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Fix attention mask traversal for three nested selects #2437

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Merged
umangyadav merged 7 commits into from
Jul 31, 2026
+209 −26
Merged

Fix attention mask traversal for three nested selects #2437

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868969d
Fix sliding-window sequence length mask detection
umangyadav Jul 23, 2026
ba15f65
Fix attention mask traversal for three nested selects
umangyadav Jul 23, 2026
381912b
[NFC] Make three-mask folding checks order-independent
umangyadav Jul 23, 2026
e41ac51
Merge branch 'develop' into users/umayadav/fix-attention-three-mask
umangyadav Jul 27, 2026
0b362d8
Merge branch 'develop' into users/umayadav/fix-attention-three-mask
umangyadav Jul 28, 2026
5c4f12b
[NFC] Remove duplicate sliding-window mask reconciliation
umangyadav Jul 29, 2026
a114b78
Merge branch 'develop' into users/umayadav/fix-attention-three-mask
umangyadav Jul 31, 2026
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69 changes: 44 additions & 25 deletions mlir/lib/Conversion/TosaToRock/TosaToRock.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2626,31 +2626,50 @@ struct AttentionRewritePattern : public OpRewritePattern<tosa::MatMulOp> {
// Analyze the first (outer) select
analyzeSelectForSeqLenMask(select, currentResult, opsToSkip, seqLenSkip);

// Check if the inputToContinue (input3) is another chained select with
// -inf. This handles cases where multiple mask patterns (KVCache, prefix
// causal, sliding window) use separate selects.
bool haveSeqLen = currentResult.seqLen != nullptr;
bool havePrefixOffset = currentResult.prefixOffset != nullptr;
bool haveSlidingWindow = currentResult.slidingWindowSize.has_value();

// Try chaining if we found at least one pattern but not all
bool foundAny = haveSeqLen || havePrefixOffset || haveSlidingWindow;
bool foundAll = haveSeqLen && havePrefixOffset && haveSlidingWindow;
if (foundAny && !foundAll) {
auto maybeChainedSelect = getSelectWithNegInf(inputToContinue);
if (succeeded(maybeChainedSelect)) {
auto chainedSelect = maybeChainedSelect.value();
// Try to analyze the chained select for the missing pattern
analyzeSelectForSeqLenMask(chainedSelect, currentResult, opsToSkip,
seqLenSkip);
// Only update inputToContinue if we found a complementary pattern
bool foundComplementary =
(!haveSeqLen && currentResult.seqLen) ||
(!havePrefixOffset && currentResult.prefixOffset) ||
(!haveSlidingWindow && currentResult.slidingWindowSize.has_value());
if (foundComplementary) {
currentResult.inputToContinue = chainedSelect.getInput3();
}
// Iteratively peel chained select(mask, -inf, scores) ops to detect
// separately nested KV-cache, prefix-causal, and sliding-window masks.
// Use prefixOffset as the recognition marker for a prefix-causal select
// (col > row + prefixOffset). A standard causal select (col > row) has no
// prefixOffset, so it remains in inputToContinue for getCausal() to handle
// after the sequence-length masks have been peeled.
auto recognizedMaskCount = [](const SeqLenMaskResult &result) {
return (result.seqLen ? 1 : 0) + (result.prefixOffset ? 1 : 0) +
(result.slidingWindowSize.has_value() ? 1 : 0);
};
while (recognizedMaskCount(currentResult) > 0 &&
recognizedMaskCount(currentResult) < 3) {
auto maybeChainedSelect =
getSelectWithNegInf(currentResult.inputToContinue);
if (failed(maybeChainedSelect))
break;

auto chainedSelect = maybeChainedSelect.value();
int before = recognizedMaskCount(currentResult);
analyzeSelectForSeqLenMask(chainedSelect, currentResult, opsToSkip,
seqLenSkip);
// Leave an unrecognized or duplicate mask in the elementwise region.

@justinrosner justinrosner Jul 29, 2026

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Not necessarily urgent for this PR, but in rocmlirTriton (assuming we are going to port this change there) we may run into errors with the duplicate/unrecognized masks being left in the elementwise region. The regularize* passes cannot sink transforms through non-splat constants (I've seen errors with this before when we were first testing the FusionZoo kernels with the rocmlirTriton prototype)

if (recognizedMaskCount(currentResult) == before)
break;
currentResult.inputToContinue = chainedSelect.getInput3();
}

// Sliding-window masking is defined relative to currentSeqLen. Reconcile
// the validated operand after all masks have been analyzed so the result is
// independent of the select nesting order.
if (currentResult.slidingWindowSize) {
if (currentResult.seqLen) {
if (!sameSeqLenBlockArg(currentResult.seqLen,
currentResult.slidingWindowSeqLen, seqLenSkip))
return failure();
// A single attention op cannot represent different clamps for the
// KV-cache and sliding-window masks.
if (currentResult.seqLenClipMin != currentResult.slidingWindowClipMin ||
currentResult.seqLenClipMax != currentResult.slidingWindowClipMax)
return failure();
} else {
currentResult.seqLen = currentResult.slidingWindowSeqLen;
currentResult.seqLenClipMin = currentResult.slidingWindowClipMin;
currentResult.seqLenClipMax = currentResult.slidingWindowClipMax;

@justinrosner justinrosner Jul 29, 2026

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I'm confused here. Doesn't this same block of logic already exist below? Why do we need the same thing twice?

