Implement decomposition from onnx.Sum to sequence of onnx.Add (#2964

) Signed-off-by: Sam <srcarroll314@gmail.com> Co-authored-by: Alexandre Eichenberger <alexe@us.ibm.com>
onnx · Oct 3, 2024 · 265ee60 · 265ee60
1 parent 56a610c
commit 265ee60
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diff --git a/src/Dialect/ONNX/Transforms/Decompose.cpp b/src/Dialect/ONNX/Transforms/Decompose.cpp
@@ -985,6 +985,30 @@ struct GroupNormIntoLayerNormPattern2
   }
 };
 
+/// Decompose `onnx.Sum` to a sequence of `onnx.Add`
+struct SumToAddPattern : public OpRewritePattern<ONNXSumOp> {
+  using OpRewritePattern<ONNXSumOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(
+      ONNXSumOp sumOp, PatternRewriter &rewriter) const final {
+    SmallVector<Value> inputs(sumOp.getData_0());
+    assert(inputs.size() > 0 && "expected at least one input");
+    Value result = inputs[0];
+    if (inputs.size() > 1) {
+      inputs.erase(inputs.begin());
+      for (auto input : inputs) {
+        result = rewriter.create<ONNXAddOp>(sumOp.getLoc(), result, input);
+      }
+    }
+    auto resultType = mlir::cast<ShapedType>(sumOp.getResult().getType());
+    if (resultType != result.getType())
+      result = rewriter.create<ONNXCastOp>(
+          sumOp.getLoc(), resultType, result, 1, resultType.getElementType());
+    rewriter.replaceOp(sumOp, result);
+    return success();
+  }
+};
+
 // =============================================================================
 // Pattern for replacing CastLikeOp by CastOp.
 // =============================================================================
@@ -1093,6 +1117,7 @@ void DecomposeONNXToONNXPass::runOnOperation() {
   target.addIllegalOp<ONNXSplitV11Op>();
   target.addIllegalOp<ONNXSplitV13Op>();
   target.addIllegalOp<ONNXSqueezeV11Op>();
+  target.addIllegalOp<ONNXSumOp>();
   target.addIllegalOp<ONNXUnsqueezeV11Op>();
   target.addIllegalOp<ONNXUpsampleOp>();
   target.addIllegalOp<ONNXUpsampleV7Op>();
@@ -1165,6 +1190,7 @@ void onnx_mlir::getDecomposeONNXToONNXPatterns(
   patterns.insert<InstanceNormIntoLayerNormPattern>(context);
   patterns.insert<GroupNormIntoLayerNormPattern1>(context);
   patterns.insert<GroupNormIntoLayerNormPattern2>(context);
+  patterns.insert<SumToAddPattern>(context);
 
   // TODO: consider whether to include SoftmaxPattern here
 }

diff --git a/test/mlir/onnx/onnx_decompose.mlir b/test/mlir/onnx/onnx_decompose.mlir
@@ -698,3 +698,50 @@ func.func @test_castlike(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf16>) -> tensor
   // CHECK: onnx.Return [[RES]] : tensor<*xf16>
 }
 
+// -----
+
+func.func @test_sum(%arg0: tensor<128x10xf32>, %arg1: tensor<64x128x10xf32>, %arg2: tensor<10xf32>, %arg3: tensor<64x1x1xf32>) -> tensor<64x128x10xf32> {
+  %0 = "onnx.Sum"(%arg0, %arg1, %arg2, %arg3) : (tensor<128x10xf32>, tensor<64x128x10xf32>, tensor<10xf32>, tensor<64x1x1xf32>) -> tensor<64x128x10xf32>
+  onnx.Return %0 : tensor<64x128x10xf32> 
+  // CHECK-LABEL:       func @test_sum
+  // CHECK-SAME:     (%[[ARG0:.*]]: {{.*}}, %[[ARG1:.*]]: {{.*}}, %[[ARG2:.*]]: {{.*}}, %[[ARG3:.*]]: {{.*}})
+  // CHECK-NEXT:      %[[SUM0:.*]] = "onnx.Add"(%[[ARG0]], %[[ARG1]])
+  // CHECK-NEXT:      %[[SUM1:.*]] = "onnx.Add"(%[[SUM0]], %[[ARG2]])
+  // CHECK-NEXT:      %[[SUM2:.*]] = "onnx.Add"(%[[SUM1]], %[[ARG3]])
+  // CHECK-NEXT:      onnx.Return %[[SUM2]]
+}
+
+// -----
+
+func.func @test_sum_to_unranked(%arg0: tensor<128x10xf32>, %arg1: tensor<64x128x10xf32>, %arg2: tensor<10xf32>, %arg3: tensor<64x1x1xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Sum"(%arg0, %arg1, %arg2, %arg3) : (tensor<128x10xf32>, tensor<64x128x10xf32>, tensor<10xf32>, tensor<64x1x1xf32>) -> tensor<*xf32>
+  onnx.Return %0 : tensor<*xf32> 
+  // CHECK-LABEL:       func @test_sum
+  // CHECK-SAME:     (%[[ARG0:.*]]: {{.*}}, %[[ARG1:.*]]: {{.*}}, %[[ARG2:.*]]: {{.*}}, %[[ARG3:.*]]: {{.*}})
+  // CHECK-NEXT:      %[[SUM0:.*]] = "onnx.Add"(%[[ARG0]], %[[ARG1]])
+  // CHECK-NEXT:      %[[SUM1:.*]] = "onnx.Add"(%[[SUM0]], %[[ARG2]])
+  // CHECK-NEXT:      %[[SUM2:.*]] = "onnx.Add"(%[[SUM1]], %[[ARG3]])
+  // CHECK-NEXT:      %[[CAST:.*]] = "onnx.Cast"(%[[SUM2]]) {saturate = 1 : si64, to = f32} : (tensor<64x128x10xf32>) -> tensor<*xf32>
+  // CHECK-NEXT:      onnx.Return %[[CAST]]
+}
+
+// -----
+
+func.func @test_sum_single_input(%arg0: tensor<64x128x10xf32>) -> tensor<64x128x10xf32> {
+  %0 = "onnx.Sum"(%arg0) : (tensor<64x128x10xf32>) -> tensor<64x128x10xf32>
+  onnx.Return %0 : tensor<64x128x10xf32> 
+  // CHECK-LABEL:       func @test_sum_single_input
+  // CHECK-SAME:     (%[[ARG0:.*]]: {{.*}})
+  // CHECK-NEXT:      onnx.Return %[[ARG0]]
+}
+
+// -----
+
+func.func @test_sum_single_input_to_unranked(%arg0: tensor<64x128x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Sum"(%arg0) : (tensor<64x128x10xf32>) -> tensor<*xf32>
+  onnx.Return %0 : tensor<*xf32> 
+  // CHECK-LABEL:       func @test_sum_single_input_to_unranked
+  // CHECK-SAME:     (%[[ARG0:.*]]: {{.*}})
+  // CHECK-NEXT:      %[[CAST:.*]] = "onnx.Cast"(%[[ARG0]]) {saturate = 1 : si64, to = f32} : (tensor<64x128x10xf32>) -> tensor<*xf32>
+  // CHECK-NEXT:      onnx.Return %[[CAST]]
+}