derek.holloway
7502bf64a4
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199 lines
14 KiB
MLIR
199 lines
14 KiB
MLIR
// RUN: mlir-opt -split-input-file -transform-interpreter -cse %s | FileCheck %s
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// Test the same patterns on generic convolution ops by first generalizing the
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// named ops. This avoids duplicating lit tests for linalg.generic conv ops.
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// RUN: mlir-opt --split-input-file --linalg-generalize-named-ops --transform-interpreter -cse %s | FileCheck %s
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///----------------------------------------------------------------------------------------
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/// ATM, all tests in this file require masking. As the support for masking is
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/// limited to depthwise convs, only that variant of convolutions is tested
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/// ATM.
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///
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/// TODO1: Add more types of convolutions (transform.structured.vectorize
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/// usually doesn't require masking when vector sizes are not specified)
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/// TODO2: Add support for masking non-depthwise convs.
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///----------------------------------------------------------------------------------------
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func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor(%input: tensor<1x8x?xi8>,
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%filter: tensor<1x?xi8>,
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%output: tensor<1x8x?xi8>) -> (tensor<1x8x?xi8>) {
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%res = linalg.depthwise_conv_1d_nwc_wc
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{dilations = dense<1> : vector<1xi64>,
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strides = dense<1> : vector<1xi64>}
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ins(%input, %filter : tensor<1x8x?xi8>, tensor<1x?xi8>)
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outs(%output : tensor<1x8x?xi8>) -> tensor<1x8x?xi8>
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return %res : tensor<1x8x?xi8>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.depthwise_conv_1d_nwc_wc", "linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
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transform.structured.vectorize %0 vector_sizes [1, 8, 4, 1] : !transform.any_op
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transform.yield
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}
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}
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// CHECK-LABEL: func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor(
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// CHECK-SAME: %[[INPUT:.*]]: tensor<1x8x?xi8>,
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// CHECK-SAME: %[[FILTER:.*]]: tensor<1x?xi8>,
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// CHECK-SAME: %[[OUTPUT:.*]]: tensor<1x8x?xi8>) -> tensor<1x8x?xi8> {
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[PAD:.*]] = arith.constant 0 : i8
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/// Create a mask for the input tensor
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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// CHECK: %[[CH_DIM_IN:.*]] = tensor.dim %[[INPUT]], %[[C2]] : tensor<1x8x?xi8>
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// CHECK: %[[C8:.*]] = arith.constant 8 : index
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// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_IN]] : vector<1x8x4xi1>
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/// Read the input tensor
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// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
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/// Create a mask for the filter tensor
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// CHECK: %[[CH_DIM_FLT:.*]] = tensor.dim %[[FILTER]], %[[C1]] : tensor<1x?xi8>
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// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C1]], %[[CH_DIM_FLT]] : vector<1x4xi1>
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/// Read the filter tensor
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// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : tensor<1x?xi8>, vector<1x4xi8> } : vector<1x4xi1> -> vector<1x4xi8>
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/// Create a mask for the output tensor
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// CHECK: %[[CH_DIM_OUT:.*]] = tensor.dim %[[OUTPUT]], %[[C2]] : tensor<1x8x?xi8>
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// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_OUT]] : vector<1x8x4xi1>
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// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
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/// Convolution
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// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x4xi8> to vector<1x8x4xi8>
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// CHECK: %[[FLT_1:.*]] = vector.extract %[[VEC_FLT]][0] : vector<4xi8> from vector<1x4xi8>
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// CHECK: %[[OUT_1:.*]] = vector.extract_strided_slice %[[VEC_OUT]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x4xi8> to vector<1x8x4xi8>
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// CHECK: %[[FLT_1_B:.*]] = vector.broadcast %[[FLT_1]] : vector<4xi8> to vector<1x8x4xi8>