@justinrosner justinrosner Jul 29, 2026

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Is this just a merge conflict artifact from updating the branch before?

@umangyadav umangyadav Jul 29, 2026

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Yes, Fixed it

}
}

Expand Down
94 changes: 94 additions & 0 deletions mlir/test/Conversion/TosaToRock/tosa-to-rock-attention-three-mask.mlir
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Original file line number Diff line number Diff line change
@@ -0,0 +1,94 @@
// RUN: sed s/##TOKEN_ARCH##/%arch/g %s | rocmlir-opt --tosa-to-rock -verify-diagnostics | FileCheck %s

// Three nested select ops must all be folded into a single rock.attention.
// From inner to outer, the masks are prefix-causal, KV-cache, and sliding
// window. All four corresponding attention properties must be preserved.
// CHECK-LABEL: func @attention_three_mask
// CHECK: rock.attention
// CHECK-DAG: currentSeqLen = (%{{.*}}
// CHECK-DAG: prefixOffset = (%{{.*}}
// CHECK-DAG: causal
// CHECK-DAG: slidingWindowSize = 3
// CHECK: qk = elementwise {
// CHECK-NOT: tosa.select
// CHECK: rock.yield

module {
func.func @attention_three_mask(%arg0: tensor<1xi32>, %arg1: tensor<9216xf16>, %arg2: tensor<2048xf16>, %arg3: tensor<2048xf16>, %arg4: tensor<2xi32>) -> tensor<7168xf16> attributes {rock.kernel, rock.arch = "##TOKEN_ARCH##"} {
%0 = "tosa.const"() <{values = dense<[[0, 1, 2, 3, 4, 5, 6, 7]]> : tensor<1x8xi32>}> : () -> tensor<1x8xi32>
%1 = "tosa.const"() <{values = dense<1.000000e+00> : tensor<2x14x4x8xf32>}> : () -> tensor<2x14x4x8xf32>
%2 = "tosa.const"() <{values = dense<0xFC00> : tensor<2x14x4x8xf16>}> : () -> tensor<2x14x4x8xf16>
%3 = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf16>}> : () -> tensor<1xf16>
%4 = "tosa.const"() <{values = dense<1.000000e+00> : tensor<2x2x7x64x8xf16>}> : () -> tensor<2x2x7x64x8xf16>
%5 = "tosa.const"() <{values = dense<1.000000e+00> : tensor<2x2x7x8x64xf16>}> : () -> tensor<2x2x7x8x64xf16>
%6 = "tosa.const"() <{values = dense<1> : tensor<2x14x4x8xi8>}> : () -> tensor<2x14x4x8xi8>
%7 = "tosa.const"() <{values = dense<1.250000e-01> : tensor<2x14x4x8xf16>}> : () -> tensor<2x14x4x8xf16>
%8 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
%9 = "tosa.const"() <{values = dense<1> : tensor<4x8xi32>}> : () -> tensor<4x8xi32>
%10 = "tosa.const"() <{values = dense<1> : tensor<4x1xi32>}> : () -> tensor<4x1xi32>
%11 = "tosa.const"() <{values = dense<1> : tensor<2x8xi32>}> : () -> tensor<2x8xi32>
%12 = "tosa.const"() <{values = dense<[[0], [1], [2], [3]]> : tensor<4x1xi32>}> : () -> tensor<4x1xi32>
%swoff = "tosa.const"() <{values = dense<-3> : tensor<1x1xi32>}> : () -> tensor<1x1xi32>
%expanded = tensor.expand_shape %arg1 [[0, 1, 2, 3]] output_shape [2, 4, 18, 64] : tensor<9216xf16> into tensor<2x4x18x64xf16>
%13 = tosa.transpose %expanded {perms = array<i32: 0, 2, 1, 3>} : (tensor<2x4x18x64xf16>) -> tensor<2x18x4x64xf16>
%expanded_0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [1, 1] : tensor<1xi32> into tensor<1x1xi32>
