Re-enable the 80 char line width limit (#3305)

vllm/model_executor/layers/quantization/awq.py

@@ -6,7 +6,8 @@ from torch.nn.parameter import Parameter
 from vllm._C import ops
 from vllm.model_executor.layers.linear import (LinearMethodBase,
                                                set_weight_attrs)
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 
 
 class AWQConfig(QuantizationConfig):
@@ -50,7 +51,8 @@ class AWQConfig(QuantizationConfig):
     def get_config_filenames() -> List[str]:
         return [
             "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
-            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
+            # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
+            "quantize_config.json",
         ]
 
     @classmethod

vllm/model_executor/layers/quantization/gptq.py

@@ -31,8 +31,8 @@ class GPTQConfig(QuantizationConfig):
         self.pack_factor = Fraction(32, self.weight_bits)
         if self.weight_bits not in [2, 3, 4, 8]:
             raise ValueError(
-                "Currently, only 2/3/4/8-bit weight quantization is supported for "
-                f"GPTQ, but got {self.weight_bits} bits.")
+                "Currently, only 2/3/4/8-bit weight quantization is "
+                f"supported for GPTQ, but got {self.weight_bits} bits.")
 
     def __repr__(self) -> str:
         return (f"GPTQConfig(weight_bits={self.weight_bits}, "
@@ -101,7 +101,8 @@ class GPTQLinearMethod(LinearMethodBase):
                 "The input size is not aligned with the quantized "
                 "weight shape. This can be caused by too large "
                 "tensor parallel size.")
-        if output_size_per_partition % self.quant_config.pack_factor.numerator != 0:
+        if (output_size_per_partition % self.quant_config.pack_factor.numerator
+                != 0):
             raise ValueError(
                 "The output size is not aligned with the quantized "
                 "weight shape. This can be caused by too large "
@@ -114,7 +115,8 @@ class GPTQLinearMethod(LinearMethodBase):
         exllama_state = ExllamaState.UNINITIALIZED
         scale_and_zero_size = input_size // group_size
         scale_and_zero_input_dim = None
-        if input_size != input_size_per_partition and self.quant_config.group_size != -1:
+        if (input_size != input_size_per_partition
+                and self.quant_config.group_size != -1):
             # For act-order models, we cannot use Exllama for row parallel layer
             if self.quant_config.desc_act:
                 exllama_state = ExllamaState.UNUSED

vllm/model_executor/layers/quantization/marlin.py

@@ -5,7 +5,8 @@ from torch.nn.parameter import Parameter
 
 from vllm._C import ops
 from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 
 
 class MarlinConfig(QuantizationConfig):
@@ -22,8 +23,9 @@ class MarlinConfig(QuantizationConfig):
         self.group_size = group_size
         if self.group_size != 128 and self.group_size != -1:
             raise ValueError(
-                "Currently, only group size 128 and -1 (channelwise) is supported for "
-                f"Marlin, but got group_size of {self.group_size}")
+                "Currently, only group size 128 and -1 (channelwise) "
+                "is supported for Marlin, but got group_size of "
+                f"{self.group_size}")
 
         # 4 Bits packed into 32 bit datatype.
         self.pack_factor = 32 // 4
@@ -37,7 +39,8 @@ class MarlinConfig(QuantizationConfig):
         # Min in_features dim
         self.min_k_threads = 128
 
-        # Max parallel problems to solve at once (improves large batch performance)
+        # Max parallel problems to solve at once (improves large
+        # batch performance)
         self.max_parallel = 16
 
         # Permutation length used by the marlin kernels.
@@ -102,22 +105,26 @@ class MarlinLinearMethod(LinearMethodBase):
         # Validate output_size_per_partition
         if output_size_per_partition % self.quant_config.min_n_threads != 0:
             raise ValueError(
-                f"Weight output_size_per_partition = {output_size_per_partition} is not divisible by min_n_threads = {self.quant_config.min_n_threads}."
-            )
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"min_n_threads = {self.quant_config.min_n_threads}.")
         if output_size_per_partition % self.quant_config.pack_factor != 0:
             raise ValueError(
-                f"Weight output_size_per_partition = {output_size_per_partition} is not divisible by pack_factor = {self.quant_config.pack_factor}."
-            )
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"pack_factor = {self.quant_config.pack_factor}.")
 
