hxzd5568 opened a new pull request, #16882:
URL: https://github.com/apache/tvm/pull/16882
### Expected behavior
Data flows should be evident and unambiguous from the execution graph dumped
from TVM debugger.
### Actual behavior
Only-reshape node has no ordinal numbers and can not be distinguished.
```python
######################################
###### A buggy naming rule
######################################
import tvm
from tvm import relay,runtime
import os
import numpy as np
import queue
import shutil
import os.path
import random
from tvm import transform, relay, parser, cpu, TVMError, IRModule
from argparse import Namespace, ArgumentParser
from typing import Iterable, List, cast, Optional, Dict
import sys
from tvm.contrib.debugger.debug_executor import GraphModuleDebug
from tvm.contrib.graph_executor import GraphModule
sys.path.append('../')
TensorDict = Dict[str, np.ndarray]
target = tvm.target.Target("llvm", host="llvm")
layout = None
dev = tvm.cpu(0)
import time
Required_pass1 =
['EliminateCommonSubexpr','CombineParallelDense','CombineParallelBatchMatmul','CombineParallelConv2D']
def build_workload(mod, params=None, Disabled_pass=['SimplifyExpr']):
with transform.PassContext(opt_level=1,
config={"relay.FuseOps.max_depth": 1},
required_pass=Required_pass1,disabled_pass=Disabled_pass):
lib1 = relay.build(mod, target)
with transform.PassContext(opt_level=5,
config={"relay.FuseOps.max_depth": 1},
):#disabled_pass=Disabled_pass
lib5 = relay.build(mod, target)
return lib1, lib5
def replay_withdebugger(mod, params=None):
factorymod1, factorymod5 = build_workload(\
mod,params= params)
GraphModuleDebug( factorymod1["debug_create"]("default", dev),
[dev],factorymod1["get_graph_json"](),
dump_root='./pr03'+'/L1/')
def test_mod1():
def mod1():
shape = (4,3)
x = relay.var("x", shape=shape, dtype="float32")
y = relay.var("y", shape=shape, dtype="float32")
m = relay.sqrt(relay.abs(y))
n = relay.divide(x,m)
s = relay.reshape(n, newshape=(3, 4))
l = relay.round(relay.nn.relu(relay.tan(relay.sum(s,axis=[1]))))
g= relay.reshape(l, newshape=(1, 3))
return tvm.IRModule.from_expr(relay.tan(g))
mod = mod1()
# replay(mod,params)
replay_withdebugger(mod)
print('case 1')
test_mod1()
```
This script restores a json file.
The following is a snippet of the executor graph.
...
{
"op": "tvmgen_default_fused_sum",
"inputs": ["reshape_nop"],
},
{
"op": "tvmgen_default_fused_tan",
"inputs": ["tvmgen_default_fused_sum"],
},
{
"op": "tvmgen_default_fused_tan_1",
"inputs": [ "reshape_nop"],
}
...
From it, the nodes named tvmgen_default_fused_tan_1 and
tvmgen_default_fused_sum have the same input node.
However, this is wrong. From the relay ir, we know tan_1 and sum have
different predecessors.
This error occurs because the reshape node is named without distinguishing
between different reshapes.
After modifying graph_executor_codegen.cc, the dumped graph is able to
correctly represent the data flow relationships.
...
{
"op": "tvmgen_default_fused_sum",
"inputs": [ "reshape_nop_0" ],
},
{
"op": "tvmgen_default_fused_tan",
"inputs": ["tvmgen_default_fused_sum" ],
},
{
"op": "tvmgen_default_fused_tan_1",
"inputs": ["reshape_nop_1"],
}
...
It is essential to fix this bug for tracing numerical errors introduced by
the compliation.
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