fix scalar tensor.mean #8970

Flowingsun007 · 2022-08-19T14:30:36Z

fix issue：MetaKD问题记录 models#378 (comment) 中记录的问题
test case

…neflow into dev_fix_scalar_tensor.mean

Flowingsun007 · 2022-08-19T15:11:49Z

oneflow/core/functional/impl/common.cpp

 for (int32_t i = 0; i < naxis; i++) {
- reduce_axis[i] = JUST(maybe_wrap_dim(axis[i], ndim));
- axis_num[reduce_axis[i]]++;


bug的原因在这一行，当x.mean(-1)里的tensor x是scalar tensor时，则参数axis：{1}； ndim=0，reduce_axis为空vector，reduce_axis[i]会引发segment fault。

github-actions · 2022-08-20T04:00:58Z

Code got formatted by CI. Please request CI again if you still want to have this PR merged. If the PR is from a forked repo, please download the patch files from the GitHub Actions web page and apply them locally.

BBuf · 2022-08-20T07:45:07Z

oneflow/core/functional/impl/common.cpp

@@ -32,22 +32,21 @@ bool IsInplaceValid(const std::shared_ptr<Tensor>& x) {
 Maybe<std::vector<int32_t>> CheckAxis(const std::vector<int32_t>& axis, const int32_t& ndim) {
 const int32_t naxis = axis.size();

+ int32_t reduce_ndim = naxis;
+ if (naxis == 0 || ndim == 0) reduce_ndim = ndim;


这里能解释下吗？没反应过来

哦，这里其实就是把两个判断合到一起了，如果naxis == 0，则reduce_ndim设置为ndim；如果ndim == 0（scalar tensor的情况），则reduce_ndim == 0(也==ndim)

marigoold · 2022-08-21T16:32:49Z

oneflow/core/functional/impl/common.cpp

+ const int32_t axis_i = JUST(maybe_wrap_dim(axis[i], ndim));
+ axis_num[axis_i]++;
+ CHECK_OR_RETURN(axis_num[axis_i] < 2) << Error::RuntimeError() << "dim " << axis_i
+ << " appears multiple times in the list of dims";


这里 else 分支似乎对返回值没有影响？

这里应该是写错了（漏了），我再看一下

github-actions · 2022-08-22T11:36:27Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/8970/

github-actions · 2022-08-22T11:37:34Z

Speed stats:

GPU Name: GeForce GTX 1080 

✔️ OneFlow resnet50 time: 128.8ms (= 12876.3ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 144.5ms (= 14453.6ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.12 (= 144.5ms / 128.8ms)

OneFlow resnet50 time: 75.8ms (= 7580.4ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 85.3ms (= 8533.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.13 (= 85.3ms / 75.8ms)

OneFlow resnet50 time: 49.4ms (= 9870.6ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 54.2ms (= 10846.4ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.10 (= 54.2ms / 49.4ms)

OneFlow resnet50 time: 36.8ms (= 7357.1ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 44.8ms (= 8960.6ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.22 (= 44.8ms / 36.8ms)

OneFlow resnet50 time: 28.4ms (= 5673.8ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 36.1ms (= 7219.6ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.27 (= 36.1ms / 28.4ms)

OneFlow swin dataloader time: 0.412s (= 82.425s / 200, num_workers=1)
PyTorch swin dataloader time: 0.149s (= 29.862s / 200, num_workers=1)
Relative speed: 0.362 (= 0.149s / 0.412s)

OneFlow swin dataloader time: 0.117s (= 23.477s / 200, num_workers=4)
PyTorch swin dataloader time: 0.040s (= 7.914s / 200, num_workers=4)
Relative speed: 0.337 (= 0.040s / 0.117s)

OneFlow swin dataloader time: 0.060s (= 12.043s / 200, num_workers=8)
PyTorch swin dataloader time: 0.022s (= 4.405s / 200, num_workers=8)
Relative speed: 0.366 (= 0.022s / 0.060s)

❌ OneFlow resnet50 time: 137.0ms (= 13703.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 163.9ms (= 16388.8ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.20 (= 163.9ms / 137.0ms)

OneFlow resnet50 time: 85.4ms (= 8541.1ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 112.1ms (= 11210.9ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.31 (= 112.1ms / 85.4ms)

OneFlow resnet50 time: 59.0ms (= 11808.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 77.9ms (= 15582.3ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.32 (= 77.9ms / 59.0ms)

OneFlow resnet50 time: 45.5ms (= 9093.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 71.0ms (= 14200.9ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.56 (= 71.0ms / 45.5ms)

OneFlow resnet50 time: 38.8ms (= 7763.2ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 68.9ms (= 13783.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.78 (= 68.9ms / 38.8ms)

wyg1997 · 2022-08-23T02:22:33Z

oneflow/core/functional/impl/common.cpp

@@ -35,23 +35,23 @@ bool IsScalarTensor(const std::shared_ptr<Tensor>& x) {

 Maybe<std::vector<int32_t>> CheckAxis(const std::vector<int32_t>& axis, const int32_t& ndim) {
 const int32_t naxis = axis.size();
-
+ int32_t reduce_ndim = naxis;
+ if (naxis == 0 || ndim == 0) reduce_ndim = ndim;


没有加大括号

if后只有一句表达式，感觉不加也行😂

我们代码的标准是要加的

wyg1997 · 2022-08-23T02:29:40Z

oneflow/core/functional/impl/common.cpp

+ CHECK_OR_RETURN_ERROR(axis_num[axis_i] < 2) << Error::RuntimeError() << "dim " << axis_i
+ << " appears multiple times in the list of dims";


