Mismatch about the evaluation results

[yuzc19]

Hi,

Thanks for your great work!

I am trying to reproduce the results in pythia-160m-zero-shot using lit-gpt repo. However, while the results of step13000 checkpoint match well with your repo. The results of step143000 checkpoint fall behind the numbers in your repo by large margins. For example, it can only achieve a 0.121 accuracy on Lambada (OpenAI) from my test, while the number is 0.328 in your repo. Why is that?

From the paper, I think 143000 steps equal one epoch (also the same as the main branch in the huggingface). Am I correct?

Thank you!

[uSaiPrashanth]

Could you try evaluating the model with lm-eval-harness and share the resultant json here?

These are the resultant jsons directly uploaded from this script And I am confident with the results updated.

[yuzc19]

Yeah, I am evaluating model with lm-eval-harness and this is my output:

"results": {
"piqa": {
"acc": 0.5903155603917302,
"acc_stderr": 0.0,
"acc_norm": 0.5903155603917302,
"acc_norm_stderr": 0.0
},
"winogrande": {
"acc": 0.4956590370955012,
"acc_stderr": 0.0
},
"arc_easy": {
"acc": 0.37457912457912457,
"acc_stderr": 0.0,
"acc_norm": 0.3686868686868687,
"acc_norm_stderr": 0.0
},
"arc_challenge": {
"acc": 0.19965870307167236,
"acc_stderr": 0.0,
"acc_norm": 0.23890784982935154,
"acc_norm_stderr": 0.0
},
"logiqa": {
"acc": 0.19201228878648233,
"acc_stderr": 0.0,
"acc_norm": 0.22119815668202766,
"acc_norm_stderr": 0.0
},
"lambada_openai": {
"ppl": 106.66464362385898,
"ppl_stderr": 0.0,
"acc": 0.12128856976518533,
"acc_stderr": 0.0
},
"wsc": {
"acc": 0.5865384615384616,
"acc_stderr": 0.0
},
"sciq": {
"acc": 0.555,
"acc_stderr": 0.0,
"acc_norm": 0.625,
"acc_norm_stderr": 0.0
}
},

[uSaiPrashanth]

Could you share the full json file?

[yuzc19]

Below the results are some configurations:

