Summary:
As title
This is a BC-breaking change because graph produced by "capture_pre_autograd_graph" cannot be input to quantization anymore. But this is ok, since this API is deprecated for a while and is going to be deleted. We have removed all call sites of it.
We remove the deprecated API references in code, docs, and tests.
We also removed two tests that specific to capture_pre_autograd_graph API.
Test Plan: CI
Differential Revision: D65351887
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139505
Approved by: https://github.com/tugsbayasgalan, https://github.com/andrewor14, https://github.com/jerryzh168
Summary:
In the new training ir, we produce `torch.ops.aten.batch_norm.default` instead of `torch.ops.aten._native_batch_norm_legit.default` or `torch.ops.aten._native_batch_norm_legit_no_training.default`.
So we need to change the pattern match to accomodate the new op.
- Add `torch.ops.aten.batch_norm.default` to pattern matcher list so it's identified as a batch norm node
- `torch.ops.aten.batch_norm.default` doesn't have a getitem user anymore, so when removing the bn norm, we need to do `bn_node.replace_all_uses_with(conv_node)` instead of `getitem_node.replace_all_uses_with(conv_node)`
The behavior of capture_pre_autograd_graph is consistent for each run.
If the run is a fbcode test, then capture_pre_autograd_graph uses training IR. This means both _get_aten_graph_module_for_pattern and replace_pattern_with_filters see the same training IR.
If the run is not a fbcode test, then both would see the old IR.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d_binary2
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d_unary
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_linear_unary
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_dynamic_quant_linear
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_dynamic_quant_linear
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_flatten_recipe
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_linear_unary
```
Reviewed By: andrewor14, tugsbayasgalan
Differential Revision: D61291077
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134157
Approved by: https://github.com/tugsbayasgalan
Part of #134054.
This corresponds to the pytorch mypy changes from D61493706. Updating takes so
long and touches so many files that it's impossible to land as a whole without conflicting with some other intermediate change.
So landing these 'type: ignore' for pytorch in advance of them actually being needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134202
Approved by: https://github.com/Skylion007
Summary: Previously, when folding BN into conv, we rely on DCE
to clean up the unused BN node from the graph. This works if
the model is already in eval mode, but fails if the model is
still in train mode because DCE doesn't remove nodes with
potential side effects (in this case `_native_batch_norm_legit`).
This required users to move the model to eval mode before calling
convert in order to get a properly DCE'd graph.
To solve this, we manually erase the BN node after folding
instead of relying on DCE. This relaxes the ordering constraints
between `move_exported_model_to_eval` and `convert_pt2e`.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d.test_fold_bn_erases_bn_node
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn2d.test_fold_bn_erases_bn_node
Reviewers: jerryzh168, yushangdi
Subscribers: jerryzh168, yushangdi, supriyar
Differential Revision: [D60520149](https://our.internmc.facebook.com/intern/diff/D60520149)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131651
Approved by: https://github.com/yushangdi, https://github.com/leslie-fang-intel
Summary: Previously, when folding BN into conv, we rely on DCE
to clean up the unused BN node from the graph. This works if
the model is already in eval mode, but fails if the model is
still in train mode because DCE doesn't remove nodes with
potential side effects (in this case `_native_batch_norm_legit`).
This required users to move the model to eval mode before calling
convert in order to get a properly DCE'd graph.
To solve this, we manually erase the BN node after folding
instead of relying on DCE. This relaxes the ordering constraints
between `move_exported_model_to_eval` and `convert_pt2e`.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d.test_fold_bn_erases_bn_node
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn2d.test_fold_bn_erases_bn_node
Reviewers: jerryzh168, yushangdi
Subscribers: jerryzh168, yushangdi, supriyar
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131651
Approved by: https://github.com/yushangdi
The current call passes in `['/actual/path']` to os.walk which is a string pointing to no path and thus silently leads to and empty traversal.
There is an unused function just above that handles that, so I guess this is what was supposed to be called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126103
Approved by: https://github.com/suo
Summary:
also added some utils in xnnpack_quantizer_utils.py
* annotate_conv_tranpsose_bn_relu and annotate_conv_transpose_bn -> this is for QAT
* annotate_conv_transpose_relu
conv_transpose + bn weights fusion is performed automatically and can not be disabled currently
we can add support to allow disable this fusion later if needed
Test Plan:
python test/test_quantization.py -k test_conv_transpose_bn_fusion
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122046
Approved by: https://github.com/andrewor14
Summary: This commit adds a util for PT2E quantization users
to call `model.train()` and `model.eval()` without error.
