chore: add pre-commit (#1569)

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -69,7 +69,12 @@ def _load_multi_mqa_gptq(
         qzeros = torch.cat([q_tensor, kv_tensor], dim=1)
         qzeros = qzeros.to(device=weights.device)
 
-        bits, groupsize, _, quant_method, = weights._get_gptq_params()
+        (
+            bits,
+            groupsize,
+            _,
+            quant_method,
+        ) = weights._get_gptq_params()
         if quant_method == "gptq":
             g_idx = weights.get_tensor(f"{prefix}.c_attn.g_idx")
             g_idx = g_idx.to(device=weights.device)
@@ -306,9 +311,9 @@ class MLP(nn.Module):
             if "gelu" not in act
             else lambda x: torch.nn.functional.gelu(
                 x,
-                approximate="tanh"
-                if act in ["gelu_fast", "gelu_pytorch_tanh"]
-                else "none",
+                approximate=(
+                    "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
+                ),
             )
         )
 

server/text_generation_server/models/custom_modeling/idefics_config.py

@@ -66,6 +66,7 @@ class IdeficsVisionConfig(PretrainedConfig):
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
     """
+
     model_type = "idefics"
     attribute_map = {
         "hidden_size": "embed_dim",
@@ -125,6 +126,7 @@ class IdeficsPerceiverConfig(PretrainedConfig):
         qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
             Whether or not to use qk layer norms in perceiver
     """
+
     model_type = "idefics"
 
     def __init__(
@@ -219,6 +221,7 @@ class IdeficsConfig(PretrainedConfig):
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
+
     model_type = "idefics"
     is_composition = True
 

server/text_generation_server/models/custom_modeling/idefics_modeling.py

@@ -123,10 +123,10 @@ def expand_inputs_for_generation(
             raise ValueError(
                 "If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined."
             )
-        encoder_outputs[
-            "last_hidden_state"
-        ] = encoder_outputs.last_hidden_state.index_select(
-            0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
+        encoder_outputs["last_hidden_state"] = (
+            encoder_outputs.last_hidden_state.index_select(
+                0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
+            )
         )
         model_kwargs["encoder_outputs"] = encoder_outputs
     return input_ids, model_kwargs

server/text_generation_server/models/custom_modeling/idefics_processing.py

@@ -133,6 +133,7 @@ class IdeficsProcessor(ProcessorMixin):
             An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
         image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
     """
+
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "IdeficsImageProcessor"
     tokenizer_class = "LlamaTokenizerFast"

server/text_generation_server/models/custom_modeling/mamba_modeling.py

@@ -19,10 +19,12 @@ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
 import math
 from dataclasses import dataclass
 
+
 @dataclass
 class InferenceParams:
     """Inference parameters that are passed to the main model in order
     to efficienly calculate and store the context during inference."""
+
     max_seqlen: int
     max_batch_size: int
     conv_states: torch.Tensor
@@ -137,13 +139,28 @@ class MambaBlock(nn.Module):
     def step(self, hidden_states, conv_state, ssm_state):
         xz = self.in_proj(hidden_states.squeeze(1))
         x, z = xz.chunk(2, dim=-1)  # (B D)
-        x = causal_conv1d_update(x, conv_state, self.conv1d.weight.squeeze(1), self.conv1d.bias, self.activation)
+        x = causal_conv1d_update(
+            x,
+            conv_state,
+            self.conv1d.weight.squeeze(1),
+            self.conv1d.bias,
+            self.activation,
+        )
         x_db = self.x_proj(x)  # (B dt_rank+2*d_state)
         dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)
         dt = F.linear(dt, self.dt_proj.weight)
         A = self.negA
         y = selective_state_update(
-          ssm_state, x, dt, A, B, C, self.D, z=z, dt_bias=self.dt_proj.bias, dt_softplus=True
+            ssm_state,
+            x,
+            dt,
+            A,
+            B,
+            C,
+            self.D,
+            z=z,
+            dt_bias=self.dt_proj.bias,
+            dt_softplus=True,
         )
         out = self.out_proj(y)
         return out.unsqueeze(1), conv_state.clone(), ssm_state.clone()