%14 = tosa.mul %expanded_0, %10, %8 : (tensor<1x1xi32>, tensor<4x1xi32>, tensor<1xi8>) -> tensor<4x1xi32>
%15 = tosa.add %14, %12 : (tensor<4x1xi32>, tensor<4x1xi32>) -> tensor<4x1xi32>
%16 = tosa.mul %15, %9, %8 : (tensor<4x1xi32>, tensor<4x8xi32>, tensor<1xi8>) -> tensor<4x8xi32>
%17 = tosa.mul %0, %9, %8 : (tensor<1x8xi32>, tensor<4x8xi32>, tensor<1xi8>) -> tensor<4x8xi32>
%18 = tosa.greater %17, %16 : (tensor<4x8xi32>, tensor<4x8xi32>) -> tensor<4x8xi1>
%19 = tosa.cast %18 : (tensor<4x8xi1>) -> tensor<4x8xi32>
%20 = tosa.cast %19 : (tensor<4x8xi32>) -> tensor<4x8xi8>
%expanded_1 = tensor.expand_shape %20 [[0, 1, 2], [3]] output_shape [1, 1, 4, 8] : tensor<4x8xi8> into tensor<1x1x4x8xi8>
%21 = tosa.mul %expanded_1, %6, %8 : (tensor<1x1x4x8xi8>, tensor<2x14x4x8xi8>, tensor<1xi8>) -> tensor<2x14x4x8xi8>
%22 = tosa.mul %0, %11, %8 : (tensor<1x8xi32>, tensor<2x8xi32>, tensor<1xi8>) -> tensor<2x8xi32>
%expanded_2 = tensor.expand_shape %arg4 [[0, 1]] output_shape [2, 1] : tensor<2xi32> into tensor<2x1xi32>
%23 = tosa.mul %expanded_2, %11, %8 : (tensor<2x1xi32>, tensor<2x8xi32>, tensor<1xi8>) -> tensor<2x8xi32>
%24 = tosa.greater %22, %23 : (tensor<2x8xi32>, tensor<2x8xi32>) -> tensor<2x8xi1>
%25 = tosa.cast %24 : (tensor<2x8xi1>) -> tensor<2x8xi32>
%26 = tosa.cast %25 : (tensor<2x8xi32>) -> tensor<2x8xi8>
%expanded_3 = tensor.expand_shape %26 [[0, 1, 2], [3]] output_shape [2, 1, 1, 8] : tensor<2x8xi8> into tensor<2x1x1x8xi8>
%27 = tosa.mul %expanded_3, %6, %8 : (tensor<2x1x1x8xi8>, tensor<2x14x4x8xi8>, tensor<1xi8>) -> tensor<2x14x4x8xi8>
%sw0 = tosa.add %expanded_2, %swoff : (tensor<2x1xi32>, tensor<1x1xi32>) -> tensor<2x1xi32>
%sw1 = tosa.mul %sw0, %11, %8 : (tensor<2x1xi32>, tensor<2x8xi32>, tensor<1xi8>) -> tensor<2x8xi32>
%sw2 = tosa.greater %sw1, %22 : (tensor<2x8xi32>, tensor<2x8xi32>) -> tensor<2x8xi1>
%sw3 = tosa.cast %sw2 : (tensor<2x8xi1>) -> tensor<2x8xi32>
%sw4 = tosa.cast %sw3 : (tensor<2x8xi32>) -> tensor<2x8xi8>
%expanded_sw = tensor.expand_shape %sw4 [[0, 1, 2], [3]] output_shape [2, 1, 1, 8] : tensor<2x8xi8> into tensor<2x1x1x8xi8>
%sw5 = tosa.mul %expanded_sw, %6, %8 : (tensor<2x1x1x8xi8>, tensor<2x14x4x8xi8>, tensor<1xi8>) -> tensor<2x14x4x8xi8>
%extracted_slice = tensor.extract_slice %13[0, 0, 0, 0] [2, 14, 4, 64] [1, 1, 1, 1] : tensor<2x18x4x64xf16> to tensor<2x14x4x64xf16>
%expanded_4 = tensor.expand_shape %arg2 [[0, 1, 2, 3, 4]] output_shape [2, 2, 1, 8, 64] : tensor<2048xf16> into tensor<2x2x1x8x64xf16>
%28 = tosa.mul %expanded_4, %5, %8 : (tensor<2x2x1x8x64xf16>, tensor<2x2x7x8x64xf16>, tensor<1xi8>) -> tensor<2x2x7x8x64xf16>
%expanded_5 = tensor.expand_shape %arg3 [[0, 1, 2, 3, 4]] output_shape [2, 2, 1, 8, 64] : tensor<2048xf16> into tensor<2x2x1x8x64xf16>
%29 = tosa.transpose %expanded_5 {perms = array<i32: 0, 1, 2, 4, 3>} : (tensor<2x2x1x8x64xf16>) -> tensor<2x2x1x64x8xf16>
%30 = tosa.mul %29, %4, %8 : (tensor<2x2x1x64x8xf16>, tensor<2x2x7x64x8xf16>, tensor<1xi8>) -> tensor<2x2x7x64x8xf16>
