Summary:
Previously we actually did not really support this, this PR added the support.
Next
* clean up insert observer logic
* add allow_transitive_sharing boolean flag to allow people to turn this op for certain edges
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_shared_qspec_transitivity
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D50250789](https://our.internmc.facebook.com/intern/diff/D50250789)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111172
Approved by: https://github.com/kimishpatel
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:
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: 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:
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: 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
