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added_tokens.json

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+{
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config.json

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+{
+  "_name_or_path": "/share/shared_models/qwen-2-7b-instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_qwen2.Qwen2Config",
+    "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM"
+  },
+  "attention_dropout": 0.0,
+  "beacon_attend_prev": true,
+  "beacon_attn": "full-coverage",
+  "beacon_embed_init": "eos",
+  "beacon_parallel_window": 1,
+  "beacon_param": [
+    "q",
+    "k",
+    "v"
+  ],
+  "beacon_pos": "interleave",
+  "beacon_ratio": [
+    2,
+    4,
+    8,
+    16,
+    32
+  ],
+  "beacon_ratio_mix": "adapt-1024",
+  "beacon_sink_size": 0,
+  "beacon_stride": 2048,
+  "beacon_window": 2048,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration_qwen2.py

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+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group 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.
+""" Qwen2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json",
+}
+
+
+class Qwen2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
+    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen2Model, Qwen2Config
+
+    >>> # Initializing a Qwen2 style configuration
+    >>> configuration = Qwen2Config()
+
+    >>> # Initializing a model from the Qwen2-7B style configuration
+    >>> model = Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        use_sliding_window=False,
+        sliding_window=4096,
+        rope_scaling=None,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        beacon_window=1024,
+        beacon_stride=1024,
+        beacon_attn="full-coverage",
+        beacon_ratio=[2,4,8,16,32],
+        beacon_ratio_mix="step-random",
+        beacon_param=[],
+        beacon_embed_init="eos",
+        beacon_sink_size=0,
+        beacon_attend_prev=True,
+        beacon_pos="interleave",
+        beacon_parallel_window=1,
+        beacon_accum=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+        self.rope_scaling = rope_scaling
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.beacon_window = beacon_window
+        self.beacon_stride = beacon_stride
+        self.beacon_attn = beacon_attn
+        self.beacon_ratio = beacon_ratio
+        self.beacon_ratio_mix = beacon_ratio_mix
+        self.beacon_param = beacon_param
+        self.beacon_embed_init = beacon_embed_init
+        self.beacon_sink_size = beacon_sink_size
+        self.beacon_attend_prev = beacon_attend_prev
+        self.beacon_pos = beacon_pos
+        self.beacon_parallel_window = beacon_parallel_window
+        self.beacon_accum = beacon_accum
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "4.39.3"
+}

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+  }
+}

modeling_beacon.py

@@ -0,0 +1,1108 @@
+import os
+import torch
+import time
+import numpy as np
+import torch.distributed as dist
+from transformers.utils import logging
+from transformers import AutoTokenizer
+from itertools import cycle
+from typing import List
+
+logger = logging.get_logger(__name__)
+
+
+class Memory(torch.nn.Module):
+    def __init__(
+        self, 
+        model_config, 
+        k_seq_dim:int=2, 
+        v_seq_dim:int=2, 
+    ):
+        """Setup necessary attributes."""
+        super().__init__()
+
+        self.config = model_config
+
+        # initialize necessary parameters
+        self.k_seq_dim = k_seq_dim
+        self.v_seq_dim = v_seq_dim
+        self.rng = np.random.default_rng(42)
+
+        self.beacon_token = torch.tensor([self.config.vocab_size])
+
+        self._post_validation()
+        self.reset()
+
+    def _post_validation(self, verbose=True):
+        assert self.config.beacon_window >= self.config.beacon_stride, f"Make sure the beacon_window {self.config.beacon_window} >= beacon_stride {self.config.beacon_stride}!"
+        for ratio in self.config.beacon_ratio:
+            assert ratio >= 0, f"Make sure all beacon ratios are greater than or equal to 0, found {self.config.beacon_ratio}!"
+        assert self.config.beacon_attn in ["segmentation", "step-expansion", "full-coverage"], f"beacon_attn {self.config.beacon_attn} not implemented!"
+        assert self.config.beacon_ratio_mix in ["instance-random", "step-random", "sequence"] or "adapt-" in self.config.beacon_ratio_mix, f"beacon_ratio_mix {self.config.beacon_ratio_mix} not implemented!"
+        # assert self.config.beacon_pos in ["append", "interleave"], f"beacon_pos {self.config.beacon_pos} not implemented!"
+        if self.config.beacon_pos == "interleave":
+            assert self.config.beacon_window == self.config.beacon_stride, f"Make sure the beacon_window equals to beacon_stride when using interleaving mode."
+        if self.config.beacon_parallel_window > 1:
+            assert self.config._attn_implementation != "flash_attention_2", f"Currently parallel window does not support flash_attention_2!"
+
+        self._cpu = torch.device("cpu")
+
+        if verbose:
+            info = f"applying activation beacon on {self.config.beacon_param} (the beacon embedding is initialized from {'bos' if self.config.beacon_embed_init == 'bos' else 'eos'} embedding, the beacon tokens are positioned with '{self.config.beacon_pos}' method), with window size {self.config.beacon_window}, stride {self.config.beacon_stride}, {self.config.beacon_attn} attention{' (attending to previous beacons)' if self.config.beacon_attend_prev else ' (no attending to previous beacons)'}, sink size {self.config.beacon_sink_size}, compression ratio {self.config.beacon_ratio} (mixed by {self.config.beacon_ratio_mix})..."
+            logger.info(info)
+
+    def set(self, verbose=True, **kwargs):
+        """
+        Set attributes out of the constructor.
+        """
+        for k, v in kwargs.items():
+            setattr(self.config, k, v)
+        self._post_validation(verbose=verbose)
+
+    def reset(self):
+        """Initialize attributes for a new sequence."""
+        # the cursor pointing to the start of the current window
+        self.start_idx = 0
+        # the cursor pointing to the end of the current window
+        self.end_idx = 0
+        # the beacon sizes of all strides
+        self.all_beacon_sizes = []
+        # the loss per batch
+        self.batch_loss = None
+        # the valid token number per batch
+        self.valid_token_num = None
+        # the step index for processing the input_ids
+        self.step_idx = 0
+        # used in set_compression_ratio
+        self.compression_ratio = None
+        # the previous inputs is a full window or not, defaults to True
+        self.is_full_window = True
+        # the number of raw activations to preserve in update_memory (only useful when beacon_stride < beacon_window)
+        self.raw_size_to_cache = 0
+
+        # the number of tokens in previous stride that should be compressed by the upcoming beacon
+        self.interleave_remainder = 0
+        # compression ratio for the unfinished window
+        self.interleave_compression_ratio = None
+        self.beacon_indices = None
+
+        self.all_input_ids = None
+        self.all_attention_mask = None
+        self.all_labels = None
+
+        # NOTE: will be reset in prepare()
+        self.beacon_skip_first = None
+        self.beacon_skip_last = None
+
+        # the raw activations of recent tokens
+        self.raw_activations = [(None, None) for _ in range(self.config.num_hidden_layers)]
+        # the attention sink activations
+        self.sink_activations = [(None, None) for _ in range(self.config.num_hidden_layers)]
+        # the beacon activations
+        self.beacon_activations = [(None, None) for _ in range(self.config.num_hidden_layers)]
+
+    @property
+    def all_sequence_length(self):
+        if self.all_input_ids is None:
+            return 0
+        else:
+            return self.all_input_ids.shape[1]
+
+    @property
+    def batch_size(self):
+        if self.all_input_ids is None:
+            return 0
+        else:
+            return self.all_input_ids.shape[0]
+
+    @property
+    def finish(self):
+        is_finish = self.end_idx == self.all_sequence_length
+        return is_finish
+
+    @property
+    def dtype(self):
+        return self.config.torch_dtype
+
+    @property
+    def min_value(self):
+        return torch.finfo(self.dtype).min
+
+    @property
+    def max_position_embeddings(self):
+        max_position_embeddings = self.config.max_position_embeddings
+        if getattr(self.config, "rope_scaling", None) is not None:
+            scaling_factor = self.config.rope_scaling["factor"]
+            max_position_embeddings = max_position_embeddings * scaling_factor
+        return max_position_embeddings
+
+    @property
+    def beacon_window(self):
+        if (
+            self.beacon_skip_last is not None
+            and self.start_idx < self.beacon_skip_last
+            and self.start_idx + self.config.beacon_window > self.beacon_skip_last
+        ):
+            return self.beacon_skip_last - self.start_idx
+        else:
+            return self.config.beacon_window
+
+    @property
+    def beacon_stride(self):
+        if (
+            self.beacon_skip_last is not None
+            and self.start_idx < self.beacon_skip_last
+            and self.start_idx + self.config.beacon_window > self.beacon_skip_last
+        ):
+            return self.beacon_skip_last - self.start_idx
+        else:
+            return self.config.beacon_stride
+ 
+    def get_memory_size(self):
+        """
+        Sink memory size, beacon memory size and raw memory size.
+        """
+        sink_memory_size = 0
+        beacon_memory_size = 0
+        raw_memory_size = 0
+        if self.sink_activations[0][0] is not None:
+            sink_memory_size += self.sink_activations[0][0].shape[self.k_seq_dim]
+        if self.beacon_activations[0][0] is not None:
+            beacon_memory_size += self.beacon_activations[0][0].shape[self.k_seq_dim]
+        if self.raw_activations[0][0] is not None:
+            raw_memory_size += self.raw_activations[0][0].shape[self.k_seq_dim]
+        return sink_memory_size, beacon_memory_size, raw_memory_size
+
+    def prepare(self, input_ids, attention_mask, labels, skip_first=None, skip_last=None):
+        """
+        Prepare inputs for the model. These inputs belong to the same sequence.
+        """
+        # assert input_ids.shape[0] == 1, "Make sure the batch size is 1!"
+        # assert attention_mask is None or (attention_mask == 1).all(), "Make sure there is no padding!"
+
+        self._device = input_ids.device
+
+        # accumulate input_ids
+        if self.all_input_ids is None:
+            self.all_input_ids = input_ids.cpu()
+        else:
+            self.all_input_ids = torch.cat([self.all_input_ids, input_ids.cpu()], dim=1)
+
+        # accumulate attention_mask
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids, device=torch.device("cpu"))
+        if self.all_attention_mask is None:
+            self.all_attention_mask = attention_mask.cpu()
+        else:
+            self.all_attention_mask = torch.cat([self.all_attention_mask, attention_mask.cpu()], dim=1)
+
+        # accumulate labels if exisits
+        if labels is not None:
+            # rotate labels in advance so that the loss of the last token is not ignored in every window
+            labels = torch.cat([labels[:, 1:].cpu(), torch.tensor([-100]).expand(labels.shape[0], 1)], dim=1)
+            if self.all_labels is None:
+                self.all_labels = labels.cpu()
+            else:
+                self.all_labels = torch.cat([self.all_labels, labels], dim=1)
+            assert self.all_input_ids.shape[1] == self.all_labels.shape[1], f"Found inconsistent all_input_ids {self.all_input_ids.shape} and all_labels {self.all_labels.shape}!"
+        
+        # how many tokens to skip at the beginning of the sequence? (They will be packed in a single chunk and processed by the model, after which their activations will be cached in sink_activations.)
+        if skip_first is not None:
+            assert self.config.beacon_parallel_window == 1, f"Make sure the parallel window is set to 1 when using beacon_skip!"
+            assert self.config.beacon_window == self.config.beacon_stride, f"Make sure the beacon_window equals to beacon_stride when using beacon_skip."
+            assert self.config.beacon_sink_size == 0, f"Make sure the beacon_sink_size is set to 0 when using beacon_skip!"
+        # stop compression after how many tokens
+        if skip_last is not None:
+            skip_first = skip_first if skip_first is not None else 0
+            # assert (skip_last - skip_first) % self.config.beacon_window == 0, f"skip_last ({skip_last}) - skip_first ({skip_first}) = {skip_last - skip_first} is not divisible by window size {self.config.beacon_window}"
+            assert self.config.beacon_sink_size == 0, "Make sure the beacon_sink_size is zero when using skip_last!"
