1.bias,
+                residual=residual,
+                x1=hidden_states2,
+                weight1=weight2,
+                bias1=bias2,
+                eps=self.norm1.eps,
+                dropout_p=self.dropout1.p if self.training else 0.0,
+                prenorm=True,
+                residual_in_fp32=self.residual_in_fp32,
+                is_rms_norm=isinstance(self.norm1, RMSNorm),
+            )
+            if self.tied_norm:
+                hidden_states2 = hidden_states1
+            else:
+                (hidden_states2,) = rest
+        if mixer_kwargs is None:
+            mixer_kwargs = {}
+        hidden_states1 = self.mixer(hidden_states1, **mixer_kwargs)
+        hidden_states2 = self.mlp(hidden_states2)
+        return hidden_states1, hidden_states2, residual

configuration_xlm_roberta.py

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+from typing import Any, Dict, List, Optional, Union
+
+import torch
+from transformers import PretrainedConfig
+
+
+class XLMRobertaFlashConfig(PretrainedConfig):
+
+    model_type = "xlm-roberta"
+
+    def __init__(
+        self,
+        vocab_size: int = 250002,
+        hidden_size: int = 1024,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        intermediate_size: int = 4096,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 8194,
+        type_vocab_size: int = 1,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-05,
+        pad_token_id: int = 1,
+        bos_token_id: int = 0,
+        eos_token_id: int = 2,
+        position_embedding_type: str = "rotary",
+        rotary_emb_base: float = 10000.0,
+        use_cache: bool = True,
+        use_reentrant: bool = False,
+        classifier_dropout: Optional[float] = None,
+        lora_adaptations: Optional[List[str]] = None,
+        task_instructions: Optional[Dict[str, str]] = None,
+        lora_rank: int = 4,
+        lora_dropout_p: float = 0.0,
+        lora_alpha: int = 1,
+        lora_main_params_trainable: bool = False,
+        load_trained_adapters: bool = False,
+        use_flash_attn: bool = True,
+        torch_dtype: Optional[Union[str, torch.dtype]] = None,
+        emb_pooler: Optional[str] = None,
+        matryoshka_dimensions: Optional[List[int]] = None,
+        truncate_dim: Optional[int] = None,
+        **kwargs: Dict[str, Any],
+    ):
+        """
+        Initialize the XLMRobertaFlashConfig configuration.
+
+        Args:
+            vocab_size (int): Size of the vocabulary.
+            hidden_size (int): Dimensionality of the encoder layers and the pooler layer.
+            num_hidden_layers (int): Number of hidden layers in the Transformer encoder.
+            num_attention_heads (int): Number of attention heads for each attention layer in the Transformer encoder.
+            intermediate_size (int): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer.
+            hidden_act (str): The activation function to use.
+            hidden_dropout_prob (float): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob (float): The dropout ratio for the attention probabilities.
+            max_position_embeddings (int): The maximum length of the position embeddings.
+            type_vocab_size (int): The vocabulary size of the token type ids.
+            initializer_range (float): The standard deviation for initializing all weight matrices.
+            layer_norm_eps (float): The epsilon used by the layer normalization layers.
+            pad_token_id (int): The ID of the padding token.
+            bos_token_id (int): The ID of the beginning-of-sequence token.
+            eos_token_id (int): The ID of the end-of-sequence token.
+            position_embedding_type (str): Type of position embeddings. Options are 'absolute', 'alibi', or 'rotary'.
+            rotary_emb_base (float): Base for rotary embeddings.
+            use_cache (bool): Whether or not the model should return the last key/values attentions (not used by all models).
+            use_reentrant (bool): Whether or not the model should enable the 'use_reentrant' flag in gradient checkpointing.
+            classifier_dropout (Optional[float]): The dropout ratio for the classification head.
+            lora_adaptations (Optional[List[str]]): LoRA adaptations configuration.
+            lora_prompts (Optional[Dict[str, str]]): LoRA prompts configuration.
+            lora_rank (int): Rank for LoRA adaptations.
+            lora_dropout_p (float): Dropout probability for LoRA adaptations.
+            lora_alpha (int): Alpha parameter for LoRA.
+            lora_main_params_trainable (bool): Whether to make the main model parameters trainable when using LoRA.
+            load_trained_adapters (bool): Whether to load trained adapters.
+            use_flash_attn (bool): Whether to use FlashAttention.
+            torch_dtype (Optional[Union[str, torch.dtype]]): Data type for the tensors.
+            emb_pooler (Optional[str]): Pooling layer configuration.
+            matryoshka_dimensions (Optional[List[int]]): Configuration for matryoshka dimension reduction.
+            truncate_dim (Optional[int]): Dimension to truncate embeddings to, if any.
+            **kwargs (Dict[str, Any]): Additional keyword arguments passed to the configuration.
+        """
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **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.hidden_act = hidden_act
+        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.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.rotary_emb_base = rotary_emb_base
+        self.use_cache = use_cache
+        self.use_reentrant = use_reentrant
+        self.classifier_dropout = classifier_dropout
+        self.load_trained_adapters = load_trained_adapters
+        self.lora_adaptations = lora_adaptations
+        self.task_instructions = task_instructions
+        self.lora_rank = lora_rank
+        self.lora_dropout_p = lora_dropout_p
+        self.lora_alpha = lora_alpha
+        self.lora_main_params_trainable = lora_main_params_trainable
+        self.use_flash_attn = use_flash_attn
+        self.emb_pooler = emb_pooler
+        self.matryoshka_dimensions = matryoshka_dimensions
+        self.truncate_dim = truncate_dim
+        if (
+            torch_dtype
+            and hasattr(torch, torch_dtype)
+            and type(getattr(torch, torch_dtype)) is torch.dtype
+        ):
+            self.torch_dtype = getattr(torch, torch_dtype)
+        else:
+            self.torch_dtype = torch_dtype
+        if not self.use_flash_attn or not torch.cuda.is_available():
+            self.torch_dtype = torch.float32

convert_roberta_weights_to_flash.py

@@ -0,0 +1,170 @@
+import re
+from collections import OrderedDict
+from transformers import PretrainedConfig
+from transformers import XLMRobertaForMaskedLM, XLMRobertaForSequenceClassification
+
+from .configuration_xlm_roberta import XLMRobertaFlashConfig as BertConfig
+from .modeling_xlm_roberta import XLMRobertaForMaskedLM as FlashXLMRobertaForMaskedLM
+from .modeling_xlm_roberta import XLMRobertaForSequenceClassification as FlashXLMRobertaForSequenceClassification
+import torch
+
+import click
+
+## inspired by https://github.com/Dao-AILab/flash-attention/blob/85881f547fd1053a7b4a2c3faad6690cca969279/flash_attn/models/bert.py
+
+
+def remap_state_dict(state_dict, config: PretrainedConfig):
+    """
+    Map the state_dict of a Huggingface BERT model to be flash_attn compatible.
+    """
+
+    # LayerNorm
+    def key_mapping_ln_gamma_beta(key):
+        key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key)
+        key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key)
+        return key
+
+    state_dict = OrderedDict(
+        (key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items()
+    )
+
+    # Layers
+    def key_mapping_layers(key):
+        return re.sub(r"^roberta.encoder.layer.", "roberta.encoder.layers.", key)
+
+    state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
+
+    # LayerNorm
+    def key_mapping_ln(key):
+        key = re.sub(r"^roberta.embeddings.LayerNorm.", "roberta.emb_ln.", key)
+        key = re.sub(
+            r"^roberta.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)",
+            r"roberta.encoder.layers.\1.norm1.\2",
+            key,
+        )
+        key = re.sub(
+            r"^roberta.encoder.layers.(\d+).output.LayerNorm.(weight|bias)",
+            r"roberta.encoder.layers.\1.norm2.\2",
+            key,
+        )
+        key = re.sub(
+            r"^cls.predictions.transform.LayerNorm.(weight|bias)",
+            r"cls.predictions.transform.layer_norm.\1",
+            key,
+        )
+        return key
+
+    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
+
+    # MLP
+    def key_mapping_mlp(key):
+        key = re.sub(
+            r"^roberta.encoder.layers.(\d+).intermediate.dense.(weight|bias)",
+            r"roberta.encoder.layers.\1.mlp.fc1.\2",
+            key,
+        )
+        key = re.sub(
+            r"^roberta.encoder.layers.(\d+).output.dense.(weight|bias)",
+            r"roberta.encoder.layers.\1.mlp.fc2.\2",
+            key,
+        )
+        return key
+
+    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
+
+    # Attention
+    last_layer_subset = getattr(config, "last_layer_subset", False)
+    for d in range(config.num_hidden_layers):
+        Wq = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.query.weight")
+        Wk = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.key.weight")
+        Wv = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.value.weight")
+        bq = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.query.bias")
+        bk = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.key.bias")
+        bv = state_dict.pop(f"roberta.encoder.layers.{d}.attention.self.value.bias")
+        if not (last_layer_subset and d == config.num_hidden_layers - 1):
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wqkv.weight"] = torch.cat(
+                [Wq, Wk, Wv], dim=0
+            )
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wqkv.bias"] = torch.cat(
+                [bq, bk, bv], dim=0
+            )
+        else:
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wq.weight"] = Wq
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wkv.weight"] = torch.cat(
+                [Wk, Wv], dim=0
+            )
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wq.bias"] = bq
+            state_dict[f"roberta.encoder.layers.{d}.mixer.Wkv.bias"] = torch.cat(
+                [bk, bv], dim=0
+            )
+
+    def key_mapping_attn(key):
+        return re.sub(
+            r"^roberta.encoder.layers.(\d+).attention.output.dense.(weight|bias)",
+            r"roberta.encoder.layers.\1.mixer.out_proj.\2",
+            key,
+        )
+
+    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
+
+    def key_mapping_decoder_bias(key):
+        return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key)
+
+    state_dict = OrderedDict(
+        (key_mapping_decoder_bias(k), v) for k, v in state_dict.items()
+    )
+
+    # Word embedding
+    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
+    if pad_vocab_size_multiple > 1:
+        word_embeddings = state_dict["roberta.embeddings.word_embeddings.weight"]
+        state_dict["roberta.embeddings.word_embeddings.weight"] = F.pad(
+            word_embeddings, (0, 0, 0, config.vocab_size - word_embeddings.shape[0])
+        )
+        decoder_weight = state_dict["cls.predictions.decoder.weight"]
+        state_dict["cls.predictions.decoder.weight"] = F.pad(
+            decoder_weight, (0, 0, 0, config.vocab_size - decoder_weight.shape[0])
+        )
+        # If the vocab was padded, we want to set the decoder bias for those padded indices to be
+        # strongly negative (i.e. the decoder shouldn't predict those indices).
+        # TD [2022-05-09]: I don't think it affects the MLPerf training.
+        decoder_bias = state_dict["cls.predictions.decoder.bias"]
+        state_dict["cls.predictions.decoder.bias"] = F.pad(
+            decoder_bias, (0, config.vocab_size - decoder_bias.shape[0]), value=-100.0
+        )
+
+    return state_dict
+
+
+@click.command()
+@click.option('--model_name', default='FacebookAI/xlm-roberta-base', help='model name')
+@click.option('--revision', default='main', help='revision')
+@click.option('--task', default='masked_lm', help='task')
+@click.option('--output', default='converted_roberta_weights.bin', help='model name')
+def main(model_name, revision, task, output):
+    
+    if task == 'masked_lm':
+        roberta_model = XLMRobertaForMaskedLM.from_pretrained(model_name, revision=revision)
+    elif task == 'sequence_classification':
+        roberta_model = XLMRobertaForSequenceClassification.from_pretrained(model_name, revision=revision,num_labels=1)
+    config = BertConfig.from_dict(roberta_model.config.to_dict())
+    state_dict = roberta_model.state_dict()
+    new_state_dict = remap_state_dict(state_dict, config)
+    
+    if task == 'masked_lm':
+        flash_model = FlashXLMRobertaForMaskedLM(config)
+    elif task == 'sequence_classification':
+        flash_model = FlashXLMRobertaForSequenceClassification(config)
+
+    for k, v in flash_model.state_dict().items():
+        if k not in new_state_dict:
+            print(f'Use old weights from {k}')
+            new_state_dict[k] = v
+
+    flash_model.load_state_dict(new_state_dict)
+
+    torch.save(new_state_dict, output)
+
+
+if __name__ == '__main__':
+    main()

embedding.py

@@ -0,0 +1,95 @@
+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/embedding.py
+# Commit id: f1a73d074002226c42ce65a1df170ecff9f022c0
+
+# Copyright (c) 2022, Tri Dao.
+
+import torch
+import torch.nn as nn
+from transformers.models.xlm_roberta.modeling_xlm_roberta import \
+    create_position_ids_from_input_ids
+
+
+class XLMRobertaEmbeddings(nn.Module):
+    def __init__(
+        self,
+        embed_dim,
+        vocab_size,
+        max_position_embeddings,
+        type_vocab_size,
+        padding_idx=None,
+        device=None,
+        dtype=None,
+    ):
+        """
+        If max_position_embeddings <= 0, there's no position embeddings
+        If type_vocab_size <= 0, there's no token type embeddings
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs
+        )
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        if self.max_position_embeddings > 0:
+            self.position_embeddings = nn.Embedding(
+                max_position_embeddings, embed_dim, **factory_kwargs
+            )
+        if self.type_vocab_size > 0:
+            self.token_type_embeddings = nn.Embedding(
+                type_vocab_size, embed_dim, **factory_kwargs
+            )
+
+    def forward(
+        self, input_ids, position_ids=None, token_type_ids=None, adapter_mask=None
+    ):
+        """
+        input_ids: (batch, seqlen)
+        position_ids: (batch, seqlen)
+        token_type_ids: (batch, seqlen)
+        adapter_mask: (batch, 1)
+        """
+        batch_size, seqlen = input_ids.shape
+        if adapter_mask is not None:
+            unique_tasks = torch.unique(adapter_mask)
+            embedding_dtype = next(self.word_embeddings.parameters()).dtype
+            embeddings = torch.empty(
+                *input_ids.shape,
+                self.word_embeddings.embedding_dim,
+                dtype=embedding_dtype,
+                device=input_ids.device
+            )
+            for task_id in unique_tasks:
+                task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                task_input_ids = input_ids[task_indices]
+                task_embeddings = self.word_embeddings(task_input_ids, task_id=task_id)
+                embeddings[task_indices] = task_embeddings
+        else:
+            embeddings = self.word_embeddings(input_ids)
+        if self.max_position_embeddings > 0:
+            if position_ids is None:
+                position_ids = create_position_ids_from_input_ids(
+                    input_ids, padding_idx=self.word_embeddings.padding_idx
+                ).to(input_ids.device)
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = embeddings + position_embeddings
+        if self.type_vocab_size > 0:
+            if token_type_ids is None:
+                token_type_ids = torch.zeros(
+                    seqlen, dtype=torch.long, device=input_ids.device
+                )
+
+            if adapter_mask is not None:
+                unique_tasks = torch.unique(adapter_mask)
+                for task_id in unique_tasks:
+                    task_token_type_embeddings = self.token_type_embeddings(
+                        token_type_ids, task_id=task_id
+                    )
+                    task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                    embeddings[task_indices] = (
+                        embeddings[task_indices] + task_token_type_embeddings
+                    )
+            else:
+                token_type_embeddings = self.token_type_embeddings(token_type_ids)
+                embeddings = embeddings + token_type_embeddings
+        return embeddings

