torch jit (xx% faster inference)

RWKV-v4neo/src/model_run.py

@@ -9,14 +9,19 @@ from torch.nn import functional as F
 import torch.nn as nn
 from typing import List, Dict
 
-# try:
+MyModule = nn.Module
-#     import torchdynamo
-#     MyFunction = torchdynamo.optimize(os.environ["RWKV_RUN_BACKEND"]) # !!!BUGGY!!! wrong output
-# except:
 def __nop(ob):
     return ob
 MyFunction = __nop
 
+# # try torchdynamo
+# import torchdynamo
+# MyFunction = torchdynamo.optimize(os.environ["RWKV_RUN_BACKEND"]) # !!!BUGGY!!! wrong output
+
+# try torch jit --> faster!!
+MyModule = torch.jit.ScriptModule
+MyFunction = torch.jit.script_method
+
 RWKV_HEAD_QK_DIM = 0
 print(f'\nRWKV_HEAD_QK_DIM {RWKV_HEAD_QK_DIM}\n')
 
@@ -26,7 +31,7 @@ RWKV_RESCALE_LAYER = 6 # set x=x/2 every X layer
 
 ############################################################################################################
 
-class RWKV_RNN(nn.Module):
+class RWKV_RNN(MyModule):
     def __init__(self, args):
         super().__init__()
 
@@ -113,7 +118,7 @@ class RWKV_RNN(nn.Module):
     # state[] 0=ffn_xx 1=att_xx 2=att_aa 3=att_bb 4=att_pp
 
     @MyFunction
-    def FF(self, x, state, i, time_mix_k, time_mix_r, kw, vw, rw):
+    def FF(self, x, state, i:int, time_mix_k, time_mix_r, kw, vw, rw):
         if self.FLOAT_MODE == "bf16":
             xk = x * time_mix_k + state[5*i+0].type(torch.bfloat16) * (1 - time_mix_k)
             xr = x * time_mix_r + state[5*i+0].type(torch.bfloat16) * (1 - time_mix_r)
@@ -134,7 +139,7 @@ class RWKV_RNN(nn.Module):
         return r * kv
 
     @MyFunction
-    def SA(self, x, state, i, time_mix_k, time_mix_v, time_mix_r, time_first, time_decay, kw, vw, rw, ow):
+    def SA(self, x, state, i:int, time_mix_k, time_mix_v, time_mix_r, time_first, time_decay, kw, vw, rw, ow):
         if self.FLOAT_MODE == "bf16":
             xk = x * time_mix_k + state[5*i+1].type(torch.bfloat16) * (1 - time_mix_k)
             xv = x * time_mix_v + state[5*i+1].type(torch.bfloat16) * (1 - time_mix_v)