Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44846
The save function traverses the model state dict to pick out the observer stats
load function traverse the module hierarchy to load the state dict into module attributes depending on observer type
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_save_observer_state_dict
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23746821
fbshipit-source-id: 05c571b62949a2833602d736a81924d77e7ade55
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44835
This is for feature parity with fx graph mode quantization
Test Plan: Imported from OSS
Reviewed By: z-a-f
Differential Revision: D23745086
fbshipit-source-id: ae2fc86129f9896d5a9039b73006a4da15821307
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44856
Support following format of qconfig_dict
```python
qconfig_dict = {
# optional, global config
"": qconfig?,
# optional, used for module and function types
# could also be split into module_types and function_types if we prefer
"object_type": [
(nn.Conv2d, qconfig?),
(F.add, qconfig?),
...,
],
# optional, used for module names
"module_name": [
("foo.bar", qconfig?)
...,
],
# optional, matched in order, first match takes precedence
"module_name_regex": [
("foo.*bar.*conv[0-9]+", qconfig?)
...,
]
# priority (in increasing order): global, object_type, module_name_regex, module_name
# qconfig == None means fusion and quantization should be skipped for anything
# matching the rule
}
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23751304
fbshipit-source-id: 5b98f4f823502b12ae2150c93019c7b229c49c50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44766
There might be modules that are not symbolically traceable, e.g. LSTM (since it has
input dependent control flows), to support quantization in these cases, user will provide
the corresponding observed and quantized version of the custom module, the observed
custom module with observers already inserted in the module and the quantized version will
have the corresponding ops quantized. And use
```
from torch.quantization import register_observed_custom_module_mapping
from torch.quantization import register_quantized_custom_module_mapping
register_observed_custom_module_mapping(CustomModule, ObservedCustomModule)
register_quantized_custom_module_mapping(CustomModule, QuantizedCustomModule)
```
to register the custom module mappings, we'll also need to define a custom delegate class
for symbolic trace in order to prevent the custom module from being traced:
```python
class CustomDelegate(DefaultDelegate):
def is_leaf_module(self, m):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, CustomModule)
m = symbolic_trace(original_m, delegate_class=CustomDelegate)
```
Test Plan: Imported from OSS
Reviewed By: z-a-f
Differential Revision: D23723455
fbshipit-source-id: 50d666e29b94cbcbea5fb6bcc73b00cff87eb77a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44956
Makes buffer shapes for HistogramObserver have the
same shapes in uninitialized versus initialized states.
This is useful because the detectron2 checkpointer assumes
that these states will stay the same, so it removes the
need for manual hacks around the shapes changing.
Test Plan:
```
python test/test_quantization.py TestObserver.test_histogram_observer_consistent_buffer_shape
```
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23785382
fbshipit-source-id: 1a83fd4f39b244b00747c368d5d305a07d877c92
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44773
The model is created and prepared using fx APIs and then scripted for training.
In order to test QAT on scriptmodel we need to be able to disable/enable fake_quant
and observer modules on it.
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_qat_and_script
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23741354
fbshipit-source-id: 3fee7aa9b049d9901313b977710f4dc1c4501532
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44749
Ensure fx module is scriptable after calling prepare_qat on it
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_qat_and_script
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23718380
fbshipit-source-id: abf63ffb21e707f7def8f6c88246877f5aded58c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44537
Originally, the `min_val`, `max_val`, `min_vals`, `max_vals`
attributes of observers were Tensors but not buffers. They had custom
state_dict save/load code to ensure their state was saved.
At some point, these attributes became buffers, and the custom
save/load code remained. This introduced a subtle bug:
* create model A, move it to a device (cpu/cuda) and save its state_dict
* create model B, load its state dict.
* `min_val|min_vals|max_val|max_vals` would always be loaded to model A's device, even if the rest of model B was on a different device
* the above is inconsistent with how save/load on different devices is expected to work (see https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-across-devices)
In practice, the case people would sometimes hit is:
* model A is on CPU, state dict is saved
* model B is created and moved to GPU, state_dict from model A is loaded
* assertions throw when operations are attempted across different devices
This PR fixes the behavior by removing the custom save/load where
possible and letting the default `nn.Module` save/load code handle
device assignment. We special case `PerChannelMinMaxObserver` and its
children to allow for loading buffers or different size, which is
normal.
There are some followups to also enable this for HistogramObserver
and FakeQuantize, which can be done in separate PRs due to higher
complexity.
Test Plan:
```
python test/test_quantization.py TestObserver.test_state_dict_respects_device_affinity
```
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23644493
fbshipit-source-id: 0dbb6aa309ad569a91a663b9ee7e44644080032e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44217
Move the tests to static ones as well
Test Plan:
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23547386
fbshipit-source-id: 41f81c31e1613098ecf6a7eff601c7dcd4b09c76
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44208
Add quantized module in static quantization namespace. Embedding
quantization requires only weights to be quantized so it is static.
