Summary:
Now that quantization works on pre-dispatch aten IR, moving to full set
of aten ops is ok. Plus when tracing models like ViT, the linear
projections of of k, q, v uses functional.linear and not nn.Linear,
which results not being able to extract nodes corresponding to linear.
Test Plan:
quant tests
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D49252194](https://our.internmc.facebook.com/intern/diff/D49252194)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109254
Approved by: https://github.com/jerryzh168
Summary:
Previously we can only use native pytorch int dtypes that has corresponding quantized dtypes (e.g. quint8, qint8), this
PR removes this assumption in observers/fake_quants so that users can use all pytorch native dtypes (except for int64, we can add it later if need)
the main addition here is int16.
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108453
Approved by: https://github.com/kimishpatel
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:
In prepararation for metadata porting diff, it is required that weight
quant annotation happens via edge quantization, i.e. input_qspec_map.
Reason: Metadata is ported via associating DQ node's metadata with its
consumer while associating Q node's metadata with its producer.
Furthermore, such porting must be qualified via user intent to see if
the consumder of DQ, or producer of Q, actually specified intent of
quantization
By making quantization annotation on linear node's weight via
input_qspec_map, we can enable associating DQ of [weight -> Q -> DQ],
with the linear module.
Test Plan:
CI
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D48488414](https://our.internmc.facebook.com/intern/diff/D48488414)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107105
Approved by: https://github.com/jerryzh168
Summary:
Previously we run propagate_annotation by default in quantization flow to propagate annotations for ops like reshape, view etc.
Not all quantizers would need this so we moved this to xnnpack_quantizer_utils for now.
Next Step:
* make propagate_annotation function configurable with a custom list of ops
* remove unneeded ops in `_is_share_obs_or_fq_op`
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D48856985](https://our.internmc.facebook.com/intern/diff/D48856985)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108320
Approved by: https://github.com/kimishpatel
**Summary**
Add linear and linear-unary post-op quantization recipe to x86 inductor quantizer. For PT2E with Inductor. With this, the quantization path will add `quant-dequant` pattern for linear and linear-unary post op.
**Test plan**
python test/test_quantization.py -k test_linear_with_quantizer_api
python test/test_quantization.py -k test_linear_unary_with_quantizer_api
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106781
Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5, https://github.com/jerryzh168
ghstack dependencies: #105818
Summary: This fixes the no bias case for conv annotations.
Previously this would result in an index out of bounds, since
the new aten.conv2d op may not have the bias arg (unlike the
old aten.convolution op). This was not caught because of a lack
of test cases, which are added in this commit.
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_qat_conv_no_bias
python test/test_quantization.py TestQuantizePT2E.test_qat_conv_bn_relu_fusion_no_conv_bias
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel
Differential Revision: [D48696874](https://our.internmc.facebook.com/intern/diff/D48696874)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107971
Approved by: https://github.com/jerryzh168
Summary:
Currently in quantizer/quantize_pt2e we import things from specific quantizers (XNNPACKQuantizer, QuantizationConfig) etc.
this PR removes them so it's clearer that they are not part of the core quantization code base
This PR also removed get_supported_operators from main Quantizer since we haven't seen a clear need for this API
Test Plan:
CIs
Imported from OSS
Differential Revision: D48340367
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107259
Approved by: https://github.com/kimishpatel
Summary:
This is to allow sharing these annotate functions by other quantizers so that writing a new quantizer is easier
note that these annotation functions will be maintained by XNNPACKQuantizer developers instead of AO team
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106642
Approved by: https://github.com/andrewor14
Summary:
Added support to allow users to set configurations based on module type in XNNPACKQuantizer, can also serve as an example
for implementing new quantizers
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_xnnpack_quantizer_set_module_type
Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106094
Approved by: https://github.com/andrewor14
ghstack dependencies: #106087
Summary:
Added support to allow users to set configurations based on module name in XNNPACKQuantizer, can also serve as an example
for implementing new quantizers
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_xnnpack_quantizer_set_module_name
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106087
Approved by: https://github.com/andrewor14
Summary: moving quantizer to torch.ao.quantization to make it a public api, since pt2e is a folder for implementations
Test Plan:
CIs
sanity check: "buck test //executorch/backends/xnnpack/test:test_xnnpack_quantized_models -- test_resnet18"
Differential Revision: D47727838
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105885
Approved by: https://github.com/andrewor14
