jeiku
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ToxicNoRobotsRosaHermesBoros_3B

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-# coding=utf-8
-# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# This code is based off the following work:
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
-""" PyTorch StableLM Epoch model. """
-from typing import Optional, Tuple, Union
-import math
-import warnings
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging, is_flash_attn_greater_or_equal_2_10
-from .configuration_stablelm_epoch import StableLMEpochConfig
-try:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-except:
-    flash_attn_func, flash_attn_varlen_func = None, None
-    index_first_axis, pad_input, unpad_input = None, None, None
-logger = logging.get_logger(__name__)
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    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))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """Make causal mask used for bi-directional self-attention."""
-    batch_size, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
-    batch_size, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
-    inverted_mask = 1.0 - expanded_mask
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        max_position_embeddings: int,
-        base: int = 10_000,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
-        )
-    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
-        # 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, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
-    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
-        # x: [batch_size, num_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-def rotate_half(x: torch.Tensor):
-    """Rotates half the hidden dims of the input."""
-    x1, x2 = torch.chunk(x, 2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-class MLP(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-        self.act_fn = nn.SiLU()
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-class Attention(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        self.is_causal = True
-        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}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
-        self._init_rope()
-    def _init_rope(self):
-        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
-        self.rotary_emb = RotaryEmbedding(
-            self.rotary_ndims,
-            max_position_embeddings=self.config.max_position_embeddings,
-            base=self.config.rope_theta,
-        )
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        position_ids: torch.LongTensor,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        query_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
-        if past_key_value is not None:
-            # Reuse k, v, self_attention
-            key_states = torch.cat((past_key_value[0], key_states), dim=2)
-            value_states = torch.cat((past_key_value[1], value_states), dim=2)
-        past_key_value = (key_states, value_states) if use_cache else None
-        # Repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_mask
-        # Upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-        # Merge heads
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        # Final linear projection
-        attn_output = self.o_proj(attn_output)
-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
-class FlashAttention2(Attention):
-    """
-    Reference: https://github.com/huggingface/transformers/blob/5d36025ca13d05151b7a0c761e90d429c4644a30/src/transformers/models/llama/modeling_llama.py#L456
-    """
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # FlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-        output_attentions = False
-        bsz, q_len, _ = hidden_states.size()
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        query_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
-        if past_key_value is not None:
-            # Reuse k, v, self_attention
-            key_states = torch.cat((past_key_value[0], key_states), dim=2)
-            value_states = torch.cat((past_key_value[1], value_states), dim=2)
-        past_key_value = (key_states, value_states) if use_cache else None
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        dropout_rate = self.attention_dropout if self.training else 0.0
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
-        )
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in FlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-        return attn_output
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-ATTENTION_CLASSES = {
-    "eager": Attention,
-    "flash_attention_2": FlashAttention2,
-}
-class DecoderLayer(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__()
-        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config=config)
-        self.mlp = MLP(config)
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-        )
-        hidden_states = residual + hidden_states
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-        if use_cache:
-            outputs += (present_key_value,)
-        return outputs
-class StableLMEpochPreTrainedModel(PreTrainedModel):
-    """An abstract class to handle weights initialization and a simple interface
-    for downloading and loading pretrained models.
-    """
-    config_class = StableLMEpochConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            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, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
-        if isinstance(module, StableLMEpochModel):
-            module.gradient_checkpointing = value
-class StableLMEpochModel(StableLMEpochPreTrainedModel):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__(config)
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
-        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-    def get_input_embeddings(self):
-        return self.embed_tokens
-    def set_input_embeddings(self, value: nn.Module):
-        self.embed_tokens = value
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_shape: torch.Size,
-        inputs_embeds: torch.Tensor,
-        past_key_values_length: int,
-    ):
-        # Create causal mask
-        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-        if attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(
-                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
-            ).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        return combined_attention_mask
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        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
-        # Retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both decoder_input_ids and decoder_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 decoder_input_ids or decoder_inputs_embeds"
-            )
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        # Embed positions
-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        else:
-            if attention_mask is None:
-                attention_mask = torch.ones(
-                    (batch_size, seq_length_with_past),
-                    dtype=torch.bool,
-                    device=inputs_embeds.device,
-                )
-            attention_mask = self._prepare_decoder_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-        hidden_states = inputs_embeds
-        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
-        # Decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            past_key_value = (
-                past_key_values[idx] if past_key_values is not None else None
-            )
-            if self.gradient_checkpointing and self.training:
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, past_key_value, output_attentions)
-                    return custom_forward
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-        hidden_states = self.norm(hidden_states)
-        # Add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
-                if v is not None
-            )
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__(config)
-        self.model = StableLMEpochModel(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_input_embeddings(self):
-        return self.model.embed_tokens
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-    def get_output_embeddings(self):
-        return self.lm_head
-    def set_output_embeddings(self, new_embeddings: nn.Module):
-        self.lm_head = new_embeddings
-    def get_decoder(self):
-        return self.model
-    def set_decoder(self, decoder):
-        self.model = decoder
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        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
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states).float()
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        # Trim decoder_input_ids if past is used
-        if past_key_values is not None:
-            past_length = past_key_values[0][0].shape[2]
-            # Some generation methods already pass only the last input ID
-            if input_ids.shape[1] > past_length:
-                remove_prefix_length = past_length
-            else:
-                # Default to old behavior: keep only final ID
-                remove_prefix_length = input_ids.shape[1] - 1
-            input_ids = input_ids[:, remove_prefix_length:]
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # Create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-        model_inputs.update(
-            {
-                "attention_mask": attention_mask,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "position_ids": position_ids,
-            }
-        )
-        return model_inputs
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(
-                    past_state.index_select(0, beam_idx.to(past_state.device))
-                    for past_state in layer_past
-                ),
-            )
-        return reordered_past
-StableLMEpochConfig.register_for_auto_class()
-StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")