[TF] Add bullet operator (•) for matrix multiplication (#17173)

Add `•` operator for matmul, and remove `Tensor.dot` and `⊗` completely.

Author: rxwei

stdlib/public/TensorFlow/Ops.swift

@@ -265,21 +265,14 @@ public func matmul<Scalar : Numeric>(
   return Raw.matMul(left, right)
 }
 
-infix operator ⊗ : MultiplicationPrecedence
+infix operator • : MultiplicationPrecedence
 
 public extension Tensor where Scalar : Numeric {
-  @_inlineable @inline(__always)
-  @available(*, renamed: "matmul(_:_:)")
-  func dot(_ other: Tensor) -> Tensor {
-    return matmul(self, other)
-  }
-
   /// Performs matrix multiplication between two tensors and produces the
   /// result.
   @_inlineable @inline(__always)
-  @available(*, renamed: "matmul(_:_:)")
-  static func ⊗ (lhs: Tensor, rhs: Tensor) -> Tensor {
-    return lhs.dot(rhs)
+  static func • (lhs: Tensor, rhs: Tensor) -> Tensor {
+    return matmul(lhs, rhs)
   }
 }
 

test/TensorFlow/crashers.swift

@@ -33,7 +33,7 @@ public func postdom_crash1(w1: Tensor<Float>, inputBatch: Tensor<Float>) {
   // expected-warning @-2 {{'inputBatch' implicitly copied to the accelerator}}
   let iterationCount = 1000
   for _ in 0..<iterationCount {
-    _ = inputBatch ⊗ w1  // expected-note 2 {{value used here}}
+    _ = inputBatch • w1  // expected-note 2 {{value used here}}
   }
 }
 
@@ -91,10 +91,10 @@ public func testStraightLineXORTraining() {
 
   // Training loop
   for _ in 0..<iterationCount {
-    let mmul1 = inputBatch ⊗ w1
+    let mmul1 = inputBatch • w1
     let l1 = mmul1 + b1
     let o1 = sigmoid(l1)
-    let mmul2 = o1 ⊗ w2
+    let mmul2 = o1 • w2
     let l2 = mmul2 + b2
     let pred = sigmoid(l2)
 
@@ -109,15 +109,15 @@ public func testStraightLineXORTraining() {
     let dL2 = dPred * pred * (1 - pred)
     let dMmul2 = dL2
     let dB2 = dL2
-    let dO1 = dMmul2 ⊗ w2.transposed(withPermutations: 1, 0)
-    let dW2 = o1.transposed(withPermutations: 1, 0) ⊗ dMmul2
+    let dO1 = dMmul2 • w2.transposed(withPermutations: 1, 0)
+    let dW2 = o1.transposed(withPermutations: 1, 0) • dMmul2
     let dL1 = dO1 * l1 * (1 - l1)
     let dMmul1 = dL1
     let dB1 = dL1
 
     // Statically detected shape mismatch!
     // expected-error @+1 {{(op: 'MatMul') with input shapes: [4,2], [4,4]}}
-    let dW1 = inputBatch ⊗ dMmul1
+    let dW1 = inputBatch • dMmul1
 
     // Descent
     w1 -= (dW1 * learningRate)

test/TensorFlow/no_copy.swift

@@ -167,8 +167,8 @@ struct Classifier {
   var b2 = Tensor<Float>(zeros: [1, 10])
 
   func prediction(for input: Tensor<Float>) -> Tensor<Float> {
-    let h1 = sigmoid(input ⊗ w1 + b1)
-    return sigmoid(h1 ⊗ w2 + b2)
+    let h1 = sigmoid(input • w1 + b1)
+    return sigmoid(h1 • w2 + b2)
   }
 
   mutating func train(images: Tensor<Float>, labels: Tensor<Float>,
@@ -177,17 +177,17 @@ struct Classifier {
     var epochCount = epochCount
     repeat {
       // Forward pass
-      let z1 = images ⊗ w1 + b1
+      let z1 = images • w1 + b1
       let h1 = sigmoid(z1)
-      let z2 = h1 ⊗ w2 + b2
+      let z2 = h1 • w2 + b2
       let pred = sigmoid(z2)
 
       // Backward pass
       let dz2 = pred - labels
-      let dw2 = h1.transposed(withPermutations: 1, 0) ⊗ dz2
+      let dw2 = h1.transposed(withPermutations: 1, 0) • dz2
       let db2 = dz2.sum(squeezingAxes: 0)
-      let dz1 = dz2.dot(w2.transposed(withPermutations: 1, 0)) * h1 * (1 - h1)
-      let dw1 = images.transposed(withPermutations: 1, 0) ⊗ dz1
+      let dz1 = matmul(dz2, w2.transposed(withPermutations: 1, 0)) * h1 * (1 - h1)
+      let dw1 = images.transposed(withPermutations: 1, 0) • dz1
       let db1 = dz1.sum(squeezingAxes: 0)
 
       // Gradient descent

test/TensorFlowRuntime/tensor_debuglog.swift

@@ -26,7 +26,7 @@ TensorTests.testAllBackends("XWPlusB") {
   // Shape: 2
   let b = Tensor<Float>([0.5, 0.5])
   // Do xW+b!
-  let result = x ⊗ w + b
+  let result = x • w + b
   expectEqual([1, 2], result.shape)
   expectEqual([12.5, 6.5], result.scalars)
 }

test/TensorFlowRuntime/tensor_xla_debuglog.swift

@@ -27,7 +27,7 @@ XLATests.test("XWPlusB_XLA") {
   // Shape: 2
   let b = Tensor<Float>([0.5, 0.5])
   // Do xW+b!
-  let result = x ⊗ w + b
+  let result = x • w + b
   expectEqual([1, 2], result.shape)
   expectEqual([12.5, 6.5], result.scalars)
 #endif