mha.py

@@ -0,0 +1,830 @@
+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py
+# Commit id: 6bbc532388e61185a92e2a563126739967b4c8c5
+# Rotary varlen support from https://github.com/Dao-AILab/flash-attention/pull/556
+
+# Copyright (c) 2023, Tri Dao.
+
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+try:
+    from flash_attn import (flash_attn_kvpacked_func,
+                            flash_attn_qkvpacked_func,
+                            flash_attn_varlen_kvpacked_func,
+                            flash_attn_varlen_qkvpacked_func,
+                            flash_attn_with_kvcache)
+except ImportError:
+    flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func = None, None
+    flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None
+    flash_attn_with_kvcache = None
+
+try:
+    from flash_attn.ops.fused_dense import (ColumnParallelLinear, FusedDense,
+                                            RowParallelLinear)
+except ImportError:
+    FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None
+
+from .rotary import RotaryEmbedding
+
+
+# From https://github.com/ofirpress/attention_with_linear_biases/blob/4b92f28a005ead2567abe2359f633e73e08f3833/fairseq/models/transformer.py#L742
+def get_alibi_slopes(nheads):
+    def get_slopes_power_of_2(nheads):
+        start = 2 ** (-(2 ** -(math.log2(nheads) - 3)))
+        ratio = start
+        return [start * ratio**i for i in range(nheads)]
+
+    if math.log2(nheads).is_integer():
+        return get_slopes_power_of_2(nheads)
+    else:
+        closest_power_of_2 = 2 ** math.floor(math.log2(nheads))
+        return (
+            get_slopes_power_of_2(closest_power_of_2)
+            + get_alibi_slopes(2 * closest_power_of_2)[0::2][
+                : nheads - closest_power_of_2
+            ]
+        )
+
+
+class FlashSelfAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(
+        self,
+        causal=False,
+        softmax_scale=None,
+        attention_dropout=0.0,
+        window_size=(-1, -1),
+        alibi_slopes=None,
+        deterministic=False,
+    ):
+        super().__init__()
+        assert (
+            flash_attn_varlen_qkvpacked_func is not None
+        ), "FlashAttention is not installed"
+        assert flash_attn_qkvpacked_func is not None, "FlashAttention is not installed"
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+        self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
+        self.window_size = window_size
+        self.deterministic = deterministic
+
+    def forward(self, qkv, causal=None, cu_seqlens=None, max_seqlen=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value.
+                If cu_seqlens is None and max_seqlen is None, then qkv has shape (B, S, 3, H, D).
+                If cu_seqlens is not None and max_seqlen is not None, then qkv has shape
+                (total, 3, H, D), where total is the sum of the sequence lengths in the batch.
+            causal: if passed, will override self.causal
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into qkv.
+            max_seqlen: int. Maximum sequence length in the batch.
+        Returns:
+        --------
+            out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
+                else (B, S, H, D).
+        """
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+        causal = self.causal if causal is None else causal
+        unpadded = cu_seqlens is not None
+        if self.alibi_slopes is not None:
+            self.alibi_slopes = self.alibi_slopes.to(torch.float32)
+        if unpadded:
+            assert cu_seqlens.dtype == torch.int32
+            assert max_seqlen is not None
+            assert isinstance(max_seqlen, int)
+            return flash_attn_varlen_qkvpacked_func(
+                qkv,
+                cu_seqlens,
+                max_seqlen,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+        else:
+            return flash_attn_qkvpacked_func(
+                qkv,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+
+
+class FlashCrossAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(
+        self,
+        causal=False,
+        softmax_scale=None,
+        attention_dropout=0.0,
+        alibi_slopes=None,
+        window_size=(-1, -1),
+        deterministic=False,
+    ):
+        super().__init__()
+        assert (
+            flash_attn_varlen_kvpacked_func is not None
+        ), "FlashAttention is not installed"
+        assert flash_attn_kvpacked_func is not None, "FlashAttention is not installed"
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+        self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
+        self.window_size = window_size
+        self.deterministic = deterministic
+
+    def forward(
+        self,
+        q,
+        kv,
+        causal=None,
+        cu_seqlens=None,
+        max_seqlen=None,
+        cu_seqlens_k=None,
+        max_seqlen_k=None,
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q: The tensor containing the query. (B, Sq, H, D)
+            kv: The tensor containing the key and value. (B, Sk, 2, H_k, D)
+            causal: if passed, will override self.causal
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into q.
+            max_seqlen: int. Maximum sequence length in the batch of q.
+            cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into kv.
+            max_seqlen_k: int. Maximum sequence length in the batch of k and v.
+        """
+        assert q.dtype in [torch.float16, torch.bfloat16]
+        assert q.is_cuda and kv.is_cuda
+        causal = self.causal if causal is None else causal
+        unpadded = cu_seqlens is not None
+        if self.alibi_slopes is not None:
+            self.alibi_slopes = self.alibi_slopes.to(torch.float32)
+        if unpadded:
+            assert cu_seqlens.dtype == torch.int32
+            assert max_seqlen is not None
+            assert isinstance(max_seqlen, int)
+            assert cu_seqlens_k is not None
+            assert cu_seqlens_k.dtype == torch.int32
+            assert max_seqlen_k is not None
+            assert isinstance(max_seqlen, int)
+            return flash_attn_varlen_kvpacked_func(
+                q,
+                kv,
+                cu_seqlens,
+                cu_seqlens_k,
+                max_seqlen,
+                max_seqlen_k,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+        else:
+            batch_size, seqlen_q = q.shape[0], q.shape[1]
+            seqlen_k = kv.shape[1]
+            assert kv.shape[0] == batch_size and kv.shape[4] == q.shape[3]
+            return flash_attn_kvpacked_func(
+                q,
+                kv,
+                self.drop.p if self.training else 0.0,
+                causal=causal,
+                softmax_scale=self.softmax_scale,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+
+
+class SelfAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(self, qkv, causal=None, key_padding_mask=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, S)
+        """
+        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
+        causal = self.causal if causal is None else causal
+        q, k, v = qkv.unbind(dim=2)
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+        if key_padding_mask is not None:
+            padding_mask = torch.full(
+                (batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device
+            )
+            padding_mask.masked_fill_(key_padding_mask, 0.0)
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+        if causal:
+            # "triu_tril_cuda_template" not implemented for 'BFloat16'
+            # So we have to construct the mask in float
+            causal_mask = torch.triu(
+                torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1
+            )
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + causal_mask.to(dtype=scores.dtype)
+        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
+        attention_drop = self.drop(attention)
+        output = torch.einsum("bhts,bshd->bthd", attention_drop, v)
+        return output
+
+
+class CrossAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(self, q, kv, causal=None, key_padding_mask=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q: The tensor containing the query. (B, Sq, H, D)
+            kv: The tensor containing the key and value. (B, Sk, 2, H_k, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, Sk)
+        """
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        causal = self.causal if causal is None else causal
+        seqlen_k = kv.shape[1]
+        assert kv.shape[0] == batch_size and kv.shape[4] == q.shape[3]
+        if kv.shape[3] != q.shape[2]:  # MQA/GQA
+            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
+        k, v = kv.unbind(dim=2)
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+        if key_padding_mask is not None:
+            padding_mask = torch.full(
+                (batch_size, seqlen_k),
+                -10000.0,
+                dtype=scores.dtype,
+                device=scores.device,
+            )
+            padding_mask.masked_fill_(key_padding_mask, 0.0)
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+        if causal:
+            # causal mask needs to take into account the difference between seqlen_q and seqlen_k
+            row_idx = rearrange(
+                torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
+            )
+            col_idx = torch.arange(seqlen_k, device=kv.device, dtype=torch.long)
+            sk = (
+                seqlen_k
+                if key_padding_mask is None
+                else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
+            )
+            causal_mask = col_idx > row_idx + sk - seqlen_q
+            scores = scores.masked_fill(causal_mask, -10000.0)
+        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
+        attention_drop = self.drop(attention)
+        output = torch.einsum("bhts,bshd->bthd", attention_drop, v)
+        return output
+
+
+class LinearResidual(nn.Linear):
+    """Wrap nn.Linear to return the residual as well. For compatibility with FusedDense."""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return super().forward(input), input
+
+
+def _update_kv_cache(kv, inference_params, layer_idx):
+    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+    # Pre-allocate memory for key-values for inference.
+    num_heads, head_dim = kv.shape[-2:]
+    if layer_idx not in inference_params.key_value_memory_dict:
+        kv_cache = torch.empty(
+            inference_params.max_batch_size,
+            inference_params.max_seqlen,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
+        )
+        inference_params.key_value_memory_dict[layer_idx] = kv_cache
+    else:
+        kv_cache = inference_params.key_value_memory_dict[layer_idx]
+    # Adjust key and value for inference
+    batch_start = inference_params.batch_size_offset
+    batch_end = batch_start + kv.shape[0]
+    sequence_start = inference_params.seqlen_offset
+    sequence_end = sequence_start + kv.shape[1]
+    assert batch_end <= kv_cache.shape[0]
+    assert sequence_end <= kv_cache.shape[1]
+    assert kv_cache is not None
+    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
+    return kv_cache[batch_start:batch_end, :sequence_end, ...]
+
+
+class MHA(nn.Module):
+    """Multi-head self-attention and cross-attention"""
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        num_heads_kv=None,
+        cross_attn=False,
+        qkv_proj_bias=True,
+        out_proj_bias=True,
+        dropout=0.0,
+        softmax_scale=None,
+        causal=False,
+        layer_idx=None,
+        dwconv=False,
+        rotary_emb_dim=0,
+        rotary_emb_base=10000.0,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        use_alibi=False,
+        window_size=(-1, -1),
+        fused_bias_fc=False,
+        use_flash_attn=False,
+        return_residual=False,
+        checkpointing=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        """
+        num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads.
+        return_residual: whether to return the input x along with the output. This is for
+            performance reason: for post-norm architecture, returning the input allows us
+            to fuse the backward of nn.Linear with the residual connection.
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.cross_attn = cross_attn
+        self.causal = causal
+        self.layer_idx = layer_idx
+        self.dwconv = dwconv
+        self.rotary_emb_dim = rotary_emb_dim
+        self.use_flash_attn = use_flash_attn
+        self.return_residual = return_residual
+        self.checkpointing = checkpointing
+        if use_alibi:
+            assert use_flash_attn, "ALiBi code path requires flash_attn"
+            alibi_slopes = torch.tensor(get_alibi_slopes(num_heads), device=device)
+        else:
+            alibi_slopes = None
+        if window_size != (-1, -1):
+            assert (
+                use_flash_attn
+            ), "Local (sliding window) attention code path requires flash_attn"
+
+        self.num_heads = num_heads
+        self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
+        assert (
+            self.num_heads % self.num_heads_kv == 0
+        ), "num_heads must be divisible by num_heads_kv"
+        assert (
+            self.embed_dim % num_heads == 0
+        ), "embed_dim must be divisible by num_heads"
+        self.head_dim = self.embed_dim // num_heads
+        qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
+        kv_dim = 2 * self.head_dim * self.num_heads_kv
+
+        if self.rotary_emb_dim > 0:
+            assert (
+                not cross_attn
+            ), "MHA with rotary embedding does not support cross-attention yet"
+            assert RotaryEmbedding is not None, "rotary_emb is not installed"
+            self.rotary_emb = RotaryEmbedding(
+                self.rotary_emb_dim,
+                base=rotary_emb_base,
+                scale_base=rotary_emb_scale_base,
+                interleaved=rotary_emb_interleaved,
+                device=device,
+                use_flash_attn=use_flash_attn,
+            )
+
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+        linear_resid_cls = (
+            LinearResidual
+            if not fused_bias_fc
+            else partial(FusedDense, return_residual=True)
+        )
+        wqkv_cls = linear_cls if not self.return_residual else linear_resid_cls
+        inner_attn_cls = (
+            partial(
+                FlashSelfAttention, alibi_slopes=alibi_slopes, window_size=window_size
+            )
+            if use_flash_attn
+            else SelfAttention
+        )
+        inner_cross_attn_cls = (
+            partial(
+                FlashCrossAttention, alibi_slopes=alibi_slopes, window_size=window_size
+            )
+            if use_flash_attn
+            else CrossAttention
+        )
+        if not self.cross_attn:
+            self.Wqkv = wqkv_cls(
+                embed_dim, qkv_dim, bias=qkv_proj_bias, **factory_kwargs
+            )
+        else:
+            self.Wq = linear_cls(
+                embed_dim, embed_dim, bias=qkv_proj_bias, **factory_kwargs
+            )
+            self.Wkv = wqkv_cls(embed_dim, kv_dim, bias=qkv_proj_bias, **factory_kwargs)
+        if self.dwconv:
+            if self.num_heads_kv == self.num_heads:
+                self.dwconv_qkv = nn.Conv1d(
+                    qkv_dim, qkv_dim, kernel_size=3, padding=2, groups=qkv_dim
+                )
+            else:
+                self.dwconv_q = nn.Conv1d(
+                    embed_dim, embed_dim, kernel_size=3, padding=2, groups=embed_dim
+                )
+                self.dwconv_kv = nn.Conv1d(
+                    kv_dim, kv_dim, kernel_size=3, padding=2, groups=kv_dim
+                )
+        self.inner_attn = inner_attn_cls(
+            causal=causal,
+            softmax_scale=softmax_scale,
+            attention_dropout=dropout,
+        )
+        self.inner_cross_attn = inner_cross_attn_cls(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+        self.out_proj = linear_cls(
+            embed_dim, embed_dim, bias=out_proj_bias, **factory_kwargs
+        )
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        dtype = self.out_proj.weight.dtype if dtype is None else dtype
+        device = self.out_proj.weight.device
+        return torch.empty(
+            batch_size,
+            max_seqlen,
+            2,
+            self.num_heads_kv,
+            self.head_dim,
+            dtype=dtype,
+            device=device,
+        )
+
+    def _update_kv_cache(self, kv, inference_params):
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+        assert not self.dwconv, "Generation does not support dwconv yet"
+        assert (
+            self.layer_idx is not None
+        ), "Generation requires layer_idx in the constructor"
+        return _update_kv_cache(kv, inference_params, self.layer_idx)
+
+    def _apply_rotary_update_kvcache_attention(self, q, kv, inference_params):
+        """
+        Fast path that combine 3 steps: apply rotary to Q and K, update kv cache, and apply attention.
+        q: (batch_size, seqlen_q, nheads, head_dim)
+        kv: (batch_size, seqlen_k, 2, nheads_kv, head_dim)
+        """
+        assert inference_params is not None and inference_params.seqlen_offset > 0
+        assert self.use_flash_attn
+        if self.rotary_emb_dim > 0:
+            assert self.rotary_emb.scale is None, "This code path does not support xPos"
+            self.rotary_emb._update_cos_sin_cache(
+                inference_params.max_seqlen, device=q.device, dtype=q.dtype
+            )
+            rotary_cos, rotary_sin = (
+                self.rotary_emb._cos_cached,
+                self.rotary_emb._sin_cached,
+            )
+        else:
+            rotary_cos, rotary_sin = None, None
+        batch = q.shape[0]
+        kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+        cache_seqlens = (
+            inference_params.lengths_per_sample[:batch]
+            if inference_params.lengths_per_sample is not None
+            else inference_params.seqlen_offset
+        )
+        alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+        context = flash_attn_with_kvcache(
+            q,
+            kv_cache[:, :, 0],
+            kv_cache[:, :, 1],
+            kv[:, :, 0],
+            kv[:, :, 1],
+            rotary_cos=rotary_cos,
+            rotary_sin=rotary_sin,
+            cache_seqlens=cache_seqlens,
+            softmax_scale=self.inner_cross_attn.softmax_scale,
+            causal=self.inner_cross_attn.causal,
+            rotary_interleaved=(
+                self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False
+            ),
+            alibi_slopes=alibi_slopes,
+        )
+        return context
+
+    def _update_kvcache_attention(self, q, kv, inference_params):
+        """Write kv to inference_params, then do attention"""
+        if (
+            inference_params.seqlen_offset == 0
+            or flash_attn_with_kvcache is None
+            or not self.use_flash_attn
+        ):
+            # TODO: this only uses seqlen_offset and not lengths_per_sample.
+            kv = self._update_kv_cache(kv, inference_params)
+            return self.inner_cross_attn(q, kv)
+        else:
+            batch = q.shape[0]
+            kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+            cache_seqlens = (
+                inference_params.lengths_per_sample[:batch]
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+            alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+            return flash_attn_with_kvcache(
+                q,
+                kv_cache[:, :, 0],
+                kv_cache[:, :, 1],
+                kv[:, :, 0],
+                kv[:, :, 1],
+                cache_seqlens=cache_seqlens,
+                softmax_scale=self.inner_cross_attn.softmax_scale,
+                causal=self.inner_cross_attn.causal,
+                alibi_slopes=alibi_slopes,
+            )
+
+    def forward(
+        self,
+        x,
+        x_kv=None,
+        key_padding_mask=None,
+        cu_seqlens=None,
+        max_seqlen=None,
+        mixer_subset=None,
+        inference_params=None,
+        adapter_mask=None,
+        **kwargs,
+    ):
+        """
+        Arguments:
+            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
+                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
+                is the is the sum of the sequence lengths in the batch.
+            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into x. Only applicable when using
+                FlashAttention.
+            max_seqlen: int. Maximum sequence length in the batch.
+            key_padding_mask: boolean mask, True means to keep, False means to mask out.
+                (batch, seqlen). Only applicable when not using FlashAttention.
+            mixer_subset: for cross-attention only. If not None, will take a subset of x
+                before applying the query projection. Useful for e.g., ViT where we only care
+                about the CLS token in the last layer.
+            inference_params: for generation. Adapted from Megatron-LM (and Apex)
+            https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
+        """
+        if cu_seqlens is not None:
+            assert max_seqlen is not None
+            assert key_padding_mask is None
+            assert self.use_flash_attn
+            assert not self.dwconv
+        if key_padding_mask is not None:
+            assert cu_seqlens is None
+            assert max_seqlen is None
+            assert not self.use_flash_attn
+        if inference_params is not None:
+            assert key_padding_mask is None
+            assert cu_seqlens is None and max_seqlen is None
+            assert not self.dwconv
+
+        kwargs = (
+            {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen, **kwargs}
+            if self.use_flash_attn
+            else {"key_padding_mask": key_padding_mask, **kwargs}
+        )
+        seqlen_offset = (
+            0
+            if inference_params is None
+            else (
+                inference_params.lengths_per_sample
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+        )
+        rotary_max_seqlen = (
+            inference_params.max_sequence_len
+            if inference_params is not None
+            else max_seqlen
+        )
+        if not self.cross_attn and self.num_heads_kv == self.num_heads:
+            assert x_kv is None and mixer_subset is None
+
+            if adapter_mask is not None:
+                unique_tasks = torch.unique(adapter_mask)
+                qkv_dtype = next(self.Wqkv.parameters()).dtype
+                qkv = torch.empty(
+                    *x.shape[:-1],
+                    self.Wqkv.out_features,
+                    dtype=qkv_dtype,
+                    device=x.device,
+                )
+                for task_id in unique_tasks:
+                    task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                    task_tensor = x[task_indices]
+                    if not self.return_residual:
+                        task_qkv = self.Wqkv(task_tensor, task_id=task_id)
+                    else:
+                        task_qkv, _ = self.Wqkv(
+                            task_tensor, task_id=task_id, residual=True
+                        )
+                    qkv[task_indices] = task_qkv
+            else:
+                if not self.return_residual:
+                    qkv = self.Wqkv(x)
+                else:
+                    if hasattr(self.Wqkv, "parametrizations"):
+                        qkv, x = self.Wqkv(x, residual=True)
+                    else:
+                        qkv, x = self.Wqkv(x)
+
+            if self.dwconv:
+                qkv = rearrange(
+                    self.dwconv_qkv(rearrange(qkv, "b s d -> b d s"))[..., :-2],
+                    "b d s -> b s d",
+                ).contiguous()
+            qkv = rearrange(
+                qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim
+            )
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    qkv = self.rotary_emb(
+                        qkv,
+                        seqlen_offset=seqlen_offset,
+                        cu_seqlens=cu_seqlens,
+                        max_seqlen=rotary_max_seqlen,
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_attn(qkv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_attn, qkv, **kwargs
+                        )
+                else:
+                    context = self._update_kvcache_attention(
+                        qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                    )
+            else:
+                context = self._apply_rotary_update_kvcache_attention(
+                    qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                )
+        else:
+            if self.cross_attn:
+                if not self.return_residual:
+                    q = self.Wq(x if mixer_subset is None else x[:, mixer_subset])
+                    kv = self.Wkv(x_kv if x_kv is not None else x)
+                else:
+                    if x_kv is not None:
+                        kv, x_kv = self.Wkv(x_kv)
+                    else:
+                        kv, x = self.Wkv(x)
+                    q = self.Wq(x if mixer_subset is None else x[:, mixer_subset])
+            else:
+                assert self.num_heads_kv != self.num_heads
+                if not self.return_residual:
+                    qkv = self.Wqkv(x)
+                else:
+                    qkv, x = self.Wqkv(x)
+                q = qkv[..., : self.num_heads * self.head_dim]
+                kv = qkv[..., self.num_heads * self.head_dim :]
+            q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
+            kv = rearrange(
+                kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim
+            )
+            if self.dwconv:
+                q = rearrange(
+                    self.dwconv_q(rearrange(q, "b s d -> b d s"))[..., :-2],
+                    "b d s -> b s d",
+                ).contiguous()
+                kv = rearrange(
+                    self.dwconv_kv(rearrange(kv, "b s d -> b d s"))[..., :-2],
+                    "b d s -> b s d",
+                ).contiguous()
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    q, kv = self.rotary_emb(
+                        q,
+                        kv,
+                        seqlen_offset=seqlen_offset,
+                        cu_seqlens=cu_seqlens,
+                        max_seqlen=rotary_max_seqlen,
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_cross_attn(q, kv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_cross_attn, q, kv, **kwargs
+                        )
+                else:
+                    context = self._update_kvcache_attention(q, kv, inference_params)
+            else:
+                context = self._apply_rotary_update_kvcache_attention(
+                    q, kv, inference_params
+                )
+
+        inp = rearrange(context, "... h d -> ... (h d)")
+        if adapter_mask is not None:
+            unique_tasks = torch.unique(adapter_mask)
+            out_dtype = next(self.out_proj.parameters()).dtype
+            out = torch.empty(
+                *inp.shape[:-1],
+                self.out_proj.out_features,
+                dtype=out_dtype,
+                device=inp.device,
+            )
+            for task_id in unique_tasks:
+                task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                task_tensor = inp[task_indices]
+                task_out = self.out_proj(task_tensor, task_id=task_id)
+                out[task_indices] = task_out
+        else:
+            out = self.out_proj(inp)
+        return out if not self.return_residual else (out, x)

