From 164153a3c7306f279d72af2159ec94b532b2ee29 Mon Sep 17 00:00:00 2001 From: alan <77715908+lihaoxin2020@users.noreply.github.com> Date: Thu, 24 Oct 2024 18:17:02 +0000 Subject: [PATCH] add before test --- pyproject.toml | 1 + .../pubmedqa/mc_3shot/config.json | 1 + .../pubmedqa/mc_3shot/requests.jsonl.gz | Bin 0 -> 389430 bytes .../pubmedqa/rc_3shot/config.json | 1 + .../pubmedqa/rc_3shot/requests.jsonl.gz | Bin 0 -> 698551 bytes .../scifact/mc_3shot/config.json | 1 + .../scifact/mc_3shot/requests.jsonl.gz | Bin 0 -> 296897 bytes .../scifact/rc_3shot/config.json | 1 + .../scifact/rc_3shot/requests.jsonl.gz | Bin 0 -> 508875 bytes src/examples/train.py | 16 + src/olmo_core/train/callbacks/__init__.py | 2 + src/olmo_core/train/callbacks/downstream.py | 764 ++++++++++++++++++ 12 files changed, 787 insertions(+) create mode 100644 src/eval/oe_eval_tasks/pubmedqa/mc_3shot/config.json create mode 100644 src/eval/oe_eval_tasks/pubmedqa/mc_3shot/requests.jsonl.gz create mode 100644 src/eval/oe_eval_tasks/pubmedqa/rc_3shot/config.json create mode 100644 src/eval/oe_eval_tasks/pubmedqa/rc_3shot/requests.jsonl.gz create mode 100644 src/eval/oe_eval_tasks/scifact/mc_3shot/config.json create mode 100644 src/eval/oe_eval_tasks/scifact/mc_3shot/requests.jsonl.gz create mode 100644 src/eval/oe_eval_tasks/scifact/rc_3shot/config.json create mode 100644 src/eval/oe_eval_tasks/scifact/rc_3shot/requests.jsonl.gz create mode 100644 src/olmo_core/train/callbacks/downstream.py diff --git a/pyproject.toml b/pyproject.toml index 5af9c589..e6382aac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ license = { file = "LICENSE" } dependencies = [ "numpy<2.0", "torch>=2.4,<=2.6.0.dev20241009", + "torchmetrics<=1.5.1", "cached-path", "requests", "packaging", diff --git a/src/eval/oe_eval_tasks/pubmedqa/mc_3shot/config.json b/src/eval/oe_eval_tasks/pubmedqa/mc_3shot/config.json new file mode 100644 index 00000000..8e726832 --- /dev/null +++ b/src/eval/oe_eval_tasks/pubmedqa/mc_3shot/config.json @@ -0,0 +1 @@ +{"task_name": "pubmedqa:mc", "task_hash": "6f91fd6326a60b11e8c2d33d632467c0", "task_config": {"task_name": "pubmedqa:mc", "task_core": "pubmedqa", "limit": 1000, "split": "test", "num_shots": 3, "fewshot_seed": 1234, "primary_metric": "acc_raw", "random_subsample_seed": 1234, "context_kwargs": {}, "generation_kwargs": {}, "metric_kwargs": {}, "native_id_field": "id", "fewshot_source": "SciLLM:PubMedQA", "dataset_path": "bigbio/pubmed_qa", "dataset_name": "pubmed_qa_labeled_fold0_source", "use_chat_format": null, "version": 0, "revision": null, "metadata": {"description": "PubMedQA (MC) using SciLLM", "regimes": ["SciLLM-v0.1"], "alias": "pubmedqa:mc::scillm"}}, "current_date": "2024-10-13 03:45:58 UTC", "num_instances": 500} \ No newline at end of file diff --git a/src/eval/oe_eval_tasks/pubmedqa/mc_3shot/requests.jsonl.gz b/src/eval/oe_eval_tasks/pubmedqa/mc_3shot/requests.jsonl.gz new file mode 100644 index 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    3Ghw+p9Y1YwA1`hU5=PJyUh#S@23U^TQq+ ztL364dnIz9M?RR7>V=`{7^OK=9r!3IE;?N21oYNrVZS!8j}(xYJ!gppv){rLQ$X2m z!kPm_rMTIDLq1rV`8+HqpHz%|_o7L^XjXZB0q2T^GgTZ(;g*@M84$c4n%N%d(3UD| zVC~(@@fytipwfBZK0y>&^rcgW%)ewaE>r|_ozFpluQ0Q?f{P5=M^ literal 0 HcmV?d00001 diff --git a/src/examples/train.py b/src/examples/train.py index deec8307..f4eb162a 100644 --- a/src/examples/train.py +++ b/src/examples/train.py @@ -34,6 +34,7 @@ GPUMemoryMonitorCallback, GradClipperCallback, LMEvaluatorCallbackConfig, + DownstreamEvaluatorCallbackConfig, ProfilerCallback, SchedulerCallback, SequenceLengthSchedulerCallback, @@ -89,6 +90,11 @@ def build_config(run_name: str, overrides: List[str]) -> ExperimentConfig: num_workers=4, ) + downstream_evaluators = [ + "pubmedqa_mc", + "scifact_rc", + ] + trainer_config = ( TrainerConfig( save_folder=f"/tmp/{run_name}", @@ -147,6 +153,16 @@ def build_config(run_name: str, overrides: List[str]) -> ExperimentConfig: eval_duration=Duration.steps(10), ), ) + .with_callback( + "downstream", + DownstreamEvaluatorCallbackConfig( + labels=downstream_evaluators, + eval_batch_size=4, + tokenizer="Qwen/Qwen2.5-1.5B", + eval_interval=250, + # eval_duration=Duration.steps(10), + ), + ) ) return ExperimentConfig( diff --git a/src/olmo_core/train/callbacks/__init__.py b/src/olmo_core/train/callbacks/__init__.py index 44028505..aad39fb2 100644 --- a/src/olmo_core/train/callbacks/__init__.py +++ b/src/olmo_core/train/callbacks/__init__.py @@ -3,6 +3,7 @@ from .comet import CometCallback, CometNotificationSetting from .config_saver import ConfigSaverCallback from .console_logger import ConsoleLoggerCallback +from .downstream import DownstreamEvaluatorCallbackConfig from .evaluator_callback import EvaluatorCallback, LMEvaluatorCallbackConfig from .float8_handler import Float8HandlerCallback from .garbage_collector import GarbageCollectorCallback @@ -24,6 +25,7 @@ "CometNotificationSetting", "ConfigSaverCallback", "ConsoleLoggerCallback", + "DownstreamEvaluatorCallbackConfig", "EvaluatorCallback", "Float8HandlerCallback", "LMEvaluatorCallbackConfig", diff --git a/src/olmo_core/train/callbacks/downstream.py b/src/olmo_core/train/callbacks/downstream.py new file mode 100644 index 00000000..133cecd3 --- /dev/null +++ b/src/olmo_core/train/callbacks/downstream.py @@ -0,0 +1,764 @@ +from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Union +from typing import TYPE_CHECKING, List, Optional +import abc +import os +import json, gzip + +from contextlib import contextmanager +from pathlib import Path +from typing import Generator + +import importlib_resources +from importlib_resources.abc import Traversable +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch.utils.data import DataLoader, DistributedSampler + +from sklearn.metrics import f1_score +from torchmetrics import Metric +import datasets + +from olmo.util import load_hf_dataset +from olmo.tokenizer import Tokenizer + +from dataclasses import dataclass, field +from olmo_core.distributed.utils import ( + get_world_size, + get_default_device, + get_fs_local_rank, + get_rank, +) +from olmo_core.eval import Evaluator +from olmo_core.exceptions import OLMoConfigurationError +import logging +from ..common import Duration +from .callback import Callback, CallbackConfig +from .evaluator_callback import EvaluatorCallback + +if TYPE_CHECKING: + from ..trainer import Trainer +log = logging.getLogger(__name__) + + +# Map from oe-eval metrics to metrics used here +METRIC_FROM_OE_EVAL = {"acc_raw": "acc", "acc_per_char": "len_norm", "acc_uncond": "pmi_dc"} +LOG_2_OF_E = 1.44269504089 + + +class ICLMetric(Metric): + # update method does not require access to global metric state + full_state_update: bool = False + + def __init__(self, metric_type="acc") -> None: + """metric_type: f1, acc, len_norm, pmi_dc, ce_loss, bpb""" + super().__init__(sync_on_compute=True) + + self.metric_type = metric_type + + self.add_state("loglikelihoods", default=[], dist_reduce_fx=None) + self.add_state("labels", default=[], dist_reduce_fx=None) + + def reset( + self, + ): + self.loglikelihoods = [] + self.labels = [] + + def update(self, batch: Dict[str, Any], lm_logits: torch.Tensor, dc_lm_logits=None): + lm_logits = F.log_softmax(lm_logits, dim=-1) + + if self.metric_type == "pmi_dc": + assert dc_lm_logits is not None, "PMI_DC acc type selected but no domain conditional logits provided" + + for idx, (doc_id, cont_id) in enumerate(zip(batch["doc_id"], batch["cont_id"])): + # [cont_len]: continuation is padded for batching + cont_tokens = batch["continuation"][idx][: batch["cont_len"][idx]] + # get logits from LM for the continuation: [cont_len, vocab] + # batch['input_ids'][idx] -> ctx + cont + padding + # -1 