Summary:
Previously we only checked dtype and is_dynamic to decide if two quantization spec are equivalent
this may not work in some cases, e.g. when people use different qscheme or quant_min/quant_max
This PR added checks for other fields as well
Test Plan:
regression tests
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D62530974](https://our.internmc.facebook.com/intern/diff/D62530974)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135736
Approved by: https://github.com/sxu
Summary:
Fixes https://github.com/pytorch/pytorch/issues/134778
The previous D62304294 broke some executorch tests. It has already been reverted.
In this diff, `_collect_param_buffer_metadata()` is modified in a way that when a `call_function` node is encountered and its input nodes include `get_attr`. We skip the fields that have been collected previously and only collect rest of the fields. This prevents over-writing.
Test Plan:
```
buck2 test 'fbcode//mode/dev-nosan' fbcode//executorch/backends/xnnpack/test:test_xnnpack_ops
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_re_export_preserve_handle
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_run_decompositions_preserve_handle
```
Differential Revision: D62514208
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135720
Approved by: https://github.com/zhxchen17, https://github.com/jerryzh168
Summary:
Skip test_prepare_qat_conv_bn_fusion_getitem_placeholder when we use training ir, since it's only for bn-getitem pattern, but the pattern doesn't exist in training ir.
Remove BLOCK_LIST since it's empty.
Now all internal unittests will use training ir.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' caffe2/test/quantization:test_quantization -- -r test_prepare_qat_conv_bn_fusion_getitem_placeholder
buck2 run 'fbcode//mode/dev-nosan' caffe2/test:quantization_pt2e_qat -- -r test_prepare_qat_conv_bn_fusion_getitem_placeholder
```
Differential Revision: D62387987
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135729
Approved by: https://github.com/tugsbayasgalan
Summary: as title
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:test_export -- -r test_conv_dynamic
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:fx -- -r matcher
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r x86
```
CI
Differential Revision: D62448302
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135623
Approved by: https://github.com/tugsbayasgalan
Summary: In new export_for_training, "stack_trace" does not exist in node meta anymore.
Test Plan:
```
buck run fbcode//mode/dev-nosan fbcode//caffe2/test:quantization_pt2e -- -r test_constant_prop_preserve_metadata
```
Reviewed By: angelayi
Differential Revision: D62219974
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135268
Approved by: https://github.com/angelayi
Summary:
In graph of TestXNNPACKQuantizer.test_dynamic_linear_with_con test, some quantized_decomposed.quantize_per_tensor.default ops are becoming quantized_decomposed.dequantize_per_tensor.tensor ops when using the new training ir.
This is because we lift params/buffers before calling make_fx. So previously, for the graph that’s passed to make_fx,`graph.L__self___linear1.weight` is a tensor
now in training ir, graph.L__self___linear1.weight is a FakeTensor. This caused the node overload to be different.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_dynamic_linear_with_conv
```
Differential Revision: D61364547
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134525
Approved by: https://github.com/tugsbayasgalan, https://github.com/jerryzh168
Summary:
Fixed some quantization tests for new training ir:
Fix batch norm node pattern matcher. In training ir, we have `aten.batch_norm` node instead of `aten._native_batch_norm_legit` and `aten._native_batch_norm_legit_no_training`.
Test Plan:
```
buck run fbcode//mode/dev-nosan fbcode//caffe2/test:quantization_pt2e
```
Reviewed By: tugsbayasgalan
Differential Revision: D62209819
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135184
Approved by: https://github.com/tugsbayasgalan
Summary:
This diff adds two new operators torch.ops._quantized.wrapped_linear_prepack and torch.ops._quantized.wrapped_quantized_linear_prepacked. It is a decomposition of the op torch.ops._quantized.wrapped_quantized_linear added in the previous diff.
We decomposed in this way as packed weight could be computed early so we don;t need to do it in every forward in AOTI
Reviewed By: jerryzh168
Differential Revision: D61395887
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134232
Approved by: https://github.com/houseroad
Summary:
Make quantization tests compatible with the new training IR.
With the new batch norm node `torch.ops.aten.batch_norm.default`, we don't need an additional getitem node after the bn node, so tests need to be fixed to not check for the getitem node.
We added a capture_pre_autograd_graph_using_training_ir() function, which returns True when we are using the training ir, and False otherwise. This way, the code supports both training ir and the old ir.
