remove landmark attn and xpos rope implementations (#1010)

README.md

@@ -798,11 +798,6 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 # Whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
-# Landmark attention (only llama)
-landmark_attention:
-# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-# LLaMA only
-xpos_rope:
 
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:

src/axolotl/cli/__init__.py

@@ -103,14 +103,6 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
 
-    if cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
-
-        set_model_mem_id(model, tokenizer)
-        model.set_mem_cache_args(
-            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
-        )
-
     model = model.to(cfg.device)
 
     while True:
@@ -176,14 +168,6 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
 
-    if cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
-
-        set_model_mem_id(model, tokenizer)
-        model.set_mem_cache_args(
-            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
-        )
-
     model = model.to(cfg.device)
 
     def generate(instruction):

src/axolotl/core/trainer_builder.py

@@ -9,7 +9,7 @@ import math
 import sys
 from abc import abstractmethod
 from dataclasses import dataclass, field
-from functools import partial, wraps
+from functools import wraps
 from pathlib import Path
 from typing import Optional
 
@@ -780,26 +780,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
 
-        if self.cfg.is_llama_derived_model and self.cfg.landmark_attention:
-            from axolotl.monkeypatch.llama_landmark_attn import (
-                add_mem_tokens,
-                get_mem_id,
-                set_model_mem_id,
-            )
-
-            set_model_mem_id(self.model, self.tokenizer)
-
-            LOG.info("Adding landmark attention tokens to dataset")
-
-            for dataset in [self.train_dataset, self.eval_dataset]:
-                dataset = dataset.map(
-                    partial(
-                        add_mem_tokens, mem_freq=50, mem_id=get_mem_id(self.tokenizer)
-                    ),
-                    batched=False,
-                    num_proc=32,
-                )
-
         trainer_cls = self._get_trainer_cls()
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls

src/axolotl/monkeypatch/llama_landmark_attn.py

@@ -1,1249 +0,0 @@
-# pylint: skip-file
-# coding=utf-8
-# Copyright 2022 EleutherAI 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 LLaMA model.
-Taken from https://github.com/epfml/landmark-attention/blob/main/llama/llama_mem.py and modified.
-"""
-import math
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import LlamaTokenizer
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.models.llama.configuration_llama import LlamaConfig
-from transformers.models.llama.modeling_llama import (
-    LLAMA_INPUTS_DOCSTRING,
-    LLAMA_START_DOCSTRING,
-    LlamaMLP,
-    LlamaPreTrainedModel,
-    LlamaRMSNorm,
-    LlamaRotaryEmbedding,
-    _expand_mask,
-    _make_causal_mask,
-    rotate_half,
-)
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-
-LOG = logging.getLogger("axolotl")
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-MEM_TOKEN = "<landmark>"  # nosec
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    if q is None:
-        q_embed = None
-    else:
-        q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
-    """
-    Landmark grouped softmax function.
-    """
-
-    # Note that forward, setup_context, and backward are @staticmethods
-    @staticmethod
-    def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
-        new_shape = list(x.shape)
-        new_shape[dim] = mem_cnt  # max_mem_cnt.item()
-        max_by_group = x.new_zeros((*new_shape,))
-        max_by_group.scatter_reduce_(
-            src=x, index=resp_mem_idx, dim=dim, reduce="amax", include_self=False
-        )
-
-        maxes = torch.gather(max_by_group, dim, resp_mem_idx)
-        # x_exp = torch.exp(x - torch.where(torch.isinf(maxes), 0, maxes))
-        x_exp = torch.exp((x - maxes).to(torch.float32))
-
-        cumsum_by_group = torch.zeros_like(max_by_group, dtype=x_exp.dtype)
-
-        cumsum_by_group.scatter_add_(
-            dim,
-            resp_mem_idx,
-            x_exp,
-        )
-        denom = torch.gather(cumsum_by_group, dim, resp_mem_idx)
-
-        # probs = torch.where(denom < 0.5, 0, x_exp / denom)
-        probs = x_exp / denom
-
-        ctx.mem_cnt = mem_cnt
-        ctx.dim = dim
-        ctx.save_for_backward(resp_mem_idx, probs)
-
-        return probs
-
-    @staticmethod
-    def backward(ctx, grad_probs):
-        mem_cnt = ctx.mem_cnt
-        dim = ctx.dim
-        resp_mem_idx, probs = ctx.saved_tensors
-        grad_x = grad_dim = grad_mem_cnt = grad_resp_mem_idx = None
-
-        if ctx.needs_input_grad[0] or ctx.needs_input_grad[4]:
-            grad_pair = grad_probs * probs
-
-            new_shape = list(probs.shape)
-            new_shape[dim] = mem_cnt  # max_mem_cnt.item()
-            cumsum_by_group = grad_pair.new_zeros((*new_shape,))
-            cumsum_by_group.scatter_add_(dim, resp_mem_idx, grad_pair)
-
-        if ctx.needs_input_grad[0]:
-            grad_sum = torch.gather(cumsum_by_group, dim, resp_mem_idx)
-            grad_x = grad_pair - probs * grad_sum
-        assert not ctx.needs_input_grad[1]
-        assert not ctx.needs_input_grad[2]
-        assert not ctx.needs_input_grad[3]
-
-        return grad_x, grad_dim, grad_mem_cnt, grad_resp_mem_idx
-
-
-def landmark_grouped_softmax(x, dim, is_mem, last_section_mask):
-    last_and_rest_mask = last_section_mask  # | mask
-
-    full_access_mask = is_mem | last_and_rest_mask
-
-    max_mem_cnt = 16
-    mem_group_idx = torch.cumsum(is_mem, dim=dim)
-    mem_bucket_id = max_mem_cnt - 1
-    resp_mem_idx = torch.where(
-        last_and_rest_mask,
-        max_mem_cnt - 1,
-        torch.where(is_mem, mem_bucket_id, mem_group_idx),
-    )
-    probs = LandmarkGroupedSoftmaxFunction.apply(x, dim, max_mem_cnt, resp_mem_idx)
-
-    new_shape = list(x.shape)
-    new_shape[dim] = max_mem_cnt
-    group_prob = probs.new_zeros((*new_shape,))
-    group_prob.scatter_(
-        dim, torch.where(is_mem, mem_group_idx - 1, max_mem_cnt - 1), probs
-    )
-    probs = probs.mul(
-        torch.where(
-            full_access_mask,
-            last_section_mask,
-            torch.gather(group_prob, dim, resp_mem_idx),
-        )
-    )
-
-    return probs
-
-
-class LlamaAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = config.max_position_embeddings
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=False
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=False
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=False
-        )
-        self.o_proj = nn.Linear(
-            self.num_heads * self.head_dim, self.hidden_size, bias=False
-        )
-        self.rotary_emb = LlamaRotaryEmbedding(
-            self.head_dim, max_position_embeddings=self.max_position_embeddings
-        )
-
-        self.mem_freq = None
-        self.top_k = None
-        self.max_cache_size = None
-
-    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 set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
-        self.mem_freq = mem_freq
-        self.top_k = top_k
-        self.max_cache_size = max_cache_size
-
-    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: bool = False,
-        use_cache: bool = False,
-        is_mem: Optional[torch.Tensor] = None,