in both indices: lm_logits will be left shited 1 pos as 0th pos in input generates next token in the 0th pos of lm_logits + lm_cont_logits = lm_logits[idx][ + batch["ctx_len"][idx] - 1 : batch["ctx_len"][idx] + batch["cont_len"][idx] - 1 + ] + + log_likelihood: torch.Tensor + if self.metric_type == "pmi_dc": + assert dc_lm_logits is not None + # get domain conditional continuation logits: [cont_len, vocab] + dc_lm_cont_logits = dc_lm_logits[idx][ + batch["dc_len"][idx] - 1 : batch["dc_len"][idx] + batch["cont_len"][idx] - 1 + ] + + # gather log-probs at continuation token indices but divide by domain conditional prob + log_likelihood = ( + torch.gather(lm_cont_logits, 1, cont_tokens.unsqueeze(-1)).sum() + / torch.gather(dc_lm_cont_logits, 1, cont_tokens.unsqueeze(-1)).sum() + ) + elif self.metric_type == "acc" or self.metric_type == "f1": + # gather log-probs at continuation token indices + log_likelihood = torch.gather(lm_cont_logits, 1, cont_tokens.unsqueeze(-1)).sum() + elif self.metric_type == "len_norm" or self.metric_type == "ce_loss": + log_likelihood = ( + torch.gather(lm_cont_logits, 1, cont_tokens.unsqueeze(-1)).sum() / batch["cont_str_len"][idx] + ) + if self.metric_type == "ce_loss": + log_likelihood = -log_likelihood + elif self.metric_type == "bpb": + # bits per byte + log_likelihood = ( + -torch.gather(lm_cont_logits, 1, cont_tokens.unsqueeze(-1)).sum() + / batch["cont_byte_len"][idx] + * LOG_2_OF_E + ) + else: + raise ValueError(self.metric_type) + + # because metric states cannot be dict/list of tuples, store this tuple as tensor: (doc_id, cont_id, metric_state) + self.loglikelihoods.append( + torch.Tensor((doc_id, cont_id, log_likelihood)).to(batch["continuation"][idx].device) + ) + self.labels.append( + torch.LongTensor((doc_id, cont_id, batch["label_id"][idx])).to(batch["label_id"][idx].device) + ) + + def compute(self) -> torch.Tensor: + # states should have been synced from all accelerators at this point + # account for duplicates here because of DistributedSampler compensating for drop_last=False + loglikelihood_dict: Dict[int, Dict[int, float]] = {} + label_dict = {} + + # collect labels + for doc_id, cont_id, label_id in self.labels: + if doc_id.item() not in label_dict: + label_dict[doc_id.item()] = label_id.item() + + # collect loglikelihoods + for doc_id, cont_id, loglikelihood in self.loglikelihoods: + if int(doc_id.item()) not in loglikelihood_dict: + loglikelihood_dict[int(doc_id.item())] = {} + + if int(cont_id.item()) not in loglikelihood_dict[int(doc_id.item())]: + loglikelihood_dict[int(doc_id.item())][int(cont_id.item())] = loglikelihood + + # compute acc + correct = [] + preds: Optional[List[float]] = None + labels: Optional[List[int]] = None + if self.metric_type == "f1": + preds = [] + labels = [] + + for doc_id in loglikelihood_dict: + # each doc_id might have a different number of continuation + num_continuations = len(loglikelihood_dict[doc_id].keys()) + loglikelihoods = torch.tensor([-float("inf")] * num_continuations) + + skip_document = False + for cont_id in loglikelihood_dict[doc_id]: + try: + loglikelihoods[cont_id] = loglikelihood_dict[doc_id][cont_id] + except IndexError: + # We didn't process all of the continuations, so skip this document. + skip_document = True + break + + if skip_document: + continue + if self.metric_type in ["ce_loss", "bpb"]: + correct.append(loglikelihoods[0]) # Only one answer is scored + else: + correct.append(1.0 if torch.argmax(loglikelihoods).item() == label_dict[doc_id] else 0.0) + + if self.metric_type == "f1": + assert preds is not None + assert labels is not None + preds.append(torch.argmax(loglikelihoods).item()) + labels.append(label_dict[doc_id]) + + if self.metric_type == "f1": + assert preds is not None + assert labels is not None + # for NLI tasks, continuations are yes, no, neither, so idx=0 