Instructions to use Spico/LLaMA-MoE-v1-2_8-DynamicSFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Spico/LLaMA-MoE-v1-2_8-DynamicSFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Spico/LLaMA-MoE-v1-2_8-DynamicSFT", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Spico/LLaMA-MoE-v1-2_8-DynamicSFT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Spico/LLaMA-MoE-v1-2_8-DynamicSFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Spico/LLaMA-MoE-v1-2_8-DynamicSFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Spico/LLaMA-MoE-v1-2_8-DynamicSFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Spico/LLaMA-MoE-v1-2_8-DynamicSFT

SGLang

How to use Spico/LLaMA-MoE-v1-2_8-DynamicSFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Spico/LLaMA-MoE-v1-2_8-DynamicSFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Spico/LLaMA-MoE-v1-2_8-DynamicSFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Spico/LLaMA-MoE-v1-2_8-DynamicSFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Spico/LLaMA-MoE-v1-2_8-DynamicSFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Spico/LLaMA-MoE-v1-2_8-DynamicSFT with Docker Model Runner:
```
docker model run hf.co/Spico/LLaMA-MoE-v1-2_8-DynamicSFT
```

LLaMA-MoE-v1-2_8-DynamicSFT / diff.patch

Spico

Upload folder using huggingface_hub

b386a77 verified over 2 years ago

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diff --git a/.gitignore b/.gitignore
index c243024..8c28ce3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -175,6 +175,7 @@ debug.py
 wandb/
 nohup.out
 lm-evaluation-harness/
+bigcode-evaluation-harness/
 results/**/*.json
 results/**/*.jsonl
 results/**/*.db
diff --git a/README.md b/README.md
index 8813a32..b276a78 100644
--- a/README.md
+++ b/README.md
@@ -26,6 +26,11 @@ bash scripts/data.sh
 git clone https://github.com/EleutherAI/lm-evaluation-harness.git
 cd lm-evaluation-harness
 pip install -e .
+# commit: 9cfa52b
+git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
+cd bigcode-evaluation-harness
+# change `pyext==0.5` in `bigcode-evaluation-harness/requirements.txt`, ref: https://github.com/bigcode-project/bigcode-evaluation-harness/pull/181
+pip install -e .
 ```
 
 ## 📃 TODO
diff --git a/scripts/eval.sh b/scripts/eval.sh
deleted file mode 100644
index 4f41b37..0000000
--- a/scripts/eval.sh
+++ /dev/null
@@ -1,96 +0,0 @@
-# nohup srun -p MoE --gres gpu:1 bash scripts/eval.sh all /mnt/petrelfs/share_data/quxiaoye/models/Sheared-LLaMA-2.7B True results/Sheared-LLaMA-2.7B 1>logs/eval-all-Sheared-LLaMA-2.7B.log 2>&1 &
-
-mmlu() {
-    # MMLU: https://github.com/princeton-nlp/LLM-Shearing/blob/20ebd2645a8ff5fa65874e1347f9891b80e01805/icl_eval/run_eval.sh#L18
-    MODEL=$1
-    TRUST_REMOTE_CODE=$2
-    RESULT_DIR=$3
-    mkdir -p $RESULT_DIR
-
-    lm_eval \
-        --model hf \
-        --model_args pretrained=$MODEL,trust_remote_code=$TRUST_REMOTE_CODE \
-        --tasks mmlu_computer_security,mmlu_high_school_chemistry,mmlu_philosophy,mmlu_elementary_mathematics,mmlu_prehistory,mmlu_formal_logic,mmlu_high_school_mathematics,mmlu_econometrics,mmlu_moral_scenarios,mmlu_college_mathematics,mmlu_high_school_government_and_politics,mmlu_us_foreign_policy,mmlu_high_school_world_history,mmlu_conceptual_physics,mmlu_college_medicine,mmlu_international_law,mmlu_abstract_algebra,mmlu_logical_fallacies,mmlu_machine_learning,mmlu_medical_genetics,mmlu_public_relations,mmlu_college_biology,mmlu_marketing,mmlu_electrical_engineering,mmlu_anatomy,mmlu_high_school_us_history,mmlu_high_school_biology,mmlu_miscellaneous,mmlu_high_school_psychology,mmlu_sociology,mmlu_business_ethics,mmlu_high_school_geography,mmlu_human_aging,mmlu_high_school_statistics,mmlu_moral_disputes,mmlu_professional_psychology,mmlu_global_facts,mmlu_college_physics,mmlu_nutrition,mmlu_high_school_macroeconomics,mmlu_world_religions,mmlu_professional_medicine,mmlu_high_school_computer_science,mmlu_college_chemistry,mmlu_human_sexuality,mmlu_high_school_microeconomics,mmlu_astronomy,mmlu_professional_accounting,mmlu_high_school_european_history,mmlu_jurisprudence,mmlu_professional_law,mmlu_high_school_physics,mmlu_virology,mmlu_management,mmlu_college_computer_science,mmlu_clinical_knowledge,mmlu_security_studies \
