derek.holloway
7502bf64a4
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194 lines
15 KiB
MLIR
194 lines
15 KiB
MLIR
// RUN: mlir-opt -transform-preload-library='transform-library-paths=%p/transpose-matmul-a.mlir' -transform-interpreter -split-input-file %s | FileCheck %s --check-prefixes=CHECK,TRANSPOSE-A
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// RUN: mlir-opt -transform-preload-library='transform-library-paths=%p/transpose-matmul-b.mlir' -transform-interpreter -split-input-file %s | FileCheck %s --check-prefixes=CHECK,TRANSPOSE-B
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// TRANSPOSE-A-DAG: #[[$MA:.*]] = affine_map<(d0, d1, d2) -> (d2, d0)>
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// TRANSPOSE-A-DAG: #[[$MB:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)>
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// TRANSPOSE-A-DAG: #[[$MC:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// TRANSPOSE-A-DAG: #[[$BMA:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d3, d1)>
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// TRANSPOSE-A-DAG: #[[$BMB:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>
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// TRANSPOSE-A-DAG: #[[$BMC:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
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// TRANSPOSE-B-DAG: #[[$MA:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
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// TRANSPOSE-B-DAG: #[[$MB:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)>
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// TRANSPOSE-B-DAG: #[[$MC:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// TRANSPOSE-B-DAG: #[[$BMA:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
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// TRANSPOSE-B-DAG: #[[$BMB:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
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// TRANSPOSE-B-DAG: #[[$BMC:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
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// CHECK-LABEL: func.func @matmul_static(
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// CHECK-SAME: %[[A:.*]]: tensor<16x8xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<8x16xf32>) -> tensor<16x16xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C_INIT:.*]] = tensor.empty() : tensor<16x16xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<16x16xf32>) -> tensor<16x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty() : tensor<8x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<16x8xf32>) outs(%[[A_TRANSP_INIT]] : tensor<8x16xf32>) permutation = [1, 0]
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// TRANSPOSE-A: %[[C:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<8x16xf32>, tensor<8x16xf32>) outs(%[[C_ZERO]] : tensor<16x16xf32>) -> tensor<16x16xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty() : tensor<16x8xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<8x16xf32>) outs(%[[B_TRANSP_INIT]] : tensor<16x8xf32>) permutation = [1, 0]
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// TRANSPOSE-B: %[[C:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<16x8xf32>, tensor<16x8xf32>) outs(%[[C_ZERO]] : tensor<16x16xf32>) -> tensor<16x16xf32>
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// CHECK: return %[[C]] : tensor<16x16xf32>
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// CHECK: }
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func.func @matmul_static(%A: tensor<16x8xf32>, %B: tensor<8x16xf32>) -> (tensor<16x16xf32>) {
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%cst = arith.constant 0.0 : f32
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%init = tensor.empty() : tensor<16x16xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<16x16xf32>) -> tensor<16x16xf32>
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%0 = linalg.matmul ins(%A, %B : tensor<16x8xf32>, tensor<8x16xf32>) outs(%C : tensor<16x16xf32>) -> tensor<16x16xf32>
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return %0 : tensor<16x16xf32>
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}
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//-----
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// CHECK-LABEL: func.func @matmul_dynamic(
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// CHECK-SAME: %[[A:.*]]: tensor<?x?xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[A_DIM0:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x?xf32>
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// CHECK: %[[B_DIM1:.*]] = tensor.dim %[[B]], %[[C1]] : tensor<?x?xf32>
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// CHECK: %[[C_INIT:.*]] = tensor.empty(%[[A_DIM0]], %[[B_DIM1]]) : tensor<?x?xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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// TRANSPOSE-A: %[[A_DIM1:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<?x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty(%[[A_DIM1]], %[[A_DIM0]]) : tensor<?x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<?x?xf32>) outs(%[[A_TRANSP_INIT]] : tensor<?x?xf32>) permutation = [1, 0]
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// TRANSPOSE-A: %[[C:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[C_ZERO]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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// TRANSPOSE-B: %[[B_DIM0:.*]] = tensor.dim %[[B]], %[[C0]] : tensor<?x?xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty(%[[B_DIM1]], %[[B_DIM0]]) : tensor<?x?xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<?x?xf32>) outs(%[[B_TRANSP_INIT]] : tensor<?x?xf32>) permutation = [1, 0]
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// TRANSPOSE-B: %[[C:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[C_ZERO]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK: return %[[C]] : tensor<?x?xf32>
