Fix commandr import; format code

python/sglang/srt/managers/router/model_rpc.py

@@ -510,9 +510,13 @@ class ModelRpcServer:
         batch.prepare_for_decode()
 
         # Forward
-        logits, (_, _, decode_top_logprobs, _, last_logprobs) = (
-            self.model_runner.forward(batch, ForwardMode.DECODE)
-        )
+        logits, (
+            _,
+            _,
+            decode_top_logprobs,
+            _,
+            last_logprobs,
+        ) = self.model_runner.forward(batch, ForwardMode.DECODE)
         next_token_ids, _ = batch.sample(logits)
         next_token_ids = next_token_ids.cpu().tolist()
 

python/sglang/srt/models/commandr.py

@@ -24,27 +24,30 @@ from typing import List, Optional, Tuple
 
 import torch
 import torch.utils.checkpoint
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from torch.nn.parameter import Parameter
-from transformers import CohereConfig
-
-from vllm.model_executor.parallel_utils.parallel_state import (get_tensor_model_parallel_rank,
-                              get_tensor_model_parallel_world_size)
+from transformers import PretrainedConfig
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.linear import (
+    LinearMethodBase,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
 from vllm.model_executor.layers.rotary_embedding import get_rope
-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_rank,
+    get_tensor_model_parallel_world_size,
+)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
-
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
 
 
 @torch.compile
@@ -53,14 +56,12 @@ def layer_norm_func(hidden_states, weight, variance_epsilon):
     hidden_states = hidden_states.to(torch.float32)
     mean = hidden_states.mean(-1, keepdim=True)
     variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
-    hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
-                                                         variance_epsilon)
+    hidden_states = (hidden_states - mean) * torch.rsqrt(variance + variance_epsilon)
     hidden_states = weight.to(torch.float32) * hidden_states
     return hidden_states.to(input_dtype)
 
 
 class LayerNorm(nn.Module):
-
     def __init__(self, param_shape=None, eps=1e-5):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(param_shape))
@@ -68,8 +69,9 @@ class LayerNorm(nn.Module):
         set_weight_attrs(self.weight, {"weight_loader": self.weight_loader})
 
     def forward(self, hidden_states, residuals=None):
-        hidden_states = layer_norm_func(hidden_states, self.weight,
-                                        self.variance_epsilon)
+        hidden_states = layer_norm_func(
+            hidden_states, self.weight, self.variance_epsilon
+        )
         return hidden_states, residuals
 
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
@@ -79,15 +81,13 @@ class LayerNorm(nn.Module):
         if shard_dim is not None:
             shard_size = param_data.shape[shard_dim]
             start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(shard_dim, start_idx,
-                                                 shard_size)
+            loaded_weight = loaded_weight.narrow(shard_dim, start_idx, shard_size)
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
 
 # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
 class CohereMLP(nn.Module):
-
     def __init__(
         self,
         config,
@@ -119,10 +119,9 @@ class CohereMLP(nn.Module):
 
 
 class CohereAttention(nn.Module):
-
     def __init__(
         self,
-        config: CohereConfig,
+        config: PretrainedConfig,
         layer_id: int = 0,
         linear_method: Optional[LinearMethodBase] = None,
     ):
@@ -148,8 +147,8 @@ class CohereAttention(nn.Module):
         self.kv_size = self.num_kv_heads * self.head_dim
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = getattr(
-            config, "model_max_length", None) or getattr(
-                config, "max_position_embeddings", 8192)
+            config, "model_max_length", None
+        ) or getattr(config, "max_position_embeddings", 8192)
         self.rope_theta = config.rope_theta
         self.rope_scaling = getattr(config, "rope_scaling", None)
         self.use_qk_norm = getattr(config, "use_qk_norm", False)
@@ -183,12 +182,13 @@ class CohereAttention(nn.Module):
             layer_id=layer_id,
         )
         if self.use_qk_norm:
-            self.q_norm = LayerNorm(param_shape=(self.num_heads,
-                                                 self.head_dim),
-                                    eps=config.layer_norm_eps)
-            self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
-                                                 self.head_dim),
-                                    eps=config.layer_norm_eps)
+            self.q_norm = LayerNorm(
+                param_shape=(self.num_heads, self.head_dim), eps=config.layer_norm_eps
+            )
+            self.k_norm = LayerNorm(
+                param_shape=(self.num_kv_heads, self.head_dim),
+                eps=config.layer_norm_eps,
+            )
 
     def _apply_qk_norm(self, q, k):
         q = q.view(*q.shape[:-1], -1, self.head_dim)
@@ -216,19 +216,23 @@ class CohereAttention(nn.Module):
 
 
 class CohereDecoderLayer(nn.Module):
-
-    def __init__(self,
-                 config: CohereConfig,
-                 layer_id: int = 0,
-                 linear_method: Optional[LinearMethodBase] = None):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = CohereAttention(config, layer_id=layer_id, linear_method=linear_method)
+        self.self_attn = CohereAttention(
+            config, layer_id=layer_id, linear_method=linear_method
+        )
 