-        --num_fewshot 5 \
-        --device cuda:0 \
-        --batch_size auto \
-        --verbosity DEBUG \
-        --output_path $RESULT_DIR/mmlu.json
-}
-
-bbh() {
-    # Big Bench Hard (BBH): https://arxiv.org/pdf/2210.09261.pdf
-    MODEL=$1
-    TRUST_REMOTE_CODE=$2
-    RESULT_DIR=$3
-    mkdir -p $RESULT_DIR
-
-    lm_eval \
-        --log_samples \
-        --model hf \
-        --model_args pretrained=$MODEL,trust_remote_code=$TRUST_REMOTE_CODE \
-        --tasks bbh_fewshot_boolean_expressions,bbh_fewshot_causal_judgement,bbh_fewshot_date_understanding,bbh_fewshot_disambiguation_qa,bbh_fewshot_dyck_languages,bbh_fewshot_formal_fallacies,bbh_fewshot_geometric_shapes,bbh_fewshot_hyperbaton,bbh_fewshot_logical_deduction_five_objects,bbh_fewshot_logical_deduction_seven_objects,bbh_fewshot_logical_deduction_three_objects,bbh_fewshot_movie_recommendation,bbh_fewshot_multistep_arithmetic_two,bbh_fewshot_navigate,bbh_fewshot_object_counting,bbh_fewshot_penguins_in_a_table,bbh_fewshot_reasoning_about_colored_objects,bbh_fewshot_ruin_names,bbh_fewshot_salient_translation_error_detection,bbh_fewshot_snarks,bbh_fewshot_sports_understanding,bbh_fewshot_temporal_sequences,bbh_fewshot_tracking_shuffled_objects_five_objects,bbh_fewshot_tracking_shuffled_objects_seven_objects,bbh_fewshot_tracking_shuffled_objects_three_objects,bbh_fewshot_web_of_lies,bbh_fewshot_word_sorting \
-        --device cuda:0 \
-        --batch_size auto \
-        --verbosity DEBUG \
-        --output_path $RESULT_DIR/bbh.json
-}
-
-reasoning() {
-    MODEL=$1
-    TRUST_REMOTE_CODE=$2
-    RESULT_DIR=$3
-    mkdir -p $RESULT_DIR
-
-    lm_eval \
-        --log_samples \
-        --model hf \
-        --model_args pretrained=$MODEL,trust_remote_code=$TRUST_REMOTE_CODE \
-        --tasks gsm8k_cot \
-        --device cuda:0 \
-        --batch_size auto \
-        --verbosity DEBUG \
-        --output_path $RESULT_DIR/reasoning.json
-}
-
-qa() {
-    MODEL=$1
-    TRUST_REMOTE_CODE=$2
-    RESULT_DIR=$3
-    mkdir -p $RESULT_DIR
-
-    lm_eval \
-        --log_samples \
-        --model hf \
-        --model_args pretrained=$MODEL,trust_remote_code=$TRUST_REMOTE_CODE \
-        --tasks arc_easy,arc_challenge,boolq \
-        --num_fewshot 0 \
-        --device cuda:0 \
-        --batch_size auto \
-        --verbosity DEBUG \
-        --output_path $RESULT_DIR/qa.json
-}
-
-EVAL_TASK=$1
-shift 1
-start=$(date +%s)
-case $EVAL_TASK in
-    mmlu)
-        mmlu $* ;;
-    bbh)
-        bbh $* ;;
-    reasoning)
-        reasoning $* ;;
-    qa)
-        qa $* ;;
-    all)
-        mmlu $*
-        bbh $*
-        reasoning $*
-        qa $*
-        ;;
-    *)
-        echo "$EVAL_TASK not recognized!";;
-esac
-end=$(date +%s)
-echo "Elapsed Time: $(($end-$start)) seconds"
diff --git a/scripts/four_mix/freeze_gate.sh b/scripts/four_mix/freeze_gate.sh
index d94d78c..70afb8e 100644
--- a/scripts/four_mix/freeze_gate.sh
+++ b/scripts/four_mix/freeze_gate.sh
@@ -83,8 +83,11 @@ num_gpus=4
 
     python -m src.eval.gen_mt_ans \
         --model-path $output_dir \
-        --model-id $task_name \
-        --num-gpus-total $num_gpus
+        --model-id $task_name
+
+    python -m src.eval.gen_alpaca_eval_ans \
+        --model-path $output_dir \
+        --model-id $task_name
 }
 
 # nohup srun -p MoE --ntasks-per-node=1 --cpus-per-task=16 --mem=128G --nodes=1 --gres=gpu:4 bash "/mnt/petrelfs/zhutong/adaptive-sft-for-moe/scripts/one_data_steps_dynamic.sh" "llama_moe_orca_epochs_cluster_4" "auto" "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new" "data/open_orca_clustered/4" "data/open_orca_clustered_eval/4" 1>logs/llama_moe_orca_cluster_4_dynamic.log 2>&1 &
diff --git a/scripts/gen_mt_bench_ans.sh b/scripts/gen_mt_bench_ans.sh
deleted file mode 100644
index f251644..0000000
--- a/scripts/gen_mt_bench_ans.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/bash
-
-#SBATCH --job-name=moe_gen
-#SBATCH --output=logs/%x-%j.log