server/text_generation_server/models/custom_modeling/mpt_modeling.py

@@ -2,6 +2,7 @@
 
 Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
 """
+
 import math
 import os
 import warnings

server/text_generation_server/models/flash_causal_lm.py

@@ -35,7 +35,6 @@ from text_generation_server.utils.dist import MEMORY_FRACTION
 tracer = trace.get_tracer(__name__)
 
 
-
 @dataclass
 class FlashCausalLMBatch(Batch):
     batch_id: int
@@ -1213,8 +1212,9 @@ class FlashCausalLM(Model):
             # accept each new token for this specific request since we may
             # have more than one new token per request with speculative decoding
             for next_token_id in _next_token_ids:
-                batch.next_token_chooser = batch.next_token_chooser.advance_grammar_single(i, next_token_id)
-
+                batch.next_token_chooser = (
+                    batch.next_token_chooser.advance_grammar_single(i, next_token_id)
+                )
 
             # Update values
             batch.input_lengths[i] = input_length + n_accepted_ids

server/text_generation_server/models/galactica.py

@@ -92,7 +92,9 @@ class GalacticaCausalLMBatch(CausalLMBatch):
             requests_idx_mapping[r.id] = i
             # Add escape_custom_split_sequence to the CausalLMBatch logic
             inputs.append(escape_custom_split_sequence(r.inputs))
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device, tokenizer))
+            next_token_choosers.append(
+                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
+            )
             stopping_criteria = StoppingCriteria.from_pb(
                 r.stopping_parameters, tokenizer
             )

server/text_generation_server/models/idefics_causal_lm.py

@@ -114,7 +114,9 @@ class IdeficsCausalLMBatch(Batch):
         for i, r in enumerate(pb.requests):
             requests_idx_mapping[r.id] = i
             inputs.append(r.inputs)
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device, tokenizer))
+            next_token_choosers.append(
+                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
+            )
             stopping_criteria = StoppingCriteria.from_pb(
                 r.stopping_parameters, tokenizer
             )
@@ -401,9 +403,9 @@ class IdeficsCausalLMBatch(Batch):
                 pixel_values = batch.pixel_values.new_zeros(
                     (total_batch_size, max_num_images, 3, 224, 224)
                 )
-            pixel_values[
-                start_index:end_index, :curr_batch_max_num_images
-            ] = batch.pixel_values
+            pixel_values[start_index:end_index, :curr_batch_max_num_images] = (
+                batch.pixel_values
+            )
 
             if image_attention_mask is None:
                 image_attention_mask = batch.image_attention_mask.new_zeros(
@@ -500,14 +502,14 @@ class IdeficsCausalLMBatch(Batch):
                 # We slice the keys to remove the padding from previous batches
                 past_seq_len = batch.max_input_length - 1
                 if batch.keys_head_dim_last:
-                    padded_past_keys[
-                        start_index:end_index, :, -past_seq_len:, :
-                    ] = past_keys[:, :, -past_seq_len:, :]
+                    padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
+                        past_keys[:, :, -past_seq_len:, :]
+                    )
                 else:
                     # BLOOM case
-                    padded_past_keys[
-                        start_index:end_index, :, :, -past_seq_len:
-                    ] = past_keys[:, :, :, -past_seq_len:]
+                    padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
+                        past_keys[:, :, :, -past_seq_len:]
+                    )
                 del past_keys
 
                 start_index = end_index
@@ -525,9 +527,9 @@ class IdeficsCausalLMBatch(Batch):
                 end_index = start_index + len(batch)
                 # We slice the past values to remove the padding from previous batches
                 past_seq_len = batch.max_input_length - 1
-                padded_past_values[
-                    start_index:end_index, :, -past_seq_len:, :
-                ] = past_values[:, :, -past_seq_len:, :]
+                padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
+                    past_values[:, :, -past_seq_len:, :]
+                )
                 del past_values
 