Instead, these will automatically call the equivalent
`move_exported_model_to_train/eval` for the user, which only
switch behavior for special ops like dropout and batchnorm.
This enables users to onboard to the PT2E flow more easily.
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_allow_exported_model_train_eval
Reviewers: jerryzh168, tugsbayasgalan, zhxchen17
Subscribers: jerryzh168, tugsbayasgalan, zhxchen17, supriyar
Differential Revision: [D53426636](https://our.internmc.facebook.com/intern/diff/D53426636)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119091
Approved by: https://github.com/jerryzh168, https://github.com/tugsbayasgalan, https://github.com/zhxchen17
Summary:
`_fold_conv_bn_qat` is taking a long time currently, so skipping it when it's not necessary,
we can have follow up fixes to actually reduce the patterns or cache the patterns if possible
Test Plan:
uncomment the print in `test_speed`, run
python test/test_quantization.py -k test_speed
and make sure the convert time is low, e.g. 0.1s instead of 8-9 seconds
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116440
Approved by: https://github.com/andrewor14
Summary: Previously the PT2 QAT code only supported conv2d-bn.
This commit extends all existing QAT fusion support to conv1d-bn,
including support for all variants like relu, no bias, literal
args, cuda etc. This commit also refactors the code such that
we can support conv3d-bn easily in the future.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar
Differential Revision: [](https://our.internmc.facebook.com/intern/diff/)
Differential Revision: [D51428979](https://our.internmc.facebook.com/intern/diff/D51428979)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113714
Approved by: https://github.com/jerryzh168
Fixes recent broken unit tests caused by PR #109908 because cudnn and miopen have separate batch norm functions.
```
2023-10-05T09:35:01.6606614Z _______________ TestQuantizePT2EQAT.test_qat_conv_bn_fusion_cuda _______________
2023-10-05T09:35:01.6606948Z Traceback (most recent call last):
2023-10-05T09:35:01.6607362Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 323, in test_qat_conv_bn_fusion_cuda
2023-10-05T09:35:01.6607767Z self._verify_symmetric_xnnpack_qat_graph(
2023-10-05T09:35:01.6608217Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 130, in _verify_symmetric_xnnpack_qat_graph
2023-10-05T09:35:01.6608658Z self._verify_symmetric_xnnpack_qat_graph_helper(
2023-10-05T09:35:01.6609105Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 173, in _verify_symmetric_xnnpack_qat_graph_helper
2023-10-05T09:35:01.6609623Z m = prepare_qat_pt2e(m, quantizer)
2023-10-05T09:35:01.6610171Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/quantize_pt2e.py", line 178, in prepare_qat_pt2e
2023-10-05T09:35:01.6610561Z _fuse_conv_bn_qat(model)
2023-10-05T09:35:01.6611072Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 501, in _fuse_conv_bn_qat
2023-10-05T09:35:01.6611497Z m = _fuse_conv_bn_qat_helper(m, is_cuda=True)
2023-10-05T09:35:01.6612065Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 575, in _fuse_conv_bn_qat_helper
2023-10-05T09:35:01.6612492Z _get_conv_bn_getitem_nodes(r.replacements)
2023-10-05T09:35:01.6613058Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 383, in _get_conv_bn_getitem_nodes
2023-10-05T09:35:01.6613465Z assert bn_node is not None
2023-10-05T09:35:01.6613716Z AssertionError
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110653
Approved by: https://github.com/jerryzh168, https://github.com/pruthvistony
Summary: Today, we get different batch norm ops depending on
the device the model is placed on at export time. Exporting
`model.cpu()` gives `_native_batch_norm_legit`, while exporting
`model.cuda()` gives `cudnn_batch_norm`. QAT fusion currently
only supports the former and silently ignores the latter. This
commit fixes this by additionally matching on the latter op
during QAT fusion.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT.test_qat_conv_bn_fusion
python test/test_quantization.py TestQuantizePT2EQAT.test_qat_conv_bn_relu_fusion
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar
Differential Revision: [D49615145](https://our.internmc.facebook.com/intern/diff/D49615145)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109908
Approved by: https://github.com/jerryzh168
Summary:
This commit fixes two silent correctness problems with
the current implementation of `move_model_to_eval`:
(1) Previously the user had to manually call `eliminate_dead_code`
before calling `move_model_to_eval`, otherwise the dropout pattern
won't actually get eliminated. This is because subgraph rewriter
complains the match is not self-contained, and so silently does
not do the replacement.