-#SBATCH --error=logs/%x-%j.log
-
-#SBATCH --partition=MoE
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=16
-#SBATCH --mem=64G
-
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --quotatype=auto
-
-{
-    # python -m fastchat.llm_judge.gen_model_answer \
-    #     --model-path outputs/sheared_llama_sharegpt/moe_sft-2411306 \
-    #     --model-id sheared_llama_sharegpt
-
-    # python -m fastchat.llm_judge.gen_model_answer \
-    #     --model-path outputs/sheared_llama_uniform_mix/moe_sft-2421072 \
-    #     --model-id sheared_llama_uniform_mix
-
-    bash scripts/cp_model_files.sh outputs/llama_moe/moe_sft-2409782
-    python -m fastchat.llm_judge.gen_model_answer \
-        --model-path outputs/llama_moe/moe_sft-2409782 \
-        --model-id llama_moe_uniform_mix
-}
-
-# nohup srun -p MoE -n1 -N1 --gres=gpu:1 --quotatype spot python -m fastchat.llm_judge.gen_model_answer --model-path outputs/sheared_llama_sharegpt/moe_sft-2411306 --model-id sheared_llama_sharegpt 1>logs/mt_bench_gen_sheared_llama_sharegpt.log 2>&1 &
-# nohup srun -p MoE -n1 -N1 --gres=gpu:1 --quotatype spot python -m fastchat.llm_judge.gen_model_answer --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/llama_moe_sharegpt/moe_sft-2411309 --model-id llama_moe_sharegpt 1>logs/mt_bench_gen_llama_moe_sharegpt.log 2>&1 &
diff --git a/scripts/multi.sh b/scripts/multi.sh
index bcd83b8..e399761 100644
--- a/scripts/multi.sh
+++ b/scripts/multi.sh
@@ -100,5 +100,8 @@ nohup srun -p MoE --ntasks-per-node=1 --cpus-per-task=16 --mem=128G --nodes=1 --
 nohup srun -p MoE --gres gpu:1 python -m src.eval.gen_mt_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/llama_moe_four_mix_uniform/bash-2485396 --model-id llama_moe_four_mix_uniform 1>logs/gen_mt_ans-llama_moe_four_mix_uniform.log 2>&1 &
 nohup srun -p MoE --gres gpu:1 python -m src.eval.gen_mt_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/sheared_four_mix_uniform/bash-2485397 --model-id sheared_four_mix_uniform 1>logs/gen_mt_ans-sheared_four_mix_uniform.log 2>&1 &
 
-nohup srun -p MoE --gres gpu:1 python -m src.eval.get_alpaca_eval_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/llama_moe_four_mix_uniform/bash-2485396 --model-id llama_moe_four_mix_uniform 1>logs/gen_alpaca_eval-llama_moe_four_mix_uniform.log 2>&1 &
-nohup srun -p MoE --gres gpu:1 python -m src.eval.get_alpaca_eval_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/sheared_four_mix_uniform/bash-2485397 --model-id sheared_four_mix_uniform 1>logs/gen_alpaca_eval-sheared_four_mix_uniform.log 2>&1 &
+nohup srun -p MoE --gres gpu:1 python -m src.eval.gen_alpaca_eval_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/llama_moe_four_mix_uniform/bash-2485396 --model-id llama_moe_four_mix_uniform 1>logs/gen_alpaca_eval-llama_moe_four_mix_uniform.log 2>&1 &
+nohup srun -p MoE --gres gpu:1 python -m src.eval.gen_alpaca_eval_ans --model-path /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048/sheared_four_mix_uniform/bash-2485397 --model-id sheared_four_mix_uniform 1>logs/gen_alpaca_eval-sheared_four_mix_uniform.log 2>&1 &
+
+nohup srun -p MoE --gres gpu:1 bash scripts/eval/eval.sh reasoning /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad_wo_gate_noise/moe_sft-2492650 True results/llama_moe_four_mix_wo_pad_wo_gate_noise 1>logs/eval-reasoning-llama_moe_four_mix_wo_pad_wo_gate_noise.log 2>&1 &
+nohup srun -p MoE --gres gpu:1 bash scripts/eval/eval.sh reasoning /mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad/moe_sft-2491633 True results/llama_moe_four_mix_wo_pad 1>logs/eval-reasoning-llama_moe_four_mix_wo_pad.log 2>&1 &
diff --git a/src/callbacks.py b/src/callbacks.py
index a750f69..e9d0c04 100644
--- a/src/callbacks.py
+++ b/src/callbacks.py
@@ -6,6 +6,7 @@ import torch
 import numpy as np
 from loguru import logger
 from transformers.trainer_callback import TrainerCallback, TrainerState, TrainerControl
+from transformers.utils import is_flash_attn_2_available
 
 from src.utils.config import TrainingArguments
 from src.utils.io import append_jsonlines
@@ -22,6 +23,7 @@ class AdaptiveSamplingCallback(TrainerCallback):
