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remove landmark attn and xpos rope implementations #1010

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Dec 28, 2023
Merged

remove landmark attn and xpos rope implementations #1010

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5 changes: 0 additions & 5 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
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16 changes: 0 additions & 16 deletions src/axolotl/cli/__init__.py
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Expand Up @@ -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:
Expand Down Expand Up @@ -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):
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22 changes: 1 addition & 21 deletions src/axolotl/core/trainer_builder.py
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Expand Up @@ -9,7 +9,7 @@
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

Expand Down Expand Up @@ -780,26 +780,6 @@ def build(self, total_num_steps):
# 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
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