modelling_RW.py

# port of models described in RW
# We use the bloom model as a starting point for these model.
# Please refer to the bloom models for usage instructions.

import math
import warnings
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F

from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from .configuration_RW import RWConfig

logger = logging.get_logger(__name__)


# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
class Linear(nn.Linear):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        ret = input @ self.weight.T
        if self.bias is None:
            return ret
        else:
            return ret + self.bias


from einops import rearrange


# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
def rotate_half(x):
    x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
    return torch.cat(
        (-x2, x1), dim=x1.ndim - 1
    )  # dim=-1 triggers a bug in torch < 1.8.0


class RotaryEmbedding(torch.nn.Module):
    """Implementation of RotaryEmbedding from GPT-NeoX.
    This implementation is design to operate on queries and keys that are compatible with
    [batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
    """

    def __init__(
        self,
        head_dim: int,
        base=1000000,
    ):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.head_dim = head_dim
        self.seq_len_cached = None
        self.batch_size_cached = None
        self.cos_cached: torch.Tensor | None = None
        self.sin_cached: torch.Tensor | None = None
        self.max_position_embeddings = 2048
        self.base = base

    def cos_sin(
        self,
        seq_len: int,
        device="cuda",
        dtype=torch.bfloat16,
    ) -> torch.Tensor:
        if seq_len != self.seq_len_cached:
            self.seq_len_cached = seq_len
            t = torch.arange(seq_len, device=device, dtype=torch.float32)
            
            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
            #print(f"HAHA -- {t.dtype=} -- {freqs.dtype=}")
            #raise Exception("dtype checking")
            #freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            emb = torch.cat((freqs, freqs), dim=-1).to(device)

            if dtype in [torch.float16, torch.bfloat16]:
                emb = emb.float()

            self.cos_cached = emb.cos()[None, :, :]
            self.sin_cached = emb.sin()[None, :, :]

            self.cos_cached = self.cos_cached.type(dtype)
            self.sin_cached = self.sin_cached.type(dtype)

        return self.cos_cached, self.sin_cached

    def forward(self, q, k):
        batch, seq_len, head_dim = q.shape
        cos, sin = self.cos_sin(seq_len, q.device, q.dtype)
        return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)


def _make_causal_mask(
    input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
) -> torch.BoolTensor:
    batch_size, target_length = input_ids_shape
    mask = torch.empty(
        (target_length, target_length + past_key_values_length),
        dtype=torch.bool,
        device=device,
    )
    # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
    seq_ids = torch.arange(target_length, device=device)
    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]

    if past_key_values_length > 0:
        mask[:, :past_key_values_length] = False

    expanded_mask = mask[None, None, :, :].expand(
        batch_size, 1, target_length, target_length + past_key_values_length
    )
    return expanded_mask


def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
    batch_size, src_length = mask.shape
    tgt_length = tgt_length if tgt_length is not None else src_length

    expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
    return expanded_mask.expand(batch_size, 1, tgt_length, src_length)


def build_alibi_tensor(
    attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype
) -> torch.Tensor:
    batch_size, seq_length = attention_mask.shape
    closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
    base = torch.tensor(
        2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
        device=attention_mask.device,
        dtype=torch.float32,
    )
    powers = torch.arange(
        1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32
    )
    slopes = torch.pow(base, powers)

    if closest_power_of_2 != num_heads:
        extra_base = torch.tensor(
            2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
            device=attention_mask.device,
            dtype=torch.float32,
        )
        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
        extra_powers = torch.arange(
            1,
            1 + 2 * num_remaining_heads,
            2,
            device=attention_mask.device,
            dtype=torch.int32,
        )
        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)

    # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
    # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
    # => the query_length dimension will then be broadcasted correctly
    # This is more or less identical to T5's relative position bias:
    # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
    arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
    alibi = slopes[..., None].bfloat16() * arange_tensor
    return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)


def dropout_add(
    x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool
) -> torch.Tensor:
    out = F.dropout(x, p=prob, training=training)
    out = residual + out
    return out


class Attention(nn.Module):
    def __init__(self, config: RWConfig):
        super().__init__()

        self.hidden_size = config.hidden_size
        self.num_heads = config.n_head
        self.head_dim = self.hidden_size // self.num_heads
        self.split_size = self.hidden_size
        self.hidden_dropout = config.hidden_dropout

        if self.head_dim * self.num_heads != self.hidden_size:
            raise ValueError(
                f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
                f" {self.num_heads})."
            )

        self.maybe_rotary = (
            RotaryEmbedding(config.head_dim) if config.rotary else lambda q, k: (q, k)
        )