         criterion: Optional[Literal["min", "max", "mean"]] = "mean",
         sim_type: Optional[Literal["cos", "l2"]] = "cos",
     ):
+        assert is_flash_attn_2_available(), "Make sure you have flash-attn installed"
         self.criterion = criterion
         self.sim_type = sim_type
         self.prob_map = {}
@@ -74,8 +76,8 @@ class AdaptiveSamplingCallback(TrainerCallback):
         cls,
         ori_weights: np.ndarray,
         delta: np.ndarray,
-        eta: float = 1.0,
-        c: float = 1e-4,
+        eta: float = 10.0,
+        c: float = 5e-2,
     ) -> np.ndarray:
         def _softmax(vec: np.ndarray) -> np.ndarray:
             exps = np.exp(vec - np.max(vec))
diff --git a/src/core/train.py b/src/core/train.py
index 2be5558..9b1f694 100644
--- a/src/core/train.py
+++ b/src/core/train.py
@@ -7,13 +7,12 @@ from loguru import logger
 from src.utils.config import ModelArguments, DataArguments, TrainingArguments
 from src.data import (
     SubDirWeightedPackedJsonlDataset,
-    get_uniform_sampling_ratio,
     fault_tolerance_data_collator,
     CachedJsonlDataset,
     get_cached_datasets_from_dir,
 )
 from src.utils.io import trainer_save_model_safe
-from src.models import LlamaMoEForCausalLM, LlamaMoEConfig
+from src.models import LlamaMoEForCausalLM, LlamaMoEConfig, DeepseekConfig, DeepseekForCausalLM
 from src.trainer import GateLoadRecordingTrainer
 from src.callbacks import AdaptiveSamplingCallback
 
@@ -36,6 +35,9 @@ def get_model_and_tokenizer(
     elif model_type == "llama_moe":
         ConfigClass = LlamaMoEConfig
         ModelClass = LlamaMoEForCausalLM
+    elif model_type == "deepseek":
+        ConfigClass = DeepseekConfig
+        ModelClass = DeepseekForCausalLM
     else:
         raise ValueError(f"Unknown model type: {model_type}")
 
@@ -54,6 +56,21 @@ def get_model_and_tokenizer(
         config.update(additional_config)
     logger.info("Config ready")
 
+    tokenizer = transformers.AutoTokenizer.from_pretrained(
+        model_name_or_path,
+        cache_dir=cache_dir,
+        model_max_length=model_max_length,
+        padding_side=padding_side,
+        use_fast=False,
+        trust_remote_code=trust_remote_code,
+    )
+    if tokenizer.pad_token is None:
+        if tokenizer.unk_token is not None:
+            tokenizer.pad_token = tokenizer.unk_token
+        else:
+            tokenizer.pad_token = tokenizer.eos_token
+    logger.info(f"tokenizer ready, pad_token: {tokenizer.pad_token}")
+
     # Load model and tokenizer
     model = ModelClass.from_pretrained(
         model_name_or_path,
@@ -65,18 +82,6 @@ def get_model_and_tokenizer(
     )
     logger.info("model ready")
 
-    tokenizer = transformers.AutoTokenizer.from_pretrained(
-        model_name_or_path,
-        cache_dir=cache_dir,
-        model_max_length=model_max_length,
-        padding_side=padding_side,
-        use_fast=False,
-        trust_remote_code=trust_remote_code,
-    )
-    if tokenizer.pad_token != tokenizer.unk_token:
-        tokenizer.pad_token = tokenizer.unk_token
-    logger.info("tokenizer ready")
-
     return model, tokenizer
 
 
@@ -117,7 +122,9 @@ def train():
         train_dataset = SubDirWeightedPackedJsonlDataset(
             data_args.dataset_dir_or_path,
             tokenizer,
-            prob_map=get_uniform_sampling_ratio(data_args.dataset_dir_or_path),
+            # prob_map=get_uniform_sampling_ratio(data_args.dataset_dir_or_path),
+            # prob_map={"code": 0.25119094959816823, "math": 0.2674581878910902, "orca": 0.243050776175138, "sharegpt": 0.23830008633560357},
+            prob_map=data_args.prob_map,
             seed=training_args.seed,
         )
     elif datapath.is_file():
diff --git a/src/data.py b/src/data.py
index d783a21..a1a8ff7 100644
--- a/src/data.py
+++ b/src/data.py
@@ -20,6 +20,7 @@ def preprocess(
     instances,
     tokenizer: transformers.PreTrainedTokenizer,
 ) -> Dict:
+    tokenizer_legacy = getattr(tokenizer, "legacy", None)
     conv = Conversation()
     roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
 
