Dev mv code from modules to functional #10420

lihuizhao · 2024-01-25T03:06:00Z

移动nn.modules下的代码到nn.functional下

代码移动：

将interpolate、affine_grid、grid_sample、linear、layer_norm、group_norm、embedding、sparse_softmax_cross_entropy、upsample、relu6函数从nn.modules下移动到nn.functional。
将对应的modules类中的forward()函数修改为调用nn.functional下的函数的形式。
在nn.modules中新增Interpolate、AffineGrid、GridSample、SparseSoftmaxCrossEntropy类，并实现代码功能

测试代码：

在test_upsample.py文件中添加upsample函数测试代码
在test_interpolate.py中添加Interpolate类测试代码，pytorch中没有Interpolate类
在test_affine_grid.py中添加AffineGrid类测试代码，pytorch中没有AffineGrid类
pytorch中没有GridSample类
pytorch中没有SparseSoftmaxCrossEntropy类
在test_layer_norm.py文件中添加LayerNorm类的测试代码

…d vectorize=False

github-actions · 2024-01-25T09:03:16Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.5ms (= 4351.2ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.4ms (= 5736.1ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.32 (= 57.4ms / 43.5ms)

OneFlow resnet50 time: 26.2ms (= 2624.2ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 37.4ms (= 3738.5ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.42 (= 37.4ms / 26.2ms)

OneFlow resnet50 time: 18.7ms (= 3735.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 35.4ms (= 7076.5ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.89 (= 35.4ms / 18.7ms)

OneFlow resnet50 time: 16.3ms (= 3261.6ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 34.0ms (= 6803.7ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 2.09 (= 34.0ms / 16.3ms)

OneFlow resnet50 time: 17.4ms (= 3481.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 28.7ms (= 5742.7ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.65 (= 28.7ms / 17.4ms)

OneFlow swin dataloader time: 0.202s (= 40.333s / 200, num_workers=1)
PyTorch swin dataloader time: 0.128s (= 25.642s / 200, num_workers=1)
Relative speed: 0.636 (= 0.128s / 0.202s)

OneFlow swin dataloader time: 0.055s (= 10.962s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.517s / 200, num_workers=4)
Relative speed: 0.594 (= 0.033s / 0.055s)

OneFlow swin dataloader time: 0.031s (= 6.155s / 200, num_workers=8)
PyTorch swin dataloader time: 0.016s (= 3.297s / 200, num_workers=8)
Relative speed: 0.536 (= 0.016s / 0.031s)

❌ OneFlow resnet50 time: 49.1ms (= 4905.1ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.3ms (= 6432.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.31 (= 64.3ms / 49.1ms)

OneFlow resnet50 time: 35.8ms (= 3582.2ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 45.6ms (= 4560.3ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.27 (= 45.6ms / 35.8ms)

OneFlow resnet50 time: 28.2ms (= 5633.7ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 39.1ms (= 7811.1ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.39 (= 39.1ms / 28.2ms)

OneFlow resnet50 time: 24.9ms (= 4976.3ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.4ms (= 7680.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.54 (= 38.4ms / 24.9ms)

OneFlow resnet50 time: 24.3ms (= 4852.6ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 35.9ms (= 7175.0ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.48 (= 35.9ms / 24.3ms)

marigoold · 2024-01-26T03:45:33Z

python/oneflow/nn/functional/sparse.py

这个文件名为啥叫 sparse？

原来这个函数是放在nn/modules/sparse.py中的，它就命名为sparse，我把它迁移到nn/functional下，没有改原文件名

marigoold · 2024-01-26T03:46:05Z

python/oneflow/nn/functional/upsampling.py

可以跟 interpolate 合并到一起

…dev_mv_code_from_modules_to_functional

github-actions · 2024-01-29T07:41:56Z

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10420/

github-actions · 2024-01-29T08:22:00Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.9ms (= 4391.9ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.2ms (= 5718.4ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.30 (= 57.2ms / 43.9ms)

OneFlow resnet50 time: 26.3ms (= 2628.4ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.1ms (= 3807.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.45 (= 38.1ms / 26.3ms)

OneFlow resnet50 time: 18.2ms (= 3647.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 36.2ms (= 7249.2ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.99 (= 36.2ms / 18.2ms)

OneFlow resnet50 time: 17.1ms (= 3424.8ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 34.3ms (= 6851.8ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 2.00 (= 34.3ms / 17.1ms)

OneFlow resnet50 time: 16.3ms (= 3266.6ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 30.2ms (= 6045.3ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.85 (= 30.2ms / 16.3ms)

OneFlow swin dataloader time: 0.200s (= 39.981s / 200, num_workers=1)
PyTorch swin dataloader time: 0.127s (= 25.400s / 200, num_workers=1)
Relative speed: 0.635 (= 0.127s / 0.200s)

