> ## Documentation Index > Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt > Use this file to discover all available pages before exploring further. # Downstream Validation Using Bigcode Eval Harness This page walks through the steps for performing downstream validation on the Cerebras Wafer-Scale cluster using [BigCode’s Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness) (BCEH). BCEH is a framework for evaluating code generation models. While you can configure BCEH as part of your training (see [Pretraining with Downstream Validation](../../model-zoo/core-workflows/pretraining-with-downstream-validation) from the Cerebras Model Zoo. The examples in this guide will perform downstream validation on [LLaMA3 8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). By the end of this guide, you will be able to leverage the BCEH framework to perform standalone downstream validation on your models on CS-X. ## Prerequisites Please ensure that you have installed the Cerebras Model Zoo package by going through the installation guide. Note that BCEH version tested and packaged in the Cerebras Model Zoo is the pinned to [this commit](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/84b96da31b7f840b55c5733325346176140cdb6b). Please also read through the [Trainer Overview](../model-zoo/trainer-overview) and [Trainer Configuration Overview](../../model-zoo/trainer-configuration-overview)), as these guides will help understand how we configure running BCEH standalone. This guide configures the downstream BCEH run using V2 YAML. While release 2.3 includes support for legacy V1 YAML, please convert the configuration to V2 using `convert_legacy_params_to_trainer_params` from the script `src/cerebras/modelzoo/trainer/utils.py`. ## Configure the Run This section covers the required steps for setting up an BCEH run to perform standalone downstream validation on code generation tasks. In particular, you will need to write a YAML configuration file to configure an instance of the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) callback. The example in this section configure code evaluation on the bigcode eval harness task `humaneval` using a single CSX. If you aren’t interested in seeing the break down of the configuration, feel free to skip ahead to the [Putting it All Together](#wsc-modelzoo-bceh-pretrain-full-yaml) section to see the full YAML configuration. ### Configure the CSX Backend The first step is to specify the CSX backend and resources required for the run. Please create a YAML configuration file with the following cluster config: ```yaml theme={null} trainer: init: backend: backend_type: CSX cluster_config: num_csx: 1 ``` This example uses a single CSX, but you can readily update `num_csx` to run BCEH on multiple CSXs for improved performance. ### Configure the Model Starting from release 2.4.0, specifying `model_name` is now deprecated and replaced with `name`. Next, please add the following model configuration in the YAML for [LLaMA3 8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) with 8K context length: ```yaml theme={null} trainer: init: backend: # CSX ... model: name: llama # This setting is required # Embedding vocab_size: 128256 hidden_size: 4096 position_embedding_type: "rotary" pos_scaling_factor: 1.0 rope_theta: 500000.0 rotary_dim: 128 share_embedding_weights: false max_position_embeddings: 8192 embedding_dropout_rate: 0.0 embedding_layer_norm: false # Decoder num_hidden_layers: 32 dropout_rate: 0.0 layer_norm_epsilon: 1.0e-5 norm_type: "rmsnorm" # Decoder - Attention num_heads: 32 attention_type: "scaled_dot_product" attention_module: "multiquery_attention" attention_dropout_rate: 0.0 use_projection_bias_in_attention: false use_ffn_bias_in_attention: false extra_attention_params: num_kv_groups: 8 # Decoder - ffn filter_size: 14336 nonlinearity: "swiglu" use_ffn_bias: false # Task-specific use_bias_in_output: false loss_scaling: "num_tokens" loss_weight: 1.0 # Initializer initializer_range: 0.02 ``` To run downstream validation harness, you must specify the `name` setting in the model configuration. Valid names corresponding to the supported models include: * `btlm` * `bloom` * `gpt2` * `gptj` * `falcon` * `gpt3` * `gpt-neox` * `llama` * `mistral` * `mpt` * `jais` * `santacoder` * `starcoder` ### Configure the BCEH Callback BCEH is implemented as an extension to the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) callback. Add the following configuration for the `BigCodeEvalHarness` callback. ```yaml theme={null} trainer: init: backend: # CSX ... model: # Llama3-8B name: llama # This setting is required ... callbacks: - BigCodeEvalHarness: # BigCode Eval Harness settings (exposed via CLI) bigcode_args: tasks: humaneval # CSX-specific eval harness settings (exposed via CLI) keep_data_dir: false hf_cache_dir: # Dataloader settings batch_size: 4 shuffle: false max_sequence_length: 8192 num_workers: 1 data_dir: tokenizer_file_path: eos_id: 128001 pretrained_model_name_or_path: null ``` The `bigcode_args` section exposes the following settings configure the BCEH run: | BigCode Eval Harness CLI Arguments | Description | | --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--prefix` | Number of examples to be added to the fewshot context string. Defaults to 0 | | `--temperature` | Sampling temperature used for generation. | | `--top_p` | Top-p parameter used for nucleus sampling. | | `--top_k` | Top-k parameter used for generation. | | `--n_samples` | Number of completions to generate for each sample. Defaults to 1. | | `--seed` | Random seed used for evaluation. | | `--tasks` | Comma separated string specifying BigCode Eval Harness tasks. | | `--instruction_tokens` | A series of instruction tokens used for instruction-tuning benchamrks separated by comma e.g. \,\,\ | | `--max_length_generation` | Maximum length of generated sequence (prompt+generation). | | `--limit` | Number of samples to solve and evaluate from the benchmark. | | `--limit_start` | Optional offset to start from when limiting the number of samples. | | `--save_every_k_tasks` | Optional saving after every k tasks. | | `--load_generations_path` | Path of file with previously generated solutions, if provided generation is skipped and only evaluation is done | | `--load_data_path` | Path of additional data to load for the tasks. | | `--metric_output_path` | Path to save the results. | | `--load_generations_intermediate_paths` | List of paths for saving the intermediate code generations. | | `--save_generations_path` | Path for saving the model’s output code generations. | | `--save_references_path` | Path for saving the references solutions/tests. | | `--prompt` | Prompt type to use for generation in HumanEvalPack tasks. | | `--check_references` | Don’t run generation but benchmark groundtruth (useful for debugging). | You can either specify the settings here or pass them via CLI arguments to the standalone BCEH run script. The callback configuration accepts dataloader settings that you must specify in the YAML to set up input data preprocessing for the run: | DataLoader Settings | Description | | ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `data_dir` | This setting is required. Provide a path to the mounted directory visible to the worker containers where eval harness task data samples are dumped after preprocessing. Use the `mount_dirs` argument to specify a dir mount, similar to our existing flows. | | `tokenizer_file_path` | Path to a custom tokenizer (JSON) file. If you provide a custom tokenizer, then you must also specify `eos_id`; otherwise, you must provide a pretrained tokenizer from Hugging Face in `pretrained_model_name_or_path`. | | `pretrained_model_name_or_path` | Hugging Face (HF) pretrained model name or path. This setting is required if you do not specify `tokenizer_file_path`. For detailed description, see [HF AutoTokenizers](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path). | | `eos_id` | End-of-sentence (eos) token ID to signal the termination of a sequence. This setting is required if you specify a custom tokenizer in `tokenizer_file_path`. You can set this by looking for the ID corresponding to the eos token in the custom tokenizer JSON file. | | `max_sequence_length` | Maximum length of the input sequence. This setting is required for preprocessing input data samples from the specified eval harness tasks. You should align the `max_sequence_length` field to the `max_position_embeddings` value in the model configuration of the YAML. If you don’t specify `max_sequence_length`, the flow defaults to this `max_position_embeddings` setting. | Additionally, you may optionally specify the following, CSX-specific eval harness setting: * `keep_data_dir`: Use this to preserve the preprocessed eval harness task data samples, i.e. the directory specified under `data_dir`. Defaults to False, i.e. data samples are deleted after the run. ### (Optional) Configure HuggingFace (HF) Cache Directory BCEH utilizes HF’s APIs to download task data and other configurations. This data is by default cached under `$HOME/.cache/huggingface`. However, you may