more pre-commit

lm_eval/api/task.py

@@ -905,7 +905,9 @@ class ConfigurableTask(Task):
 
             for key, result in zip(self._metric_fn_list.keys(), results):
                 _dict = self._metric_fn_list[key].compute(
-                    references=[gold], predictions=[result], **self._metric_fn_kwargs[key]
+                    references=[gold],
+                    predictions=[result],
+                    **self._metric_fn_kwargs[key],
                 )
 
                 result_dict = {**result_dict, **_dict}

lm_eval/evaluator.py

@@ -183,9 +183,7 @@ def evaluate(
     # get lists of each type of request
     for task_name, task in task_dict.items():
         versions[task_name] = task.VERSION
-        configs[task_name] = dict(
-            task.dump_config()
-        )
+        configs[task_name] = dict(task.dump_config())
 
         # deterministically shuffle docs and chop off the first `limit` because sometimes docs are in some kind of order
         # task_docs = list(task_doc_func())

lm_eval/models/hf_merged.py

@@ -27,6 +27,7 @@ class HFLM(LM):
     """
 
     AUTO_MODEL_CLASS = None
+
     def __init__(
         self,
         device="cuda",
@@ -79,7 +80,10 @@ class HFLM(LM):
         else:
             self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
 
-        assert self.AUTO_MODEL_CLASS in [transformers.AutoModelForCausalLM, transformers.AutoModelForSeq2SeqLM]
+        assert self.AUTO_MODEL_CLASS in [
+            transformers.AutoModelForCausalLM,
+            transformers.AutoModelForSeq2SeqLM,
+        ]
 
         self._model = self.AUTO_MODEL_CLASS.from_pretrained(
             pretrained, revision=revision, low_cpu_mem_usage=low_cpu_mem_usage
@@ -175,9 +179,7 @@ class HFLM(LM):
         return self._world_size
 
     def tok_encode(self, string: str, left_truncate_len=None):
-        """
-
-        """
+        """ """
         if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
             add_special_tokens = False
         elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
@@ -197,23 +199,9 @@ class HFLM(LM):
         elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
             return self.tokenizer.decode(tokens, skip_special_tokens=True)
 
-    def _model_call(self, inps, attn_mask = None ,labels = None):
-        """
-        inps: a torch tensor of shape [batch, sequence_ctx]
-        the size of sequence may vary from call to call
-
-        labels: a torch tensor of shape [batch, sequence_cont]
-        the size of sequence may vary from call to call
-
-        returns: a torch tensor of shape [batch, sequence, vocab] with the
-        logits returned from the model
-        """
-        with torch.no_grad():
-            return self.model(input_ids = inps, attention_mask = attn_mask, labels = labels).logits
-        
     def _model_call(self, inps, attn_mask=None, labels=None):
         """
-        inps: torch.Tensor
+        :param inps: torch.Tensor
             A torch tensor of shape [batch, (sequence_ctx + sequence_cont)] or of shape
             [batch, sequence_ctx]. the size of sequence may vary from call to call
         :param attn_mask: torch.Tensor, optional
@@ -229,7 +217,9 @@ class HFLM(LM):
         with torch.no_grad():
             if attn_mask or labels:
                 assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
-                return self.model(input_ids = inps, attention_mask = attn_mask, labels = labels).logits
+                return self.model(
+                    input_ids=inps, attention_mask=attn_mask, labels=labels
+                ).logits
             else:
                 assert self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                 return self.model(inps).logits
@@ -254,15 +244,19 @@ class HFLM(LM):
 
     def _select_cont_toks(self, logits, contlen=None, inplen=None):
         if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
-            assert (contlen and inplen), "Must pass input len and cont. len to select scored logits for causal LM" 
+            assert (
+                contlen and inplen
+            ), "Must pass input len and cont. len to select scored logits for causal LM"
             # discard right-padding.
             # also discard the input/context tokens. we'll only score continuations.
             logits = logits[inplen - contlen : inplen]
         elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
-            assert (contlen and not inplen), "Selecting scored logits for Seq2SeqLM requires only cont. len"
+            assert (
+                contlen and not inplen
+            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
             # only discard right-padding.
             # the logits input to this fn only contain decoder-side tokens.
-            logits = logits[: contlen]
+            logits = logits[:contlen]
 
         return logits
 
@@ -296,7 +290,7 @@ class HFLM(LM):
                 )
             )
 
-            #TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
+            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
             rolling_token_windows = [(None,) + x for x in rolling_token_windows]
 
             pad_amnt = 0
@@ -386,7 +380,7 @@ class HFLM(LM):
                     inp = torch.tensor(
                         (context_enc)[-self.max_length :],
                         dtype=torch.long,
-                        device=self.device
+                        device=self.device,
                     )
                     (inplen,) = inp.shape
                     cont = torch.tensor(
@@ -400,9 +394,17 @@ class HFLM(LM):
 
                     conts.append(cont)
 
