Support Steering-Control-Based Dataflow Representation & Mapping by ShangkunLi · Pull Request #144 · coredac/dataflow

ShangkunLi · 2025-10-07T11:35:11Z

In this PR, we extend our current predicate-based dataflow representation to support steering-control-based dataflow representation by:

Introduce specific operations, including true/false_steer, invariant, carry, and merge
Implement transform-to-steer-control pass to transform our predicate-based dataflow IR into the steering control manner
Implement remove-predicated-type pass to transform the predicate data type to the source generic data type
Some minor changes in mapping_util.cpp to enable map steer-control-based dataflow IR

For example, the source ir of a simple loop is:

module {
  func.func @simple_add_loop() -> i64 attributes {accelerator = "neura"} {
    %0 = "neura.constant"() <{predicate = true, value = 16 : i64}> : () -> !neura.data<i64, i1>
    %1 = "neura.grant_once"(%0) : (!neura.data<i64, i1>) -> !neura.data<i64, i1>
    %2 = "neura.constant"() <{predicate = true, value = 1 : i64}> : () -> !neura.data<i64, i1>
    %3 = "neura.grant_once"(%2) : (!neura.data<i64, i1>) -> !neura.data<i64, i1>
    %4 = "neura.constant"() <{predicate = true, value = 1 : i64}> : () -> !neura.data<i64, i1>
    %5 = "neura.grant_once"(%4) : (!neura.data<i64, i1>) -> !neura.data<i64, i1>
    %6 = "neura.constant"() <{predicate = true, value = 0 : i64}> : () -> !neura.data<i64, i1>
    %7 = "neura.grant_once"(%6) : (!neura.data<i64, i1>) -> !neura.data<i64, i1>
    %8 = neura.reserve : !neura.data<i64, i1>
    %9 = "neura.phi"(%8, %1) : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i64, i1>
    %10 = neura.reserve : !neura.data<i64, i1>
    %11 = "neura.phi"(%10, %5) : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i64, i1>
    %12 = neura.reserve : !neura.data<i64, i1>
    %13 = "neura.phi"(%12, %7) : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i64, i1>
    %14 = "neura.icmp"(%13, %9) <{cmpType = "slt"}> : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i1, i1>
    %15 = neura.grant_predicate %11, %14 : !neura.data<i64, i1>, !neura.data<i1, i1> -> !neura.data<i64, i1>
    %16 = neura.grant_predicate %13, %14 : !neura.data<i64, i1>, !neura.data<i1, i1> -> !neura.data<i64, i1>
    %17 = neura.grant_predicate %3, %14 : !neura.data<i64, i1>, !neura.data<i1, i1> -> !neura.data<i64, i1>
    %18 = neura.grant_predicate %1, %14 : !neura.data<i64, i1>, !neura.data<i1, i1> -> !neura.data<i64, i1>
    %19 = "neura.not"(%14) : (!neura.data<i1, i1>) -> !neura.data<i1, i1>
    %20 = neura.grant_predicate %11, %19 : !neura.data<i64, i1>, !neura.data<i1, i1> -> !neura.data<i64, i1>
    %21 = "neura.add"(%15, %15) : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i64, i1>
    %22 = "neura.add"(%16, %17) : (!neura.data<i64, i1>, !neura.data<i64, i1>) -> !neura.data<i64, i1>
    neura.ctrl_mov %22 -> %12 : !neura.data<i64, i1> !neura.data<i64, i1>
    neura.ctrl_mov %21 -> %10 : !neura.data<i64, i1> !neura.data<i64, i1>
    neura.ctrl_mov %18 -> %8 : !neura.data<i64, i1> !neura.data<i64, i1>
    "neura.return"(%20) : (!neura.data<i64, i1>) -> ()
  }
}

