Delete .ipynb_checkpoints

.ipynb_checkpoints/README-checkpoint.md

@@ -1,156 +0,0 @@
----
-library_name: transformers
-tags: []
----
-
-# Falcon-11B-Base-V1.1
-The Falcon-11B-Base-V1 Large Language Model (LLM) is a pretrained generative text model with 11.1 billion parameters. 
-
-## Model Specifications
-- Base Model (not instruct tuned)
-- Flash Attention 2
-- Untied LM-Head and Word Embeddings (This adds 300M parameters over the 10.8B)
-- 11.1B Parameters
-- Rope Theta 500,042
-
-
-
-### Inference Model
-Inference the model with `trust_remote_code=True` to use our modeling code. We show an example below with the most basic hyperparameters.
-
-```python
-import os
-import sys
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-#Load Model and Tokenizer
-base_model_id = "ruliadai/falcon-base-v1.1"
-
-model = AutoModelForCausalLM.from_pretrained(
-    base_model_id, 
-    device_map="auto",
-    torch_dtype=torch.bfloat16, 
-    trust_remote_code=True,
-    attn_implementation="flash_attention_2",
-    )
-
-tokenizer = AutoTokenizer.from_pretrained(
-    base_model_id,
-    padding_side="left",
-    device_map="auto",
-    )
-tokenizer.pad_token = tokenizer.eos_token
-
-#Run Inference
-while True:
-    prompt = input("Instruction: ")
-    model_input = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)
-    model.eval()
-    print(model.generation_config)
-    with torch.no_grad():
-        print(tokenizer.decode(
-            model.generate(**model_input,max_new_tokens=800, temperature=0.0, do_sample=False, repetition_penalty=1.15)[0], use_cache=True)
-            )
-```
-
-### How to run inference
-
-Setup and activate your venv/or conda env
-
-```bash
-python3 -m venv env \
-  && source env/bin/activate
-```
-
-Install torch:
-```bash
-pip3 install torch torchvision torchaudio
-```
-Note that you may need to install torch according to your system req/drivers (https://pytorch.org/get-started/locally/)
-
-
-Install requirements:
-```bash
-pip3 install --upgrade --force-reinstall transformers accelerate flash-attn hf_transfer
-```
-
-Run script:
-
-```bash
-
-HF_HUB_ENABLE_HF_TRANSFER=1 HF_TOKEN=<YOUR_HF_TOKEN> python3 inference.py
-```
-
-
-If flash-attn is broken:
-```bash
-pip3 uninstall flash-attn
-pip3 cache purge
-pip3 install flash-attn
-```
-
-
-## Model Evaluation
-
-### Measured Benchmarks (by Ruliad)
-
-| MODEL           | AVERAGE    | MMLU (5-s) | TQA (0-s)  | ARC (25-s) | GSM8K (5-s)| HS (10-s)  | WG (5-s)   |
-| --------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |
-| Falcon-Base-v1.1  | 0.6440     | 0.5683     | 0.5263     | 0.6041     | 0.5542     | 0.8280     | 0.7806     |
-| Llama-3-8B      | 0.6300     | 0.6513     | 0.4385     | 0.5904     | 0.5034     | 0.8223     | 0.7751     |
-| Mistral-7B-v0.1 | 0.6130     | 0.6233     | 0.4258     | 0.6220     | 0.3859     | 0.8332     | 0.7861     |
-
-### Evaluation Replication
-
-**Install Eval Harness**
-
-To install the `lm-eval` package from the github repository, run:
-```bash
-git clone https://github.com/EleutherAI/lm-evaluation-harness
-cd lm-evaluation-harness
-pip install -e .
-pip install hf_transfer accelerate transformers flash_attn
-```
-**Benchmarking**
-
-To evaluate our model:
-
-Evaluating MMLU, GSM8K and WG on 5-Shot
-```bash
-HF_HUB_ENABLE_HF_TRANSFER=1 HF_TOKEN=<YOUR_HF_TOKEN> accelerate launch -m lm_eval --model hf-auto \
-    --model_args pretrained=ruliadai/falcon-base-v1.1,trust_remote_code=True \
-    --tasks mmlu,gsm8k,winogrande \
-    --device cuda:0 \
-    --num_fewshot 5 \
-    --batch_size 1
-```
-
-Evaluating TQA on 0-Shot
-```bash
-HF_HUB_ENABLE_HF_TRANSFER=1 HF_TOKEN=<YOUR_HF_TOKEN> accelerate launch -m lm_eval --model hf-auto \
-    --model_args pretrained=ruliadai/falcon-base-v1.1,trust_remote_code=True \
-    --tasks truthfulqa_mc2 \
-    --device cuda:0 \
-    --batch_size 1
-```
-
-Evaluating HS on 10-Shot
-```bash
-HF_HUB_ENABLE_HF_TRANSFER=1 HF_TOKEN=<YOUR_HF_TOKEN> accelerate launch -m lm_eval --model hf-auto \
-    --model_args pretrained=ruliadai/falcon-base-v1.1,trust_remote_code=True \
-    --tasks hellaswag \
-    --device cuda:0 \
-    --num_fewshot 10 \
-    --batch_size 1
-```
-
-Evaluating ARC on 25-Shot
-```bash
-HF_HUB_ENABLE_HF_TRANSFER=1 HF_TOKEN=<YOUR_HF_TOKEN> accelerate launch -m lm_eval --model hf-auto \
-    --model_args pretrained=ruliadai/falcon-base-v1.1,trust_remote_code=True \
-    --tasks arc_challenge \
-    --device cuda:0 \
-    --num_fewshot 25 \
-    --batch_size 1
-```

