Sandbox::https://github.com/pytorch/pytorch/pull/95751

Chillee · 2023-03-01T01:24:39Z

Stack from ghstack (oldest at bottom):

Overall, an example usage. Note that this also captures backwards FLOPs.

import torchvision.models as models
import torch
from torch.utils.flop_counter import FlopCounterMode

inp = torch.randn(1, 3, 224, 224, device='cpu')
mod = models.resnet18()

flop_counter = FlopCounterMode(mod, depth=1)
with flop_counter:
    mod(inp).sum().backward()

You can control the depth of the module hierarchy with the depth attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs.

Other APIs

FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions
FlopCounterMode.get_table(depth=...): Explicitly get the table as a string
FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]]
FlopCounterMode.register_hierarchy(f, name): Allows you to register additional "hierarchies" for a function.

[ghstack-poisoned]

pytorch-bot · 2023-03-01T01:24:42Z

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[ghstack-poisoned]

Overall, an example usage. Note that this *also* captures backwards FLOPs. ``` import torchvision.models as models import torch from torch.utils.flop_counter import FlopCounterMode inp = torch.randn(1, 3, 224, 224, device='cpu') mod = models.resnet18() flop_counter = FlopCounterMode(mod, depth=1) with flop_counter: mod(inp).sum().backward() ``` <img width="326" alt="image" src="https://user-images.githubusercontent.com/6355099/222023068-3491e405-f195-4e11-b679-36b19a1380c7.png"> You can control the depth of the module hierarchy with the `depth` attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs. <img width="366" alt="image" src="https://user-images.githubusercontent.com/6355099/222023306-3d880bb6-f534-4f98-bf10-83c4353acefc.png"> ## Other APIs FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions FlopCounterMode.get_table(depth=...): Explicitly get the table as a string FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]] [ghstack-poisoned]

Overall, an example usage. Note that this *also* captures backwards FLOPs. ``` import torchvision.models as models import torch from torch.utils.flop_counter import FlopCounterMode inp = torch.randn(1, 3, 224, 224, device='cpu') mod = models.resnet18() flop_counter = FlopCounterMode(mod, depth=1) with flop_counter: mod(inp).sum().backward() ``` <img width="326" alt="image" src="https://user-images.githubusercontent.com/6355099/222023068-3491e405-f195-4e11-b679-36b19a1380c7.png"> You can control the depth of the module hierarchy with the `depth` attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs. <img width="366" alt="image" src="https://user-images.githubusercontent.com/6355099/222023306-3d880bb6-f534-4f98-bf10-83c4353acefc.png"> ## Other APIs FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions FlopCounterMode.get_table(depth=...): Explicitly get the table as a string FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]] FlopCounterMode.register_hierarchy(f, name): Allows you to register additional "hierarchies" for a function. [ghstack-poisoned]

ghstack-source-id: e17f258 Pull Request resolved: #95751

ezyang · 2023-03-01T02:54:21Z

Who do you want to do a close code review on this?

yinghai · 2023-03-01T03:02:52Z

+    return flop_count
+
+
+flop_mapping = {


Can we also add support to scaled_dot_product_attention since it's more and more used nowadays?

Einsum isn’t needed since it’s a “compositeimplicit” op and decomposes into other operators. There’s an example of counting einsum flops in the tests.

I'm gonna do the attention ones in a follow-up PR.

This actually leads to an important design issue: I think there should be an API to return "unsupported" ops. Similar to https://github.com/facebookresearch/fvcore/blob/51092b5515cbb493f73de079743dd6b11cc4bbf1/fvcore/nn/jit_analysis.py#L98

The reason is that users are mainly interacting with high-level modules and ops, and aren't aware of what low-level ops are called. Imagine one day torch adds a new op for transformer, or adds a different implementation of einsum that doesn't decompose: the change should be transparent, but it makes the flop counter become a silent error!

So in fvcore we provide unsupported ops and print a warning about them by default. To make the results more meaningful, we have a list of trivial ops that are always ignored and will not appear in "unsupported ops" (https://github.com/facebookresearch/fvcore/blob/51092b5515cbb493f73de079743dd6b11cc4bbf1/fvcore/nn/jit_analysis.py#L28).

@ppwwyyxx Hmm... I think I might just add all the ops as "unsupported ops".

I think one benefit of putting it in core is that in principle, we should hopefully be able to keep the operator list up to date better.

Yeah, impl changes should be caught by tests, we have an einsum test in this PR, but not matmul?

Chillee · 2023-03-01T04:27:14Z

@ezyang Not sure I really need a close code review from anybody (in particular) 🤔 Maybe @ngimel

ppwwyyxx · 2023-03-01T07:37:49Z

+        func_packet = func._overloadpacket
+        if func_packet in self.flop_mapping:
+            flop_count_func = self.flop_mapping[func_packet]
+            args_shape, out_shape = tree_map(get_shape, (args, normalize_tuple(out)))


Is it a good idea to always apply get_shape on tensors? For example I wonder if there would be an op that takes a scalar boolean tensor and condition two different behaviors (with different flops) on it.

I don't think it's a big deal - if that ever shows up we can just pass through scalar tensors.

ghstack-source-id: 7d04ddd Pull Request resolved: #95751

Chillee · 2023-03-02T18:25:35Z

@pytorchbot merge

pytorchmergebot · 2023-03-02T18:33:58Z

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albanD

I need to upgrade the public API script again to pick these up...

albanD · 2023-03-02T19:56:24Z

+    aten.convolution_backward: conv_backward_flop,
+}
+
+def normalize_tuple(x):


Please document public API or make them private if they shouldn't be public.

ah that's what you meant

I can also just set __all__ right?