这一段复用

oneflow/oneflow/core/common/wrap_dim_utils.h

Lines 45 to 58 in b9f51dd

static inline Maybe<std::bitset<dim_bitset_size>> dim_list_to_bitset(

const std::vector<int32_t>& dims, int64_t ndims) {

CHECK_LE_OR_RETURN(ndims, (int64_t)dim_bitset_size)

<< Error::RuntimeError() << "Only tensors with up to " << dim_bitset_size

<< " dims are supported";

std::bitset<dim_bitset_size> seen;

for (int32_t i = 0; i < dims.size(); i++) {

size_t dim = JUST(maybe_wrap_dim(dims[i], ndims));

CHECK_OR_RETURN(!seen[dim]) << Error::RuntimeError() << "The dim " << dim

<< " appears multiple times in the list of dims";

seen[dim] = true;

}

return seen;

}

这个函数吧，有 wrap_dim 和 axis_num 的检察，另外这里判断 axis_num[axis_i] < 2 也不太好

…neflow into dev_fix_scalar_tensor.mean

github-actions · 2022-08-23T07:23:47Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/8970/

github-actions · 2022-08-23T07:25:18Z

Speed stats:

GPU Name: GeForce GTX 1080 

❌ OneFlow resnet50 time: 129.5ms (= 12954.4ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 143.1ms (= 14314.7ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.11 (= 143.1ms / 129.5ms)

OneFlow resnet50 time: 76.4ms (= 7637.9ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 89.6ms (= 8963.1ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.17 (= 89.6ms / 76.4ms)

OneFlow resnet50 time: 48.7ms (= 9736.6ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 59.6ms (= 11911.8ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.22 (= 59.6ms / 48.7ms)

OneFlow resnet50 time: 35.6ms (= 7123.4ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 47.0ms (= 9401.0ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.32 (= 47.0ms / 35.6ms)

OneFlow resnet50 time: 29.3ms (= 5865.9ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 37.5ms (= 7497.2ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.28 (= 37.5ms / 29.3ms)

OneFlow swin dataloader time: 0.258s (= 51.662s / 200, num_workers=1)
PyTorch swin dataloader time: 0.151s (= 30.166s / 200, num_workers=1)
Relative speed: 0.584 (= 0.151s / 0.258s)

OneFlow swin dataloader time: 0.071s (= 14.164s / 200, num_workers=4)
PyTorch swin dataloader time: 0.041s (= 8.216s / 200, num_workers=4)
Relative speed: 0.580 (= 0.041s / 0.071s)

OneFlow swin dataloader time: 0.039s (= 7.896s / 200, num_workers=8)
PyTorch swin dataloader time: 0.023s (= 4.507s / 200, num_workers=8)
Relative speed: 0.571 (= 0.023s / 0.039s)

❌ OneFlow resnet50 time: 138.9ms (= 13886.3ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 168.3ms (= 16831.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.21 (= 168.3ms / 138.9ms)

OneFlow resnet50 time: 87.1ms (= 8714.5ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 103.6ms (= 10361.5ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.19 (= 103.6ms / 87.1ms)

OneFlow resnet50 time: 58.5ms (= 11707.1ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 78.8ms (= 15765.1ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.35 (= 78.8ms / 58.5ms)

OneFlow resnet50 time: 45.1ms (= 9027.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 70.3ms (= 14067.5ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.56 (= 70.3ms / 45.1ms)

OneFlow resnet50 time: 39.5ms (= 7894.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 66.9ms (= 13389.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.70 (= 66.9ms / 39.5ms)

fix scalar tensor.mean

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Flowingsun007 requested review from hjchen2, BBuf, daquexian and jackalcooper as code owners August 19, 2022 14:30

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Merge branch 'master' into dev_fix_scalar_tensor.mean

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Flowingsun007 requested a review from oneflow-ci-bot August 19, 2022 14:31

Flowingsun007 added 2 commits August 19, 2022 15:07

refine

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Merge branch 'dev_fix_scalar_tensor.mean' of github.com:Oneflow-Inc/o…

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…neflow into dev_fix_scalar_tensor.mean

Flowingsun007 commented Aug 19, 2022

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Merge branch 'master' into dev_fix_scalar_tensor.mean

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Merge branch 'master' into dev_fix_scalar_tensor.mean

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fix

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fix scalar tensor.mean #8970

fix scalar tensor.mean #8970

Flowingsun007 commented Aug 19, 2022

Flowingsun007 Aug 19, 2022

github-actions bot commented Aug 20, 2022

BBuf Aug 20, 2022

Flowingsun007 Aug 22, 2022

marigoold Aug 21, 2022 •

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wyg1997 Aug 23, 2022

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github-actions bot commented Aug 23, 2022

		CHECK_OR_RETURN_ERROR(axis_num[axis_i] < 2) << Error::RuntimeError() << "dim " << axis_i
		<< " appears multiple times in the list of dims";

	static inline Maybe<std::bitset<dim_bitset_size>> dim_list_to_bitset(
	const std::vector<int32_t>& dims, int64_t ndims) {
	CHECK_LE_OR_RETURN(ndims, (int64_t)dim_bitset_size)
	<< Error::RuntimeError() << "Only tensors with up to " << dim_bitset_size
	<< " dims are supported";
	std::bitset<dim_bitset_size> seen;
	for (int32_t i = 0; i < dims.size(); i++) {
	size_t dim = JUST(maybe_wrap_dim(dims[i], ndims));
	CHECK_OR_RETURN(!seen[dim]) << Error::RuntimeError() << "The dim " << dim
	<< " appears multiple times in the list of dims";
	seen[dim] = true;
	}
	return seen;
	}

fix scalar tensor.mean #8970

fix scalar tensor.mean #8970

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