-        last_section_mask: Optional[torch.Tensor] = None,
-        offload_cache_to_cpu: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = (
-            self.q_proj(hidden_states)
-            .view(bsz, q_len, self.num_heads, self.head_dim)
-            .transpose(1, 2)
-        )
-        key_states = (
-            self.k_proj(hidden_states)
-            .view(bsz, q_len, self.num_heads, self.head_dim)
-            .transpose(1, 2)
-        )
-        value_states = (
-            self.v_proj(hidden_states)
-            .view(bsz, q_len, self.num_heads, self.head_dim)
-            .transpose(1, 2)
-        )
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-            if len(past_key_value) > 2:
-                kv_seq_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        key_states_before_pos = key_states
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, position_ids
-        )
-        # [bsz, nh, t, hd]
-
-        attn_prefix = None
-        if past_key_value is not None:
-            # reuse k, v, self_attention
-            if self.mem_freq is None:
-                cache_len = past_key_value[0].shape[2]
-                if self.max_cache_size is not None:
-                    cache_len = min(cache_len, self.max_cache_size)
-                if is_mem is not None:
-                    is_mem = torch.cat(
-                        (is_mem.new_zeros((1, 1, q_len, cache_len)), is_mem), dim=-1
-                    )
-                    last_section_mask = torch.cat(
-                        (
-                            last_section_mask.new_ones((1, 1, q_len, cache_len)),
-                            last_section_mask,
-                        ),
-                        dim=-1,
-                    )
-
-                past_key_states = torch.cat([past_key_value[0], key_states], dim=2)
-                past_value_states = torch.cat([past_key_value[1], value_states], dim=2)
-                key_states = past_key_states[:, :, -(q_len + cache_len) :]
-                value_states = past_value_states[:, :, -(q_len + cache_len) :]
-                expected_att_size = (bsz, self.num_heads, q_len, cache_len + q_len)
-            else:
-                orig_value_states = value_states
-
-                incomplete_len = past_key_value[0].shape[2] % (self.mem_freq + 1)
-                full_len = past_key_value[0].shape[2] - incomplete_len
-                past_key_mem, past_key_incomplete = torch.split(
-                    past_key_value[0], (full_len, incomplete_len), dim=2
-                )
-                past_value_mem, past_value_incomplete = torch.split(
-                    past_key_value[1], (full_len, incomplete_len), dim=2
-                )
-
-                if offload_cache_to_cpu:
-                    past_key_value = (
-                        past_key_incomplete,
-                        past_value_incomplete,
-                        *past_key_value[2:],
-                    )
-
-                if incomplete_len > 0:
-                    assert q_len + incomplete_len <= (self.mem_freq + 1)
-                is_mem = torch.cat(
-                    (is_mem.new_zeros((1, 1, q_len, incomplete_len)), is_mem), dim=-1
-                )
-                last_section_mask = torch.cat(
-                    (
-                        last_section_mask.new_ones((1, 1, q_len, incomplete_len)),
-                        last_section_mask,
-                    ),
-                    dim=-1,
-                )
-
-                if len(past_key_value) > 2:
-                    full_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
-                past_key_incomplete_pos = torch.arange(
-                    full_len,
-                    full_len + incomplete_len,
-                    dtype=torch.long,
-                    device=position_ids.device,
-                ).unsqueeze(0)
-                _, past_key_incomplete = apply_rotary_pos_emb(
-                    None, past_key_incomplete, cos, sin, past_key_incomplete_pos
-                )
-                key_states = torch.cat((past_key_incomplete, key_states), dim=2)
-                value_states = torch.cat((past_value_incomplete, value_states), dim=2)
-
-                past_key_mem = past_key_mem.view(
-                    bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim
-                )
-                past_value_mem = past_value_mem.view(
-                    bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim
-                )
-
-                if len(past_key_value) > 2:
-                    mem_key_nopos = torch.cat(
-                        (
-                            past_key_value[2],
-                            past_key_mem.select(dim=3, index=self.mem_freq),
-                        ),
-                        dim=2,
-                    )
-                    past_key_mem_offload = past_key_value[3]
-                    past_key_mem = torch.cat(
-                        (
-                            past_key_mem_offload,
-                            past_key_mem.to(past_key_mem_offload.device),
-                        ),
-                        dim=2,
-                    )