After the transformation:

module {
  func.func @simple_add_loop() -> i64 attributes {accelerator = "neura"} {
    %0 = neura.reserve : i64
    %1 = neura.reserve : i64
    %2 = neura.reserve : i1
    %3 = "neura.constant"() <{value = 16 : i64}> : () -> i64
    %4 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %5 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %6 = "neura.constant"() <{value = 0 : i64}> : () -> i64
    %7 = neura.invariant %4, %2 : i64, i1 -> i64
    %8 = neura.invariant %3, %2 : i64, i1 -> i64
    %9 = neura.carry %5, %2, %0 : i64, i1, i64 -> i64
    %10 = neura.carry %6, %2, %1 : i64, i1, i64 -> i64
    %11 = "neura.icmp"(%10, %8) <{cmpType = "slt"}> : (i64, i64) -> i1
    neura.ctrl_mov %11 -> %2 : i1 i1
    %12 = "neura.not"(%11) : (i1) -> i1
    %13 = "neura.add"(%9, %9) : (i64, i64) -> i64
    neura.ctrl_mov %13 -> %0 : i64 i64
    %14 = "neura.add"(%10, %7) : (i64, i64) -> i64
    neura.ctrl_mov %14 -> %1 : i64 i64
    "neura.return"(%9) : (i64) -> ()
  }
}

And we can map it on to the 4x4 CGRA:

