Move deabstraction of non-tensorflow convention functions to the TFPartitioning pipeline. #21299

bgogul · 2018-12-13T20:31:16Z

This PR changes the invocation of deabstraction and partitioning passes as follows:

Diagnostic Pipeline:

Deabstraction of tensorflow-convention functions

Perf Pipeline:

Partitioning of tensorflow-convention functions (the next step is to move this to diagnostic pipeline so that we can have a proper -Onone mode)
Deabstraction and partitioning of non-tensorflow convention functions.

To recover most of the previous behavior with respect to sends and receives for non-tensorflow convention functions, the following optimizations are explicitly invoked after deabstraction (see Deabtraction::doIt()):

  PredictableMemoryOptimizations();
  PropagateSSAValues()

Specifically, the TFPartitionPipeline looks like this now:

SILPassPipelinePlan SILPassPipelinePlan::getTFPartitionPassPipeline() {
  SILPassPipelinePlan P;
  P.startPipeline("TensorFlow Partitioning");
  P.addTFDeabstraction();
  P.addTFPartition();
  return P;
}

Moreover, the perfInliner was modified to prevent inlining of some functions that the deabstraction relies on.

Tests:

Ignore the regressions in send/recv warnings in the tests as they are going away.
In some tests, the basic blocks got re-ordered because the deabstraction sees optimized SILFunction for non-tensorflow convention functions.

bgogul · 2018-12-13T20:37:42Z

@swift-ci please test tensorflow

mhong

A very nice step forward!

test/TensorFlow/playground_1.swift

lib/SILOptimizer/PassManager/PassPipeline.cpp

mhong · 2018-12-13T22:14:22Z

lib/SILOptimizer/Mandatory/TFDeabstraction.h

+namespace tf {
+
+/// A helper class to launch deabstraction on functions.
+class TFDeabstractionHelper {


how about calling this class TFDeabstraction? That matches the file name, and also "helper" suggests its scope is more local (like an impl detail).

Update: after reading the code changes in the cc file, i feel a better option is to move this helper class (can keep the helper name) to the cc file.

The only external user is then isSpecialNoInlineCallee() -- can we move it to TFUtilities.h or some other suitable header file? If not, i'm fine with creating this new header file for it.

Also, given the helper class seems only used in TFDeabstractionPass::run(), should we just define these public APIs in the TFDeabstractionPass class instead, and avoid creating a new class? I can be convinced either way.

lib/SILOptimizer/Mandatory/TFDeabstraction.h

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

bgogul · 2018-12-14T22:13:05Z

Please hold off further review on this PR. The changes that I did to the performance inliner (unsurprisingly) causes regressions in swift optimizer tests. I am looking into it.

bgogul · 2018-12-19T19:30:39Z

@swift-ci please test tensorflow

bgogul · 2018-12-20T00:32:04Z

@swift-ci please test tensorflow

bgogul · 2018-12-20T00:33:34Z

lib/AST/TensorFlow.cpp

@@ -220,6 +220,14 @@ bool tf::flattenTensorFlowValueAggregate(Type ty,
 /// parameter of result contains any TensorFlow value type.
 bool TypeContainsTensorFlowValue::containsTensorFlowValue(
    Type ty, bool checkHigherOrderFunctions) {
+  llvm::SmallPtrSet<NominalTypeDecl*, 8> parentDecls;


I will split this out into a separate CL.

bgogul

@mhong, I have resolved all the comments and the optimization errors. PTAL.

bgogul · 2018-12-20T00:57:02Z

@swift-ci please test tensorflow

mhong

Left another around of comments. Thanks.

mhong · 2018-12-20T00:52:13Z

include/swift/AST/TensorFlow.h

@@ -92,7 +92,13 @@ namespace tf {
    bool containsTensorFlowValue(Type ty, bool checkHigherOrderFunctions);

  private:
-    bool structContainsTensorFlowValue(StructDecl *decl);
+    bool containsTensorFlowValueImpl(


please document parentDecls -- what it does, and whether we mutate it. the func name seems to suggests this func is a predicate, and would not have side effects.

IIUC, parentDecls is used as a cache. Should we call it a cache instead of "parent sth"?

No, it is not a cache. It keeps track of the nesting structure so that we can detect recursive data structures.

In any case, ignore the file in this PR. I have opened a separate PR for this: #21449

(I had to include the files here to make sure I can run the tests.)

mhong · 2018-12-20T00:56:36Z

lib/AST/TensorFlow.cpp

+bool TypeContainsTensorFlowValue::structContainsTensorFlowValue(
+    StructDecl *decl, llvm::SmallPtrSetImpl<NominalTypeDecl *> &parentDecls) {
+  // If we have a cycle, break it here.
+  if (parentDecls.count(decl) > 0) {


can you give an example of a cycle? I thought structs cannot have such self references

#21449 has an example.

mhong · 2018-12-20T00:58:03Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

@@ -154,7 +154,7 @@ namespace {

    void promoteToSSA(ArrayRef<AllocStackInst *> allocs);
    void prepareStackAllocForPromotion(AllocStackInst *alloc);
-    void propagateSSAValues();
+    void propagateSSAValues(SmallVectorImpl<SILInstruction *> &relevantInsts);


please document relevantInsts. if it's for read-only, consider using ArrayRef instead.

would ArrayRef work here?

also as a nit: the word "relevant" does not seem to carry much weight. how about just insts?

mhong · 2018-12-20T01:00:47Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+  //
+  // e.g., the inlining of functions (like allocateUninitializedArray) may
+  // result in the following code snippet in a function:
+  //     %9 = alloc_stack $Int32


can you provide an example on why we should care about optimizing such scalar (non-tensor) insts here? is it for tensorflow convention function?

the larger question is that if we start applying more optimizations that used to belong only to the optimization pipeline, could that be "slippery slope" in that we might keep adding more such logic to the DA pass over time?

It'd be nice if we could try and make the DA pass compose with (and complimentary to) the other compiler passes as much as possible.

This specific optimization mainly avoids the introduction of sends and receives by eliminating redundant allocations and creating an SSA value for attribute values. This is actually quite similar to promoteToSSA function. (May be it is sufficient to call promoteToSSA again. Let me try it.)

When working on this PR, I simply tried to avoid introducing new sends and receives as much as possible in our tests. As a result of that approach, I identified that these optimizations were necessary. Given that we won't be running the optimization pipeline for tensor-flow convention functions in -Onone mode and still be able to extract graphs, we will have to perform some of the transformations here.

For tensorflow convention functions, I agree we should extend the DA pass to handle it, rather than duplicating more optimizer pipeline code. This is especially true for load/store related issues, which DA is designed to handle.

also, do we already have a test example, that show we'll need more optimizing work before we can extract a graph function?

I have the same question as Mingsheng. with partitioning running after the performance optimizer, it seems like these sorts of things should already be done. Why is this necessary? What is the bad thing that happens if we don't call into "optimizeMemoryAllocations" here? Is this a phase ordering issue that can be resolved some other way?

Why is this necessary?
What is the bad thing that happens if we don't call into "optimizeMemoryAllocations" here?

If we don't do this here, we introduce new sends and receives. This is mostly OK if correctness is not affected, as sending/receiving TensorHandles should be cheap. However, in some cases, this is a problem. For example, when operations need to be run on TPU, partitioner is not able to determine shapes in some cases because of an intervening send or receive.

Here are technical reasons as to why it is needed here. Note that we prevent the PerformanceInliner from inlining certain functions (such as array inits), and therefore, function-level optimizations (like optimizeMemoryAllocations) do not see the body of these functions and do not have an opportunity to optimize the body of such functions. When we inline the functions during deabstraction, code snippets such as the following occur in the function being partitioned:

// %9 = alloc_stack $Int32 // store %0 to %9 : $*Int32 // %11 = load %9 : $*Int32 // %12 = struct_extract %11 : $Int32, #Int32._value

Because no optimizations run, the loads and stores are not eliminated and causes sends/receives to be introduced.

I think some of the optimizeMemoryAllocations might be subsumed by PromotableMemoryFinder and PropagateSSAValues, but I haven't tried it yet.

mhong · 2018-12-20T01:05:03Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+  /// deabstracted. If the flag forceTFFunctions is true, forces partitioning of
+  /// functions that operate on Tensors even if it would have been rejected
+  /// otherwise.
+  bool deabstract(SILFunction &fn, bool forceTFFunctions);


can deabstract be a private method instead?

mhong · 2018-12-20T01:11:44Z

lib/SILOptimizer/Transforms/PerformanceInliner.cpp

+  //  - Does this operate on Tensor values?
+  //
+  // Helper that returns true if function belongs to TensorFlow module.
+  auto isTensorFlowFunction = [](SILFunction *func) {


adding such domain specific (tensor specific) knowledge to this "general purpose" mechanism of performance inliner does not feel ideal. This might prevent us from being able to upstream this patch later.

is there a way to refactor the code so that the caller / constructor of SILInliner can pass in a callback (or a bit) to decide whether func is eligible for inlining? The call-site from TF can then pass in a callback which does the "tensor op check" as written here.

Yes, it is not ideal, but I prefer to leave this as is for now.

I feel this is an important issue concering architecture ("layering") that we'll want to resolve ASAP. If you feel strongly about submitting this patch first, please go ahead, as long as we can follow up and address this one shortly.

I agree with Mingsheng that this is a horrible hack, but I'm ok with it to unblock work - so long as there is a known path out of this. What is the plan to resolve this?

Chris, are you worried about having tensorflow specific logic here? That is easily fixed by folding this logic this into the TensorFunctionClassifier::isSpecialNoInlineCallee.

Or, are you concerned about the fact that we prevent some inlining from happening? I don't have a good answer for that right now.

mhong · 2018-12-20T01:14:00Z

test/TensorFlow/control_flow.swift

@@ -78,7 +78,7 @@ public func weighPetOnlyDefault(pet: Pet) {

 // CHECK-LABEL: ---- ANALYSIS STATE FOR FUNCTION {{.*}}testCondBranch
 // CHECK:       bb0:
-// CHECK:       [Copy]    cond_br {{.*}}, bb1, bb2
+// CHECK:       [Copy]    cond_br {{.*}}, bb2, bb1


do you know why BB numbers are changing for some tests (consider adding the explanation to PR description)?

I believe this has to do with the fact that the deabstraction sees optimized SIL function for non-tensorflow convention functions.

mhong · 2018-12-20T01:14:51Z

test/TensorFlow/crashers.swift

@@ -6,6 +6,7 @@ import TensorFlow

 var someGlobal = Tensor<Int32>(1)

+// expected-warning @+1 {{value implicitly copied to the host}}


the changes in sends/recvs warnings are fairly distracting -- should i ignore all of them when reviewing this patch?

Ideally, we could send a patch to remove all such warnings first, as discussed.

Just ignore these for now. The good thing is that the runtime tests pass, which gives me some confidence that the refactoring is still generating correct code.

(I will send out a separate PR removing these send/receive warnings along with adding an internal counter for debugging/testing purposes.)

How do we confirm that this patch has not overall regressed in the # of sends/recvs -- would it make sense to introduce some stats counter infra first?

mhong · 2018-12-20T01:15:20Z

test/TensorFlow/diagnostics.swift

@@ -49,7 +49,8 @@ public func testDevice() {
 // should be a single copy-to-host compiler warning.
 public func SR8412_CopyToHost() {
  for _ in 0...10 {
-    let x = Tensor(1)  // expected-warning {{value implicitly copied to the host}}
+		// This gets moved outside the loop by the compiler optimizations. So, no warnings. 


tabs are used here and in some other places in this patch

Oops...fixed now.

bgogul

Investigating the failures in GPU and mac runs, but I have addressed all your comments otherwise.

PTAL.

bgogul · 2018-12-21T06:26:21Z

include/swift/AST/TensorFlow.h

@@ -92,7 +92,13 @@ namespace tf {
    bool containsTensorFlowValue(Type ty, bool checkHigherOrderFunctions);

  private:
-    bool structContainsTensorFlowValue(StructDecl *decl);
+    bool containsTensorFlowValueImpl(


No, it is not a cache. It keeps track of the nesting structure so that we can detect recursive data structures.

In any case, ignore the file in this PR. I have opened a separate PR for this: #21449

(I had to include the files here to make sure I can run the tests.)

bgogul · 2018-12-21T06:28:11Z

lib/AST/TensorFlow.cpp

+bool TypeContainsTensorFlowValue::structContainsTensorFlowValue(
+    StructDecl *decl, llvm::SmallPtrSetImpl<NominalTypeDecl *> &parentDecls) {
+  // If we have a cycle, break it here.
+  if (parentDecls.count(decl) > 0) {


#21449 has an example.

bgogul · 2018-12-21T06:43:47Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+  //
+  // e.g., the inlining of functions (like allocateUninitializedArray) may
+  // result in the following code snippet in a function:
+  //     %9 = alloc_stack $Int32


This specific optimization mainly avoids the introduction of sends and receives by eliminating redundant allocations and creating an SSA value for attribute values. This is actually quite similar to promoteToSSA function. (May be it is sufficient to call promoteToSSA again. Let me try it.)

When working on this PR, I simply tried to avoid introducing new sends and receives as much as possible in our tests. As a result of that approach, I identified that these optimizations were necessary. Given that we won't be running the optimization pipeline for tensor-flow convention functions in -Onone mode and still be able to extract graphs, we will have to perform some of the transformations here.

bgogul · 2018-12-21T06:44:12Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+  /// deabstracted. If the flag forceTFFunctions is true, forces partitioning of
+  /// functions that operate on Tensors even if it would have been rejected
+  /// otherwise.
+  bool deabstract(SILFunction &fn, bool forceTFFunctions);


bgogul · 2018-12-21T06:45:19Z

lib/SILOptimizer/Transforms/PerformanceInliner.cpp

+  //  - Does this operate on Tensor values?
+  //
+  // Helper that returns true if function belongs to TensorFlow module.
+  auto isTensorFlowFunction = [](SILFunction *func) {


Yes, it is not ideal, but I prefer to leave this as is for now.

bgogul · 2018-12-21T06:46:17Z

test/TensorFlow/control_flow.swift

@@ -78,7 +78,7 @@ public func weighPetOnlyDefault(pet: Pet) {

 // CHECK-LABEL: ---- ANALYSIS STATE FOR FUNCTION {{.*}}testCondBranch
 // CHECK:       bb0:
-// CHECK:       [Copy]    cond_br {{.*}}, bb1, bb2
+// CHECK:       [Copy]    cond_br {{.*}}, bb2, bb1


I believe this has to do with the fact that the deabstraction sees optimized SIL function for non-tensorflow convention functions.

bgogul · 2018-12-21T06:47:46Z

test/TensorFlow/crashers.swift

@@ -6,6 +6,7 @@ import TensorFlow

 var someGlobal = Tensor<Int32>(1)

+// expected-warning @+1 {{value implicitly copied to the host}}


Just ignore these for now. The good thing is that the runtime tests pass, which gives me some confidence that the refactoring is still generating correct code.

(I will send out a separate PR removing these send/receive warnings along with adding an internal counter for debugging/testing purposes.)

bgogul · 2018-12-21T06:51:30Z

test/TensorFlow/diagnostics.swift

@@ -49,7 +49,8 @@ public func testDevice() {
 // should be a single copy-to-host compiler warning.
 public func SR8412_CopyToHost() {
  for _ in 0...10 {
-    let x = Tensor(1)  // expected-warning {{value implicitly copied to the host}}
+		// This gets moved outside the loop by the compiler optimizations. So, no warnings. 


Oops...fixed now.

mhong

Left a few more comments, in terms of code behavior (verify no regression in sends/recvs) and readability (layering design). You can decide which ones to address before vs after submitting this patch.

lattner · 2019-01-03T17:19:39Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+    return;
+
+  TFDeabstractionHelper helper(*this, module);
+  if (PM->getStageName() == "TensorFlow Partitioning") {


Instead of matching on stage name, it would be cleaner to just have two passes, and insert them at the right part of hte pass pipeline. The optimizer version of the partitioning pass should be a function pass, and the mandatory version should be a module pass, right?

lattner

I'd really like to understand why hard integration of optimizeMemoryAllocations is required.

Please also split the deabstraction pass into two different passes (calling into a shared implementation).

Thanks!

Remove a redundant check as well.

Now we get multiple error diagnostics about configuration instruction as we call it from multiple places. This needs to be sorted out.

….swift

bgogul

Given our recent focus on tracing, some of the changes in this PR (e.g., related to optimizations and send/receives) could be deferred to later. However, I just wanted to reply to the comments and questions, when I have everything fresh in my memory.

I'd really like to understand why hard integration of optimizeMemoryAllocations is required.

Please see my reply to your review comment.

Please also split the deabstraction pass into two different passes (calling into a shared implementation).

I have factored out deabstraction as a utility and I am calling into it from partitioning now.

bgogul · 2019-01-03T17:58:54Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+  //
+  // e.g., the inlining of functions (like allocateUninitializedArray) may
+  // result in the following code snippet in a function:
+  //     %9 = alloc_stack $Int32


Why is this necessary?
What is the bad thing that happens if we don't call into "optimizeMemoryAllocations" here?

If we don't do this here, we introduce new sends and receives. This is mostly OK if correctness is not affected, as sending/receiving TensorHandles should be cheap. However, in some cases, this is a problem. For example, when operations need to be run on TPU, partitioner is not able to determine shapes in some cases because of an intervening send or receive.

Here are technical reasons as to why it is needed here. Note that we prevent the PerformanceInliner from inlining certain functions (such as array inits), and therefore, function-level optimizations (like optimizeMemoryAllocations) do not see the body of these functions and do not have an opportunity to optimize the body of such functions. When we inline the functions during deabstraction, code snippets such as the following occur in the function being partitioned:

// %9 = alloc_stack $Int32 // store %0 to %9 : $*Int32 // %11 = load %9 : $*Int32 // %12 = struct_extract %11 : $Int32, #Int32._value

Because no optimizations run, the loads and stores are not eliminated and causes sends/receives to be introduced.

I think some of the optimizeMemoryAllocations might be subsumed by PromotableMemoryFinder and PropagateSSAValues, but I haven't tried it yet.

bgogul · 2019-01-04T01:37:08Z

lib/SILOptimizer/Mandatory/TFDeabstraction.cpp

+    return;
+
+  TFDeabstractionHelper helper(*this, module);
+  if (PM->getStageName() == "TensorFlow Partitioning") {


bgogul · 2019-01-04T23:27:10Z

lib/SILOptimizer/Transforms/PerformanceInliner.cpp

+  //  - Does this operate on Tensor values?
+  //
+  // Helper that returns true if function belongs to TensorFlow module.
+  auto isTensorFlowFunction = [](SILFunction *func) {


Chris, are you worried about having tensorflow specific logic here? That is easily fixed by folding this logic this into the TensorFunctionClassifier::isSpecialNoInlineCallee.

Or, are you concerned about the fact that we prevent some inlining from happening? I don't have a good answer for that right now.

bgogul requested review from lattner and mhong December 13, 2018 20:44

mhong approved these changes Dec 13, 2018

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bgogul force-pushed the deabs_part branch from b6f4ef5 to 1e5cdb3 Compare December 19, 2018 19:30

bgogul force-pushed the deabs_part branch from 1e5cdb3 to fa4953b Compare December 20, 2018 00:27

bgogul commented Dec 20, 2018

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mhong reviewed Dec 20, 2018

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bgogul commented Dec 21, 2018

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mhong approved these changes Dec 21, 2018

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bgogul force-pushed the deabs_part branch from 95d51df to 176ed28 Compare January 2, 2019 20:57

lattner reviewed Jan 3, 2019

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lattner requested changes Jan 3, 2019

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bgogul added 12 commits January 4, 2019 13:37

Refactored deabstraction and partition passes.

8f1857b

Disable inlining for special callees during PerformanceInliner phase

f39102f

Do not inline WellKnownFunctions in the perf inliner as well.

7e5b117

Remove a redundant check as well.

Make deabstraction and some optimizations to be part of partitioning

bf7131a

Fix expectation in TensorFlow/control_flow.swift

ef0ce24

Fixed expectations in integration.swift

be949dd

Change expectations in diagnostics.swift

48ca3b7

Fix expectations in diagnostics_with_deabstraction.swift (temporary)

a964126

Now we get multiple error diagnostics about configuration instruction as we call it from multiple places. This needs to be sorted out.

Fix test expectations: crashers.swift & sese_loop_canonicalization.

82dc9c4

Disable failing expectations for playground.swift test.

c081f0a

Fix send/recv warnings in Runtime/sese_regions.swift test

3c1cd5d

Fix send warning expectation in optimization_disabled.swift

da4ab56

bgogul added 11 commits January 4, 2019 13:37

Add TODO in playground_1.swift

5a1cb0a

Prevent inlining for functions in TensorFlow or with tensor ops.

266600b

Invoke optimize memory allocations after graph_op formation.

b3fd0b1

Invoke propagateSSAValues on newly formed graph_op instructions.

46d48f5

Fix new send/recv regression in TensorFlowRuntime/sese_regions.swift

d419b3e

Reverse change to optimization_disabled.swift

208c9f8

Fix send recv warnings in crashers.swift & sese_loop_canonicalization…

d825cb1

….swift

Get rid of TFDeabstraction and move some utilities to TFUtilities.

fd6a643

Addressed style-related issues in review comments.

133ebba

Disable a loop test for Mac (https://bugs.swift.org/browse/SR-8986)

5d8d524

Refactoring deabstraction as a helper function.

6e13a6d

bgogul force-pushed the deabs_part branch from 176ed28 to 6e13a6d Compare January 4, 2019 23:21

bgogul commented Jan 7, 2019

View reviewed changes

dan-zheng force-pushed the tensorflow branch from 6dcf239 to 04dca63 Compare November 17, 2019 02:40

compnerd closed this Feb 22, 2020

		@@ -6,6 +6,7 @@ import TensorFlow

		var someGlobal = Tensor<Int32>(1)

		// expected-warning @+1 {{value implicitly copied to the host}}

Move deabstraction of non-tensorflow convention functions to the TFPartitioning pipeline. #21299

Move deabstraction of non-tensorflow convention functions to the TFPartitioning pipeline. #21299

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bgogul commented Dec 13, 2018 •

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