.ipynb_checkpoints/config-checkpoint.json

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-{
-  "_name_or_path": "tiiuae/falcon-11B",
-  "activation": "gelu",
-  "alibi": false,
-  "architectures": [
-    "FalconForCausalLM"
-  ],
-  "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoConfig": "tiiuae/falcon-11B--configuration_falcon.FalconConfig",
-    "AutoModel": "tiiuae/falcon-11B--modeling_falcon.FalconModel",
-    "AutoModelForCausalLM": "tiiuae/falcon-11B--modeling_falcon.FalconForCausalLM",
-    "AutoModelForQuestionAnswering": "tiiuae/falcon-11B--modeling_falcon.FalconForQuestionAnswering",
-    "AutoModelForSequenceClassification": "tiiuae/falcon-11B--modeling_falcon.FalconForSequenceClassification",
-    "AutoModelForTokenClassification": "tiiuae/falcon-11B--modeling_falcon.FalconForTokenClassification"
-  },
-  "bias": false,
-  "bos_token_id": 11,
-  "eos_token_id": 11,
-  "ff_factor": 4,
-  "ffn_hidden_size": 16384,
-  "hidden_dropout": 0.0,
-  "hidden_size": 4096,
-  "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "max_position_embeddings": 8192,
-  "model_type": "falcon",
-  "multi_query": true,
-  "new_decoder_architecture": true,
-  "num_attention_heads": 32,
-  "num_hidden_layers": 60,
-  "num_kv_heads": 8,
-  "num_ln_in_parallel_attn": 1,
-  "parallel_attn": true,
-  "rope_scaling": null,
-  "rope_theta": 500042.0,
-  "rotary_base": 5000042,
-  "tie_word_embeddings": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.39.2",
-  "use_cache": true,
-  "vocab_size": 65024
-}

.ipynb_checkpoints/generation_config-checkpoint.json

@@ -1,6 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 11,
-  "eos_token_id": 11,
-  "transformers_version": "4.40.1"
-}

.ipynb_checkpoints/model.safetensors.index-checkpoint.json

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.ipynb_checkpoints/modeling_falcon-checkpoint.py

