ruff & yapf

benchmarks/kernels/benchmark_mixtral_moe_rocm.py

@@ -4,41 +4,50 @@
 import sys
 
 import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
 import torch.nn.functional as F
 import triton
 import triton.language as tl
 from tqdm import tqdm
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-from tuning_utils import (get_full_tuning_space, prune_configs, union_of_list_of_dicts)
+from tuning_utils import (
+    get_full_tuning_space,
+    prune_configs,
+    union_of_list_of_dicts,
+)
 
 import vllm._moe_C as moe_kernels
 from vllm._C import ops
-from vllm.model_executor.layers.fused_moe import (get_config_file_name,
-                                                  invoke_fused_moe_kernel,
-                                                  moe_align_block_size)
+from vllm.model_executor.layers.fused_moe import (
+    get_config_file_name,
+    invoke_fused_moe_kernel,
+    moe_align_block_size,
+)
 
 os.environ["HIP_FORCE_DEV_KERNARG"] = "1"
 os.environ["DEBUG_CLR_GRAPH_PACKET_CAPTURE"] = "1"
 
+
 def main(args):
     world_size = args.numGPU
-    mp.spawn(wrapper, args=(args,), nprocs=world_size, join=False)
+    mp.spawn(wrapper, args=(args, ), nprocs=world_size, join=False)
+
 
-
 def wrapper(rank, args):
     dist.init_process_group("nccl", world_size=args.numGPU, rank=rank)
     device_id = rank
-
-    batches = [1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536, 2048, 3072, 4096]
+
+    batches = [
+        1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536, 2048,
+        3072, 4096
+    ]
     for i in range(device_id, len(batches), args.numGPU):
         tune_batch(batches[i], model=args.model, TP=args.modelTP)
 
 
-def tune_batch(bs, model, TP):    
+def tune_batch(bs, model, TP):
     device_id = torch.distributed.get_rank()
-    
+
     if model == '8x7B':
         d_model = 4096
         model_intermediate_size = 14336
@@ -69,7 +78,9 @@ def tune_batch(bs, model, TP):
     best_config = None
     best_time_us = 1e20
 
-    progress_bar = tqdm(total=len(configs), desc=f"bs={bs:4d} device={device_id}", position=device_id)
+    progress_bar = tqdm(total=len(configs),
+                        desc=f"bs={bs:4d} device={device_id}",
+                        position=device_id)
 
     with torch.cuda.device(device_id):
         for config in configs:
@@ -135,7 +146,7 @@ def run_timing(
 
     device_ = "cuda"
     dtype_ = torch.float16
-    
+
     hidden_states = torch.rand(
         (bs, d_model),
         device=device_,
@@ -194,7 +205,7 @@ def run_timing(
         topk_weights,
         topk_ids,
         token_expert_indicies,
-        gating_output.float(), 
+        gating_output.float(),
     )
     del token_expert_indicies  # Not used. Will be used in the future.
 

benchmarks/kernels/tuning_utils.py

@@ -1,5 +1,3 @@
-
-
 ## Utilize method from rocm/Triton tuning script
 def get_full_tuning_space():
     configs = []