fix

Author: Longsheng Du

include/gc/Dialect/OneDNNGraph/OneDNNGraphTypes.td

@@ -18,7 +18,7 @@ include "OneDNNGraphDialect.td"
 //===----------------------------------------------------------------------===//
 
 def OneDNNGraph_DataType : AnyTypeOf<[
-			                F16,
+                            F16,
                             BF16, 
                             F32, 
                             SI<32>, 

lib/gc/Dialect/OneDNNGraph/OneDNNGraphDialect.cpp

@@ -10,23 +10,6 @@
 #include "gc/Dialect/OneDNNGraph/OneDNNGraphOps.h"
 #include "gc/Dialect/OneDNNGraph/OneDNNGraphTypes.h"
 
-#include "mlir/Dialect/Quant/QuantOps.h"
-#include "mlir/Dialect/Tensor/IR/Tensor.h"
-#include "mlir/Dialect/Tosa/IR/TosaOps.h"
-#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
-#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"
-#include "mlir/Dialect/Utils/IndexingUtils.h"
-#include "mlir/IR/BuiltinTypes.h"
-#include "mlir/IR/DialectImplementation.h"
-#include "mlir/IR/Matchers.h"
-#include "mlir/IR/PatternMatch.h"
-#include "mlir/IR/TypeUtilities.h"
-#include "mlir/Interfaces/InferTypeOpInterface.h"
-#include "mlir/Transforms/InliningUtils.h"
-#include "llvm/ADT/APFloat.h"
-#include "llvm/ADT/DenseMap.h"
-#include "llvm/ADT/TypeSwitch.h"
-
 using namespace mlir;
 using namespace mlir::onednn_graph;
 

test/gc/Dialect/OneDNNGraph/onednn-graph-mlp.mlir

@@ -4,6 +4,12 @@
 func.func @mlp(%in: tensor<128x512xbf16>, 
                %weight0: tensor<512x64xbf16>, %bias0: tensor<64xbf16>,
                %weight1: tensor<64x256xbf16>, %bias1: tensor<256xbf16>) -> tensor<128x256xbf16> {
+  // CHECK: [[MM1:%.+]] = onednn_graph.matmul 
+  // CHECK: [[RL1:%.+]] = onednn_graph.relu [[MM1]]
+  // CHECK: [[MM2:%.+]] = onednn_graph.matmul 
+  // CHECK: [[AD2:%.+]] = onednn_graph.add [[MM2]]
+  // CHECK: [[RL2:%.+]] = onednn_graph.relu [[AD2]]
+  // CHECK: return [[RL2]]
   %0 = onednn_graph.matmul %in, %weight0, %bias0 
        : (tensor<128x512xbf16>, tensor<512x64xbf16>, tensor<64xbf16>) -> tensor<128x64xbf16>
   %1 = onednn_graph.relu %0 : (tensor<128x64xbf16>) -> tensor<128x64xbf16>
@@ -17,6 +23,10 @@ func.func @mlp(%in: tensor<128x512xbf16>,
 // CHECK-LABEL: @mlp_transpose_a
 func.func @mlp_transpose_a(%in: tensor<512x128xbf16>, 
                %weight0: tensor<512x256xbf16>, %bias0: tensor<256xbf16>) -> tensor<128x256xbf16> {
+  // CHECK: [[MM1:%.+]] = onednn_graph.matmul 
+  // CHECK: {transpose_a = true}
+  // CHECK-NEXT: [[RL1:%.+]] = onednn_graph.relu [[MM1]]
+  // CHECK-NEXT: return [[RL1]]
   %0 = onednn_graph.matmul %in, %weight0, %bias0 {transpose_a = true}  
        : (tensor<512x128xbf16>, tensor<512x256xbf16>, tensor<256xbf16>) -> tensor<128x256xbf16>
   %1 = onednn_graph.relu %0 : (tensor<128x256xbf16>) -> tensor<128x256xbf16>
@@ -26,6 +36,10 @@ func.func @mlp_transpose_a(%in: tensor<512x128xbf16>,
 // CHECK-LABEL: @mlp_transpose_b
 func.func @mlp_transpose_b(%in: tensor<128x512xbf16>, 
                %weight0: tensor<256x512xbf16>, %bias0: tensor<256xbf16>) -> tensor<128x256xbf16> {
+  // CHECK: [[MM1:%.+]] = onednn_graph.matmul 
+  // CHECK: {transpose_b = true}
+  // CHECK-NEXT: [[RL1:%.+]] = onednn_graph.relu [[MM1]]
+  // CHECK-NEXT: return [[RL1]]
   %0 = onednn_graph.matmul %in, %weight0, %bias0 {transpose_b = true}  
        : (tensor<128x512xbf16>, tensor<256x512xbf16>, tensor<256xbf16>) -> tensor<128x256xbf16>
   %1 = onednn_graph.relu %0 : (tensor<128x256xbf16>) -> tensor<128x256xbf16>
@@ -35,6 +49,10 @@ func.func @mlp_transpose_b(%in: tensor<128x512xbf16>,
 // CHECK-LABEL: @mlp_transpose_a_b
 func.func @mlp_transpose_a_b(%in: tensor<512x128xbf16>, 
                %weight0: tensor<256x512xbf16>, %bias0: tensor<256xbf16>) -> tensor<128x256xbf16> {
+  // CHECK: [[MM1:%.+]] = onednn_graph.matmul 
+  // CHECK: {transpose_a = true, transpose_b = true}
+  // CHECK-NEXT: [[RL1:%.+]] = onednn_graph.relu [[MM1]]
+  // CHECK-NEXT: return [[RL1]]
   %0 = onednn_graph.matmul %in, %weight0, %bias0 {transpose_a = true, transpose_b = true}  
        : (tensor<512x128xbf16>, tensor<256x512xbf16>, tensor<256xbf16>) -> tensor<128x256xbf16>
   %1 = onednn_graph.relu %0 : (tensor<128x256xbf16>) -> tensor<128x256xbf16>