         self.mlp = CohereMLP(config, linear_method=linear_method)
-        self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
-                                         eps=config.layer_norm_eps)
+        self.input_layernorm = LayerNorm(
+            param_shape=(config.hidden_size), eps=config.layer_norm_eps
+        )
 
     def forward(
         self,
@@ -253,23 +257,26 @@ class CohereDecoderLayer(nn.Module):
 
 
 class CohereModel(nn.Module):
-
     def __init__(
         self,
-        config: CohereConfig,
+        config: PretrainedConfig,
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         self.config = config
         self.vocab_size = config.vocab_size
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
-                                                   config.hidden_size)
-        self.layers = nn.ModuleList([
-            CohereDecoderLayer(config, i, linear_method=linear_method)
-            for i in range(config.num_hidden_layers)
-        ])
-        self.norm = LayerNorm(param_shape=(config.hidden_size),
-                              eps=config.layer_norm_eps)
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.layers = nn.ModuleList(
+            [
+                CohereDecoderLayer(config, i, linear_method=linear_method)
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = LayerNorm(
+            param_shape=(config.hidden_size), eps=config.layer_norm_eps
+        )
 
     def forward(
         self,
@@ -292,10 +299,9 @@ class CohereModel(nn.Module):
 
 
 class CohereForCausalLM(nn.Module):
-
     def __init__(
         self,
-        config: CohereConfig,
+        config: PretrainedConfig,
         linear_method: Optional[LinearMethodBase] = None,
     ) -> None:
         super().__init__()
@@ -311,7 +317,11 @@ class CohereForCausalLM(nn.Module):
         positions: torch.Tensor,
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
-        hidden_states = self.model(input_ids, positions, input_metadata,)
+        hidden_states = self.model(
+            input_ids,
+            positions,
+            input_metadata,
+        )
         return self.logits_processor(
             input_ids, hidden_states, self.model.embed_tokens.weight, input_metadata
         )
@@ -334,7 +344,8 @@ class CohereForCausalLM(nn.Module):
         params_dict = dict(self.named_parameters())
         loaded_params = set()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format, revision):
+            model_name_or_path, cache_dir, load_format, revision
+        ):
             for param_name, shard_name, shard_id in stacked_params_mapping:
                 if shard_name not in name:
                     continue
@@ -355,8 +366,7 @@ class CohereForCausalLM(nn.Module):
                 if name.endswith(".bias") and name not in params_dict:
                     continue
                 param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
 

python/sglang/srt/models/dbrx.py

@@ -171,7 +171,6 @@ class DbrxExperts(nn.Module):
 
 
 class DbrxAttention(nn.Module):
-
     def __init__(
         self,
         config: DbrxConfig,
@@ -251,7 +250,6 @@ class DbrxAttention(nn.Module):
 
 
 class DbrxFusedNormAttention(nn.Module):
-
     def __init__(
         self,
         config: DbrxConfig,
@@ -284,7 +282,6 @@ class DbrxFusedNormAttention(nn.Module):
 
 
 class DbrxBlock(nn.Module):
-
     def __init__(
         self,
         config: DbrxConfig,
@@ -312,7 +309,6 @@ class DbrxBlock(nn.Module):
 
 
 class DbrxModel(nn.Module):
-
     def __init__(
         self,
         config: DbrxConfig,
@@ -351,7 +347,6 @@ class DbrxModel(nn.Module):
 
 
 class DbrxForCausalLM(nn.Module):
-
     def __init__(
         self,
         config: DbrxConfig,

test/srt/model/bench_llama_low_api.py

@@ -66,9 +66,9 @@ class BenchBatch:
             p_idx = prefix_req_idx[i // fork_num].item()
             n_idx = self.req_pool_indices[i].item()
             req_to_token[n_idx, :prefix_len] = req_to_token[p_idx, :prefix_len]
-            req_to_token[n_idx, prefix_len : prefix_len + extend_len] = (
-                self.out_cache_loc[i * extend_len : (i + 1) * extend_len]
-            )
+            req_to_token[
+                n_idx, prefix_len : prefix_len + extend_len
+            ] = self.out_cache_loc[i * extend_len : (i + 1) * extend_len]
 
     def update_decode(self, predict_ids, batch_size):
         assert predict_ids.shape[0] == batch_size
@@ -81,9 +81,9 @@ class BenchBatch:
             self.out_cache_cont_start,
             self.out_cache_cont_end,
         ) = self.token_to_kv_pool.alloc_contiguous(batch_size)
-        self.req_to_token_pool.req_to_token[self.req_pool_indices, self.seq_lens] = (
-            self.out_cache_loc
-        )
+        self.req_to_token_pool.req_to_token[
+            self.req_pool_indices, self.seq_lens
+        ] = self.out_cache_loc
         self.seq_lens.add_(1)