[DRAFT] prompt migration engine

.gitignore

@@ -3,3 +3,5 @@ __pycache__
 .ipynb_checkpoints
 wandb/
 artifacts/
+
+**/.env

end-to-end-use-cases/benchmarks/llm_eval_harness/.gitignore

@@ -0,0 +1,2 @@
+**/eval_results/**
+**/old_eval_results/**

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/eval_config.yaml

@@ -1,9 +1,9 @@
-model_name: "meta-llama/Llama-3.1-8B-Instruct" # The name of the model to evaluate. This must be a valid Meta Llama 3 based model name in the HuggingFace model hub."
+model_name: "meta-llama/Llama-3.3-70B-Instruct" # The name of the model to evaluate. This must be a valid Meta Llama 3 based model name in the HuggingFace model hub."
 
-evals_dataset: "meta-llama/Llama-3.1-8B-Instruct-evals" # The name of the 3.1 evals dataset to evaluate, please make sure this eval dataset corresponds to the model loaded. This must be a valid dataset name in the Llama 3.x Evals collection.
+evals_dataset: "meta-llama/Llama-3.1-70B-Instruct-evals" # The name of the 3.1 evals dataset to evaluate, please make sure this eval dataset corresponds to the model loaded. This must be a valid dataset name in the Llama 3.x Evals collection.
 # Must be one of the following ["meta-llama/Llama-3.1-8B-Instruct-evals","meta-llama/Llama-3.1-70B-Instruct-evals","meta-llama/Llama-3.1-405B-Instruct-evals","meta-llama/Llama-3.1-8B-evals","meta-llama/Llama-3.1-70B-evals","meta-llama/Llama-3.1-405B-evals","meta-llama/Llama-3.2-1B-evals","meta-llama/Llama-3.2-3B-evals", "meta-llama/Llama-3.2-1B-Instruct-evals", "meta-llama/Llama-3.2-3B-Instruct-evals"]
 
-tasks: "meta_instruct" # Available tasks for 3.1 instruct model: "meta_math_hard", "meta_gpqa_cot", "meta_mmlu_pro_instruct", "meta_ifeval"; or just use "meta_instruct" to run all of them.
+tasks: "meta_mmlu_pro_instruct" # Available tasks for 3.1 instruct model: "meta_math_hard", "meta_gpqa_cot", "meta_mmlu_pro_instruct", "meta_ifeval"; or just use "meta_instruct" to run all of them.
 # Available tasks for 3.1 pretrain model: "meta_bbh", "meta_mmlu_pro_pretrain"; or just use "meta_pretrain" to run all of them.
 # Available tasks for 3.2 instruct model: "meta_mmlu", "meta_math", "meta_gpqa"; or just use "meta_instruct" to run all of them.
 # Available tasks for 3.2 pretrain model: "meta_mmlu"; or just use "meta_pretrain" to run all of them

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/bbh/bbh_3shot_cot.yaml

@@ -0,0 +1,28 @@
+dataset_path: meta-llama/Llama-3.1-70B-evals
+dataset_name: Llama-3.1-70B-evals__bbh__details
+task: meta_bbh
+output_type: generate_until
+process_docs: !function utils.process_docs
+test_split: latest
+doc_to_text: !function utils.doc_to_text
+doc_to_target: answer
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        regex_pattern: 'the answer is (.*?)\.'
+      - function: "take_first"
+generation_kwargs:
+  until: "\n\nQ: "
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 512
+num_fewshot: 0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/bbh/utils.py

@@ -0,0 +1,21 @@
+import random
+import re
+
+import datasets
+
+
+
+def doc_to_text(doc: dict) -> str:
+    return doc["input_final_prompts"][0]
+
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc: dict) -> dict:
+        out_doc = {
+            "problem": doc["input_question"],
+            "answer": doc["input_correct_responses"][0],
+        }
+        return out_doc
+    dataset = dataset.select_columns(["input_question", "input_correct_responses", "input_final_prompts", "is_correct","input_question_hash","output_prediction_text"])
+    dataset = dataset.rename_column("is_correct","previously_is_correct")
+    dataset = dataset.map(_process_doc)
+    return dataset.map(_process_doc)

