Add MedAlign scenario

src/helm/benchmark/adaptation/adapter_spec.py

@@ -13,7 +13,7 @@
 ADAPT_MULTIPLE_CHOICE_SEPARATE_ORIGINAL: str = "multiple_choice_separate_original"
 ADAPT_MULTIPLE_CHOICE_SEPARATE_CALIBRATED: str = "multiple_choice_separate_calibrated"
 ADAPT_RANKING_BINARY: str = "ranking_binary"
-
+ADAPT_EHR_INSTRUCTION: str = "ehr_instruction"
 ADAPT_MULTIPLE_CHOICE_SEPARATE_METHODS: List[str] = [
     ADAPT_MULTIPLE_CHOICE_SEPARATE_ORIGINAL,
     ADAPT_MULTIPLE_CHOICE_SEPARATE_CALIBRATED,

src/helm/benchmark/adaptation/adapters/adapter_factory.py

@@ -1,4 +1,5 @@
 from helm.benchmark.adaptation.adapter_spec import (
+    ADAPT_EHR_INSTRUCTION,
     ADAPT_GENERATION,
     ADAPT_CHAT,
     ADAPT_GENERATION_MULTIMODAL,
@@ -27,6 +28,7 @@
 )
 from helm.benchmark.adaptation.adapters.multiple_choice_separate_adapter import MultipleChoiceSeparateAdapter
 from helm.benchmark.window_services.tokenizer_service import TokenizerService
+from helm.benchmark.adaptation.adapters.ehr_instruction_adapter import EHRInstructionAdapter
 
 
 class AdapterFactory:
@@ -38,7 +40,9 @@ def get_adapter(adapter_spec: AdapterSpec, tokenizer_service: TokenizerService)
         method: str = adapter_spec.method
         adapter: Adapter
 
-        if method == ADAPT_GENERATION:
+        if method == ADAPT_EHR_INSTRUCTION:
+            adapter = EHRInstructionAdapter(adapter_spec, tokenizer_service)
+        elif method == ADAPT_GENERATION:
             adapter = GenerationAdapter(adapter_spec, tokenizer_service)
         elif method == ADAPT_CHAT:
             adapter = ChatAdapter(adapter_spec, tokenizer_service)

src/helm/benchmark/adaptation/adapters/ehr_instruction_adapter.py

@@ -0,0 +1,108 @@
+from typing import List, Optional
+
+from helm.benchmark.adaptation.adapters.generation_adapter import GenerationAdapter
+from helm.benchmark.adaptation.prompt import Prompt
+from helm.benchmark.adaptation.request_state import RequestState
+from helm.benchmark.scenarios.scenario import TRAIN_SPLIT, Instance
+from helm.benchmark.window_services.window_service import EncodeResult
+from helm.common.tokenization_request import TokenizationToken
+
+
+# in the prompt templates for EHR instructions, this is the placeholder for the EHR part
+# which we use to compute accurate tokenized sequence lengths
+PROMPT_TEMPLATE_EHR_PLACEHOLDER = "{ehr}"
+
+
+class EHRInstructionAdapter(GenerationAdapter):
+    """
+    Each instance consists of the following:
+
+    EHRInstructionInput:
+        question: the question to answer or instruction to follow
+        ehr: the XML-tagged EHR to use as context to answer the question
+        prompt_template: a string template for how to combine the question + ehr
+
+    Reference output:
+        text: the 'golden' clinician response to the question
+
+    This Adapter combines the above into RequestStates with logic to truncate the EHR specifically
+    to fit in the context window with enough room for the instruction/question and the specified
+    amount of generated tokens.
+    """
+
+    def adapt(self, instances: List[Instance], parallelism: int) -> List[RequestState]:
+        """
+        Main adaptation method which takes all instances and turns them into `RequestState` objects.
+        """
+        # sanity check, since for now we assume that there are no training instances at all
+        if any(instance.split == TRAIN_SPLIT for instance in instances):
+            raise RuntimeError(f"Got train instances for {self.__class__.__name__} - expected only eval instances.")
+
+        # use superclass implementation here
+        return super().adapt(instances, parallelism)
+
+    def construct_prompt(
+        self,
+        train_instances: List[Instance],  # unused
+        eval_instance: Instance,
+        include_output: bool,  # unused
+        reference_index: Optional[int],  # unused
+    ) -> Prompt:
+        """
+        Uses the instance to construct a prompt for a given eval instance.
+
+        Parameters
+        ----------
+        eval_instance: Instance
+            the instance we wish to use to construct the prompt
+        """
+        # start by simply getting the inputs
+        question = eval_instance.input.text
+        assert eval_instance.extra_data is not None
+        ehr_text: str = eval_instance.extra_data["ehr"]
+        prompt_template: str = eval_instance.extra_data["prompt_template"]
+        full_prompt_text = prompt_template.format(question=question, ehr=ehr_text)
+
+        # insert the question and see how many tokens we have so far
+        prompt_with_instr_no_ehr_placeholder = prompt_template.format(question=question, ehr="")
+        num_tokens_no_ehr = self.window_service.get_num_tokens(prompt_with_instr_no_ehr_placeholder)
+
+        # number of tokens we can allow the EHR part to be
+        target_ehr_num_tokens = (
+            self.window_service.max_request_length - self.adapter_spec.max_tokens - num_tokens_no_ehr
+        )
+
+        # round-trip tokenization to get the correct token length we need
+        # NOTE: we truncate from the left side so that the most recent pieces of the EHR are included in the context
+        # as opposed to the canonical way of truncating from the right. This is done to match the MedAlign method.
+        full_ehr_tokens: EncodeResult = self.window_service.encode(ehr_text, max_length=None, truncation=False)
+        truncated_ehr_tokens: List[TokenizationToken] = full_ehr_tokens.tokens[-target_ehr_num_tokens:]
+        ehr_truncated: str
+        ehr_truncated = self.window_service.decode(truncated_ehr_tokens)
+
+        # create the truncated prompt
+        truncated_prompt_text = prompt_template.format(question=question, ehr=ehr_truncated)
+        num_truncations = 1
+        while (
+            num_extra_tokens := self.adapter_spec.max_tokens
+            + self.window_service.get_num_tokens(truncated_prompt_text)
+            - self.window_service.max_request_length
+        ) > 0:
+            truncated_ehr_tokens = truncated_ehr_tokens[num_extra_tokens:]
+            ehr_truncated = self.window_service.decode(truncated_ehr_tokens)
+            truncated_prompt_text = prompt_template.format(question=question, ehr=ehr_truncated)
+            num_truncations += 1
+
+        # naively construct the full non-truncated prompt
+        prompt = Prompt(
+            global_prefix=self.adapter_spec.global_prefix,
+            global_suffix=self.adapter_spec.global_suffix,
+            instance_prefix=self.adapter_spec.instance_prefix,
+            substitutions=self.adapter_spec.substitutions,
+            instructions_block=self.adapter_spec.instructions,
+            train_instance_blocks=[],
+            eval_instance_block=full_prompt_text,
+            truncated_text=truncated_prompt_text,
+        )
+
+        return prompt