@@ -1,1670 +0,0 @@
-# coding=utf-8
-# Copyright 2023 the Falcon authors and 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.
-"""PyTorch Falcon model."""
-
-import math
-import warnings
-from typing import TYPE_CHECKING, 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_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-)
-from .configuration_falcon import FalconConfig
-
-
-if TYPE_CHECKING:
-    from transformers.configuration_utils import PretrainedConfig
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-logger = logging.get_logger(__name__)
-
-FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "tiiuae/falcon-40b",
-    "tiiuae/falcon-40b-instruct",
-    "tiiuae/falcon-7b",
-    "tiiuae/falcon-7b-instruct",
-    "tiiuae/falcon-rw-7b",
-    "tiiuae/falcon-rw-1b",
-]
-_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
-_CONFIG_FOR_DOC = "FalconConfig"
-
-
-# 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 FalconLinear(nn.Linear):
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        hidden_states = input @ self.weight.T
-        if self.bias is None:
-            return hidden_states
-        return hidden_states + self.bias
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-@torch.jit.script
-def get_max_seqlen_in_batch(attention_mask: torch.Tensor) -> torch.Tensor:
-    max_num = int(torch.max(attention_mask).item())
-    batch_size, _ = attention_mask.shape
-    counts = torch.zeros((batch_size, max_num), dtype=torch.int32)
-
-    for i in range(1, max_num + 1):
-        mask = attention_mask == i
-        counts[:, i - 1] = torch.sum(mask, dim=-1).to(dtype=torch.int32)
-
-    result = counts.flatten()
-    nonzero_indices = torch.nonzero(result).squeeze(-1)
-    return result[nonzero_indices]
-
-
-@torch.jit.script
-def _get_unpad_data(attention_mask: torch.Tensor):
-    device = attention_mask.device
-    seqlens_in_batch = get_max_seqlen_in_batch(attention_mask)
-    indices = torch.nonzero(attention_mask.flatten()).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = (
-        F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-        .to(device=device)
-        .detach()
-    )
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Falcon
-class FalconRotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, 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, dtype=torch.int64).float().to(device) / 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, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_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=x.dtype)
-
-        return (
-            self.cos_cached[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
-
-
-# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon
-# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
-class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
-    """FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-
-        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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon
-# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
-class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding):
-    """FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-
-        if seq_len > self.max_position_embeddings:
-            base = self.base * (
-                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
-            ) ** (self.dim / (self.dim - 2))
-            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-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)
-
-
-# Copied from transformers.models.bloom.modeling_bloom.dropout_add
-def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
-    """
-    Dropout add function
-
-    Args:
-        x (`torch.tensor`, *required*):
-            input tensor
-        residual (`torch.tensor`, *required*):
-            residual tensor
-        prob (`float`, *required*):
-            dropout probability
-        training (`bool`, *required*):
-            training mode
-    """
-    out = F.dropout(x, p=prob, training=training)
-    out = residual + out
-    return out
-
-
-class FalconAttention(nn.Module):
-    def __init__(self, config: FalconConfig):
-        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.split_size = self.hidden_size
-        self.hidden_dropout = config.hidden_dropout
-        self.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-        self._use_sdpa = config._attn_implementation == "sdpa"
-
-        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})."
-            )
-
-        if config.rotary:
-            self._init_rope()
-
-        # Layer-wise attention scaling
-        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
-        self.beta = self.inv_norm_factor
-        if config.new_decoder_architecture:
-            qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
-        elif config.multi_query:
-            qkv_out_dim = self.hidden_size + 2 * self.head_dim
-        else:
-            qkv_out_dim = 3 * self.hidden_size
-        self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
-        self.new_decoder_architecture = config.new_decoder_architecture
-        self.multi_query = config.multi_query
-        self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
-        self.attention_dropout = nn.Dropout(config.attention_dropout)
-        self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Falcon
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = FalconRotaryEmbedding(
-                self.head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = FalconLinearScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = FalconDynamicNTKScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    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]
-        """
-        if self.new_decoder_architecture:
-            batch, seq_len, _ = fused_qkv.shape
-            qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
-            query = qkv[:, :, :, :-2]
-            key = qkv[:, :, :, [-2]]
-            value = qkv[:, :, :, [-1]]
-            key = torch.broadcast_to(key, query.shape)
-            value = torch.broadcast_to(value, query.shape)
-
-            query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
-            return query, key, value
-        elif not self.multi_query:
-            batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
-            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
-            return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
-        else:
-            batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
-            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
-            return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
-
-    # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
-    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Merge heads together over the last dimension
-
-        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: Optional[torch.Tensor],
-        attention_mask: torch.Tensor,
-        position_ids: Optional[torch.LongTensor] = None,
-        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        use_cache: bool = False,
-        output_attentions: bool = False,
-        **kwargs,
-    ):
-        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.`"
-            )
-
-        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