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// CHECK: }
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func.func @matmul_dynamic(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>) -> (tensor<?x?xf32>) {
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%cst = arith.constant 0.0 : f32
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d0 = tensor.dim %A, %c0 : tensor<?x?xf32>
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%d1 = tensor.dim %B, %c1 : tensor<?x?xf32>
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%init = tensor.empty(%d0, %d1) : tensor<?x?xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
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%0 = linalg.matmul ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%C : tensor<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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//-----
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// CHECK-LABEL: func.func @matmul_mixed(
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// CHECK-SAME: %[[A:.*]]: tensor<?x8xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<8x16xf32>) -> tensor<?x16xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[A_DIM0:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x8xf32>
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// CHECK: %[[C_INIT:.*]] = tensor.empty(%[[A_DIM0]]) : tensor<?x16xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<?x16xf32>) -> tensor<?x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty(%[[A_DIM0]]) : tensor<8x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<?x8xf32>) outs(%[[A_TRANSP_INIT]] : tensor<8x?xf32>) permutation = [1, 0]
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// TRANSPOSE-A: %[[B0:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<8x?xf32>, tensor<8x16xf32>) outs(%[[C_ZERO]] : tensor<?x16xf32>) -> tensor<?x16xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty() : tensor<16x8xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<8x16xf32>) outs(%[[B_TRANSP_INIT]] : tensor<16x8xf32>) permutation = [1, 0]
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// TRANSPOSE-B: %[[B0:.*]] = linalg.matmul indexing_maps = [#[[$MA]], #[[$MB]], #[[$MC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<?x8xf32>, tensor<16x8xf32>) outs(%[[C_ZERO]] : tensor<?x16xf32>) -> tensor<?x16xf32>
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// CHECK: return %[[B0]] : tensor<?x16xf32>
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// CHECK: }
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func.func @matmul_mixed(%A: tensor<?x8xf32>, %B: tensor<8x16xf32>) -> (tensor<?x16xf32>) {
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%cst = arith.constant 0.0 : f32
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d0 = tensor.dim %A, %c0 : tensor<?x8xf32>
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%init = tensor.empty(%d0) : tensor<?x16xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<?x16xf32>) -> tensor<?x16xf32>
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%0 = linalg.matmul ins(%A, %B : tensor<?x8xf32>, tensor<8x16xf32>) outs(%C : tensor<?x16xf32>) -> tensor<?x16xf32>
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return %0 : tensor<?x16xf32>
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}
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//-----
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// CHECK-LABEL: func.func @batch_matmul_static(
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// CHECK-SAME: %[[A:.*]]: tensor<2x16x8xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<2x8x16xf32>) -> tensor<2x16x16xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C_INIT:.*]] = tensor.empty() : tensor<2x16x16xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<2x16x16xf32>) -> tensor<2x16x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty() : tensor<2x8x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<2x16x8xf32>) outs(%[[A_TRANSP_INIT]] : tensor<2x8x16xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-A: %[[C:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<2x8x16xf32>, tensor<2x8x16xf32>) outs(%[[C_ZERO]] : tensor<2x16x16xf32>) -> tensor<2x16x16xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty() : tensor<2x16x8xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<2x8x16xf32>) outs(%[[B_TRANSP_INIT]] : tensor<2x16x8xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-B: %[[C:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<2x16x8xf32>, tensor<2x16x8xf32>) outs(%[[C_ZERO]] : tensor<2x16x16xf32>) -> tensor<2x16x16xf32>
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// CHECK: return %[[C]] : tensor<2x16x16xf32>
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// CHECK: }
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func.func @batch_matmul_static(%A: tensor<2x16x8xf32>, %B: tensor<2x8x16xf32>) -> (tensor<2x16x16xf32>) {
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%cst = arith.constant 0.0 : f32
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%init = tensor.empty() : tensor<2x16x16xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<2x16x16xf32>) -> tensor<2x16x16xf32>
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%0 = linalg.batch_matmul ins(%A, %B : tensor<2x16x8xf32>, tensor<2x8x16xf32>) outs(%C : tensor<2x16x16xf32>) -> tensor<2x16x16xf32>
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return %0 : tensor<2x16x16xf32>
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}
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//-----
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// CHECK-LABEL: func.func @batch_matmul_dynamic(
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// CHECK-SAME: %[[A:.*]]: tensor<?x?x?xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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// CHECK: %[[A_DIM0:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x?x?xf32>