src/helm/benchmark/metrics/comet_metric.py

@@ -0,0 +1,125 @@
+import logging
+from typing import List
+
+import comet
+from torch import nn
+
+from helm.benchmark.adaptation.adapter_spec import AdapterSpec
+from helm.benchmark.adaptation.request_state import RequestState
+from helm.benchmark.adaptation.scenario_state import ScenarioState
+from helm.benchmark.metrics.metric import Metric, MetricResult
+from helm.benchmark.metrics.metric_name import MetricName
+from helm.benchmark.metrics.metric_service import MetricService
+from helm.benchmark.metrics.statistic import Stat
+from helm.common.hierarchical_logger import hlog
+from helm.common.request import RequestResult
+
+
+class CometMetric(Metric):
+    """COMET machine translation metric using a regression model.
+    The model takes a triplet of source sentence, translation, and reference
+    and computes a score in the range [0, 1] reflecting the quality of the predicted
+    translation.
+
+    Paper:
+    @inproceedings{rei-etal-2022-comet,
+        title = "{COMET}-22: Unbabel-{IST} 2022 Submission for the Metrics Shared Task",
+        author = "Rei, Ricardo  and
+            C. de Souza, Jos{\'e} G.  and
+            Alves, Duarte  and
+            Zerva, Chrysoula  and
+            Farinha, Ana C  and
+            Glushkova, Taisiya  and
+            Lavie, Alon  and
+            Coheur, Luisa  and
+            Martins, Andr{\'e} F. T.",
+            editor = {Koehn, Philipp  and
+            Barrault, Lo{\"\i}c  and
+            Bojar, Ond{\v{r}}ej  and
+            Bougares, Fethi  and
+            Chatterjee, Rajen  and
+            Costa-juss{\`a}, Marta R.  and
+            Federmann, Christian  and
+            Fishel, Mark  and
+            Fraser, Alexander  and
+            Freitag, Markus  and
+            Graham, Yvette  and
+            Grundkiewicz, Roman  and
+            Guzman, Paco  and
+            Haddow, Barry  and
+            Huck, Matthias  and
+            Jimeno Yepes, Antonio  and
+            Kocmi, Tom  and
+            Martins, Andr{\'e}  and
+            Morishita, Makoto  and
+            Monz, Christof  and
+            Nagata, Masaaki  and
+            Nakazawa, Toshiaki  and
+            Negri, Matteo  and
+            N{\'e}v{\'e}ol, Aur{\'e}lie  and
+            Neves, Mariana  and
+            Popel, Martin  and
+            Turchi, Marco  and
+            Zampieri, Marcos},
+        booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
+        month = dec,
+        year = "2022",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2022.wmt-1.52",
+    }
+    """
+
+    METRIC_NAME = "comet"
+
+    def __init__(self, task: str, model_name: str = "Unbabel/wmt22-comet-da", device: str = "cpu"):
+        self.model_name = model_name
+        self.comet_scorer: nn.Module = self._load_model(model_name)
+        self.num_gpus = 0 if device == "cpu" else 1
+
+        # suppress warnings from PyTorch Lightning which spams terminal
+        logging.getLogger("lightning.pytorch.utilities.rank_zero").setLevel(logging.WARNING)
+        logging.getLogger("lightning.pytorch.accelerators.cuda").setLevel(logging.WARNING)
+
+    @staticmethod
+    def _load_model(model_name: str) -> nn.Module:
+        """Load Comet model from the checkpoint.
+
+        Returns:
+            The loaded model.