@@ -72,7 +73,7 @@ def preprocess(
             # "-2" is hardcoded for the Llama tokenizer to make the offset correct.
             instruction_len = len(tokenizer(parts[0]).input_ids) - 2
 
-            if i != 0 and not tokenizer.legacy:
+            if i != 0 and not tokenizer_legacy:
                 # The legacy and non-legacy modes handle special tokens differently
                 instruction_len -= 1
 
@@ -80,7 +81,7 @@ def preprocess(
             target[cur_len : cur_len + instruction_len] = IGNORE_TOKEN_ID
             cur_len += turn_len
 
-            if i != 0 and not tokenizer.legacy:
+            if i != 0 and not tokenizer_legacy:
                 # The legacy and non-legacy modes handle special tokens differently
                 cur_len -= 1
 
diff --git a/src/eval/get_alpaca_eval_ans.py b/src/eval/get_alpaca_eval_ans.py
deleted file mode 100644
index 1ff3e5e..0000000
--- a/src/eval/get_alpaca_eval_ans.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import argparse
-from pathlib import Path
-
-import torch
-import datasets
-from tqdm import tqdm
-
-from src.core.train import get_model_and_tokenizer
-from src.utils.conversation import Conversation
-from src.utils.io import dump_json
-
-
-@torch.inference_mode()
-def run_eval(model_path, model_id, max_new_tokens):
-    model, tokenizer = get_model_and_tokenizer(
-        "auto",
-        model_path,
-        torch_dtype=torch.bfloat16,
-        trust_remote_code=True,
-    )
-    model.cuda()
-    model.eval()
-
-    conv = Conversation()
-    outputs = []
-    eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval")["eval"]
-    for example in tqdm(eval_set, desc="Eval"):
-        conv.append_message(conv.roles[0], example["instruction"])
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-        input_ids = tokenizer([prompt], return_tensors="pt").input_ids
-        conv.clear_msg()
-        # generate here is a placeholder for your models generations
-        output_ids = model.generate(
-            input_ids.cuda(),
-            do_sample=False,
-            temperature=0.0,
-            max_new_tokens=max_new_tokens,
-        )
-        if model.config.is_encoder_decoder:
-            output_ids = output_ids[0]
-        else:
-            output_ids = output_ids[0][len(input_ids[0]) :]  # noqa: E203
-        # be consistent with the template's stop_token_ids
-        if conv.stop_token_ids:
-            stop_token_ids_index = [
-                i
-                for i, id in enumerate(output_ids)
-                if id in conv.stop_token_ids
-            ]
-            if len(stop_token_ids_index) > 0:
-                output_ids = output_ids[: stop_token_ids_index[0]]
-
-        output = tokenizer.decode(
-            output_ids,
-            spaces_between_special_tokens=False,
-        )
-        if conv.stop_str and isinstance(conv.stop_str, list):
-            stop_str_indices = sorted(
-                [
-                    output.find(stop_str)
-                    for stop_str in conv.stop_str
-                    if output.find(stop_str) > 0
-                ]
-            )
-            if len(stop_str_indices) > 0:
-                output = output[: stop_str_indices[0]]
-        elif conv.stop_str and output.find(conv.stop_str) > 0:
-            output = output[: output.find(conv.stop_str)]
-
-        for special_token in tokenizer.special_tokens_map.values():
-            if isinstance(special_token, list):
-                for special_tok in special_token:
-                    output = output.replace(special_tok, "")
-            else:
-                output = output.replace(special_token, "")
-        output = output.strip()
-
-        if conv.name == "xgen" and output.startswith("Assistant:"):
-            output = output.replace("Assistant:", "", 1).strip()
-
-        example["output"] = output
-        outputs.append(example)
-
-    outpath = Path("results/alpaca_eval") / f"{model_id}.json"
-    dump_json(outputs, outpath, indent=2)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model-path",
-        type=str,
-        required=True,
-        help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
-    )
-    parser.add_argument(
-        "--model-id", type=str, required=True, help="A custom name for the model."
-    )
-    parser.add_argument(
-        "--max-new-token",
-        type=int,
-        default=1024,
-        help="The maximum number of new generated tokens.",
-    )
-
-    args = parser.parse_args()
-
-    run_eval(
-        model_path=args.model_path,
-        model_id=args.model_id,
-        max_new_tokens=args.max_new_token,
-    )
diff --git a/src/eval/show.py b/src/eval/show.py
index d500054..ea0c210 100644
--- a/src/eval/show.py
+++ b/src/eval/show.py
@@ -55,13 +55,13 @@ def collect_results(result_dir: str, verbose: bool = True) -> dict:
             avg = sum(vals) / len(vals)
         tot_vals.append(avg)
         if verbose:
-            logger.info(f"task: {name}, num: {len(tasks.split(','))}, avg: {avg:.3%}")
+            logger.info(f"task: {name}, num: {len(tasks.split(','))}, avg: {100 * avg:.3f} %")
 
     if len(tot_vals) == 0:
         tot_avg = 0.0
     else:
         tot_avg = sum(tot_vals) / len(tot_vals)
-    logger.info(f"total avg: {tot_avg:.3%}")
+    logger.info(f"total avg: {100 * tot_avg:.3f} %")
 
 
 if __name__ == "__main__":
diff --git a/src/models/deepseek/modeling_deepseek.py b/src/models/deepseek/modeling_deepseek.py
index 1dae56e..20498b2 100644
--- a/src/models/deepseek/modeling_deepseek.py
+++ b/src/models/deepseek/modeling_deepseek.py
@@ -20,6 +20,7 @@
 """ PyTorch DeepSeek model."""
