Hi there,
I'm running the below code and shows the error. May I confirm what I'm
missing? Thanks your help!
=
Python 3.7.7 (default, Mar 10 2020, 15:16:38)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "lice
Ok so I commented out the `tune_graph()` call and use `sch_log` which is the
schedule from `tune_kernels()`
```
with autotvm.apply_graph_best(sch_log):
logging.info("Compiling the schedule")
with relay.build_config(opt_level=3):
graph, lib, params = relay.build_module
No reason. `constexpr` implies `const` in object declarations.
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Thanks! I'll give that a try.
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Thanks for sharing. The failure is while calling tune_graph. The graph tuning
assumes the data to be float32.
Additionally, last time I tried, the graph tuning cant work with QNN ops. One
way to handle this is to call QnnCanonilcalize
(python/tvm/relay/qnn/transform.py) before calling graph tu
Here is the script that reproduces the issue:
```
import sys
import os
import tvm
from tvm import relay
from tvm import autotvm
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tvm.autotvm.graph_tuner import DPTuner, PBQPTuner
import tflite.Model
#
# This fun
Right now, I think the process fails in `relay.build_module.build(mod,
target=target, params=params)` That is after the code I showed above. I just
verified that the layout transformation takes place by comparing the both
`relay_NHWC.txt` and `relay_NCHW.txt`.
Let me create a minimal script s
Hmm, this is weird. My script seems to work well. Is it possible for you to
share the script? If not, can you reach the printing on relay_NHWC.txt for
quantized model, or it fails before that?
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Hi eveyrone,
I would like to know whether int4/int16 quantization was possible using
"`relay.quantize.quantize`". So far, I have gone through the documentation in
https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/quantize/quantize.py
But I have a few questions:
1) What is
I have double checked the type and made sure the NHWC -> NCHW is applied:
```
assert input_type == "uint8", "Quantized models use uint8 input_type"
mod, params =\
relay.frontend.from_tflite(tflite_model,
shape_dict={input_name: dshape},
See DNNL example below. Since TVM runtime is sequential, there is no
synchronization of any kind. You just deal with pointers to tensors via DLTensor
https://github.com/apache/incubator-tvm/tree/master/src/runtime/contrib/dnnl
https://github.com/apache/incubator-tvm/tree/master/src/relay/backen
It seems that annotate_target tutorial has not been uploaded. I am wondering
how one runtime communicates with another runtime. Something like a host
runtime and a kernel runtime？Host runtime uses jit to generate kernel's code?
Or they use other synchronization mechanisms?