-        num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
-        # 3 x [batch_size, seq_length, num_heads, head_dim]
-        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
-
-        batch_size, query_length, _, _ = query_layer.shape
-
-        query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
-        key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
-        value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
-
-        kv_seq_len = key_layer.shape[-2]
-        if layer_past is not None:
-            kv_seq_len += layer_past[0].shape[-2]
-        if alibi is None:
-            cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
-            query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
-
-        if layer_past is not None:
-            past_key, past_value = layer_past
-            # concatenate along seq_length dimension:
-            #  - key: [batch_size, self.num_heads, kv_length, head_dim]
-            #  - value: [batch_size, self.num_heads, kv_length, head_dim]
-            key_layer = torch.cat((past_key, key_layer), dim=-2)
-            value_layer = torch.cat((past_value, value_layer), dim=-2)
-
-        kv_length = key_layer.shape[-2]
-        if use_cache:
-            present = (key_layer, value_layer)
-        else:
-            present = None
-
-        if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None:
-            # For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask,
-            # Reference: https://github.com/pytorch/pytorch/issues/112577.
-            query_layer = query_layer.contiguous()
-            key_layer = key_layer.contiguous()
-            value_layer = value_layer.contiguous()
-
-        if alibi is None:
-            if self._use_sdpa and not output_attentions:
-                attn_output = F.scaled_dot_product_attention(
-                    query_layer,
-                    key_layer,
-                    value_layer,
-                    attention_mask,
-                    0.0,
-                    # The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case query_length == 1.
-                    is_causal=self.is_causal and attention_mask is None and query_length > 1,
-                )
-
-                attention_scores = None
-            else:
-                attention_scores = query_layer @ key_layer.transpose(-1, -2)
-                attention_scores /= math.sqrt(self.head_dim)
-
-                attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
-                # It is unclear why neither dropout nor head_mask is applied here (while it is with alibi).
-                attn_output = attention_scores @ value_layer
-
-            attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
-            attn_output = attn_output.permute(0, 2, 1, 3)
-            attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
-
-            attn_output = self.dense(attn_output)
-
-            if output_attentions:
-                return attn_output, present, attention_scores
-            else:
-                return attn_output, present
-
-        else:
-            if self._use_sdpa and not output_attentions and head_mask is None:
-                attn_output = F.scaled_dot_product_attention(
-                    query_layer,
-                    key_layer,
-                    value_layer,
-                    attn_mask=attention_mask,
-                    dropout_p=self.attention_dropout.p if self.training else 0.0,
-                    is_causal=self.is_causal and attention_mask is None and query_length > 1,
-                )
-                attn_output = attn_output.transpose(1, 2)
-                attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
-
-                attn_output = self.dense(attn_output)
-            else:
-                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, query_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)
-
-                attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
-                attention_logits *= self.inv_norm_factor
-                attention_probs = F.softmax(attention_logits + attention_mask, 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, num_heads, q_length, kv_length]
-                attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
-
-                # matmul: [batch_size * num_heads, q_length, head_dim]
-                attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
-
-                # change view [batch_size, q_length, num_heads * head_dim]
-                attn_output = self._merge_heads(attn_output)
-
-                attn_output = self.dense(attn_output)
-
-            if output_attentions:
-                return attn_output, present, attention_probs
-            else:
-                return attn_output, present
-
-
-class FalconFlashAttention2(FalconAttention):
-    """
-    Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    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,
-        alibi: Optional[torch.Tensor],
-        attention_mask: torch.Tensor,
-        position_ids: Optional[torch.LongTensor] = None,
-        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        use_cache: bool = False,
-        output_attentions: bool = False,
-        **kwargs,
-    ):
-        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")
-
-        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
-        num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
-        # 3 x [batch_size, seq_length, num_heads, head_dim]
-        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
-
-        batch_size, query_length, _, _ = query_layer.shape
-
-        query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
-        key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
-        value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
-
-        kv_seq_len = key_layer.shape[-2]
-        if layer_past is not None:
-            kv_seq_len += layer_past[0].shape[-2]
-        if alibi is None:
-            cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
-            query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
-
-        if layer_past is not None and use_cache:
-            past_key, past_value = layer_past
-            # concatenate along seq_length dimension:
-            #  - key: [batch_size, self.num_heads, kv_length, head_dim]
-            #  - value: [batch_size, self.num_heads, kv_length, head_dim]
-            key_layer = torch.cat((past_key, key_layer), dim=-2)
-            value_layer = torch.cat((past_value, value_layer), dim=-2)
-
-        past_key_value = (key_layer, value_layer) 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_layer = query_layer.transpose(1, 2)
-        key_layer = key_layer.transpose(1, 2)
-        value_layer = value_layer.transpose(1, 2)
-
-        if alibi is not None:
-            raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
-
-        attn_dropout = self.config.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in float16 just to be sure everything works as expected.
-        input_dtype = query_layer.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.query_key_value.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_layer = query_layer.to(target_dtype)
-            key_layer = key_layer.to(target_dtype)