choose to specify a different directory for this cached data via the [`HFCacheDir`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) callback: ```yaml theme={null} trainer: init: backend: # CSX ... model: # Llama3-8B ... callbacks: - BigCodeEvalHarness: ... - HFCacheDir: cache_dir: ``` ### Configure Generation Settings All BCEH tasks are generative (autoregressive) in nature. In order to run generative inference on CSX, you must specify the following inference settings in the model config of YAML file: * `start_token` - ID of the special token that indicates where to start inferring for each sample, as described above. You may specify a list of token IDs instead of a single ID. If you do, the model will start inference at the first token that matches any one of the provided IDs. The model will pad inferred predictions with the first ID in the list. * `stop_sequences` - List of sequences (each one being a list of token IDs). If any one of these sequences is emitted by the model, inference will stop for that sample. For example, suppose you would like to stop inferring after either a newline character (e.g. token id 1), or a combination of a period (e.g. token id 2) followed by a space (e.g. token id 3). In this case, set stop\_sequences to \[\[1], \[2, 3]]. To stop inferring after seeing a newline character only, set stop\_sequences to \[\[1]]. To disable this feature, set `stop_sequences` to an empty list \[]. Additionally, the following optional parameters may be set: * `max_tokens` - Maximum tokens to infer for each sample. * `loop_dim` - Indicates the sequence dimension in the input and output data. Default value is 1. If set to 0, indicates that both input and output data is transposed (i.e. `sequence X samples` instead of `samples X sequence`). Please update the YAML file to add these inference settings: ```yaml theme={null} trainer: init: backend: # CSX ... model: # Llama3-8B name: llama ... # Inference Settings start_token: 128256 # Set to `vocab_size` stop_sequences: [] # Left empty as stop_sequences are overridden from the BCEH task max_tokens: 256 # Default from HF implementations loop_dim: 1 callbacks: - BigCodeEvalHarness: # BigCode Eval Harness settings bigcode_args: tasks: humaneval ... ... ``` 1. For `start_token`, it is ideal to choose a value that’s not going to be generated by the model, i.e. `vocab_size` in the example above. 2. The code generation task itself defines `stop_sequences`. For instance, see the specification of stop tokens for [humaneval](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/334efb7feb1c6d18fc4be2c71ae73248f3de8440/bigcode_eval/tasks/humaneval.py#L52) . The flow will internally override the `stop_sequences` config with the value from the task, so you can also specify an arbitrary, valid value in the YAML as shown above. ### Putting it All Together Here’s what the full YAML configuration looks like once you follow this guide for configuring the individual pieces: ```yaml theme={null} trainer: init: backend: # CSX backend_type: CSX cluster_config: num_csx: 1 model: name: llama # This setting is required # Embedding vocab_size: 128256 hidden_size: 4096 position_embedding_type: "rotary" pos_scaling_factor: 1.0 rope_theta: 500000.0 rotary_dim: 128 share_embedding_weights: false max_position_embeddings: 8192 embedding_dropout_rate: 0.0 embedding_layer_norm: false # Decoder num_hidden_layers: 32 dropout_rate: 0.0 layer_norm_epsilon: 1.0e-5 norm_type: "rmsnorm" # Decoder - Attention num_heads: 32 attention_type: "scaled_dot_product" attention_module: "multiquery_attention" attention_dropout_rate: 0.0 use_projection_bias_in_attention: false use_ffn_bias_in_attention: false extra_attention_params: num_kv_groups: 8 # Decoder - ffn filter_size: 14336 nonlinearity: "swiglu" use_ffn_bias: false # Task-specific use_bias_in_output: false loss_scaling: "num_tokens" loss_weight: 1.0 # Initializer initializer_range: 0.02 # Inference Settings start_token: 128256 # Set to `vocab_size` stop_sequences: [] # Left empty as stop_sequences are overridden from the BCEH task max_tokens: 256 # Default from HF implementations loop_dim: 1 callbacks: - BigCodeEvalHarness: # BigCode Eval Harness settings (exposed via CLI) bigcode_args: tasks: humaneval # CSX-specific eval harness settings (exposed via CLI) keep_data_dir: false hf_cache_dir: # Dataloader settings batch_size: 4 shuffle: false max_sequence_length: 8192 num_workers: 1 data_dir: tokenizer_file_path: eos_id: 128001 pretrained_model_name_or_path: null ``` ## Running BCEH on CS-X Once the YAML configuration is