-                    past_value_mem = torch.cat(
-                        (
-                            past_key_value[4],
-                            past_value_mem.to(past_key_mem_offload.device),
-                        ),
-                        dim=2,
-                    )
-                else:
-                    mem_key_nopos = past_key_mem.select(dim=3, index=self.mem_freq)
-
-                num_mems = past_key_mem.shape[2]
-                top_k = min(self.top_k, num_mems)
-                prefix_len = full_len - (top_k + 1) * (self.mem_freq + 1)
-                mem_indices = torch.cat(
-                    (
-                        position_ids.new_zeros((max(0, num_mems - top_k),)),
-                        torch.arange(
-                            1,
-                            top_k + 1,
-                            device=query_states.device,
-                            dtype=position_ids.dtype,
-                        ),
-                    ),
-                    dim=0,
-                )
-                mem_pos = (mem_indices * (self.mem_freq + 1) + self.mem_freq).unsqueeze(
-                    0
-                ).expand(bsz, -1) + prefix_len
-                _, mem_key = apply_rotary_pos_emb(
-                    None, mem_key_nopos, cos, sin, mem_pos
-                )
-                mem_attn_weights = torch.matmul(
-                    query_states, mem_key.transpose(2, 3)
-                ) / math.sqrt(self.head_dim)
-
-                if offload_cache_to_cpu:
-                    aggregate = "max_over_tokens"
-                else:
-                    aggregate = None
-                if aggregate == "max_over_tokens":
-                    token_retrievers = 1
-                    head_retrievers = self.num_heads
-                    mem_attn_weights = torch.nn.functional.softmax(
-                        mem_attn_weights, dim=-1
-                    )
-                    mem_attn_weights = mem_attn_weights.amax(dim=2, keepdim=True)
-                elif aggregate is None:
-                    token_retrievers = q_len
-                    head_retrievers = self.num_heads
-                else:
-                    raise NotImplementedError()
-
-                mem_selected_idx = (
-                    mem_attn_weights.topk(dim=-1, k=top_k)[1]
-                    .sort(dim=-1)[0]
-                    .view(bsz, head_retrievers, token_retrievers, top_k)
-                )
-
-                selected_indices = torch.arange(
-                    0,
-                    top_k * (self.mem_freq + 1),
-                    device=query_states.device,
-                    dtype=position_ids.dtype,
-                )
-                selected_indices = torch.where(
-                    mem_selected_idx >= num_mems - top_k, self.mem_freq + 1, 0
-                ).unsqueeze(-1) + selected_indices.view(
-                    1, 1, 1, top_k, self.mem_freq + 1
-                )
-                selected_indices = (
-                    selected_indices.view(
-                        bsz, head_retrievers, token_retrievers, -1
-                    ).expand(bsz, self.num_heads, q_len, -1)
-                    + prefix_len
-                )
-
-                mem_selected_idx = mem_selected_idx.to(past_key_mem.device)
-
-                mem_selected_idx = mem_selected_idx.view(
-                    bsz, self.num_heads, token_retrievers, top_k, 1, 1
-                ).expand(
-                    bsz,
-                    self.num_heads,
-                    token_retrievers,
-                    top_k,
-                    self.mem_freq + 1,
-                    self.head_dim,
-                )
-                selected_keys = past_key_mem.unsqueeze(2).expand(
-                    bsz,
-                    self.num_heads,
-                    token_retrievers,
-                    -1,
-                    self.mem_freq + 1,
-                    self.head_dim,
-                )
-                selected_keys = selected_keys.take_along_dim(
-                    mem_selected_idx, dim=3
-                ).to(query_states.device)
-                selected_values = (
-                    past_value_mem.unsqueeze(2)
-                    .expand(
-                        bsz,
-                        self.num_heads,
-                        token_retrievers,
-                        -1,
-                        self.mem_freq + 1,
-                        self.head_dim,
-                    )
-                    .take_along_dim(mem_selected_idx, dim=3)
-                    .to(query_states.device)
-                )
-
-                selected_keys = selected_keys.view(
-                    bsz, self.num_heads, token_retrievers, -1, self.head_dim
-                ).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
-                selected_keys = apply_rotary_pos_emb(
-                    None, selected_keys.unsqueeze(1), cos, sin, selected_indices
-                )[1].squeeze(1)
-                selected_values = selected_values.view(
-                    bsz, self.num_heads, token_retrievers, -1, self.head_dim
-                ).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