        # Layer-wise attention scaling
        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.beta = self.inv_norm_factor

        self.query_key_value = Linear(
            self.hidden_size,
            (config.n_head_kv * 2 + config.n_head) * self.head_dim,
            bias=config.bias,
        )
        self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
        self.attention_dropout = nn.Dropout(config.attention_dropout)
        self.num_kv = config.n_head_kv

    def _split_heads(
        self, fused_qkv: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Split the last dimension into (num_heads, head_dim), results share same memory
        storage as `fused_qkv`

        Args:
            fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]

        Returns:
            query: [batch_size, seq_length, num_heads, head_dim]
            key: [batch_size, seq_length, num_heads, head_dim]
            value: [batch_size, seq_length, num_heads, head_dim]
        """
        batch, seq_len, _ = fused_qkv.shape
        qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv + 2, 64)
        q = qkv[:, :, :, :-2]
        k = qkv[:, :, :, [-2]]
        v = qkv[:, :, :, [-1]]
        k = torch.broadcast_to(k, q.shape)
        v = torch.broadcast_to(v, q.shape)

        q, k, v = [
            rearrange(
                x,
                "batch seq_len group num_heads head_dim ->\
                batch seq_len (group num_heads) head_dim",
                head_dim=self.head_dim,
            )
            for x in [q, k, v]
        ]
        return q, k, v

    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
        """
        Merge heads together over the last dimenstion

        Args:
            x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]

        Returns:
            torch.tensor: [batch_size, seq_length, num_heads * head_dim]
        """
        # What we want to achieve is:
        # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
        batch_size_and_num_heads, seq_length, _ = x.shape
        batch_size = batch_size_and_num_heads // self.num_heads

        # First view to decompose the batch size
        # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
        x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)

        # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
        x = x.permute(0, 2, 1, 3)

        # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
        return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
    ):
        fused_qkv = self.query_key_value(
            hidden_states
        )  # [batch_size, seq_length, 3 x hidden_size]

        # 3 x [batch_size, seq_length, num_heads, head_dim]
        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)

        batch_size, q_length, _, _ = query_layer.shape

        query_layer = query_layer.transpose(1, 2).reshape(
            batch_size * self.num_heads, q_length, self.head_dim
        )
        key_layer = key_layer.transpose(1, 2).reshape(
            batch_size * self.num_heads,
            q_length,
            self.head_dim,
        )
        value_layer = value_layer.transpose(1, 2).reshape(
            batch_size * self.num_heads, q_length, self.head_dim
        )

        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)

        if layer_past is not None:
            past_key, past_value = layer_past
            # concatenate along seq_length dimension:
            #  - key: [batch_size * self.num_heads, head_dim, kv_length]
            #  - value: [batch_size * self.num_heads, kv_length, head_dim]
            key_layer = torch.cat((past_key, key_layer), dim=1)
            value_layer = torch.cat((past_value, value_layer), dim=1)

        _, kv_length, _ = key_layer.shape

        if use_cache is True:
            present = (key_layer, value_layer)
        else:
            present = None

        if alibi is None:
            query_layer_ = query_layer.reshape(
                batch_size, self.num_heads, -1, self.head_dim
            )
            key_layer_ = key_layer.reshape(
                batch_size, self.num_heads, -1, self.head_dim
            )
            value_layer_ = value_layer.reshape(
                batch_size, self.num_heads, -1, self.head_dim
            )

            attn_output = F.scaled_dot_product_attention(
                query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
            )

            x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
            x = x.permute(0, 2, 1, 3)
            attn_output = x.reshape(
                batch_size, q_length, self.num_heads * self.head_dim
            )

            output_tensor = self.dense(attn_output)

            outputs = (output_tensor, present)
            assert not output_attentions  # not supported.
            return outputs
        else:
            attention_mask_float = (
                (attention_mask * 1.0)
                .masked_fill(attention_mask, -1e9)
                .to(torch.bfloat16)
            )
            matmul_result = query_layer @ key_layer.transpose(-1, -2)

            # change view to [batch_size, num_heads, q_length, kv_length]
            attention_scores = matmul_result.view(
                batch_size, self.num_heads, q_length, kv_length
            )

            # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
            input_dtype = attention_scores.dtype
            # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
            if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
                attention_scores = attention_scores.to(torch.float32)
            # attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
            attention_probs = F.softmax(
                (attention_scores + alibi.view(batch_size, self.num_heads, 1, -1))
                * self.inv_norm_factor
                + attention_mask_float,
                dim=-1,
                dtype=hidden_states.dtype,
            )
            # [batch_size, num_heads, q_length, kv_length]
            attention_probs = self.attention_dropout(attention_probs)

            if head_mask is not None:
                attention_probs = attention_probs * head_mask