mlp.py

@@ -0,0 +1,237 @@
+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mlp.py
+# Commit id: c3b219665292c61a51153d0ded4473c494296382
+
+# Copyright (c) 2023, Tri Dao.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed import ProcessGroup
+
+try:
+    from flash_attn.ops.activations import swiglu
+except ImportError:
+    swiglu = None
+
+try:
+    from flash_attn.ops.fused_dense import (ColumnParallelLinear,
+                                            RowParallelLinear)
+except ImportError:
+    ColumnParallelLinear, RowParallelLinear = None, None
+
+try:
+    from flash_attn.ops.fused_dense import FusedMLP, ParallelFusedMLP
+except ImportError:
+    FusedMLP, ParallelFusedMLP = None, None
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        activation=F.gelu,
+        bias1=True,
+        bias2=True,
+        return_residual=False,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        out_features = out_features if out_features is not None else in_features
+        hidden_features = (
+            hidden_features if hidden_features is not None else in_features * 4
+        )
+        self.return_residual = return_residual
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias1, **factory_kwargs)
+        self.activation = activation
+        self.fc2 = nn.Linear(
+            hidden_features, out_features, bias=bias2, **factory_kwargs
+        )
+
+    def forward(self, x, adapter_mask=None):
+        if adapter_mask is not None:
+            unique_tasks = torch.unique(adapter_mask)
+            fc1_dtype = next(self.fc1.parameters()).dtype
+            y = torch.empty(
+                *x.shape[:-1], self.fc1.out_features, dtype=fc1_dtype, device=x.device
+            )
+            for task_id in unique_tasks:
+                task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                task_tensor = x[task_indices]
+                task_y = self.fc1(task_tensor, task_id=task_id)
+                y[task_indices] = task_y
+        else:
+            y = self.fc1(x)
+
+        y = self.activation(y)
+
+        if adapter_mask is not None:
+            unique_tasks = torch.unique(adapter_mask)
+            fc2_dtype = next(self.fc2.parameters()).dtype
+            out = torch.empty(
+                *y.shape[:-1], self.fc2.out_features, dtype=fc2_dtype, device=y.device
+            )
+            for task_id in unique_tasks:
+                task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                task_tensor = y[task_indices]
+                task_out = self.fc2(task_tensor, task_id=task_id)
+                out[task_indices] = task_out
+        else:
+            out = self.fc2(y)
+
+        return out if not self.return_residual else (out, x)
+
+
+class ParallelMLP(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        activation=F.gelu,
+        process_group: ProcessGroup = None,
+        sequence_parallel=True,
+        bias1=True,
+        bias2=True,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        assert ColumnParallelLinear is not None, "Need to install fused_dense"
+        assert RowParallelLinear is not None, "Need to install fused_dense"
+        out_features = out_features if out_features is not None else in_features
+        hidden_features = (
+            hidden_features if hidden_features is not None else in_features * 4
+        )
+        self.fc1 = ColumnParallelLinear(
+            in_features,
+            hidden_features,
+            process_group,
+            bias=bias1,
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
+        self.activation = activation
+        self.fc2 = RowParallelLinear(
+            hidden_features,
+            out_features,
+            process_group,
+            bias=bias2,
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
+
+    def forward(self, x):
+        y = self.fc1(x)
+        y = self.activation(y)
+        y = self.fc2(y)
+        return y
+
+
+class GatedMlp(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        activation=F.sigmoid,
+        bias1=True,
+        bias2=True,
+        multiple_of=128,
+        return_residual=False,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        out_features = out_features if out_features is not None else in_features
+        hidden_features = (
+            hidden_features if hidden_features is not None else int(8 * in_features / 3)
+        )
+        hidden_features = (
+            (hidden_features + multiple_of - 1) // multiple_of * multiple_of
+        )
+        self.return_residual = return_residual
+        self.fc1 = nn.Linear(
+            in_features, 2 * hidden_features, bias=bias1, **factory_kwargs
+        )
+        self.activation = activation
+        self.fc2 = nn.Linear(
+            hidden_features, out_features, bias=bias2, **factory_kwargs
+        )
+
+    def forward(self, x):
+        y = self.fc1(x)
+        if self.activation == F.sigmoid:  # Special case for GLU
+            y = F.glu(y, dim=-1)
+        elif (
+            self.activation == F.silu and swiglu is not None
+        ):  # Special case for SwiGLU
+            y, gate = y.chunk(2, dim=-1)
+            y = swiglu(gate, y)
+        else:
+            y, gate = y.chunk(2, dim=-1)
+            y = y * self.activation(gate)
+        y = self.fc2(y)
+        return y if not self.return_residual else (y, x)
+
+
+class ParallelGatedMlp(nn.Module):
+    """Parallel GatedMlp"""
+
+    def __init__(
+        self,
+        in_features,
+        process_group,
+        hidden_features=None,
+        out_features=None,
+        activation=F.sigmoid,
+        bias1=True,
+        bias2=True,
+        multiple_of=128,
+        sequence_parallel=True,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        out_features = out_features if out_features is not None else in_features
+        hidden_features = (
+            hidden_features if hidden_features is not None else int(8 * in_features / 3)
+        )
+        hidden_features = (
+            (hidden_features + multiple_of - 1) // multiple_of * multiple_of
+        )
+        if ColumnParallelLinear is None or RowParallelLinear is None:
+            raise ImportError("fused_dense is not installed")
+        self.fc1 = ColumnParallelLinear(
+            in_features,
+            2 * hidden_features,
+            process_group,
+            bias=bias1,
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
+        self.activation = activation
+        self.fc2 = RowParallelLinear(
+            hidden_features,
+            out_features,
+            process_group,
+            bias=bias2,
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
+
+    def forward(self, x):
+        y = self.fc1(x)
+        if self.activation == F.sigmoid:  # Special case for GLU
+            y = F.glu(y, dim=-1)
+        else:
+            y, gate = y.chunk(2, dim=-1)
+            y = y * self.activation(gate)
+        y = self.fc2(y)
+        return y

