Optimization for Roberta unstick->reshape->transpose->reshape->stick

docs/Dialects/zhigh.md

@@ -782,13 +782,6 @@ Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterfac
 
 Effects: `MemoryEffects::Effect{}`
 
-#### Attributes:
-
-<table>
-<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
-<tr><td><code>op_type</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
-</table>
-
 #### Operands:
 
 | Operand | Description |
@@ -814,13 +807,6 @@ Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterfac
 
 Effects: `MemoryEffects::Effect{}`
 
-#### Attributes:
-
-<table>
-<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
-<tr><td><code>op_type</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
-</table>
-
 #### Operands:
 
 | Operand | Description |
@@ -857,6 +843,40 @@ Effects: `MemoryEffects::Effect{}`
 | :----: | ----------- |
 | `Out` | unranked tensor of 16-bit float values or 1D tensor of 16-bit float values with layout _1D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2D or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3D or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2DS or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NCHW or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NHWC or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout HWCK or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout FICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout ZRH or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BFICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BZRH
 
+### `zhigh.Reshape` (::onnx_mlir::zhigh::ZHighReshapeOp)
+
+_ZHigh Reshape operation for Z Tensors_
+
+ZHigh operation to perform a converts a Z Tensor from one type to an equivalent type
+with a provided shape. The data is never copied or modified. When no layout is specified,
+the output preserve the same layout as the source input.
+
+Traits: `AlwaysSpeculatableImplTrait`
+
+Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `ShapeHelperOpInterface`, `ShapeInferenceOpInterface`
+
+Effects: `MemoryEffects::Effect{}`
+
+#### Attributes:
+
+<table>
+<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
+<tr><td><code>layout</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
+</table>
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+| `source` | unranked tensor of 16-bit float values or 1D tensor of 16-bit float values with layout _1D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2D or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3D or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2DS or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NCHW or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NHWC or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout HWCK or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout FICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout ZRH or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BFICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BZRH
+| `shape` | tensor of 64-bit signless integer values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+| `result` | unranked tensor of 16-bit float values or 1D tensor of 16-bit float values with layout _1D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2D or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3D or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4D or unranked tensor of 16-bit float values or 2D tensor of 16-bit float values with layout _2DS or unranked tensor of 16-bit float values or 3D tensor of 16-bit float values with layout _3DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout _4DS or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NCHW or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout NHWC or unranked tensor of 16-bit float values or 4D tensor of 16-bit float values with layout HWCK or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout FICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout ZRH or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BFICO or unranked tensor of 16-bit float values or 2D/3D tensor of 16-bit float values with layout BZRH
+
 ### `zhigh.Sigmoid` (::onnx_mlir::zhigh::ZHighSigmoidOp)
 
 _ZHigh Sigmoid operation_

docs/Dialects/zlow.md

@@ -770,7 +770,6 @@ Traits: `MemRefsNormalizable`
 <table>
 <tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
 <tr><td><code>layout</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
-<tr><td><code>op_type</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
 </table>
 
 #### Operands:
@@ -795,7 +794,6 @@ Traits: `MemRefsNormalizable`
 <table>
 <tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
 <tr><td><code>layout</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
-<tr><td><code>op_type</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
 </table>
 
 #### Operands:
@@ -832,6 +830,29 @@ Interfaces: `MemoryEffectOpInterface`
 | `shape` | memref of 64-bit signless integer values
 | `Out` | memref of dlfloat16 type values
 
+### `zlow.reshape` (::onnx_mlir::zlow::ZLowReshapeOp)
+
+_ZLow Reshape operation_
+
+ZLow operation to perform a reshape (no data movement).
+
+Traits: `MemRefsNormalizable`
+
+#### Attributes:
+
+<table>
+<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
+<tr><td><code>layout</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
+</table>
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+| `X` | memref of dlfloat16 type values
+| `shape` | memref of 64-bit signless integer values
+| `Out` | memref of dlfloat16 type values
+
 ### `zlow.sigmoid` (::onnx_mlir::zlow::ZLowSigmoidOp)
 
 _ZLow sigmoid operation_

src/Accelerators/NNPA/Conversion/ONNXToZHigh/RewriteONNXForZHigh.td

@@ -95,17 +95,6 @@ def replaceONNXBatchNormalizationInferenceModePattern : Pattern<
 //
 //===----------------------------------------------------------------------===//
 
-
-// Create an ONNX Shape Op with type
-def CreateShapeOp: NativeCodeCall<
-  "$_builder.create<mlir::ONNXShapeOp>($_loc, $0, $1, IntegerAttr(), 0)"
->;
-
-// Get a type for a tensor that stores the shape of another tensor.
-def GetShapeTypeOf: NativeCodeCall<
-  "RankedTensorType::get({mlir::cast<ShapedType>($0.getType()).getRank()}, $_builder.getIntegerType(64))"
->;
-
 // Check unidirectional broadcasting from the first to second tensor.
 def IsUniBroadcastingFromFirstToSecond: Constraint<
   CPred<"isUniBroadcatableFirstToSecond($0, $1)">,

src/Accelerators/NNPA/Conversion/ZHighToZLow/ZHighToZLow.cpp

@@ -1093,6 +1093,47 @@ struct ZHighToZLowUnaryOpLowering : public ConversionPattern {
   }
 };
 