-            value_layer = value_layer.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_layer, key_layer, value_layer, attention_mask, query_length, dropout=attn_dropout
-        )
-
-        attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
-        attn_output = self.dense(attn_weights)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, past_key_value, attn_weights
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
-    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 (`float`):
-                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 LlamaFlashAttention2 __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
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
-    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),
-        )
-
-
-class FalconMLP(nn.Module):
-    def __init__(self, config: FalconConfig):
-        super().__init__()
-        hidden_size = config.hidden_size
-
-        self.upscale = FalconLinear(
-            hidden_size, config.ff_factor * hidden_size, bias=config.bias
-        )
-        self.act = nn.GELU()
-        self.downscale = FalconLinear(
-            config.ff_factor * 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.upscale(x))
-        x = self.downscale(x)
-        return x
-
-FALCON_ATTENTION_CLASSES = {
-    "eager": FalconAttention,
-    "sdpa": FalconAttention,  # FalconAttention originally implemented both a forward with & without SDPA
-    "flash_attention_2": FalconFlashAttention2,
-}
-
-
-class FalconDecoderLayer(nn.Module):
-    def __init__(self, config: FalconConfig):
-        super().__init__()
-        hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-
-        self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config)
-        self.mlp = FalconMLP(config)
-        self.hidden_dropout = config.hidden_dropout
-        self.config = config
-
-        if config.new_decoder_architecture and config.num_ln_in_parallel_attn == 2:
-            # The layer norm before self-attention
-            self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-            # The layer norm before the MLP
-            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        else:
-            self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-            if not config.parallel_attn:
-                self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        alibi: Optional[torch.Tensor],
-        attention_mask: torch.Tensor,
-        position_ids: Optional[torch.LongTensor] = None,
-        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        use_cache: bool = False,
-        output_attentions: bool = False,
-        **kwargs,
-    ):
-        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.`"
-            )
-
-        residual = hidden_states
-
-        if self.config.num_ln_in_parallel_attn == 2:
-            attention_layernorm_out = self.ln_attn(hidden_states)
-            mlp_layernorm_out = self.ln_mlp(hidden_states)
-        else:
-            attention_layernorm_out = self.input_layernorm(hidden_states)
-
-        # Self attention.
-        attn_outputs = self.self_attention(
-            attention_layernorm_out,
-            layer_past=layer_past,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            alibi=alibi,
-            head_mask=head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            **kwargs,
-        )
-
-        attention_output = attn_outputs[0]
-
-        if self.config.num_ln_in_parallel_attn == 1:
-            if self.config.parallel_attn:
-                mlp_layernorm_out = attention_layernorm_out
-            else:
-                residual = dropout_add(
-                    attention_output, residual, self.config.attention_dropout, training=self.training
-                )
-                mlp_layernorm_out = self.post_attention_layernorm(residual)
-
-        outputs = attn_outputs[1:]
-
-        # MLP.
-        mlp_output = self.mlp(mlp_layernorm_out)
-
-        if self.config.new_decoder_architecture or self.config.parallel_attn:
-            mlp_output += attention_output
-
-        output = dropout_add(mlp_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
-
-
-FALCON_START_DOCSTRING = r"""
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`FalconConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-FALCON_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
-            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]`
-            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
-
-            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
-            `input_ids`.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`):
-            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
-            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
-            their past given to this model should not be passed as `input_ids` as they have already been computed.
-
-            Each element of `past_key_values` is a tuple (past_key, past_value):
-            - past_key: [batch_size * num_heads, head_dim, kv_length]
-            - past_value: [batch_size * num_heads, kv_length, head_dim]
-        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-
-            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
-            `past_key_values`).
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class FalconPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = FalconConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["FalconDecoderLayer"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-
-    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, FalconLinear):
-            # 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)
-
-    # Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
-    @classmethod
-    def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig":
-        # NOTE: Falcon supported SDPA from PyTorch 2.0. We keep it like that for backward compatibility (automatically use SDPA for torch>=2.0).
-        if hard_check_only:
-            if not is_torch_greater_or_equal_than_2_0:
-                raise ImportError("PyTorch SDPA requirements in Transformers are not met. Please install torch>=2.0.")
-
-        if not is_torch_greater_or_equal_than_2_0:
-            return config
-
-        _is_bettertransformer = getattr(cls, "use_bettertransformer", False)
-        if _is_bettertransformer:
-            return config
-
-        if not hard_check_only:
-            config._attn_implementation = "sdpa"
-        return config
-
-
-@add_start_docstrings(
-    "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
-    FALCON_START_DOCSTRING,
-)
-class FalconModel(FalconPreTrainedModel):
-    def __init__(self, config: FalconConfig):
-        super().__init__(config)
-
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.use_alibi = config.alibi
-
-        # Embedding + LN Embedding
-        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
-
-        # Transformer blocks
-        self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self._use_sdpa = config._attn_implementation == "sdpa"
-
-        # 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 set_input_embeddings(self, new_embeddings: torch.Tensor):
-        self.word_embeddings = new_embeddings
-
-    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPastAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    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,