modeling_lora.py

@@ -0,0 +1,426 @@
+import math
+import os
+from functools import partial
+from typing import Iterator, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.utils.parametrize as parametrize
+from torch import nn
+from torch.nn import Parameter
+from torch.nn import functional as F
+from transformers import PretrainedConfig
+
+from .configuration_xlm_roberta import XLMRobertaFlashConfig
+from .modeling_xlm_roberta import (
+    XLMRobertaFlashConfig,
+    XLMRobertaModel,
+    XLMRobertaPreTrainedModel,
+)
+
+
+def initialized_weights(
+    shape: Tuple[int], num_adaptations: int, init: str = "kaiming"
+) -> torch.Tensor:
+    weight_data = []
+    for _ in range(num_adaptations):
+        new_adaption = torch.zeros(shape)
+        if init == "kaiming":
+            nn.init.kaiming_uniform_(new_adaption, a=math.sqrt(5))
+        elif init == "normal":
+            nn.init.normal_(new_adaption)
+        else:
+            raise NotImplementedError
+        weight_data.append(new_adaption)
+    return torch.stack(weight_data, dim=0)
+
+
+class LoRAParametrization(nn.Module):
+    """
+    This LoRA implementation was inspired by  https://github.com/cccntu/minLoRA
+    The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software
+    and associated documentation files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+    and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+    The above copyright notice and this permission notice shall be included in all copies or substantial
+    portions of the Software.
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
+    LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+    WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+    """
+
+    def __init__(
+        self,
+        fan_in: int,
+        fan_out: int,
+        layer_type: str = "linear",
+        num_adaptations: int = 1,
+        rank: int = 4,
+        dropout_p: float = 0.0,
+        alpha: float = 1,
+    ):
+        super().__init__()
+        # if weight is stored as (fan_out, fan_in), the memory layout of A & B follows (W + BA)x
+        # otherwise, it's x(W + AB). This allows us to tie the weights between linear layers and embeddings
+        fan_in_fan_out = layer_type == "embedding"
+        self.swap = (lambda x: (x[1], x[0])) if fan_in_fan_out else (lambda x: x)
+
+        if layer_type == "linear":
+            self.lora_A = nn.Parameter(
+                initialized_weights((rank, fan_in), num_adaptations, init="kaiming")
+            )
+            self.lora_B = nn.Parameter(torch.zeros((num_adaptations, fan_out, rank)))
+        elif layer_type == "embedding":
+            self.lora_A = nn.Parameter(torch.zeros((num_adaptations, fan_in, rank)))
+            self.lora_B = nn.Parameter(
+                initialized_weights(
+                    (rank, fan_out), num_adaptations=num_adaptations, init="normal"
+                )
+            )
+        else:
+            raise NotImplementedError
+
+        self.lora_alpha, self.rank = alpha, rank
+        self.scaling = alpha / rank
+        self.lora_dropout = nn.Dropout(p=dropout_p) if dropout_p > 0 else lambda x: x
+        self.dropout_fn = self._dropout if dropout_p > 0 else lambda x: x
+        self.register_buffer(
+            "lora_dropout_mask",
+            torch.ones(self.swap((1, fan_in)), dtype=self.lora_A.dtype),
+            persistent=False,
+        )
+
+    def _dropout(self, A):
+        # to mimic the original implementation: A @ dropout(x), we do (A * dropout(ones)) @ x
+        return A * self.lora_dropout(self.lora_dropout_mask)
+
+    def lora_forward(self, X, current_task):
+        return (
+            X
+            + torch.matmul(
+                *self.swap(
+                    (
+                        self.lora_B[current_task],
+                        self.dropout_fn(self.lora_A[current_task]),
+                    )
+                )
+            ).view(X.shape)
+            * self.scaling
+        )
+
+    def forward(self, X):
+        return X
+
+    @classmethod
+    def from_linear(
+        cls,
+        layer: nn.Module,
+        num_adaptations: int,
+        rank: int,
+        dropout_p: float,
+        alpha: float,
+    ):
+        assert isinstance(layer, nn.Linear)
+        fan_out, fan_in = layer.weight.shape
+        return cls(
+            fan_in,
+            fan_out,
+            num_adaptations=num_adaptations,
+            layer_type="linear",
+            rank=rank,
+            dropout_p=dropout_p,
+            alpha=alpha,
+        )
+
+    @classmethod
+    def from_embedding(
+        cls,
+        layer: nn.Module,
+        num_adaptations: int,
+        rank: int,
+        dropout_p: float,
+        alpha: float,
+    ):
+        assert isinstance(layer, nn.Embedding)
+        fan_in, fan_out = layer.weight.shape
+        return cls(
+            fan_in,
+            fan_out,
+            num_adaptations=num_adaptations,
+            layer_type="embedding",
+            rank=rank,
+            dropout_p=dropout_p,
+            alpha=alpha,
+        )
+
+    @classmethod
+    def add_to_layer(
+        cls,
+        layer: nn.Module,
+        num_adaptations: int,
+        rank: int,
+        dropout_p: float,
+        alpha: float,
+    ):
+        """
+        Registering LoRA adapters to all embedding and linear layers.
+        Additionally, we implement a custom forward function for LoRA parametrization.
+        This function modifies the layer's forward pass to optionally use task-specific
+        parameters. When a `task_id` is provided, it employs a LoRA parametrization
+        to modify the original weights according to the specific task. This allows
+        the layer to adapt dynamically to different tasks at runtime. If no `task_id`
+        is specified, the layer uses its original weights.
+        """
+        if isinstance(layer, nn.Linear):
+            parametrize.register_parametrization(
+                layer,
+                "weight",
+                cls.from_linear(
+                    layer,
+                    num_adaptations=num_adaptations,
+                    rank=rank,
+                    dropout_p=dropout_p,
+                    alpha=alpha,
+                ),
+            )
+
+            def new_forward(self, input, task_id=None, residual=False):
+                if task_id is not None:
+                    weights = self.parametrizations.weight[0].lora_forward(
+                        self.weight, current_task=task_id
+                    )
+                else:
+                    weights = self.weight
+
+                out = F.linear(input, weights, self.bias)
+
+                if residual:
+                    return out, input
+                return out
+
+            layer.forward = new_forward.__get__(layer, layer.__class__)
+
+        elif isinstance(layer, nn.Embedding):
+            parametrize.register_parametrization(
+                layer,
+                "weight",
+                cls.from_embedding(
+                    layer,
+                    num_adaptations=num_adaptations,
+                    rank=rank,
+                    dropout_p=dropout_p,
+                    alpha=alpha,
+                ),
+            )
+
+            def new_forward(self, input, task_id=None):
+                if task_id is not None:
+                    weights = self.parametrizations.weight[0].lora_forward(
+                        self.weight, current_task=task_id
+                    )
+                else:
+                    weights = self.weight
+
+                out = F.embedding(
+                    input,
+                    weights,
+                    self.padding_idx,
+                    self.max_norm,
+                    self.norm_type,
+                    self.scale_grad_by_freq,
+                    self.sparse,
+                )
+
+                return out
+
+            layer.forward = new_forward.__get__(layer, layer.__class__)
+
+
+class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
+    """
+    A wrapper class around the Jina XLM-RoBERTa model that integrates LoRA (Low-Rank Adaptation) adapters.
+    """
+
+    def __init__(
+        self,
+        config: XLMRobertaFlashConfig,
+        roberta: Optional[XLMRobertaModel] = None,
+        add_pooling_layer: bool = True,
+    ):
+        super().__init__(config)
+        if roberta is None:
+            self.roberta = XLMRobertaModel(config, add_pooling_layer=add_pooling_layer)
+        else:
+            self.roberta = roberta
+
+        self._lora_adaptations = config.lora_adaptations
+        if (
+            not isinstance(self._lora_adaptations, list)
+            or len(self._lora_adaptations) < 1
+        ):
+            raise ValueError(
+                f"`lora_adaptations` must be a list and contain at least one element"
+            )
+        self._task_instructions = config.task_instructions
+        if (
+            not isinstance(self._task_instructions, dict)
+            or len(self._task_instructions) != len(self._lora_adaptations)
+            or not all(
+                [v in self._lora_adaptations for v in self._task_instructions.keys()]
+            )
+        ):
+            raise ValueError(
+                f"`task_instructions` must be a dict and contain the same number of elements "
+                f"as `lora_adaptations` with all keys in `task_instructions` present in `lora_adaptations`."
+            )
+        self._adaptation_map = {
+            name: idx for idx, name in enumerate(self._lora_adaptations)
+        }
+        self._rank = config.lora_rank
+        self._dropout_p = config.lora_dropout_p
+        self._alpha = config.lora_alpha
+        self._register_lora(
+            num_adaptations=len(self._lora_adaptations),
+            rank=self._rank,
+            dropout_p=self._dropout_p,
+            alpha=self._alpha,
+        )
+        self.main_params_trainable = config.lora_main_params_trainable
+
+    @property
+    def rotary_emb_base(self):
+        return self.roberta.rotary_emb_base
+
+    @rotary_emb_base.setter
+    def rotary_emb_base(self, base):
+        self.roberta.rotary_emb_base = base
+
+    @property
+    def main_params_trainable(self):
+        return self._main_params_trainable
+
+    @main_params_trainable.setter
+    def main_params_trainable(self, val: bool):
+        """Whether the main parameters (i.e. those that are not LoRA) should be trainable.
+        This method sets the `requires_grad_` attribute of the main weights
+        and controls which parameters are returned in `self.parameters()`.
+        :param val: Whether or not to make the parameters trainable.
+        :return: None
+        """
+        self._main_params_trainable = val
+        for name, param in super().named_parameters():
+            if "lora" not in name:
+                param.requires_grad_(val)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ):
+        for key in list(kwargs.keys()):
+            if key in config.to_dict():
+                config.update({key: kwargs.pop(key)})
+        if config.load_trained_adapters:  # checkpoint already contains LoRA adapters
+            return super().from_pretrained(
+                pretrained_model_name_or_path,
+                *model_args,
+                config=config,
+                cache_dir=cache_dir,
+                ignore_mismatched_sizes=ignore_mismatched_sizes,
+                force_download=force_download,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                use_safetensors=use_safetensors,
+                **kwargs,
+            )
+        else:  # initializing new adapters
+            roberta = XLMRobertaModel.from_pretrained(
+                pretrained_model_name_or_path,
+                *model_args,
+                use_flash_attn=config.use_flash_attn,
+                **kwargs,
+            )
+            return cls(config, roberta=roberta)
+
+    def _register_lora(self, num_adaptations, rank, dropout_p, alpha):
+        self.apply(
+            partial(
+                LoRAParametrization.add_to_layer,
+                num_adaptations=num_adaptations,
+                rank=rank,
+                dropout_p=dropout_p,
+                alpha=alpha,
+            )
+        )
+
+    def forward(self, *args, **kwargs):
+        return self.roberta(*args, **kwargs)
+
+    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+        for _, param in self.named_parameters(recurse=recurse):
+            yield param
+
+    def named_parameters(
+        self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
+    ) -> Iterator[Tuple[str, Parameter]]:
+        for name, param in super().named_parameters(
+            prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate
+        ):
+            if "lora" in name or self.main_params_trainable:
+                yield name, param
+
+    @torch.inference_mode()
+    def encode(
+        self,
+        sentences: Union[str, List[str]],
+        *args,
+        task: Optional[str] = None,
+        **kwargs,
+    ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
+        """
+        Computes sentence embeddings.
+        sentences(`str` or `List[str]`):
+            Sentence or sentences to be encoded
+        task(`str`, *optional*, defaults to `None`):
+            Specifies the task for which the encoding is intended. If `task` is not provided,
+            all LoRA adapters are disabled, and the model reverts to its original,
+            general-purpose weights.
+        """
+        if task and task not in self._lora_adaptations:
+            raise ValueError(
+                f"Unsupported task '{task}'. "
+                f"Supported tasks are: {', '.join(self.config.lora_adaptations)}."
+                f"Alternatively, don't pass the `task` argument to disable LoRA."
+            )
+        adapter_mask = None
+        if task:
+            task_id = self._adaptation_map[task]
+            num_examples = 1 if isinstance(sentences, str) else len(sentences)
+            adapter_mask = torch.full(
+                (num_examples,), task_id, dtype=torch.int32, device=self.device
+            )
+            if isinstance(sentences, str):
+                sentences = self._task_instructions[task] + sentences
+            else:
+                sentences = [
+                    self._task_instructions[task] + sentence for sentence in sentences
+                ]
+        return self.roberta.encode(
+            sentences, *args, adapter_mask=adapter_mask, **kwargs
+        )
+