+        self.beacon_skip_first = skip_first
+        self.beacon_skip_last = skip_last
+
+    def set_compression_ratio(self, start_idx, end_idx):
+        """Choose a condensing ratio from self.config.beacon_ratio"""
+        def filter_ratio(ratios, stride):
+            valid_ratios = []
+            for ratio in ratios:
+                # stride must be bigger than condensing ratio because we there must be at least one beacon
+                if stride < ratio:
+                    continue
+                # the stride must be evenly divisible by condensing ratio
+                if ratio > 0 and (stride % ratio) != 0:
+                    continue
+                # when training, ratio=0 is valid if previous windows contain beacon or later windows contain beacon
+                if ratio == 0 and self.training:
+                    previous_has_zero = -1 in self.all_beacon_sizes
+                    following_has_nonzero = (start_idx + stride + self.beacon_window) <= self.all_sequence_length
+                    if previous_has_zero or (not following_has_nonzero):
+                        continue
+                valid_ratios.append(ratio)
+            assert len(valid_ratios), f"Cannot find valid condensing ratio (among {ratios}) for stride {stride}!"
+            return valid_ratios
+
+        def get_max_length(ratios):
+            max_lengths = []
+            for compression_ratio in ratios:
+                if compression_ratio > 0:
+                    # NOTE: here we must use the scaled position embeddings
+                    max_lengths.append((self.max_position_embeddings - self.beacon_window) * compression_ratio + self.beacon_window)
+                else:
+                    max_lengths.append(self.max_position_embeddings)
+            return max_lengths
+
+        if len(self.config.beacon_ratio) == 1:
+            return self.config.beacon_ratio[0]
+
+        ratio_mix = self.config.beacon_ratio_mix
+
+        beacon_ratio = filter_ratio(self.config.beacon_ratio, self.beacon_stride)
+
+        if ratio_mix == "instance-random":
+            if self.compression_ratio is None:
+                beacon_ratio = self.rng.choice(beacon_ratio).tolist()
+                self.compression_ratio = beacon_ratio
+            else:
+                beacon_ratio = self.compression_ratio
+
+        elif ratio_mix == "step-random":
+            beacon_ratio = self.rng.choice(beacon_ratio).tolist()
+        
+        elif ratio_mix == "sequence":
+            if self.compression_ratio is None:
+                self.compression_ratio = cycle(beacon_ratio)
+            beacon_ratio = next(self.compression_ratio)
+
+        elif "adapt" in ratio_mix:
+            if self.compression_ratio is None:
+                future_length = int(ratio_mix.split("-")[1])
+                sequence_length = self.all_input_ids.shape[1] + future_length
+                max_lengths = get_max_length(beacon_ratio)
+                # ascendingly sort the max lengths
+                valid_max_lengths_and_indices = [x for x in enumerate(max_lengths) if x[1] >= sequence_length]
+                if len(valid_max_lengths_and_indices):
+                    minimum_length_index = min(valid_max_lengths_and_indices, key=lambda x: x[1])[0]
+                    # use the minimal possible length for this sequence (the smallest fold ratio)
+                    beacon_ratio = beacon_ratio[minimum_length_index]
+                else:
+                    beacon_ratio = max(beacon_ratio)
+                    # logger.warning(f"Failed to find valid fold window and size for sequence length {sequence_length}, as the maximum theoretical length is {max(max_lengths)}. Fall back to use the maximum one: {beacon_ratio}.")
+                self.compression_ratio = beacon_ratio
+            else:
+                beacon_ratio = self.compression_ratio
+
+        return beacon_ratio
+
+    def step(self):
+        # parallel does not support stride < window
+        # parallel does not support non-compression
+        # the input_ids is not long enough for parallel
+        if (
+            self.config.beacon_parallel_window > 1 
+            and self.config.beacon_stride == self.config.beacon_window
+            and 0 not in self.config.beacon_ratio
+            and self.all_input_ids[:, self.end_idx:].shape[1] >= self.config.beacon_parallel_window * self.config.beacon_window
+        ):
+            input_ids_list = []
+            attention_mask_list = []
+            position_ids_list = []
+            labels_list = []
+
+            beacon_size_list = []
+            beacon_indices_list = []
+
+            for i in range(self.config.beacon_parallel_window):
+                if i == 0:
+                    _input_ids, _attention_mask, _position_ids, _past_key_values, _labels = self._step()
+                else:
+                    _input_ids, _attention_mask, _position_ids, _past_key_values, _labels = self._step(ignore_memory=True)
+
+                input_ids_list.append(_input_ids)
+                attention_mask_list.append(_attention_mask)
+                position_ids_list.append(_position_ids)
+                labels_list.append(_labels)
+                beacon_size_list.append(_past_key_values[0][2])
+                beacon_indices_list.append(_past_key_values[0][3])
+
+                if i == 0:
+                    past_key_values = _past_key_values
+                    if past_key_values[0][0] is None:
+                        mem_size = 0
+                    else:
+                        mem_size = past_key_values[0][0].shape[self.k_seq_dim]
+
+                else:
+                    # no memory
+                    assert _past_key_values[0][0] is None
+            
+            batch_size = self.all_input_ids.shape[0]
+            # NOTE: we do not need to repliace beacon tokens for the last window
+            seq_len = sum(x.shape[1] for x in input_ids_list) + sum(beacon_size_list) - beacon_size_list[-1]
+
+            input_ids = _input_ids.new_zeros((batch_size, seq_len)) + self.beacon_token.to(_input_ids.device)
+            # all 0
+            attention_mask = _attention_mask.new_zeros((batch_size, 1, seq_len, mem_size + seq_len)) + self.min_value
+            position_ids = torch.arange(mem_size + seq_len, device=self._device).expand(batch_size, mem_size + seq_len)
+            # 2 indicates the beacon token is used for replication
+            beacon_indices = beacon_indices_list[0].new_zeros(seq_len) + 2
+            if _labels is not None:
+                # -100 because no loss on beacon tokens
+                labels = _labels.new_zeros((batch_size, seq_len)) - 100
+            else:
+                labels = None
+
+            start_idx = 0
+            position_offset = mem_size
+            for i in range(self.config.beacon_parallel_window):
+                beacon_size = beacon_size_list[i]
+
+                # populate input_ids
+                _input_ids = input_ids_list[i]
+                cur_seq_len = _input_ids.shape[1]
+                input_ids[:, start_idx: start_idx + cur_seq_len] = _input_ids
+                
+                # populate attention_mask and position_ids
+                _attention_mask = attention_mask_list[i]
+                _position_ids = position_ids_list[i]
+                # the attention mask in the first window contains the mask for memory, which is redundant here
+                if i == 0:
+                    _attention_mask = _attention_mask[:, :, :, mem_size:]
+                    _position_ids = _position_ids[:, mem_size:] - mem_size
+
+                attention_mask[:, :, start_idx: start_idx + cur_seq_len, mem_size + start_idx: mem_size + start_idx + cur_seq_len] = _attention_mask
+                position_ids[:, mem_size + start_idx: mem_size + start_idx + cur_seq_len] = _position_ids + position_offset
+
+                # populate beacon_indices
+                _beacon_indices = beacon_indices_list[i]
+                beacon_indices[start_idx: start_idx + cur_seq_len] = _beacon_indices
+
+                # populate labels
+                if labels is not None:
+                    # populate labels
+                    _labels = labels_list[i]
+                    labels[:, start_idx: start_idx + cur_seq_len] = _labels
+
+                # NOTE: when there is sink activations, we need to bias the position_ids for the first window
+                if i == 0 and self.config.beacon_sink_size > 0 and self.sink_activations[0][0] is None:
+                    position_offset += 1
+
+                # modify the attention and position for replicated beacon tokens
+                if i != self.config.beacon_parallel_window - 1:
+                    replicate_beacon_row_start = start_idx + cur_seq_len
+                    replicate_beacon_col_start = mem_size + start_idx + cur_seq_len
+                    # NOTE: any attention mask is okay for replicated beacon tokens, but for convenience we use the causal mask
+                    attention_mask[:, :, replicate_beacon_row_start: replicate_beacon_row_start + beacon_size, replicate_beacon_col_start: replicate_beacon_col_start + beacon_size] = _attention_mask.new_full((beacon_size, beacon_size), self.min_value).triu(1)
+                    # NOTE: all future tokens can attend to the replicated beacon tokens
+                    attention_mask[:, :, replicate_beacon_row_start + beacon_size:, replicate_beacon_col_start: replicate_beacon_col_start + beacon_size] = 0
+                    # NOTE: the position of replicated beacon tokens start from 0
+                    position_ids[:, mem_size + start_idx + cur_seq_len: mem_size + start_idx + cur_seq_len + beacon_size] = torch.arange(position_offset, position_offset + beacon_size, device=_input_ids.device)[None:]
+
+                start_idx += cur_seq_len + beacon_size
+                position_offset += beacon_size
+
+            # the memory is visible to all subsequent tokens
+            attention_mask[:, :, :, :max(mem_size, self.config.beacon_sink_size)] = 0
+
+            # NOTE: modify beacon_indices
+            for i, (key, value, _, _) in enumerate(past_key_values):
+                past_key_values[i] = (key, value, sum(beacon_size_list), beacon_indices)
+
+            # NOTE: update _beacon_indices so that the next-token logits can be properly sliced out in self.output()
+            self.beacon_indices = beacon_indices
+            
+            return input_ids, attention_mask, position_ids, past_key_values, labels
+
+        else:
+            return self._step()
+
+    def _step(self, ignore_memory=False):
+        """
+        Yield inputs for the current sliding window, including the input_ids, attention_mask, position_ids, and past_key_values.
+        """
+        #============================================#
+        # Check whether the inputs fulfills a window.
+        #============================================#
+
+        # the starting position of the current window w.r.t. the start of the current input sequence
+        start_idx = self.start_idx
+        # the end position of the current window w.r.t. the start of the current input sequence
+        end_idx = start_idx + self.beacon_window
+        # indicates if the current window is completely filled by raw activations and new tokens
+        # we only append beacon tokens for full windows
+        if end_idx > self.all_sequence_length:
+            # the input is shorter than the initial window size
+            end_idx = self.all_sequence_length
+            is_full_window = False
+        else:
+            is_full_window = True
+
+        # NOTE: in training, the entire sequence is input to the model at once
+        # In the last window, we do not need to append beacons because they will not be used at all
+        if self.training and end_idx == self.all_sequence_length:
+            next_start_idx = start_idx
+            is_full_window = False
+            raw_size_to_cache = -1
+            beacon_size = 0
+            compression_ratio = -1
+        
+        # NOTE: we do not compress the beacon_skip_first tokens at the beginning of the sequence
+        elif self.step_idx == 0 and self.beacon_skip_first is not None:
+            end_idx = start_idx + self.beacon_skip_first
+            assert end_idx <= self.all_sequence_length
+            next_start_idx = end_idx
+            is_full_window = True
+            raw_size_to_cache = -1
+            beacon_size = 0
+            compression_ratio = -1
+        
+        # NOTE: we do not compress tokens after beacon_skip_last tokens
+        elif self.beacon_skip_last is not None and start_idx >= self.beacon_skip_last:
+            end_idx = min(start_idx + self.beacon_window, self.all_sequence_length)
+            next_start_idx = end_idx
+            is_full_window = False
+            raw_size_to_cache = -1
+            beacon_size = 0
+            compression_ratio = -1
+
+        else:
+            #============================================#
+            # Set compression ratio
+            #============================================#
+            if self.config.beacon_pos == "append":
+                if is_full_window:
+                    # determine compression ratio for the current window
+                    beacon_stride = self.beacon_stride
+                    compression_ratio = self.set_compression_ratio(start_idx=start_idx, end_idx=end_idx)
+
+                    if compression_ratio > 0:
+                        # the stride must be evenly divisible by compression_ratio
+                        beacon_size = beacon_stride // compression_ratio
+                    else:
+                        # the raw activations are used as beacon activations
+                        beacon_size = -1
+
+                    # forward start_idx and end_idx
+                    next_start_idx = start_idx + beacon_stride
+                    # how many raw activations to save
+                    raw_size_to_cache = end_idx - next_start_idx
+                else:
+                    # no stride because the sequence has finished
+                    next_start_idx = start_idx
+                    # cache all raw activations
+                    raw_size_to_cache = -1
+                    beacon_size = 0
+                    compression_ratio = 0
+
+            elif self.config.beacon_pos == "interleave":
+                # the number of raw tokens in the input_ids
+                input_size = end_idx - self.end_idx
+                # set compression ratio once the previous window has finished, otherwise, reuse the interleave_compression_ratio if the input belongs to an unfinished window
+                if self.is_full_window:
+                    compression_ratio = self.set_compression_ratio(start_idx=start_idx, end_idx=end_idx)
+                    self.interleave_compression_ratio = compression_ratio
+                else:
+                    compression_ratio = self.interleave_compression_ratio
+
+                # the beacon size is non-zero even if the window is not full
+                if compression_ratio > 0:
+                    # this number of beacon tokens will be inserted among the raw tokens
+                    beacon_size = (input_size + self.interleave_remainder) // compression_ratio
+                else:
+                    # the raw activations are used as beacon activations
+                    beacon_size = -1
+
+                if is_full_window:
+                    # move forward one window
+                    next_start_idx = start_idx + self.beacon_stride
+                    # no save raw activations
+                    raw_size_to_cache = 0
+                else:
+                    # no stride because the sequence has not finished
+                    next_start_idx = start_idx
+                    # cache all recent raw activations to be used in the next window
+                    raw_size_to_cache = -1
+
+        #============================================#
+        # Slice out input_ids (raw tokens in the current window)
+        #============================================#
+        input_ids = self.all_input_ids[:, self.end_idx: end_idx].to(self._device)
+        attention_mask = self.all_attention_mask[:, self.end_idx: end_idx].to(self._device)
+        if self.all_labels is not None:
+            labels = self.all_labels[:, self.end_idx: end_idx].to(self._device)
+        else:
+            labels = None
+        batch_size = input_ids.shape[0]
+
+        #============================================#
+        # Insert beacon tokens if necessary.