complete, use the Model Zoo CLI to run standalone BCEH: ```bash theme={null} cszoo bigcode_eval --help usage: cszoo bigcode_eval [-h] [--prefix PREFIX] [--max_tokens MAX_TOKENS] [--temperature TEMPERATURE] [--top_p TOP_P] [--top_k TOP_K] [--n_samples N_SAMPLES] [--seed SEED] [--tasks task1,task2] [--instruction_tokens INSTRUCTION_TOKENS] [--limit LIMIT] [--limit_start LIMIT_START] [--save_every_k_tasks SAVE_EVERY_K_TASKS] [--load_generations_path LOAD_GENERATIONS_PATH] [--load_data_path LOAD_DATA_PATH] [--metric_output_path METRIC_OUTPUT_PATH] [--load_generations_intermediate_paths [LOAD_GENERATIONS_INTERMEDIATE_PATHS [LOAD_GENERATIONS_INTERMEDIATE_PATHS ...]]] [--save_generations_path SAVE_GENERATIONS_PATH] [--save_references_path SAVE_REFERENCES_PATH] [--prompt PROMPT] [--check_references] [--keep_data_dir] [--target_device {CSX}] [-o MODEL_DIR] [--checkpoint_path CHECKPOINT_PATH] [--load_checkpoint_states LOAD_CHECKPOINT_STATES] [--logging LOGGING] [--compile_only] [--validate_only] [--job_labels JOB_LABELS [JOB_LABELS ...]] [--job_priority {p1,p2,p3}] [--mount_dirs MOUNT_DIRS [MOUNT_DIRS ...]] [--python_paths PYTHON_PATHS [PYTHON_PATHS ...]] [--credentials_path CREDENTIALS_PATH] [--mgmt_address MGMT_ADDRESS] [--disable_version_check] [--num_csx NUM_CSX] [--debug_args [DEBUG_ARGS [DEBUG_ARGS ...]]] [--debug_args_path DEBUG_ARGS_PATH] [--ini [INI [INI ...]]] [--transfer_processes TRANSFER_PROCESSES] [--config CONFIG] params ``` 1. We support only a subset of BigCode’s [command line interface (CLI) arguments](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/main.py) as listed above. 2. You may also specify these arguments in the YAML under the `bigcode_args` key of the `BigCodeEvalHarness` configuration, but please note that the CLI setting will override the settings in the YAML. 3. The `params` argument is required. Use it to specify the path to the YAML configuration file. 4. Use the `--checkpoint_path` CLI argument to specify the path to the checkpoint file to load model weights from. If a checkpoint path is not provided, we support checkpoint autoloading in this flow such that the latest checkpoint file will be picked up from the specified `model_dir`. ### Code Generation on CSX We only support BCEH’s generation-only flow to generate model outputs and dump these generations to JSON files. Use the `--save_generations_path` argument to specify the path for saving the generations. If no absolute path is provided, the generations will be dumped inside of `model_dir`. The code execution and evaluation flow is run on CPU, preferably in a sandboxed environment, using these dumped model outputs. ### Example Let’s assume that the YAML configuration file above is written to `./llama3_8B_bceh.yaml`. Then, to run code generation for task `humaneval` set up a bash script as follows: ```bash theme={null} cszoo bigcode_eval \ --target_device CSX \ --params ./llama3_8B_bceh.yaml \ --checkpoint_path \ --python_paths \ --mount_dirs ``` By default, the model will perform greedy sampling of the inferred tokens, i.e. for all of the model’s outputs, pick the token with the highest probability. In order to perform non-greedy sampling, you can pass in values for `temperature`, `top_k` or `top_p` sampling to either the bash script or under `bigcode_args` of the YAML. For example: ```bash theme={null} cszoo bigcode_eval \ --target_device CSX \ --params ./llama3_8B_bceh.yaml \ --checkpoint_path \ --python_paths \ --mount_dirs \ --temperature 0.7 \ --top_k 10 \ --top_p 0.95 ``` ### Code Execution and Evaluation on CPU To obtain the final eval scores, please clone [BigCode’s official repository](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main). Some BigCode tasks, such as `humaneval`, require executing the model’s generated code for processing the final eval scores. Thus, for security purposes, use the instructions [here](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main?tab=readme-ov-file#evaluation-only) to set up a containerized environment. Finally, to obtain the final eval scores, invoke the BCEH’s main script using the `--load_generations_path` CLI argument. ### Example Output Please make note of the path of the model’s output generations in the logs, i.e. `/20240627_051131/bigcode_0/generations_humaneval_1.json`. Set up a bash script as follows to invoke the BCEH script: ```bash theme={null} cszoo bigcode_eval \ --tasks humaneval \ --check_references \ --load_generations_path /20240627_051131/bigcode_0/generations_humaneval_1.json \ --metric_output_path ./evaluation_results.json \ --params ``` The final code eval results will be saved in file `./evaluation_results.json` are as follows: ```json theme={null} ... { "humaneval": { "pass@1": 0.13414634146341464 }, "config": { "prefix": "", "do_sample": true, "temperature": 0.2, "top_k": 0, "top_p": 0.95, "n_samples": 1, "eos": "<|endoftext|>", "seed": 0, "model": "codeparrot/codeparrot-small", "modeltype": "causal", "peft_model": null, "revision": null, "use_auth_token": false, "trust_remote_code": false, "tasks": "humaneval", "instruction_tokens": null, "batch_size": 1, "max_length_generation": 512, "precision": "fp32", "load_in_8bit": false, "load_in_4bit": false, "left_padding": false, "limit": null, "limit_start": 0, "save_every_k_tasks": -1, "postprocess": true, "allow_code_execution": true, "generation_only": false, "load_generations_path": "/20240627_051131/bigcode_0/generations_humaneval_1.json", "load_data_path": null, "metric_output_path": "evaluation_results.json", "save_generations": false, "load_generations_intermediate_paths": null, "save_generations_path": "generations.json", "save_references": false, "save_references_path": "references.json", "prompt": "prompt", "max_memory_per_gpu": null, "check_references": false } } ``` 1. The `config` section of the final output JSON contains all the default values from BigCode’s [CLI](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/main.py). Feel free to ignore this section and consider only the final eval score, i.e. `"pass@1": 0.13414634146341464` above. 2. Please ensure that `--check_references` is set for tasks that require code execution. 3. Setting up a sandboxed environment to run code executions is optional. 4. Please ensure that the `--tasks` argument specifies the correct task for which you produced the model’s output generations on CSX, i.e. `humaneval` in the example above. ### Run Multiple Generative Tasks You can run multiple tasks by configuring multiple [`BigCodeEvalHarness`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) callbacks (one per task) in the YAML. For example, you may update the YAML config as such to run downstream validation on generative tasks `mpbb` and `humaneval`: ```yaml theme={null} trainer: init: backend: # CSX ... model: # Llama3-8B name: llama # This setting is required ... # Inference Settings start_token: 128256 # Set to `vocab_size` stop_sequences: [] # Arbitrary value that will be overridden from the task spec max_tokens: 256 # Default from HF implementations loop_dim: 1 callbacks: - BigCodeEvalHarness: # BigCode Eval Harness settings (exposed via CLI) bceh_args: tasks: humaneval, mbpp # CSX-specific eval harness settings (exposed via CLI) keep_data_dir: false hf_cache_dir: # Dataloader settings batch_size: 4 shuffle: false max_sequence_length: 8192 num_workers: 1 data_dir: tokenizer_file_path: eos_id: 128001 pretrained_model_name_or_path: null ``` ## Supported Tasks * You may perform downstream validation on all BCEH tasks except for `DS1000`, since it requires Python version 3.7.10, but we support 3.8.0 in our packaged environment. ## Adding New Tasks Please refer to BigCode’s new task implementation guide [here](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/docs/guide.md) to add new tasks. ## Limitations * You may only specify running a single generative task at a time. You should configure multiple BCEH tasks via separate callbacks, as shown above. * We only support the generation flow for BCEH. In order to run the code execution and evalution flow, please create a separate clone of the BCEH repository to obtain the final eval scores using the outputs generated on CSX. * Please turn on grad accumulation and choose a small micro batch size (between 16 to 32) under the `flags` configuration of the `BigCodeEvalHarness` callback of the YAML. ## Conclusion In summary, by following this guide you have run standalone downstream validation for the Llama3-8B model on BCEH’s task `humaneval`. You should now be comfortable in configuring more downstream BCEH runs on your model of choice on even more code eval tasks. ## What’s next? To run downstream validation on general eval datasets and tasks, please see check out: * [Downstream Validation using Eleuther Eval Harness](../../model-zoo/core-workflows/downstream-validation-using-eleuther-eval-harness) You can also perform downstream validation using BCEH as part of your pretraining runs with upstream validation. Check out the following guide: * [Pretraining with Downstream Validation](../../model-zoo/core-workflows/downstream-validation-using-eleuther-eval-harness)