module {
  func.func @simple_add_loop() -> i64 attributes {accelerator = "neura", mapping_info = {compiled_ii = 4 : i32, mapping_mode = "spatial-only", mapping_strategy = "heuristic", rec_mii = 2 : i32, res_mii = 1 : i32, x_tiles = 4 : i32, y_tiles = 4 : i32}} {
    %0 = neura.reserve : i64
    %1 = neura.reserve : i64
    %2 = neura.reserve : i1
    %3 = "neura.constant"() <{value = 16 : i64}> {mapping_locs = [{id = 0 : i32, resource = "tile", time_step = 0 : i32, x = 0 : i32, y = 0 : i32}]} : () -> i64
    %4 = "neura.constant"() <{value = 1 : i64}> {mapping_locs = [{id = 11 : i32, resource = "tile", time_step = 1 : i32, x = 3 : i32, y = 2 : i32}]} : () -> i64
    %5 = "neura.constant"() <{value = 1 : i64}> {mapping_locs = [{id = 5 : i32, resource = "tile", time_step = 1 : i32, x = 1 : i32, y = 1 : i32}]} : () -> i64
    %6 = "neura.constant"() <{value = 0 : i64}> {mapping_locs = [{id = 13 : i32, resource = "tile", time_step = 0 : i32, x = 1 : i32, y = 3 : i32}]} : () -> i64
    %7 = "neura.data_mov"(%4) {mapping_locs = [{id = 35 : i32, resource = "link", time_step = 1 : i32}]} : (i64) -> i64
    %8 = neura.invariant %7, %2 {mapping_locs = [{id = 10 : i32, resource = "tile", time_step = 2 : i32, x = 2 : i32, y = 2 : i32}]} : i64, i1 -> i64
    %9 = "neura.data_mov"(%3) {mapping_locs = [{id = 0 : i32, resource = "link", time_step = 0 : i32}]} : (i64) -> i64
    %10 = neura.invariant %9, %2 {mapping_locs = [{id = 1 : i32, resource = "tile", time_step = 1 : i32, x = 1 : i32, y = 0 : i32}]} : i64, i1 -> i64
    %11 = "neura.data_mov"(%5) {mapping_locs = [{id = 14 : i32, resource = "link", time_step = 1 : i32}]} : (i64) -> i64
    %12 = neura.carry %11, %2, %0 {mapping_locs = [{id = 6 : i32, resource = "tile", time_step = 2 : i32, x = 2 : i32, y = 1 : i32}]} : i64, i1, i64 -> i64
    %13 = "neura.data_mov"(%6) {mapping_locs = [{id = 42 : i32, resource = "link", time_step = 0 : i32}]} : (i64) -> i64
    %14 = neura.carry %13, %2, %1 {mapping_locs = [{id = 9 : i32, resource = "tile", time_step = 1 : i32, x = 1 : i32, y = 2 : i32}]} : i64, i1, i64 -> i64
    %15 = "neura.data_mov"(%14) {mapping_locs = [{id = 27 : i32, resource = "link", time_step = 1 : i32}, {id = 25 : i32, resource = "link", time_step = 2 : i32}]} : (i64) -> i64
    %16 = "neura.data_mov"(%10) {mapping_locs = [{id = 2 : i32, resource = "link", time_step = 1 : i32}, {id = 1 : i32, resource = "link", time_step = 2 : i32}]} : (i64) -> i64
    %17 = "neura.icmp"(%15, %16) <{cmpType = "slt"}> {mapping_locs = [{id = 4 : i32, resource = "tile", time_step = 3 : i32, x = 0 : i32, y = 1 : i32}]} : (i64, i64) -> i1
    neura.ctrl_mov %17 -> %2 {mapping_locs = [{id = 10 : i32, resource = "link", time_step = 3 : i32}, {id = 16 : i32, resource = "link", time_step = 4 : i32}, {id = 28 : i32, resource = "link", time_step = 5 : i32}]} : i1 i1
    %18 = "neura.data_mov"(%17) {mapping_locs = [{id = 12 : i32, resource = "link", time_step = 3 : i32}]} : (i1) -> i1
    %19 = "neura.not"(%18) {mapping_locs = [{id = 8 : i32, resource = "tile", time_step = 4 : i32, x = 0 : i32, y = 2 : i32}]} : (i1) -> i1
    %20 = "neura.data_mov"(%12) {mapping_locs = [{id = 19 : i32, resource = "link", time_step = 2 : i32}, {id = 64 : i32, resource = "register", time_step = 3 : i32}, {id = 64 : i32, resource = "register", time_step = 4 : i32}]} : (i64) -> i64
    %21 = "neura.data_mov"(%12) {mapping_locs = [{id = 17 : i32, resource = "link", time_step = 2 : i32}, {id = 15 : i32, resource = "link", time_step = 3 : i32}, {id = 3 : i32, resource = "link", time_step = 4 : i32}]} : (i64) -> i64
    %22 = "neura.add"(%20, %21) {mapping_locs = [{id = 2 : i32, resource = "tile", time_step = 5 : i32, x = 2 : i32, y = 0 : i32}]} : (i64, i64) -> i64
    neura.ctrl_mov %22 -> %0 {mapping_locs = [{id = 7 : i32, resource = "link", time_step = 5 : i32}]} : i64 i64
    %23 = "neura.data_mov"(%14) {mapping_locs = [{id = 30 : i32, resource = "link", time_step = 1 : i32}, {id = 41 : i32, resource = "link", time_step = 2 : i32}]} : (i64) -> i64
    %24 = "neura.data_mov"(%8) {mapping_locs = [{id = 34 : i32, resource = "link", time_step = 2 : i32}]} : (i64) -> i64
    %25 = "neura.add"(%23, %24) {mapping_locs = [{id = 14 : i32, resource = "tile", time_step = 3 : i32, x = 2 : i32, y = 3 : i32}]} : (i64, i64) -> i64
    neura.ctrl_mov %25 -> %1 {mapping_locs = [{id = 45 : i32, resource = "link", time_step = 3 : i32}, {id = 31 : i32, resource = "link", time_step = 4 : i32}]} : i64 i64
    %26 = "neura.data_mov"(%12) {mapping_locs = [{id = 18 : i32, resource = "link", time_step = 2 : i32}]} : (i64) -> i64
    "neura.return"(%26) {mapping_locs = [{id = 7 : i32, resource = "tile", time_step = 3 : i32, x = 3 : i32, y = 1 : i32}]} : (i64) -> ()
  }
}

tancheng · 2025-10-07T15:37:11Z

Can you please draw a diagram or slide, listing the before/after IRs side-by-side, and highlight which dataflow IR correspond to which steering IR? And also briefly explain the meaning of each new op, e.g., carry/merge.

And I didn't see true/false_steer in your example.

lib/NeuraDialect/Mapping/mapping_util.cpp

include/NeuraDialect/NeuraOps.td

test/neura/steer_ctrl/loop_with_return_value.mlir

ShangkunLi · 2025-10-08T05:36:55Z

Can you please draw a diagram or slide, listing the before/after IRs side-by-side, and highlight which dataflow IR correspond to which steering IR? And also briefly explain the meaning of each new op, e.g., carry/merge.

And I didn't see true/false_steer in your example.

Added the true/false_steer test in latest commit.

Our steering operation semantics follow the definition in RipTide, as shown in the above figures.

A correspondence between the predicate-based dataflow IR and the steering-based dataflow IR is shown below.

lib/NeuraDialect/Transforms/MapToAcceleratorPass.cpp

lib/NeuraDialect/Transforms/TransformCtrlToDataFlowPass.cpp

test/neura/steer_ctrl/loop_with_return_value.mlir

tancheng · 2025-10-08T05:57:26Z

Can you please draw a diagram or slide, listing the before/after IRs side-by-side, and highlight which dataflow IR correspond to which steering IR? And also briefly explain the meaning of each new op, e.g., carry/merge.
And I didn't see true/false_steer in your example.

Added the true/false_steer test in latest commit.

Our steering operation semantics follow the definition in RipTide, as shown in the above figures.

A correspondence between the predicate-based dataflow IR and the steering-based dataflow IR is shown below.

So our ctrl_mov is basically the true_steer?
And how do you recognize/identify the invariant? Everything are grant_predicate in the dataflow, how to distinguish them into different carry/invariant?

ShangkunLi · 2025-10-08T06:14:09Z

So our ctrl_mov is basically the true_steer?

And how do you recognize/identify the invariant? Everything are grant_predicate in the dataflow, how to distinguish them into different carry/invariant?

ctrl_mov and reserve are still structural non-materialized operations to denote the data dependency between an operation and its backward users.
We can detect those phi (%reserve, %init_value) -> grant_predicate (%phi, %cond) -> ctrl_mov %grant -> %reserve patterns, which are transformed to invariant (%init_value, %cond)

tancheng · 2025-10-08T06:22:24Z

So our ctrl_mov is basically the true_steer?

And how do you recognize/identify the invariant? Everything are grant_predicate in the dataflow, how to distinguish them into different carry/invariant?

ctrl_mov and reserve are still structural non-materialized operations to denote the data dependency between an operation and its backward users.

We can detect those phi (%reserve, %init_value) -> grant_predicate (%phi, %cond) -> ctrl_mov %grant -> %reserve patterns, which are transformed to invariant (%init_value, %cond)

Then what pattern would be true_steer?
Thanks~! I got the invariant's identification, which basically needs to identify a grant_once for the init_value, right?
- Then, what is needed to identify carry? and false_steer?

ShangkunLi · 2025-10-08T06:31:49Z

So our ctrl_mov is basically the true_steer?

And how do you recognize/identify the invariant? Everything are grant_predicate in the dataflow, how to distinguish them into different carry/invariant?

ctrl_mov and reserve are still structural non-materialized operations to denote the data dependency between an operation and its backward users.

We can detect those phi (%reserve, %init_value) -> grant_predicate (%phi, %cond) -> ctrl_mov %grant -> %reserve patterns, which are transformed to invariant (%init_value, %cond)

Then what pattern would be true_steer?

Thanks~! I got the invariant's identification, which basically needs to identify a grant_once for the init_value, right?

Then, what is needed to identify carry? and false_steer?

At the beginning of the transformation, I remove all the grant_once ops, so it is identifying constant for the init_value
For carry, we identify the phi -> grant_predicate -> other computations -> ctrl_mov -> reserve. And create a carry (%init_value, %cond, %carried_value) for this pattern. The %carried_value is the source value of the reserve op.
For true/false_steer, we identify those grant_predicate (%value, %cond) patterns. If %cond is a not operation, we create false_steer for it, otherwise a true_steer. This process is the last process after carry and invariant identification and transformation, so we can avoid handling grant_predicates in loops.

ShangkunLi · 2025-10-08T06:37:49Z

@tancheng One problem here, when I try to map the steering-based dataflow IR, it seems the ALAP sort didn't respect the producer-consumer dependency. So I cannot map it...