                 # Update values
@@ -603,9 +605,11 @@ class IdeficsCausalLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto"
-            if torch.cuda.is_available() and torch.cuda.device_count() > 1
-            else None,
+            device_map=(
+                "auto"
+                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                else None
+            ),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
@@ -836,9 +840,9 @@ class IdeficsCausalLM(Model):
 
         # Update attention_mask as we added a new token to input_ids
         batch.attention_mask[:, -batch.padding_right_offset] = 1
-        batch.image_attention_mask[
-            :, -batch.padding_right_offset, :
-        ] = batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
+        batch.image_attention_mask[:, -batch.padding_right_offset, :] = (
+            batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
+        )
         # Decrease right offset
         batch.padding_right_offset -= 1
 

server/text_generation_server/models/mamba.py

@@ -15,7 +15,10 @@ from text_generation_server.utils import (
 )
 from text_generation_server.models.globals import ENABLE_CUDA_GRAPHS, MEM_POOL
 import time
-from text_generation_server.models.custom_modeling.mamba_modeling import MambaModel, InferenceParams
+from text_generation_server.models.custom_modeling.mamba_modeling import (
+    MambaModel,
+    InferenceParams,
+)
 from text_generation_server.models import Model
 from typing import Any, List, Optional, Tuple, Type, Dict
 from text_generation_server.models.types import (
@@ -28,21 +31,35 @@ from text_generation_server.utils.tokens import batch_top_tokens, Sampling
 from dataclasses import dataclass
 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
 
-def new_inference_params(n_blocks: int, batch_size: int, d_inner: int, d_conv: int, d_state: int, seqlen_offset: int, dtype: torch.dtype, device: torch.device):
+
+def new_inference_params(
+    n_blocks: int,
+    batch_size: int,
+    d_inner: int,
+    d_conv: int,
+    d_state: int,
+    seqlen_offset: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
     max_seqlen = 0
     conv_states = torch.zeros(
-        (n_blocks,
-        batch_size,
-        d_inner,
-        d_conv,),
+        (
+            n_blocks,
+            batch_size,
+            d_inner,
+            d_conv,
+        ),
         device=device,
         dtype=dtype,
     )
     ssm_states = torch.zeros(
-        (n_blocks,
-        batch_size,
-        d_inner,
-        d_state,),
+        (
+            n_blocks,
+            batch_size,
+            d_inner,
+            d_state,
+        ),
         device=device,
         dtype=dtype,
     )
@@ -52,7 +69,6 @@ def new_inference_params(n_blocks: int, batch_size: int, d_inner: int, d_conv: i
         seqlen_offset=seqlen_offset,
         conv_states=conv_states,
         ssm_states=ssm_states,
-
     )
     return inference_params
 
@@ -124,7 +140,9 @@ class MambaBatch(Batch):
         for i, r in enumerate(pb.requests):
             requests_idx_mapping[r.id] = i
             inputs.append(r.inputs)
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device, tokenizer))
+            next_token_choosers.append(
+                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
+            )
             stopping_criteria = StoppingCriteria.from_pb(
                 r.stopping_parameters, tokenizer
             )
@@ -251,7 +269,9 @@ class MambaBatch(Batch):
 
         # TODO
         # Kept it simple by just updating the state, maybe updating the other CPU values is necessary.
-        self.inference_params.conv_states = self.inference_params.conv_states[:, indices]
+        self.inference_params.conv_states = self.inference_params.conv_states[
+            :, indices
+        ]
         self.inference_params.ssm_states = self.inference_params.ssm_states[:, indices]
         return self
 
@@ -280,13 +300,20 @@ class MambaBatch(Batch):
         max_seqlen = 0
         seqlen_offset = 0
 