+        #============================================#
+        # t1 = time.time()
+
+        if self.config.beacon_pos == "append":
+            # append beacons if necessary
+            if is_full_window and beacon_size > 0:
+                input_ids = torch.cat([input_ids, self.beacon_token.expand(batch_size, beacon_size).to(input_ids.device, dtype=input_ids.dtype)], dim=1)
+                # NOTE: prepend 1 to attention_mask because we have past_key_values
+                attention_mask = torch.cat([attention_mask, attention_mask.new_ones(batch_size, beacon_size)], dim=1)
+                if labels is not None:
+                    labels = torch.cat([labels, labels.new_zeros(batch_size, beacon_size) - 100], dim=1)
+
+        elif self.config.beacon_pos == "interleave":
+            input_len = input_ids.shape[1]
+            if beacon_size > 0:
+                # insert beacon tokens in between raw tokens
+                input_ids_with_beacons = input_ids.new_full((input_ids.shape[0], input_len + beacon_size), self.beacon_token.item())
+                raw_token_indices = torch.arange(input_ids_with_beacons.shape[1], device=input_ids.device)
+                interleave_start_idx = compression_ratio - self.interleave_remainder
+                raw_token_indices = raw_token_indices[raw_token_indices % (compression_ratio + 1) != interleave_start_idx].unsqueeze(0).expand_as(input_ids)
+                input_ids_with_beacons = input_ids_with_beacons.scatter(dim=1, index=raw_token_indices, src=input_ids)
+                input_ids = input_ids_with_beacons
+                # attention mask
+                attention_mask_with_beacons = attention_mask.new_full((attention_mask.shape[0], attention_mask.shape[1] + beacon_size), 1)
+                attention_mask_with_beacons = attention_mask_with_beacons.scatter(dim=1, index=raw_token_indices, src=attention_mask)
+                attention_mask = attention_mask_with_beacons
+                # labels
+                if labels is not None:
+                    labels_with_beacons = labels.new_full((labels.shape[0], labels.shape[1] + beacon_size), -100)
+                    labels_with_beacons = labels_with_beacons.scatter(dim=1, index=raw_token_indices, src=labels)
+                    labels = labels_with_beacons
+
+            if compression_ratio > 0:
+                # update the reminder
+                self.interleave_remainder = (input_len + self.interleave_remainder) % compression_ratio
+
+        # NOTE: skip computing loss in the very first window because the beacon tokens will be used in the next window
+        if self.training and self.step_idx == 0 and not (self.config.beacon_pos == 'interleave' and self.config.beacon_attn == 'full-coverage'):
+            labels[:] = -100
+
+        # t2 = time.time()
+
+        #============================================#
+        # Prepare beacon_indices for interleave beacon_pos, a boolean mask where True indicates the beacon tokens.
+        # The mask is applied on the inputs of the entire window, including the cached activations and the input_ids.
+        #============================================#
+        beacon_indices = (input_ids[0] == self.beacon_token.item()).long()
+        if self.is_full_window:
+            self.beacon_indices = torch.tensor([], dtype=torch.long, device=input_ids.device)
+        # the beacon_indices always tracks the beacon tokens in both the cached activations and the input_ids
+        beacon_indices = torch.cat([self.beacon_indices, beacon_indices])
+        # record the beacon_indices for the next window
+        self.beacon_indices = beacon_indices
+        if is_full_window and beacon_size == -1:
+            # NOTE: the first beacon_stride raw tokens serve as beacon tokens
+            # we use -1 to indicate these raw tokens, so that the attention mask and position ids will not be modified
+            beacon_indices[:self.beacon_stride] = -1
+
+        # t3 = time.time()
+
+        #============================================#
+        # Prepare past_key_values.
+            # beacon_size: how many beacon tokens are there in the input_ids
+            # beacon_indices: the boolean mask for the entire window where True indicates the beacon tokens (for append, the beacon_indices corresponds to input_ids, while for 'interleave', the beacon_indices corresponds to the entire window including both the input_ids and the cached activations)
+        #============================================#
+        past_key_values = []
+        for layer_idx in range(self.config.num_hidden_layers):
+            if ignore_memory:
+                key, value = None, None
+            else:
+                sink_key, sink_value = self.sink_activations[layer_idx]
+                beacon_key, beacon_value = self.beacon_activations[layer_idx]
+                raw_key, raw_value = self.raw_activations[layer_idx]
+
+                key = cat_tensor([
+                    sink_key, beacon_key, raw_key,
+                ], dim=self.k_seq_dim)
+                value = cat_tensor([
+                    sink_value, beacon_value, raw_value,
+                ], dim=self.v_seq_dim)
+
+            layer_past_key_values = (key, value, beacon_size, beacon_indices)
+            past_key_values.append(layer_past_key_values)
+
+        # t4 = time.time()
+        
+        #============================================#
+        # Prepare attention_mask and position_ids.
+        #============================================#
+        first_key = past_key_values[0][0]
+        mem_size = first_key.shape[self.k_seq_dim] if first_key is not None else 0
+        if mem_size > 0:
+            attention_mask = torch.cat([attention_mask.new_ones(batch_size, mem_size), attention_mask], dim=1)
+
+        input_length = input_ids.shape[1]
+        position_ids = torch.arange(attention_mask.shape[-1], dtype=torch.long, device=self._device).repeat(batch_size, 1)
+
+        if self.config._attn_implementation == "flash_attention_2":
+            assert self.config.beacon_attn == "full-coverage", f"Make sure to set beacon_attn='full-coverage' when using flash attention! Found {self.config.beacon_attn}."
+            if 0 in attention_mask:
+                pass
+            else:
+                attention_mask = None
+        elif self.config._attn_implementation == "sdpa" and self.config.beacon_pos == "append" and beacon_size <= 0 and (input_length == 1 or mem_size == 0):
+            attention_mask = None
+        else:
+            attention_mask, position_ids = self._make_4d_attention_mask_and_position_ids(
+                attention_mask, 
+                position_ids, 
+                mem_size, 
+                beacon_size, 
+                compression_ratio, 
+            )
+
+        # t5 = time.time()
+
+        # print(f"prepare inputs {t2-t1}, prepare indices {t3-t2}, prepare memory {t4-t3}, prepare attention mask {t5-t4}")
+
+        #============================================#
+        # Update necessary attributes.
+        #============================================#
+        # keep track of whether the current inputs is a full_window
+        self.is_full_window = is_full_window
+        # keep track of the raw_size_to_cache
+        self.raw_size_to_cache = raw_size_to_cache
+        # involked in self.output()
+        self.all_beacon_sizes.append(beacon_size)
+        # update start_idx and end_idx
+        # NOTE: the update of start_idx will influence self.beacon_window and self.beacon_stride in case self.beacon_skip_last is not None
+        # Therefore, we must make sure all calls to self.beacon_window and self.beacon_stride happen before the update of start_idx
+        self.start_idx = next_start_idx
+        self.end_idx = end_idx
+        self.step_idx += 1
+
+        # print(f"start_idx:          {start_idx}")
+        # print(f"next_start_idx:     {next_start_idx}")
+        # print(f"beacon_size:        {beacon_size}")
+        # print(f"raw_size_to_cache:  {raw_size_to_cache}")
+        # print(f"interleave_remainder:{self.interleave_remainder}")
+        # print(f"input_ids:          {input_ids}")
+        # print(f"beacon_indices:     {beacon_indices}")
+        # print(f"position_ids:       {position_ids}")
+        # print(f"attention_mask:\n{attention_mask == 0}")
+        # x = input()
+        # if x == "s":
+        #     return
+
+        return input_ids, attention_mask, position_ids, past_key_values, labels
+
+    def update_memory(self, past_key_values):
+        """
+        Accumulate beacon activations and raw activations.
+        """
+        for layer_idx, (key, value, beacon_size, beacon_indices) in enumerate(past_key_values):
+            # NOTE: the past_key_values are incrementally returned (only the new keys and values are returned)
+            previous_raw_key, previous_raw_value = self.raw_activations[layer_idx]
+
+            if self.beacon_skip_first is not None and self.sink_activations[layer_idx][0] is None:
+                assert key.shape[self.k_seq_dim] == self.beacon_skip_first
+                assert value.shape[self.k_seq_dim] == self.beacon_skip_first
+                self.sink_activations[layer_idx] = [
+                    key,
+                    value,
+                ]
+                # NOTE: no need to update raw activations and beacon activations as all activations are kept as sink activations
+                continue
+
+            if self.beacon_activations[layer_idx][0] is None and self.config.beacon_sink_size > 0:
+                # save the sink activations
+                # NOTE: we do not slice the key/value activations, which may cause duplication when beacon_ratio=-1 for the first window, but it's okay
+                self.sink_activations[layer_idx] = [
+                    slice_tensor(key, end=self.config.beacon_sink_size, dim=self.k_seq_dim),
+                    slice_tensor(value, end=self.config.beacon_sink_size, dim=self.v_seq_dim),
+                ]
+
+            if not self.is_full_window:
+                # this means the current input does not fulfill a window
+                # thus, the key and value are all raw activations, and we accumulate them until the window is fulfilled
+                assert self.raw_size_to_cache == -1
+                raw_key = cat_tensor([
+                    previous_raw_key,
+                    key
+                ], dim=self.k_seq_dim)
+                raw_value = cat_tensor([
+                    previous_raw_value, 
+                    value
+                ], dim=self.v_seq_dim)
+                self.raw_activations[layer_idx] = (raw_key, raw_value)
+
+            else:
+                # NOTE: use the correct previous_beacon_key and value!
+                previous_beacon_key, previous_beacon_value = self.beacon_activations[layer_idx]
+                
+                beacon_key, beacon_value, raw_key, raw_value = self._extract_beacon_and_raw_memory(
+                    key, 
+                    value, 
+                    previous_beacon_key, 
+                    previous_beacon_value, 
+                    previous_raw_key, 
+                    previous_raw_value, 
+                    beacon_indices,
+                )
+
+                self.beacon_activations[layer_idx] = (beacon_key, beacon_value)
+                self.raw_activations[layer_idx] = (raw_key, raw_value)
+
+    def update_loss(self, batch_loss, valid_token_num):
+        """
+        Accumulate loss for later perplexity computation and backward pass.
+        """
+        if self.batch_loss is None:
+            # NOTE: multiply valid_token_num because batch_loss is divided by it in advance
+            self.batch_loss = batch_loss * valid_token_num
+            self.valid_token_num = valid_token_num
+        else:
+            # NOTE: avoid in-place operations, otherwise there will be gradient errors in training
+            self.batch_loss = self.batch_loss + batch_loss * valid_token_num
+            self.valid_token_num = self.valid_token_num + valid_token_num
+
+    def output(self, model_outputs):
+        """
+        Override loss with accumulated loss. Update the next-token logits.