assigned to pos label + score = f1_score(labels, preds, pos_label=0, average="macro") + else: + score = sum(correct) / len(correct) + + return torch.tensor(score) + + +class ICLMultiChoiceTaskDataset(metaclass=abc.ABCMeta): + """Only supports zero-shot for now.""" + + metric_type: str + + def __init__( + self, + tokenizer: Tokenizer, + dataset_path: str, + dataset_name: Union[str, Sequence[str], None] = None, + model_ctx_len: int = 2048, + split="validation", + metric_type=None, # Override default metric type + prompts=[None], # List of prompt variants to use + ): + super().__init__() + + self.tokenizer = tokenizer + self.dataset_path = dataset_path + self.dataset_name = dataset_name + self.model_ctx_len = model_ctx_len + self.prompts = prompts + self.current_prompt = None + if metric_type is not None: + self.metric_type = metric_type + self.log_instances = 0 # Set to > 0 to log the first few instances as a sanity check + + self.samples: List[Dict[str, Any]] = [] + dataset_names: Sequence[Optional[str]] + if isinstance(dataset_name, str) or dataset_name is None: + dataset_names = [dataset_name] + else: + dataset_names = dataset_name + + dataset_list = [] + for ds_name in dataset_names: + dataset = load_hf_dataset(self.dataset_path, ds_name, split) + dataset_list.append(dataset) + self.dataset = datasets.concatenate_datasets(dataset_list) + + # prep examples + self.prep_examples() + + def __getitem__(self, index): + return self.samples[index] + + def __len__(self): + return len(self.samples) + + def prep_examples(self): + """Append doc_ids to each example so that they are processed together in the metric""" + doc_id = 0 + for doc in self.dataset: + for prompt in self.prompts: + self.current_prompt = prompt + # from EAI harness + # how this all works: + # CTX CONT + # inp 0 1 2 3|4 5 6 7 8 9 <- last token is deleted by inp[:, :-1] + # gpt2 \ \ + # logits 1 2 3|4 5 6 7 8 9 <- the ctx half gets tossed out by the + # cont_toks 4 5 6 7 8 9 [:, -len(continuation_enc):, :self.vocab_size] slice + + continuations = self.doc_to_continuations(doc) + label_id = self.doc_to_label(doc) + doc_text = self.doc_to_text(doc) + ctx = self.token_encode(doc_text) + dc = self.token_encode(self.doc_to_domain_conditional(doc)) + if self.log_instances > 0: + self.log_instances -= 1 + ds_name = self.dataset_name + if isinstance(ds_name, list): + ds_name = ds_name[0] + # log.info( + # f"Sample doc from ({self.dataset_path}, {ds_name}, {self.current_prompt}):" + # + f"\ndoc_text: {doc_text}\ncontinuations: {continuations}" + # ) + + for cont_id, continuation_str in enumerate(continuations): + cont_str_len = len(continuation_str) - 1 # continuation contain leading blank + cont_byte_len = len(continuation_str[1:].encode("utf-8")) + continuation = self.token_encode(continuation_str) + + # query, remove last token from continuation, truncate from left is longer than model ctx length + query = ctx + continuation[:-1] + query = query[-self.model_ctx_len :] + # this will be different from len(ctx) when truncated by model_ctx_len + actual_ctx_len = len(query) - len(continuation) + 1 + + # get domain conditional query + # we don't expect this to be longer than self.model_ctx_len and it won't make sense to truncate from left + dc_query = dc + continuation[:-1] + + # form a sample + self.samples.append( + { + "doc_id": doc_id, + "cont_id": cont_id, + "ctx": ctx, + "continuation": continuation, + "ctx_len": actual_ctx_len, + "dc_len": len(dc), + "cont_len": len( + continuation + ), # even if query has last token removed, LM will output same cont len + "cont_str_len": cont_str_len, + "cont_byte_len": cont_byte_len, + "query": query, # remove last token from continuation + "dc_query": dc_query, + "label_id": label_id, + } + ) + + doc_id += 1 + + def pad_tokens_until_max(self, tokens, max_len=2048): + """truncate from left