Einsum decomposition

docs/SupportedONNXOps-cpu.md

@@ -1,5 +1,5 @@
 <!--- Automatically generated, do not edit. -->
-<!--- python documentOps.py --arch cpu --input /workdir/onnx-mlir/test/backend/inference_backend.py --path /workdir/onnx-mlir/utils --notes --unsupported -->
+<!--- python documentOps.py --arch cpu --input test/backend/inference_backend.py --path utils --notes --unsupported -->
 
 # Supported ONNX Operation for Target *cpu*.
 
@@ -49,7 +49,7 @@ Onnx-mlir currently supports ONNX operations targeting up to opset 16. Limitatio
 | **Div** |14 |No support for short integers. | |
 | **Dropout** |13 |Does not support masked and training. | |
 | **DynamicQuantizeLinear** |11 | | |
-| **EinSum** |unsupported | | |
+| **Einsum** |12 |Limited to the types supported by ReduceSum and MatMul (which we decompose to in most cases) which exclude integers with width < 32. | |
 | **Elu** |6 | | |
 | **Equal** |13 | | |
 | **Erf** |13 | | |

src/Conversion/ONNXToKrnl/RNN/GRU.cpp

@@ -419,7 +419,8 @@ void calculateState<GruState, GruActivationPack, GruWeightPack, GruBiasPack>(
       MemRefType::get({batchSize, 3 * hiddenSize}, elementType);
 
   // Common matrix multiplications.
-  Value XtWT = create.onnx.matmul(matrixAllGatesType, Xt, weightPack.WT);
+  Value XtWT = create.onnx.toMemref(
+      create.onnx.matmul(matrixAllGatesType, Xt, weightPack.WT));
   Value one = create.math.constant(elementType, 1);
 
   // Lower and upper bounds derived from Ht tensor.
@@ -437,7 +438,8 @@ void calculateState<GruState, GruActivationPack, GruWeightPack, GruBiasPack>(
     // Ht = (1 - zt) (.) ht + zt (.) Ht-1"
     // In this case, we can do all matrix multiplications first, then fuse all
     // element-wise computations into a single nested loop.
-    Value HtRT = create.onnx.matmul(matrixAllGatesType, Ht, weightPack.RT);
+    Value HtRT = create.onnx.toMemref(
+        create.onnx.matmul(matrixAllGatesType, Ht, weightPack.RT));
 
     // Do element-wise computations. Fuse them into a single nested loop.
     ValueRange loops = create.krnl.defineLoops(htRank);
@@ -509,8 +511,10 @@ void calculateState<GruState, GruActivationPack, GruWeightPack, GruBiasPack>(
     // In this case, besides computing matrix multiplications, we need to
     // compute rt and (rt (.) Ht-1) first, then fuse the remaining element-wise
     // computations into a single nested loop.
-    Value HtRz = create.onnx.matmul(matrixType, Ht, weightPack.Rz);
-    Value HtRr = create.onnx.matmul(matrixType, Ht, weightPack.Rr);
+    Value HtRz =
+        create.onnx.toMemref(create.onnx.matmul(matrixType, Ht, weightPack.Rz));
+    Value HtRr =
+        create.onnx.toMemref(create.onnx.matmul(matrixType, Ht, weightPack.Rr));
     Value rt, rtHt;
     if (hasAllConstantDimensions(matrixType)) {
       rt = insertAllocAndDealloc(matrixType, loc, rewriter, false);
@@ -553,7 +557,8 @@ void calculateState<GruState, GruActivationPack, GruWeightPack, GruBiasPack>(
         });
 
     // Emit (rt (.) Ht-1)*(Rh^T)
-    Value rtHtRh = create.onnx.matmul(matrixType, rtHt, weightPack.Rh);
+    Value rtHtRh = create.onnx.toMemref(
+        create.onnx.matmul(matrixType, rtHt, weightPack.Rh));
 
     // Do element-wise computations. Fuse them into a single nested loop.
     ValueRange loops2 = create.krnl.defineLoops(htRank);

src/Conversion/ONNXToKrnl/RNN/LSTM.cpp

@@ -476,8 +476,10 @@ void calculateState<LstmState, LstmActivationPack, LstmWeightPack,
   // Xt * (Wi^T ++ Wo^T ++ Wf^T ++ Wc^T)
   // Ht * (Ri^T ++ Ro^T ++ Rf^T ++ Rc^T)
   // where '++' is matrix concatenation.
-  Value XtWT = create.onnx.matmul(matrixAllGatesType, Xt, weightPack.WT);
-  Value HtRT = create.onnx.matmul(matrixAllGatesType, Ht, weightPack.RT);
+  Value XtWT = create.onnx.toMemref(
+      create.onnx.matmul(matrixAllGatesType, Xt, weightPack.WT));
+  Value HtRT = create.onnx.toMemref(
+      create.onnx.matmul(matrixAllGatesType, Ht, weightPack.RT));
 
   // Do element-wise computations. Fuse them into a single nested loop.
   // Lower and upper bounds derived from Ht tensor.

src/Conversion/ONNXToKrnl/RNN/RNN.cpp

@@ -320,8 +320,10 @@ void calculateState<RnnState, RnnActivationPack, RnnWeightPack, RnnBiasPack>(
   unsigned htRank = matrixType.getRank();
 