-        position_ids: Optional[torch.LongTensor] = 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,
-    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
-        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))
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        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
-        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
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[-2]
-
-        if self.use_alibi:
-            mask = (
-                torch.ones(
-                    (batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
-                )
-                if attention_mask is None
-                else attention_mask
-            )
-            alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
-        else:
-            alibi = None
-            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)
-
-        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
-        elif self._use_sdpa and not output_attentions:
-            # output_attentions=True can not be supported when using SDPA, and we fall back on
-            # the manual implementation that requires a 4D causal mask in all cases.
-            if alibi is None:
-                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    inputs_embeds,
-                    past_key_values_length,
-                )
-            elif head_mask is None:
-                alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
-
-                attention_mask_2d = attention_mask
-                # We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched.
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-                )
-
-                # We take care to integrate alibi bias in the attention_mask here.
-                if attention_mask_2d is None:
-                    attention_mask = alibi / math.sqrt(self.config.hidden_size // self.num_heads)
-                else:
-                    min_dtype = torch.finfo(alibi.dtype).min
-                    attention_mask = torch.masked_fill(
-                        alibi / math.sqrt(self.config.hidden_size // self.num_heads),
-                        attention_mask < -1,
-                        min_dtype,
-                    )
-
-                    # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
-                    # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
-                    if seq_length > 1 and attention_mask.device.type == "cuda":
-                        attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype)
-            else:
-                # PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case.
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-                )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-            )
-
-        # 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.num_hidden_layers)
-
-        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:
-                outputs = self._gradient_checkpointing_func(
-                    block.__call__,
-                    hidden_states,
-                    alibi,
-                    attention_mask,
-                    position_ids,
-                    head_mask[i],
-                    layer_past,
-                    use_cache,
-                    output_attentions,
-                )
-            else:
-                outputs = block(
-                    hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    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,
-        )
-
-
-@add_start_docstrings(
-    "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
-    FALCON_START_DOCSTRING,
-)
-class FalconForCausalLM(FalconPreTrainedModel):
-    _tied_weights_keys = None  # ["lm_head.weight"]
-
-    def __init__(self, config: FalconConfig):
-        super().__init__(config)
-        self.transformer = FalconModel(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_key_values: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> dict:
-        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:]
-
-        # Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE.
-        if not self.transformer.use_alibi and 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[:, -input_ids.shape[1] :]
-
-        return {
-            "input_ids": input_ids,
-            "position_ids": position_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "attention_mask": attention_mask,
-        }
-
-    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=CausalLMOutputWithCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    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,
-        position_ids: Optional[torch.LongTensor] = 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,
-    ) -> 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]`
-        """
-
-        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,
-            position_ids=position_ids,
-            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:
-            # 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`.
-        """
-
-        # 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 past
-        )
-        return reordered_past
-
-
-@add_start_docstrings(
-    """
-    The Falcon Model transformer with a sequence classification head on top (linear layer).
-
-    [`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-1) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    FALCON_START_DOCSTRING,
-)
-class FalconForSequenceClassification(FalconPreTrainedModel):
-    def __init__(self, config: FalconConfig):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.transformer = FalconModel(config)
-        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    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,
-    ) -> 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).
-        """
-
-        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:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            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,
-        )
-
-
-@add_start_docstrings(
-    """
-    Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    FALCON_START_DOCSTRING,
-)
-class FalconForTokenClassification(FalconPreTrainedModel):
-    def __init__(self, config: FalconConfig):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.transformer = FalconModel(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) 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()
-
-    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    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,
-    ) -> 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).
-        """
-
-        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,
-        )
-
-
-@add_start_docstrings(
-    """
-    The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like
-    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    FALCON_START_DOCSTRING,
-)
-class FalconForQuestionAnswering(FalconPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = FalconModel(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        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,
-            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,
-        )