OneFlow swin dataloader time: 0.053s (= 10.515s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.429s / 200, num_workers=4)
Relative speed: 0.611 (= 0.032s / 0.053s)

OneFlow swin dataloader time: 0.031s (= 6.179s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.370s / 200, num_workers=8)
Relative speed: 0.545 (= 0.017s / 0.031s)

❌ OneFlow resnet50 time: 49.1ms (= 4906.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.7ms (= 6471.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.32 (= 64.7ms / 49.1ms)

OneFlow resnet50 time: 36.6ms (= 3660.8ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 46.4ms (= 4644.4ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.27 (= 46.4ms / 36.6ms)

OneFlow resnet50 time: 28.1ms (= 5615.5ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 41.1ms (= 8227.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.47 (= 41.1ms / 28.1ms)

OneFlow resnet50 time: 25.1ms (= 5026.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.7ms (= 7747.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.54 (= 38.7ms / 25.1ms)

OneFlow resnet50 time: 23.5ms (= 4699.9ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.2ms (= 7231.4ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.54 (= 36.2ms / 23.5ms)

github-actions · 2024-01-29T11:39:31Z

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10420/

github-actions · 2024-01-29T12:15:22Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.6ms (= 4363.8ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 58.0ms (= 5798.4ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.33 (= 58.0ms / 43.6ms)

OneFlow resnet50 time: 26.7ms (= 2669.0ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.7ms (= 3866.5ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.45 (= 38.7ms / 26.7ms)

OneFlow resnet50 time: 18.9ms (= 3772.6ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 36.0ms (= 7191.5ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.91 (= 36.0ms / 18.9ms)

OneFlow resnet50 time: 15.5ms (= 3096.7ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 31.1ms (= 6219.8ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 2.01 (= 31.1ms / 15.5ms)

OneFlow resnet50 time: 15.9ms (= 3188.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.5ms (= 5898.0ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.85 (= 29.5ms / 15.9ms)

OneFlow swin dataloader time: 0.202s (= 40.367s / 200, num_workers=1)
PyTorch swin dataloader time: 0.128s (= 25.525s / 200, num_workers=1)
Relative speed: 0.632 (= 0.128s / 0.202s)

OneFlow swin dataloader time: 0.054s (= 10.752s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.700s / 200, num_workers=4)
Relative speed: 0.623 (= 0.033s / 0.054s)

OneFlow swin dataloader time: 0.031s (= 6.247s / 200, num_workers=8)
PyTorch swin dataloader time: 0.016s (= 3.264s / 200, num_workers=8)
Relative speed: 0.522 (= 0.016s / 0.031s)

❌ OneFlow resnet50 time: 49.1ms (= 4906.6ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 65.5ms (= 6553.1ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.34 (= 65.5ms / 49.1ms)

OneFlow resnet50 time: 35.7ms (= 3570.9ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 45.7ms (= 4567.6ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.28 (= 45.7ms / 35.7ms)

OneFlow resnet50 time: 28.3ms (= 5666.2ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 41.3ms (= 8266.1ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.46 (= 41.3ms / 28.3ms)

OneFlow resnet50 time: 25.8ms (= 5163.1ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 40.7ms (= 8136.5ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.58 (= 40.7ms / 25.8ms)

OneFlow resnet50 time: 24.5ms (= 4898.9ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 35.9ms (= 7185.9ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.47 (= 35.9ms / 24.5ms)

…dev_mv_code_from_modules_to_functional

marigoold · 2024-02-02T08:44:57Z

python/oneflow/nn/functional/normalization.py

+        return res
+
+
+def layer_norm(input, normalized_shape: tuple, weight=None, bias=None, eps=1e-05):


这里可以加一下文档，参照上面的 group norm

以及 type hinting

marigoold · 2024-02-02T08:49:49Z

python/oneflow/nn/functional/sparse.py

+    norm_type=2.0,
+    scale_grad_by_freq=False,
+    sparse=False,
+):


同样，这里加上 type hinting，另外文档中有 example 代码，结尾要加上

if __name__ == "__main__": import doctest doctest.testmod(raise_on_error=True)

marigoold · 2024-02-02T08:50:43Z

python/oneflow/nn/functional/linear.py

+import oneflow as flow
+
+
+def linear(input, weight, bias=None):


type hinting 以及结尾的测试

github-actions · 2024-02-02T09:05:34Z

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10420/

…ctional' into dev_mv_code_from_modules_to_functional

github-actions · 2024-02-05T05:51:16Z

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10420/

levi131

type hinting 部分没有问题

github-actions · 2024-02-26T03:42:41Z

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10420/

github-actions · 2024-02-26T03:48:37Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.3ms (= 4328.0ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.7ms (= 5769.8ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.33 (= 57.7ms / 43.3ms)

