[GitHub] [incubator-tvm] trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass blows up call stack
trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass
blows up call stack
URL: https://github.com/apache/incubator-tvm/issues/4534#issuecomment-571817854
I was getting a segfault during `relay.build()` while trying to run
resnet152_v1 with the script below. Smaller models worked fine. Once I
increased my machine's stack limit using `ulimit -s unlimited`, the segfaults
stopped. The stack limit was 8192 kilobytes originally. Might be related?
```
import numpy as np
import tvm
from tvm import relay
from tvm.contrib import graph_runtime
import mxnet
from mxnet.gluon.model_zoo.vision import get_model
input_shape = (1, 3, 224, 224)
block = get_model('resnet152_v1', pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape},
dtype='float32')
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod, "cuda", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.gpu(0))
mod.set_input(**params)
i_data = np.random.uniform(0, 1, input_shape).astype('float32')
for i in range(10):
mod.run(data=i_data)
```
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[GitHub] [incubator-tvm] trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass blows up call stack
trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass
blows up call stack
URL: https://github.com/apache/incubator-tvm/issues/4534#issuecomment-571817854
I was getting a segfault during `relay.build()` while trying to run
resnet152_v1 with the script below. Smaller models worked fine. Once I
increased my machine's stack limit using `ulimit -s unlimited`, the segfaults
stopped. The stack limit was 8192 kilobytes originally.
```
import numpy as np
import tvm
from tvm import relay
from tvm.contrib import graph_runtime
import mxnet
from mxnet.gluon.model_zoo.vision import get_model
input_shape = (1, 3, 224, 224)
block = get_model('resnet152_v1', pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape},
dtype='float32')
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod, "cuda", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.gpu(0))
mod.set_input(**params)
i_data = np.random.uniform(0, 1, input_shape).astype('float32')
for i in range(10):
mod.run(data=i_data)
```
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To respond to the message, please log on to GitHub and use the
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[GitHub] [incubator-tvm] trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass blows up call stack
trevor-m edited a comment on issue #4534: [Relay] Dead code elimination pass
blows up call stack
URL: https://github.com/apache/incubator-tvm/issues/4534#issuecomment-571817854
I was getting a segfault during `relay.build()` while trying to run
resnet152_v1 with the script below. Smaller models worked fine. Once I
increased the stack limit using `ulimit -s unlimited`, the segfaults stopped.
```
import numpy as np
import tvm
from tvm import relay
from tvm.contrib import graph_runtime
import mxnet
from mxnet.gluon.model_zoo.vision import get_model
input_shape = (1, 3, 224, 224)
block = get_model('resnet152_v1', pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape},
dtype='float32')
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod, "cuda", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.gpu(0))
mod.set_input(**params)
i_data = np.random.uniform(0, 1, input_shape).astype('float32')
for i in range(10):
mod.run(data=i_data)
```
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
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