+        """
+        # override loss
+        if self.batch_loss is not None:
+            # here the batch_loss is the summation of all token losses in each element
+            loss = self.batch_loss.sum() / self.valid_token_num.sum()
+
+            # NOTE: prevent nan
+            batch_loss = self.batch_loss / self.valid_token_num
+            if (self.valid_token_num == 0).any():
+                batch_loss = batch_loss.masked_fill(self.valid_token_num == 0, 0.)
+
+            # NOTE: we must use dict to override values, otherwise trainer cannot find loss
+            model_outputs["loss"] = loss
+            model_outputs["batch_loss"] = batch_loss
+
+        # override last_hidden_states (used in generation)
+        beacon_size = self.all_beacon_sizes[-1]
+        # remove logits corresponding to beacon tokens
+        if beacon_size > 0:
+            logits = model_outputs["logits"]
+            beacon_indices = self.beacon_indices[-logits.shape[1]:]
+            model_outputs["logits"] = logits[:, beacon_indices == 0]
+
+        return model_outputs
+
+    def _make_4d_attention_mask_and_position_ids(
+        self, 
+        attention_mask, 
+        position_ids,
+        mem_size, 
+        beacon_size, 
+        compression_ratio, 
+    ):
+        """
+        Convert attention_mask into causal 4D attention_mask (batch_size, head_num, query_len, key_len).
+        """
+        tgt_size = attention_mask.size(-1) - mem_size
+        dtype = self.dtype
+        min_value = self.min_value
+        device = self._device
+        batch_size, src_size = attention_mask.size()
+
+        # square for memory, and lower triangular for input_ids
+        causal_mask = torch.full((tgt_size, tgt_size), min_value, device=device, dtype=dtype)
+        mask_cond = torch.arange(causal_mask.size(-1), device=device)
+        causal_mask.masked_fill_(mask_cond < (mask_cond + 1).view(causal_mask.size(-1), -1), 0)
+        causal_mask = torch.cat([torch.zeros(tgt_size, mem_size, dtype=dtype, device=device), causal_mask], dim=-1)
+        causal_mask = causal_mask[None, None, ...].expand(batch_size, 1, tgt_size, src_size)
+        # 1 for non-padding tokens
+        expand_mask = attention_mask[:, None, None, :].expand(batch_size, 1, tgt_size, src_size)
+        invert_mask = 1.0 - expand_mask
+        invert_mask.masked_fill_(invert_mask.bool(), min_value)
+
+        attention_mask = causal_mask.masked_fill(invert_mask.bool(), min_value)
+
+        if self.config.beacon_attn == "step-expansion":
+            # each beacon can attend to one more sub-interval than its predecessor
+
+            if self.config.beacon_pos == "append" and beacon_size > 0:
+                window_size = self.beacon_window
+                window_size_with_beacon = window_size + beacon_size
+                beacon_start_idx = -beacon_size
+                # batch_size, head_num, window_size
+                reference_attention_mask = attention_mask[..., -beacon_size - 1, -window_size_with_beacon: -beacon_size]
+
+                # compression_ratio, 2 * compression_ratio, ..., beacon_size * compression_ratio
+                beacon_arange = torch.arange(1, beacon_size + 1, device=device) * compression_ratio
+                # 0, 1, 2, ..., window_size - 1
+                ordinal_arange = torch.arange(window_size, device=device)
+                # beacon_size, window_size
+                valid_pos = ordinal_arange.expand(beacon_size, window_size) < beacon_arange.unsqueeze(-1)
+                # beacon_size, window_size
+                ordinal_attention_mask = torch.where(valid_pos, 0, min_value)
+                # NOTE: add reference attention_mask so that padding tokens are considered
+                ordinal_attention_mask = ordinal_attention_mask[None, None, ...] + reference_attention_mask.unsqueeze(-2)
+
+                if self.config.beacon_attend_prev:
+                    beacon_attention_mask = attention_mask.new_full((beacon_size, beacon_size), min_value).triu(1)
+                    # the beacon token is next to the last ordinal token it attends to
+                    ordinal_position_ids = position_ids[:, -window_size_with_beacon: -beacon_size]
+                    beacon_position_ids = ordinal_position_ids[:, compression_ratio - 1::compression_ratio] + torch.arange(1, beacon_size + 1, device=device)[None]
+                    position_ids[:, beacon_start_idx:] = beacon_position_ids
+                else:
+                    beacon_attention_mask = attention_mask.new_full((beacon_size, beacon_size), min_value).fill_diagonal_(0)
+                    # the beacon token is next to the last ordinal token it attends to
+                    ordinal_position_ids = position_ids[:, -window_size_with_beacon: -beacon_size]
+                    beacon_position_ids = ordinal_position_ids[:, compression_ratio - 1::compression_ratio] + 1
+                    position_ids[:, beacon_start_idx:] = beacon_position_ids
+
+                attention_mask[..., beacon_start_idx:, -window_size_with_beacon: -beacon_size] = ordinal_attention_mask
+                attention_mask[..., beacon_start_idx:, beacon_start_idx:] = beacon_attention_mask
+
+            # NOTE: the attention mask should be modified when there is beacon token within the window, not in the input_ids
+            elif self.config.beacon_pos == "interleave" and (self.beacon_indices == 1).any():
+                assert self.config.beacon_attend_prev == False, f"Make sure beacon_attend_prev is False if using 'interleave' beacon pos!"
+
+                beacon_indices = self.beacon_indices
+
+                cur_position_ids = position_ids[:, -len(beacon_indices):]
+                base_position = cur_position_ids[:, 0] - 1
+                # NOTE: alternate position so that the position of raw tokens are consistent
+                position_template = cur_position_ids.new_ones(cur_position_ids.shape)
+                position_template[:, compression_ratio + 1::compression_ratio + 1] = 0
+                cur_position_ids = base_position + position_template.cumsum(-1)
+                position_ids[:, -len(beacon_indices):] = cur_position_ids
+
+                cur_input_length = len(beacon_indices)
+                cur_attention_mask = attention_mask[..., -cur_input_length:, -cur_input_length:]
+                # mask all beacon columns
+                cur_attention_mask[..., beacon_indices] = min_value
+                # beacon tokens can attend to themselves
+                input_ids_attention_mask = cur_attention_mask[..., -tgt_size:, -tgt_size:]
+                input_ids_attention_mask[..., range(tgt_size), range(tgt_size)] = 0
+
+        elif self.config.beacon_attn == "segmentation":
+            # each beacon can attend to its corresponding sub-interval
+
+            if self.config.beacon_pos == "append" and beacon_size > 0:
+                window_size = self.beacon_window
+                window_size_with_beacon = window_size + beacon_size
+                beacon_start_idx = -beacon_size
+                # batch_size, head_num, window_size
+                reference_attention_mask = attention_mask[..., -beacon_size - 1, -window_size_with_beacon: -beacon_size]
+
+                # beacon_size, compression_ratio
+                indices = torch.arange(compression_ratio * beacon_size, device=device).view(beacon_size, -1)
+                # beacon_size, window_size
+                ordinal_attention_mask = attention_mask.new_full((beacon_size, window_size), min_value)
+                ordinal_attention_mask.scatter_(dim=-1, index=indices, value=0)
+
+                # NOTE: add reference attention_mask so that padding tokens are considered
+                ordinal_attention_mask = ordinal_attention_mask[None, None, ...] + reference_attention_mask.unsqueeze(-2)
+
+                if self.config.beacon_attend_prev:
+                    beacon_attention_mask = attention_mask.new_full((beacon_size, beacon_size), min_value).triu(1)
+                    # the beacon token is next to the last ordinal token it attends to
+                    beacon_position_ids = position_ids.new_full(beacon_size, fill_value=compression_ratio + mem_size)
+                    beacon_position_ids = beacon_position_ids + torch.arange(beacon_size)
+                    position_ids[:, beacon_start_idx:] = beacon_position_ids
+                else:
+                    beacon_attention_mask = attention_mask.new_full((beacon_size, beacon_size), min_value).fill_diagonal_(0)
+                    # the beacon token is next to the last ordinal token it attends to
+                    beacon_position_ids = position_ids.new_full(beacon_size, fill_value=compression_ratio + mem_size)
+                    position_ids[:, beacon_start_idx:] = beacon_position_ids
+
+                attention_mask[..., beacon_start_idx:, -window_size_with_beacon: -beacon_size] = ordinal_attention_mask
+                attention_mask[..., beacon_start_idx:, beacon_start_idx:] = beacon_attention_mask
+                # beacons of different ratios are blind to others
+                attention_mask[..., beacon_start_idx:, -beacon_size: beacon_start_idx] = min_value
+
+            elif self.config.beacon_pos == "interleave":
+                raise NotImplementedError
+
+        elif self.config.beacon_attn == "full-coverage":
+            pass
+
+        return attention_mask, position_ids
+
+    def _extract_beacon_and_raw_memory(
+        self, 
+        key, 
+        value, 
+        previous_beacon_key, 
+        previous_beacon_value, 
+        previous_raw_key, 
+        previous_raw_value, 
+        beacon_indices,
+    ):
+        """Extract beacon and raw memory from the returned key and value when the window is full."""
+        key = cat_tensor([
+            previous_raw_key, 
+            key
+        ], dim=self.k_seq_dim)
+        value = cat_tensor([
+            previous_raw_value, 
+            value
+        ], dim=self.v_seq_dim)
+
+        # NOTE: we use magic slice instead of boolean index here for efficiency
+        beacon_key = slice_tensor(key, index=torch.logical_or(beacon_indices == 1, beacon_indices == -1), dim=self.k_seq_dim)
+        beacon_value = slice_tensor(value, index=torch.logical_or(beacon_indices == 1, beacon_indices == -1), dim=self.v_seq_dim)
+
+        if self.config.beacon_accum:
+            beacon_key = cat_tensor([previous_beacon_key, beacon_key], dim=self.k_seq_dim)
+            beacon_value = cat_tensor([previous_beacon_value, beacon_value], dim=self.v_seq_dim)
+
+        if self.raw_size_to_cache > 0:
+            raw_key = slice_tensor(key, index=beacon_indices == 0, dim=self.k_seq_dim)
+            raw_key = slice_tensor(raw_key, start=-raw_size_to_cache, dim=self.k_seq_dim)
+
+            raw_value = slice_tensor(value, index=beacon_indices == 0, dim=self.v_seq_dim)
+            raw_value = slice_tensor(raw_value, start=-raw_size_to_cache, dim=self.v_seq_dim)        
+
+        else:
+            raw_key = None
+            raw_value = None
+
+        return beacon_key, beacon_value, raw_key, raw_value
+
+
+def slice_tensor(x, start=None, end=None, step=None, index=None, dim=2):
+    if x is None:
+        return None
+    if end == 0:
+        return None
+    if start == x.shape[dim]:
+        return None
+    if start is not None and start == end:
+        return None
+    if dim == 2:
+        if index is not None:
+            return x[:, :, index]
+        elif start is None and end is not None:
+            if step is None:
+                return x[:, :, :end, ...]
+            else:
+                return x[:, :, :end:step, ...]
+        elif start is not None and end is None:
+            if step is None:
+                return x[:, :, start:, ...]
+            else:
+                return x[:, :, start::step, ...]
+        elif start is not None and end is not None:
+            if step is None:
+                return x[:, :, start:end, ...]
+            else:
+                return x[:, :, start:end:step, ...]
+    elif dim == 1:
+        if index is not None:
+            return x[:, :, index]
+        elif start is None and end is not None:
+            if step is None:
+                return x[:, :end, ...]
+            else:
+                return x[:, :end:step, ...]
+        elif start is not None and end is None:
+            if step is None:
+                return x[:, start:, ...]
+            else:
+                return x[:, start::step, ...]
+        elif start is not None and end is not None:
+            if step is None:
+                return x[:, start:end, ...]
+            else:
+                return x[:, start:end:step, ...]
