This page walks through the steps for performing downstream validation on the Cerebras Wafer-Scale cluster using BigCode’s 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 from the Cerebras Model Zoo. The examples in this guide will perform downstream validation on LLaMA3 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.

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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. Please also read through the Trainer Overview and 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.

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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 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 section to see the full YAML configuration.

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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: 
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.

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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 with 8K context length: 
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

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Configure the BCEH Callback

BCEH is implemented as an extension to the Trainer callback. Add the following configuration for the BigCodeEvalHarness callback. 
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: <path_to_directory_for_caching_HF_data>
        # Dataloader settings
        batch_size: 4
        shuffle: false
        max_sequence_length: 8192
        num_workers: 1
        data_dir: <path_to_mounted_dir>
        tokenizer_file_path: <path_to_llama3_tokenizer_json_file>
        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 ArgumentsDescription
--prefixNumber of examples to be added to the fewshot context string. Defaults to 0
--temperatureSampling temperature used for generation.
--top_pTop-p parameter used for nucleus sampling.
--top_kTop-k parameter used for generation.
--n_samplesNumber of completions to generate for each sample. Defaults to 1.
--seedRandom seed used for evaluation.
--tasksComma separated string specifying BigCode Eval Harness tasks.
--instruction_tokensA series of instruction tokens used for instruction-tuning benchamrks separated by comma e.g. <user_message>,<end_user_message>,<assistant_message>
--max_length_generationMaximum length of generated sequence (prompt+generation).
--limitNumber of samples to solve and evaluate from the benchmark.
--limit_startOptional offset to start from when limiting the number of samples.
--save_every_k_tasksOptional saving after every k tasks.
--load_generations_pathPath of file with previously generated solutions, if provided generation is skipped and only evaluation is done
--load_data_pathPath of additional data to load for the tasks.
--metric_output_pathPath to save the results.
--load_generations_intermediate_pathsList of paths for saving the intermediate code generations.
--save_generations_pathPath for saving the model’s output code generations.
--save_references_pathPath for saving the references solutions/tests.
--promptPrompt type to use for generation in HumanEvalPack tasks.
--check_referencesDon’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 SettingsDescription
data_dirThis 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_pathPath 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_pathHugging 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.
eos_idEnd-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_lengthMaximum 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.

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(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 callback: 
trainer:
  init:
    backend: # CSX
      ...
    model: # Llama3-8B
      ...
    callbacks:
    - BigCodeEvalHarness:
        ...
    - HFCacheDir:
        cache_dir: <path_to_directory_for_caching_HF_data>

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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: 
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 . 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.

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Putting it All Together

Here’s what the full YAML configuration looks like once you follow this guide for configuring the individual pieces: 
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: <path_to_directory_for_caching_HF_data>
        # Dataloader settings
        batch_size: 4
        shuffle: false
        max_sequence_length: 8192
        num_workers: 1
        data_dir: <path_to_mounted_dir>
        tokenizer_file_path: <path_to_llama3_tokenizer_json_file>
        eos_id: 128001
        pretrained_model_name_or_path: null

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Running BCEH on CS-X

Once the YAML configuration is complete, use the Model Zoo CLI to run standalone BCEH: 
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 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.

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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.

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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: 
cszoo bigcode_eval \
  --target_device CSX \
  --params ./llama3_8B_bceh.yaml \
  --checkpoint_path <path_to_checkpoint_file> \
  --python_paths <path(s)_to_export_to_PYTHONPATH_in_appliance_containers> \
  --mount_dirs <path(s)_to_mount_to_appliance_containers>
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: 
cszoo bigcode_eval \
  --target_device CSX \
  --params ./llama3_8B_bceh.yaml \
  --checkpoint_path <path_to_checkpoint_file> \
  --python_paths <path(s)_to_export_to_PYTHONPATH_in_appliance_containers> \
  --mount_dirs <path(s)_to_mount_to_appliance_containers> \
  --temperature 0.7 \
  --top_k 10 \
  --top_p 0.95

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Code Execution and Evaluation on CPU

To obtain the final eval scores, please clone BigCode’s official repository. 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 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.

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Example Output

Please make note of the path of the model’s output generations in the logs, i.e. <path_to_bigcode_model_dir>/20240627_051131/bigcode_0/generations_humaneval_1.json. Set up a bash script as follows to invoke the BCEH script: 
cszoo bigcode_eval \
  --tasks humaneval \
  --check_references \
  --load_generations_path <path_to_bigcode_model_dir>/20240627_051131/bigcode_0/generations_humaneval_1.json \
  --metric_output_path ./evaluation_results.json \
  --params <path_to_params_yaml>
The final code eval results will be saved in file ./evaluation_results.json are as follows: 
...
{
"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": "<path_to_bigcode_model_dir>/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. 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.

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Run Multiple Generative Tasks

You can run multiple tasks by configuring multiple BigCodeEvalHarness 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: 
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: <path_to_directory_for_caching_HF_data>
        # Dataloader settings
        batch_size: 4
        shuffle: false
        max_sequence_length: 8192
        num_workers: 1
        data_dir: <path_to_mounted_dir>
        tokenizer_file_path: <path_to_llama3_tokenizer_json_file>
        eos_id: 128001
        pretrained_model_name_or_path: null

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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.

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Adding New Tasks

Please refer to BigCode’s new task implementation guide here to add new tasks.

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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.

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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.

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What’s next?

To run downstream validation on general eval datasets and tasks, please see check out: You can also perform downstream validation using BCEH as part of your pretraining runs with upstream validation. Check out the following guide: