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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/01/17 05:22:25 UTC
[GitHub] [tvm] codgeek opened a new issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in deploy_detection.py
codgeek opened a new issue #7301:
URL: https://github.com/apache/tvm/issues/7301
**Reprocuce code:**
`cd ${TVM_HOME} && python vta/tutorials/frontend/legacy/deploy_detection.py`
**description:**
I am using the [newest version code on 'Fri Jan 15 07:59:28 2021'](https://github.com/apache/tvm/tree/c9474639dd3761b78a457ab274603d87a3dcf9b8) with pynq Z1 board. Error occurs inside `graph_pack()` when running yolo-v3-tiny in deploy_detection.py.
```
File "vta/tutorials/frontend/legacy/deploy_detection.py", line 243, in <module>
stop_name_idx=pack_dictMODEL_NAME,
File "tvm/vta/python/vta/top/graphpack.py", line 599, in graph_pack
expr = run_opt_pass(expr, transform.InferType())
File "tvm/vta/python/vta/top/graphpack.py", line 30, in run_opt_pass
mod = opt_pass(mod)
File "tvm/python/tvm/ir/transform.py", line 127, in call
return _ffi_transform_api.RunPass(self, mod)
File "tvm/python/tvm/ffi/ctypes/packed_func.py", line 237, in call
raise get_last_ffi_error()
File "tvm/src/relay/analysis/type_solver.cc", line 622
TVMError:
Check failed: false == false: [00:55:37] tvm/src/relay/op/tensor/transform.cc:703:
Check failed: oshape_sum == data_shape_sum (172380 vs. 173056) : Input tensor shape and reshaped shape are not compatible, reshape data_shape:[1, 1, 16, 16, 26, 26], oshape:[1, 255, 26, 26]
```
I've add more shape info in `src/relay/op/tensor/transform.cc` as follows:
```c++
diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index ecfde359d..7e150e2c9 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -386,7 +386,7 @@ bool TransposeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
// check dimension match
ICHECK(!axes.defined() || static_cast<int>(axes.size()) == ndim)
<< "Dimension mismatch: axes has " << axes.size() << " elements"
- << ", but data.ndim = " << ndim;
+ << ", but data.ndim = " << ndim << ", transpose data_shape:" << data->shape << ", axes:" << axes;
// construct int_axes
std::vector<int> int_axes;
int_axes.reserve(ndim);
@@ -701,7 +701,7 @@ bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
}
if (!found_dynamic) {
ICHECK_EQ(oshape_sum, data_shape_sum)
- << "Input tensor shape and reshaped shape are not compatible";
+ << "Input tensor shape and reshaped shape are not compatible" << ", reshape data_shape:" << data_shape << ", oshape:" << oshape;;
}
reporter->Assign(types[1], TensorType(oshape, data->dtype));
```
**problem:**
**A relay IR is trying to reshape source tensor with shape `1, 1, 16, 16, 26, 26]` in to shape [1, 255, 26, 26] while the high demision is 16*16=256.
I've tried to find the error op, it seems the %162-th op in compiled module has output shape of (1, 255, 26, 26), The error problem is why it's shape is transformed into 256 at previous stages?** Hoping programers knowing vta or relay could offer some help， thanks a lot!
```
print("quant modules:", mod)
# Perform graph packing and constant folding for VTA target
mod = graph_pack(
mod["main"],
env.BATCH,
env.BLOCK_OUT,
env.WGT_WIDTH,
start_name=pack_dict[MODEL_NAME][0],
stop_name=pack_dict[MODEL_NAME][1],
start_name_idx=pack_dict[MODEL_NAME][2],
stop_name_idx=pack_dict[MODEL_NAME][3],
)
```
> %160 = (%156, %157, %159);
> %161 = concatenate(%160, axis=2) /* ty=Tensor[(1, 3, 85, 26, 26), float32] */;
> %162 = reshape(%161, newshape=[-1, 255, 26, 26]) /* ty=Tensor[(1, 255, 26, 26), float32] */;
> %163 = cast(%111, dtype="int8") /* ty=Tensor[(1, 256, 13, 13), int8] */;
**related changes:**
after some searching, this is some related issues i’ve found.
@fprotopapa had fixed another issue #7059 in `pack_dict` configure, change the cast inedx from 185 to 186. 👍 Did your `deploy_detection.py` run successfully after this change? looking forward to your practice, many thanks!
@huajsj had add the yolo-v3-tiny support in[ this commit ](https://github.com/apache/tvm/commit/09c55fd1f3354d2280bb792a252590ac6bd68e58)
some additional configs are:
I‘ve verified `deploy_detection.py` my macbook pro`Darwin Kernel Version 16.4.0:` and ubuntu linux server ` Ubuntu 18.04.2 LTS (GNU/Linux 5.4.0-58-generic x86_64)`, the same problem occurs.