"versions": {
"arc_challenge": 0,
"logiqa": 0,
"wsc": 0,
"sciq": 0,
"piqa": 0,
"lambada_openai": 0,
"arc_easy": 0,
"winogrande": 0
},
"config": {
"model": "neox",
"model_args": {
"distributed_backend": "nccl",
"local_rank": 0,
"rank": 0,
"lazy_mpu_init": false,
"short_seq_prob": 0.1,
"eod_mask_loss": false,
"adlr_autoresume": false,
"adlr_autoresume_interval": 1000,
"seed": 1234,
"onnx_safe": false,
"deepscale": false,
"deepscale_config": null,
"deepspeed_mpi": false,
"deepspeed_slurm": false,
"user_script": "evaluate.py",
"iteration": 0,
"do_train": null,
"do_valid": null,
"do_test": null,
"save_iters": [
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],
"global_num_gpus": 8,
"text_gen_type": "unconditional",
"temperature": 0.0,
"top_p": 0.0,
"top_k": 0,
"return_logits": false,
"maximum_tokens": 64,
"prompt_end": "\n",
"sample_input_file": null,
"sample_output_file": "samples.txt",
"num_samples": 1,
"recompute": false,
"eval_results_prefix": "",
"eval_tasks": [
"lambada_openai",
"piqa",
"winogrande",
"wsc",
"arc_easy",
"arc_challenge",
"sciq",
"logiqa"
],
"use_wandb": false,
"wandb_group": null,
"wandb_team": null,
"wandb_project": "neox",
"wandb_host": "https://api.wandb.ai",
"wandb_init_all_ranks": false,
"git_hash": "444c0ef",
"log_dir": "logs",
"tensorboard_dir": "tensorboard",
"log_interval": 10,
"log_grad_pct_zeros": false,
"log_param_norm": false,
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"log_optimizer_states": false,
"log_gradient_noise_scale": false,
"gradient_noise_scale_n_batches": 5,
"gradient_noise_scale_cpu_offload": false,
"pipe_parallel_size": 1,
"model_parallel_size": 1,
"pipe_partition_method": "type:transformer|mlp",
"world_size": 8,
"is_pipe_parallel": true,
"data_path": "data/enwik8/enwik8_text_document",
"use_shared_fs": true,
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"label_data_paths": null,
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"weight_by_num_documents": false,
"weighted_sampler_alpha": 0.3,
"data_impl": "mmap",
"mmap_warmup": false,
"save": "checkpoints",
"config_files": {
"slurm_local.yml": "{\n "data_path": "data/enwik8/enwik8_text_document",\n "vocab_file": "../Lightning-Pretrain/checkpoints/pythia/tokenizer.json",\n # "merge_file": "data/gpt2-merges.txt",\n "load": "checkpoints/neox_converted/pythia/160m",\n "save": "checkpoints",\n "checkpoint_validation_with_forward_pass": false,\n "tensorboard_dir": "tensorboard",\n "log_dir": "logs",\n "use_wandb": false,\n "wandb_host": "https://api.wandb.ai\",\n "wandb_project": "neox"\n}\n",
"160M.yml": "{\n "pipe_parallel_size": 1,\n "model_parallel_size": 1,\n\n "num_layers": 12,\n "hidden_size": 768,\n "num_attention_heads": 12,\n "seq_length": 2048,\n "max_position_embeddings": 2048,\n "pos_emb": "rotary",\n "rotary_pct": 0.25,\n "no_weight_tying": true,\n "gpt_j_residual": true,\n "output_layer_parallelism": "column",\n\n "attention_config": [[["flash"], 12]],\n\n "scaled_upper_triang_masked_softmax_fusion": true,\n "bias_gelu_fusion": true,\n\n "init_method": "small_init",\n "output_layer_init_method": "wang_init",\n\n "optimizer": {\n "type": "Adam",\n "params": {\n "lr": 0.0006,\n "betas": [0.9, 0.95],\n "eps": 1.0e-8\n }\n },\n "min_lr": 0.00006,\n\n # "zero_optimization": {\n # "stage": 1,\n # "allgather_partitions": true,\n # "allgather_bucket_size": 500000000,\n # "overlap_comm": true,\n # "reduce_scatter": true,\n # "reduce_bucket_size": 500000000,\n # "contiguous_gradients": true,\n # "cpu_offload": false\n # },\n\n "train_micro_batch_size_per_gpu": 32,\n "gas": 1,\n "data_impl": "mmap",\n "num_workers": 1,\n\n "checkpoint_activations": true,\n "checkpoint_num_layers": 1,\n "partition_activations": true,\n "synchronize_each_layer": true,\n\n "gradient_clipping": 1.0,\n "weight_decay": 0.1,\n "hidden_dropout": 0,\n "attention_dropout": 0,\n\n "fp16": {\n "fp16": true,\n "enabled": true,\n "loss_scale": 0,\n "loss_scale_window": 1000,\n "initial_scale_power": 12,\n "hysteresis": 2,\n "min_loss_scale": 1\n },\n\n "train_iters": 143000,\n "lr_decay_iters": 143000,\n "distributed_backend": "nccl",\n "lr_decay_style": "cosine",\n "warmup": 0.01,\n "checkpoint_factor": 1000,\n "extra_save_iters": [0,1,2,4,8,16,32,64,128,256,512],\n "eval_interval": 143000,\n "eval_iters": 10,\n\n "log_interval": 10,\n "steps_per_print": 10,\n "wall_clock_breakdown": true,\n\n "tokenizer_type": "HFTokenizer"\n}\n"
},
"load": "checkpoints/neox_converted/pythia/160m",
"checkpoint_validation_with_forward_pass": false,
"checkpoint_scale": "linear",
"checkpoint_factor": 1000,
"extra_save_iters": [
0,
1,
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64,
128,
256,
512
],
"no_save_optim": false,
"no_save_rng": false,
"no_load_optim": true,
"no_load_rng": false,
"finetune": false,
"batch_size": 32,
"train_iters": 143000,
"eval_iters": 10,
"keep_last_n_checkpoints": null,
"eval_interval": 143000,
"split": "969, 30, 1",
"vocab_file": "../Lightning-Pretrain/checkpoints/pythia/tokenizer.json",
"merge_file": null,
"num_workers": 1,
"exit_interval": null,
"attention_dropout": 0,
"hidden_dropout": 0,
"weight_decay": 0.1,
"checkpoint_activations": false,
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"deepspeed_activation_checkpointing": true,
"contiguous_checkpointing": false,
"checkpoint_in_cpu": false,
"synchronize_each_layer": true,
"profile_backward": false,
"partition_activations": false,
"gas": 1,
"clip_grad": 1.0,
"hysteresis": 2,
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"loss_scale": null,
"loss_scale_window": 1000.0,
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"char_level_ppl": false,
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"mup_init_scale": 1.0,
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"tokenizer_type": "HFTokenizer",
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"zero_reduce_bucket_size": 500000000,
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"lr": 0.001,
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"precision": "fp16",
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"norm": "layernorm",
"layernorm_epsilon": 1e-05,
"rms_norm_epsilon": 1e-08,
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"pos_emb": "rotary",
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"opt_pos_emb_offset": 0,
"no_weight_tying": true,
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],
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"make_vocab_size_divisible_by": 128,
"activation": "gelu",
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}