The code is:

module {
  func.func @simple_add_loop() -> i64 attributes {accelerator = "neura", dataflow_mode = "steering"} {
    %0 = neura.reserve : i64
    %1 = neura.reserve : i64
    %2 = neura.reserve : i1
    %3 = "neura.constant"() <{value = 16 : i64}> : () -> i64
    %4 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %5 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %6 = "neura.constant"() <{value = 0 : i64}> : () -> i64
    %7 = neura.invariant %4, %2 : i64, i1 -> i64
    %8 = neura.invariant %3, %2 : i64, i1 -> i64
    %9 = neura.carry %5, %2, %0 : i64, i1, i64 -> i64
    %10 = neura.carry %6, %2, %1 : i64, i1, i64 -> i64
    %11 = "neura.icmp"(%10, %8) <{cmpType = "slt"}> : (i64, i64) -> i1
    neura.ctrl_mov %11 -> %2 : i1 i1
    %12 = neura.false_steer %9, %11 : i64, i1 -> i64
    %13 = "neura.add"(%9, %9) : (i64, i64) -> i64
    neura.ctrl_mov %13 -> %0 : i64 i64
    %14 = "neura.add"(%10, %7) : (i64, i64) -> i64
    neura.ctrl_mov %14 -> %1 : i64 i64
    "neura.return"(%12) : (i64) -> ()
  }
}

The ALAP level of %7 = neura.invariant %4, %2 : i64, i1 -> i64 is 3, but we get 2 for %11 = "neura.icmp"(%10, %8) <{cmpType = "slt"}> : (i64, i64) -> i1. So when we try to map icmp, its backward user %7 is unmapped, which causes error.

tancheng · 2025-10-08T06:49:54Z

So our ctrl_mov is basically the true_steer?

And how do you recognize/identify the invariant? Everything are grant_predicate in the dataflow, how to distinguish them into different carry/invariant?

ctrl_mov and reserve are still structural non-materialized operations to denote the data dependency between an operation and its backward users.

We can detect those phi (%reserve, %init_value) -> grant_predicate (%phi, %cond) -> ctrl_mov %grant -> %reserve patterns, which are transformed to invariant (%init_value, %cond)

Then what pattern would be true_steer?

Thanks~! I got the invariant's identification, which basically needs to identify a grant_once for the init_value, right?

Then, what is needed to identify carry? and false_steer?

At the beginning of the transformation, I remove all the grant_once ops, so it is identifying constant for the init_value

For carry, we identify the phi -> grant_predicate -> other computations -> ctrl_mov -> reserve. And create a carry (%init_value, %cond, %carried_value) for this pattern. The %carried_value is the source value of the reserve op.

For true/false_steer, we identify those grant_predicate (%value, %cond) patterns. If %cond is a not operation, we create false_steer for it, otherwise a true_steer. This process is the last process after carry and invariant identification and transformation, so we can avoid handling grant_predicates in loops.

Then why there is not true_steer but only false_steer in your example? how to distinguish it from carry?

tancheng · 2025-10-08T06:53:35Z

@tancheng One problem here, when I try to map the steering-based dataflow IR, it seems the ALAP sort didn't respect the producer-consumer dependency. So I cannot map it...

The code is:

module {
  func.func @simple_add_loop() -> i64 attributes {accelerator = "neura", dataflow_mode = "steering"} {
    %0 = neura.reserve : i64
    %1 = neura.reserve : i64
    %2 = neura.reserve : i1
    %3 = "neura.constant"() <{value = 16 : i64}> : () -> i64
    %4 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %5 = "neura.constant"() <{value = 1 : i64}> : () -> i64
    %6 = "neura.constant"() <{value = 0 : i64}> : () -> i64
    %7 = neura.invariant %4, %2 : i64, i1 -> i64
    %8 = neura.invariant %3, %2 : i64, i1 -> i64
    %9 = neura.carry %5, %2, %0 : i64, i1, i64 -> i64
    %10 = neura.carry %6, %2, %1 : i64, i1, i64 -> i64
    %11 = "neura.icmp"(%10, %8) <{cmpType = "slt"}> : (i64, i64) -> i1
    neura.ctrl_mov %11 -> %2 : i1 i1
    %12 = neura.false_steer %9, %11 : i64, i1 -> i64
    %13 = "neura.add"(%9, %9) : (i64, i64) -> i64
    neura.ctrl_mov %13 -> %0 : i64 i64
    %14 = "neura.add"(%10, %7) : (i64, i64) -> i64
    neura.ctrl_mov %14 -> %1 : i64 i64
    "neura.return"(%12) : (i64) -> ()
  }
}

The ALAP level of %7 = neura.invariant %4, %2 : i64, i1 -> i64 is 3, but we get 2 for %11 = "neura.icmp"(%10, %8) <{cmpType = "slt"}> : (i64, i64) -> i1. So when we try to map icmp, its backward user %7 is unmapped, which causes error.