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// CHECK: %[[A_DIM1:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<?x?x?xf32>
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// CHECK: %[[B_DIM2:.*]] = tensor.dim %[[B]], %[[C2]] : tensor<?x?x?xf32>
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// CHECK: %[[C_INIT:.*]] = tensor.empty(%[[A_DIM0]], %[[A_DIM1]], %[[B_DIM2]]) : tensor<?x?x?xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
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// TRANSPOSE-A: %[[A_DIM2:.*]] = tensor.dim %[[A]], %[[C2]] : tensor<?x?x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty(%[[A_DIM0]], %[[A_DIM2]], %[[A_DIM1]]) : tensor<?x?x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<?x?x?xf32>) outs(%[[A_TRANSP_INIT]] : tensor<?x?x?xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-A: %[[C:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[C_ZERO]] : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
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// TRANSPOSE-B: %[[B_DIM0:.*]] = tensor.dim %[[B]], %[[C0]] : tensor<?x?x?xf32>
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// TRANSPOSE-B: %[[B_DIM1:.*]] = tensor.dim %[[B]], %[[C1]] : tensor<?x?x?xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty(%[[B_DIM0]], %[[B_DIM2]], %[[B_DIM1]]) : tensor<?x?x?xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<?x?x?xf32>) outs(%[[B_TRANSP_INIT]] : tensor<?x?x?xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-B: %[[C:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[C_ZERO]] : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
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// CHECK: return %[[C]] : tensor<?x?x?xf32>
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// CHECK: }
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func.func @batch_matmul_dynamic(%A: tensor<?x?x?xf32>, %B: tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>) {
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%cst = arith.constant 0.0 : f32
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c2 = arith.constant 2 : index
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%d0 = tensor.dim %A, %c0 : tensor<?x?x?xf32>
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%d1 = tensor.dim %A, %c1 : tensor<?x?x?xf32>
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%d2 = tensor.dim %B, %c2 : tensor<?x?x?xf32>
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%init = tensor.empty(%d0, %d1, %d2) : tensor<?x?x?xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
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%0 = linalg.batch_matmul ins(%A, %B : tensor<?x?x?xf32>, tensor<?x?x?xf32>) outs(%C : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
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return %0 : tensor<?x?x?xf32>
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}
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//-----
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// CHECK-LABEL: func.func @batch_matmul_mixed(
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// CHECK-SAME: %[[A:.*]]: tensor<2x?x8xf32>,
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// CHECK-SAME: %[[B:.*]]: tensor<2x8x16xf32>) -> tensor<2x?x16xf32> {
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// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[A_DIM1:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<2x?x8xf32>
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// CHECK: %[[C_INIT:.*]] = tensor.empty(%[[A_DIM1]]) : tensor<2x?x16xf32>
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// CHECK: %[[C_ZERO:.*]] = linalg.fill ins(%[[C0_F32]] : f32) outs(%[[C_INIT]] : tensor<2x?x16xf32>) -> tensor<2x?x16xf32>
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// TRANSPOSE-A: %[[A_TRANSP_INIT:.*]] = tensor.empty(%[[A_DIM1]]) : tensor<2x8x?xf32>
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// TRANSPOSE-A: %[[A_TRANSP:.*]] = linalg.transpose ins(%[[A]] : tensor<2x?x8xf32>) outs(%[[A_TRANSP_INIT]] : tensor<2x8x?xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-A: %[[B0:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A_TRANSP]], %[[B]] : tensor<2x8x?xf32>, tensor<2x8x16xf32>) outs(%[[C_ZERO]] : tensor<2x?x16xf32>) -> tensor<2x?x16xf32>
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// TRANSPOSE-B: %[[B_TRANSP_INIT:.*]] = tensor.empty() : tensor<2x16x8xf32>
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// TRANSPOSE-B: %[[B_TRANSP:.*]] = linalg.transpose ins(%[[B]] : tensor<2x8x16xf32>) outs(%[[B_TRANSP_INIT]] : tensor<2x16x8xf32>) permutation = [0, 2, 1]
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// TRANSPOSE-B: %[[B0:.*]] = linalg.batch_matmul indexing_maps = [#[[$BMA]], #[[$BMB]], #[[$BMC]]] ins(%[[A]], %[[B_TRANSP]] : tensor<2x?x8xf32>, tensor<2x16x8xf32>) outs(%[[C_ZERO]] : tensor<2x?x16xf32>) -> tensor<2x?x16xf32>
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// CHECK: return %[[B0]] : tensor<2x?x16xf32>
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// CHECK: }
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func.func @batch_matmul_mixed(%A: tensor<2x?x8xf32>, %B: tensor<2x8x16xf32>) -> (tensor<2x?x16xf32>) {
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%cst = arith.constant 0.0 : f32
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d1 = tensor.dim %A, %c1 : tensor<2x?x8xf32>
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%init = tensor.empty(%d1) : tensor<2x?x16xf32>
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%C = linalg.fill ins(%cst : f32) outs(%init : tensor<2x?x16xf32>) -> tensor<2x?x16xf32>
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%0 = linalg.batch_matmul ins(%A, %B : tensor<2x?x8xf32>, tensor<2x8x16xf32>) outs(%C : tensor<2x?x16xf32>) -> tensor<2x?x16xf32>
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return %0 : tensor<2x?x16xf32>
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}
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