if len(tokens) > model_ctx_len, max_len is not considered then + queries are already truncated at max length of model_ctx_len + this acts as additional check for all types of sequences in the batch + """ + if len(tokens) > self.model_ctx_len: + return tokens[-self.model_ctx_len :] + else: + # pad to max_len, but check again if this padding exceeded self.model_ctx_len + # this time truncate from right side of the sequence because additional padding caused len(tokens) > self.model_ctx_len + tokens = tokens + [self.tokenizer.pad_token_id] * (max_len - len(tokens)) + + if len(tokens) > self.model_ctx_len: + tokens = tokens[: self.model_ctx_len] + + return tokens + + def collate_fn(self, data): + # pad to max length + # 'ctx', 'continuation', 'query' can all have variable length + max_ctx_len = 0 + max_cont_len = 0 + max_query_len = 0 + max_dc_query_len = 0 + + for sample in data: + if len(sample["ctx"]) > max_ctx_len: + max_ctx_len = len(sample["ctx"]) + + if len(sample["continuation"]) > max_cont_len: + max_cont_len = len(sample["continuation"]) + + if len(sample["query"]) > max_query_len: + max_query_len = len(sample["query"]) + + if len(sample["dc_query"]) > max_dc_query_len: + max_dc_query_len = len(sample["dc_query"]) + + doc_ids = [] + cont_ids = [] + ctxs = [] + continuations = [] + ctx_lens = [] + dc_lens = [] + cont_lens = [] + cont_str_lens = [] + cont_byte_lens = [] + queries = [] + dc_queries = [] + label_ids = [] + + # pad according to max_lengths + for sample in data: + doc_ids.append(sample["doc_id"]) + cont_ids.append(sample["cont_id"]) + + ctxs.append(torch.LongTensor(self.pad_tokens_until_max(sample["ctx"], max_len=max_ctx_len))) + continuations.append( + torch.LongTensor(self.pad_tokens_until_max(sample["continuation"], max_len=max_cont_len)) + ) + + ctx_lens.append(sample["ctx_len"]) + dc_lens.append(sample["dc_len"]) + cont_lens.append(sample["cont_len"]) + cont_str_lens.append(sample["cont_str_len"]) + cont_byte_lens.append(sample["cont_byte_len"]) + + queries.append(torch.LongTensor(self.pad_tokens_until_max(sample["query"], max_len=max_query_len))) + dc_queries.append( + torch.LongTensor(self.pad_tokens_until_max(sample["dc_query"], max_len=max_dc_query_len)) + ) + + label_ids.append(sample["label_id"]) + + batch = { + "doc_id": torch.LongTensor(doc_ids), + "cont_id": torch.LongTensor(cont_ids), + "ctx": torch.stack(ctxs), + "continuation": torch.stack(continuations), + "ctx_len": torch.LongTensor(ctx_lens), + "dc_len": torch.LongTensor(dc_lens), + "cont_len": torch.LongTensor(cont_lens), # since query has last token removed from continuation + "cont_str_len": torch.LongTensor(cont_str_lens), + "cont_byte_len": torch.LongTensor(cont_byte_lens), + "input_ids": torch.stack(queries), + "dc_input_ids": torch.stack(dc_queries), + "label_id": torch.LongTensor(label_ids), + } + + return batch + + def token_encode(self, string: str) -> List[int]: + return self.tokenizer.encode(string, add_special_tokens=False) + + def token_decode(self, tokens: List[int]) -> str: + return self.tokenizer.decode(tokens) + + @abc.abstractmethod + def doc_to_text(self, doc) -> str: + """Match EAI eval harness + returns a single context string + """ + raise NotImplementedError + + @abc.abstractmethod + def doc_to_continuations(self, doc) -> List[str]: + """Match EAI eval harness + returns a list of continuations + """ + raise NotImplementedError + + @abc.abstractmethod + def doc_to_label(self, doc) -> int: + """Match EAI eval harness + returns continuation id which corresponds to true label + """ + raise NotImplementedError + + def doc_to_domain_conditional(self, doc) -> str: + """Provide string for domain conditional normalization + by default its blank string, continuation normalized by prob conditioned on a blank + """ + del doc + return " " + + +class OEEvalTask(ICLMultiChoiceTaskDataset): + """Generic