 import math
 import warnings
+from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -297,7 +298,7 @@ class DeepseekMLP(nn.Module):
         self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
         self.act_fn = ACT2FN[config.hidden_act]
 
-    def forward(self, x):
+    def forward(self, x, **kwargs):
         if self.config.pretraining_tp > 1:
             slice = self.intermediate_size // self.config.pretraining_tp
             gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
@@ -328,7 +329,9 @@ class DeepseekMLP(nn.Module):
         else:
             down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
 
-        return down_proj
+        bsz, seq_len, _ = x.shape
+        load = torch.zeros(bsz * seq_len, self.config.n_routed_experts)
+        return down_proj, load
 
 
 class MoEGate(nn.Module):
@@ -356,7 +359,10 @@ class MoEGate(nn.Module):
         init.kaiming_uniform_(self.weight, a=math.sqrt(5))
 
     def forward(self, hidden_states):
-        bsz, seq_len, h = hidden_states.shape
+        if len(hidden_states.shape) == 2:
+            bsz, h = hidden_states.shape
+        else:
+            bsz, seq_len, h = hidden_states.shape
         ### compute gating score
         hidden_states = hidden_states.view(-1, h)
         logits = F.linear(hidden_states, self.weight, None)
@@ -404,7 +410,10 @@ class MoEGate(nn.Module):
                 aux_loss = (Pi * fi).sum() * self.alpha
         else:
             aux_loss = None
-        return topk_idx, topk_weight, aux_loss
+        _zeros = torch.zeros_like(logits)
+        _scores_filtered = _zeros.scatter(dim=1, index=topk_idx, src=topk_weight)
+        load = (_scores_filtered > 0).sum(0)
+        return topk_idx, topk_weight, aux_loss, load
 
 
 class AddAuxiliaryLoss(torch.autograd.Function):
@@ -450,10 +459,19 @@ class DeepseekMoE(nn.Module):
                 config=config, intermediate_size=intermediate_size
             )
 
-    def forward(self, hidden_states):
+    def forward(self, hidden_states, attention_mask=None):
+        bsz, seq_len, hsz = hidden_states.shape
+        hidden_states = hidden_states.reshape(-1, hsz)
+        flattened_mask = None
+        flattened_shape = None
+        if attention_mask is not None and len(attention_mask.shape) == 2:
+            flattened_mask = attention_mask.flatten()
+            flattened_shape = flattened_mask.shape
+            hidden_states = hidden_states[flattened_mask.bool()]
+
         identity = hidden_states
         orig_shape = hidden_states.shape
-        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+        topk_idx, topk_weight, aux_loss, load = self.gate(hidden_states)
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         flat_topk_idx = topk_idx.view(-1)
         if self.training:
@@ -472,7 +490,15 @@ class DeepseekMoE(nn.Module):
             ).view(*orig_shape)
         if self.config.n_shared_experts is not None:
             y = y + self.shared_experts(identity)
-        return y
+
+        if flattened_mask is not None:
+            _y = torch.zeros(flattened_shape + (hsz,), dtype=y.dtype, device=y.device)
+            _y[flattened_mask.bool()] = y
+            y = _y
+
+        y = y.reshape(bsz, seq_len, hsz)
+
+        return y, load
 
     @torch.no_grad()
     def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
@@ -1163,7 +1189,7 @@ class DeepseekDecoderLayer(nn.Module):
         # Fully Connected
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
+        hidden_states, load = self.mlp(hidden_states, attention_mask=attention_mask)
         hidden_states = residual + hidden_states
 
         outputs = (hidden_states,)
@@ -1174,6 +1200,8 @@ class DeepseekDecoderLayer(nn.Module):
         if use_cache:
             outputs += (present_key_value,)
 
+        outputs += (load,)
+
         return outputs
 
 
@@ -1220,6 +1248,11 @@ class DeepseekPreTrainedModel(PreTrainedModel):
                 module.weight.data[module.padding_idx].zero_()
 
 
+@dataclass
+class BaseMoEModelOutputWithPast(BaseModelOutputWithPast):
+    gate_load: Optional[torch.Tensor] = None
+
+
 Deepseek_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -1429,6 +1462,7 @@ class DeepseekModel(DeepseekPreTrainedModel):
         # decoder layers
         all_hidden_states = () if output_hidden_states else None
         all_self_attns = () if output_attentions else None
+        gate_load = ()
         next_decoder_cache = None
 
         for decoder_layer in self.layers:
@@ -1463,6 +1497,8 @@ class DeepseekModel(DeepseekPreTrainedModel):
             if output_attentions:
                 all_self_attns += (layer_outputs[1],)
 
+            gate_load += (layer_outputs[-1],)
+
         hidden_states = self.norm(hidden_states)
 
         # add hidden states from the last decoder layer
@@ -1482,14 +1518,20 @@ class DeepseekModel(DeepseekPreTrainedModel):
                 for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
                 if v is not None
             )
-        return BaseModelOutputWithPast(
+        return BaseMoEModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=next_cache,
             hidden_states=all_hidden_states,
             attentions=all_self_attns,
+            gate_load=gate_load,
         )
 
 
+@dataclass
+class MoECausalLMOutputWithPast(CausalLMOutputWithPast):
+    gate_load: Optional[torch.Tensor] = None
+
+
 class DeepseekForCausalLM(DeepseekPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
@@ -1620,12 +1662,13 @@ class DeepseekForCausalLM(DeepseekPreTrainedModel):
             output = (logits,) + outputs[1:]
             return (loss,) + output if loss is not None else output
 
-        return CausalLMOutputWithPast(
+        return MoECausalLMOutputWithPast(
             loss=loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
+            gate_load=outputs.gate_load,
         )
 
     def prepare_inputs_for_generation(
diff --git a/src/utils/config.py b/src/utils/config.py
index 3ea5283..d4060d9 100644
--- a/src/utils/config.py
+++ b/src/utils/config.py
@@ -6,6 +6,7 @@ import torch
 import transformers
 
 from src.utils.io import load_json
+from src.data import get_uniform_sampling_ratio
 
 
 @dataclass
@@ -33,7 +34,9 @@ class ModelArguments:
     )
     attn_impl: str = field(
         default="flash_attention_2",
-        metadata={"help": "attention implementation, choice from [eager, flash_attention_2, sdpa] (default: `flash_attention_2`)"}
+        metadata={
+            "help": "attention implementation, choice from [eager, flash_attention_2, sdpa] (default: `flash_attention_2`)"
+        },
     )
 
     def __post_init__(self):
@@ -56,6 +59,18 @@ class DataArguments:
         default="data/merged",
         metadata={"help": "Path to dataset directory or a single jsonl file"},
     )
+    prob_map: str = field(
+        default=None,
+        metadata={"help": "Path to the probability map file"},
+    )
+
+    def __post_init__(self):
+        if self.prob_map is not None:
+            if not pathlib.Path(self.prob_map).exists():
+                raise ValueError(f"Probability map file {self.prob_map} not found")
+            self.prob_map = load_json(self.prob_map)
+        else:
+            self.prob_map = get_uniform_sampling_ratio(self.dataset_dir_or_path)
 
 
 @dataclass
@@ -70,9 +85,7 @@ class TrainingArguments(transformers.TrainingArguments):
     )
     max_eval_steps_per_type: int = field(
         default=10,
-        metadata={
-            "help": "Maximum number of steps to perform during evaluation."
-        },
+        metadata={"help": "Maximum number of steps to perform during evaluation."},
     )
     dynamic_sampling_sim_type: Literal["cos", "l2"] = field(
         default="l2",
@@ -88,7 +101,5 @@ class TrainingArguments(transformers.TrainingArguments):
     )
     freeze_gate: bool = field(
         default=False,
-        metadata={
-            "help": "Whether to freeze the gate during training."
-        },
+        metadata={"help": "Whether to freeze the gate during training."},
     )
diff --git a/src/utils/visualization.py b/src/utils/visualization.py
index 794f6c8..02bd236 100644
--- a/src/utils/visualization.py
+++ b/src/utils/visualization.py
@@ -180,6 +180,86 @@ def gate_load_stats(model_dir, data_dir, result_dir, update_strategy: str = "cos
         )
 
 
+def sampling_info_stats(filepath: str, data_type: str, output_dir: str):
+    from pathlib import Path
+    import numpy as np
+    from src.utils.io import load_jsonlines
+
+    Path(output_dir).mkdir(exist_ok=True, parents=True)
+
+    data = load_jsonlines(filepath)
+    step2data = {ins["step"]: ins for ins in data}
+
+    data_types = sorted(data[0]["old_prob_map"].keys())
+    data_type_idx = data_types.index(data_type)
+
+    probs = []
+    loads = []
+    sims = []
+    steps = sorted(step2data.keys())
+    for step in steps:
+        ins = step2data[step]
+        probs.append(ins["old_prob_map"][data_type])
+        loads.append(ins["name2load"][data_type])
+        sims.append(ins["sim"][data_type_idx])
+
+    # probs
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(steps, probs)
+    ax.set_title(f"Sampling Probability of {data_type}")
+    ax.set_xlabel("step")
+    fig.savefig(f"{output_dir}/prob-{data_type}.png")
+
+    # loads
+    def cv_square(data):
+        return np.var(data, axis=1) / (np.mean(data, axis=1)**2 + 1e-10)
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(steps, cv_square(loads))
+    ax.set_title(f"cv(load)^2 of {data_type}")