+    else:
+        raise NotImplementedError
+
+def cat_tensor(list_of_tensors, dim=-1):
+    list_of_tensors = [t for t in list_of_tensors if t is not None]
+    if len(list_of_tensors) > 1:
+        result = torch.cat(list_of_tensors, dim=dim)
+    elif len(list_of_tensors) == 1:
+        result = list_of_tensors[0]
+    else:
+        result = None
+    return result
+
+def slice_activations(activations, start=None, end=None, k_seq_dim=2, v_seq_dim=2):
+    new_activations = []
+    for key, value in activations:
+        new_key = slice_tensor(key, start=start, end=end, dim=k_seq_dim)
+        new_value = slice_tensor(value, start=start, end=end, dim=v_seq_dim)
+        new_activations.append([new_key, new_value])
+    return new_activations
+
+def cat_activations(list_of_activations, k_seq_dim=2, v_seq_dim=2):
+    assert all(len(x) == len(list_of_activations[0]) for x in list_of_activations), f"Make sure all activations have the same number of layers! Found {[len(x) for x in list_of_activations]}."
+
+    new_activations = []
+    for layer_idx in range(len(list_of_activations[0])):
+        keys = [x[layer_idx][0] for x in list_of_activations]
+        values = [x[layer_idx][1] for x in list_of_activations]
+
+        new_key = cat_tensor(keys, dim=k_seq_dim)
+        new_value = cat_tensor(values, dim=v_seq_dim)
+        new_activations.append([new_key, new_value])
+    return new_activations
+
+def interleave_activations(main_activations, augment_activations, main_spans, augment_spans, k_seq_dim=2, v_seq_dim=2, device=torch.device("cuda")):
+    """ Interleave main_activations and augment_activations according to main_span and augment_span.
+
+    Args:
+        main_span: a list of tuples (start_idx, end_idx). when start_idx and end_idx is None, the augment_activations will be plugged in.
+        augment_span: a list of tuples (start_idx, end_idx)
+    """
+    assert len(main_activations) == len(augment_activations) , f"Make sure main and augment activations have the same number of layers! Found {len(main_activations)} and {len(augment_activations)}!"
+    assert sum(x[0] is None and x[1] is None for x in main_spans) == len(augment_spans), f"Make sure the number of slots for augmentation (start_idx=None and end_idx=None in main_spans) matches the number of augmentations. Found {sum(x for x in main_spans if x[0] is None and x[1] is None)} slots but {len(augment_spans)} augmentations!"
+
+    new_activations = []
+    for layer_idx in range(len(main_activations)):
+        main_key, main_value = main_activations[layer_idx]
+        augment_key, augment_value = augment_activations[layer_idx]
+
+        sliced_keys = []
+        sliced_values = []
+
+        augment_idx = 0
+        for start, end in main_spans:
+            if start is None and end is None:
+                # this means the augment key/value should be plugged in
+                augment_start, augment_end = augment_spans[augment_idx]
+                sliced_key = slice_tensor(
+                    augment_key, 
+                    start=augment_start, 
+                    end=augment_end,
+                    dim=k_seq_dim
+                ).to(device)
+                sliced_value = slice_tensor(
+                    augment_value, 
+                    start=augment_start, 
+                    end=augment_end,
+                    dim=v_seq_dim
+                ).to(device)
+
+            else:
+                sliced_key = slice_tensor(
+                    main_key, 
+                    start=start, 
+                    end=end,
+                    dim=k_seq_dim
+                )
+                sliced_value = slice_tensor(
+                    main_value, 
+                    start=start, 
+                    end=end,
+                    dim=v_seq_dim
+                )
+
+            sliced_keys.append(sliced_key)
+            sliced_values.append(sliced_value)
+
+        new_key = cat_tensor(sliced_keys, dim=k_seq_dim)
+        new_value = cat_tensor(sliced_values, dim=v_seq_dim)
+        new_activations.append([new_key, new_value])
+
+    return new_activations
+
+def softmax(x:np.ndarray, axis=-1, temperature=1):
+    if isinstance(x, list):
+        x = np.array(x)
+    x = x / temperature
+    x = x - x.max(axis=axis, keepdims=True)
+    y = np.exp(x)
+    return y / y.sum(axis=axis, keepdims=True)
+
+def l1_norm(x):
+    sum_x = sum(x)
+    x = [y/sum_x for y in x]
+    return x

modeling_qwen2.py

@@ -0,0 +1,1330 @@
+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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 Qwen2 model."""
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.integrations import is_deepspeed_zero3_enabled
+from .configuration_qwen2 import Qwen2Config
+
+
+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
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+from ..modeling_beacon import Memory
+from ..modeling_utils import optional_grad_ctx, compute_loss, get_rope, ModelOutput
+
+
+logger = logging.get_logger(__name__)
+
+
+_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
+_CONFIG_FOR_DOC = "Qwen2Config"
+
+QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "Qwen/Qwen2-7B-beta",
+    # See all Qwen2 models at https://huggingface.co/models?filter=qwen2
+]
+
+
+# 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.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2
+class Qwen2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Qwen2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.Qwen2MLP with Qwen2->Qwen2
+class Qwen2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+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 Qwen2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        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.rope_theta = config.rope_theta
+        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=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = get_rope(self.head_dim, config.rope_theta, config.max_position_embeddings, getattr(config, "rope_scaling", None))
+
+        # NOTE: add extra parameters for beacon tokens
+        # skip post initialization to speed up loading
+        if "q" in config.beacon_param:
+            self.beacon_q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=self.q_proj.bias is not None)
+            # NOTE: initialize the beacon parameters as zero
+            self.beacon_q_proj.weight.data.zero_()
+            self.beacon_q_proj._is_hf_initialized = True
+        if "k" in config.beacon_param:
+            self.beacon_k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.k_proj.bias is not None)
+            self.beacon_k_proj.weight.data.zero_()
+            self.beacon_k_proj._is_hf_initialized = True
+        if "v" in config.beacon_param:
+            self.beacon_v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.v_proj.bias is not None)
+            self.beacon_v_proj.weight.data.zero_()
+            self.beacon_v_proj._is_hf_initialized = True
+        if "o" in config.beacon_param:
+            self.beacon_o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=self.o_proj.bias is not None)
+            self.beacon_o_proj.weight.data.zero_()
+            self.beacon_o_proj._is_hf_initialized = True
+
+    def _init_beacon_proj(self, missing_keys):
+        """Initialize the beacon projection weight with that of the ordinal projection."""
+        beacon_param = self.config.beacon_param
+        
+        if is_deepspeed_zero3_enabled():
+            # FIXME: after deepspeed initialization, some weights becomes non-zero
+            # For Mistral, there are rows that are full of zeros
+            # For Mistral, there are values bigger than 1e29...
+
+            import deepspeed
+            if "q" in beacon_param:
+                params = [self.beacon_q_proj.weight, self.q_proj.weight]
+                if self.q_proj.bias is not None:
+                    params.extend([self.beacon_q_proj.bias, self.q_proj.bias])
+                with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                    # FIXME: after deepspeed initialization, some weights becomes non-zero, but there are rows that are full of zeros
+                    if (self.beacon_q_proj.weight.sum(-1) == 0).any() or (self.beacon_q_proj.weight > 1e29).any():
+                        self.beacon_q_proj.weight.data[:] = self.q_proj.weight.data
+                        if self.q_proj.bias is not None:
+                            self.beacon_q_proj.bias.data[:] = self.q_proj.bias.data
+            if "k" in beacon_param:
+                params = [self.beacon_k_proj.weight, self.k_proj.weight]
+                if self.k_proj.bias is not None:
+                    params.extend([self.beacon_k_proj.bias, self.k_proj.bias])
+                with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                    # FIXME: after deepspeed initialization, some weights becomes non-zero, but there are rows that are full of zeros
+                    if (self.beacon_k_proj.weight.sum(-1) == 0).any() or (self.beacon_k_proj.weight > 1e29).any():
+                        self.beacon_k_proj.weight.data[:] = self.k_proj.weight.data
+                        if self.k_proj.bias is not None:
+                            self.beacon_k_proj.bias.data[:] = self.k_proj.bias.data
+            if "v" in beacon_param:
+                params = [self.beacon_v_proj.weight, self.v_proj.weight]
+                if self.v_proj.bias is not None:
+                    params.extend([self.beacon_v_proj.bias, self.v_proj.bias])
+                with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                    # FIXME: after deepspeed initialization, some weights becomes non-zero, but there are rows that are full of zeros
+                    if (self.beacon_v_proj.weight.sum(-1) == 0).any() or (self.beacon_v_proj.weight > 1e29).any():
+                        self.beacon_v_proj.weight.data[:] = self.v_proj.weight.data
+                        if self.v_proj.bias is not None:
+                            self.beacon_v_proj.bias.data[:] = self.v_proj.bias.data
+            if "o" in beacon_param:
+                params = [self.beacon_o_proj.weight, self.o_proj.weight]
+                if self.o_proj.bias is not None:
+                    params.extend([self.beacon_o_proj.bias, self.o_proj.bias])
+                with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                    # FIXME: after deepspeed initialization, some weights becomes non-zero, but there are rows that are full of zeros
+                    if (self.beacon_o_proj.weight.sum(-1) == 0).any() or (self.beacon_o_proj.weight > 1e29).any():
+                        self.beacon_o_proj.weight.data[:] = self.o_proj.weight.data
+                        if self.o_proj.bias is not None:
+                            self.beacon_o_proj.bias.data[:] = self.o_proj.bias.data
+        else:
+            # only copy the value in-place, without tieing the weight
+            if "q" in beacon_param and any("beacon_q_proj" in missing_key for missing_key in missing_keys):
+                # FIXME: some beacon weights are not initialized as zero for mistral model, why? 