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// CHECK: %[[MULI:.*]] = arith.muli %[[IN_1]], %[[FLT_1_B]] : vector<1x8x4xi8>
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// CHECK: %[[ADDI:.*]] = arith.addi %[[MULI]], %[[OUT_1]] : vector<1x8x4xi8>
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// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[ADDI]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<1x8x4xi8> into vector<1x8x4xi8>
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// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x8x4xi8>, tensor<1x8x?xi8> } : vector<1x8x4xi1> -> tensor<1x8x?xi8>
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// CHECK: return %[[OUT]] : tensor<1x8x?xi8>
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// -----
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func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor_scalable(
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%input: tensor<1x8x?xi8>,
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%filter: tensor<1x?xi8>,
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%output: tensor<1x8x?xi8>) -> (tensor<1x8x?xi8>) {
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%res = linalg.depthwise_conv_1d_nwc_wc
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{dilations = dense<1> : vector<1xi64>,
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strides = dense<1> : vector<1xi64>}
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ins(%input, %filter : tensor<1x8x?xi8>, tensor<1x?xi8>)
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outs(%output : tensor<1x8x?xi8>) -> tensor<1x8x?xi8>
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return %res : tensor<1x8x?xi8>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.depthwise_conv_1d_nwc_wc", "linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
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transform.structured.vectorize %0 vector_sizes [1, 8, [4], 1] : !transform.any_op
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transform.yield
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}
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}
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// CHECK-LABEL: func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor_scalable(
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// CHECK-SAME: %[[INPUT:.*]]: tensor<1x8x?xi8>,
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// CHECK-SAME: %[[FILTER:.*]]: tensor<1x?xi8>,
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// CHECK-SAME: %[[OUTPUT:.*]]: tensor<1x8x?xi8>) -> tensor<1x8x?xi8> {
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[PAD:.*]] = arith.constant 0 : i8
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/// Create a mask for the input tensor
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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// CHECK: %[[CH_DIM_IN:.*]] = tensor.dim %[[INPUT]], %[[C2]] : tensor<1x8x?xi8>
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// CHECK: %[[C8:.*]] = arith.constant 8 : index
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// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_IN]] : vector<1x8x[4]xi1>
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/// Read the input tensor
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// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
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/// Create a mask for the filter tensor
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// CHECK: %[[CH_DIM_FLT:.*]] = tensor.dim %[[FILTER]], %[[C1]] : tensor<1x?xi8>
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// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C1]], %[[CH_DIM_FLT]] : vector<1x[4]xi1>
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/// Read the filter tensor
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// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : tensor<1x?xi8>, vector<1x[4]xi8> } : vector<1x[4]xi1> -> vector<1x[4]xi8>
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/// Create a mask for the output tensor
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// CHECK: %[[CH_DIM_OUT:.*]] = tensor.dim %[[OUTPUT]], %[[C2]] : tensor<1x8x?xi8>
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// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_OUT]] : vector<1x8x[4]xi1>
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/// Read the output tensor
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// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
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/// Convolution
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// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x[4]xi8> to vector<1x8x[4]xi8>
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// CHECK: %[[FLT_1:.*]] = vector.extract %[[VEC_FLT]][0] : vector<[4]xi8> from vector<1x[4]xi8>
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// CHECK: %[[OUT_1:.*]] = vector.extract_strided_slice %[[VEC_OUT]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x[4]xi8> to vector<1x8x[4]xi8>
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// CHECK: %[[FLT_1_B:.*]] = vector.broadcast %[[FLT_1]] : vector<[4]xi8> to vector<1x8x[4]xi8>
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// CHECK: %[[MULI:.*]] = arith.muli %[[IN_1]], %[[FLT_1_B]] : vector<1x8x[4]xi8>
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// CHECK: %[[ADDI:.*]] = arith.addi %[[MULI]], %[[OUT_1]] : vector<1x8x[4]xi8>
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// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[ADDI]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<1x8x[4]xi8> into vector<1x8x[4]xi8>
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// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x8x[4]xi8>, tensor<1x8x?xi8> } : vector<1x8x[4]xi1> -> tensor<1x8x?xi8>
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// CHECK: return %[[OUT]] : tensor<1x8x?xi8>