modeling_xlm_roberta.py

@@ -0,0 +1,1254 @@
+# This implementation was adopted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/models/bert.py
+# Commit id: abbc1311731867310635f9edc2a9ec18317c8c48
+# Copyright (c) 2022, Tri Dao.
+# This BERT implementation is based on our MLPerf 2.0 and MLPerf 2.1 BERT implementation.
+# https://github.com/mlcommons/training_results_v2.0/blob/main/HazyResearch/benchmarks/bert/implementations/pytorch/modeling.py
+# https://github.com/mlcommons/training_results_v2.1/blob/main/Azure-HazyResearch/benchmarks/bert/implementations/ND96amsr_A100_v4/modeling.py
+
+# Inspired by https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py
+
+import importlib.util
+import logging
+import re
+from collections import OrderedDict
+from collections.abc import Sequence
+from functools import partial
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers import AutoTokenizer, PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.bert.modeling_bert import (
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    BertForPreTrainingOutput,
+)
+from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaLMHead
+
+from .rotary import RotaryEmbedding
+from .block import Block
+from .configuration_xlm_roberta import XLMRobertaFlashConfig
+from .embedding import XLMRobertaEmbeddings
+from .mha import MHA
+from .mlp import FusedMLP, Mlp
+from .xlm_padding import index_first_axis_residual, pad_input, unpad_input
+
+try:
+    from flash_attn.ops.fused_dense import FusedDense
+except ImportError:
+    FusedDense = None
+
+try:
+    from flash_attn.ops.triton.layer_norm import layer_norm_fn
+except ImportError:
+    layer_norm_fn = None
+
+
+try:
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
+except ImportError:
+    CrossEntropyLoss = torch.nn.CrossEntropyLoss
+
+try:
+    from tqdm.autonotebook import trange
+except ImportError:
+    trange = None
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_use_flash_attn(config: XLMRobertaFlashConfig):
+    if not getattr(config, "use_flash_attn", False) or not torch.cuda.is_available():
+        return False
+    if importlib.util.find_spec("flash_attn") is None:
+        logger.warning(
+            "flash_attn is not installed. Using PyTorch native attention implementation."
+        )
+        return False
+    return True
+
+
+def create_mixer_cls(config, cross_attn=False, return_residual=False):
+    use_flash_attn = get_use_flash_attn(config)
+    fused_bias_fc = getattr(config, "fused_bias_fc", False)
+    rotary_kwargs = {}
+    if config.position_embedding_type == "rotary":
+        rotary_kwargs["rotary_emb_dim"] = getattr(
+            config, "rotary_emb_dim", config.hidden_size / config.num_attention_heads
+        )
+        rotary_kwargs["rotary_emb_base"] = config.rotary_emb_base
+        rotary_kwargs["rotary_emb_scale_base"] = getattr(
+            config, "rotary_emb_scale_base", None
+        )
+        rotary_kwargs["rotary_emb_interleaved"] = getattr(
+            config, "rotary_emb_interleaved", False
+        )
+    mixer_cls = partial(
+        MHA,
+        num_heads=config.num_attention_heads,
+        cross_attn=cross_attn,
+        dropout=config.attention_probs_dropout_prob,
+        causal=False,
+        fused_bias_fc=fused_bias_fc,
+        use_flash_attn=use_flash_attn,
+        return_residual=return_residual,
+        use_alibi=config.position_embedding_type == "alibi",
+        **rotary_kwargs,
+    )
+    return mixer_cls
+
+
+def create_mlp_cls(config, layer_idx=None, return_residual=False):
+    inner_dim = config.intermediate_size
+    fused_mlp = getattr(config, "fused_mlp", False)
+    if fused_mlp:
+        assert config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"], (
+            "fused_mlp only " "supports approximate gelu"
+        )
+    if not fused_mlp:
+        approximate = (
+            "tanh"
+            if config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"]
+            else "none"
+        )
+        mlp_cls = partial(
+            Mlp,
+            hidden_features=inner_dim,
+            activation=partial(F.gelu, approximate=approximate),
+            return_residual=return_residual,
+        )
+    else:
+        if FusedMLP is None:
+            raise ImportError("fused_dense is not installed")
+        mlp_checkpoint_lvl = getattr(config, "mlp_checkpoint_lvl", 0)
+        # mlp_checkpoint_lvl could be a list, which contains the checkpoint_lvl for each layer
+        if isinstance(mlp_checkpoint_lvl, Sequence):
+            assert layer_idx is not None
+            mlp_checkpoint_lvl = mlp_checkpoint_lvl[layer_idx]
+        mlp_cls = partial(
+            FusedMLP,
+            hidden_features=inner_dim,
+            checkpoint_lvl=mlp_checkpoint_lvl,
+            return_residual=return_residual,
+        )
+    return mlp_cls
+
+
+def create_block(config, layer_idx=None):
+    last_layer_subset = getattr(config, "last_layer_subset", False)
+    cross_attn = last_layer_subset and layer_idx == config.num_hidden_layers - 1
+    # TD [2022-12-19]: For cross attention (last layer), we actually want to return the
+    # residual x_kv, not residual x. But it's annoying to change the API (and it only affects
+    # one layer) so we just choose not to return residual in this case.
+    return_residual = not cross_attn
+    mixer_cls = create_mixer_cls(config, cross_attn, return_residual=return_residual)
+    mlp_cls = create_mlp_cls(config, layer_idx, return_residual=return_residual)
+    norm_cls = partial(nn.LayerNorm, eps=config.layer_norm_eps)
+    block = Block(
+        config.hidden_size,
+        mixer_cls,
+        mlp_cls,
+        norm_cls=norm_cls,
+        prenorm=False,
+        resid_dropout1=config.hidden_dropout_prob,
+        resid_dropout2=config.hidden_dropout_prob,
+        fused_dropout_add_ln=getattr(config, "fused_dropout_add_ln", False),
+        return_residual=return_residual,
+    )
+    return block
+
+
+# https://github.com/huggingface/transformers/blob/7032e0203262ebb2ebf55da8d2e01f873973e835/src/transformers/models/bert/modeling_bert.py#L748
+def _init_weights(module, initializer_range=0.02):
+    if isinstance(module, nn.Linear):
+        nn.init.normal_(module.weight, std=initializer_range)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif isinstance(module, nn.Embedding):
+        nn.init.normal_(module.weight, std=initializer_range)
+        if module.padding_idx is not None:
+            nn.init.zeros_(module.weight[module.padding_idx])
+
+
+class XLMRobertaEncoder(nn.Module):
+    def __init__(self, config: XLMRobertaFlashConfig):
+        super().__init__()
+        self.use_flash_attn = get_use_flash_attn(config)
+        self.use_reentrant = config.use_reentrant
+        self.layers = nn.ModuleList(
+            [create_block(config, layer_idx=i) for i in range(config.num_hidden_layers)]
+        )
+        self._grad_checkpointing = False
+
+    @property
+    def gradient_checkpointing(self):
+        return self._grad_checkpointing
+
+    @gradient_checkpointing.setter
+    def gradient_checkpointing(self, value):
+        self._grad_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states,
+        key_padding_mask=None,
+        subset_mask=None,
+        adapter_mask=None,
+        output_hidden_states: Optional[bool] = None,
+    ):
+        """If subset_mask is not None, we only want output for the subset of the sequence.
+        This means that we only compute the last layer output for these tokens.
+        subset_mask: (batch, seqlen), dtype=torch.bool
+        """
+
+        all_hidden_states = () if output_hidden_states else None
+
+        if output_hidden_states and subset_mask:
+            raise ValueError('output_hidden_states is not supported for subset_masks')
+
+        if key_padding_mask is None or not self.use_flash_attn:
+            mixer_kwargs = {"adapter_mask": adapter_mask}
+            if key_padding_mask is not None:
+                mixer_kwargs["key_padding_mask"] = key_padding_mask.bool()
+            for layer in self.layers:
+                if output_hidden_states:
+                    all_hidden_states = all_hidden_states + (hidden_states,)
+                if self._grad_checkpointing:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        layer,
+                        hidden_states,
+                        use_reentrant=self.use_reentrant,
+                        mixer_kwargs=mixer_kwargs,
+                    )
+                else:
+                    hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            if subset_mask is not None:
+                hidden_states = hidden_states[subset_mask]
+        else:
+            batch, seqlen = hidden_states.shape[:2]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            hidden_states, indices, cu_seqlens, max_seqlen_in_batch, cu_adapter_mask = (
+                unpad_input(hidden_states, key_padding_mask, adapter_mask)
+            )
+            mixer_kwargs = {
+                "cu_seqlens": cu_seqlens,
+                "max_seqlen": max_seqlen_in_batch,
+                "adapter_mask": cu_adapter_mask,
+            }
+
+            if subset_mask is None:
+                for layer in self.layers:
+                    if self._grad_checkpointing:
+                        hidden_states = torch.utils.checkpoint.checkpoint(
+                            layer,
+                            hidden_states,
+                            use_reentrant=self.use_reentrant,
+                            mixer_kwargs=mixer_kwargs,
+                        )
+                    else:
+                        hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
+                    if output_hidden_states:
+                        all_hidden_states = all_hidden_states + (
+                            pad_input(hidden_states, indices, batch, seqlen),
+                        )
+                hidden_states = pad_input(hidden_states, indices, batch, seqlen)
+            else:
+                for layer in self.layers[:-1]:
+                    if self._grad_checkpointing:
+                        hidden_states = torch.utils.checkpoint.checkpoint(
+                            layer,
+                            hidden_states,
+                            use_reentrant=self.use_reentrant,
+                            mixer_kwargs=mixer_kwargs,
+                        )
+                    else:
+                        hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
+                if key_padding_mask is not None:
+                    subset_idx = torch.nonzero(
+                        subset_mask[key_padding_mask], as_tuple=False
+                    ).flatten()
+                    subset_seqlens = (subset_mask & key_padding_mask).sum(
+                        dim=-1, dtype=torch.int32
+                    )
+                    subset_cu_seqlens = F.pad(
+                        torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32),
+                        (1, 0),
+                    )
+                else:
+                    subset_idx = torch.nonzero(subset_mask, as_tuple=False).flatten()
+                    subset_seqlens = subset_mask.sum(dim=-1, dtype=torch.int32)
+                    subset_cu_seqlens = F.pad(
+                        torch.cumsum(subset_seqlens, dim=0, dtype=torch.torch.int32),
+                        (1, 0),
+                    )
+                hidden_states_subset, hidden_states = index_first_axis_residual(
+                    hidden_states, subset_idx
+                )
+                # It's ok to set max_seqlen_q to be much larger
+                mixer_kwargs = {
+                    "x_kv": hidden_states,
+                    "cu_seqlens": subset_cu_seqlens,
+                    "max_seqlen": max_seqlen_in_batch,
+                    "cu_seqlens_k": cu_seqlens,
+                    "max_seqlen_k": max_seqlen_in_batch,
+                }
+                if self._grad_checkpointing:
+                    torch.utils.checkpoint.checkpoint(
+                        self.layers[-1],
+                        hidden_states_subset,
+                        use_reentrant=self.use_reentrant,
+                        mixer_kwargs=mixer_kwargs,
+                    )
+                else:
+                    hidden_states = self.layers[-1](
+                        hidden_states_subset, mixer_kwargs=mixer_kwargs
+                    )
+        return all_hidden_states if output_hidden_states else hidden_states
+
+
+class XLMRobertaPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        fused_bias_fc = getattr(config, "fused_bias_fc", False)
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+        self.dense = linear_cls(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states, pool=True, adapter_mask=None):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0] if pool else hidden_states
+        if adapter_mask is not None:
+            unique_tasks = torch.unique(adapter_mask)
+            pool_dtype = next(self.dense.parameters()).dtype
+            pooled_output = torch.empty(
+                first_token_tensor.shape[0],
+                self.dense.out_features,
+                dtype=pool_dtype,
+                device=first_token_tensor.device,
+            )
+            for task_id in unique_tasks:
+                task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0]
+                task_first_token_tensor = first_token_tensor[task_indices]
+                task_pooled_output = self.dense(
+                    task_first_token_tensor, task_id=task_id
+                )
+                pooled_output[task_indices] = task_pooled_output
+        else:
+            pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class XLMRobertaPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        fused_bias_fc = getattr(config, "fused_bias_fc", False)
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+        self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
+        if self.fused_dropout_add_ln and layer_norm_fn is None:
+            raise ImportError("Triton is not installed")
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+        self.dense = linear_cls(config.hidden_size, config.hidden_size)
+        approximate = (
+            "tanh"
+            if config.hidden_act in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"]
+            else "none"
+        )
+        self.transform_act_fn = nn.GELU(approximate=approximate)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        if not self.fused_dropout_add_ln:
+            hidden_states = self.layer_norm(hidden_states)
+        else:
+            hidden_states = layer_norm_fn(
+                hidden_states,
+                self.layer_norm.weight,
+                self.layer_norm.bias,
+                eps=self.layer_norm.eps,
+            )
+        return hidden_states
+
+
+class XLMRobertaLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        fused_bias_fc = getattr(config, "fused_bias_fc", False)
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+
+        self.transform = XLMRobertaPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = linear_cls(config.hidden_size, config.vocab_size, bias=True)
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class XLMRobertaPreTrainingHeads(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = XLMRobertaLMPredictionHead(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class XLMRobertaPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and
+    a simple interface for dowloading and loading pretrained models.
+    """
+
+    config_class = XLMRobertaFlashConfig
+    base_model_prefix = "roberta"
+    supports_gradient_checkpointing = True
+    _supports_param_buffer_assignment = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, XLMRobertaEncoder):
+            module.gradient_checkpointing = value
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        *args,
+        **kwargs,
+    ):
+        if not "torch_dtype" in kwargs:
+            kwargs["torch_dtype"] = "auto"
+        return super().from_pretrained(*args, **kwargs)
+
+
+class XLMRobertaModel(XLMRobertaPreTrainedModel):
+    def __init__(self, config: XLMRobertaFlashConfig, add_pooling_layer=True):
+        super().__init__(config)
+        self.pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
+        if config.vocab_size % self.pad_vocab_size_multiple != 0:
+            config.vocab_size += self.pad_vocab_size_multiple - (
+                config.vocab_size % self.pad_vocab_size_multiple
+            )
+        self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
+        if self.fused_dropout_add_ln and layer_norm_fn is None:
+            raise ImportError("Triton is not installed")
+        assert config.hidden_act in [
+            "gelu",
+            "gelu_new",
+            "gelu_fast",
+            "gelu_pytorch_tanh",
+        ]
+        self.embeddings = XLMRobertaEmbeddings(
+            config.hidden_size,
+            config.vocab_size,
+            (
+                config.max_position_embeddings
+                if config.position_embedding_type == "absolute"
+                else -1
+            ),
+            config.type_vocab_size,
+            padding_idx=config.pad_token_id,
+        )
+        self.emb_drop = nn.Dropout(config.hidden_dropout_prob)
+        self.emb_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.encoder = XLMRobertaEncoder(config)
+        self.pooler = XLMRobertaPooler(config) if add_pooling_layer else None
+
+        self.apply(partial(_init_weights, initializer_range=config.initializer_range))
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.name_or_path, trust_remote_code=True
+        )
+        self._rotary_emb_base = config.rotary_emb_base
+
+    @torch.inference_mode()
+    def encode(
+        self: "XLMRobertaModel",
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: Optional[bool] = None,
+        output_value: str = "sentence_embedding",
+        convert_to_numpy: bool = True,
+        convert_to_tensor: bool = False,
+        device: Optional[torch.device] = None,
+        normalize_embeddings: bool = True,
+        truncate_dim: Optional[int] = None,
+        adapter_mask: Optional[torch.Tensor] = None,
+        task: Optional[str] = None,
+        **tokenizer_kwargs,
+    ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
+        """
+        Computes sentence embeddings
+        Args:
+            sentences(`str` or `List[str]`):
+                Sentence or sentences to be encoded
+            batch_size(`int`, *optional*, defaults to 32):
+                Batch size for the computation
+            show_progress_bar(`bool`, *optional*, defaults to None):
+                Show a progress bar when encoding sentences.
+                If set to None, progress bar is only shown when
+                `logger.level == logging.INFO` or `logger.level == logging.DEBUG`.
+            output_value(`str`, *optional*, defaults to 'sentence_embedding'):
+                Default sentence_embedding, to get sentence embeddings.
+                Can be set to token_embeddings to get wordpiece token embeddings.
+                Set to None, to get all output values
+            convert_to_numpy(`bool`, *optional*, defaults to True):
+                If true, the output is a list of numpy vectors.
+                Else, it is a list of pytorch tensors.
+            convert_to_tensor(`bool`, *optional*, defaults to False):
+                If true, you get one large tensor as return.
+                Overwrites any setting from convert_to_numpy
+            device(`torch.device`, *optional*, defaults to None):
+                Which torch.device to use for the computation
+            normalize_embeddings(`bool`, *optional*, defaults to True):
+                If set to true, returned vectors will have length 1. In that case, the
+                faster dot-product (util.dot_score) instead of cosine similarity can
+                be used.
+            truncate_dim(`int`, *optional*, defaults to None):
+                The dimension to truncate sentence embeddings to. `None` does no truncation.
+            tokenizer_kwargs(`Dict[str, Any]`, *optional*, defaults to {}):
+                Keyword arguments for the tokenizer
+        Returns:
+            By default, a list of tensors is returned.
+            If convert_to_tensor, a stacked tensor is returned.
+            If convert_to_numpy, a numpy matrix is returned.
+        """
+        is_training = self.training
+        self.eval()
+
+        if show_progress_bar is None:
+            show_progress_bar = (
