From a974cf5b66733dc7dc685e92f91377e51dcb2bf7 Mon Sep 17 00:00:00 2001 From: samsja <55492238+samsja@users.noreply.github.com> Date: Tue, 10 Dec 2024 10:51:03 -0800 Subject: [PATCH] add eval (#175) * add eval * fix llama2 compatibility * update readme * fix llama2 * Update README.md * update instruction --------- Co-authored-by: Johannes Hagemann --- README.md | 16 ++ pyproject.toml | 4 + scripts/export_dcp.py | 33 ++- uv.lock | 554 ++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 598 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index a1035756..06fca245 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,22 @@ Ensure you have at least two GPU to run the full test suite: ```bash uv run pytest ``` + + +### Eval + +To eval you need first to convert the checkpoint to a huggingface compatible model. + +```bash +uv run python scripts/export_dcp.py @configs/10B/H100.toml --ckpt.path CONVERTED_MODEL_PATH --ckpt.resume CHECKPOINT_PATH --torch_dtype bfloat16 --ckpt.interval 1 +``` + + +``` +uv run accelerate launch -m lm_eval --model hf --model_args pretrained=CONVERTED_MODEL_PATH,add_bos_token=True --tasks hellaswag --num_fewshot 10 +``` + + ## Environment variables ### Global Store Initialization | Environment Variable | Description | Default Value | diff --git a/pyproject.toml b/pyproject.toml index da393919..e4e0786e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,13 +22,17 @@ dependencies = [ ] [project.optional-dependencies] + + all = [ "wandb", "asyncio>=3.4.3", "aiohttp>=3.10.5", "requests>=2.32.3", + "lm-eval" ] + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/scripts/export_dcp.py b/scripts/export_dcp.py index 51538baa..cf7460dd 100644 --- a/scripts/export_dcp.py +++ b/scripts/export_dcp.py @@ -51,7 +51,9 @@ def _get_ffn_dim(hidden_dim: int, ffn_dim_multiplier: float, multiple_of: int) - return hidden_dim -def convert_config_zb_to_hf(zb_config: ModelArgs, with_debug_automap: bool = False) -> LlamaConfig: +def convert_config_zb_to_hf( + zb_config: ModelArgs, with_debug_automap: bool = False, type_model: str = "llama3" +) -> LlamaConfig: """Convert ZeroBand config to HuggingFace config""" config = LlamaConfig() config.hidden_size = zb_config.dim @@ -63,8 +65,14 @@ def convert_config_zb_to_hf(zb_config: ModelArgs, with_debug_automap: bool = Fal config.rms_norm_eps = zb_config.norm_eps config.rope_theta = float(zb_config.rope_theta) config.max_position_embeddings = zb_config.max_seq_len - config.bos_token_id = 128000 - config.eos_token_id = [128001, 128008, 128009] + + if type_model == "llama2": + config.bos_token_id = [1] + config.eos_token_id = [2] + else: + config.bos_token_id = [128000] + config.eos_token_id = [128001, 128008, 128009] + config.architectures = ["LlamaForCausalLM"] # Rope scaling @@ -76,11 +84,12 @@ def convert_config_zb_to_hf(zb_config: ModelArgs, with_debug_automap: bool = Fal if with_debug_automap: config.auto_map = { "AutoConfig": "PrimeIntellect/prime-llama-debug--configuration_llama.LlamaConfig", - "AutoModelForCausalLM": "PrimeIntellect/prime-llama-debug--modeling_llama.LlamaForCausalLM" + "AutoModelForCausalLM": "PrimeIntellect/prime-llama-debug--modeling_llama.LlamaForCausalLM", } return config + @torch.no_grad def convert_qk_from_complex_to_rotate_half(linear_weight: torch.FloatTensor, head_dim: int) -> torch.FloatTensor: """Converts the Q/K weight from complex to rotate half form. @@ -99,8 +108,12 @@ def convert_qk_from_complex_to_rotate_half(linear_weight: torch.FloatTensor, hea # This applies the riffle shuffle permutation to the outputs of the linear for each attn head # Even numbers go to the top half, odd numbers go to the bottom half for i in range(num_heads): - new_weight[i * head_dim:(i * head_dim + hhd), :].copy_(linear_weight[i * head_dim + 0:(i + 1) * head_dim:2, :]) - new_weight[i * head_dim + hhd:(i + 1) * head_dim, :].copy_(linear_weight[i * head_dim + 1:(i + 1) * head_dim:2, :]) + new_weight[i * head_dim : (i * head_dim + hhd), :].copy_( + linear_weight[i * head_dim + 0 : (i + 1) * head_dim : 2, :] + ) + new_weight[i * head_dim + hhd : (i + 1) * head_dim, :].copy_( + linear_weight[i * head_dim + 1 : (i + 1) * head_dim : 2, :] + ) return new_weight @@ -127,9 +140,11 @@ def main(config: ExportConfig): seq_length=config.data.seq_length, attn_fn=config.train.attn_fn, ) - + # Convert ZeroBand config to HuggingFace config - hf_config = convert_config_zb_to_hf(model_config, with_debug_automap=config.with_debug_automap) + hf_config = convert_config_zb_to_hf( + model_config, with_debug_automap=config.with_debug_automap, type_model=config.type_model + ) hf_config.to_json_file(save_path / "config.json") # Load checkpoint @@ -152,7 +167,7 @@ def main(config: ExportConfig): index_json = {} total_size = 0 state_dict = {remap_keys_llama(k): v for k, v in state_dict.items()} - if not config.with_debug_automap: # The debug uses complex rotary impl + if not 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