(2) We wish to error when the user calls `model.train()` or
`model.eval()` on an exported model. This error is raised
correctly immediately after export today, but no longer raised
after the user calls prepare or convert.
We fix (1) by moving the `eliminate_dead_code` call into
`move_model_to_eval`, and fix (2) by ensuring the respective
errors are thrown after prepare and convert as well.
Additionally, this commit renames `move_model_to_eval` to
`move_exported_model_to_eval` to be more explicit.
bypass-github-export-checks
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_disallow_eval_train
python test/test_quantization.py TestQuantizePT2E.test_move_exported_model_to_eval
Imported from OSS
Differential Revision: D49097293
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108891
Approved by: https://github.com/jerryzh168
Summary:
During convert step observers are first replaced by Q-DQ pair. In some
scenarios like following output DQ has a fan out.
---> OP2 -> Q -> DQ
/
OP -> Q -> DQ -
\
---> OP3 -> Q -> DQ
If either op OP2 or OP3 are configured to be quantized, then the input
is expected to quantized. In this case quantized equivalent of some
pattern, that quantizer asked to be quantized, should look like:
[DQ -> {pattern} -> Q]. However, in scenario like above where DQ node
is shared between multiple "quantized" patterns, boundary of "quantized"
pattern is not clear because DQ now belongs to multiple quantized
patterns.
This poses challenge for:
- Porting metadata: which "quantized" partition this DQ node belongs
- Quantized representation, equivalently, needs to identify
self-contained quantized pattern that is replaced by its equivalent pattern
that captures compute in the quantized precision.
Test Plan:
test_duplicate_dq_pass
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D48663147](https://our.internmc.facebook.com/intern/diff/D48663147)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107900
Approved by: https://github.com/jerryzh168, https://github.com/andrewor14, https://github.com/leslie-fang-intel
ghstack dependencies: #107105, #107106, #107899
Summary: This commit adds a public facing
`torch.ao.quantization.move_model_to_eval` util function
for QAT users. Instead of calling model.eval() on an exported
model (which doesn't work, see
https://github.com/pytorch/pytorch/issues/103681), the user
would call this new util function instead. This ensures special
ops such as dropout and batchnorm (not supported yet) will have
the right behavior when the graph is later used for inference.
Note: Support for an equivalent `move_model_to_train` will be
added in the future. This is difficult to do for dropout
currently because the eval pattern of dropout is simply a clone
op, which we cannot just match and replace with a dropout op.
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_move_model_to_eval
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar
Differential Revision: [D48814735](https://our.internmc.facebook.com/intern/diff/D48814735)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108184
Approved by: https://github.com/jerryzh168
**Summary**
The latest check-in a0cfaf0688 for the conv-bn folding assumes the graph is captured by the new graph capture API `torch._export.capture_pre_autograd_graph`. Since we still need to use the original graph capture API `torch._dynamo_export` in 2.1 release. So, this check-in made negative impact to workloads' performance heavily. Made this PR to fix this issue by trying to make the conv-bn folding function workable with both new and original graph capture API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107951
Approved by: https://github.com/jgong5, https://github.com/jerryzh168
ghstack dependencies: #106836, #106838, #106958
When exporting dropout with cpu tensor, we get following graph module
```
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[512, 10]):
empty_memory_format: f32[512, 10] = torch.ops.aten.empty.memory_format([512, 10], dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False, memory_format = torch.contiguous_format)
bernoulli_p: f32[512, 10] = torch.ops.aten.bernoulli.p(empty_memory_format, 0.9); empty_memory_format = None
div_scalar: f32[512, 10] = torch.ops.aten.div.Scalar(bernoulli_p, 0.9); bernoulli_p = None
mul_tensor: f32[512, 10] = torch.ops.aten.mul.Tensor(arg0_1, div_scalar); arg0_1 = div_scalar = None
return (mul_tensor,)
```
In addition, if we export with eval() mode, we will have an empty graph.
However, when exporting with cuda tensor, we got
```
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[512, 10]):
native_dropout_default = torch.ops.aten.native_dropout.default(arg0_1, 0.1, True); arg0_1 = None
getitem: f32[512, 10] = native_dropout_default[0]; native_dropout_default = None
return (getitem,)
```
and exporting under eval() mode will still have a dropout node in graph.
This PR make exporting with CPU tensor also produce aten.native_dropout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106274
Approved by: https://github.com/ezyang