+                logger.getEffectiveLevel() == logging.INFO
+                or logger.getEffectiveLevel() == logging.DEBUG
+            )
+
+        if convert_to_tensor:
+            convert_to_numpy = False
+
+        if output_value != "sentence_embedding":
+            convert_to_tensor = False
+            convert_to_numpy = False
+
+        input_was_string = False
+        if isinstance(sentences, str) or not hasattr(sentences, "__len__"):
+            sentences = [sentences]
+            input_was_string = True
+
+        if device is not None:
+            self.to(device)
+
+        permutation = np.argsort([-len(i) for i in sentences])
+        inverse_permutation = np.argsort(permutation)
+        sentences = [sentences[idx] for idx in permutation]
+
+        tokenizer_kwargs["padding"] = tokenizer_kwargs.get("padding", True)
+        tokenizer_kwargs["max_length"] = tokenizer_kwargs.get(
+            "max_length", self.tokenizer.init_kwargs.get("model_max_length", 8192)
+        )
+        tokenizer_kwargs["truncation"] = tokenizer_kwargs.get("truncation", True)
+
+        all_embeddings = []
+
+        if trange is not None:
+            range_iter = trange(
+                0,
+                len(sentences),
+                batch_size,
+                desc="Encoding",
+                disable=not show_progress_bar,
+            )
+        else:
+            range_iter = range(0, len(sentences), batch_size)
+
+        for i in range_iter:
+            encoded_input = self.tokenizer(
+                sentences[i : i + batch_size],
+                return_tensors="pt",
+                **tokenizer_kwargs,
+            ).to(self.device)
+            lora_arguments = (
+                {"adapter_mask": adapter_mask[i : i + batch_size]}
+                if adapter_mask is not None
+                else {}
+            )
+            token_embs = self.forward(**encoded_input, **lora_arguments)[0]
+
+            # Accumulate in fp32 to avoid overflow
+            token_embs = token_embs.float()
+
+            if output_value == "token_embeddings":
+                raise NotImplementedError
+            elif output_value is None:
+                raise NotImplementedError
+            else:
+                if self.config.emb_pooler == "cls":
+                    embeddings = self.cls_pooling(
+                        token_embs, encoded_input["attention_mask"]
+                    )
+                else:
+                    embeddings = self.mean_pooling(
+                        token_embs, encoded_input["attention_mask"]
+                    )
+
+            all_embeddings.extend(embeddings)
+
+        all_embeddings = [all_embeddings[idx] for idx in inverse_permutation]
+
+        truncate_dim = truncate_dim or self.config.truncate_dim
+        if truncate_dim:
+            all_embeddings = self.truncate_embeddings(all_embeddings, truncate_dim)
+
+        if normalize_embeddings:
+            all_embeddings = [
+                torch.nn.functional.normalize(embedding, p=2, dim=0)
+                for embedding in all_embeddings
+            ]
+
+        if convert_to_tensor:
+            all_embeddings = torch.stack(all_embeddings)
+        elif convert_to_numpy:
+            all_embeddings = np.asarray([emb.cpu().numpy() for emb in all_embeddings])
+
+        if input_was_string:
+            all_embeddings = all_embeddings[0]
+
+        self.train(is_training)
+        return all_embeddings
+
+    def truncate_embeddings(self, embeddings, truncate_dim):
+        if not self.config.matryoshka_dimensions:
+            logger.warning(
+                "Matryoshka embeddings are not supported, so dimension truncation will not be performed."
+            )
+            return embeddings
+        elif truncate_dim in self.config.matryoshka_dimensions:
+            return [tensor[:truncate_dim] for tensor in embeddings]
+        else:
+            raise ValueError(
+                f"The provided `truncate_dim` value of {truncate_dim} is not supported. "
+                f"Supported dimensions are {self.config.matryoshka_dimensions}."
+            )
+
+    def mean_pooling(
+        self, token_embeddings: torch.Tensor, attention_mask: torch.Tensor
+    ):
+        input_mask_expanded = (
+            attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        )
+        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+            input_mask_expanded.sum(1), min=1e-9
+        )
+
+    def cls_pooling(self, token_embeddings: torch.Tensor, attention_mask: torch.Tensor):
+        return token_embeddings[:, 0]
+
+    @property
+    def rotary_emb_base(self):
+        return self._rotary_emb_base
+
+    @rotary_emb_base.setter
+    def rotary_emb_base(self, base):
+        if not isinstance(base, (int, float)):
+            raise TypeError("Base must be an integer or float")
+        logger.info(f"Changing RoPE base value to {base}")
+        for layer in self.encoder.layers:
+            layer.mixer.rotary_emb.base = base
+        self._rotary_emb_base = base
+
+    def forward(
+        self,
+        input_ids,
+        position_ids=None,
+        token_type_ids=None,
+        attention_mask=None,
+        masked_tokens_mask=None,
+        return_dict=None,
+        output_hidden_states=None,
+        **kwargs,
+    ):
+        """If masked_tokens_mask is not None (i.e. last_layer_subset == True in XLMForPreTraining),
+        we only want the output for the masked tokens. This means that we only compute the last
+        layer output for these tokens.
+        masked_tokens_mask: (batch, seqlen), dtype=torch.bool
+        """
+        adapter_mask = kwargs.pop("adapter_mask", None)
+        if kwargs:
+            for key, value in kwargs.items():
+                if value is not None:
+                    logger.warning(
+                        "Flash attention implementation does not support kwargs: %s",
+                        key,
+                    )
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        hidden_states = self.embeddings(
+            input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            adapter_mask=adapter_mask,
+        )
+        # TD [2022-12:18]: Don't need to force residual in fp32
+        # BERT puts embedding LayerNorm before embedding dropout.
+        if not self.fused_dropout_add_ln:
+            hidden_states = self.emb_ln(hidden_states)
+        else:
+            hidden_states = layer_norm_fn(
+                hidden_states, self.emb_ln.weight, self.emb_ln.bias, eps=self.emb_ln.eps
+            )
+        hidden_states = self.emb_drop(hidden_states)
+
+        if masked_tokens_mask is not None:
+            batch_size, seqlen = input_ids.shape[:2]
+            # We also need the first column for the CLS token
+            first_col_mask = torch.zeros(
+                batch_size, seqlen, dtype=torch.bool, device=input_ids.device
+            )
+            first_col_mask[:, 0] = True
+            subset_mask = masked_tokens_mask | first_col_mask
+        else:
+            subset_mask = None
+
+        sequence_output = self.encoder(
+            hidden_states,
+            key_padding_mask=attention_mask,
+            subset_mask=subset_mask,
+            adapter_mask=adapter_mask,
+            output_hidden_states=output_hidden_states,
+        )
+
+        if output_hidden_states:
+            all_hidden_states = sequence_output
+            sequence_output = sequence_output[-1]
+        else:
+            all_hidden_states = None
+
+        if masked_tokens_mask is None:
+            pooled_output = (
+                self.pooler(sequence_output, adapter_mask=adapter_mask)
+                if self.pooler is not None
+                else None
+            )
+        else:
+            # TD [2022-03-01]: the indexing here is very tricky.
+            if attention_mask is not None:
+                subset_idx = subset_mask[attention_mask]
+                pool_input = sequence_output[first_col_mask[attention_mask][subset_idx]]
+                sequence_output = sequence_output[
+                    masked_tokens_mask[attention_mask][subset_idx]
+                ]
+            else:
+                pool_input = sequence_output[first_col_mask[subset_mask]]
+                sequence_output = sequence_output[masked_tokens_mask[subset_mask]]
+            pooled_output = (
+                self.pooler(pool_input, pool=False, adapter_mask=adapter_mask)
+                if self.pooler is not None
+                else None
+            )
+
+        if not return_dict:
+            return sequence_output, pooled_output
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=all_hidden_states,
+        )
+
+
+class XLMRobertaForMaskedLM(XLMRobertaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `XLMRobertaForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
+        self.lm_head = XLMRobertaLMHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.roberta.embeddings.word_embeddings
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(prediction_scores.device)
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+def remap_state_dict(state_dict, config: PretrainedConfig):
+    """
+    Map the state_dict of a Huggingface BERT model to be flash_attn compatible.
+    """
+
+    # LayerNorm
+    def key_mapping_ln_gamma_beta(key):
+        key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key)
+        key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key)
+        return key
+
+    state_dict = OrderedDict(
+        (key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items()
+    )
+
+    # Layers
+    def key_mapping_layers(key):
+        return re.sub(r"^bert.encoder.layer.", "bert.encoder.layers.", key)
+
+    state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
+
+    # LayerNorm
+    def key_mapping_ln(key):
+        key = re.sub(r"^bert.embeddings.LayerNorm.", "bert.emb_ln.", key)
+        key = re.sub(
+            r"^bert.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)",
+            r"bert.encoder.layers.\1.norm1.\2",
+            key,
+        )
+        key = re.sub(
+            r"^bert.encoder.layers.(\d+).output.LayerNorm.(weight|bias)",
+            r"bert.encoder.layers.\1.norm2.\2",
+            key,
+        )
+        key = re.sub(
+            r"^cls.predictions.transform.LayerNorm.(weight|bias)",
+            r"cls.predictions.transform.layer_norm.\1",
+            key,
+        )
+        return key
+
+    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
+
+    # MLP
+    def key_mapping_mlp(key):
+        key = re.sub(
+            r"^bert.encoder.layers.(\d+).intermediate.dense.(weight|bias)",
+            r"bert.encoder.layers.\1.mlp.fc1.\2",
+            key,
+        )
+        key = re.sub(
+            r"^bert.encoder.layers.(\d+).output.dense.(weight|bias)",
+            r"bert.encoder.layers.\1.mlp.fc2.\2",
+            key,
+        )
+        return key
+
+    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
+
+    # Attention
+    last_layer_subset = getattr(config, "last_layer_subset", False)
+    for d in range(config.num_hidden_layers):
+        Wq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.weight")
+        Wk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.weight")
+        Wv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.weight")
+        bq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.bias")
+        bk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.bias")
+        bv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.bias")
+        if not (last_layer_subset and d == config.num_hidden_layers - 1):
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.weight"] = torch.cat(
+                [Wq, Wk, Wv], dim=0
+            )
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wqkv.bias"] = torch.cat(
+                [bq, bk, bv], dim=0
+            )
+        else:
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wq.weight"] = Wq
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.weight"] = torch.cat(
+                [Wk, Wv], dim=0
+            )
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wq.bias"] = bq
+            state_dict[f"bert.encoder.layers.{d}.mixer.Wkv.bias"] = torch.cat(
+                [bk, bv], dim=0
+            )
+
+    def key_mapping_attn(key):
+        return re.sub(
+            r"^bert.encoder.layers.(\d+).attention.output.dense.(weight|bias)",
+            r"bert.encoder.layers.\1.mixer.out_proj.\2",
+            key,
+        )
+
+    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
+
+    def key_mapping_decoder_bias(key):
+        return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key)
+
+    state_dict = OrderedDict(
+        (key_mapping_decoder_bias(k), v) for k, v in state_dict.items()
+    )
+
+    # Word embedding
+    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
+    if pad_vocab_size_multiple > 1:
+        word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
+        state_dict["bert.embeddings.word_embeddings.weight"] = F.pad(
+            word_embeddings, (0, 0, 0, config.vocab_size - word_embeddings.shape[0])
+        )
+        decoder_weight = state_dict["cls.predictions.decoder.weight"]
+        state_dict["cls.predictions.decoder.weight"] = F.pad(
+            decoder_weight, (0, 0, 0, config.vocab_size - decoder_weight.shape[0])
+        )
+        # If the vocab was padded, we want to set the decoder bias for those padded indices to be
+        # strongly negative (i.e. the decoder shouldn't predict those indices).
+        # TD [2022-05-09]: I don't think it affects the MLPerf training.
+        decoder_bias = state_dict["cls.predictions.decoder.bias"]
+        state_dict["cls.predictions.decoder.bias"] = F.pad(
+            decoder_bias, (0, config.vocab_size - decoder_bias.shape[0]), value=-100.0
+        )
+
+    return state_dict
+
+
+def inv_remap_state_dict(state_dict, config: PretrainedConfig):
+    """
+    Map the state_dict of a flash_attn model to be Huggingface BERT compatible.
+
+    This function is meant to be the inverse of remap_state_dict.
+    """
+    # Word embedding
+    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
+    if pad_vocab_size_multiple > 1:
+        word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
+        decoder_weight = state_dict["cls.predictions.decoder.weight"]
+        decoder_bias = state_dict["cls.predictions.decoder.bias"]
+        # unpad embeddings
+        state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings[
+            : config.orig_vocab_size, :
+        ]
+        state_dict["cls.predictions.decoder.weight"] = decoder_weight[
+            : config.orig_vocab_size, :
+        ]
+        state_dict["cls.predictions.decoder.bias"] = decoder_bias[
+            : config.orig_vocab_size
+        ]
+
+    for d in range(config.num_hidden_layers):
+        last_layer_subset = getattr(config, "last_layer_subset", False)
+        if not last_layer_subset or d != (config.num_hidden_layers - 1):
+            Wqkv_weights = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wqkv.weight")
+            Wqkv_biases = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wqkv.bias")
+            state_dict[f"bert.encoder.layers.{d}.attention.self.query.weight"] = (
+                Wqkv_weights[: Wqkv_weights.shape[0] // 3, :]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.key.weight"] = (
+                Wqkv_weights[
+                    Wqkv_weights.shape[0] // 3 : 2 * Wqkv_weights.shape[0] // 3, :
+                ]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.value.weight"] = (
+                Wqkv_weights[2 * Wqkv_weights.shape[0] // 3 :, :]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.query.bias"] = (
+                Wqkv_biases[: Wqkv_biases.shape[0] // 3]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.key.bias"] = (
+                Wqkv_biases[Wqkv_biases.shape[0] // 3 : 2 * Wqkv_biases.shape[0] // 3]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.value.bias"] = (
+                Wqkv_biases[2 * Wqkv_biases.shape[0] // 3 :]
+            )
+        else:
+            Wq_weight = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wq.weight")
+            Wkv_weights = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wkv.weight")
+            Wq_bias = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wq.bias")
+            Wkv_biases = state_dict.pop(f"bert.encoder.layers.{d}.mixer.Wkv.bias")
+            state_dict[f"bert.encoder.layers.{d}.attention.self.query.weight"] = (
+                Wq_weight
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.key.weight"] = (
+                Wkv_weights[: Wkv_weights.shape[0] // 2, :]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.value.weight"] = (
+                Wkv_weights[Wkv_weights.shape[0] // 2 :, :]
+            )
+            state_dict[f"bert.encoder.layers.{d}.attention.self.query.bias"] = Wq_bias
+            state_dict[f"bert.encoder.layers.{d}.attention.self.key.bias"] = Wkv_biases[
+                : Wkv_biases.shape[0] // 2
+            ]
+            state_dict[f"bert.encoder.layers.{d}.attention.self.value.bias"] = (
+                Wkv_biases[Wkv_biases.shape[0] // 2 :]
+            )
+
+    def inv_key_mapping_ln(key):
+        key = re.sub(r"bert.emb_ln.", "bert.embeddings.LayerNorm.", key)
+        key = re.sub(
+            r"bert.encoder.layers.(\d+).norm1.(weight|bias)",
+            r"bert.encoder.layers.\1.attention.output.LayerNorm.\2",
+            key,
+        )
+        key = re.sub(
+            r"bert.encoder.layers.(\d+).norm2.(weight|bias)",
+            r"bert.encoder.layers.\1.output.LayerNorm.\2",
+            key,
+        )
+        key = re.sub(
+            r"cls.predictions.transform.layer_norm.(weight|bias)",
+            r"cls.predictions.transform.LayerNorm.\1",
+            key,
+        )
+        return key
+
+    def inv_key_mapping_ln_gamma_beta(key):
+        key = re.sub(r"LayerNorm.weight$", "LayerNorm.gamma", key)
+        key = re.sub(r"LayerNorm.bias$", "LayerNorm.beta", key)
+        return key
+
+    def inv_key_mapping_layers(key):
+        return re.sub(r"bert.encoder.layers.", "bert.encoder.layer.", key)
+
+    def inv_key_mapping_mlp(key):
+        key = re.sub(
+            r"bert.encoder.layer.(\d+).mlp.fc1.(weight|bias)",
+            r"bert.encoder.layer.\1.intermediate.dense.\2",
+            key,
+        )
+        key = re.sub(
+            r"bert.encoder.layer.(\d+).mlp.fc2.(weight|bias)",
+            r"bert.encoder.layer.\1.output.dense.\2",
+            key,
+        )
+        return key
+
+    def inv_key_mapping_attn(key):
+        return re.sub(
+            r"bert.encoder.layer.(\d+).mixer.out_proj.(weight|bias)",
+            r"bert.encoder.layer.\1.attention.output.dense.\2",
+            key,
+        )
+
+    def inv_key_mapping_decoder_bias(key):
+        return re.sub(r"cls.predictions.decoder.bias", "cls.predictions.bias", key)
+
+    state_dict = OrderedDict(
+        (inv_key_mapping_ln(key), value) for key, value in state_dict.items()
+    )
+    state_dict = OrderedDict(
+        (inv_key_mapping_ln_gamma_beta(key), value) for key, value in state_dict.items()
+    )
+    state_dict = OrderedDict(
+        (inv_key_mapping_layers(key), value) for key, value in state_dict.items()
+    )
+    state_dict = OrderedDict(
+        (inv_key_mapping_mlp(key), value) for key, value in state_dict.items()
+    )
+    state_dict = OrderedDict(
+        (inv_key_mapping_attn(key), value) for key, value in state_dict.items()
+    )
+    state_dict = OrderedDict(
+        (inv_key_mapping_decoder_bias(key), value) for key, value in state_dict.items()
+    )
+
+    return state_dict
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->XLMRoberta
+class XLMRobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        fused_bias_fc = getattr(config, "fused_bias_fc", False)
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+        self.dense = linear_cls(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = linear_cls(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
+class XLMRobertaForSequenceClassification(XLMRobertaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
+        self.classifier = XLMRobertaClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        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 regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.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 = MSELoss()
+                if self.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 = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