+        """
+        return comet.load_from_checkpoint(comet.download_model(model_name))
+
+    def evaluate(
+        self, scenario_state: ScenarioState, metric_service: MetricService, eval_cache_path: str, parallelism: int
+    ) -> MetricResult:
+        hlog(
+            f"Setting parallelism from {parallelism} to 1, since "
+            f"evaluating {self.__class__.__name__} with parallelism > 1 seg faults."
+        )
+        return super().evaluate(scenario_state, metric_service, eval_cache_path, parallelism=1)
+
+    def evaluate_generation(
+        self,
+        adapter_spec: AdapterSpec,
+        request_state: RequestState,
+        metric_service: MetricService,
+        eval_cache_path: str,
+    ) -> List[Stat]:
+        """Compute the COMET score for this instance"""
+        assert len(request_state.instance.references) == 1
+        ref = request_state.instance.references[0].output.text
+        src = request_state.instance.input.text
+
+        result = request_state.result
+        if not isinstance(result, RequestResult):
+            raise TypeError(f"Expected a valid result, but got {result}!")
+        mt = result.completions[0].text.strip()
+
+        # comet requires this exac5 format
+        data = [dict(ref=ref, src=src, mt=mt)]
+        output = self.comet_scorer.predict(data, gpus=self.num_gpus, progress_bar=False)  # type: ignore
+        comet_score = output[0][0]  # extract the actual score
+
+        metric_result = [Stat(MetricName(self.METRIC_NAME)).add(comet_score)]
+
+        return metric_result

src/helm/benchmark/run_specs/classic_run_specs.py

@@ -14,6 +14,7 @@
     ADAPT_MULTIPLE_CHOICE_SEPARATE_ORIGINAL,
     ADAPT_RANKING_BINARY,
     AdapterSpec,
+    ADAPT_EHR_INSTRUCTION,
 )
 from helm.benchmark.adaptation.adapters.binary_ranking_adapter import BinaryRankingAdapter
 from helm.benchmark.adaptation.common_adapter_specs import (
@@ -1218,6 +1219,41 @@ def get_medication_qa_spec() -> RunSpec:
     )
 
 
+def get_medalign_adapter_spec() -> AdapterSpec:
+    return AdapterSpec(
+        method=ADAPT_EHR_INSTRUCTION,
+        max_train_instances=0,
+        max_eval_instances=None,
+        num_outputs=1,
+        max_tokens=256,  # MedAlign default number of generation tokens
+    )
+
+
+def get_comet_metric_specs(args: Dict[str, Any]) -> List[MetricSpec]:
+    return [MetricSpec(class_name="helm.benchmark.metrics.comet_metric.CometMetric", args=args)]
+
+
+@run_spec_function("medalign")
+def get_medalign_spec(prompt_template: str = "generic.txt") -> RunSpec:
+    from helm.common.gpu_utils import get_torch_device_name
+
+    scenario_spec = ScenarioSpec(
+        class_name="helm.benchmark.scenarios.medalign_scenario.MedAlignScenario",
+        args={"prompt_template": prompt_template},
+    )
+
+    adapter_spec = get_medalign_adapter_spec()
+
+    metric_args = {"task": "medalign", "device": get_torch_device_name()}
+    return RunSpec(
+        name="medalign",
+        scenario_spec=scenario_spec,
+        adapter_spec=adapter_spec,
+        metric_specs=get_summarization_metric_specs(metric_args) + get_comet_metric_specs(metric_args),
+        groups=["medalign", "med_helm"],
+    )
+
+
 @run_spec_function("lextreme")
 def get_lextreme_spec(subset: str) -> RunSpec:
     from helm.benchmark.scenarios.lextreme_scenario import (