   // Do matrix multiplications.
-  Value XtWi = create.onnx.matmul(matrixType, Xt, weightPack.Wi);
-  Value HtRi = create.onnx.matmul(matrixType, Ht, weightPack.Ri);
+  Value XtWi =
+      create.onnx.toMemref(create.onnx.matmul(matrixType, Xt, weightPack.Wi));
+  Value HtRi =
+      create.onnx.toMemref(create.onnx.matmul(matrixType, Ht, weightPack.Ri));
 
   // Do element-wise computations. Fuse them into a single nested loop.
   // Lower and upper bounds derived from Ht tensor.

src/Dialect/ONNX/CMakeLists.txt

@@ -16,6 +16,7 @@ add_onnx_mlir_library(OMONNXOps
   ONNXDialect.cpp
   ONNXOps.cpp
   ONNXOpsHelper.cpp
+  ONNXEinsumOpHelper.cpp
   ONNXTypes.cpp
   Rewrite.cpp
 

src/Dialect/ONNX/DialectBuilder.cpp

@@ -86,7 +86,7 @@ Value OnnxBuilder::matmul(Type Y, Value A, Value B, bool useGemm) const {
         /*transB=*/
         IntegerAttr::get(b.getIntegerType(64, /*isSigned=*/true),
             APInt(64, 0, /*isSigned=*/true)));
-  return toMemref(b.create<ONNXMatMulOp>(loc, toTensor(Y), aValue, bValue));
+  return b.create<ONNXMatMulOp>(loc, toTensor(Y), aValue, bValue);
 }
 
 Value OnnxBuilder::min(ValueRange inputs) const {
@@ -109,11 +109,24 @@ Value OnnxBuilder::mul(Value A, Value B) const {
   return b.create<ONNXMulOp>(loc, toTensor(A), toTensor(B));
 }
 
+Value OnnxBuilder::reduceSum(Type outputType, Value data, Value axes,
+    bool keepdims, bool noop_with_empty_axes) const {
+  int64_t i_keepdims = keepdims; // 0 if false, 1 if true
+  int64_t i_noop_with_empty_axes = noop_with_empty_axes; // ditto
+  return b.create<ONNXReduceSumOp>(loc, toTensor(outputType), toTensor(data),
+      toTensor(axes), i_keepdims, i_noop_with_empty_axes);
+}
+
 Value OnnxBuilder::reshape(Type outputType, Value input, Value shape) const {
   return b.create<ONNXReshapeOp>(
       loc, toTensor(outputType), toTensor(input), toTensor(shape));
 }
 
+Value OnnxBuilder::squeeze(Type outputType, Value data, Value axes) const {
+  return b.create<ONNXSqueezeOp>(
+      loc, toTensor(outputType), toTensor(data), toTensor(axes));
+}
+
 Value OnnxBuilder::sub(Value A, Value B) const {
   assert((A.getType().cast<ShapedType>().getElementType() ==
              B.getType().cast<ShapedType>().getElementType()) &&
@@ -162,4 +175,15 @@ Value OnnxBuilder::toMemref(Value input) const {
       .getResult(0);
 }
 
+Value OnnxBuilder::unsqueeze(Type outputType, Value data, Value axes) const {
+  return b.create<ONNXUnsqueezeOp>(
+      loc, toTensor(outputType), toTensor(data), toTensor(axes));
+}
+
+Value OnnxBuilder::where(
+    Type outputType, Value condition, Value X, Value Y) const {
+  return b.create<ONNXWhereOp>(
+      loc, toTensor(outputType), toTensor(condition), toTensor(X), toTensor(Y));
+}
+
 } // namespace onnx_mlir

src/Dialect/ONNX/DialectBuilder.hpp

@@ -52,10 +52,19 @@ struct OnnxBuilder : onnx_mlir::DialectBuilder {
   // ONNXMulOp
   mlir::Value mul(mlir::Value A, mlir::Value B) const;
 
+  // ONNXReduceSumOp
+  mlir::Value reduceSum(mlir::Type outputType, mlir::Value data,
+      mlir::Value axes, bool keepdims = true,
+      bool noop_with_empty_axes = false) const;
+
   // ONNXReshapeOp
   mlir::Value reshape(
       mlir::Type outputType, mlir::Value input, mlir::Value shape) const;
 
+  // ONNXSqueezeOp
+  mlir::Value squeeze(
+      mlir::Type outputType, mlir::Value data, mlir::Value axes) const;
+
   // ONNXSubOp
   mlir::Value sub(mlir::Value A, mlir::Value B) const;
 
@@ -66,8 +75,18 @@ struct OnnxBuilder : onnx_mlir::DialectBuilder {
   mlir::Type toTensor(mlir::Type input) const;
   // Convert a Value to MemrefType if it is of TensorType.
   mlir::Value toMemref(mlir::Value input) const;
+
+  // ONNXTransposeOp
   mlir::Value transpose(
       mlir::Type outputType, mlir::Value input, mlir::ArrayAttr perm) const;
+
+  // ONNXUnsqueezeOp
+  mlir::Value unsqueeze(
+      mlir::Type outputType, mlir::Value data, mlir::Value axes) const;
+
+  // ONNXWhereOp
+  mlir::Value where(mlir::Type outputType, mlir::Value condition, mlir::Value X,
+      mlir::Value Y) const;
 };
 
 // Recursive class specialized for OnnxBuilder refereed to as onnx.