-                    padding_len_cont = max(padding_len_cont, contlen) if padding_len_cont is not None else contlen
+                    padding_len_cont = (
+                        max(padding_len_cont, contlen)
+                        if padding_len_cont is not None
+                        else contlen
+                    )
 
-                padding_len_inp = max(padding_len_inp, inplen) if padding_len_inp is not None else inplen
+                padding_len_inp = (
+                    max(padding_len_inp, inplen)
+                    if padding_len_inp is not None
+                    else inplen
+                )
 
                 inps.append(inp)  # [1, inp_length]
                 cont_toks_list.append(continuation_enc)
@@ -411,13 +413,24 @@ class HFLM(LM):
             # create encoder attn mask and batched conts, if seq2seq
             call_kwargs = {}
             if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
-                batched_inps = utils.pad_and_concat(padding_len_inp, inps, padding_side="right") # [batch, padding_len_inp]
+                batched_inps = utils.pad_and_concat(
+                    padding_len_inp, inps, padding_side="right"
+                )  # [batch, padding_len_inp]
             elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                 # TODO: left-pad encoder inps and mask?
-                batched_inps = utils.pad_and_concat(padding_len_inp, inps) # [batch, padding_len_inp]
-                batched_conts = utils.pad_and_concat(padding_len_cont, conts) # [batch, padding_len_cont]
-                batched_encoder_mask = utils.pad_and_concat(padding_len_inp, encoder_attns) # [batch, padding_len_inp]
-                call_kwargs = {"attn_mask": batched_encoder_mask, "labels": batched_conts}
+                batched_inps = utils.pad_and_concat(
+                    padding_len_inp, inps
+                )  # [batch, padding_len_inp]
+                batched_conts = utils.pad_and_concat(
+                    padding_len_cont, conts
+                )  # [batch, padding_len_cont]
+                batched_encoder_mask = utils.pad_and_concat(
+                    padding_len_inp, encoder_attns
+                )  # [batch, padding_len_inp]
+                call_kwargs = {
+                    "attn_mask": batched_encoder_mask,
+                    "labels": batched_conts,
+                }
 
             multi_logits = F.log_softmax(
                 self._model_call(batched_inps, **call_kwargs), dim=-1
@@ -431,11 +444,13 @@ class HFLM(LM):
                 contlen = len(cont_toks)
                 # take only logits in the continuation
                 # (discard context toks if decoder-only ; discard right-padding)
-                ctx_len = inplen if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM else None
+                ctx_len = (
+                    inplen
+                    if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
+                    else None
+                )
                 logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
-                logits = logits.unsqueeze(
-                    0
-                )  # [1, seq, vocab]
+                logits = logits.unsqueeze(0)  # [1, seq, vocab]
 
                 # Check if per-token argmax is exactly equal to continuation
                 greedy_tokens = logits.argmax(dim=-1)

lm_eval/models/seq2seq.py

@@ -19,6 +19,7 @@ from accelerate import Accelerator
 @register_model("hf-seq2seq", "seq2seq")
 class Seq2SeqHFLM(LM):
     _DEFAULT_MAX_LENGTH: int = 2048
+
     def __init__(
         self,
         device="cuda",
@@ -111,7 +112,8 @@ class Seq2SeqHFLM(LM):
 
     @property
     def max_length(self):
-        return self._DEFAULT_MAX_LENGTH #TODO: Is this a good default?
+        return self._DEFAULT_MAX_LENGTH  # TODO: Is this a good default?
+
     @property
     def max_gen_toks(self):
         return 256
@@ -138,7 +140,7 @@ class Seq2SeqHFLM(LM):
     def tok_decode(self, tokens):
         return self.tokenizer.decode(tokens, skip_special_tokens=True)
 
-    def _model_call(self, inps, attn_mask = None ,labels = None):
+    def _model_call(self, inps, attn_mask=None, labels=None):
         """
         inps: a torch tensor of shape [batch, sequence_ctx]
         the size of sequence may vary from call to call
@@ -150,7 +152,9 @@ class Seq2SeqHFLM(LM):
         logits returned from the model
         """
         with torch.no_grad():
-            return self.model(input_ids = inps, attention_mask = attn_mask, labels = labels).logits
+            return self.model(
+                input_ids=inps, attention_mask=attn_mask, labels=labels
+            ).logits
 
     def _model_generate(self, context, max_length, stop, **generation_kwargs):
         # we require users to pass do_sample=True explicitly
@@ -208,7 +212,7 @@ class Seq2SeqHFLM(LM):
                 )
             )
 