            # change view [batch_size x num_heads, q_length, kv_length]
            attention_probs_reshaped = attention_probs.view(
                batch_size * self.num_heads, q_length, kv_length
            )

            # matmul: [batch_size * num_heads, q_length, head_dim]
            context_layer = attention_probs_reshaped @ value_layer

            # change view [batch_size, num_heads, q_length, head_dim]
            context_layer = self._merge_heads(context_layer)

            output_tensor = self.dense(context_layer)

            outputs = (output_tensor, present)
            if output_attentions:
                outputs += (attention_probs,)

            return outputs


class MLP(nn.Module):
    def __init__(self, config: RWConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
        self.act = nn.GELU()
        self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
        self.hidden_dropout = config.hidden_dropout

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.act(self.dense_h_to_4h(x))
        x = self.dense_4h_to_h(x)
        return x


class DecoderLayer(nn.Module):
    def __init__(self, config: RWConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.num_heads = config.n_head
        self.self_attention = Attention(config)

        self.mlp = MLP(config)

        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm
        )
        self.hidden_dropout = config.hidden_dropout

        self.config = config

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
    ):
        ln_attn = self.ln_attn(hidden_states)
        ln_mlp = self.ln_mlp(hidden_states)

        residual = hidden_states

        # Self attention.
        attn_outputs = self.self_attention(
            ln_attn,
            layer_past=layer_past,
            attention_mask=attention_mask,
            alibi=alibi,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )

        attention_output = attn_outputs[0]

        outputs = attn_outputs[1:]

        # MLP.
        mlp_output = self.mlp(ln_mlp)

        output = dropout_add(
            mlp_output + attention_output,
            residual,
            self.config.hidden_dropout,
            training=self.training,
        )

        if use_cache:
            outputs = (output,) + outputs
        else:
            outputs = (output,) + outputs[1:]

        return outputs  # hidden_states, present, attentions


class RWPreTrainedModel(PreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"h.*.self_attention.scale_mask_softmax.causal_mask",
        r"lm_head.weight",
    ]
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = RWConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DecoderLayer"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear) or isinstance(module, Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
        if isinstance(module, RWModel):
            module.gradient_checkpointing = value

    @staticmethod
    def _convert_to_standard_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        """
        Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
        num_heads, ...]))
        """
        batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
        num_heads = batch_size_times_num_heads // batch_size
        # key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
        # value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
        return tuple(
            (
                layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
                layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
            )
            for layer_past in past_key_value
        )

    @staticmethod
    def _convert_to_rw_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
        batch_size_times_num_heads = batch_size * num_heads
        # key:  [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
        # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
        return tuple(
            (
                layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
                layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
            )
            for layer_past in past_key_value
        )


class RWModel(RWPreTrainedModel):
    def __init__(self, config: RWConfig):
        super().__init__(config)

        self.embed_dim = config.hidden_size
        self.num_heads = config.n_head
        self.alibi = config.alibi

        # Embedding + LN Embedding
        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)

        # Transformer blocks
        self.h = nn.ModuleList(
            [DecoderLayer(config) for _ in range(config.num_hidden_layers)]
        )

        # Final Layer Norm
        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.word_embeddings

    def _prepare_attn_mask(
        self,
        attention_mask: torch.Tensor,
        input_shape: Tuple[int, int],
        past_key_values_length: int,
    ) -> torch.BoolTensor:
        # create causal mask
        # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
        combined_attention_mask = None
        device = attention_mask.device
        _, src_length = input_shape

        if src_length > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                device=device,
                past_key_values_length=past_key_values_length,
            )

        # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
        expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
        combined_attention_mask = (
            expanded_attn_mask
            if combined_attention_mask is None
            else expanded_attn_mask | combined_attention_mask
        )

        return combined_attention_mask

    def set_input_embeddings(self, new_embeddings: torch.Tensor):
        self.word_embeddings = new_embeddings

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
        if deprecated_arguments.pop("position_ids", False) is not False:
            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
            warnings.warn(
                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
                " passing `position_ids`.",
                FutureWarning,
            )
        if len(deprecated_arguments) > 0:
            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")

        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if past_key_values is None:
            past_key_values = tuple([None] * len(self.h))

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape batch_size x num_heads x N x N
        # head_mask has shape n_layer x batch x num_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        hidden_states = inputs_embeds