-                attn_prefix = torch.matmul(
-                    query_states.unsqueeze(3), selected_keys.transpose(3, 4)
-                ).squeeze(3) / math.sqrt(self.head_dim)
-                is_mem_prefix = (
-                    torch.cat(
-                        (is_mem.new_zeros((self.mem_freq,)), is_mem.new_ones((1,)))
-                    )
-                    .unsqueeze(0)
-                    .repeat((top_k, 1))
-                )
-                is_mem_prefix = is_mem_prefix.view(1, 1, 1, -1).expand(1, 1, q_len, -1)
-                is_mem = torch.cat((is_mem_prefix, is_mem), dim=-1)
-                last_section_mask = torch.cat(
-                    (
-                        last_section_mask.new_zeros(
-                            (1, 1, q_len, top_k * (self.mem_freq + 1))
-                        ),
-                        last_section_mask,
-                    ),
-                    dim=-1,
-                )
-                expected_att_size = (bsz, self.num_heads, q_len, q_len + incomplete_len)
-
-                past_key_states = torch.cat(
-                    [past_key_value[0], key_states_before_pos], dim=2
-                )
-                past_value_states = torch.cat(
-                    [past_key_value[1], orig_value_states], dim=2
-                )
-
-                if offload_cache_to_cpu:
-                    past_key_value = (
-                        (
-                            past_key_states,
-                            past_value_states,
-                            mem_key_nopos,
-                            past_key_mem.to("cpu"),
-                            past_value_mem.to("cpu"),
-                            *past_key_value[5:],
-                        )
-                        if use_cache
-                        else None
-                    )
-                else:
-                    past_key_value = (
-                        (past_key_states, past_value_states) if use_cache else None
-                    )
-
-        else:
-            if self.mem_freq is None:
-                past_key_states = key_states
-            else:
-                past_key_states = key_states_before_pos
-            past_value_states = value_states
-            expected_att_size = (bsz, self.num_heads, q_len, kv_seq_len)
-            past_key_value = (past_key_states, past_value_states) if use_cache else None
-
-        attn_weights = torch.matmul(
-            query_states, key_states.transpose(2, 3)
-        ) / math.sqrt(self.head_dim)
-        if attn_weights.size() != expected_att_size:
-            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[..., -attn_weights.shape[-1] :]
-            attn_weights = torch.max(
-                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
-            )
-        if attn_prefix is not None:
-            attn_weights = torch.cat((attn_prefix, attn_weights), dim=-1)
-        # upcast attention to fp32
-        if is_mem is None:
-            raise ValueError("Don't use this without landmarks")
-
-        attn_weights = landmark_grouped_softmax(
-            attn_weights,
-            dim=-1,
-            is_mem=is_mem.expand(-1, self.num_heads, -1, -1),
-            last_section_mask=last_section_mask,
-        ).to(query_states.dtype)
-
-        if attn_prefix is not None:
-            attn_prefix, attn_weights = torch.split(
-                attn_weights,
-                (attn_prefix.shape[-1], attn_weights.shape[-1] - attn_prefix.shape[-1]),
-                dim=-1,
-            )
-        attn_output = torch.matmul(attn_weights, value_states)
-        if attn_prefix is not None:
-            attn_output += torch.matmul(
-                attn_prefix.unsqueeze(3), selected_values
-            ).squeeze(3)
-
-        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)
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class LlamaDecoderLayer(nn.Module):
-    """
-    Llama Decoder layer
-    """
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = LlamaAttention(config=config)
-        self.mlp = LlamaMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-        )
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-
-    def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
-        self.self_attn.set_mem_cache_args(mem_freq, top_k, max_cache_size)
-
-    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,
-        is_mem: Optional[torch.Tensor] = None,
-        last_section_mask: Optional[torch.Tensor] = None,
-        offload_cache_to_cpu: bool = False,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
-        """
-        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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
-            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,
-            is_mem=is_mem,
-            last_section_mask=last_section_mask,
-            offload_cache_to_cpu=offload_cache_to_cpu,
-        )
-        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
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaModel(LlamaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-
-    Args:
-        config: LlamaConfig
-    """
-