-            #TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder
+            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder
             rolling_token_windows = [(None,) + x for x in rolling_token_windows]
 
             pad_amnt = 0
@@ -283,8 +287,16 @@ class Seq2SeqHFLM(LM):
                 ).to(self.device)
                 (contlen,) = cont.shape
 
-                max_batch_length_inp = max(max_batch_length_inp, inplen) if max_batch_length_inp is not None else inplen
-                max_batch_length_cont = max(max_batch_length_cont, contlen) if max_batch_length_cont is not None else contlen
+                max_batch_length_inp = (
+                    max(max_batch_length_inp, inplen)
+                    if max_batch_length_inp is not None
+                    else inplen
+                )
+                max_batch_length_cont = (
+                    max(max_batch_length_cont, contlen)
+                    if max_batch_length_cont is not None
+                    else contlen
+                )
 
                 inps.append(inp)  # [1, inp_len]
                 conts.append(cont)  # [1, cont_len]
@@ -292,13 +304,22 @@ class Seq2SeqHFLM(LM):
 
                 cont_toks_list.append(continuation_enc)
 
-            batched_inps = utils.pad_and_concat(max_batch_length_inp, inps) # [batch, padding_length]
-            batched_conts = utils.pad_and_concat(max_batch_length_cont, conts) # [batch, padding_length]
-            batched_encoder_mask = utils.pad_and_concat(max_batch_length_inp, encoder_attns)
+            batched_inps = utils.pad_and_concat(
+                max_batch_length_inp, inps
+            )  # [batch, padding_length]
+            batched_conts = utils.pad_and_concat(
+                max_batch_length_cont, conts
+            )  # [batch, padding_length]
+            batched_encoder_mask = utils.pad_and_concat(
+                max_batch_length_inp, encoder_attns
+            )
             # need to make attention mask here too
 
             multi_logits = F.log_softmax(
-                self._model_call(batched_inps, attn_mask = batched_encoder_mask, labels = batched_conts), dim=-1
+                self._model_call(
+                    batched_inps, attn_mask=batched_encoder_mask, labels=batched_conts
+                ),
+                dim=-1,
             ).cpu()  # [batch, padding_length, vocab]
 
             for (cache_key, _, _), logits, cont_toks in zip(
@@ -307,9 +328,7 @@ class Seq2SeqHFLM(LM):
 
                 # Slice to original seq length
                 contlen = len(cont_toks)
-                logits = logits[: contlen].unsqueeze(
-                    0
-                )  # [1, seq, vocab]
+                logits = logits[:contlen].unsqueeze(0)  # [1, seq, vocab]
 
                 # Check if per-token argmax is exactly equal to continuation
                 greedy_tokens = logits.argmax(dim=-1)
@@ -383,4 +402,3 @@ class Seq2SeqHFLM(LM):
             res.append(s)
 
         return re_ord.get_original(res)
-    

lm_eval/tasks/__init__.py

@@ -22,7 +22,7 @@ def include_task_folder(task_dir):
     Calling this function
     """
     for root, subdirs, file_list in os.walk(task_dir):
-        if (len(file_list) > 0):
+        if len(file_list) > 0:
             for f in file_list:
                 if f.endswith(".yaml"):
                     yaml_path = os.path.join(root, f)

lm_eval/utils.py

@@ -18,11 +18,12 @@ import torch
 from omegaconf import OmegaConf
 from jinja2 import BaseLoader, Environment, StrictUndefined
 from itertools import islice
-import torch 
+
 import transformers
 
 from lm_eval.logger import eval_logger
 
+
 class ExitCodeError(Exception):
     pass
 
@@ -416,13 +417,16 @@ def create_iterator(raw_iterator, rank, world_size, limit=None):
     """
     return islice(raw_iterator, rank, limit, world_size)
 
-def pad_and_concat(max_length:int, tensors: List[torch.Tensor], padding_side="right"):
+
+def pad_and_concat(max_length: int, tensors: List[torch.Tensor], padding_side="right"):
     """
     Method for padding a list of tensors given the maximum tensor
     length in the batch. Used for batching inputs and continuations in
     seq2seq models.
     """
-    assert padding_side == "left" or padding_side == "right", f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
+    assert (
+        padding_side == "left" or padding_side == "right"
+    ), f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
 
     for i, tensor in enumerate(tensors):
         tensor_len = tensor.shape[0]
@@ -456,7 +460,7 @@ def pad_and_concat(max_length:int, tensors: List[torch.Tensor], padding_side="ri
         else:
             tensors[i] = tensor.unsqueeze(0)
 
-    return torch.cat(tensors, dim = 0)
+    return torch.cat(tensors, dim=0)
 
 
 def clear_torch_cache():