.ipynb_checkpoints/special_tokens_map-checkpoint.json

@@ -1,24 +0,0 @@
-{
-  "additional_special_tokens": [
-    ">>TITLE<<",
-    ">>ABSTRACT<<",
-    ">>INTRODUCTION<<",
-    ">>SUMMARY<<",
-    ">>COMMENT<<",
-    ">>ANSWER<<",
-    ">>QUESTION<<",
-    ">>DOMAIN<<",
-    ">>PREFIX<<",
-    ">>SUFFIX<<",
-    ">>MIDDLE<<"
-  ],
-  "bos_token": ">>",
-  "eos_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "<|endoftext|>"
-}

.ipynb_checkpoints/tokenizer_config-checkpoint.json

@@ -1,135 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": ">>TITLE<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": ">>ABSTRACT<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": ">>INTRODUCTION<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": ">>SUMMARY<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": ">>COMMENT<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "5": {
-      "content": ">>ANSWER<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "6": {
-      "content": ">>QUESTION<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "7": {
-      "content": ">>DOMAIN<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "8": {
-      "content": ">>PREFIX<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "9": {
-      "content": ">>SUFFIX<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "10": {
-      "content": ">>MIDDLE<<",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "11": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "500": {
-      "content": ">>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "additional_special_tokens": [
-    ">>TITLE<<",
-    ">>ABSTRACT<<",
-    ">>INTRODUCTION<<",
-    ">>SUMMARY<<",
-    ">>COMMENT<<",
-    ">>ANSWER<<",
-    ">>QUESTION<<",
-    ">>DOMAIN<<",
-    ">>PREFIX<<",
-    ">>SUFFIX<<",
-    ">>MIDDLE<<"
-  ],
-  "bos_token": ">>",
-  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ 'User: \n' + message['content'] }}\n{% elif message['role'] == 'system' %}\n{{ 'System: ' + message['content'] }}\n{% elif message['role'] == 'assistant' %}\n{{ 'Falcon:\n'  + message['content']}}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ 'Falcon:' }}\n{% endif %}\n{% endfor %}",
-  "clean_up_tokenization_spaces": true,
-  "device_map": "cuda:2",
-  "eos_token": "<|endoftext|>",
-  "model_input_names": [
-    "input_ids",
-    "attention_mask"
-  ],
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "<|endoftext|>",
-  "padding_side": "left",
-  "tokenizer_class": "PreTrainedTokenizerFast"
-}