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        # Compute alibi tensor: check build_alibi_tensor documentation
        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values[0] is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length
        if attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), device=hidden_states.device
            )
        else:
            attention_mask = attention_mask.to(hidden_states.device)

        if self.alibi:
            alibi = build_alibi_tensor(
                attention_mask, self.num_heads, dtype=hidden_states.dtype
            )
        else:
            alibi = None

        causal_mask = self._prepare_attn_mask(
            attention_mask,
            input_shape=(batch_size, seq_length),
            past_key_values_length=past_key_values_length,
        )

        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warning(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(
                            *inputs,
                            use_cache=use_cache,
                            output_attentions=output_attentions,
                        )

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    alibi,
                    causal_mask,
                    head_mask[i],
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=causal_mask,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    alibi=alibi,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (
                    outputs[2 if use_cache else 1],
                )

        # Add last hidden state
        hidden_states = self.ln_f(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    presents,
                    all_hidden_states,
                    all_self_attentions,
                ]
                if v is not None
            )

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class RWForCausalLM(RWPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"h.*.self_attention.scale_mask_softmax.causal_mask",
        r"lm_head.weight",
    ]

    def __init__(self, config: RWConfig):
        super().__init__(config)
        self.transformer = RWModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings: torch.Tensor):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> dict:
        # only last token for input_ids if past is not None
        if past:
            input_ids = input_ids[:, -1].unsqueeze(-1)

            # the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
            if past[0][0].shape[0] == input_ids.shape[0]:
                past = self._convert_to_rw_cache(past)

        return {
            "input_ids": input_ids,
            "past_key_values": past,
            "use_cache": kwargs.get("use_cache"),
            "attention_mask": attention_mask,
        }

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        input_tokens: Optional[torch.LongTensor] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        if deprecated_arguments.pop("position_ids", False) is not False:
            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
            warnings.warn(
                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
                " passing `position_ids`.",
                FutureWarning,
            )
        if len(deprecated_arguments) > 0:
            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            if (input_tokens is not None):
                stt = input_tokens
                # Shift so that tokens < n predict n and also only calculate output loss
                shift_logits = lm_logits[..., stt :-1, :].contiguous()
                shift_labels = labels[..., stt + 1 :].contiguous()
            else:
                # Shift so that tokens < n predict n
                shift_logits = lm_logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
            batch_size, seq_length, vocab_size = shift_logits.shape

            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(batch_size * seq_length, vocab_size),
                shift_labels.view(batch_size * seq_length),
            )

        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    def _reorder_cache(
        self,
        past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...],
        beam_idx: torch.LongTensor,
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        standardized_past = self._convert_to_standard_cache(
            past, batch_size=len(beam_idx)
        )

        # Get a copy of `beam_idx` on all the devices where we need those indices.
        device_to_beam_idx = {
            past_state.device: beam_idx.to(past_state.device)
            for layer_past in past
            for past_state in layer_past
        }
        reordered_past = tuple(
            (
                layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
                layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
            )
            for layer_past in standardized_past
        )
        return self._convert_to_rw_cache(reordered_past)


class RWForSequenceClassification(RWPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"h.*.self_attention.scale_mask_softmax.causal_mask",
        r"lm_head.weight",
    ]

    def __init__(self, config: RWConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = RWModel(config)
        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
        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).
        """
        if deprecated_arguments.pop("position_ids", False) is not False:
            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
            warnings.warn(
                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
                " passing `position_ids`.",
                FutureWarning,
            )
        if len(deprecated_arguments) > 0:
            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError(
                "Cannot handle batch sizes > 1 if no padding token is defined."
            )
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (
                    torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
                )
            else:
                sequence_lengths = -1
                logger.warning(
                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )

        pooled_logits = logits[
            torch.arange(batch_size, device=logits.device), sequence_lengths
        ]

        loss = None
        if labels is not None:
            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(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


class RWForTokenClassification(RWPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"h.*.self_attention.scale_mask_softmax.causal_mask",
        r"lm_head.weight",
    ]

    def __init__(self, config: RWConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = RWModel(config)
        if (
            hasattr(config, "classifier_dropout")
            and config.classifier_dropout is not None
        ):
            classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        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).
        """
        if deprecated_arguments.pop("position_ids", False) is not False:
            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
            warnings.warn(
                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
                " passing `position_ids`.",
                FutureWarning,
            )
        if len(deprecated_arguments) > 0:
            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            batch_size, seq_length = labels.shape
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                logits.view(batch_size * seq_length, self.num_labels),
                labels.view(batch_size * seq_length),
            )

        if not return_dict:
            output = (logits,) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


class RWForQuestionAnswering(RWPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"h.*.self_attention.scale_mask_softmax.causal_mask",
        r"lm_head.weight",
    ]

    def __init__(self, config):
        super().__init__(config)
        self.transformer = RWModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, 2)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            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.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )