[Quant] Add DQ duplication pass #107900

kimishpatel · 2023-08-24T21:45:10Z

Stack from ghstack (oldest at bottom):

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

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @anijain2305

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: [ghstack-poisoned]

pytorch-bot · 2023-08-24T21:45:14Z

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kimishpatel · 2023-08-24T21:50:21Z

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jerryzh168 · 2023-08-24T22:13:40Z

torch/ao/quantization/pt2e/utils.py

+    return False
+
+
+def _find_q_dq_node_for_user(


I feel this will be hard to debug and maintain, what is the problem with the existing duplicate dq implementation: https://github.com/pytorch/pytorch/blob/main/torch/ao/quantization/pt2e/qat_utils.py#L580?

oh I looked again, is this for test only? can you move this to test files?

No this is also used later in the stack for metdata porting.

jerryzh168 · 2023-08-24T22:23:02Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+    _copy_and_replace_use(gm, producer, user)
+
+
+class DuplicateDQPass(_ExportPassBase):


I'm wondering if it makes sense to just always duplicate dequant for now, or do a duplicate and then deduplicate like we have did before in https://github.com/pytorch/pytorch/blob/main/torch/ao/quantization/pt2e/qat_utils.py#L580-L627

Oh you mean, move this before fold_conv_bn? If so yes. makes sense.

I was wondering if we can do:

duplicate all dequant -> port metadata -> deduplicate dequantize ops when we didn't find metadatas on them

so that we don't need to refer to quantization_annotation here again

why is that better? It might be cleaner if we dont duplicate and dedup.

so current quantization_annotation doesn't really convey any information about share or not share dq nodes, I think it will be better to not rely on it

actually I'm wondering when does this have to be landed? I'm thinking of doing a proper design for this part next week, right now this information is missing from QuantizationSpec

there is no sharing of dq nodes at all. The only reason annotation is taken into account is so that you dont unnecessarily duplicate dq nodes. I can very much remove quantization_annotation from decision making, it will just have performance imlication

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) [ghstack-poisoned]

jerryzh168 · 2023-08-30T20:13:03Z

torch/ao/quantization/pt2e/utils.py

+    "filter_sym_size_users",
+    "find_q_dq_node_for_user",
+    "is_valid_annotation",


nit: could you make these private?

I did. and having to export it from util was complaining that if this is to be used elsewhere remove _. If you have suggestions let me know and i can do it in subsequent diffs

I see, maybe I can try to remove, I do remember we have been successfully using private utils somewhere

jerryzh168 · 2023-08-30T20:20:50Z

torch/ao/quantization/pt2e/utils.py

+    return False
+
+
+def find_q_dq_node_for_user(


I feel this function is a bit too complicated and might be error prone as well, is there a way to simplify this?

it is just finding q, dq from [producer -> .... -> q -> dq - > user]. I can add doc string if you want

yeah a docstring would be helpful

jerryzh168 · 2023-08-30T20:22:29Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+
+
+"""
+This API is specifically to replace a specific use of producer node


is this comment still up to date?

oh the internal api part. yes let me update that

jerryzh168 · 2023-08-30T20:26:17Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+"""
+
+
+def _copy_and_replace_use(


also maybe merge this function with _maybe_duplicate_dq?

jerryzh168 · 2023-08-30T20:27:00Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+
+
+def _maybe_duplicate_dq(
+    gm: torch.fx.GraphModule, producer: torch.fx.Node, user: torch.fx.Node


nit: renaming producer to dq_node might be clearer

sounds good

jerryzh168 · 2023-08-30T20:30:46Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            if node.op == "call_function" and node.target in _DEQUANTIZE_OPS:
+                dq_users = filter_sym_size_users(node)


is this needed? can we just replace all users? also after the IR changes, we don't have sym size nodes anymore right?

well I dont know if the sym size node will show up due to user code. like querying size on a tensor. I think here we are just making a call of not duplicating dq for sym size nodes. Either choice is fine. I would say we can go with this for now and change the behavior later

jerryzh168 · 2023-08-30T20:33:30Z

torch/ao/quantization/pt2e/duplicate_dq_pass.py

+    """
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:


also I'm wondering if you can reuse https://github.com/pytorch/pytorch/blob/main/torch/ao/quantization/pt2e/qat_utils.py#L585 actually, I think we don't need to maintain multiple implementations of the same thing

So that doesnt account for all the things that is being done here. things like sym_size filtering, accounting valid annotation etc. I agree with unifying the code though, but I would imagine we can update that code or move the fold_conv_bn after duplicate dq. although that needs some code update because in fold conv bn it removes duplicate dq at some point.

mcr229 · 2023-08-30T21:09:50Z

Does this mean DQ duplication will be a default in prepare pt2e?

jerryzh168 · 2023-08-30T21:13:34Z

Does this mean DQ duplication will be a default in prepare pt2e?

yeah, this will be handled in quant flow for normal cases

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) [ghstack-poisoned]

torch/ao/quantization/__init__.py

kimishpatel · 2023-08-31T16:03:11Z

@kimishpatel has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

andrewor14

Sorry Kimish but I think we want to keep move_model_to_eval under torch.ao.quantization directly (cc @jerryzh168). We're also cherry picking this back in 2.1 so it would break BC for those users if we moved this. Could we look into other ways to work around the circular dependency issue?

andrewor14 · 2023-08-31T19:17:32Z

torch/ao/quantization/pt2e/utils.py

+    return node_users
+
+
+def _is_valid_annotation(annotation: QuantizationAnnotation) -> bool:


This is the only place where we use QuantizationAnnotation right? Maybe we can leave this as Any for now since it's an internal function?