OneFlow resnet50 time: 26.6ms (= 2659.6ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.0ms (= 3795.0ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.43 (= 38.0ms / 26.6ms)

OneFlow resnet50 time: 19.1ms (= 3823.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 36.4ms (= 7284.9ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.91 (= 36.4ms / 19.1ms)

OneFlow resnet50 time: 17.8ms (= 3561.5ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 32.4ms (= 6475.2ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.82 (= 32.4ms / 17.8ms)

OneFlow resnet50 time: 16.9ms (= 3387.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.1ms (= 5821.8ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.72 (= 29.1ms / 16.9ms)

OneFlow swin dataloader time: 0.214s (= 42.828s / 200, num_workers=1)
PyTorch swin dataloader time: 0.130s (= 25.928s / 200, num_workers=1)
Relative speed: 0.605 (= 0.130s / 0.214s)

OneFlow swin dataloader time: 0.056s (= 11.215s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.462s / 200, num_workers=4)
Relative speed: 0.576 (= 0.032s / 0.056s)

OneFlow swin dataloader time: 0.032s (= 6.413s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.319s / 200, num_workers=8)
Relative speed: 0.518 (= 0.017s / 0.032s)

❌ OneFlow resnet50 time: 49.3ms (= 4925.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 63.9ms (= 6390.4ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.30 (= 63.9ms / 49.3ms)

OneFlow resnet50 time: 36.8ms (= 3684.4ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 48.5ms (= 4850.3ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.32 (= 48.5ms / 36.8ms)

OneFlow resnet50 time: 28.2ms (= 5648.3ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 44.5ms (= 8901.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.58 (= 44.5ms / 28.2ms)

OneFlow resnet50 time: 26.2ms (= 5241.4ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 40.9ms (= 8182.3ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.56 (= 40.9ms / 26.2ms)

OneFlow resnet50 time: 24.2ms (= 4847.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.4ms (= 7687.6ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.59 (= 38.4ms / 24.2ms)

lihuizhao added 30 commits January 2, 2024 03:25

add jvp interface and test

fa04b47

add jvp interface and test

57ec099

add annotation information

256d75a

add jacobian

cc7b43d

add vhp

4f4c1d5

add vhp test

49dfb97

add hvp

f26d539

add hvp test and fix vhp test

65d90ce

fix jacobian interface when the condition is strategy=reverse-mode an…

c6b8298

…d vectorize=False

fix jacobian interface when the condition is strategy=reverse-mode an…

b5bac0e

…d vectorize=False

add jacobian tests and hessian tests

5906339

format code

e69a37c

code format

4bbb368

change jvp doc

030dc61

add jvp/jacobian/hessian/vhp/hvp to autograd.rst

6b8db1f

add annotation

233d423

remove _batched_autograd_grad() function

f54c58b

format

81fe941

Merge branch 'master' into dev_add_jvp_jacobian_hessian

6efac49

fix

6ab73d6

Remove extra code

ab874fb

move interpolate function code from modules to functional

3e3b457

move affine_grid code from modules to functional

c3d03ac

Modify comment

c14a50b

format

f069447

move grid_sample code from modules to functional

325b526

move sparse_softmax_cross_entropy code from modules to functional

46281cf

move layer_norm code from modules to functional

28e58a4

move embedding code from modules to functional

7583963

move linear code from modules to functional

eb91311

lihuizhao added api python enhancement labels Jan 25, 2024

marigoold reviewed Jan 26, 2024

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可以跟 interpolate 合并到一起

lihuizhao added 6 commits January 26, 2024 04:10

merge upsample and interpolate

0c109d9

Compact code

38e7b31

add upsample func test code

6f0e8a9

Merge branch 'master' of https://github.com/Oneflow-Inc/oneflow into …

c40ec4e

…dev_mv_code_from_modules_to_functional

add interpolate module test code

56f9fdc

add AffineGrid class test

e13b1dd

improve LayerNorm class code

ba92dce

lihuizhao and others added 3 commits January 30, 2024 04:33

add LayerNorm class test code

07a32fc

Merge branch 'master' of https://github.com/Oneflow-Inc/oneflow into …

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…dev_mv_code_from_modules_to_functional

Merge branch 'master' into dev_mv_code_from_modules_to_functional

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lihuizhao added 2 commits February 5, 2024 10:54

Merge remote-tracking branch 'upstram/dev_mv_code_from_modules_to_fun…

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…ctional' into dev_mv_code_from_modules_to_functional

Add 'Type hints', 'documentation', 'example code'

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Dev mv code from modules to functional #10420

Dev mv code from modules to functional #10420

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		return res


		def layer_norm(input, normalized_shape: tuple, weight=None, bias=None, eps=1e-05):

Dev mv code from modules to functional #10420

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Dev mv code from modules to functional #10420

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移动nn.modules下的代码到nn.functional下

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