remove rng state

nanovllm/engine/model_runner.py

@@ -77,7 +77,6 @@ class ModelRunner:
         assert self.world_size > 1 and not self.rank
         data = pickle.dumps([method_name, *args])
         n = len(data)
-        assert n + 4 <= self.shm.size
         self.shm.buf[0:4] = n.to_bytes(4, "little")
         self.shm.buf[4:n+4] = data
         for event in self.event:
@@ -87,7 +86,6 @@ class ModelRunner:
         if self.world_size > 1 and self.rank == 0:
             self.write_shm(method_name, *args)
         method = getattr(self, method_name, None)
-        assert callable(method)
         return method(*args)
 
     def warmup_model(self):
@@ -109,6 +107,7 @@ class ModelRunner:
         num_kv_heads = hf_config.num_key_value_heads // self.world_size
         block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * num_kv_heads * hf_config.head_dim * hf_config.torch_dtype.itemsize
         config.num_kvcache_blocks = int(total * config.gpu_memory_utilization - used - peak + current) // block_bytes
+        assert config.num_kvcache_blocks > 0
         self.kv_cache = torch.zeros(2, hf_config.num_hidden_layers, config.num_kvcache_blocks, self.block_size, num_kv_heads, hf_config.head_dim)
         layer_id = 0
         for module in self.model.modules():
@@ -119,10 +118,7 @@ class ModelRunner:
 
     def prepare_block_tables(self, seqs: list[Sequence]):
         max_len = max(len(seq.block_table) for seq in seqs)
-        block_tables = [
-            seq.block_table + [-1] * (max_len - len(seq.block_table))
-            for seq in seqs
-        ]
+        block_tables = [seq.block_table + [-1] * (max_len - len(seq.block_table)) for seq in seqs]
         block_tables = torch.tensor(block_tables, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         return block_tables
 
@@ -219,12 +215,6 @@ class ModelRunner:
 
     @torch.inference_mode()
     def capture_cudagraph(self):
-        get_rng_state = torch.cuda.get_rng_state
-        set_rng_state = torch.cuda.set_rng_state
-        rng_state = torch.cuda.get_rng_state()
-        torch.cuda.get_rng_state = lambda: rng_state
-        torch.cuda.set_rng_state = lambda _: None
-    
         config = self.config
         hf_config = config.hf_config
         max_bs = min(self.config.max_num_seqs, 512)
@@ -259,6 +249,3 @@ class ModelRunner:
             block_tables=block_tables,
             outputs=outputs,
         )
-
-        torch.cuda.get_rng_state = get_rng_state
-        torch.cuda.set_rng_state = set_rng_state