Let me see if there is a work around

Hey @kimishpatel, I have this PR which should take care of the circular deps issue: #108363. Please rebase on top of this when you have the chance, then you don't have to do anything special

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) [ghstack-poisoned]

kimishpatel · 2023-08-31T23:30:50Z

@kimishpatel has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

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) cc voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx chenyang78 aakhundov [ghstack-poisoned]

kimishpatel · 2023-09-01T00:49:38Z

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kimishpatel · 2023-09-01T01:53:02Z

@leslie-fang-intel can you help look at this failure https://github.com/pytorch/pytorch/actions/runs/6043973442/job/16402138568?pr=107900?

leslie-fang-intel · 2023-09-01T01:59:13Z

Sure, I will take a look soon @kimishpatel

leslie-fang-intel · 2023-09-01T05:31:47Z

Hi @kimishpatel, I think we need to make corresponding changes in test_mkldnn_pattern_matcher.py. Please kindly refer to this commit: leslie-fang-intel@59e5ce9 to adjust the match numbers. Please let me if anything missing.

andrewor14

move_model_to_eval changes look good to me

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) cc voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx chenyang78 aakhundov [ghstack-poisoned]

kimishpatel · 2023-09-01T15:14:26Z

Hi @kimishpatel, I think we need to make corresponding changes in test_mkldnn_pattern_matcher.py. Please kindly refer to this commit: leslie-fang-intel@59e5ce9 to adjust the match numbers. Please let me if anything missing.

Thanks Leslie. Please review changes to test_mkldnn_pattern_matcher.py.

As I was reviewing those, I realize that decomposition for quantize/dequantize does not match that of here, https://github.com/pytorch/pytorch/blob/main/torch/ao/quantization/pt2e/representation/rewrite.py#L428. Specifically in inductor decomp of quantize_per_tensor, addition of zero point still happens in fp, which I dont think is accurate because of the rounding errors that can happen on rouding zero point to fp and then whatever rounding happens during add. So I think we should sync those. cc: @jerryzh168

Also, please see how this diff affects quantization for x86 inductor. I see there was a pass to remove convert node that appear in quant/dequant pattern. But since now quant can more than 1 dequant users, that pattern wont match. If you still need to remove such redundant convert nodes, you will have to change the pattern/replacement

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) cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov anijain2305 [ghstack-poisoned]

kimishpatel · 2023-09-01T15:40:58Z

@kimishpatel has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

kimishpatel · 2023-09-01T20:20:39Z

@kimishpatel has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

leslie-fang-intel · 2023-09-01T21:35:40Z

test/inductor/test_mkldnn_pattern_matcher.py

            # 1. Pair of to_int8 and to_fp32 at conv input * 1, extra input of add * 1, and graph output * 1
            #    matched in pointless_convert pass at
            #    torch/_inductor/fx_passes/joint_graph.py: [convert_element_type, convert_element_type_1]
+            #    NB: since quant workflow now duplicates DQ node, for each user, we wont necessarily see


Hi @kimishpatel, could you also update the comments here https://github.com/leslie-fang-intel/pytorch/blob/59e5ce92f8b5ca68205df0abc444d0b55f16fc58/test/inductor/test_mkldnn_pattern_matcher.py#L514-L515 and other similar places? so, it will be aligned with expected count values.

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…107107) Summary: This diff adds adding metadata to q-dq nodes by inferring the quatization intent from node annotations. Annotations on the node are way for user to specify how a node or subgraph is supposed to be quantized. We continue to use that information to copy metadata on Q/DQ node from appropriate nodes. Test Plan: Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D48488416](https://our.internmc.facebook.com/intern/diff/D48488416) Pull Request resolved: #107107 Approved by: https://github.com/jerryzh168 ghstack dependencies: #107105, #107106, #107899, #107900

kimishpatel requested a review from jerryzh168 as a code owner August 24, 2023 21:45

kimishpatel mentioned this pull request Aug 24, 2023

[Quant] Use input_qspec_map for weight quantization of linear #107105

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[Quantization] Make annotation util functions return annotated nodes #107106

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[Quant] Bug fix #107899

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[Quantization] Add metadata porting for nodes added by quantization #107107

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[quant][be] Avoid circular deps from move_model_to_eval #108363

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		_copy_and_replace_use(gm, producer, user)


		class DuplicateDQPass(_ExportPassBase):



		"""
		This API is specifically to replace a specific use of producer node



		def _maybe_duplicate_dq(
		gm: torch.fx.GraphModule, producer: torch.fx.Node, user: torch.fx.Node

		return node_users


		def _is_valid_annotation(annotation: QuantizationAnnotation) -> bool:

[Quant] Add DQ duplication pass #107900

[Quant] Add DQ duplication pass #107900

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