Why would this happen? I think the current sorting is not pure ALAP, it is kind of mixed, right? (we can disable the test and fix it in next PR though)

ShangkunLi · 2025-10-08T06:53:49Z

Then why there is not true_steer but only false_steer in your example? how to distinguish it from carry?

There is another case in test/neura/steer_ctrl/for_with_if.mlir https://github.com/coredac/dataflow/pull/144/files#diff-24a95dd03aee90bcabee7106cdc76ac6f4301fe6b502e5f1a6b330af61d49f52 which leverages the true_steer op.

tancheng · 2025-10-08T06:55:26Z

Then why there is not true_steer but only false_steer in your example? how to distinguish it from carry?

There is another case in test/neura/steer_ctrl/for_with_if.mlir https://github.com/coredac/dataflow/pull/144/files#diff-24a95dd03aee90bcabee7106cdc76ac6f4301fe6b502e5f1a6b330af61d49f52 which leverages the true_steer op.

I just mean in your simple example, why there is no true_steer? Is it because pattern match for carry has higher priority?

ShangkunLi · 2025-10-08T07:01:45Z

Then why there is not true_steer but only false_steer in your example? how to distinguish it from carry?

There is another case in test/neura/steer_ctrl/for_with_if.mlir https://github.com/coredac/dataflow/pull/144/files#diff-24a95dd03aee90bcabee7106cdc76ac6f4301fe6b502e5f1a6b330af61d49f52 which leverages the true_steer op.

I just mean in your simple example, why there is no true_steer? Is it because pattern match for carry has higher priority?

Yes, the transformation order is:

transforming loop-control related ops into carry and invariant
transforming complementary grant_predicate + phi into merge
- Pattern: %0 = grant_predicate(%val0, %cond), %1 = grant_predicate(%val1, %not_cond), phi(%0, %1)
- Transformed: %result = merge (%cond, %val0, %val1)
transforming rest grant_predicates into true/false_steer

Support Steering-Control-Based Dataflow Representation & Mapping

ShangkunLi added 5 commits October 7, 2025 16:06

prototype steer control transformation

5ff1133

add comments

6a3bb7a

add test for steer control transform

57b7f7e

support steer control mapping

a4316f6

[clean] remove some debugging code

7eec1c2

ShangkunLi marked this pull request as ready for review October 7, 2025 11:40

ShangkunLi requested a review from tancheng October 7, 2025 13:24

tancheng reviewed Oct 7, 2025

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ShangkunLi added 3 commits October 8, 2025 10:30

add dataflow mode attribute

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add test for true/false_steer

773a4a6

[fix] fix some erros in transform

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tancheng reviewed Oct 8, 2025

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add dataflow mode checking

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tancheng approved these changes Oct 8, 2025

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ShangkunLi merged commit e05ca6a into coredac:main Oct 8, 2025
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ShangkunLi added a commit that referenced this pull request Mar 12, 2026

Merge pull request #144 from ShangkunLi/steer-control

4be34a7

Support Steering-Control-Based Dataflow Representation & Mapping

ShangkunLi added a commit that referenced this pull request Mar 12, 2026

Merge pull request #144 from ShangkunLi/steer-control

47c6224

Support Steering-Control-Based Dataflow Representation & Mapping

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ShangkunLi commented Oct 7, 2025

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tancheng commented Oct 7, 2025

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ShangkunLi commented Oct 8, 2025

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tancheng commented Oct 8, 2025

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ShangkunLi commented Oct 8, 2025

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ShangkunLi commented Oct 8, 2025

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ShangkunLi commented Oct 8, 2025

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tancheng commented Oct 8, 2025

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tancheng commented Oct 8, 2025

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ShangkunLi commented Oct 8, 2025

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tancheng commented Oct 8, 2025

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ShangkunLi commented Oct 8, 2025

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