class for OE evaluation tasks""" + + def __init__( + self, + tokenizer: Tokenizer, + dataset_path: str, + dataset_name: Union[str, Sequence[str], None] = None, + model_ctx_len: int = 2048, + split=None, + metric_type=None, + prompts=[None], # List of prompt variants to use + ): + self.tokenizer = tokenizer + self.dataset_path = dataset_path + self.dataset_name = dataset_name + self.model_ctx_len = model_ctx_len + self.log_instances = 0 # Set to > 0 to log the first few instances as a sanity check + + self.samples: List[Dict[str, Any]] = [] + dataset_names: Sequence[Optional[str]] + if isinstance(dataset_name, str) or dataset_name is None: + dataset_names = [dataset_name] + else: + dataset_names = dataset_name + + requests_list = [] + configs = [] + for ds_name in dataset_names: + config, requests = load_oe_eval_requests(self.dataset_path, ds_name, split) + requests_list.append(requests) + configs.append(config) + if metric_type is not None: + self.metric_type = metric_type + else: + # Use metric type from associated task config + for config in configs: + if config is not None: + metric_type_raw = config["task_config"].get("primary_metric") + if metric_type_raw is not None: + # acc, len_norm, pmi_dc + metric_type = METRIC_FROM_OE_EVAL[metric_type_raw] + if self.metric_type is not None and self.metric_type != metric_type: + raise ValueError(f"Conflicting metric types: {self.metric_type} and {metric_type}") + self.metric_type = metric_type + self.dataset = requests_list + + # prep examples + self.prep_examples() + + def prep_examples(self): + current_doc_id_offset = 0 + max_doc_id = 0 + for requests in self.dataset: + current_doc_id_offset += max_doc_id + max_doc_id = 0 # Max doc id seen in this dataset + for request in requests: + doc = request["doc"] + doc_id = request["doc_id"] + if doc_id >= 1000000: + # Hacky implementation of unconditional requests in oe-eval + # Not supported here for now + continue + if doc_id > max_doc_id: + max_doc_id = doc_id + assert ( + request["request_type"] == "loglikelihood" + ), f"Unsupported request type: {request['request_type']}" + + # from EAI harness + # how this all works: + # CTX CONT + # inp 0 1 2 3|4 5 6 7 8 9 <- last token is deleted by inp[:, :-1] + # gpt2 \ \ + # logits 1 2 3|4 5 6 7 8 9 <- the ctx half gets tossed out by the + # cont_toks 4 5 6 7 8 9 [:, -len(continuation_enc):, :self.vocab_size] slice + + request_dict = request["request"] + continuation_str = request_dict["continuation"] + label_id = request["label"] + cont_id = request["idx"] + if self.metric_type in ["ce_loss", "bpb"]: + if label_id != cont_id: + # Skip non-target continuations for ce_loss and bpb + continue + else: + # Treat as instance with just one continuation + cont_id = 0 + label_id = 0 + doc_text = request_dict["context"] + ctx = self.token_encode(doc_text) + dc = self.token_encode(self.doc_to_domain_conditional(doc)) + if self.log_instances > 0: + self.log_instances -= 1 + ds_name = self.dataset_name + if isinstance(ds_name, list): + ds_name = ds_name[0] + log.info( + f"Sample doc from ({self.dataset_path}, {ds_name}):" + + f"\ndoc_text: {doc_text}\ncontinuation: {continuation_str}" + ) + cont_str_len = len(continuation_str) - 1 # continuation contain leading blank + cont_byte_len = len(continuation_str[1:].encode("utf-8")) + continuation = self.token_encode(continuation_str) + + # query, remove last token from continuation, truncate from left is longer than model ctx length + query = ctx + continuation[:-1] + query = query[-self.model_ctx_len :] + # this will be different from len(ctx) when truncated by model_ctx_len + actual_ctx_len = len(query) - len(continuation) + 1 + + # get domain conditional query + # we don't expect this to be longer than self.model_ctx_len and it won't make sense to truncate from left + dc_query = dc + continuation[:-1] + + # form a sample + self.samples.append( + { + "doc_id": doc_id + current_doc_id_offset, + "cont_id": cont_id, + "ctx": ctx, + "continuation": continuation, + "ctx_len": actual_ctx_len, + "dc_len": len(dc), + "cont_len": len( + continuation + ), # even if query has last token removed, LM will output same cont len + "cont_str_len": cont_str_len, + "cont_byte_len": cont_byte_len, + "query": query, # remove last token from continuation + "dc_query": dc_query, + "label_id": label_id, + } + ) + + def doc_to_text(self, doc) -> str: + raise NotImplementedError + + def doc_to_continuations(self, doc) -> List[str]: + raise NotImplementedError + + def doc_to_label(self, doc) -> int: + raise NotImplementedError + + +class DownstreamEvaluator(Evaluator): + """ + Language modeling evaluator that computes cross entropy loss and perplexity over one or more + datasets. + + .. important:: + The :data:`batches` generated from these evaluators must contain a "metadata" field which + should be a list of dictionaries, and each dictionary item in the list should contain + a string field called "label" which indicates which dataset the data file is associated + with, and should be included in the ``labels`` argument to this class. + + :param labels: All of the task labels. + """ + + def __init__( + self, + *, + name: str, + batches: Iterable[Dict[str, Any]], + labels: Sequence[str], + metrics: List[Metric], + device: Optional[torch.device] = None, + dp_process_group: Optional[dist.ProcessGroup] = None, + ): + super().__init__( + name=name, batches=batches, device=device, dp_process_group=dp_process_group + ) + assert len(labels) == len(metrics) + self.metrics = { + label: metric for (label, metric) in zip(labels, metrics) + } + + @classmethod + def from_oe_dataset( + cls, + dataset: OEEvalTask, + *, + name: str, + batch_size: int, + device: Optional[torch.device] = None, + dp_process_group: Optional[dist.ProcessGroup] = None, + seed: int = 0, + # num_threads: Optional[int] = None, + num_workers: int = 0, + prefetch_factor: Optional[int] = None, + ) -> "DownstreamEvaluator": + ds_eval_sampler = DistributedSampler( + dataset, + drop_last=False, + shuffle=False, + num_replicas=get_world_size(), + rank=get_rank(), + seed=seed, + ) + ds_eval_dataloader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=dataset.collate_fn, + num_workers=num_workers, + sampler=ds_eval_sampler, + pin_memory=True, + prefetch_factor=prefetch_factor, + # persistent_workers=data_config.persistent_workers, + # timeout=data_config.timeout, + ) + metric = ICLMetric(metric_type=dataset.metric_type) + + evaluator = DownstreamEvaluator( + name=name, + batches=ds_eval_dataloader, + labels=[name], + metrics=[metric.to(device)], + device=device, + dp_process_group=dp_process_group, + ) + return evaluator + + def update_metrics( + self, batch: Dict[str, Any], ce_loss: torch.Tensor, logits: torch.Tensor + ) -> None: + del ce_loss + for metric in self.metrics.values(): + metric.update(batch, logits) # type: ignore + + def compute_metrics(self) -> Dict[str, torch.Tensor]: + out: Dict[str, torch.Tensor] = {} + for label in sorted(self.metrics.keys()): + metric = self.metrics[label] + key = f"eval/downstream/{label}_{metric.metric_type}" + if metric.metric_type in ["ce_loss", "bpb"]: + key = key.replace("/downstream/", f"/downstream_{metric.metric_type}/") + out[f'{label}/{key}'] = metric.compute() + return out + + def reset_metrics(self) -> None: + for metric in self.metrics.values(): + metric.reset() + + +@dataclass +class DownstreamEvaluatorCallbackConfig(CallbackConfig): + labels: List[str] + tokenizer: str + eval_batch_size: int + eval_interval: int = 1000 + eval_duration: Duration = field(default_factory=lambda: Duration.epochs(1)) + log_interval: int = 5 + + def build(self, trainer: "Trainer") -> Callback: + evaluators = [] + for