Merge branch 'main' into shanea/storage-cleaner-unshard-improvements

Author: 2015aroras

CHANGELOG.md

@@ -13,11 +13,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added the option to directly pass input embeddings to `OLMo` and `OLMoForCausalLM`.
 - Added support for Python 3.8.
 - Added code to throw an error if `output_attentions` is set to `True` in forward call to `OLMoForCausalLM`. This functionality hasn't been implemented yet.
+- Fixed running with data loading workers on LUMI
 
 ### Added
 - Added `output_hidden_states` argument and associated functionality to `OLMo` and `OLMoForCausalLM` to return model intermediate hidden states.
-- Added MMLU downstream evaluation tasks.
+- Added MMLU downstream evaluation tasks, with prompt variations.
 - Added support for PyTorch v2.2.
+- Added ability to show logs from all ranks
+
+
 
 ## [v0.2.4](https://github.com/allenai/OLMo/releases/tag/v0.2.4) - 2024-02-02
 

olmo/eval/downstream.py

@@ -1,4 +1,5 @@
 import abc
+import logging
 import re
 from typing import Any, ClassVar, Dict, List, Optional, Sequence, Union
 
@@ -10,6 +11,8 @@
 
 from ..tokenizer import Tokenizer
 
+log = logging.getLogger(__name__)
+
 
 class ICLMetric(Metric):
     # update method does not require access to global metric state
@@ -152,13 +155,17 @@ def __init__(
         dataset_name: Union[str, Sequence[str], None] = None,
         model_ctx_len: int = 2048,
         split="validation",
+        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
+        self.log_instances = 5  # Log the first few instances as a sanity check
 
         self.samples: List[Dict[str, Any]] = []
         dataset_names: Sequence[Optional[str]]
@@ -174,6 +181,7 @@ def __init__(
                     path=self.dataset_path,
                     name=ds_name,
                     split=split,
+                    trust_remote_code=True,
                 )
             )
         self.dataset = datasets.concatenate_datasets(dataset_list)
@@ -191,51 +199,65 @@ 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:
-            # 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)
-            ctx = self.token_encode(self.doc_to_text(doc))
-            dc = self.token_encode(self.doc_to_domain_conditional(doc))
-
-            for cont_id, continuation_str in enumerate(continuations):
-                cont_str_len = len(continuation_str) - 1  # continuation contain leading blank
-                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 :]
-
-                # 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": len(ctx),
-                        "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,
-                        "query": query,  # remove last token from continuation
-                        "dc_query": dc_query,
-                        "label_id": label_id,
-                    }
-                )
-
-            doc_id += 1
+            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
+                    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,
+                            "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
@@ -655,7 +677,7 @@ def __init__(self, tokenizer, dataset_path="sciq", dataset_name=None):
         )
 
     def doc_to_text(self, doc):
-        return doc["support"] + "\nQuestion: " + doc["question"] + "\nAnswer:".strip()
+        return doc["support"].strip() + "\nQuestion: " + doc["question"] + "\nAnswer:"
 
     def doc_to_continuations(self, doc):
         # add spaces in front of continuation
@@ -1055,7 +1077,14 @@ class MMLU(ICLMultiChoiceTaskDataset):
         "other": ["other", "business", "health"],
     }
 
-    def __init__(self, tokenizer, dataset_path="hails/mmlu_no_train", dataset_name=None, split="validation"):
+    def __init__(
+        self,
+        tokenizer,
+        dataset_path="hails/mmlu_no_train",
+        dataset_name=None,
+        split="validation",
+        prompt_variations=None,
+    ):
         dataset_names = []
         # Collect the relevant categories
         if dataset_name in MMLU._categories:
@@ -1069,10 +1098,40 @@ def __init__(self, tokenizer, dataset_path="hails/mmlu_no_train", dataset_name=N
             for name, cats in MMLU._subcategories.items():
                 if dataset_name in cats:
                     dataset_names.append(name)
-        super().__init__(tokenizer=tokenizer, dataset_path=dataset_path, dataset_name=dataset_names, split=split)
+        self.dev_set = {}
+        if prompt_variations == 1:
+            prompts = [None, "inst", "inst+1", "inst+2", "inst+3", "inst+4", "inst+5"]
+            # Need to grab the dev set for the few-shot prompts
+            for name in dataset_names:
+                self.dev_set[name] = datasets.load_dataset(
+                    path=dataset_path, name=name, split="dev", trust_remote_code=True
+                )
+        super().__init__(
+            tokenizer=tokenizer,
+            dataset_path=dataset_path,
+            dataset_name=dataset_names,
+            split=split,
+            prompts=prompts,
+        )
 