-        (n_blocks, _, d_inner, d_conv) = (
-            batches[0].inference_params.conv_states.shape
-        )
+        (n_blocks, _, d_inner, d_conv) = batches[0].inference_params.conv_states.shape
         (_, _, _, d_state) = batches[0].inference_params.ssm_states.shape
         dtype = batches[0].inference_params.conv_states.dtype
         device = batches[0].inference_params.conv_states.device
-        inference_params = new_inference_params(n_blocks=n_blocks, batch_size=total_batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=device, dtype=dtype)
+        inference_params = new_inference_params(
+            n_blocks=n_blocks,
+            batch_size=total_batch_size,
+            d_state=d_state,
+            d_conv=d_conv,
+            d_inner=d_inner,
+            seqlen_offset=seqlen_offset,
+            device=device,
+            dtype=dtype,
+        )
 
         # Batch tensors
         input_ids = None
@@ -334,13 +361,20 @@ class MambaBatch(Batch):
                 max_input_length - batch.max_input_length
             ) * len(batch)
 
-            inference_params.max_seqlen = max(inference_params.max_seqlen, batch.inference_params.max_seqlen)
+            inference_params.max_seqlen = max(
+                inference_params.max_seqlen, batch.inference_params.max_seqlen
+            )
             assert batch.inference_params.seqlen_offset != 0, "Invalid seqlen offset"
-            inference_params.seqlen_offset = max(inference_params.seqlen_offset, batch.inference_params.seqlen_offset)
-
+            inference_params.seqlen_offset = max(
+                inference_params.seqlen_offset, batch.inference_params.seqlen_offset
+            )
 
-            inference_params.conv_states[:, start_index:end_index] = batch.inference_params.conv_states
-            inference_params.ssm_states[:, start_index:end_index] = batch.inference_params.ssm_states
+            inference_params.conv_states[:, start_index:end_index] = (
+                batch.inference_params.conv_states
+            )
+            inference_params.ssm_states[:, start_index:end_index] = (
+                batch.inference_params.ssm_states
+            )
 
             start_index = end_index
 
@@ -452,36 +486,39 @@ class Mamba(Model):
 
         # Important seqlen_offset to go through the update mecanism with the state
         seqlen_offset = 1
-        inference_params = new_inference_params(n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype)
+        inference_params = new_inference_params(
+            n_blocks=n_blocks,
+            batch_size=batch_size,
+            d_state=d_state,
+            d_conv=d_conv,
+            d_inner=d_inner,
+            seqlen_offset=seqlen_offset,
+            device=self.device,
+            dtype=self.dtype,
+        )
 
         graph = torch.cuda.CUDAGraph()
 
         torch.cuda.synchronize()
         # Run once outside to warmup
-        self.model.forward(
-            input_ids=input_ids,
-            inference_params=inference_params
-        )
+        self.model.forward(input_ids=input_ids, inference_params=inference_params)
         torch.cuda.synchronize()
 
         with torch.cuda.graph(graph, pool=MEM_POOL):
             logits = self.model.forward(
-                input_ids=input_ids,
-                inference_params=inference_params
+                input_ids=input_ids, inference_params=inference_params
             )
         torch.cuda.synchronize()
         graph_dict = {
             "input_ids": input_ids,
             "inference_params": inference_params,
             "graph": graph,
-            "logits": logits
+            "logits": logits,
         }
         self.cuda_graphs[batch_size] = graph_dict
 
     def forward(
-        self,
-        input_ids: torch.Tensor,
-        inference_params: Any
+        self, input_ids: torch.Tensor, inference_params: Any
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         bs = input_ids.shape[0]
         padded_bs = bs
@@ -504,15 +541,21 @@ class Mamba(Model):
 