         # Validate input_size_per_partition
         if input_size_per_partition % self.quant_config.min_k_threads != 0:
             raise ValueError(
-                f"Weight input_size_per_partition = {input_size_per_partition} is not divisible by min_k_threads = {self.quant_config.min_k_threads}."
-            )
-        if self.quant_config.group_size != -1 and input_size_per_partition % self.quant_config.group_size != 0:
-            raise ValueError(
-                f"Weight input_size_per_partition = f{input_size_per_partition} is not divisible by group_size = {self.quant_config.group_size}."
-            )
+                f"Weight input_size_per_partition = "
+                f"{input_size_per_partition} is not divisible by "
+                f"min_k_threads = {self.quant_config.min_k_threads}.")
+        if (self.quant_config.group_size != -1 and
+                input_size_per_partition % self.quant_config.group_size != 0):
+            raise ValueError(f"Weight input_size_per_partition = "
+                             f"{input_size_per_partition} is not divisible by "
+                             f"group_size = {self.quant_config.group_size}.")
 
         # Check that we have at least 4 tiles horizontally in the shard
         num_tiles_per_perm = self.quant_config.perm_len // (
@@ -149,7 +156,9 @@ class MarlinLinearMethod(LinearMethodBase):
         )
 
         # Determine if channelwise or not
-        input_groups = 1 if self.quant_config.group_size == -1 else input_size_per_partition // self.quant_config.group_size
+        input_groups = (1 if self.quant_config.group_size == -1 else
+                        input_size_per_partition //
+                        self.quant_config.group_size)
 
         scales = Parameter(
             torch.empty(

vllm/model_executor/layers/quantization/squeezellm.py

@@ -6,7 +6,8 @@ from torch.nn.parameter import Parameter
 from vllm._C import ops
 from vllm.model_executor.layers.linear import (LinearMethodBase,
                                                set_weight_attrs)
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.utils import is_hip
 
 

vllm/model_executor/layers/sampler.py

@@ -6,7 +6,8 @@ import torch.nn as nn
 
 from vllm.model_executor.parallel_utils.communication_op import (
     tensor_model_parallel_gather)
-from vllm.model_executor.sampling_metadata import SamplingMetadata, SamplingTensors
+from vllm.model_executor.sampling_metadata import (SamplingMetadata,
+                                                   SamplingTensors)
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import (Logprob, PromptLogprobs, SampleLogprobs,
                            SamplerOutput, SequenceData, SequenceGroupOutput,

vllm/model_executor/models/baichuan.py

@@ -333,7 +333,8 @@ class BaiChuanBaseForCausalLM(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
             if name == "lm_head.weight":
-                # Unlike Baichuan, Baichuan2 normalizes the head weights. Refer to:
+                # Unlike Baichuan, Baichuan2 normalizes the head weights.
+                # Refer to:
                 # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
                 # Distinguish between Baichuan and Baichuan2 by checking the
                 # vocab size. This is suggested by

vllm/model_executor/models/deepseek.py

@@ -119,7 +119,8 @@ class DeepseekMoE(nn.Module):
                                      linear_method=None)
 
         if config.n_shared_experts is not None:
-            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.n_shared_experts)
             self.shared_experts = DeepseekMLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
@@ -273,8 +274,9 @@ class DeepseekDecoderLayer(nn.Module):
             max_position_embeddings=max_position_embeddings,
             linear_method=linear_method,
         )
-        if (config.n_routed_experts is not None and  \
-            layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0):
+        if (config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0):
             self.mlp = DeepseekMoE(config=config, linear_method=linear_method)
         else:
             self.mlp = DeepseekMLP(

vllm/model_executor/models/gpt_j.py

@@ -143,7 +143,8 @@ class GPTJBlock(nn.Module):
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
+        inner_dim = (4 * config.n_embd
+                     if config.n_inner is None else config.n_inner)
         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
         self.attn = GPTJAttention(config, linear_method)
         self.mlp = GPTJMLP(inner_dim, config, linear_method)

vllm/model_executor/models/internlm2.py

@@ -305,7 +305,8 @@ class InternLM2ForCausalLM(nn.Module):
                 param = params_dict[name]
                 if "wqkv" in name:
                     config = self.config
-                    kv_groups = config.num_attention_heads // config.num_key_value_heads
+                    kv_groups = (config.num_attention_heads //
+                                 config.num_key_value_heads)
                     head_dim = config.hidden_size // config.num_attention_heads
                     loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
                                                        head_dim,

vllm/model_executor/models/olmo.py

@@ -52,7 +52,8 @@ from vllm.model_executor.layers.linear import (
 )
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_world_size, )
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -81,7 +82,8 @@ class SwiGLU(nn.Module):
 
 class OlmoAttention(nn.Module):
     """
-    This is the attention block where the output is computed as ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    This is the attention block where the output is computed as
+    ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
     (plus another skip connection).
     """
 
@@ -94,11 +96,12 @@ class OlmoAttention(nn.Module):
         self.config = config
         self.hidden_size = config.d_model
         assert config.d_model % config.n_heads == 0
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
-        )
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         self.total_num_heads = self.config.n_heads
         assert self.total_num_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
         self.head_dim = self.hidden_size // self.total_num_heads
 