     def doc_to_text(self, doc):
-        return "Question: " + doc["question"] + "\nAnswer:"
+        output_text = "Question: " + doc["question"] + "\nAnswer:"
+        if self.current_prompt is not None:
+            prefix = ""
+            if "inst" in self.current_prompt:
+                subject = doc.get("subject").replace("_", " ")
+                prefix = f"The following are multiple choice questions (with answers) about {subject}:\n\n"
+            num_shots = re.findall("\\+(\\d+)", self.current_prompt)
+            if num_shots:
+                dev_set = self.dev_set.get(doc.get("subject"), [])
+                num_shots_int = int(num_shots[0])
+                for idx, dev_doc in enumerate(dev_set):
+                    if idx >= num_shots_int:
+                        break
+                    answer = dev_doc["choices"][dev_doc["answer"]]
+                    prefix += "Question: " + dev_doc["question"] + "\nAnswer: " + answer + "\n\n"
+            output_text = prefix + output_text
+        return output_text
 
     def doc_to_continuations(self, doc):
         # add spaces in front of continuation
@@ -1108,4 +1167,8 @@ def doc_to_domain_conditional(self, doc):
     "mmlu_humanities": (MMLU, {"dataset_name": "humanities"}),
     "mmlu_social_sciences": (MMLU, {"dataset_name": "social_sciences"}),
     "mmlu_other": (MMLU, {"dataset_name": "other"}),
+    "mmlu_stem_var": (MMLU, {"dataset_name": "stem", "prompt_variations": 1}),
+    "mmlu_humanities_var": (MMLU, {"dataset_name": "humanities", "prompt_variations": 1}),
+    "mmlu_social_sciences_var": (MMLU, {"dataset_name": "social_sciences", "prompt_variations": 1}),
+    "mmlu_other_var": (MMLU, {"dataset_name": "other", "prompt_variations": 1}),
 }

olmo/util.py

@@ -59,6 +59,7 @@ def __repr__(self) -> str:
 class LogFilterType(StrEnum):
     rank0_only = "rank0_only"
     local_rank0_only = "local_rank0_only"
+    all_ranks = "all_ranks"
 
 
 def log_extra_field(field_name: str, field_value: Any) -> None:
@@ -126,11 +127,12 @@ def local_rank0_filter(record: logging.LogRecord) -> int:
         else:
             return 0
 
-    filter = None
     if log_filter_type == LogFilterType.rank0_only:
         filter = rank0_filter
     elif log_filter_type == LogFilterType.local_rank0_only:
         filter = local_rank0_filter  # type: ignore
+    elif log_filter_type == LogFilterType.all_ranks:
+        filter = None
     else:
         raise ValueError(log_filter_type)
 

pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "google-cloud-storage",
     "tokenizers",
     "packaging",
-    "cached_path",
+    "cached_path>=1.6.2",
     "transformers",
 ]
 

scripts/train.py

@@ -2,6 +2,7 @@
 
 import gzip
 import logging
+import multiprocessing as mp
 import sys
 from pathlib import Path
 from typing import Optional, TextIO
@@ -240,6 +241,7 @@ def dummy_init_fn(module: torch.nn.Module) -> None:
 
 
 if __name__ == "__main__":
+    mp.set_start_method("spawn")
     # Initialize process group.
     dist.init_process_group(backend="nccl")