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/gpqa/gpqa_0shot.yaml

@@ -0,0 +1,29 @@
+dataset_path: meta-llama/Llama-3.1-70B-Instruct-evals
+dataset_name: Llama-3.1-70B-Instruct-evals__gpqa__details
+task: meta_gpqa
+output_type: generate_until
+process_docs: !function utils.process_docs
+test_split: latest
+doc_to_text: !function utils.doc_to_text
+doc_to_target: gold
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        group_select: -1
+        regex_pattern: ' ([A-Z])'
+      - function: "take_first"
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 2048
+num_fewshot: 0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/gpqa/utils.py

@@ -0,0 +1,19 @@
+import random
+import re
+
+import datasets
+
+def doc_to_text(doc: dict) -> str:
+    return doc["input_final_prompts"][0]
+
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc: dict) -> dict:
+        out_doc = {
+            "problem": doc["input_question"],
+            "gold": doc["input_correct_responses"][0],
+        }
+        return out_doc
+    dataset = dataset.select_columns(["input_question", "input_correct_responses", "input_final_prompts", "is_correct","input_question_hash","input_choice_list","output_prediction_text"])
+    dataset = dataset.rename_column("is_correct","previously_is_correct")
+    dataset = dataset.map(_process_doc)
+    return dataset.map(_process_doc)

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/gpqa_cot/gpqa_0shot_cot.yaml

@@ -0,0 +1,29 @@
+dataset_path: meta-llama/Llama-3.1-70B-Instruct-evals
+dataset_name: Llama-3.1-70B-Instruct-evals__gpqa__details
+task: meta_gpqa_cot
+output_type: generate_until
+process_docs: !function utils.process_docs
+test_split: latest
+doc_to_text: !function utils.doc_to_text
+doc_to_target: gold
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        group_select: -1
+        regex_pattern: 'best answer is ([A-Z])'
+      - function: "take_first"
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 2048
+num_fewshot: 0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/gpqa_cot/utils.py

@@ -0,0 +1,20 @@
+import random
+import re
+
+import datasets
+
+
+
+def doc_to_text(doc: dict) -> str:
+    return doc["input_final_prompts"][0]
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc: dict) -> dict:
+        out_doc = {
+            "problem": doc["input_question"],
+            "gold": doc["input_correct_responses"][0],
+        }
+        return out_doc
+    dataset = dataset.select_columns(["input_question", "input_correct_responses", "input_final_prompts", "is_correct","input_question_hash","input_choice_list","output_prediction_text"])
+    dataset = dataset.rename_column("is_correct","previously_is_correct")
+    dataset = dataset.map(_process_doc)
+    return dataset.map(_process_doc)

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/ifeval/ifeval.yaml

@@ -0,0 +1,32 @@
+task: meta_ifeval
+dataset_path: parquet
+dataset_kwargs:
+  data_files: ./work_dir/joined_ifeval.parquet
+output_type: generate_until
+test_split: train
+num_fewshot: 0
+doc_to_text: prompt
+doc_to_target: 0
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0.0
+  max_gen_toks: 1280
+process_results: !function utils.process_results
+metric_list:
+  - metric: prompt_level_strict_acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: inst_level_strict_acc
+    aggregation: !function utils.agg_inst_level_acc
+    higher_is_better: true
+  - metric: prompt_level_loose_acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: inst_level_loose_acc
+    aggregation: !function utils.agg_inst_level_acc
+    higher_is_better: true
+metadata:
+  version: 2.0
+fewshot_config:
+  sampler: first_n