+                # if (self.beacon_q_proj.weight == 0).all():
+                    self.beacon_q_proj.weight.data[:] = self.q_proj.weight.data
+                    if self.q_proj.bias is not None:
+                        self.beacon_q_proj.bias.data[:] = self.q_proj.bias.data
+            if "k" in beacon_param and any("beacon_k_proj" in missing_key for missing_key in missing_keys):
+                # if (self.beacon_k_proj.weight == 0).all():
+                    self.beacon_k_proj.weight.data[:] = self.k_proj.weight.data
+                    if self.k_proj.bias is not None:
+                        self.beacon_k_proj.bias.data[:] = self.k_proj.bias.data
+            if "v" in beacon_param and any("beacon_v_proj" in missing_key for missing_key in missing_keys):
+                # if (self.beacon_v_proj.weight == 0).all():
+                    self.beacon_v_proj.weight.data[:] = self.v_proj.weight.data
+                    if self.v_proj.bias is not None:
+                        self.beacon_v_proj.bias.data[:] = self.v_proj.bias.data
+            if "o" in beacon_param and any("beacon_o_proj" in missing_key for missing_key in missing_keys):
+                # if (self.beacon_o_proj.weight == 0).all():
+                    self.beacon_o_proj.weight.data[:] = self.o_proj.weight.data
+                    if self.o_proj.bias is not None:
+                        self.beacon_o_proj.bias.data[:] = self.o_proj.bias.data
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+    
+    def qkv_proj_with_beacon(self, hidden_states, beacon_size, beacon_indices):
+        if beacon_size > 0:
+            # NOTE: when beacon_pos == "interleave", the beacon_indices points to all beacon tokens in the current window (cached activations + input_ids), so we shall slice out the part corresponding to the input_ids
+            cur_beacon_indices = beacon_indices[-hidden_states.shape[1]:]
+
+            # NOTE: there is slight redundant computation because ordinal tokens should never be projected by beacon matrices, but we are doing this for efficiency
+            if "q" in self.config.beacon_param:
+                ordinal_query_states = self.q_proj(hidden_states)
+                beacon_query_states = self.beacon_q_proj(hidden_states)
+                query_states = torch.where((cur_beacon_indices == 0)[:, None], ordinal_query_states, beacon_query_states)
+                if (cur_beacon_indices == 2).any():
+                    # beacon_indices == 2 means the beacon token is used to replicate the ones in previous window for parallel encoding
+                    # we should slice out all beacon tokens then copy them to the replicate beacon tokens
+                    query_states[:, cur_beacon_indices == 2] = beacon_query_states[:, cur_beacon_indices == 1][:, :(cur_beacon_indices == 2).sum()]
+            else:
+                query_states = self.q_proj(hidden_states)
+
+            if "k" in self.config.beacon_param:
+                ordinal_key_states = self.k_proj(hidden_states)
+                beacon_key_states = self.beacon_k_proj(hidden_states)
+                key_states = torch.where((cur_beacon_indices == 0)[:, None], ordinal_key_states, beacon_key_states)
+                if (cur_beacon_indices == 2).any():
+                    # beacon_indices == 2 means the beacon token is used to replicate the ones in previous window for parallel encoding
+                    # we should slice out all beacon tokens then copy them to the replicate beacon tokens
+                    key_states[:, cur_beacon_indices == 2] = beacon_key_states[:, cur_beacon_indices == 1][:, :(cur_beacon_indices == 2).sum()]
+            else:
+                key_states = self.k_proj(hidden_states)
+
+            if "v" in self.config.beacon_param:
+                ordinal_value_states = self.v_proj(hidden_states)
+                beacon_value_states = self.beacon_v_proj(hidden_states)
+                value_states = torch.where((cur_beacon_indices == 0)[:, None], ordinal_value_states, beacon_value_states)
+                if (cur_beacon_indices == 2).any():
+                    # beacon_indices == 2 means the beacon token is used to replicate the ones in previous window for parallel encoding
+                    # we should slice out all beacon tokens then copy them to the replicate beacon tokens
+                    value_states[:, cur_beacon_indices == 2] = beacon_value_states[:, cur_beacon_indices == 1][:, :(cur_beacon_indices == 2).sum()]
+            else:
+                value_states = self.v_proj(hidden_states)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        return query_states, key_states, value_states
+    
+    def o_proj_with_beacon(self, attn_output, beacon_size, beacon_indices):
+        if beacon_size > 0:
+            # NOTE: when beacon_pos == "interleave", the beacon_indices points to all beacon tokens in the current window (cached activations + input_ids), so we shall slice out the part corresponding to the input_ids
+            cur_beacon_indices = beacon_indices[-attn_output.shape[1]:]
+
+            if "o" in self.config.beacon_param:
+                ordinal_attn_output = self.o_proj(attn_output)
+                beacon_attn_output = self.beacon_o_proj(attn_output)
+                attn_output = torch.where((cur_beacon_indices == 0)[:, None], ordinal_attn_output, beacon_attn_output)
+            else:
+                attn_output = self.o_proj(attn_output)
+        else:
+            attn_output = self.o_proj(attn_output)
+        return attn_output
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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]]]:
+        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.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+        kv_seq_len = hidden_states.shape[-2]
+        past_key, past_value, beacon_size, beacon_indices = past_key_value
+
+        if past_key is not None:
+            past_seq_len = past_key.shape[2]
+            kv_seq_len += past_seq_len
+        else:
+            past_seq_len = 0
+
+        query_states, key_states, value_states = self.qkv_proj_with_beacon(hidden_states, beacon_size, beacon_indices)
+
+        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)
+
+        # return keys and values before rope
+        # NOTE: incrementally return keys and values for efficiency 
+        past_key_value = (key_states, value_states, beacon_size, beacon_indices)
+
+        if past_key is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key, key_states], dim=2)
+            value_states = torch.cat([past_value, value_states], dim=2)
+
+        query_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+
+        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_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        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()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj_with_beacon(attn_output, beacon_size, beacon_indices)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class Qwen2SdpaAttention(Qwen2Attention):
+    """
+    Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Qwen2Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                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,
+            )
+        bsz, q_len, _ = hidden_states.size()
+        kv_seq_len = hidden_states.shape[-2]
+        past_key, past_value, beacon_size, beacon_indices = past_key_value
+        if past_key is not None:
+            past_seq_len = past_key.shape[2]
+            kv_seq_len += past_seq_len
+        else:
+            past_seq_len = 0
+
+        query_states, key_states, value_states = self.qkv_proj_with_beacon(hidden_states, beacon_size, beacon_indices)
+
+        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)
+
+        # return keys and values before rope
+        # NOTE: incrementally return keys and values for efficiency 
+        past_key_value = (key_states, value_states, beacon_size, beacon_indices)
+
+        if past_key is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key, key_states], dim=2)
+            value_states = torch.cat([past_value, value_states], dim=2)
+        
+        query_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        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()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj_with_beacon(attn_output, beacon_size, beacon_indices)
+
+        return attn_output, None, past_key_value
+
+
+class Qwen2FlashAttention2(Qwen2Attention):
+    """
+    Qwen2 flash attention module. This module inherits from `Qwen2Attention` 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.
+    """
+
+    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,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+        kv_seq_len = hidden_states.shape[-2]
+
+        past_key, past_value, beacon_size, beacon_indices = past_key_value
+        if past_key is not None:
+            past_seq_len = past_key.shape[2]
+            kv_seq_len += past_seq_len
+        else:
+            past_seq_len = 0
+
+        query_states, key_states, value_states = self.qkv_proj_with_beacon(hidden_states, beacon_size, beacon_indices)
+
+        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)
+
+        # return keys and values before rope
+        # NOTE: incrementally return keys and values for efficiency 
+        past_key_value = (key_states, value_states, beacon_size, beacon_indices)
+
+        if past_key is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key, key_states], dim=2)
+            value_states = torch.cat([past_value, value_states], dim=2)
+
+        query_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+
+        # FlashAttention will automatically handle grouped query attention
+        # key_states = repeat_kv(key_states, self.num_key_value_groups)
+        # value_states = repeat_kv(value_states, self.num_key_value_groups)
+        
+        # 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
+
+        # 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 the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (Qwen2RMSNorm handles it correctly)
+
+        input_dtype = query_states.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.q_proj.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_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        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_with_beacon(attn_output, beacon_size, beacon_indices)
+
+        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 (`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 Qwen2FlashAttention2 __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),
+        )
+
+
+QWEN2_ATTENTION_CLASSES = {
+    "eager": Qwen2Attention,
+    "sdpa": Qwen2SdpaAttention,
+    "flash_attention_2": Qwen2FlashAttention2,
+}
+
+
+class Qwen2DecoderLayer(nn.Module):
+    def __init__(self, config: Qwen2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
+            logger.warning_once(
+                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
+                "unexpected results may be encountered."
+            )
+        self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        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.`"
+            )
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            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`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        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
+
+
+QWEN2_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, pruning heads
+    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 ([`Qwen2Config`]):
+            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.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    QWEN2_START_DOCSTRING,
+)
+class Qwen2PreTrainedModel(PreTrainedModel):
+    config_class = Qwen2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+QWEN2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` 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)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        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)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        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.
+        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 [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    QWEN2_START_DOCSTRING,
+)
+class Qwen2Model(Qwen2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
+
+    Args:
+        config: Qwen2Config
+    """
+
+    def __init__(self, config: Qwen2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        # BEACON: add beacon embedding
+        self.beacon_embed_tokens = nn.Embedding(1, config.hidden_size, self.padding_idx)
+        self.beacon_embed_tokens._is_hf_initialized = True
+
+        self.layers = nn.ModuleList(
+            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _init_beacon_embed(self, missing_keys):
+        """Initialize the beacon token embedding with that of the eos token."""
+        if is_deepspeed_zero3_enabled():
+            import deepspeed
+            params = [self.beacon_embed_tokens.weight, self.embed_tokens.weight]
+            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                # deepspeed will initialize the parameters to zero
+                if (self.beacon_embed_tokens.weight == 0).all():
+                    if self.config.beacon_embed_init == "bos":
+                        self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[self.config.bos_token_id]
+                    elif self.config.beacon_embed_init == "eos":
+                        if isinstance(self.config.eos_token_id, list):
+                            eos_token_id = self.config.eos_token_id[0]
+                        else:
+                            eos_token_id = self.config.eos_token_id
+                        self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[eos_token_id]
+                    else:
+                        raise NotImplementedError(f"Make sure beacon_embed_init is either eos or bos, found {self.config.beacon_embed_init}")
+        else:
+            if any("beacon_embed_tokens" in missing_key for missing_key in missing_keys):
+                if self.config.beacon_embed_init == "bos":
+                    self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[self.config.bos_token_id]
+                elif self.config.beacon_embed_init == "eos":
+                    if isinstance(self.config.eos_token_id, list):
+                        eos_token_id = self.config.eos_token_id[0]
+                    else:
+                        eos_token_id = self.config.eos_token_id
+                    self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[eos_token_id]
+                else:
+                    raise NotImplementedError(f"Make sure beacon_embed_init is either eos or bos, found {self.config.beacon_embed_init}")
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[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
+        )
+        # BEACON: always use cache
+        use_cache = True
+
+        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 input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_key, past_value, beacon_size, beacon_indices = past_key_values[0]
+
+        # BEACON: separately embed ordinal tokens and beacon tokens because ordinal tokens do not receive gradients
+        if beacon_size > 0:
+            # NOTE: when beacon_pos == "interleave", the beacon_indices points to all beacon tokens in the current window (cached activations + input_ids), so we shall slice out the part corresponding to the input_ids
+            cur_beacon_indices = beacon_indices[-input_ids.shape[1]:]
+
+            ordinal_input_ids = input_ids[:, cur_beacon_indices == 0]
+            beacon_input_ids = input_ids[:, cur_beacon_indices > 0]
+            ordinal_inputs_embeds = self.embed_tokens(ordinal_input_ids)
+            beacon_input_embeds = self.beacon_embed_tokens(beacon_input_ids - self.config.vocab_size)
+            # create a new embedding tensor
+            inputs_embeds = beacon_input_embeds.new_zeros(*input_ids.shape, beacon_input_embeds.shape[-1])
+            inputs_embeds[:, cur_beacon_indices == 0] = ordinal_inputs_embeds
+            inputs_embeds[:, cur_beacon_indices > 0] = beacon_input_embeds
+
+        else:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # print(f"input_ids:          {input_ids}")
+        # print(f"beacon_indices:     {beacon_indices}")
+        # print(f"position_ids:       {position_ids}")
+        # print(f"attention_mask:\n{attention_mask == 0}")
+        # x = input()
+        # if x == "s":
+        #     return
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        # BEACON: still use tuple to organize cache
+        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,)
+
+            # BEACON: slice out the past_key_value of the corresponding layer
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_value,
+                    output_attentions,
+                    use_cache,
+                )
+            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 Qwen2ForCausalLM(Qwen2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen2Model(config)
+        self.vocab_size = config.vocab_size
+        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):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        """Override the default from_pretrained to extend vocab size according to beacon_size."""
+        kwargs.update(output_loading_info=True)
+        model, loading_info = super().from_pretrained(*args, **kwargs)
+
+        # NOTE: set memory after from_pretrained because there may be another transformer model inside the Memory object, which may cause weird erros during loading
+        config = model.config
+        model.memory = Memory(
+            model_config=config,
+            k_seq_dim=2,
+            v_seq_dim=2,
+        )
+
+        missing_keys = loading_info["missing_keys"]
+        # NOTE: the beacon parameters may or may not be loaded from the checkpoint
+        # if it is loaded from the checkpoint, we should not re-initilize it
+        model.model._init_beacon_embed(missing_keys)
+        # initialize weights of possible q,k,v,o,mlp
+        for layer in model.model.layers:
+            layer.self_attn._init_beacon_proj(missing_keys)
+
+        return model
+
+    def _native_forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, ModelOutput]:
+        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
+
+        # when we directly call _native_forward, the past_key_values would be None
+        if past_key_values is None:
+            # NOTE: set beacon size to 0 to avoid using any beacon parameters, see Qwen2Attention.forward
+            past_key_values = [(None, None, 0, None) for _ in range(self.config.num_hidden_layers)]
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=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)
+        logits = logits.float()
+
+        loss = None
+        batch_loss = None
+        token_loss = None
+        
+        if labels is not None:
+            loss, batch_loss, token_loss = compute_loss(logits, labels, shift=False)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return ModelOutput(
+            loss=loss,
+            batch_loss=batch_loss,
+            token_loss=token_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def _beacon_forward(self, 
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        beacon_skip_first: Optional[int] = None,
+        beacon_skip_last: Optional[int] = None,
+    ):
+        # t1 = time.time()
+
+        # initialize cache
+        self.memory.prepare(
+            input_ids=input_ids, 
+            attention_mask=attention_mask, 
+            labels=labels,
+            skip_first=beacon_skip_first,
+            skip_last=beacon_skip_last,
+        )
+
+        # t2 = time.time()
+
+        while not self.memory.finish:
+
+            # t3 = time.time()
+
+            input_ids, attention_mask, position_ids, past_key_values, labels = self.memory.step()
+
+            # t4 = time.time()
+
+            outputs = self._native_forward(
+                input_ids=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,
+                labels=labels,
+            )
+
+            # t5 = time.time()
+
+            # update past_key_values
+            self.memory.update_memory(outputs.past_key_values)
+
+            # t6 = time.time()
+
+            if labels is not None:
+                # update loss
+                self.memory.update_loss(outputs.batch_loss, (labels != -100).sum(-1))
+
+            # t7 = time.time()
+
+            # print(f"step time: {t4-t3}, forward time: {t5-t4}, update time: {t6-t5}, loss time: {t7-t6}")
+            # input()
+
+        # t8 = time.time()
+
+        # output loss, past_key_values, and perplexity
+        outputs = self.memory.output(outputs)
+
+        # t9 = time.time()
+
+        # print(f"output time:            {t9-t8}")
+        # input()
+
+        return outputs
+
+    def forward(self, **kwargs):
+        """Forward computation over a batch of sequences.
+        """
+        # only allow gradient when training
+        with optional_grad_ctx(with_grad=self.training):
+            # we can disable beacon to use the original mistral
+            if hasattr(self, "_enable_beacon") and self._enable_beacon == False:
+                return self._native_forward(**kwargs)
+            else:
+                return self._beacon_forward(**kwargs)
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        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(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        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

modeling_utils.py

@@ -0,0 +1,711 @@
+import math
+import torch
+from tqdm import tqdm
+from dataclasses import dataclass
+from contextlib import nullcontext
+from typing import Mapping, Optional, Tuple
+from accelerate import Accelerator
+from collections import defaultdict
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+
+def optional_grad_ctx(with_grad=False):
+    if with_grad:
+        return nullcontext()
+    else:
+        return torch.no_grad()
+
+def move_to_device(data, device):
+    """
+    Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
+    """
+    if isinstance(data, Mapping):
+        return type(data)({k: move_to_device(v, device) for k, v in data.items()})
+    elif isinstance(data, (tuple, list)):
+        return type(data)(move_to_device(v, device) for v in data)
+    elif isinstance(data, torch.Tensor):
+        kwargs = {"device": device}
+        return data.to(**kwargs)
+    else:
+        return data
+
+def get_shifted_labels(input_ids):
+    if isinstance(input_ids, torch.Tensor):
+        labels = input_ids.clone()
+        labels = torch.cat([labels[:, 1:], labels.new_zeros((input_ids.shape[0], 1)) - 100], dim=-1)
+    elif isinstance(input_ids, list) and isinstance(input_ids[0], int):
+        labels = input_ids.copy()
+        labels = labels[1:] + [-100]
+    elif isinstance(input_ids, list) and isinstance(input_ids[0], list):
+        labels = input_ids.copy()
+        for i, label in enumerate(labels):
+            labels[i] = labels[i][1:] + [-100]
+    else:
+        raise NotImplementedError
+    return labels
+
+def compute_loss(logits, labels, shift=False):
+    """
+    Returns:
+        token_loss: batch_size, seq_length
+    """
+    if shift:
+        labels = get_shifted_labels(labels)
+
+    labels = labels.to(logits.device)
+    batch_size = logits.shape[0]
+
+    # NOTE: the loss on -100 labels is 0 by default
+    token_loss = torch.nn.functional.cross_entropy(
+        logits.flatten(0, 1), 
+        labels.reshape(-1), 
+        reduction="none"
+    ).reshape(batch_size, -1)   # batch_size, seq_len
+
+    # print(token_loss)
+
+    valid_token_num = (labels != -100).sum(-1)  # batch_size
+    all_valid_token_num = valid_token_num.sum()
+    
+    if all_valid_token_num > 0:
+        loss = token_loss.sum() / valid_token_num.sum()
+    else:
+        loss = token_loss.sum()
+
+    batch_loss = token_loss.sum(-1) / valid_token_num
+    # prevent nan
+    if (valid_token_num == 0).any():
+        batch_loss = batch_loss.masked_fill(valid_token_num == 0, 0.)
+
+    return loss, batch_loss, token_loss
+
+
+@torch.no_grad()
+def evaluate_perplexity(model, dataloader, accelerator:Optional[Accelerator]=None):
+    if accelerator is not None and type(dataloader) == torch.utils.data.DataLoader:
+        # if the dataloader has been prepared, we shall not prepare it twice, especially in case of deepspeed
+        dataloader = accelerator.prepare(dataloader)
+
+    # if accelerator.process_index == 0:
+    #     for name, x in model.named_parameters():
+    #         print(f"{name: ^80} {x.dtype}")
+
+    all_loss = defaultdict(list)
+    for i, x in enumerate(tqdm(dataloader, desc="Computing Perplexity")):
+        # NOTE: important to reset memory for every batch
+        if hasattr(model, "memory"):
+            model.memory.reset()
+
+        # the seq id
+        index = x.pop("index")
+        # length is used to group training data, no use here
+        length = x.pop("length", None)
+
+        output = model(**x)
+
+        valid_token_num = (x["labels"] != -100).sum(-1)
+
+        # NOTE: we need the loss for each element in the batch for accurate computation, because the number of valid tokens may differ among elements
+        if hasattr(output, "batch_loss"):
+            # output from our model has batch_loss by default
+            batch_loss = output.batch_loss
+        else:
+            # output from other models does not
+            loss, batch_loss, token_loss = compute_loss(output.logits, x["labels"], shift=True)
+
+        index = index.tolist()
+        batch_loss = batch_loss.tolist()
+        valid_token_num = valid_token_num.tolist()
+
+        if accelerator is not None and accelerator.num_processes > 1:
+            # num_device * batch_size
+            index = accelerator.gather_for_metrics(index)
+            batch_loss = accelerator.gather_for_metrics(batch_loss)
+            valid_token_num = accelerator.gather_for_metrics(valid_token_num)
+
+        for _id, _loss, _num in zip(index, batch_loss, valid_token_num):
+            # loss times num is the total loss of all valid tokens
+            all_loss[_id].append((_loss * _num, _num))
+
+    all_loss = dict(all_loss)
+    for _id, loss_and_num in all_loss.items():
+        # sum up the loss for all valid tokens in the entire sequence, and divide the number of valid tokens
+        all_loss[_id] = sum([x[0] for x in loss_and_num]) / sum(x[1] for x in loss_and_num)
+    
+    # average across then take exp
+    perplexity = math.exp(sum(all_loss.values()) / len(all_loss))
+    return perplexity
+
+
+@torch.no_grad()
+def evaluate_generation(model, dataloader, accelerator:Optional[Accelerator]=None, tokenizer=None, return_new_tokens_only=True, **generation_config):
+    if accelerator is not None and type(dataloader) == torch.utils.data.DataLoader:
+        # if the dataloader has been prepared, we shall not prepare it twice, especially in case of deepspeed
+        dataloader = accelerator.prepare(dataloader)
+
+    all_indices = []
+    all_outputs = []
+
+    index = 0
+    
+    for i, x in enumerate(tqdm(dataloader, desc="Computing Generation")):
+        # if i > 3:
+        #     break
+        
+        # NOTE: important to reset memory for every batch
+        if hasattr(model, "memory"):
+            model.memory.reset()
+
+        # length is used to group training data, no use here
+        length = x.pop("length", None)
+
+        # if indices are None, we use batch size
+        indices = x.pop("index", None)
+        if indices is None:
+            indices = list(range(index, index + x['input_ids'].shape[0]))
+            index += x['input_ids'].shape[0]
+        else:
+            indices = indices.tolist()
+
+        outputs = model.generate(**x, **generation_config)
+        if return_new_tokens_only:
+            start_idx = x["input_ids"].shape[1]
+            outputs = outputs[:, start_idx:]
+
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        if accelerator is not None and accelerator.num_processes > 1:
+            outputs = accelerator.gather_for_metrics(outputs)
+            indices = accelerator.gather_for_metrics(indices)
+
+        outputs = outputs
+        indices = indices
+        all_indices.extend(indices)
+        all_outputs.extend(outputs)
+
+    return all_indices, all_outputs
+
+
+@torch.no_grad()
+def evaluate_nll(model, dataloader, accelerator:Optional[Accelerator]=None):
+    if accelerator is not None and type(dataloader) == torch.utils.data.DataLoader:
+        # if the dataloader has been prepared, we shall not prepare it twice, especially in case of deepspeed
+        dataloader = accelerator.prepare(dataloader)
+
+    # if accelerator.process_index == 0:
+    #     for name, x in model.named_parameters():
+    #         print(f"{name: ^80} {x.dtype}")
+
+    all_loss = defaultdict(list)
+    for i, x in enumerate(tqdm(dataloader, desc="Computing Perplexity")):
+        # NOTE: important to reset memory for every batch
+        if hasattr(model, "memory"):
+            model.memory.reset()
+
+        # the seq id
+        index = x.pop("index")
+        # length is used to group training data, no use here
+        length = x.pop("length", None)
+
+        output = model(**x)
+
+        valid_token_num = (x["labels"] != -100).sum()
+
+        # NOTE: we need the loss for each element in the batch for accurate computation, because the number of valid tokens may differ among elements
+        if hasattr(output, "batch_loss"):
+            # output from our model has batch_loss by default
+            batch_loss = output.batch_loss
+        else:
+            # output from other models does not
+            loss, batch_loss, token_loss = compute_loss(output.logits, x["labels"], shift=True)
+
+        if accelerator is not None and accelerator.num_processes > 1:
+            # num_device * batch_size
+            index = accelerator.gather_for_metrics(index)
+            batch_loss = accelerator.gather_for_metrics(batch_loss)
+            valid_token_num = accelerator.gather_for_metrics(valid_token_num)
+
+        for _id, _loss in zip(index.tolist(), batch_loss.tolist()):
+            # loss times num is the total loss of all valid tokens
+            all_loss[_id].append(_loss)
+
+    return all_loss
+
+
+@dataclass
+class ModelOutput(BaseModelOutputWithPast):
+    loss: Optional[torch.FloatTensor] = None
+    batch_loss: Optional[torch.FloatTensor] = None
+    token_loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+
+########## Various RoPE Scaling Methods Below (wrap the encoding process within the module for convenience) ##########
+
+def get_rope(head_dim, base, max_position_embeddings, rope_scaling=None):
+    """
+    Get rope module. {native, linear scaling, dynamic ntk scaling, yarn scaling, llama3 scaling}
+    """
+    if rope_scaling is None:
+        rope = RotaryEmbedding(
+            dim=head_dim,
+            base=base,
+            max_position_embeddings=max_position_embeddings,
+        )
+    else:
+        scaling_type = rope_scaling["type"]
+        scaling_factor = rope_scaling["factor"]
+        if scaling_type == "linear":
+            rope = LinearScalingRotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+            )
+        elif scaling_type == "dynamic":
+            rope = DynamicNTKScalingRotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+            )
+        elif scaling_type == "yarn":
+            rope = YarnRotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+            )
+        elif scaling_type == "yarn-t":
+            rope = YarnDynamicTemperatureRotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+            )
+        elif scaling_type == "yarn-t-logn":
+            rope = YarnDynamicTemperatureLogNRotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+            )
+        elif scaling_type == "llama3":
+            rope = Llama3RotaryEmbedding(
+                dim=head_dim,
+                base=base,
+                max_position_embeddings=max_position_embeddings,
+                scaling_factor=scaling_factor,
+                original_max_position_embeddings=rope_scaling.get("original_max_position_embeddings", 8192),
+                low_freq_factor=rope_scaling.get("low_freq_factor", 1),
+                high_freq_factor=rope_scaling.get("high_freq_factor", 4),
+            )
+        else:
+            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+    
+    return rope
+
+
+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)
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=32768, 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.float32).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.float32)
+        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(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+
+    def forward(self, q, k, position_ids):
+        seq_len = max(position_ids.max().item() + 1, k.shape[2])
+
+        # 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=k.device, dtype=k.dtype)
+
+        # batch_size, 1, key_len, head_dim
+        k_cos = self.cos_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+        k_sin = self.sin_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+
+        q_cos = k_cos[..., -q.shape[2]:, :]
+        q_sin = k_sin[..., -q.shape[2]:, :]
+
+        q_embed = (q * q_cos) + (rotate_half(q) * q_sin)
+        k_embed = (k * k_cos) + (rotate_half(k) * k_sin)
+        return q_embed, k_embed
+
+
+class LinearScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=32768, 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.float32)
+        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)
+
+
+class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=32768, 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.float32).to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        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)
