black

server/text_generation/models/bloom.py

@@ -234,7 +234,9 @@ class BLOOMSharded(BLOOM):
                     if name == "word_embeddings.weight":
                         model.lm_head._parameters["weight"] = tensor
 
-    def forward(self, input_ids, attention_mask, position_ids, past_key_values: Optional = None):
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
         outputs = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,

server/text_generation/models/causal_lm.py

@@ -296,7 +296,10 @@ class CausalLM(Model):
         )
         with context_manager():
             logits, past = self.forward(
-                batch.input_ids, batch.attention_mask, batch.position_ids, batch.past_key_values
+                batch.input_ids,
+                batch.attention_mask,
+                batch.position_ids,
+                batch.past_key_values,
             )
 
         # List of indices to cache
@@ -389,7 +392,7 @@ class CausalLM(Model):
                         token_ids=token_ids.squeeze(1).tolist(),
                         logprobs=logprobs,
                         reason=reason,
-                        seed=seed
+                        seed=seed,
                     )
                 )
             # add to the next batch

server/text_generation/models/galactica.py

@@ -333,7 +333,9 @@ class GalacticaSharded(Galactica):
                     if name == "model.decoder.embed_tokens.weight":
                         model.lm_head._parameters["weight"] = tensor
 
-    def forward(self, input_ids, attention_mask, position_ids, past_key_values: Optional = None):
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
         outputs = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,

server/text_generation/models/santacoder.py

@@ -39,12 +39,16 @@ class SantaCoder(CausalLM):
             }
         )
 
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=dtype,
-            load_in_8bit=quantize,
-            trust_remote_code=True,  # required
-        ).to(device).eval()
+        self.model = (
+            AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=dtype,
+                load_in_8bit=quantize,
+                trust_remote_code=True,  # required
+            )
+            .to(device)
+            .eval()
+        )
 
         super(CausalLM, self).__init__(
             tokenizer=tokenizer,

server/text_generation/models/seq2seq_lm.py

@@ -468,7 +468,7 @@ class Seq2SeqLM(Model):
                         token_ids=token_ids.tolist(),
                         logprobs=logprobs,
                         reason=reason,
-                        seed=seed
+                        seed=seed,
                     )
                 )
             # add to the next batch

server/text_generation/models/types.py

@@ -50,5 +50,5 @@ class GeneratedText:
             token_ids=self.token_ids,
             logprobs=self.logprobs,
             finish_reason=self.reason,
-            seed=self.seed
+            seed=self.seed,
         )

server/text_generation/utils.py

@@ -33,7 +33,9 @@ class Sampling:
 
     def __call__(self, logits):
         probs = torch.nn.functional.softmax(logits, dim=-1)
-        next_tokens = torch.multinomial(probs, num_samples=1, generator=self.generator).squeeze(1)
+        next_tokens = torch.multinomial(
+            probs, num_samples=1, generator=self.generator
+        ).squeeze(1)
         return next_tokens
 
     @property
@@ -47,7 +49,9 @@ class Greedy:
 
 
 class NextTokenChooser:
-    def __init__(self, temperature=1.0, top_k=None, top_p=None, do_sample=False, seed=None):
+    def __init__(
+        self, temperature=1.0, top_k=None, top_p=None, do_sample=False, seed=None
+    ):
         warpers = LogitsProcessorList()
         # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
         # all samplers can be found in `generation_utils_samplers.py`
@@ -84,7 +88,7 @@ class NextTokenChooser:
             top_k=pb.top_k,
             top_p=pb.top_p,
             do_sample=pb.do_sample,
-            seed=seed
+            seed=seed,
         )
 
 
@@ -100,10 +104,10 @@ class StopSequenceCriteria:
 
 class StoppingCriteria:
     def __init__(
-            self,
-            eos_token_id: int,
-            stop_sequence_criterias: List[StopSequenceCriteria],
-            max_new_tokens=20,
+        self,
+        eos_token_id: int,
+        stop_sequence_criterias: List[StopSequenceCriteria],
+        max_new_tokens=20,
     ):
         self.eos_token_id = eos_token_id
         self.stop_sequence_criterias = stop_sequence_criterias
@@ -128,9 +132,9 @@ class StoppingCriteria:
 
     @classmethod
     def from_pb(
-            cls,
-            pb: generate_pb2.StoppingCriteriaParameters,
-            tokenizer: PreTrainedTokenizerBase,
+        cls,
+        pb: generate_pb2.StoppingCriteriaParameters,
+        tokenizer: PreTrainedTokenizerBase,
     ) -> "StoppingCriteria":
         stop_sequence_criterias = [
             StopSequenceCriteria(sequence) for sequence in pb.stop_sequences