label in self.labels: + task_class = label_to_task_map[label] + task_class, task_kwargs = task_class + dataset = task_class(tokenizer=Tokenizer.from_pretrained(self.tokenizer), **task_kwargs) + + evaluator = DownstreamEvaluator.from_oe_dataset( + dataset, + name=label, + batch_size=self.eval_batch_size, + device=trainer.device, + ) + evaluators.append(evaluator) + return EvaluatorCallback( + evaluators=evaluators, + eval_interval=self.eval_interval, + log_interval=self.log_interval, + eval_duration=self.eval_duration, + ) + +def load_oe_eval_requests(path: str, name: Optional[str] = None, split: Optional[str] = None): + """ + Loads an oe-eval request file from `olmo_data/oe_eval_tasks`. + TODO: Add support from loading from S3 instead? + """ + dataset_rel_path = os.path.join("oe_eval_tasks", path) + if name is not None: + dataset_rel_path = os.path.join(dataset_rel_path, name) + with get_data_path(dataset_rel_path) as dataset_path: + if not dataset_path.is_dir(): + raise NotADirectoryError(f"OE Eval dataset not found in directory {dataset_rel_path}") + data_file = dataset_path / "requests.jsonl.gz" + if not data_file.is_file(): + data_file = dataset_path / "requests.jsonl" + if not data_file.is_file(): + raise FileNotFoundError( + f"OE Eval dataset file requests-{split}.jsonl(.gz) missing in directory {dataset_rel_path}" + ) + requests = [] + if data_file.suffix == ".gz": + with gzip.open(data_file, "r") as file: + for line in file: + requests.append(json.loads(line.decode("utf-8").strip())) + else: + with open(data_file, "r") as file: + for line2 in file: + requests.append(json.loads(line2.strip())) + config = None + config_file = dataset_path / "config.json" + if config_file.is_file(): + with open(config_file, "r") as file: + config = json.load(file) + return config, requests + +def _get_data_traversable(data_rel_path: str) -> Traversable: + return importlib_resources.files("eval").joinpath(data_rel_path) + +@contextmanager +def get_data_path(data_rel_path: str) -> Generator[Path, None, None]: + try: + with importlib_resources.as_file(_get_data_traversable(data_rel_path)) as path: + yield path + finally: + pass + +label_to_task_map = { + # "pubmedqa_rc": (OEEvalTask, {"dataset_path": "pubmedqa", "dataset_name": "rc_3shot", "metric_type": "acc"}), + "pubmedqa_mc": (OEEvalTask, {"dataset_path": "pubmedqa", "dataset_name": "mc_3shot", "metric_type": "acc"}), + "scifact_rc": (OEEvalTask, {"dataset_path": "scifact", "dataset_name": "rc_3shot", "metric_type": "acc"}), + # "scifact_mc": (OEEvalTask, {"dataset_path": "scifact", "dataset_name": "mc_3shot", "metric_type": "acc"}), + # "covidfact_rc": (OEEvalTask, {"dataset_path": "covidfact", "dataset_name": "rc_2shot", "metric_type": "acc"}), + # "covidfact_mc": (OEEvalTask, {"dataset_path": "covidfact", "dataset_name": "mc_2shot", "metric_type": "acc"}), + # "healthver_rc": (OEEvalTask, {"dataset_path": "healthver", "dataset_name": "rc_3shot", "metric_type": "acc"}), + # "healthver_mc": (OEEvalTask, {"dataset_path": "healthver", "dataset_name": "mc_3shot", "metric_type": "acc"}), + # "scicite_rc": (OEEvalTask, {"dataset_path": "scicite", "dataset_name": "rc_3shot", "metric_type": "acc"}), + # "scicite_mc": (OEEvalTask, {"dataset_path": "scicite", "dataset_name": "mc_3shot", "metric_type": "acc"}), + # "acl_arc_rc": (OEEvalTask, {"dataset_path": "acl_arc", "dataset_name": "rc_6shot", "metric_type": "acc"}), + # "acl_arc_mc": (OEEvalTask, {"dataset_path": "acl_arc", "dataset_name": "mc_6shot", "metric_type": "acc"}), + # "datafinder_reco_sc_rc": (OEEvalTask, {"dataset_path": "datafinder_reco_sc", "dataset_name": "rc_5shot", "metric_type": "acc"}), + # "datafinder_reco_sc_mc": (OEEvalTask, {"dataset_path": "datafinder_reco_sc", "dataset_name": "mc_5shot", "metric_type": "acc"}), +}

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