+// Reshape operation. Code similar to unary lowering, except that we use the
+// operation's specialized shape here.
+struct ZHighToZLowReshapeOpLowering : public ConversionPattern {
+  ZHighToZLowReshapeOpLowering(TypeConverter &typeConverter, MLIRContext *ctx)
+      : ConversionPattern(ZHighReshapeOp::getOperationName(), 1, ctx) {}
+
+  LogicalResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
+      ConversionPatternRewriter &rewriter) const final {
+    Location loc = op->getLoc();
+    Value input = operands[0];
+
+    // Helper builders.
+    MultiDialectBuilder<IndexExprBuilderForKrnl> create(rewriter, loc);
+
+    // Convert ZTensor type to MemRefType.
+    ZMemRefType zMemRefType =
+        convertZTensorToMemRefType(*op->result_type_begin());
+
+    // Shape helper.
+    ZHighReshapeOpShapeHelper shapeHelper(op, operands, &create.krnlIE);
+    shapeHelper.computeShapeAndAssertOnFailure();
+    SmallVector<IndexExpr, 4> &dims = shapeHelper.getOutputDims();
+
+    // Allocate a buffer for the result MemRef. Follow this pattern to be
+    // similar to all the other zlow patterns. Will remove the alloc when
+    // lowering zlow.reshape to memref.reinterpret_cast once memrefs are
+    // normalized. See code in ReshapeToReinterpretCastPattern.
+    Value alloc = insertAllocForZMemRef(zMemRefType, dims, op, rewriter);
+
+    // Note, we do not need to save the shape of the original operation, as this
+    // reshape is "no-op" that logically reorganize the shape of the operation
+    // into 2 equivalent shapes under their given layout.
+
+    // Emit a ZLow operation.
+    rewriter.create<ZLowReshapeOp>(
+        loc, input, /* shape,*/ alloc, zMemRefType.layout);
+    rewriter.replaceOp(op, alloc);
+    return success();
+  }
+};
+
 //===----------------------------------------------------------------------===//
 // Lower ZHigh ReduceMax/ReduceMin to ZLow ReduceMax/ReduceMin
 //===----------------------------------------------------------------------===//
@@ -1117,8 +1158,6 @@ struct ZHighToZLowReduceOpLowering : public ConversionPattern {
       : ConversionPattern(OP_TYPE::getOperationName(), 1, ctx) {}
   LogicalResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
-    MLIRContext *context = rewriter.getContext();
-    OP_TYPE reduceOp = mlir::cast<OP_TYPE>(op);
     Location loc = op->getLoc();
     Value data = operands[0];
 
@@ -2285,6 +2324,8 @@ void populateZHighToZLowConversionPattern(mlir::RewritePatternSet &patterns,
   patterns.insert<ZHighToZLowUnaryOpLowering<ZHighTanhOp>>(typeConverter, ctx);
   patterns.insert<ZHighToZLowUnaryOpLowering<ZHighSigmoidOp>>(
       typeConverter, ctx);
+  // Reshape operations.
+  patterns.insert<ZHighToZLowReshapeOpLowering>(typeConverter, ctx);
   // Neural network operations.
   patterns.insert<ZHighToZLowReduceOpLowering<ZHighReduceMaxOp>>(
       typeConverter, ctx);

src/Accelerators/NNPA/Dialect/ZHigh/CMakeLists.txt

@@ -26,6 +26,7 @@ add_onnx_mlir_library(OMZHighOps
   ZHighOps/QuantizedMatMul/QuantizedMatMul.cpp
   ZHighOps/QuantizedStick/QuantizedStick.cpp
   ZHighOps/Reduction/Reduction.cpp
+  ZHighOps/Reshape/Reshape.cpp
   ZHighOps/Softmax/Softmax.cpp
   ZHighOps/Stick/Stick.cpp
   ZHighOps/StickForGRU/StickForGRU.cpp

src/Accelerators/NNPA/Dialect/ZHigh/ZHigh.td

@@ -1195,4 +1195,28 @@ def ZHighFixGRUYhOp:ZHigh_Op<"FixGRUYh", [Pure,
   }];
 }
 
+def ZHighReshapeOp:ZHigh_Op<"Reshape", [Pure,
+    DeclareOpInterfaceMethods<ShapeInferenceOpInterface>,
+    DeclareOpInterfaceMethods<ShapeHelperOpInterface>]> {
+  let summary = "ZHigh Reshape operation for Z Tensors";
+  let description = [{
+    ZHigh operation to perform a converts a Z Tensor from one type to an equivalent type
+    with a provided shape. The data is never copied or modified. When no layout is specified,
+    the output preserve the same layout as the source input.
+  }];
+  let arguments = (ins AnyTypeOf<[AnyZTensor]>:$source, // Input Z Tensor to be reshaped.
+                       TensorOf<[I64]>:$shape,          // Shape of output Z Tensor.
+                       OptionalAttr<StrAttr>:$layout);  // Layout of output Z Tensor, default same as input.
+  let results = (outs AnyTypeOf<[AnyZTensor]>:$result);
+
+  let extraClassDefinition = [{
+    onnx_mlir::ONNXOpShapeHelper * ZHighReshapeOp::getShapeHelper(mlir::Operation *op, mlir::ArrayRef<mlir::Value> oper,
+        onnx_mlir::IndexExprBuilder *ieb, onnx_mlir::IndexExprScope *scope) {
+      onnx_mlir::ONNXOpShapeHelper *sh = new ZHighReshapeOpShapeHelper(op, oper, ieb, scope);
+      assert(sh && "failed to allocate shape helper");
+      return sh;
+    }
+  }];
+}
+
 #endif // ZHIGH_OPS