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/ifeval/utils.py

@@ -0,0 +1,139 @@
+import dataclasses
+from typing import Dict, Optional, Union
+
+from lm_eval.tasks.ifeval import instructions_registry
+
+
+@dataclasses.dataclass
+class InputExample:
+    key: int
+    instruction_id_list: list[str]
+    prompt: str
+    kwargs: list[Dict[str, Optional[Union[str, int]]]]
+
+
+@dataclasses.dataclass
+class OutputExample:
+    instruction_id_list: list[str]
+    prompt: str
+    response: str
+    follow_all_instructions: bool
+    follow_instruction_list: list[bool]
+
+
+def test_instruction_following_strict(
+    inp,
+    response,
+):
+    """Tests response to see if instructions are followed."""
+    instruction_list = inp.instruction_id_list
+    is_following_list = []
+
+    for index, instruction_id in enumerate(instruction_list):
+        instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
+        instruction = instruction_cls(instruction_id)
+
+        # Remove None values from kwargs to avoid unexpected keyword argument errors in build_description method.
+        kwargs = {k: v for k, v in inp.kwargs[index].items() if v}
+        instruction.build_description(**kwargs)
+        args = instruction.get_instruction_args()
+        if args and "prompt" in args:
+            instruction.build_description(prompt=inp.prompt)
+
+        if response.strip() and instruction.check_following(response):
+            is_following_list.append(True)
+        else:
+            is_following_list.append(False)
+
+    return OutputExample(
+        instruction_id_list=inp.instruction_id_list,
+        prompt=inp.prompt,
+        response=response,
+        follow_all_instructions=all(is_following_list),
+        follow_instruction_list=is_following_list,
+    )
+
+
+def test_instruction_following_loose(
+    inp,
+    response,
+):
+    """Tests response for an upper bound for following instructions."""
+    r = response.split("\n")
+    response_remove_first = "\n".join(r[1:]).strip()
+    response_remove_last = "\n".join(r[:-1]).strip()
+    response_remove_both = "\n".join(r[1:-1]).strip()
+    revised_response = response.replace("*", "")
+    revised_response_remove_first = response_remove_first.replace("*", "")
+    revised_response_remove_last = response_remove_last.replace("*", "")
+    revised_response_remove_both = response_remove_both.replace("*", "")
+    all_responses = [
+        response,
+        revised_response,
+        response_remove_first,
+        response_remove_last,
+        response_remove_both,
+        revised_response_remove_first,
+        revised_response_remove_last,
+        revised_response_remove_both,
+    ]
+    instruction_list = inp.instruction_id_list
+    is_following_list = []
+
+    for index, instruction_id in enumerate(instruction_list):
+        instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
+        instruction = instruction_cls(instruction_id)
+
+        # Remove None values from kwargs to avoid unexpected keyword argument errors in build_description method.
+        kwargs = {k: v for k, v in inp.kwargs[index].items() if v}
+        instruction.build_description(**kwargs)
+        args = instruction.get_instruction_args()
+        if args and "prompt" in args:
+            instruction.build_description(prompt=inp.prompt)
+
+        is_following = False
+        for r in all_responses:
+            if r.strip() and instruction.check_following(r):
+                is_following = True
+                break
+
+        is_following_list.append(is_following)
+
+    return OutputExample(
+        instruction_id_list=inp.instruction_id_list,
+        prompt=inp.prompt,
+        response=response,
+        follow_all_instructions=all(is_following_list),
+        follow_instruction_list=is_following_list,
+    )
+
+
+def process_results(doc, results):
+    new_kwargs = []
+    for item in doc["kwargs"]:
+        if item["nth_paragraph"]:
+            item["nth_paragraph"] = int(item["nth_paragraph"])
+        new_kwargs.append(item)
+    inp = InputExample(
+        key=doc["key"],
+        instruction_id_list=doc["instruction_id_list"],
+        prompt=doc["prompt"],
+        kwargs=new_kwargs,
+    )
+    response = results[0]
+
+    out_strict = test_instruction_following_strict(inp, response)
+    out_loose = test_instruction_following_loose(inp, response)
+
+    return {
+        "prompt_level_strict_acc": out_strict.follow_all_instructions,
+        "inst_level_strict_acc": out_strict.follow_instruction_list,
+        "prompt_level_loose_acc": out_loose.follow_all_instructions,
+        "inst_level_loose_acc": out_loose.follow_instruction_list,
+    }
+
+
+def agg_inst_level_acc(items):
+    flat_items = [item for sublist in items for item in sublist]
+    inst_level_acc = sum(flat_items) / len(flat_items)
+    return inst_level_acc

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/math_hard/math_4shot_cot.yaml

@@ -0,0 +1,21 @@
+dataset_path: parquet
+dataset_kwargs:
+  data_files: ./work_dir/joined_math.parquet
+task: meta_math
+process_docs: !function utils.process_docs
+output_type: generate_until
+test_split: train
+doc_to_text:  !function utils.doc_to_text
+process_results: !function utils.process_results
+doc_to_target: answer
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 512
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/work_dir/math_hard/math_hard_0shot_cot.yaml

@@ -0,0 +1,21 @@
+dataset_path: parquet
+dataset_kwargs:
+  data_files: ./work_dir/joined_math_hard.parquet
+task: meta_math_hard
+process_docs: !function utils.process_docs
+output_type: generate_until
+test_split: train
+doc_to_text:  !function utils.doc_to_text
+process_results: !function utils.process_results
+doc_to_target: answer
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 5120
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0