[yuzc19]

I have to say I test checkpoint-100000 (works very very well, with lambada accuracy of 0.308) and checkpoint-133000 (works somehow bad, with lambada accuracy of 0.112). I have no idea why there would be such a performance drop within 30000 steps.

[yuzc19]

I just wonder if you have uploaded the wrong checkpoint for Pythia-160M 143000 checkpoints (also the main branch).

[uSaiPrashanth]

I just evaluated step143000 again and here are the results:

{
  "results": {
    "arc_challenge": {
      "acc": 0.1825938566552901,
      "acc_stderr": 0.011289730684565,
      "acc_norm": 0.2354948805460751,
      "acc_norm_stderr": 0.012399451855004746
    },
    "arc_easy": {
      "acc": 0.43602693602693604,
      "acc_stderr": 0.010175459582759727,
      "acc_norm": 0.3977272727272727,
      "acc_norm_stderr": 0.010042861602178066
    },
    "piqa": {
      "acc": 0.6251360174102285,
      "acc_stderr": 0.011294565805619017,
      "acc_norm": 0.6175190424374319,
      "acc_norm_stderr": 0.011339019654272349
    }
  },
  "versions": {
    "arc_challenge": 0,
    "arc_easy": 0,
    "piqa": 0
  },
  "config": {
    "model": "hf-causal",
    "model_args": "pretrained=EleutherAI/pythia-160m,revision=step143000",
    "num_fewshot": 0,
    "batch_size": null,
    "batch_sizes": [],
    "device": null,
    "no_cache": false,
    "limit": null,
    "bootstrap_iters": 100000,
    "description_dict": {}
  }
}

hf-causal (pretrained=EleutherAI/pythia-160m,revision=step143000), limit: None, provide_description: False, num_fewshot: 0, batch_size: None

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	0.1826	±	0.0113
		acc_norm	0.2355	±	0.0124
arc_easy	0	acc	0.4360	±	0.0102
		acc_norm	0.3977	±	0.0100
piqa	0	acc	0.6251	±	0.0113
		acc_norm	0.6175	±	0.0113

I would suggest you to

• Try evaluating the uploaded huggingface model, to make sure your results match with mine
• Try reconverting the checkpoint back to neox again.

[yuzc19]

Yeah, the results of the HF model match well, but Neox falls behind, could it be possible?

BTW, what are Lambada (OpenAI) results here?

[uSaiPrashanth]

As mentioned and verified, there are no issues with evaluation results on this repo. So, Lambada (OpenAI) evals will remain the same as the ones mentioned in appropriate files

[inf3rnus]

fwiw I'm finding that I'm having a similar issue as @yuzc19, lambada (lambada_openai) is showing me similar results for EleutherAI/pythia-160m.

Note, I'm just trying to replicate the results in the paper because the default model for HF does not line up at all.

e.g.

result = simple_evaluate(
    model="hf-auto",
    model_args=",".join(
        [
            "pretrained=EleutherAI/pythia-160m",
            "revision=step100000",
            # "batch_size=16",
            "parallelize=True",
        ]
    ),
    tasks=["lambada_openai"],
)

gives me this

as where

result = simple_evaluate(
    model="hf-auto",
    model_args=",".join(
        [
            "pretrained=EleutherAI/pythia-160m",
            "revision=step143000",
            # "batch_size=16",
            "parallelize=True",
        ]
    ),
    tasks=["lambada_openai"],
)

gives me this

[yuzc19]

Thank you, @inf3rnus. This is exactly what I met before, and I feel like the model experiences sort of catastrophic forgetting in the steps trained after 100000. I didn't figure out why, but lambada is definitely not a quite stable evaluation task.