Black.

server/text_generation_server/models/causal_lm.py

@@ -551,7 +551,9 @@ class CausalLM(Model):
 
     def forward(
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]:
+    ) -> Tuple[
+        torch.Tensor, Optional[torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]
+    ]:
         # Model Forward
         kwargs = {
             "input_ids": input_ids,

server/text_generation_server/models/custom_modeling/bloom_modeling.py

@@ -911,10 +911,13 @@ class BloomForCausalLM(BloomPreTrainedModel):
             output = (lm_logits,) + transformer_outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
-        return CausalLMOutputWithCrossAttentions(
+        return (
+            CausalLMOutputWithCrossAttentions(
                 loss=loss,
                 logits=logits,
                 past_key_values=transformer_outputs.past_key_values,
                 hidden_states=transformer_outputs.hidden_states,
                 attentions=transformer_outputs.attentions,
-        ), speculative_logits
+            ),
+            speculative_logits,
+        )

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -613,9 +613,7 @@ class FlashRWForCausalLM(FlashRWPreTrainedModel):
 
         self.transformer = FlashRWModel(config, weights)
 
-        self.lm_head = SpeculativeHead.load(
+        self.lm_head = SpeculativeHead.load(config, prefix="lm_head", weights=weights)
-            config, prefix="lm_head", weights=weights
-        )
 
     def forward(
         self,

server/text_generation_server/models/custom_modeling/idefics_modeling.py

@@ -272,9 +272,7 @@ class IdeficsDecoupledTensorParallelLinear(nn.Module):
         weights,
     ) -> None:
         super().__init__()
-        self.fc = SpeculativeHead.load(
+        self.fc = SpeculativeHead.load(config=config, prefix="lm_head", weights=weights)
-            config=config, prefix="lm_head", weights=weights
-        )
         self.additional_fc = FastLinear.load(
             config=config,
             prefix="lm_head.additional_fc",

server/text_generation_server/models/custom_modeling/mamba_modeling.py

@@ -206,9 +206,7 @@ class MambaModel(nn.Module):
         self.norm_f = FastRMSNorm.load(
             f"{prefix}.norm_f", weights, eps=config.layer_norm_epsilon
         )
-        self.lm_head = SpeculativeHead.load(
+        self.lm_head = SpeculativeHead.load(config, f"{prefix}.embedding", weights)
-            config, f"{prefix}.embedding", weights
-        )
         self.config = config
 
     def forward(

server/text_generation_server/models/custom_modeling/t5_modeling.py

@@ -1140,7 +1140,8 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
             output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
             return ((loss,) + output) if loss is not None else output
 
-        return Seq2SeqLMOutput(
+        return (
+            Seq2SeqLMOutput(
                 loss=loss,
                 logits=logits,
                 past_key_values=decoder_outputs.past_key_values,
@@ -1150,7 +1151,9 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
                 encoder_last_hidden_state=encoder_outputs.last_hidden_state,
                 encoder_hidden_states=encoder_outputs.hidden_states,
                 encoder_attentions=encoder_outputs.attentions,
-        ), speculative_logits
+            ),
+            speculative_logits,
+        )
 
     def prepare_inputs_for_generation(
         self,

server/text_generation_server/models/flash_causal_lm.py

@@ -807,7 +807,9 @@ class FlashCausalLM(Model):
 
         return int(num_blocks * BLOCK_SIZE)
 
-    def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    def forward(
+        self, batch: FlashCausalLMBatch
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         # Model Forward
         if batch.speculative_ids is not None:
             input_ids = batch.input_ids
@@ -903,7 +905,11 @@ class FlashCausalLM(Model):
         # Replay the graph
         cuda_graph["graph"].replay()
         # Slice output to the correct shape
-        speculative_logits = cuda_graph["speculative_logits"][:bs] if cuda_graph["speculative_logits"] is not None else None
+        speculative_logits = (
+            cuda_graph["speculative_logits"][:bs]
+            if cuda_graph["speculative_logits"] is not None
+            else None
+        )
         logits = cuda_graph["logits"][:bs]
         return logits, speculative_logits
 
@@ -962,7 +968,6 @@ class FlashCausalLM(Model):
             speculative_logits,
         )
 
-
         logger.info(f"Accepted ids {accepted_ids}")
 
         batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(

server/text_generation_server/models/flash_mistral.py

@@ -412,7 +412,9 @@ class BaseFlashMistral(FlashCausalLM):
             self.cuda_graphs[bs]["speculative_logits"] = speculative_logits
         torch.cuda.synchronize()
 
-    def forward(self, batch: FlashMistralBatch) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    def forward(
+        self, batch: FlashMistralBatch
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         # Model Forward
         if batch.speculative_ids is not None:
             input_ids = batch.input_ids
@@ -515,12 +517,15 @@ class BaseFlashMistral(FlashCausalLM):
         cuda_graph["graph"].replay()
 
         # Slice output to the correct shape
-        speculative_logits = cuda_graph["speculative_logits"][:bs] if cuda_graph["speculative_logits"] is not None else None
+        speculative_logits = (
+            cuda_graph["speculative_logits"][:bs]
+            if cuda_graph["speculative_logits"] is not None
+            else None
+        )
         logits = cuda_graph["logits"][:bs]
         return logits, speculative_logits
 
 
-
 class FlashMistral(BaseFlashMistral):
     def __init__(
         self,

server/text_generation_server/models/mamba.py

@@ -560,7 +560,11 @@ class Mamba(Model):
             cuda_graph["inference_params"].ssm_states[:, :bs]
         )
         # Slice output to the correct shape
-        speculative_logits = cuda_graph["speculative_logits"][:bs] if cuda_graph["speculative_logits"] is not None else None
+        speculative_logits = (
+            cuda_graph["speculative_logits"][:bs]
+            if cuda_graph["speculative_logits"] is not None
+            else None
+        )
         logits = cuda_graph["logits"][:bs]
         return logits, speculative_logits
 
@@ -593,7 +597,9 @@ class Mamba(Model):
             batch.inference_params = inference_params
 
         # Forward pass
-        logits, speculative_logits = self.forward(input_ids, inference_params=batch.inference_params)
+        logits, speculative_logits = self.forward(
+            input_ids, inference_params=batch.inference_params
+        )
 
         # batch.inference_params = new_inference_params
         # Results

server/text_generation_server/utils/layers.py

@@ -379,6 +379,7 @@ class SuperLayer(nn.Module):
     def forward(self, x):
         return self.linear.forward(x)
 
+
 class ResBlock(torch.nn.Module):
     def __init__(self, config, prefix, weights):
         super().__init__()
@@ -426,6 +427,7 @@ class MedusaHead(torch.nn.Module):
         x = self.out(x)
         return x
 
+
 class SpeculativeHead(nn.Module):
     def __init__(self, lm_head, medusa):
         super().__init__()
@@ -440,6 +442,7 @@ class SpeculativeHead(nn.Module):
             from pathlib import Path
             from safetensors import safe_open
             import json
+
             medusa_config = str(Path(use_medusa) / "config.json")
             medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
 
@@ -460,11 +463,14 @@ class SpeculativeHead(nn.Module):
             medusa = None
         return SpeculativeHead(lm_head, medusa)
 
-    def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    def forward(
+        self, input: torch.Tensor
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         logits = self.lm_head(input)
         speculative_logits = self.medusa(input) if self.medusa is not None else None
         return logits, speculative_logits
 
+
 class TensorParallelHead(SuperLayer):
     def __init__(self, linear, process_group, should_gather: bool):
         super().__init__(linear)