+    ax.set_xlabel("step")
+    fig.savefig(f"{output_dir}/load_cv-{data_type}.png")
+
+    # sims
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(steps, np.mean(sims, axis=1))
+    ax.set_title(f"Mean Similarities with {data_type}")
+    ax.set_xlabel("step")
+    fig.savefig(f"{output_dir}/sim-{data_type}.png")
+
+
+def test_sampling_convergence():
+    from collections import defaultdict
+    from src.callbacks import AdaptiveSamplingCallback
+
+    # freeze gate
+    name2load = {"code": [0.1359794776119403, 0.1333115671641791, 0.12858208955223882, 0.10330223880597016, 0.12544776119402984, 0.12625932835820897, 0.12761194029850748, 0.11950559701492537], "orca": [0.1509941502743006, 0.11721425756978752, 0.1232988815809414, 0.12714439426545024, 0.11256554420634679, 0.14008274482465977, 0.11819552632376563, 0.11050450095474797], "math": [0.15956486572028086, 0.10727138452881943, 0.11506675888262392, 0.10958069091633744, 0.11805010139847842, 0.11915200393871546, 0.13648938539627462, 0.13482480921846976], "sharegpt": [0.15337086599959998, 0.11428233411553493, 0.12873151621889287, 0.1177436980734424, 0.11538123789498336, 0.13793986642403783, 0.12419686111124664, 0.10835362016226212]}  # fmt: skip
+    # # dynamic
+    # name2load = {"code": [0.14031716417910448, 0.1310634328358209, 0.12651119402985075, 0.10993470149253731, 0.12196828358208955, 0.12552238805970148, 0.12791977611940297, 0.11676305970149255], "orca": [0.15106234655836084, 0.11803640166095838, 0.12349968175067437, 0.12884551268450883, 0.11344072985178673, 0.1383778377231534, 0.11733170672566907, 0.1094057830448883], "math": [0.16001617686708006, 0.10756444371505268, 0.11391210568886491, 0.114803005615014, 0.11676650216277679, 0.1177863481308685, 0.13630182751708533, 0.13284959030325763], "sharegpt": [0.15440024978412215, 0.113654214863131, 0.12914741653941664, 0.12104040941178769, 0.11470799162832905, 0.13593110446537907, 0.12316259873058931, 0.10795601457724527]}  # fmt: skip
+    names = sorted(name2load.keys())
+    callback = AdaptiveSamplingCallback()
+    callback.prob_map = {"code": 0.25, "math": 0.25, "orca": 0.25, "sharegpt": 0.25}
+    name2probs = defaultdict(list)
+    for _ in range(100):
+        for name in names:
+            name2probs[name].append(callback.prob_map[name])
+        new_name2prob, _ = callback._update_prob_map(name2load)
+        callback.prob_map = new_name2prob
+    print(f"final prob_map: {callback.prob_map}")
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    for name in names:
+        ax.plot(name2probs[name], label=name)
+    ax.legend()
+    ax.set_title("Sampling Probability")
+    ax.set_xlabel("step")
+    fig.savefig("results/sampling_convergence.png")
+
+
 if __name__ == "__main__":
     # gate_load_stats(
     #     "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new",
@@ -195,12 +275,12 @@ if __name__ == "__main__":
     #     "results/gate_load_vis_llama_moe_2_8_orca_4clusters",
     # )
 
-    gate_load_stats(
-        "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new",
-        "data/four_types_mix/dev",
-        "results/debug",
-        update_strategy="l2",
-    )
+    # gate_load_stats(
+    #     "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new",
+    #     "data/four_types_mix/dev",
+    #     "results/debug",
+    #     update_strategy="l2",
+    # )
 
     # gate_load_stats(
     #     "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new",
@@ -227,3 +307,29 @@ if __name__ == "__main__":
     #     "results/gate_load_vis_llama_moe_2_8_four_types_mix_l2",
     #     update_strategy="l2"
     # )
+
+    # sampling_info_stats(
+    #     "/mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad_freeze_gate/moe_sft-2491632/sampling_info/data.jsonl",
+    #     "code",
+    #     "results/sampling_info/llama_moe_four_mix_wo_pad_freeze_gate/code",
+    # )
+
+    # sampling_info_stats(
+    #     "/mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad/moe_sft-2491633/sampling_info/data.jsonl",
+    #     "code",
+    #     "results/sampling_info/llama_moe_four_mix_wo_pad/code",
+    # )
+
+    # sampling_info_stats(
+    #     "/mnt/petrelfs/zhutong/adaptive-sft-for-moe/outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad_freeze_gate_wo_gate_noise/moe_sft-2493315/sampling_info/data.jsonl",
+    #     "code",
+    #     "results/sampling_info/llama_moe_four_mix_wo_pad_freeze_gate_wo_gate_noise/code",
+    # )
+
+    # sampling_info_stats(
+    #     "outputs/len2048_dynamic_remove_padding_tokens/llama_moe_four_mix_wo_pad_wo_gate_noise/moe_sft-2492650/sampling_info/data.jsonl",
+    #     "code",
+    #     "results/sampling_info/llama_moe_four_mix_wo_pad_wo_gate_noise/code",
+    # )
+
+    test_sampling_convergence()