```
target: xilinx pynq Z1.
host: macbook pro
sys-platform: x86_64, macOS-10.12.3-x86_64-i386-64bit
os-version: Darwin Kernel Version 16.4.0:
python: 3.6
llvm: 10.0.0
cmake: 3.15.3
tvm: build from newest version code on 'Fri Jan 15 07:59:28 2021'： (https://github.com/apache/tvm/tree/c9474639dd3761b78a457ab274603d87a3dcf9b8)
```
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[GitHub] [tvm] codgeek commented on issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
codgeek commented on issue #7301:
URL: https://github.com/apache/tvm/issues/7301#issuecomment-989555192
> @codgeek , thanks for report this issue, please check if this PR #8731 fixed the said issue.
sorry to reply after several months, It works now! thank you so much! 👍👍
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[GitHub] [tvm] codgeek closed issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
codgeek closed issue #7301:
URL: https://github.com/apache/tvm/issues/7301
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[GitHub] [tvm] codgeek edited a comment on issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
codgeek edited a comment on issue #7301:
URL: https://github.com/apache/tvm/issues/7301#issuecomment-761834331
some new discoveries:
After reset to version when yolo-v3-tiny from @huajsj is merged [09c55fd1f3354d2280bb792a252590ac6bd68e58](https://github.com/apache/tvm/commit/09c55fd1f3354d2280bb792a252590ac6bd68e58). the detection task works! Inference time on pynq Z1 is reduced to 615.49ms from 4520ms on arm_cpu version.
**As an strong infer , some changes in 'Relay transform or vta packing' might lead to the error on latest version wrote in the bug above. could any 'good soul' offer some help ?**
> Cannot find config for target=ext_dev -device=vta -keys=cpu -model=pynq_1x16_i8w8a32_15_15_18_17, workload=('conv2d_nchw_winograd.arm_cpu', A fallback configuration is used, which may bring great performance regression.
> yolov3-tiny inference graph built in 14.41s!
> Performed inference in 615.49ms (std = 0.02) for 1 samples
> Average per sample inference time: 615.49ms
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[GitHub] [tvm] huajsj commented on issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
huajsj commented on issue #7301:
URL: https://github.com/apache/tvm/issues/7301#issuecomment-897318076
@codgeek , thanks for report this issue, please check if this PR https://github.com/apache/tvm/pull/8731 fixed the said issue.
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[GitHub] [tvm] codgeek commented on issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
codgeek commented on issue #7301:
URL: https://github.com/apache/tvm/issues/7301#issuecomment-761834331
some new discoveries:
after reset to version when mege yolo-v3-tiny from @huajsj [09c55fd1f3354d2280bb792a252590ac6bd68e58](https://github.com/apache/tvm/commit/09c55fd1f3354d2280bb792a252590ac6bd68e58). the detection task works!
the inference times on pynq Z1 is reduced to 615.49ms from 4520ms on arm_cpu version.
**As an strong infer , some changes in 'Relay' might lead to the error on latest version wrote in the bug above. could any 'good soul' offer some help ?**
> Cannot find config for target=ext_dev -device=vta -keys=cpu -model=pynq_1x16_i8w8a32_15_15_18_17, workload=('conv2d_nchw_winograd.arm_cpu', A fallback configuration is used, which may bring great performance regression.
> yolov3-tiny inference graph built in 14.41s!
> Performed inference in 615.49ms (std = 0.02) for 1 samples
> Average per sample inference time: 615.49ms
----------------------------------------------------------------
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[GitHub] [tvm] codgeek edited a comment on issue #7301: [VTA][Relay] Reshape mismatch and Compile fail when running yolo-v3-tiny model in tutorial deploy_detection.py
Posted by GitBox <gi...@apache.org>.
codgeek edited a comment on issue #7301:
URL: https://github.com/apache/tvm/issues/7301#issuecomment-761834331
some new discoveries:
After reset to version when yolo-v3-tiny from @huajsj is merged [09c55fd1f3354d2280bb792a252590ac6bd68e58](https://github.com/apache/tvm/commit/09c55fd1f3354d2280bb792a252590ac6bd68e58). the detection task works! Inference time on pynq Z1 is reduced to 615.49ms from 4520ms on arm_cpu version.
**As an strong infer , some changes in 'Relay' might lead to the error on latest version wrote in the bug above. could any 'good soul' offer some help ?**
> Cannot find config for target=ext_dev -device=vta -keys=cpu -model=pynq_1x16_i8w8a32_15_15_18_17, workload=('conv2d_nchw_winograd.arm_cpu', A fallback configuration is used, which may bring great performance regression.
> yolov3-tiny inference graph built in 14.41s!
> Performed inference in 615.49ms (std = 0.02) for 1 samples
> Average per sample inference time: 615.49ms
----------------------------------------------------------------
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