Adept | Машинное обучение | Открытое ПО

Сделал детальное представление вычислительного графа и теперь его можно преобразовать в другое IR. Вот например для такого небольшого перцептрона: class MLP : public Module { public: MLP() : linear1(28 * 28, 1024), linear2(1024, 512), linear3(512, 256), linear4(256, 10) { ... } Tensor forward(Tensor input) { auto out = l1(input); out = relu(out); out = l2(out); out = relu(out); out = l3(out); out = relu(out); out = l4(out); return out; } ... }; Получается такое представление в StableHLO формате: module { func.func @main(%arg0: tensor<784x784xf32>, %arg1: tensor<1024x784xf32>, %arg2: tensor<1024xf32>, %arg3: tensor<512x1024xf32>, %arg4: tensor<512xf32>, %arg5: tensor<256x512xf32>, %arg6: tensor<256xf32>, %arg7: tensor<10x256xf32>, %arg8: tensor<10xf32>) -> tensor<784x10xf32> { %0 = stablehlo.transpose %arg1, permutation = [1, 0] : (tensor<1024x784xf32>) -> tensor<784x1024xf32> %1 = stablehlo.dot_general %arg0, %0, contracting_dims = [1] x [0] : (tensor<784x784xf32>, tensor<784x1024xf32>) -> tensor<784x1024xf32> %2 = stablehlo.broadcast_in_dim %arg2, dims = [1] : (tensor<1024xf32>) -> tensor<784x1024xf32> %3 = stablehlo.add %1, %2 : tensor<784x1024xf32> %4 = stablehlo.constant dense<0> : tensor<784x1024xf32> %5 = stablehlo.maximum %3, %4 : tensor<784x1024xf32> %6 = stablehlo.transpose %arg3, permutation = [1, 0] : (tensor<512x1024xf32>) -> tensor<1024x512xf32> %7 = stablehlo.dot_general %5, %6, contracting_dims = [1] x [0] : (tensor<784x1024xf32>, tensor<1024x512xf32>) -> tensor<784x512xf32> %8 = stablehlo.broadcast_in_dim %arg4, dims = [1] : (tensor<512xf32>) -> tensor<784x512xf32> %9 = stablehlo.add %7, %8 : tensor<784x512xf32> %10 = stablehlo.constant dense<0> : tensor<784x512xf32> %11 = stablehlo.maximum %9, %10 : tensor<784x512xf32> %12 = stablehlo.transpose %arg5, permutation = [1, 0] : (tensor<256x512xf32>) -> tensor<512x256xf32> %13 = stablehlo.dot_general %11, %12, contracting_dims = [1] x [0] : (tensor<784x512xf32>, tensor<512x256xf32>) -> tensor<784x256xf32> %14 = stablehlo.broadcast_in_dim %arg6, dims = [1] : (tensor<256xf32>) -> tensor<784x256xf32> %15 = stablehlo.add %13, %14 : tensor<784x256xf32> %16 = stablehlo.constant dense<0> : tensor<784x256xf32> %17 = stablehlo.maximum %15, %16 : tensor<784x256xf32> %18 = stablehlo.transpose %arg7, permutation = [1, 0] : (tensor<10x256xf32>) -> tensor<256x10xf32> %19 = stablehlo.dot_general %17, %18, contracting_dims = [1] x [0] : (tensor<784x256xf32>, tensor<256x10xf32>) -> tensor<784x10xf32> %20 = stablehlo.broadcast_in_dim %arg8, dims = [1] : (tensor<10xf32>) -> tensor<784x10xf32> %21 = stablehlo.add %19, %20 : tensor<784x10xf32> return %21 : tensor<784x10xf32> } } Далее его надо преобразовать тензорным компиляторм. Эксперименты продолжаются.