+
+
+class YarnRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, beta_slow=2, beta_fast=128):
+        super().__init__()
+
+        self.base = base
+        self.dim = dim
+        self.scaling_factor = scaling_factor
+        self.beta_slow = beta_slow
+        self.beta_fast = beta_fast
+        self.max_position_embeddings = max_position_embeddings
+
+        self._set_cos_sin_cache(
+            seq_len=math.ceil(max_position_embeddings * scaling_factor), device=device, dtype=torch.get_default_dtype()
+        )
+
+    def _get_factor(self):
+        # the dimension whose index is smaller than fast_dim rotates more than beta_fast
+        fast_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_fast)) / math.log(self.base))
+        fast_dim = max(math.floor(fast_dim), 0)
+        # the dimension whose index is bigger than slow_dim rotates less than beta_slow
+        slow_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_slow)) / math.log(self.base))
+        slow_dim = min(math.ceil(slow_dim), self.dim - 1)
+
+        if fast_dim == slow_dim:
+            slow_dim += 0.001
+
+        # NOTE: very important to use full precision here so that the factor is correct
+        dim_arange = torch.arange(0, self.dim // 2, dtype=torch.float32)
+        dim_factor = (dim_arange - fast_dim) / (slow_dim - fast_dim)
+        dim_factor = torch.clamp(dim_factor, 0, 1)
+
+        # align with the paper notation
+        return (1 - dim_factor)
+
+    def _get_temperature(self):
+        if self.scaling_factor <= 1:
+            return 1.0
+        return 0.07 * math.log(self.scaling_factor) + 1.0
+    
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        dim_arange = torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim
+        # dim / 2
+        freq = self.base ** dim_arange
+        theta = 1 / freq
+        interleave_theta = theta / self.scaling_factor
+
+        factor = self._get_factor().to(device)
+        yarn_theta = factor * theta + (1 - factor) * interleave_theta
+        self.register_buffer("inv_freq", yarn_theta, persistent=False)
+
+        t = torch.arange(seq_len, device=device, dtype=torch.float32)
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        # get attention temperature
+        temperature = self._get_temperature()
+
+        self.register_buffer("cos_cached", emb.cos() * temperature, persistent=False)
+        self.register_buffer("sin_cached", emb.sin() * temperature, persistent=False)
+        self.max_seq_len_cached = seq_len
+    
+    def forward(self, q, k, position_ids):
+        seq_len = max(position_ids.max().item() + 1, k.shape[2])
+
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self.scaling_factor = seq_len / self.max_position_embeddings
+            self._set_cos_sin_cache(seq_len=seq_len, device=k.device, dtype=k.dtype)
+
+        k_cos = self.cos_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+        k_sin = self.sin_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+
+        q_cos = k_cos[..., -q.shape[2]:, :]
+        q_sin = k_sin[..., -q.shape[2]:, :]
+
+        q_embed = (q * q_cos) + (rotate_half(q) * q_sin)
+        k_embed = (k * k_cos) + (rotate_half(k) * k_sin)
+        return q_embed, k_embed
+
+
+class YarnDynamicTemperatureRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, beta_slow=2, beta_fast=128):
+        super().__init__()
+
+        self.base = base
+        self.dim = dim
+        self.scaling_factor = scaling_factor
+        self.beta_slow = beta_slow
+        self.beta_fast = beta_fast
+        self.max_position_embeddings = max_position_embeddings
+
+        self._set_cos_sin_cache(
+            seq_len=math.ceil(max_position_embeddings * scaling_factor), device=device, dtype=torch.get_default_dtype()
+        )
+
+    def _get_factor(self):
+        # the dimension whose index is smaller than fast_dim rotates more than beta_fast
+        fast_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_fast)) / math.log(self.base))
+        fast_dim = max(math.floor(fast_dim), 0)
+        # the dimension whose index is bigger than slow_dim rotates less than beta_slow
+        slow_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_slow)) / math.log(self.base))
+        slow_dim = min(math.ceil(slow_dim), self.dim - 1)
+
+        if fast_dim == slow_dim:
+            slow_dim += 0.001
+
+        # NOTE: very important to use full precision here so that the factor is correct
+        dim_arange = torch.arange(0, self.dim // 2, dtype=torch.float32)
+        dim_factor = (dim_arange - fast_dim) / (slow_dim - fast_dim)
+        dim_factor = torch.clamp(dim_factor, 0, 1)
+
+        # align with the paper notation
+        return (1 - dim_factor)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        dim_arange = torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim
+        # dim / 2
+        freq = self.base ** dim_arange
+        theta = 1 / freq
+        interleave_theta = theta / self.scaling_factor
+
+        factor = self._get_factor().to(device)
+        yarn_theta = factor * theta + (1 - factor) * interleave_theta
+        self.register_buffer("inv_freq", yarn_theta, persistent=False)
+
+        positions = torch.arange(seq_len, device=device, dtype=torch.float32)
+        freqs = torch.outer(positions, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        # NOTE: get attention temperature that will be applied on the query vector
+        # temperature = torch.log(positions + 1) / math.log(self.max_position_embeddings)
+        temperature = (0.07 * torch.log((positions + 1) / self.max_position_embeddings) + 1) ** 2
+        temperature[:self.max_position_embeddings] = 1
+        self.register_buffer("temperature", temperature.unsqueeze(1), persistent=False)
+
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+        self.max_seq_len_cached = seq_len
+    
+    def forward(self, q, k, position_ids):
+        seq_len = max(position_ids.max().item() + 1, k.shape[2])
+
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self.scaling_factor = seq_len / self.max_position_embeddings
+            self._set_cos_sin_cache(seq_len=seq_len, device=k.device, dtype=k.dtype)
+
+        # batch_size, 1, key_len, head_dim
+        k_cos = self.cos_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+        k_sin = self.sin_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+
+        q_cos = k_cos[..., -q.shape[2]:, :]
+        q_sin = k_sin[..., -q.shape[2]:, :]
+
+        q_position_ids = position_ids[:, -q.shape[2]:]
+        temperature = self.temperature[q_position_ids].to(dtype=k.dtype).unsqueeze(1)
+        q_cos = q_cos * temperature
+        q_sin = q_sin * temperature
+
+        q_embed = (q * q_cos) + (rotate_half(q) * q_sin)
+        k_embed = (k * k_cos) + (rotate_half(k) * k_sin)
+        return q_embed, k_embed
+
+
+class YarnDynamicTemperatureLogNRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, beta_slow=2, beta_fast=128):
+        super().__init__()
+
+        self.base = base
+        self.dim = dim
+        self.scaling_factor = scaling_factor
+        self.beta_slow = beta_slow
+        self.beta_fast = beta_fast
+        self.max_position_embeddings = max_position_embeddings
+
+        self._set_cos_sin_cache(
+            seq_len=math.ceil(max_position_embeddings * scaling_factor), device=device, dtype=torch.get_default_dtype()
+        )
+
+    def _get_factor(self):
+        # the dimension whose index is smaller than fast_dim rotates more than beta_fast
+        fast_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_fast)) / math.log(self.base))
+        fast_dim = max(math.floor(fast_dim), 0)
+        # the dimension whose index is bigger than slow_dim rotates less than beta_slow
+        slow_dim = self.dim / 2 * (math.log(self.max_position_embeddings / (2 * math.pi * self.beta_slow)) / math.log(self.base))
+        slow_dim = min(math.ceil(slow_dim), self.dim - 1)
+
+        if fast_dim == slow_dim:
+            slow_dim += 0.001
+
+        # NOTE: very important to use full precision here so that the factor is correct
+        dim_arange = torch.arange(0, self.dim // 2, dtype=torch.float32)
+        dim_factor = (dim_arange - fast_dim) / (slow_dim - fast_dim)
+        dim_factor = torch.clamp(dim_factor, 0, 1)
+
+        # align with the paper notation
+        return (1 - dim_factor)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        dim_arange = torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim
+        # dim / 2
+        freq = self.base ** dim_arange
+        theta = 1 / freq
+        interleave_theta = theta / self.scaling_factor
+
+        factor = self._get_factor().to(device)
+        yarn_theta = factor * theta + (1 - factor) * interleave_theta
+        self.register_buffer("inv_freq", yarn_theta, persistent=False)
+
+        positions = torch.arange(seq_len, device=device, dtype=torch.float32)
+        freqs = torch.outer(positions, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        # NOTE: get attention temperature that will be applied on the query vector
+        temperature = torch.log(positions + 1) / math.log(self.max_position_embeddings)
+        # temperature = (0.07 * torch.log((positions + 1) / self.max_position_embeddings) + 1) ** 2
+        temperature[:self.max_position_embeddings] = 1
+        self.register_buffer("temperature", temperature.unsqueeze(1), persistent=False)
+
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+        self.max_seq_len_cached = seq_len
+    
+    def forward(self, q, k, position_ids):
+        seq_len = max(position_ids.max().item() + 1, k.shape[2])
+
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self.scaling_factor = seq_len / self.max_position_embeddings
+            self._set_cos_sin_cache(seq_len=seq_len, device=k.device, dtype=k.dtype)
+
+        # batch_size, 1, key_len, head_dim
+        k_cos = self.cos_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+        k_sin = self.sin_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+
+        q_cos = k_cos[..., -q.shape[2]:, :]
+        q_sin = k_sin[..., -q.shape[2]:, :]
+
+        q_position_ids = position_ids[:, -q.shape[2]:]
+        temperature = self.temperature[q_position_ids].to(dtype=k.dtype).unsqueeze(1)
+        q_cos = q_cos * temperature
+        q_sin = q_sin * temperature
+
+        q_embed = (q * q_cos) + (rotate_half(q) * q_sin)
+        k_embed = (k * k_cos) + (rotate_half(k) * k_sin)
+        return q_embed, k_embed
+
+
+class Llama3RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=8192, base=10000, device=None, scaling_factor=1.0, original_max_position_embeddings=8192, low_freq_factor=1, high_freq_factor=4):
+        super().__init__()
+
+        self.base = base
+        self.dim = dim
+        self.scaling_factor = scaling_factor
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.max_position_embeddings = max(max_position_embeddings, int(original_max_position_embeddings * scaling_factor))
+        self.low_freq_factor = low_freq_factor
+        self.high_freq_factor = high_freq_factor
+
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32).to(device) / self.dim))
+        low_freq_wavelen = self.original_max_position_embeddings / low_freq_factor
+        high_freq_wavelen = self.original_max_position_embeddings / high_freq_factor
+        new_freqs = []
+        for freq in inv_freq:
+            wavelen = 2 * math.pi / freq
+            if wavelen < high_freq_wavelen:
+                new_freqs.append(freq)
+            elif wavelen > low_freq_wavelen:
+                new_freqs.append(freq / scaling_factor)
+            else:
+                assert low_freq_wavelen != high_freq_wavelen
+                smooth = (self.original_max_position_embeddings / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                new_freqs.append((1 - smooth) * freq / scaling_factor + smooth * freq)
+        inv_freq = torch.tensor(new_freqs, dtype=inv_freq.dtype, device=inv_freq.device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        self._set_cos_sin_cache(seq_len=self.max_position_embeddings, device=device)
+
+    def _set_cos_sin_cache(self, seq_len, device):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+        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(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+    
+    def forward(self, q, k, position_ids):
+        seq_len = max(position_ids.max().item() + 1, k.shape[2])
+
+        # 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=k.device)
+
+        k_cos = self.cos_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+        k_sin = self.sin_cached[position_ids].to(dtype=k.dtype).unsqueeze(1)
+
+        q_cos = k_cos[..., -q.shape[2]:, :]
+        q_sin = k_sin[..., -q.shape[2]:, :]
+
+        q_embed = (q * q_cos) + (rotate_half(q) * q_sin)
+        k_embed = (k * k_cos) + (rotate_half(k) * k_sin)
+        return q_embed, k_embed

special_tokens_map.json

@@ -0,0 +1,20 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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