-    def __init__(self, config: LlamaConfig):
-        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
-        )
-        self.layers = nn.ModuleList(
-            [LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
-        )
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.mem_id = None
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def set_mem_id(self, mem_id):
-        self.mem_id = mem_id
-
-    def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
-        for layer in self.layers:
-            layer.set_mem_cache_args(mem_freq, top_k, max_cache_size)
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(
-        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
-    ):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(
-                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
-            ).to(inputs_embeds.device)
-            combined_attention_mask = (
-                expanded_attn_mask
-                if combined_attention_mask is None
-                else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
-    @add_start_docstrings_to_model_forward(LLAMA_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,
-        offload_cache_to_cpu: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        # retrieve input_ids and inputs_embeds
-        is_mem = None
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
-            )
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-            if self.mem_id is not None:
-                with torch.no_grad():
-                    is_mem = input_ids == self.mem_id
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-            if self.mem_id is not None:
-                raise NotImplementedError
-        else:
-            raise ValueError(
-                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
-            )
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-
-        if past_key_values is not None:
-            if is_mem is not None:
-                pass
-                # raise NotImplementedError
-            past_key_values_length = past_key_values[0][0].shape[2]
-            if len(past_key_values[0]) > 2:
-                past_key_values_length += (
-                    past_key_values[0][3].shape[2] * past_key_values[0][3].shape[3]
-                )
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past),
-                dtype=torch.bool,
-                device=inputs_embeds.device,
-            )
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-        )
-
-        last_section_mask = None
-        if is_mem is not None:
-            is_mem = is_mem.unsqueeze(1).unsqueeze(2)
-            current_len = input_ids.shape[1]
-            mem_ids = torch.where(
-                attention_mask[..., -current_len:] < -1,
-                0,
-                torch.cumsum(is_mem, -1) - is_mem.int(),
-            )
-            last_section_mask = torch.amax(mem_ids, -1, keepdim=True) == mem_ids
-            attention_mask[..., -current_len:].masked_fill_(
-                last_section_mask & is_mem,
-                torch.tensor(
-                    torch.finfo(inputs_embeds.dtype).min, device=inputs_embeds.device
-                ),
-            )
-            last_section_mask.logical_and_(attention_mask[..., -current_len:] > -1)
-            is_mem = is_mem.logical_and(attention_mask[..., -current_len:] > -1)
-
-        hidden_states = inputs_embeds
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                LOG.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            past_key_value = (
-                past_key_values[idx] if past_key_values is not None else None
-            )
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    None,
-                    output_attentions,
-                    None,
-                    is_mem,
-                    last_section_mask,
-                )
-            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,
-                    is_mem=is_mem,
-                    last_section_mask=last_section_mask,
-                    offload_cache_to_cpu=offload_cache_to_cpu,
-                )
-
-            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 LlamaForCausalLM(LlamaPreTrainedModel):
-    """
-    Llama model with a causal language modeling head.
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = LlamaModel(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.mem_id = None
-        self.mem_freq = None
-        self.top_k = None
-        self.max_seq_len = None
-
-        # 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
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(
-        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-    )
-    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,
-        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,
-        offload_cache_to_cpu: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LlamaForCausalLM
-
-        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-        >>> prompt = "Hey, are you consciours? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
-        ```"""
-
