chore: add pre-commit (#1569)

.github/workflows/tests.yaml

@@ -71,12 +71,11 @@ jobs:
           pip install pytest
           export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
           pytest -s -vv server/tests
-      - name: Run Rust fmt
+      - name: Pre-commit checks
         run: |
-          cargo fmt --check
-      - name: Run Rust clippy
-        run: |
-          cargo clippy
+          pip install pre-commit
+          pre-commit install
+          pre-commit run --all-files
       - name: Run Rust tests
         run: |
           cargo test

.gitignore

@@ -11,4 +11,3 @@ server/exllama_kernels/exllama_kernels/hip_func/
 *_hip.cuh
 server/exllama_kernels/exllama_kernels/hip_buffers.cuh
 server/exllama_kernels/exllama_kernels/exllama_ext_hip.cpp
-

.pre-commit-config.yaml

@@ -0,0 +1,18 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+        exclude: docs/source/basic_tutorials/launcher.md
+-   repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+    -   id: black
+-   repo: https://github.com/doublify/pre-commit-rust
+    rev: v1.0
+    hooks:
+    -   id: fmt
+    -   id: cargo-check
+    -   id: clippy

benchmark/Cargo.toml

@@ -29,4 +29,3 @@ tui = {package = "ratatui", version = "0.23", default-features = false, features
 tracing = "0.1.37"
 tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
 hf-hub = "0.3.1"
-

clients/python/text_generation/types.py

@@ -134,6 +134,7 @@ class Parameters(BaseModel):
                 raise ValidationError("`value` cannot be empty for `json` grammar")
         return v
 
+
 class Request(BaseModel):
     # Prompt
     inputs: str

docs/source/conceptual/flash_attention.md

@@ -9,4 +9,3 @@ Standard attention mechanism uses High Bandwidth Memory (HBM) to store, read and
 It is implemented for supported models. You can check out the complete list of models that support Flash Attention [here](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models), for models with flash prefix.
 
 You can learn more about Flash Attention by reading the paper in this [link](https://arxiv.org/abs/2205.14135).
-

integration-tests/models/test_mamba.py

@@ -54,7 +54,9 @@ async def test_mamba_all_params(fused_kernel_mamba, response_snapshot):
 
 @pytest.mark.asyncio
 @pytest.mark.private
-async def test_mamba_load(fused_kernel_mamba, generate_load, generous_response_snapshot):
+async def test_mamba_load(
+    fused_kernel_mamba, generate_load, generous_response_snapshot
+):
     responses = await generate_load(
         fused_kernel_mamba, "What is Deep Learning?", max_new_tokens=10, n=4
     )

router/src/validation.rs

@@ -27,6 +27,7 @@ pub struct Validation {
 }
 
 impl Validation {
+    #[allow(clippy::too_many_arguments)]
     pub(crate) fn new(
         workers: usize,
         tokenizer: Option<Tokenizer>,

server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_6.cuh

@@ -40,5 +40,3 @@ __forceinline__ __device__ void dequant_6bit_16
 #endif
 
 #endif
-
-

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -251,9 +251,9 @@ class LlamaMLP(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"
+                ),
             )
         )
         # Fuse gate and up proj

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -255,9 +255,9 @@ class MistralMLP(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"
+                ),
             )
         )
         # Fuse gate and up proj

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -344,9 +344,9 @@ class BlockSparseMoE(nn.Module):
         if "gelu" in act:
             self.act = 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"
+                ),
             )
         elif "silu" in act:
             self.act = torch.nn.functional.silu
@@ -600,9 +600,9 @@ class DenseMoE(nn.Module):
         if "gelu" in act:
             self.act = 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"
+                ),
             )
         elif "silu" in act:
             self.act = torch.nn.functional.silu

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -187,9 +187,9 @@ class FlashMLP(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/flash_phi_modeling.py

@@ -225,9 +225,9 @@ class PhiMLP(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/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]
@@ -533,14 +576,20 @@ class Mamba(Model):
             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/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