rotary.py

@@ -0,0 +1,659 @@
+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py
+# Commit id: 3566596ad867ee415dd3c12616dd50c610176f6c
+# Rotary varlen support from https://github.com/Dao-AILab/flash-attention/pull/556
+
+# Copyright (c) 2023, Tri Dao.
+
+from typing import Optional, Tuple, Union
+
+import torch
+from einops import rearrange, repeat
+
+if torch.cuda.is_available():
+    try:
+        from flash_attn.ops.triton.rotary import apply_rotary
+    except ImportError:
+
+        def apply_rotary(*args, **kwargs):
+            raise RuntimeError(
+                "FlashAttention is not installed. To proceed with training, please install FlashAttention. "
+                "For inference, you have two options: either install FlashAttention or disable it by setting use_flash_attn=False when loading the model."
+            )
+
+
+def rotate_half(x, interleaved=False):
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(
+            torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
+        )
+
+
+def apply_rotary_emb_torch(x, cos, sin, interleaved=False):
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos, sin = (
+        cos[: x.shape[1]],
+        sin[: x.shape[1]],
+    )
+    cos = repeat(
+        cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    sin = repeat(
+        sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
+            x[..., ro_dim:],
+        ],
+        dim=-1,
+    )
+
+
+class ApplyRotaryEmb(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x,
+        cos,
+        sin,
+        interleaved=False,
+        inplace=False,
+        seqlen_offsets: Union[int, torch.Tensor] = 0,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+    ):
+        out = apply_rotary(
+            x,
+            cos,
+            sin,
+            seqlen_offsets=seqlen_offsets,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            interleaved=interleaved,
+            inplace=inplace,
+        )
+
+        if isinstance(seqlen_offsets, int):
+            ctx.save_for_backward(
+                cos, sin, cu_seqlens
+            )  # Can't save int with save_for_backward
+            ctx.seqlen_offsets = seqlen_offsets
+        else:
+            ctx.save_for_backward(cos, sin, cu_seqlens, seqlen_offsets)
+            ctx.seqlen_offsets = None
+        ctx.interleaved = interleaved
+        ctx.inplace = inplace
+        ctx.max_seqlen = max_seqlen
+        return out if not inplace else x
+
+    @staticmethod
+    def backward(ctx, do):
+        seqlen_offsets = ctx.seqlen_offsets
+        if seqlen_offsets is None:
+            cos, sin, cu_seqlens, seqlen_offsets = ctx.saved_tensors
+        else:
+            cos, sin, cu_seqlens = ctx.saved_tensors
+        # TD [2023-09-02]: For some reason Triton (2.0.0.post1) errors with
+        # "[CUDA]: invalid device context", and cloning makes it work. Idk why. Triton 2.1.0 works.
+        if not ctx.interleaved and not ctx.inplace:
+            do = do.clone()
+
+        dx = apply_rotary(
+            do,
+            cos,
+            sin,
+            seqlen_offsets=seqlen_offsets,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=ctx.max_seqlen,
+            interleaved=ctx.interleaved,
+            inplace=ctx.inplace,
+            conjugate=True,
+        )
+        return dx, None, None, None, None, None, None, None
+
+
+def apply_rotary_emb(
+    x,
+    cos,
+    sin,
+    interleaved=False,
+    inplace=False,
+    seqlen_offsets: Union[int, torch.Tensor] = 0,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+):
+    """
+    Arguments:
+        x: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, nheads, headdim)
+        cos, sin: (seqlen_rotary, rotary_dim / 2)
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+            of 1st half and 2nd half (GPT-NeoX style).
+        inplace: if True, apply rotary embedding in-place.
+        seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Return:
+        out: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, nheads, headdim)
+    rotary_dim must be <= headdim
+    Apply rotary embedding to the first rotary_dim of x.
+    """
+    return ApplyRotaryEmb.apply(
+        x, cos, sin, interleaved, inplace, seqlen_offsets, cu_seqlens, max_seqlen
+    )
+
+
+# For backward compatibility
+apply_rotary_emb_func = apply_rotary_emb
+
+
+class ApplyRotaryEmbQKV_(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        qkv,
+        cos,
+        sin,
+        cos_k=None,
+        sin_k=None,
+        interleaved=False,
+        seqlen_offsets: Union[int, torch.Tensor] = 0,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        use_flash_attn: bool = True,
+    ):
+        # batch, seqlen, three, nheads, headdim = qkv.shape
+        assert qkv.shape[-3] == 3
+        if cos_k is None and sin_k is None and qkv.is_contiguous():
+
+            if use_flash_attn:
+                # Call 1 kernel instead of 2 kernels
+                # We need qkv to be contiguous so that when we reshape to combine (3, nheads)
+                # dimensions, we get the same tensor
+                qk = rearrange(qkv[..., :2, :, :], "... t h d -> ... (t h) d")
+                # qk = qkv[:, :, :2].reshape(batch, seqlen, -1, headdim)
+                apply_rotary(
+                    qk,
+                    cos,
+                    sin,
+                    seqlen_offsets=seqlen_offsets,
+                    interleaved=interleaved,
+                    inplace=True,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                )
+            else:
+                q_rot = apply_rotary_emb_torch(
+                    qkv[:, :, 0],
+                    cos,
+                    sin,
+                    interleaved=interleaved,
+                )
+                k_rot = apply_rotary_emb_torch(
+                    qkv[:, :, 1],
+                    cos,
+                    sin,
+                    interleaved=interleaved,
+                )
+                qkv = torch.stack((q_rot, k_rot, qkv[:, :, 2]), dim=2)
+        else:
+            cos_k = cos if cos_k is None else cos_k
+            sin_k = sin if sin_k is None else sin_k
+            q, k = qkv[..., 0, :, :], qkv[..., 1, :, :]
+            apply_rotary(
+                q,
+                cos,
+                sin,
+                seqlen_offsets,
+                interleaved=interleaved,
+                inplace=True,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+            apply_rotary(
+                k,
+                cos_k,
+                sin_k,
+                seqlen_offsets,
+                interleaved=interleaved,
+                inplace=True,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+            ctx.save_for_backward(cos, sin, cos_k, sin_k)
+        if isinstance(seqlen_offsets, int):
+            ctx.save_for_backward(cos, sin, cos_k, sin_k, cu_seqlens)
+            ctx.seqlen_offsets = seqlen_offsets
+        else:
+            ctx.save_for_backward(cos, sin, cos_k, sin_k, cu_seqlens, seqlen_offsets)
+            ctx.seqlen_offsets = None
+        ctx.max_seqlen = max_seqlen
+        ctx.interleaved = interleaved
+        return qkv
+
+    @staticmethod
+    def backward(ctx, dqkv):
+        seqlen_offsets = ctx.seqlen_offsets
+        if seqlen_offsets is None:
+            cos, sin, cos_k, sin_k, cu_seqlens, seqlen_offsets = ctx.saved_tensors
+        else:
+            cos, sin, cos_k, sin_k, cu_seqlens = ctx.saved_tensors
+        if cos_k is None and sin_k is None and dqkv.is_contiguous():
+            # Call 1 kernel instead of 2 kernels
+            # We need dqkv to be contiguous so that when we reshape to combine (3, nheads)
+            # dimensions, we get the same tensor
+            dqk = rearrange(dqkv[..., :2, :, :], "... t h d -> ... (t h) d")
+            apply_rotary(
+                dqk,
+                cos,
+                sin,
+                seqlen_offsets=seqlen_offsets,
+                interleaved=ctx.interleaved,
+                inplace=True,
+                conjugate=True,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=ctx.max_seqlen,
+            )
+        else:
+            cos_k = cos if cos_k is None else cos_k
+            sin_k = sin if sin_k is None else sin_k
+            dq, dk = dqkv[..., 0, :, :], dqkv[..., 1, :, :]
+            apply_rotary(
+                dq,
+                cos,
+                sin,
+                seqlen_offsets,
+                interleaved=ctx.interleaved,
+                inplace=True,
+                conjugate=True,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=ctx.max_seqlen,
+            )
+            apply_rotary(
+                dk,
+                cos_k,
+                sin_k,
+                seqlen_offsets,
+                interleaved=ctx.interleaved,
+                inplace=True,
+                conjugate=True,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=ctx.max_seqlen,
+            )
+        return dqkv, None, None, None, None, None, None, None, None, None
+
+
+def apply_rotary_emb_qkv_(
+    qkv,
+    cos,
+    sin,
+    cos_k=None,
+    sin_k=None,
+    interleaved=False,
+    seqlen_offsets: Union[int, torch.Tensor] = 0,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+    use_flash_attn=True,
+):
+    """
+    Arguments:
+        qkv: (batch_size, seqlen, 3, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, 3, nheads, headdim)
+        cos, sin: (seqlen, rotary_dim / 2)
+        cos_k, sin_k: (seqlen, rotary_dim / 2), optional
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead of
+            1st half and 2nd half (GPT-NeoX style).
+        seqlen_offsets: (batch_size,) or int. Each sequence in Q and K is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+            cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Return:
+        qkv: (batch_size, seqlen, 3, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, 3, nheads, headdim)
+    rotary_dim must be <= headdim
+    Apply rotary embedding *inplace* to the first rotary_dim of Q and K.
+    """
+    return ApplyRotaryEmbQKV_.apply(
+        qkv, cos, sin, cos_k, sin_k, interleaved, seqlen_offsets, cu_seqlens, max_seqlen, use_flash_attn,
+    )
+
+
+class ApplyRotaryEmbKV_(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        kv,
+        cos,
+        sin,
+        interleaved=False,
+        seqlen_offsets: Union[int, torch.Tensor] = 0,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+    ):
+        # batch, seqlen, two, nheads, headdim = kv.shape
+        assert kv.shape[-3] == 2
+        k = kv[..., 0, :, :]
+        apply_rotary(
+            k,
+            cos,
+            sin,
+            seqlen_offsets=seqlen_offsets,
+            interleaved=interleaved,
+            inplace=True,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        if isinstance(seqlen_offsets, int):
+            ctx.save_for_backward(
+                cos, sin, cu_seqlens
+            )  # Can't save int with save_for_backward
+            ctx.seqlen_offsets = seqlen_offsets
+        else:
+            ctx.save_for_backward(cos, sin, cu_seqlens, seqlen_offsets)
+            ctx.seqlen_offsets = None
+        ctx.max_seqlen = max_seqlen
+        ctx.interleaved = interleaved
+        return kv
+
+    @staticmethod
+    def backward(ctx, dkv):
+        seqlen_offsets = ctx.seqlen_offsets
+        if seqlen_offsets is None:
+            cos, sin, cu_seqlens, seqlen_offsets = ctx.saved_tensors
+        else:
+            cos, sin, cu_seqlens = ctx.saved_tensors
+        apply_rotary(
+            dkv[..., 0, :, :],
+            cos,
+            sin,
+            seqlen_offsets=seqlen_offsets,
+            interleaved=ctx.interleaved,
+            inplace=True,
+            conjugate=True,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=ctx.max_seqlen,
+        )
+        return dkv, None, None, None, None, None, None
+
+
+apply_rotary_emb_kv_ = ApplyRotaryEmbKV_.apply
+
+
+def apply_rotary_emb_kv_(
+    kv,
+    cos,
+    sin,
+    interleaved=False,
+    seqlen_offsets: Union[int, torch.Tensor] = 0,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+):
+    """
+    Arguments:
+        kv: (batch_size, seqlen, 2, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, 2, nheads, headdim)
+        cos, sin: (seqlen, rotary_dim / 2)
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead of
+            1st half and 2nd half (GPT-NeoX style).
+        seqlen_offsets: (batch_size,) or int. Each sequence in Q and K is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Return:
+        kv: (batch_size, seqlen, 2, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, 2, nheads, headdim)
+    rotary_dim must be <= headdim
+    Apply rotary embedding *inplace* to the first rotary_dim of K.
+    """
+    return ApplyRotaryEmbKV_.apply(
+        kv, cos, sin, interleaved, seqlen_offsets, cu_seqlens, max_seqlen
+    )
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    The rotary position embeddings from RoFormer_ (Su et. al).
+    A crucial insight from the method is that the query and keys are
+    transformed by rotation matrices which depend on the relative positions.
+
+    Other implementations are available in the Rotary Transformer repo_ and in
+    GPT-NeoX_, GPT-NeoX was an inspiration
+
+    .. _RoFormer: https://arxiv.org/abs/2104.09864
+    .. _repo: https://github.com/ZhuiyiTechnology/roformer
+    .. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox
+
+    If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
+    A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
+    Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        base=10000.0,
+        interleaved=False,
+        scale_base=None,
+        pos_idx_in_fp32=True,
+        device=None,
+        use_flash_attn=True,
+    ):
+        """
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+            of 1st half and 2nd half (GPT-NeoX style).
+        pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
+            otherwise they might be in lower precision.
+            This option was added because previously (before 2023-07-02), when we construct
+            the position indices, we use the dtype of self.inv_freq. In most cases this would
+            be fp32, but if the model is trained in pure bf16 (not mixed precision), then
+            self.inv_freq would be bf16, and the position indices are also in bf16.
+            Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
+            embeddings for some positions will coincide.
+            To maintain compatibility with models previously trained in pure bf16,
+            we add this option.
+        """
+        super().__init__()
+        self.dim = dim
+        self._base = float(base)
+        self.pos_idx_in_fp32 = pos_idx_in_fp32
+        self.use_flash_attn = use_flash_attn
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = self._compute_inv_freq(device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.interleaved = interleaved
+        self.scale_base = scale_base
+        scale = (
+            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
+            / (1.4 * dim)
+            if scale_base is not None
+            else None
+        )
+        self.register_buffer("scale", scale, persistent=False)
+
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+        self._cos_k_cached = None
+        self._sin_k_cached = None
+
+    @property
+    def base(self):
+        return self._base
+
+    @base.setter
+    def base(self, new_base):
+        new_base = float(new_base)
+        if new_base > 0:
+            if self._base != new_base:  # only update if the base value has changed
+                self._base = new_base
+                self._update_cos_sin_cache(
+                    self._seq_len_cached,
+                    device=self.inv_freq.device,
+                    dtype=self._cos_cached.dtype if self._cos_cached is not None else None,
+                    rotary_base_changed=True,
+                )
+        else:
+            raise ValueError("Rotary base value must be positive")
+
+    def _compute_inv_freq(self, device=None):
+        return 1.0 / (
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
+                / self.dim
+            )
+        )
+
+    def _update_cos_sin_cache(
+        self, seqlen, device=None, dtype=None, rotary_base_changed=False
+    ):
+        # Reset the tables if the sequence length has changed,
+        # if we're on a new device (possibly due to tracing for instance),
+        # or if we're switching from inference mode to training
+        # or if the rotary base value was changed
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached is None
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+            or (self.training and self._cos_cached.is_inference())
+            or rotary_base_changed
+        ):
+            self._seq_len_cached = seqlen
+            # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
+            # And the output of arange can be quite large, so bf16 would lose a lot of precision.
+            # However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
+            if rotary_base_changed:
+                self.inv_freq = self._compute_inv_freq(device=device)
+            if self.pos_idx_in_fp32:
+                t = torch.arange(seqlen, device=device, dtype=torch.float32)
+                # We want fp32 here as well since inv_freq will be multiplied with t, and the output
+                # will be large. Having it in bf16 will lose a lot of precision and cause the
+                # cos & sin output to change significantly.
+                # We want to recompute self.inv_freq if it was not loaded in fp32
+                if self.inv_freq.dtype != torch.float32:
+                    inv_freq = self._compute_inv_freq(device=device)
+                else:
+                    inv_freq = self.inv_freq
+            else:
+                t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
+                inv_freq = self.inv_freq
+
+            # Don't do einsum, it converts fp32 to fp16 under AMP
+            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            freqs = torch.outer(t, inv_freq)
+            if self.scale is None:
+                self._cos_cached = torch.cos(freqs).to(dtype)
+                self._sin_cached = torch.sin(freqs).to(dtype)
+            else:
+                power = (
+                    torch.arange(
+                        seqlen, dtype=self.scale.dtype, device=self.scale.device
+                    )
+                    - seqlen // 2
+                ) / self.scale_base
+                scale = self.scale.to(device=power.device) ** rearrange(
+                    power, "s -> s 1"
+                )
+                # We want the multiplication by scale to happen in fp32
+                self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
+                self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
+                self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
+                self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
+
+    def forward(
+        self,
+        qkv: torch.Tensor,
+        kv: Optional[torch.Tensor] = None,
+        seqlen_offset: Union[int, torch.Tensor] = 0,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        qkv: (batch, seqlen, 3, nheads, headdim) if kv is none,
+             else it's just q of shape (batch, seqlen, nheads, headdim)
+        kv: (batch, seqlen, 2, nheads, headdim)
+        seqlen_offset: (batch_size,) or int. Each sequence in x is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+            If it's a tensor of shape (batch_size,), then to update the cos / sin cache, one
+            should pass in max_seqlen, which will update the cos / sin cache up to that length.
+        Apply rotary embedding *inplace* to qkv and / or kv.
+        """
+        if cu_seqlens is not None:
+            assert max_seqlen is not None
+        seqlen = qkv.shape[1] if max_seqlen is None else max_seqlen
+        if max_seqlen is not None:
+            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
+        elif isinstance(seqlen_offset, int):
+            self._update_cos_sin_cache(
+                seqlen + seqlen_offset, device=qkv.device, dtype=qkv.dtype
+            )
+        if kv is None:
+            if self.scale is None:
+                return apply_rotary_emb_qkv_(
+                    qkv,
+                    self._cos_cached,
+                    self._sin_cached,
+                    interleaved=self.interleaved,
+                    seqlen_offsets=seqlen_offset,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    use_flash_attn=self.use_flash_attn,
+                )
+            else:
+                return apply_rotary_emb_qkv_(
+                    qkv,
+                    self._cos_cached,
+                    self._sin_cached,
+                    self._cos_k_cached,
+                    self._sin_k_cached,
+                    interleaved=self.interleaved,
+                    seqlen_offsets=seqlen_offset,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    use_flash_attn=self.use_flash_attn,
+                )
+        else:
+            q = qkv
+            q = apply_rotary_emb_func(
+                q,
+                self._cos_cached,
+                self._sin_cached,
+                interleaved=self.interleaved,
+                inplace=True,
+                seqlen_offsets=seqlen_offset,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+            if self.scale is None:
+                kv = apply_rotary_emb_kv_(
+                    kv,
+                    self._cos_cached,
+                    self._sin_cached,
+                    interleaved=self.interleaved,
+                    seqlen_offsets=seqlen_offset,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                )
+            else:
+                kv = apply_rotary_emb_kv_(
+                    kv,
+                    self._cos_k_cached,
+                    self._sin_k_cached,
+                    interleaved=self.interleaved,
+                    seqlen_offsets=seqlen_offset,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                )
+            return q, kv