Yes but then you'll see that you can't have these functions that look public and are not in __all__. So you will have to prepend them with _ anyways.

Why will I see? It seems to work for me.

You can try python test/test_public_bindings.py, I would expect that it will fail.

Seems to work for me 🤔

Overall, an example usage. Note that this *also* captures backwards FLOPs. ``` import torchvision.models as models import torch from torch.utils.flop_counter import FlopCounterMode inp = torch.randn(1, 3, 224, 224, device='cpu') mod = models.resnet18() flop_counter = FlopCounterMode(mod, depth=1) with flop_counter: mod(inp).sum().backward() ``` <img width="326" alt="image" src="https://user-images.githubusercontent.com/6355099/222023068-3491e405-f195-4e11-b679-36b19a1380c7.png"> You can control the depth of the module hierarchy with the `depth` attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs. <img width="366" alt="image" src="https://user-images.githubusercontent.com/6355099/222023306-3d880bb6-f534-4f98-bf10-83c4353acefc.png"> ## Other APIs FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions FlopCounterMode.get_table(depth=...): Explicitly get the table as a string FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]] FlopCounterMode.register_hierarchy(f, name): Allows you to register additional "hierarchies" for a function. [ghstack-poisoned]

pytorchmergebot · 2023-03-02T20:00:45Z

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albanD

Ok if CI is ok!

Chillee · 2023-03-02T21:06:06Z

@pytorchbot merge

pytorchmergebot · 2023-03-02T21:07:55Z

Merge started

Your change will be merged once all checks pass (ETA 0-4 Hours).

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

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danthe3rd · 2023-03-03T14:33:14Z

+            def forward(ctx, *args):
+                assert(self.parents[-1] == name)
+                self.parents.pop()
+                args = tree_map(lambda x: x.clone() if isinstance(x, torch.Tensor) else x, args)


quick question: why are we calling clone() here?

If the output of an autograd.Function is a view of the input, there are some restrictions on whether you can use inplace operations or not.

Overall, an example usage. Note that this *also* captures backwards FLOPs. ``` import torchvision.models as models import torch from torch.utils.flop_counter import FlopCounterMode inp = torch.randn(1, 3, 224, 224, device='cpu') mod = models.resnet18() flop_counter = FlopCounterMode(mod, depth=1) with flop_counter: mod(inp).sum().backward() ``` <img width="326" alt="image" src="https://user-images.githubusercontent.com/6355099/222023068-3491e405-f195-4e11-b679-36b19a1380c7.png"> You can control the depth of the module hierarchy with the `depth` attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs. <img width="366" alt="image" src="https://user-images.githubusercontent.com/6355099/222023306-3d880bb6-f534-4f98-bf10-83c4353acefc.png"> ## Other APIs FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions FlopCounterMode.get_table(depth=...): Explicitly get the table as a string FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]] FlopCounterMode.register_hierarchy(f, name): Allows you to register additional "hierarchies" for a function. Pull Request resolved: pytorch/pytorch#95751 Approved by: https://github.com/ngimel, https://github.com/albanD

Overall, an example usage. Note that this *also* captures backwards FLOPs. ``` import torchvision.models as models import torch from torch.utils.flop_counter import FlopCounterMode inp = torch.randn(1, 3, 224, 224, device='cpu') mod = models.resnet18() flop_counter = FlopCounterMode(mod, depth=1) with flop_counter: mod(inp).sum().backward() ``` <img width="326" alt="image" src="https://user-images.githubusercontent.com/6355099/222023068-3491e405-f195-4e11-b679-36b19a1380c7.png"> You can control the depth of the module hierarchy with the `depth` attribute (which defaults to 2). For example, if I don't limit it, this is what it outputs. <img width="366" alt="image" src="https://user-images.githubusercontent.com/6355099/222023306-3d880bb6-f534-4f98-bf10-83c4353acefc.png"> ## Other APIs FlopCounterMode(custom_mapping=...): Allows for custom flop counting functions FlopCounterMode.get_table(depth=...): Explicitly get the table as a string FlopCounterMode.flop_counts: Contains the flop information as a Dict[hierarchy: str, Dict[Op, int]] FlopCounterMode.register_hierarchy(f, name): Allows you to register additional "hierarchies" for a function. Pull Request resolved: pytorch#95751 Approved by: https://github.com/ngimel, https://github.com/albanD

Add flop counter utility

f386ee4

[ghstack-poisoned]

github-actions Bot requested review from SherlockNoMad, albanD, antoniojkim, bdhirsh, ezyang, jbschlosser, miladm, voznesenskym and wconstab March 1, 2023 01:24

Update on "Add flop counter utility"

6b7decb

[ghstack-poisoned]

Update on "Add flop counter utility"

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Chillee added the topic: new features topic category label Mar 1, 2023

Chillee requested a review from ngimel March 1, 2023 01:53

Chillee added the release notes: composability release notes category label Mar 1, 2023

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Add flop counter utility

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ghstack-source-id: e17f258 Pull Request resolved: #95751

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[RFC] Flop counters in PyTorch #93121

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Add flop counter utility

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ghstack-source-id: 7d04ddd Pull Request resolved: #95751

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Add flop formulas for sdpa #95854

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Fix node provenance tracking #95901

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🚀 Add FLOPs count to model summary Lightning-AI/pytorch-lightning#12567

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Provide more informative kernel names in Inductor #95940

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[proposal] "Name" string attribute for modules, parameters, buffers, tensors for more pleasant debugging (including distributed FSDP2 logging) #104247

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Could not measure the FLOPs of LSTM meta-pytorch/tnt#624

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