         # Layer norms.
@@ -158,7 +161,8 @@ class OlmoAttention(nn.Module):
 
 class OlmoMLP(nn.Module):
     """
-    This is the MLP block where the output is computed as ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    This is the MLP block where the output is computed as
+    ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
     (plus another skip connection).
     """
 
@@ -217,7 +221,8 @@ class OlmoMLP(nn.Module):
 
 class OlmoBlock(nn.Module):
     """
-    This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))``
+    This is a typical transformer block where the output is
+    computed as ``MLP(LN(x + Attention(LN(x))))``
     (plus another skip connection).
     """
 

vllm/model_executor/models/qwen2.py

@@ -170,7 +170,8 @@ class Qwen2DecoderLayer(nn.Module):
         self.hidden_size = config.hidden_size
         # Requires transformers > 4.32.0
         rope_theta = getattr(config, "rope_theta", 1000000)
-        use_sliding_window = config.use_sliding_window and layer_idx < config.max_window_layers
+        use_sliding_window = (config.use_sliding_window
+                              and layer_idx < config.max_window_layers)
         self.self_attn = Qwen2Attention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,

vllm/model_executor/models/stablelm.py

 # coding=utf-8
-# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team.
+# All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,7 +17,8 @@
 # This code is based off the following work:
 # https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py
 # https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
-"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM) model compatible with HuggingFace weights."""
+"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
+model compatible with HuggingFace weights."""
 from typing import List, Optional, Tuple
 
 import torch
@@ -102,8 +104,8 @@ class StablelmAttention(nn.Module):
         self.kv_size = self.num_key_value_heads * self.head_dim
         self.qkv_bias = getattr(config, "use_qkv_bias", False)
         if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+            raise ValueError(f"hidden_size must be divisible by num_heads "
+                             f"(got `hidden_size`: {self.hidden_size}"
                              f" and `num_heads`: {self.num_heads}).")
 
         self.qkv_proj = QKVParallelLinear(self.hidden_size,
@@ -192,7 +194,6 @@ class StableLMEpochModel(nn.Module):
                  config: PretrainedConfig,
                  linear_method: Optional[LinearMethodBase] = None) -> None:
         super().__init__()
-        # self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,

vllm/model_executor/models/starcoder2.py

@@ -35,7 +35,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
-from vllm.model_executor.parallel_utils.parallel_state import get_tensor_model_parallel_world_size
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput

vllm/model_executor/neuron_model_loader.py

@@ -34,7 +34,8 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
 
 def get_model(model_config: ModelConfig, device_config: DeviceConfig,
               **kwargs) -> nn.Module:
-    from transformers_neuronx.config import NeuronConfig, ContinuousBatchingConfig
+    from transformers_neuronx.config import (NeuronConfig,
+                                             ContinuousBatchingConfig)
 
     parallel_config = kwargs.get("parallel_config")
     scheduler_config = kwargs.get("scheduler_config")

vllm/model_executor/parallel_utils/communication_op.py

@@ -11,7 +11,8 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_group,
     is_cupy_nccl_enabled_for_all_reduce,
 )
-from vllm.model_executor.parallel_utils.custom_all_reduce import custom_all_reduce
+from vllm.model_executor.parallel_utils.custom_all_reduce import (
+    custom_all_reduce)
 
 
 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:

vllm/model_executor/sampling_metadata.py

@@ -114,7 +114,8 @@ class SamplingTensors:
                 do_penalties = True
             if (i < sampling_metadata.num_prompts
                     and sampling_params.prompt_logprobs is not None):
-                # For tokens in the prompt that we only need to get their logprobs
+                # For tokens in the prompt that we only need to get
+                # their logprobs
                 prompt_len = sampling_metadata.prompt_lens[i]
                 temperatures += [temperature] * (prompt_len - 1)
                 top_ps += [top_p] * (prompt_len - 1)

vllm/sampling_params.py

@@ -74,8 +74,8 @@ class SamplingParams:
         stop_token_ids: List of tokens that stop the generation when they are
             generated. The returned output will contain the stop tokens unless
             the stop tokens are special tokens.
-        include_stop_str_in_output: Whether to include the stop strings in output
-            text. Defaults to False.
+        include_stop_str_in_output: Whether to include the stop strings in
+            output text. Defaults to False.
         ignore_eos: Whether to ignore the EOS token and continue generating
             tokens after the EOS token is generated.
         max_tokens: Maximum number of tokens to generate per output sequence.