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        window_len = self.max_seq_len or input_ids.shape[1]
-        last_logits = None
-        for _, idx in enumerate(range(0, input_ids.shape[1], window_len)):
-            if idx >= 1:
-                if output_attentions or output_hidden_states:
-                    raise NotImplementedError
-                if not use_cache:
-                    raise NotImplementedError
-            outputs = self.model(
-                input_ids=input_ids[:, idx : idx + window_len],
-                attention_mask=attention_mask[
-                    :, : idx + window_len + attention_mask.shape[1] - input_ids.shape[1]
-                ]
-                if attention_mask is not None
-                else None,
-                position_ids=position_ids[:, idx : idx + window_len]
-                if position_ids is not None
-                else None,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds[:, idx : idx + window_len]
-                if inputs_embeds is not None
-                else None,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                offload_cache_to_cpu=offload_cache_to_cpu,
-            )
-            past_key_values = outputs.past_key_values
-            if last_logits is not None:
-                last_logits = torch.cat((last_logits, outputs[0]), dim=-2)
-            last_logits = outputs[0]
-
-        hidden_states = last_logits
-        logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def set_mem_id(self, mem_id):
-        self.mem_id = mem_id
-        self.model.set_mem_id(mem_id)
-
-    def set_mem_cache_args(self, max_seq_len, mem_freq, top_k, max_cache_size):
-        self.mem_freq = mem_freq
-        self.top_k = top_k
-        self.max_seq_len = max_seq_len
-        if self.max_seq_len is not None:
-            assert self.max_seq_len % (self.mem_freq + 1) == 0
-        self.model.set_mem_cache_args(mem_freq, top_k, max_cache_size)
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        total_len = input_ids.shape[1]
-        if past_key_values:
-            prev_len = input_ids.shape[1] - 1
-        else:
-            prev_len = 0
-
-        position_ids = kwargs.get("position_ids", None)
-
-        if self.mem_freq is not None:
-            if position_ids is not None:
-                raise NotImplementedError
-            # T = input_ids.shape[1]
-
-            prev_incomplete_len = prev_len % self.mem_freq
-            prev_complete_len = prev_len - prev_incomplete_len
-            incomplete_len = total_len % self.mem_freq
-            new_full_len = total_len - prev_complete_len - incomplete_len
-
-            prev_input, input_ids_with_mem, input_ids_without_mem = torch.split(
-                input_ids, (prev_complete_len, new_full_len, incomplete_len), dim=-1
-            )
-
-            bsz, _ = input_ids.size()
-            input_ids_with_mem = input_ids_with_mem.view(bsz, -1, self.mem_freq)
-            input_ids_with_mem = torch.cat(
-                (
-                    input_ids_with_mem,
-                    input_ids_with_mem.new_full(
-                        (bsz, input_ids_with_mem.shape[1], 1), self.mem_id
-                    ),
-                ),
-                dim=-1,
-            ).view(bsz, -1)
-            input_ids = torch.cat(
-                (prev_input, input_ids_with_mem, input_ids_without_mem), dim=-1
-            )
-            if attention_mask is not None:
-                attention_mask_with_mem, attention_mask_without_mem = torch.split(
-                    attention_mask,
-                    (prev_complete_len + new_full_len, incomplete_len),
-                    dim=-1,
-                )
-                attention_mask_with_mem = attention_mask_with_mem.view(
-                    bsz, -1, self.mem_freq
-                )
-                attention_mask_with_mem = torch.cat(
-                    (
-                        attention_mask_with_mem,
-                        attention_mask_with_mem.new_ones(
-                            (bsz, attention_mask_with_mem.shape[1], 1)
-                        ),
-                    ),
-                    dim=-1,
-                ).view(bsz, -1)
-                attention_mask = torch.cat(
-                    (attention_mask_with_mem, attention_mask_without_mem), dim=-1
-                )
-
-        input_ids = input_ids[:, prev_len:]
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            position_ids = position_ids[:, -input_ids.shape[1] :].unsqueeze(-1)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if (
-            inputs_embeds is not None
-            and past_key_values is None
-            and self.mem_freq is None
-        ):
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-                "offload_cache_to_cpu": kwargs.get("offload_cache_to_cpu"),
-            }
-        )
-        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) for past_state in layer_past
-                ),
-            )
-        return reordered_past
-
-
-def add_mem_tokens(example, mem_freq, mem_id):
-    ids = example["input_ids"]
-    ret = []
-    prev_idx = 0
-    for t_idx in range(mem_freq, len(ids), mem_freq):
-        ret.extend(ids[prev_idx:t_idx])