stochastic_depth.py

@@ -0,0 +1,97 @@
+# Implementation modified from torchvision:
+# https://github.com/pytorch/vision/blob/main/torchvision/ops/stochastic_depth.py
+#
+# License:
+# BSD 3-Clause License
+#
+# Copyright (c) Soumith Chintala 2016,
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import torch
+import torch.fx
+from torch import Tensor, nn
+
+
+def stochastic_depth(
+    input: Tensor, p: float, mode: str, training: bool = True
+) -> Tensor:
+    """
+    Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
+    <https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
+    branches of residual architectures.
+
+    Args:
+        input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
+                    being its batch i.e. a batch with ``N`` rows.
+        p (float): probability of the input to be zeroed.
+        mode (str): ``"batch"`` or ``"row"``.
+                    ``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
+                    randomly selected rows from the batch.
+        training: apply stochastic depth if is ``True``. Default: ``True``
+
+    Returns:
+        Tensor[N, ...]: The randomly zeroed tensor.
+    """
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
+    if mode not in ["batch", "row"]:
+        raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
+    if not training or p == 0.0:
+        return input
+
+    survival_rate = 1.0 - p
+    if mode == "row":
+        size = [input.shape[0]] + [1] * (input.ndim - 1)
+    else:
+        size = [1] * input.ndim
+    noise = torch.empty(size, dtype=input.dtype, device=input.device)
+    noise = noise.bernoulli_(survival_rate)
+    if survival_rate > 0.0:
+        noise.div_(survival_rate)
+    return input * noise
+
+
+torch.fx.wrap("stochastic_depth")
+
+
+class StochasticDepth(nn.Module):
+    """
+    See :func:`stochastic_depth`.
+    """
+
+    def __init__(self, p: float, mode: str) -> None:
+        super().__init__()
+        self.p = p
+        self.mode = mode
+
+    def forward(self, input: Tensor) -> Tensor:
+        return stochastic_depth(input, self.p, self.mode, self.training)
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})"
+        return s

xlm_padding.py

@@ -0,0 +1,236 @@
+# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/block.py
+# Commit id: c94cd09744d20f0ac587a351ff6ff2e8ad11ae1b
+
+# Previously adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+
+class IndexFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, indices):
+        ctx.save_for_backward(indices)
+        assert input.ndim >= 2
+        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
+        second_dim = other_shape.numel()
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        # return input[indices]
+        return torch.gather(
+            rearrange(input, "b ... -> b (...)"),
+            0,
+            repeat(indices, "z -> z d", d=second_dim),
+        ).reshape(-1, *other_shape)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (indices,) = ctx.saved_tensors
+        assert grad_output.ndim >= 2
+        other_shape = grad_output.shape[1:]
+        grad_output = rearrange(grad_output, "b ... -> b (...)")
+        grad_input = torch.zeros(
+            [ctx.first_axis_dim, grad_output.shape[1]],
+            device=grad_output.device,
+            dtype=grad_output.dtype,
+        )
+        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
+        # grad_input[indices] = grad_output
+        grad_input.scatter_(
+            0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output
+        )
+        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
+
+
+index_first_axis = IndexFirstAxis.apply
+
+
+class IndexPutFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, values, indices, first_axis_dim):
+        ctx.save_for_backward(indices)
+        assert indices.ndim == 1
+        assert values.ndim >= 2
+        output = torch.zeros(
+            first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype
+        )
+        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
+        output[indices] = values
+        # output.scatter_(0, repeat(indices, 'z -> z d', d=values.shape[1]), values)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (indices,) = ctx.saved_tensors
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        grad_values = grad_output[indices]
+        # grad_values = torch.gather(grad_output, 0, repeat(indices, 'z -> z d', d=grad_output.shape[1]))
+        return grad_values, None, None
+
+
+index_put_first_axis = IndexPutFirstAxis.apply
+
+
+class IndexFirstAxisResidual(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, indices):
+        ctx.save_for_backward(indices)
+        assert input.ndim >= 2
+        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
+        second_dim = other_shape.numel()
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        output = input[indices]
+        # We don't want to reshape input (b ... -> b (...)) since it could change the channel_last
+        # memory format to channel_first. In other words, input might not be contiguous.
+        # If we don't detach, Pytorch complains about output being a view and is being modified inplace
+        return output, input.detach()
+
+    @staticmethod
+    def backward(ctx, grad_output, grad_residual):
+        (indices,) = ctx.saved_tensors
+        assert grad_output.ndim >= 2
+        other_shape = grad_output.shape[1:]
+        assert grad_residual.shape[1:] == other_shape
+        grad_input = grad_residual
+        # grad_input[indices] += grad_output
+        indices = indices.reshape(indices.shape[0], *((1,) * (grad_output.ndim - 1)))
+        indices = indices.expand_as(grad_output)
+        grad_input.scatter_add_(0, indices, grad_output)
+        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
+
+
+index_first_axis_residual = IndexFirstAxisResidual.apply
+
+
+def unpad_input(hidden_states, attention_mask, adapter_mask=None):
+    """
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+    Return:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
+        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
+        max_seqlen_in_batch: int
+    """
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(
+        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
+    )
+
+    cu_adapter_mask = (
+        torch.repeat_interleave(adapter_mask, cu_seqlens[1:] - cu_seqlens[:-1])
+        if adapter_mask is not None
+        else None
+    )
+
+    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
+    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
+    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
+    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
+    # so we write custom forward and backward to make it a bit faster.
+    return (
+        index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices),
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+        cu_adapter_mask,
+    )
+
+
+def unpad_input_for_concatenated_sequences(hidden_states, attention_mask_in_length):
+    """
+    Supports concatenating short samples in one sequence. The attention_mask_in_length is utilized to mask other short samples. It helps efficient training of variant lengths-based samples (e.g., the supervised fine-tuning task in large language model).
+    The motivation for this function is explained [here](https://github.com/Dao-AILab/flash-attention/issues/432#issuecomment-1668822286).
+
+    For example, if batch = 3 and seqlen = 6, the attention_mask_in_length is:
+        ```
+        [
+          [2, 3, 0, 0, 0, 0],
+          [3, 2, 0, 0, 0, 0],
+          [6, 0, 0, 0, 0, 0]
+        ]
+        ```
+    , which refers to the 3D-attention mask:
+        ```
+        [
+          [
+            [1, 0, 0, 0, 0, 0],
+            [1, 1, 0, 0, 0, 0],
+            [0, 0, 1, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0],
+            [0, 0, 1, 1, 1, 0],
+            [0, 0, 0, 0, 0, 1]
+          ],
+          [
+            [1, 0, 0, 0, 0, 0],
+            [1, 1, 0, 0, 0, 0],
+            [1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0],
+            [0, 0, 0, 1, 1, 0],
+            [0, 0, 0, 0, 0, 1]
+          ],
+          [
+            [1, 0, 0, 0, 0, 0],
+            [1, 1, 0, 0, 0, 0],
+            [1, 1, 1, 0, 0, 0],
+            [1, 1, 1, 1, 0, 0],
+            [1, 1, 1, 1, 1, 0],
+            [1, 1, 1, 1, 1, 1]
+          ]
+        ]
+        ```.
+
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask_in_length: (batch, seqlen), int, a nonzero number (e.g., 1, 2, 3, etc.) means length of concatenated sequence in b-th batch, and 0 means none.
+    Return:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
+        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
+        max_seqlen_in_batch: int
+    """
+    length = attention_mask_in_length.sum(dim=-1)
+    seqlen = attention_mask_in_length.size(-1)
+    attention_mask_2d = torch.arange(
+        seqlen, device=length.device, dtype=length.dtype
+    ).expand(len(length), seqlen) < length.unsqueeze(1)
+    real_indices_idx = torch.nonzero(
+        attention_mask_in_length.flatten(), as_tuple=False
+    ).flatten()
+    seqlens_in_batch = attention_mask_in_length.flatten()[real_indices_idx]
+    indices = torch.nonzero(attention_mask_2d.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(
+        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
+    )
+    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
+    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
+    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
+    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
+    # so we write custom forward and backward to make it a bit faster.
+    return (
+        index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices),
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def pad_input(hidden_states, indices, batch, seqlen):
+    """
+    Arguments:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz), the indices that represent the non-masked tokens of the original padded input sequence.
+        batch: int, batch size for the padded sequence.
+        seqlen: int, maximum sequence length for the padded sequence.
+    Return:
+        hidden_states: (batch, seqlen, ...)
+    """
+    dim = hidden_states.shape[-1]
+    # output = torch.zeros((batch * seqlen), dim, device=hidden_states.device, dtype=hidden_states.dtype)
+    # output[indices] = hidden_states
+    output = index_put_first_axis(hidden_states, indices, batch * seqlen)
+    return rearrange(output, "(b s) ... -> b s ...", b=batch)