         # Copy inputs to the static inputs of the cuda graph
         # Static inputs are potentially padded
-        cuda_graph["input_ids"][: bs] = input_ids
-        cuda_graph["inference_params"].conv_states[:, : bs] = inference_params.conv_states
-        cuda_graph["inference_params"].ssm_states[:, : bs] = inference_params.ssm_states
+        cuda_graph["input_ids"][:bs] = input_ids
+        cuda_graph["inference_params"].conv_states[
+            :, :bs
+        ] = inference_params.conv_states
+        cuda_graph["inference_params"].ssm_states[:, :bs] = inference_params.ssm_states
 
         # Replay the graph
         cuda_graph["graph"].replay()
 
-        inference_params.conv_states.copy_(cuda_graph["inference_params"].conv_states[:, :bs])
-        inference_params.ssm_states.copy_(cuda_graph["inference_params"].ssm_states[:, :bs])
+        inference_params.conv_states.copy_(
+            cuda_graph["inference_params"].conv_states[:, :bs]
+        )
+        inference_params.ssm_states.copy_(
+            cuda_graph["inference_params"].ssm_states[:, :bs]
+        )
 
         # Slice output to the correct shape
         return cuda_graph["logits"][:bs]
@@ -528,19 +571,25 @@ class Mamba(Model):
 
         if batch.inference_params is None:
             # 0 is important here
-            seqlen_offset = 0 
+            seqlen_offset = 0
             n_blocks = len(self.model.blocks)
             d_state = self.model.config.d_state
             d_conv = self.model.config.d_conv
             d_inner = self.model.config.d_inner
-            inference_params = new_inference_params(n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype)
+            inference_params = new_inference_params(
+                n_blocks=n_blocks,
+                batch_size=batch_size,
+                d_state=d_state,
+                d_conv=d_conv,
+                d_inner=d_inner,
+                seqlen_offset=seqlen_offset,
+                device=self.device,
+                dtype=self.dtype,
+            )
             batch.inference_params = inference_params
 
         # Forward pass
-        logits = self.forward(
-            input_ids, inference_params=batch.inference_params
-        )
-
+        logits = self.forward(input_ids, inference_params=batch.inference_params)
 
         # batch.inference_params = new_inference_params
         # Results
@@ -694,9 +743,9 @@ class Mamba(Model):
                 generations.append(generation)
 
                 # Update values
-                batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
-                    next_token_id_squeezed.item()
-                )
+                batch.next_token_choosers[i] = batch.next_token_choosers[
+                    i
+                ].advance_grammar(next_token_id_squeezed.item())
                 batch.input_ids[i, 0] = next_token_id
                 batch.all_input_ids[i] = all_input_ids
                 batch.input_lengths[i] = new_input_length

server/text_generation_server/models/rw.py

@@ -36,9 +36,11 @@ class RW(CausalLM):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto"
-            if torch.cuda.is_available() and torch.cuda.device_count() > 1
-            else None,
+            device_map=(
+                "auto"
+                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                else None
+            ),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )

server/text_generation_server/models/seq2seq_lm.py

@@ -96,7 +96,9 @@ class Seq2SeqLMBatch(Batch):
             inputs.append(r.inputs)
             requests_idx_mapping[r.id] = i
             decoder_input_lengths.append(1)
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device, tokenizer))
+            next_token_choosers.append(
+                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
+            )
             stopping_criteria = StoppingCriteria.from_pb(
                 r.stopping_parameters, tokenizer
             )
@@ -351,9 +353,9 @@ class Seq2SeqLMBatch(Batch):
                     (total_batch_size, max_input_length),
                 )
             # Copy to correct indices
-            attention_mask[
-                start_index:end_index, -batch.max_input_length :
-            ] = batch.attention_mask[:, -batch.max_input_length :]
+            attention_mask[start_index:end_index, -batch.max_input_length :] = (
+                batch.attention_mask[:, -batch.max_input_length :]
+            )
 
