init

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/qhj/Activation-Beacon/outputs/beacon-qwen2-full_coverage-interleave-2048_2048-2,4,8,16,32-step_random-qkv-redpajama/checkpoint-11436",
+ "architectures": [
+ "Qwen2ForCausalLM"
+ ],
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_qwen2.Qwen2Config",
+ "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM"
+ },
+ "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
+ ],
+ "beacon_ratio_mix": "step-random",
+ "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": "4.43.1",
+ "use_cache": true,
+ "use_sliding_window": false,
+ "vocab_size": 152064
+}

configuration_qwen2.py

@@ -0,0 +1,171 @@
+# 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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+{
+ "bos_token_id": 151643,
+ "do_sample": true,
+ "eos_token_id": [
+ 151645,
+ 151643
+ ],
+ "pad_token_id": 151643,
+ "repetition_penalty": 1.05,
+ "temperature": 0.7,
+ "top_k": 20,
+ "top_p": 0.8,
+ "transformers_version": "4.43.1"
+}

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+ }
+}

model_args.json

@@ -0,0 +1 @@
+{"model_cache_dir": null, "dataset_cache_dir": null, "data_root": "/data/long-llm", "train_data": ["/share/qhj/rags/data/long_context/train/0830.all.30k.jsonl"], "eval_data": null, "model_name_or_path": "/share/qhj/Activation-Beacon/outputs/beacon-qwen2-full_coverage-interleave-2048_2048-2,4,8,16,32-step_random-qkv-redpajama/checkpoint-11436", "padding_side": "left", "no_use_fast": false, "access_token": null, "attn_impl": "flash_attention_2", "max_length": 40000, "chat_template": "qwen", "max_position_embeddings": null, "mistral_sliding_window": null, "rope_theta": null, "rope_method": null, "rope_factor": 1.0, "lora": null, "lora_unload": true, "load_in_4_bit": false, "dtype": "bf16", "device_map": null, "batch_size": 1, "cpu": false, "enable_tp": false, "enable_vllm": false, "vllm_mem": 0.9, "vllm_tp": 1, "vllm_len": null, "vllm_disable_ar": false, "enable_beacon": true, "beacon_window": 2048, "beacon_stride": 2048, "beacon_attn": "full-coverage", "beacon_ratio": [2, 4, 8], "beacon_ratio_mix": "step-random", "beacon_param": ["q", "k", "v"], "beacon_embed_init": "eos", "beacon_sink_size": 0, "beacon_attend_prev": true, "beacon_pos": "interleave", "beacon_parallel_window": null, "retrieval_method": null, "retrieval_topk": null, "retrieval_key_length": null, "max_new_tokens": null, "do_sample": null, "temperature": null, "top_p": null}

modeling_beacon.py

@@ -0,0 +1,1143 @@
+import os
+import torch
+import time
+import numpy as np
+import torch.distributed as dist
+from copy import deepcopy
+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._post_validation()
+ self.reset()
+ 
+ @property
+ def beacon_token(self):
+ return self.config.vocab_size
+
+ 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, **kwargs):
+ """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 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)]
+ # the raw activations of recent tokens
+ self.raw_activations = [(None, None) for _ in range(self.config.num_hidden_layers)]
+
+ # NOTE: in case we want to resume the memory from a particular state
+ for k, v in kwargs.items():
+ # NOTE: deepcopy to untie the memory state and the model memory
+ setattr(self, deepcopy(k), deepcopy(v))
+
+ def export(self):
+ """Export all necessary attributes of the memory module."""
+ return {
+ "start_idx": self.start_idx,
+ "end_idx": self.end_idx,
+ "all_beacon_sizes": self.all_beacon_sizes,
+ "batch_loss": self.batch_loss,
+ "valid_token_num": self.valid_token_num,
+ "step_idx": self.step_idx,
+ "compression_ratio": self.compression_ratio,
+ "is_full_window": self.is_full_window,
+ "raw_size_to_cache": self.raw_size_to_cache,
+ "interleave_remainder": self.interleave_remainder,
+ "interleave_compression_ratio": self.interleave_compression_ratio,
+ "beacon_indices": self.beacon_indices,
+ "all_input_ids": self.all_input_ids,
+ "all_attention_mask": self.all_attention_mask,
+ "all_labels": self.all_labels,
+ "beacon_skip_first": self.beacon_skip_first,
+ "beacon_skip_last": self.beacon_skip_last,
+ # NOTE: deepcopy to untie the memory state and the model memory
+ "sink_activations": deepcopy(self.sink_activations),
+ "beacon_activations": deepcopy(self.beacon_activations),
+ "raw_activations": deepcopy(self.raw_activations),
+ }
+
+ @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
+ # 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, input_ids.new_full((batch_size, beacon_size), self.beacon_token)], 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)
+ 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).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"input_len: {input_len}")
+ # 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 +1,1309 @@
+# 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, beacon_skip_first=None, beacon_skip_last=None, **kwargs
+ ):
+ if past_key_values:
+ input_ids = input_ids[:, -1:]
+ 
+ model_inputs = {"input_ids": input_ids, "beacon_skip_first": beacon_skip_first, "beacon_skip_last": beacon_skip_last}
+ 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
+ }
+}

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+ "add_prefix_space": false,
+ "added_tokens_decoder": {
+ "151643": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151644": {
+ "content": "<|im_start|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "151645": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [
+ "<|im_start|>",
+ "<|im_end|>"
+ ],
+ "bos_token": null,
+ "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "<|im_end|>",
+ "errors": "replace",
+ "model_max_length": 131072,
+ "pad_token": "<|endoftext|>",
+ "padding_side": "left",
+ "split_special_tokens": false,
+ "tokenizer_class": "Qwen2Tokenizer",
+ "unk_token": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2033b811b3474a96f5c2c4757d753e269ed2ef5e1aef1faaf2fc55a9c4b32b06
+size 7480