-        ret.append(mem_id)
-        prev_idx = t_idx
-    ret.extend(ids[prev_idx:])
-    # drop attention_mask
-    return {"input_ids": ret}
-
-
-def patch_llama_with_landmark_attn():
-    import transformers
-
-    transformers.models.llama.modeling_llama.LlamaForCausalLM = LlamaForCausalLM
-    transformers.models.llama.modeling_llama.LlamaModel = LlamaModel
-    transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention
-    transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
-    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb
-
-
-def set_model_mem_id(model: LlamaForCausalLM, tokenizer: LlamaTokenizer):
-    mem_id = tokenizer.convert_tokens_to_ids(MEM_TOKEN)
-    model.set_mem_id(mem_id)
-
-
-def get_mem_id(tokenizer: LlamaTokenizer):
-    return tokenizer.convert_tokens_to_ids(MEM_TOKEN)

src/axolotl/monkeypatch/xpos_rope_llama_monkey_patch.py

@@ -1,94 +0,0 @@
-# pylint: skip-file
-"""
-Copied from https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-"""
-import torch
-import transformers
-import transformers.models.llama.modeling_llama
-from einops import rearrange
-
-
-class XposRotaryEmbedding(torch.nn.Module):
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scale_base=2048,
-        use_xpos=True,
-    ):
-        super().__init__()
-        self.max_seq_len_cached = max_position_embeddings
-        self.scale_base = scale_base
-
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
-        t = torch.arange(self.max_seq_len_cached, device=device).type_as(inv_freq)
-        freqs = torch.einsum("i , j -> i j", t, inv_freq)
-        freqs = torch.cat((freqs, freqs), dim=-1)
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("freqs_cached", freqs, persistent=False)
-
-        if not use_xpos:
-            self.register_buffer("scale", None)
-            self.register_buffer("scale_cached", torch.ones(1))
-            return
-
-        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
-        power = (t - (self.max_seq_len_cached // 2)) / self.scale_base
-        scale_cached = scale ** rearrange(power, "n -> n 1")
-        scale_cached = torch.cat((scale_cached, scale_cached), dim=-1)
-
-        self.register_buffer("scale", scale, persistent=False)
-        self.register_buffer("scale_cached", scale_cached, persistent=False)
-
-    def forward(
-        self,
-        x,
-        seq_len,
-    ):
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device).type_as(
-                self.inv_freq
-            )
-            freqs = torch.einsum("i , j -> i j", t, self.inv_freq)
-            freqs = torch.cat((freqs, freqs), dim=-1).to(dtype=x.dtype)
-
-            self.register_buffer("freqs_cached", freqs)
-
-            if self.scale is None:
-                self.register_buffer(
-                    "scale_cached", torch.ones(1, device=x.device).to(dtype=x.dtype)
-                )
-
-                return self.freqs_cached.to(dtype=x.dtype), self.scale_cached
-
-            power = (t - (seq_len // 2)) / self.scale_base
-            scale = self.scale ** rearrange(power, "n -> n 1")
-            scale = torch.cat((scale, scale), dim=-1).to(dtype=x.dtype)
-            self.register_buffer("scale_cached", scale)
-
-        return self.freqs_cached.to(dtype=x.dtype), self.scale_cached.to(dtype=x.dtype)
-
-
-def rotate_half(x):
-    x1, x2 = x.chunk(2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, freqs, scale=1, position_ids=None):
-    freqs = freqs[position_ids, :]
-    if scale.shape[-1] != 1:
-        scale = scale[position_ids, :]
-
-    q_embed = (q * freqs.cos() * scale) + (rotate_half(q) * freqs.sin() * scale)
-    k_embed = (k * freqs.cos() * 1 / scale) + (rotate_half(k) * freqs.sin() * 1 / scale)
-
-    return q_embed, k_embed
-
-
-def replace_llama_rope_with_xpos_rope():
-    transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = XposRotaryEmbedding
-    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb

src/axolotl/utils/models.py

@@ -247,17 +247,6 @@ def load_model(
 
         LOG.info("patching with sdp attention")
         hijack_llama_sdp_attention()
-    elif cfg.is_llama_derived_model and cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import (
-            MEM_TOKEN,
-            patch_llama_with_landmark_attn,
-        )
-
-        LOG.info("patching with landmark attention")
-        patch_llama_with_landmark_attn()
-
-        # Note: This might overwrite previous additional_special_tokens
-        tokenizer.add_special_tokens({"additional_special_tokens": [MEM_TOKEN]})
 
     if cfg.is_mistral_derived_model and cfg.flash_attention and cfg.sample_packing:
         from axolotl.monkeypatch.mistral_attn_hijack_flash import (
@@ -279,14 +268,6 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mixtral_attn_with_multipack_flash_attn()
 
-    if cfg.is_llama_derived_model and cfg.xpos_rope:
-        from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
-            replace_llama_rope_with_xpos_rope,
-        )
-
-        LOG.info("patching with xpos rope")
-        replace_llama_rope_with_xpos_rope()
-
     if (
         cfg.is_llama_derived_model
         and (cfg.max_packed_sequence_len or cfg.sample_packing)