vllm/sequence.py

@@ -351,7 +351,8 @@ class SequenceGroup:
             self.metrics.first_token_time = time
 
     def maybe_set_first_scheduled_time(self, time: float) -> None:
-        """Sets the first scheduled time and time in queue for Request level timings."""
+        """Sets the first scheduled time and time in queue for Request
+        level timings."""
         if self.metrics.first_scheduled_time is None:
             self.metrics.first_scheduled_time = time
             self.metrics.time_in_queue = time - self.metrics.arrival_time

vllm/spec_decode/batch_expansion.py

@@ -5,8 +5,12 @@ import torch
 
 from vllm.sequence import (SamplerOutput, SequenceGroupMetadata, SequenceData)
 from vllm.worker.worker import Worker
-from vllm.spec_decode.util import nvtx_range, sampler_output_to_torch, get_all_seq_ids, split_batch_by_proposal_len
-from vllm.spec_decode.interfaces import SpeculativeScorer, SpeculativeProposals, SpeculativeScores
+from vllm.spec_decode.util import (nvtx_range, sampler_output_to_torch,
+                                   get_all_seq_ids,
+                                   split_batch_by_proposal_len)
+from vllm.spec_decode.interfaces import (SpeculativeScorer,
+                                         SpeculativeProposals,
+                                         SpeculativeScores)
 
 SeqId = int
 TargetSeqId = int
@@ -68,7 +72,8 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
         proposal_lens_list = proposals.proposal_lens.tolist()
         proposal_token_ids_list = proposals.proposal_token_ids.tolist()
 
-        spec_indices, non_spec_indices, target_seq_group_metadata_list, num_scoring_tokens = self._expand_batch(
+        (spec_indices, non_spec_indices, target_seq_group_metadata_list,
+         num_scoring_tokens) = self._expand_batch(
              seq_group_metadata_list=seq_group_metadata_list,
              proposal_token_ids_list=proposal_token_ids_list,
              proposal_lens_list=proposal_lens_list,
@@ -125,7 +130,8 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
         num_scoring_tokens = len(target_seq_group_metadata_list)
         target_seq_group_metadata_list.extend(non_spec_seqs)
 
-        return spec_indices, non_spec_indices, target_seq_group_metadata_list, num_scoring_tokens
+        return (spec_indices, non_spec_indices, target_seq_group_metadata_list,
+                num_scoring_tokens)
 
     def _contract_batch(self, original_bs: int,
                         target_sampler_output: List[SamplerOutput],
@@ -306,10 +312,11 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
         # Convert non-speculative output tokens to tensors.
         sampler_output.sampled_token_probs = non_spec_probs
         sampler_output.sampled_token_ids = non_spec_sampled_tokens
-        non_spec_target_token_ids, non_spec_target_probs = sampler_output_to_torch(
-            [sampler_output])
+        non_spec_target_token_ids, non_spec_target_probs = (
+            sampler_output_to_torch([sampler_output]))
 
-        return target_token_ids, target_probs, non_spec_target_token_ids, non_spec_target_probs
+        return (target_token_ids, target_probs, non_spec_target_token_ids,
+                non_spec_target_probs)
 
     def _create_target_seq_id_iterator(
             self, seq_ids: List[SeqId]) -> Iterator[TargetSeqId]:

vllm/spec_decode/multi_step_worker.py

@@ -5,7 +5,8 @@ import torch
 
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 from vllm.worker.worker import Worker
-from vllm.spec_decode.interfaces import SpeculativeProposals, SpeculativeProposer
+from vllm.spec_decode.interfaces import (SpeculativeProposals,
+                                         SpeculativeProposer)
 from vllm.spec_decode.util import sampler_output_to_torch
 
 
@@ -247,7 +248,8 @@ class DraftModelTop1Proposer(SpeculativeProposer):
         """
 
         # Split speculative- and non-speculative- sequences.
-        proposal_lens, nonzero_proposal_len_seqs, nonzero_proposal_len_indices = self._split_by_max_model_len(
+        (proposal_lens, nonzero_proposal_len_seqs,
+         nonzero_proposal_len_indices) = self._split_by_max_model_len(
              seq_group_metadata_list, max_proposal_len)
 
         if nonzero_proposal_len_seqs:
@@ -306,7 +308,8 @@ class DraftModelTop1Proposer(SpeculativeProposer):
             else:
                 proposal_lens.append(0)
 
-        return proposal_lens, nonzero_proposal_len_seqs, nonzero_proposal_len_indices
+        return (proposal_lens, nonzero_proposal_len_seqs,
+                nonzero_proposal_len_indices)
 
     def _merge_outputs(
         self,
@@ -356,7 +359,8 @@ class DraftModelTop1Proposer(SpeculativeProposer):
                                             device=self._device)
         entire_proposal_probs[nonzero_proposal_len_indices] = proposal_probs
 
-        proposal_tokens, proposal_probs = entire_proposal_tokens, entire_proposal_probs
+        proposal_tokens, proposal_probs = (entire_proposal_tokens,
+                                           entire_proposal_probs)
 
         proposal_lens = torch.zeros(batch_size,
                                     dtype=torch.long,