             # Create padded tensor
             if decoder_input_ids is None:
@@ -547,9 +549,11 @@ class Seq2SeqLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto"
-            if torch.cuda.is_available() and torch.cuda.device_count() > 1
-            else None,
+            device_map=(
+                "auto"
+                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                else None
+            ),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
@@ -750,7 +754,7 @@ class Seq2SeqLM(Model):
 
                 if top_n_tokens > 0:
                     all_top_tokens = []
-                    for (top_token_ids, top_token_logprobs) in zip(
+                    for top_token_ids, top_token_logprobs in zip(
                         top_token_ids, top_token_logprobs
                     ):
                         toptoken_texts = self.tokenizer.batch_decode(

server/text_generation_server/models/types.py

@@ -88,14 +88,16 @@ class Generation:
     def to_pb(self) -> generate_pb2.Generation:
         return generate_pb2.Generation(
             request_id=self.request_id,
-            prefill_tokens=self.prefill_tokens.to_pb()
-            if self.prefill_tokens is not None
-            else None,
+            prefill_tokens=(
+                self.prefill_tokens.to_pb() if self.prefill_tokens is not None else None
+            ),
             tokens=self.tokens.to_pb(),
-            generated_text=self.generated_text.to_pb()
-            if self.generated_text is not None
-            else None,
-            top_tokens=[top_tokens.to_pb() for top_tokens in self.top_tokens]
-            if self.top_tokens is not None
-            else None,
+            generated_text=(
+                self.generated_text.to_pb() if self.generated_text is not None else None
+            ),
+            top_tokens=(
+                [top_tokens.to_pb() for top_tokens in self.top_tokens]
+                if self.top_tokens is not None
+                else None
+            ),
         )

server/text_generation_server/pb/.gitignore

 *.py
 *.pyi
-*.py-e
\ No newline at end of file
+*.py-e

server/text_generation_server/utils/gptq/quant_linear.py

@@ -182,7 +182,7 @@ try:
             )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
 
             zeros = (zeros >> zeros_shifter[None, :]) & maxq
-            zeros = (zeros + 1) & maxq # eventually avoid overflow
+            zeros = (zeros + 1) & maxq  # eventually avoid overflow
 
             a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
             b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

server/text_generation_server/utils/layers.py

@@ -355,7 +355,9 @@ def get_linear(weight, bias, quantize):
                 "to use Exllama/GPTQ kernels for AWQ inference."
             )
         if not HAS_AWQ:
-            raise NotImplementedError("You do not seem to have awq installed, either install it (cd server &&  make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly")
+            raise NotImplementedError(
+                "You do not seem to have awq installed, either install it (cd server &&  make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly"
+            )
         linear = WQLinear(
             w_bit=bits,
             group_size=groupsize,

server/text_generation_server/utils/logits_process.py

@@ -516,7 +516,7 @@ class GrammarLogitProcessor(LogitsProcessor):
         if grammar_type == GrammarType.GRAMMAR_TYPE_JSON:
             schema = build_regex_from_object(schema)
         elif grammar_type == GrammarType.GRAMMAR_TYPE_REGEX:
-            pass # schema is already a regex just here for clarity
+            pass  # schema is already a regex just here for clarity
         fsm = RegexFSM(schema, tokenizer)
         logger.debug(f"Compiled FSM in {time.time() - start_time:.2f}s")
         return fsm

server/text_generation_server/utils/tokens.py

@@ -409,8 +409,12 @@ class HeterogeneousNextTokenChooser:
 
     def advance_grammar_single(self, grammar_state_index: int, next_id: int):
         if self.grammar_processor is not None:
-            self.fsm_grammar_states[grammar_state_index] = self.grammar_processor.advance_at_index(
-                next_id, self.fsm_grammar_states[grammar_state_index], grammar_state_index
+            self.fsm_grammar_states[grammar_state_index] = (
+                self.grammar_processor.advance_at_index(
+                    next_id,
+                    self.fsm_grammar_states[grammar_state_index],
+                    grammar_state_index,
+                )
             )
         return self