For now, we are just rolling out the training ir for fbcode internal tests.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_preserve_source_fn_stack
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_update_shared_qspec
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_relu_fusion
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_fusion
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_fusion_literal_args
```
Reviewed By: andrewor14, tugsbayasgalan
Differential Revision: D61292102
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134259
Approved by: https://github.com/tugsbayasgalan
Summary:
This diff adds a new operator wrapped_quantized_linear (torch.ops._quantized.wrapped_quantized_linear) and takes the following input argument: input (in fp32) , input_scale, input_zero_point, weight (in fp32), weight_scale, weight_zero_point, bias (in fp32), output_scale, output_zero_point, and out_channel. It does the following
1. Use quantize_per_tensor(input, input_scale, input_zero_point) to quantize the input tensor to int8
2. Use quantized::linear_prepack(weight, weight_scale, weight_zero_point, bias) to pack the weight and bias
3. Use quantized::linear to perform int8 quantized linear
4. dequantize
This new op is essentially a wrapper of mutiple ops. We do this as torch.export cannot handle models where it has old quantize apis.
Reviewed By: jerryzh168
Differential Revision: D61377266
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134024
Approved by: https://github.com/houseroad
Summary:
This diff aims to fix the GPU Test skips in the quantization tests under the `caffe2/test/quantization` directory. The changes made in the `TARGETS` files include adding the `should_use_remote_gpu` flag to enable remote GPU testing. This should help to resolve the skipped tests and improve the overall test coverage.
[This diff] Fixed skip count: 4
[Running total] Fixed skip count: 4
Note: Creating separate diffs for each test-group.
Test Plan:
**281475054644766**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_compare_per_channel_device_numerics (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/5629499773981783
**281475054644780**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_compare_per_tensor_device_numerics (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/11540474087422107
**281475054644853**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_quant_pin_memory (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/11540474087422477
**844425008078016**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_cuda_quantization_does_not_pin_memory (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/1407375259845199
Differential Revision: D60055277
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133158
Approved by: https://github.com/jovianjaison
Summary:
The Quantizer subclass can return a new model from `transform_for_annotation`,
and this is common if it uses any ExportPass subclass which does not mutate in-place.
Use the returned model instead of assuming its the same.
Differential Revision: D60869676
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132893
Approved by: https://github.com/jerryzh168
Summary:
- make default DCE pass check schema,
- need to rebase onto https://github.com/pytorch/pytorch/pull/131651 after it's in phabricator (for now the change is manually added).
- mark Proxy dump as NotImplemented for better error msg
- Remove Proxy from tensors when dumping models, as Proxy cannot be dumped.
More details in https://docs.google.com/document/d/1G5vmTXjzxoyVGRI2kpA1gQukK_Glyg2NrE0Oh6Nlg9A/edit?usp=sharing.
Test Plan:
CI
```
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r qat_conv2d
- test_export.py
- buck2 run 'fbcode//mode/dev-nosan' fbcode//modai/test:test_modai -- -r test_qat_stinson_htp_export
- buck2 run 'fbcode//mode/dev-nosan' fbcode//vizard_projects/ml_depth/tests:test_model -- -r test_qat_model_et
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:fx -- -r dce
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=False,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=True,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_fold_bn_erases_bn_node
```
Reviewed By: angelayi
Differential Revision: D60319175
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132764
Approved by: https://github.com/angelayi
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
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
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
Summary:
This pr fixes all the places in strict export stack where the output node's meta is not preserved correctly. However, we're getting a new error for the test we intend to fix: `buck2 run caffe2/test/quantization:test_quantization -- -r "test_re_export_preserve_handle"`:
The `get_attr` nodes has wrong metadata. I guess there are more things need to be fixed to get it working but it's beyond the scope of this PR.
Test Plan: buck2 run caffe2/test/quantization:test_quantization -- -r "test_re_export_preserve_handle"
Differential Revision: D60198221
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131706
Approved by: https://github.com/yushangdi
Summary:
This diff reverts D59561509
D59561509: [FX][export] DCE pass, check schema for node impurity (#130395) by yushangdi causes the following test failure:
Tests affected:
- [cogwheel:cogwheel_mtia_cmf_m5_shrunk_test#test_flow_with_verification](https://www.internalfb.com/intern/test/844425041436985/)
Here's the Multisect link:
https://www.internalfb.com/multisect/6533402
Here are the tasks that are relevant to this breakage:
T191383430: 10+ tests unhealthy for ads_mtia_inference
The backout may land if someone accepts it.
If this diff has been generated in error, you can Commandeer and Abandon it.
Test Plan: NA
Differential Revision: D60029318
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131341
Approved by: https://github.com/angelayi
Earlier the signature of dequantize ops for decomposed quantized Tensor was changed for wider use-cases where the output dtype can be different from torch.float and needs to be passed during dequantization.
Please refer: https://github.com/pytorch/pytorch/pull/121450
However, setting of correct output dtype for dequantize ops was still missing in convert_pt2e flow.
This change enables the users to use PT2E quantization flow with non torch.float unquantized dtype, such as torch.bfloat16.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128953
Approved by: https://github.com/jgong5, https://github.com/jerryzh168
Summary:
Add three top level APIs for numeric debugger in pt2e flow that can log intermediate output in the model
and calculate summary for metric comparisons between nodes in two graphs
* `prepare_for_propagation_comparison`
* `extract_results_from_loggers`
* `compare_results`
Test Plan:
python test/test_quantization.py -k test_prepare_for_propagation_comparison
python test/test_quantization.py -k test_extract_results_from_loggers
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130643
Approved by: https://github.com/dulinriley, https://github.com/tarun292
Summary:
* Added support for preserving it during deepcopy, need to remap the args since _numeric_debug_handle refers
to the nodes in the graph
TODO: need to fully support re-export, currently the metadata for output node is not preserved
Test Plan:
python test/test_quantization.py -k test_deepcopy_preserve_handle
python test/test_quantization.py -k test_copy_preserve_handle
all related tests:
python test/test_quantization.py -k TestGenerateNumericDebugHandle
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129287
Approved by: https://github.com/zhxchen17
Summary:
Previously we store edge id in numeric_debug_handle to support operator fusion and operator decomposition throughout the stack,
but according to feedback from customers, people prefer the simpler per-node id, and they are fine with not having the additional
support for numerical debugging for inputs and willing to hack around to achieve this.
This PR changes the structure of numeric_debug_handle to store unique_id for each node instead.
e.g.
graph:
```
node = op(input_node, weight_node)
```
Before:
```
node.meta[NUMERIC_DEBUG_HANDLE_KEY] = {input_node: id1, weight_node: id2, "output": id3}
```
After:
```
node.meta[NUMERIC_DEBUG_HANDLE_KEY] = id1
```
Test Plan:
python test/test_quantization.py -k TestGenerateNumericDebugHandle
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129811
Approved by: https://github.com/tarun292
Summary:
Previously we store edge id in numeric_debug_handle to support operator fusion and operator decomposition throughout the stack,
but according to feedback from customers, people prefer the simpler per-node id, and they are fine with not having the additional
support for numerical debugging for inputs and willing to hack around to achieve this.
This PR changes the structure of numeric_debug_handle to store unique_id for each node instead.
e.g.
graph:
```
node = op(input_node, weight_node)
```
Before:
```
node.meta[NUMERIC_DEBUG_HANDLE_KEY] = {input_node: id1, weight_node: id2, "output": id3}
```
After:
```
node.meta[NUMERIC_DEBUG_HANDLE_KEY] = id1
```
Test Plan:
python test/test_quantization.py -k TestGenerateNumericDebugHandle
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129811
Approved by: https://github.com/tarun292
Summary:
There were two problems with the HistogramObserver:
1. It does not work when someone passes a batch_size 1, tensor_size 1 data-point.
2. The Histogram doesn't seem to actually update if the range of the new x falls within the old one
These issues were both fixed.
On top of this, I greatly simplified the logic for the histogram updating. Now, it doesn't do the downsampling anymore, which saves a ton of memory and code. The accuracy can still be controlled with the upsampling ratio. This ratio was also too high for the accuracy we generally need here, I reduced the default for this.
Also the code is cleaner now, much easier to follow what's happening.
test_histogram_observer_same_inputs was likely wrong - If I pass 0s and 1s to my histogramobserver, I want them to actually count! The current test now thinks it's good to discard and ignore these values.
Test Plan: You can run the included tests.
Differential Revision: D58931336
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129387
Approved by: https://github.com/jerryzh168