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// -----
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func.func @depthwise_conv1d_nwc_wc_3x5x4xf32_memref_dilation_2(
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%input: memref<3x5x?xf32>,
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%filter: memref<2x?xf32>,
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%output: memref<3x2x?xf32>) {
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linalg.depthwise_conv_1d_nwc_wc
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{dilations = dense<2> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
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ins(%input, %filter : memref<3x5x?xf32>, memref<2x?xf32>)
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outs(%output : memref<3x2x?xf32>)
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return
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.depthwise_conv_1d_nwc_wc", "linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
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transform.structured.vectorize %0 vector_sizes [3, 2, [4], 2] : !transform.any_op
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transform.yield
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}
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}
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// CHECK-LABEL: func.func @depthwise_conv1d_nwc_wc_3x5x4xf32_memref_dilation_2(
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// CHECK-SAME: %[[INPUT:.*]]: memref<3x5x?xf32>,
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// CHECK-SAME: %[[FILTER:.*]]: memref<2x?xf32>,
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// CHECK-SAME: %[[OUTPUT:.*]]: memref<3x2x?xf32>) {
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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/// Create a mask for the input tensor
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// CHECK: %[[CH_DIM_IN:.*]] = memref.dim %[[INPUT]], %[[C2]] : memref<3x5x?xf32>
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// CHECK: %[[C3:.*]] = arith.constant 3 : index
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// CHECK: %[[C5:.*]] = arith.constant 5 : index
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// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C3]], %[[C5]], %[[CH_DIM_IN]] : vector<3x4x[4]xi1>
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/// Read the input tensor
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// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : memref<3x5x?xf32>, vector<3x4x[4]xf32> } : vector<3x4x[4]xi1> -> vector<3x4x[4]xf32>
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/// Create a mask for the filter tensor
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// CHECK: %[[CH_DIM_FLT:.*]] = memref.dim %[[FILTER]], %[[C1]] : memref<2x?xf32>
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// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C2]], %[[CH_DIM_FLT]] : vector<2x[4]xi1>
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/// Read the filter tensor
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// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : memref<2x?xf32>, vector<2x[4]xf32> } : vector<2x[4]xi1> -> vector<2x[4]xf32>
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/// Create a mask for the output tensor
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// CHECK: %[[CH_DIM_OUT:.*]] = memref.dim %[[OUTPUT]], %[[C2]] : memref<3x2x?xf32>
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// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C3]], %[[C2]], %[[CH_DIM_OUT]] : vector<3x2x[4]xi1>
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/// Read the output tensor
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// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : memref<3x2x?xf32>, vector<3x2x[4]xf32> } : vector<3x2x[4]xi1> -> vector<3x2x[4]xf32>
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/// Convolution
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// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [3, 2, 4], strides = [1, 1, 1]} : vector<3x4x[4]xf32> to vector<3x2x[4]xf32>
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// CHECK: %[[IN_2:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 2, 0], sizes = [3, 2, 4], strides = [1, 1, 1]} : vector<3x4x[4]xf32> to vector<3x2x[4]xf32>
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// CHECK: %[[FLT_1:.*]] = vector.extract %[[VEC_FLT]][0] : vector<[4]xf32> from vector<2x[4]xf32>
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// CHECK: %[[FLT_2:.*]] = vector.extract %[[VEC_FLT]][1] : vector<[4]xf32> from vector<2x[4]xf32>
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// CHECK: %[[OUT_1:.*]] = vector.extract_strided_slice %[[VEC_OUT]] {offsets = [0, 0, 0], sizes = [3, 2, 4], strides = [1, 1, 1]} : vector<3x2x[4]xf32> to vector<3x2x[4]xf32>
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// CHECK: %[[FLT_1_B:.*]] = vector.broadcast %[[FLT_1]] : vector<[4]xf32> to vector<3x2x[4]xf32>
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// CHECK: %[[FMA_1:.*]] = vector.fma %[[IN_1]], %[[FLT_1_B]], %[[OUT_1]] : vector<3x2x[4]xf32>
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// CHECK: %[[FLT_2_B:.*]] = vector.broadcast %[[FLT_2]] : vector<[4]xf32> to vector<3x2x[4]xf32>
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// CHECK: %[[FMA_2:.*]] = vector.fma %[[IN_2]], %[[FLT_2_B]], %[[FMA_1]] : vector<3x2x[4]xf32>
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// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[FMA_2]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<3x2x[4]xf32> into vector<3x2x[4]xf32>
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// CHECK: vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<3x2x[4]xf32>, memref<3x2x?xf32> } : vector<3x2x[4]xi1>
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