%collapsed = tensor.collapse_shape %extracted_slice [[0, 1], [2], [3]] : tensor<2x14x4x64xf16> into tensor<28x4x64xf16>
%collapsed_6 = tensor.collapse_shape %30 [[0, 1, 2], [3], [4]] : tensor<2x2x7x64x8xf16> into tensor<28x64x8xf16>
%31 = tosa.matmul %collapsed, %collapsed_6, %3, %3 {acc_type = f32} : (tensor<28x4x64xf16>, tensor<28x64x8xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<28x4x8xf16>
%expanded_7 = tensor.expand_shape %31 [[0, 1], [2], [3]] output_shape [2, 14, 4, 8] : tensor<28x4x8xf16> into tensor<2x14x4x8xf16>
%32 = tosa.mul %expanded_7, %7, %8 : (tensor<2x14x4x8xf16>, tensor<2x14x4x8xf16>, tensor<1xi8>) -> tensor<2x14x4x8xf16>
%33 = tosa.cast %21 : (tensor<2x14x4x8xi8>) -> tensor<2x14x4x8xi1>
%34 = tosa.select %33, %2, %32 : (tensor<2x14x4x8xi1>, tensor<2x14x4x8xf16>, tensor<2x14x4x8xf16>) -> tensor<2x14x4x8xf16>
%35 = tosa.cast %27 : (tensor<2x14x4x8xi8>) -> tensor<2x14x4x8xi1>
%36 = tosa.select %35, %2, %34 : (tensor<2x14x4x8xi1>, tensor<2x14x4x8xf16>, tensor<2x14x4x8xf16>) -> tensor<2x14x4x8xf16>
%sw6 = tosa.cast %sw5 : (tensor<2x14x4x8xi8>) -> tensor<2x14x4x8xi1>
%sw7 = tosa.select %sw6, %2, %36 : (tensor<2x14x4x8xi1>, tensor<2x14x4x8xf16>, tensor<2x14x4x8xf16>) -> tensor<2x14x4x8xf16>
%37 = tosa.cast %sw7 : (tensor<2x14x4x8xf16>) -> tensor<2x14x4x8xf32>
%38 = tosa.reduce_max %37 {axis = 3 : i32} : (tensor<2x14x4x8xf32>) -> tensor<2x14x4x1xf32>
%39 = tosa.mul %38, %1, %8 : (tensor<2x14x4x1xf32>, tensor<2x14x4x8xf32>, tensor<1xi8>) -> tensor<2x14x4x8xf32>
%40 = tosa.sub %37, %39 : (tensor<2x14x4x8xf32>, tensor<2x14x4x8xf32>) -> tensor<2x14x4x8xf32>
%41 = tosa.exp %40 : (tensor<2x14x4x8xf32>) -> tensor<2x14x4x8xf32>
%42 = tosa.reduce_sum %41 {axis = 3 : i32} : (tensor<2x14x4x8xf32>) -> tensor<2x14x4x1xf32>
%43 = tosa.mul %42, %1, %8 : (tensor<2x14x4x1xf32>, tensor<2x14x4x8xf32>, tensor<1xi8>) -> tensor<2x14x4x8xf32>
%44 = tosa.reciprocal %43 : (tensor<2x14x4x8xf32>) -> tensor<2x14x4x8xf32>
%45 = tosa.mul %41, %44, %8 : (tensor<2x14x4x8xf32>, tensor<2x14x4x8xf32>, tensor<1xi8>) -> tensor<2x14x4x8xf32>
%46 = tosa.cast %45 : (tensor<2x14x4x8xf32>) -> tensor<2x14x4x8xf16>
%collapsed_8 = tensor.collapse_shape %46 [[0, 1], [2], [3]] : tensor<2x14x4x8xf16> into tensor<28x4x8xf16>
%collapsed_9 = tensor.collapse_shape %28 [[0, 1, 2], [3], [4]] : tensor<2x2x7x8x64xf16> into tensor<28x8x64xf16>
%47 = tosa.matmul %collapsed_8, %collapsed_9, %3, %3 {acc_type = f32} : (tensor<28x4x8xf16>, tensor<28x8x64xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<28x4x64xf16>
%expanded_10 = tensor.expand_shape %47 [[0, 1], [2], [3]] output_shape [2, 14, 4, 64] : tensor<28x4x64xf16> into tensor<2x14x4x64xf16>
%48 = tosa.transpose %expanded_10 {perms = array<i32: 0, 2, 1, 3>} : (tensor<2x14x4x64xf16>) -> tensor<2x4x14x64xf16>
%collapsed_11 = tensor.collapse_shape %48 [[0, 1, 2, 3]] : tensor<2x4x14x64xf16> into tensor<7168xf16>
return %collapsed_11 : tensor<7168xf16>
}
}
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