Internally it calls the embedding_bag_byte op with the offsets set corresponding to the
indices.
Future PR will move EmbeddingBag quantization from dynamic to static as well.
Test Plan:
python test/test_quantization.py test_embedding_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23547384
fbshipit-source-id: eddc6fb144b4a771060e7bab5853656ccb4443f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44042
Missed one case last time
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23479345
fbshipit-source-id: 30e6713120c494e9fab5584de4df9b25bec83d32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44125
In `Quantizer._prepare`, `observed` was used for two different variables
with different types. Making the names a bit cleaner and removing the
name conflict.
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
```
Imported from OSS
Reviewed By: dskhudia
Differential Revision: D23504109
fbshipit-source-id: 0f73eac3d6dd5f72ad5574a4d47d33808a70174a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44092
instead submodules and weights are installed directly on the
graph_module by transferring the original modules. This makes it more
likely that scripting will succeed (since we no longer have submodules
that are not used in the trace). It also prevents layered transforms
from having to special case handling of the `root` module. GraphModules
can now be re-traced as part of the input to other transforms.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23504210
Pulled By: zdevito
fbshipit-source-id: f79e5c4cbfc52eb0ffb5d6ed89b37ce35a7dc467
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44115
Fixes device affinity in the FX prepare pass for QAT. Before this PR, observers
were always created on CPU. After this PR, observers are created on the
same device as the rest of the model. This will enable QAT prepare to
work regardless of whether users move the model to cuda before or after
calling this pass.
Test Plan:
```
python test/test_quantization.py TestQuantizeFx.test_qat_prepare_device_affinity
```
Imported from OSS
Reviewed By: supriyar
Differential Revision: D23502291
fbshipit-source-id: ec4ed20c21748a56a25e3395b35ab8640d71b5a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43927
Adds uninitialized placeholders for various state
used throughout the Quantizer object, with documentation
on what they are. No logic change.
Test Plan:
```
python test/test_quantization.py TestQuantizeFxOps
python test/test_quantization.py TestQuantizeFx
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23439473
fbshipit-source-id: d4ae83331cf20d81a7f974f88664ccddca063ffc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43914
Renames `matches` function to `is_match`, since there is also
a list named `matches` we are passing around in `Quantizer`,
and would be good to decrease name conflicts.
Test Plan:
```
python test/test_quantization.py TestQuantizeFxOps
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23435601
fbshipit-source-id: 394af11e0120cfb07dedc79d5219247330d4dfd6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43910
Adds a debug function to get a representation of all nodes in the
graph, such as
```
name op target args kwargs
x plchdr x () {}
linear_weight gt_prm linear.weight () {}
add_1 cl_fun <bi_fun add> (x, linear_weight) {}
linear_1 cl_mod linear (add_1,) {}
relu_1 cl_meth relu (linear_1,) {}
sum_1 cl_fun <bi_meth sum> (relu_1,) {'dim': -1}
topk_1 cl_fun <bi_meth topk> (sum_1, 3) {}
```
using only Python STL. This is useful for printing internal state of
graphs when working on FX code.
Has some on-by-default logic to shorten things so that node reprs for
toy models and unit tests fit into 80 chars.
Flexible on function name and location, I care more that this is
accessible from both inside PT as well as from debug scripts which
are not checked in.
Test Plan:
see
https://gist.github.com/vkuzo/ed0a50e5d6dc7442668b03bb417bd603 for
example usage
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23435029
fbshipit-source-id: 1a2df797156a19cedd705e9e700ba7098b5a1376
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43892
Run weight observer in the convert function, so user do not need to run calibration
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23429758
fbshipit-source-id: 5bc222e3b731789ff7a86463c449690a58dffb7b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43902
Trace back from the weight node util we hit getattr, reconstruct the graph module with the traced nodes
and run the graph module to pack the weight. then replace the original chain of ops with the packed weight.
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432431
fbshipit-source-id: 657f21a8287494f7f87687a9d618ca46376d3aa3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43901
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432430
fbshipit-source-id: fc99eb75cbecd6ee7a3aa6c8ec71cd499ff7e3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43789
Since it's single element.. In some cases we may not be able to resize the
buffers.
Test Plan: unit tests
Reviewed By: supriyar
Differential Revision: D23393108
fbshipit-source-id: 46cd7f73ed42a05093662213978a01ee726433eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43728
Trace back from the weight node util we hit getattr, reconstruct the graph module with the traced nodes
and run the graph module to pack the weight. then replace the original chain of ops with the packed weight.
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385090
fbshipit-source-id: 11341f0af525a02ecec36f163a9cd35dee3744a1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43581
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385091
fbshipit-source-id: b789e54e1a0f3af6b026fd568281984e253e0433
