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[TOSA] Fix avgpool2d accum in wider type #80849

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Merged
merged 1 commit into from
Feb 7, 2024
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[TOSA] Fix avgpool2d accum in wider type #80849

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4 changes: 4 additions & 0 deletions mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -890,6 +890,10 @@ class AvgPool2dConverter : public OpRewritePattern<tosa::AvgPool2dOp> {
auto countF = rewriter.create<arith::SIToFPOp>(loc, accETy, count);
poolVal = rewriter.create<arith::DivFOp>(loc, poolVal, countF)
->getResult(0);
if (accETy.getIntOrFloatBitWidth() >
resultETy.getIntOrFloatBitWidth())
poolVal =
rewriter.create<arith::TruncFOp>(loc, resultETy, poolVal);
} else {

// If we have quantization information we need to apply an offset
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91 changes: 91 additions & 0 deletions mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
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Expand Up @@ -306,6 +306,97 @@ func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>)

// -----

// CHECK-LABEL: @avg_pool_f16_f32acc
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @avg_pool_f16_f32acc(%arg0: tensor<1x6x34x62xf16>) -> (tensor<1x5x33x62xf16>) {
// Apply padding to the input:
// CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f16
// CHECK: %[[PAD:.+]] = tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
// CHECK: tensor.yield %[[F0]] : f16

// Fill the pooling target:
// CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<1x5x33x62xf32>
// CHECK: %[[FILL:.+]] = linalg.fill ins(%[[F0]] : f32) outs(%[[EMPTY]] : tensor<1x5x33x62xf32>)

// Compute the sum padding:
// CHECK: %[[KERNEL:.+]] = tensor.empty() : tensor<4x4xf32>
// CHECK: %[[POOL:.+]] = linalg.pooling_nhwc_sum
// CHECK-SAME: dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>}
// CHECK-SAME: ins(%[[PAD]], %[[KERNEL]] : tensor<1x8x36x62xf16>, tensor<4x4xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<1x5x33x62xf32>)

// Compute dimension based constants:
// CHECK: %[[I1:.+]] = arith.constant 1 : index
// CHECK: %[[DIM1:.+]] = tensor.dim %[[POOL]], %[[I1]]
// CHECK: %[[I2:.+]] = arith.constant 2 : index
// CHECK: %[[DIM2:.+]] = tensor.dim %[[POOL]], %[[I2]]
// CHECK: %[[ONE:.+]] = arith.constant 1 : index
// CHECK: %[[HEIGHT:.+]] = arith.subi %[[DIM1]], %[[ONE]] : index
// CHECK: %[[WIDTH:.+]] = arith.subi %[[DIM2]], %[[ONE]] : index

// Divide the sum pooling by the number of summed values.
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<1x5x33x62xf16>
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[POOL]] : tensor<1x5x33x62xf32>)
// CHECK-SAME: outs(%[[EMPTY]] : tensor<1x5x33x62xf16>)
// CHECK: ^bb0(%[[IN:.+]]: f32, %{{.+}}: f16)
// CHECK: %[[ZERO:.+]] = arith.constant 0

// Compute how much of the height does not include padding:
// CHECK: %[[STRIDE:.+]] = arith.constant 1
// CHECK: %[[KSIZE:.+]] = arith.constant 4
// CHECK: %[[START:.+]] = linalg.index 1
// CHECK: %[[END:.+]] = arith.subi %[[HEIGHT]], %[[START]]
// CHECK: %[[SRC_START:.+]] = arith.muli %[[START]], %[[STRIDE]]
// CHECK: %[[SRC_END:.+]] = arith.muli %[[END]], %[[STRIDE]]
// CHECK: %[[PAD_START:.+]] = arith.constant 1
// CHECK: %[[START_SUB:.+]] = arith.subi %[[SRC_START]], %[[PAD_START]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[START_SUB]], %[[ZERO]]
// CHECK: %[[OFFSET:.+]] = arith.select %[[CMP]], %[[START_SUB]], %[[ZERO]]
// CHECK: %[[START_OFFSET:.+]] = arith.addi %[[KSIZE]], %[[OFFSET]]
// CHECK: %[[PAD_END:.+]] = arith.constant 1
// CHECK: %[[END_SUB:.+]] = arith.subi %[[SRC_END]], %[[PAD_END]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[END_SUB]], %[[ZERO]]
// CHECK: %[[OFFSET:.+]] = arith.select %[[CMP]], %[[END_SUB]], %[[ZERO]]
// CHECK: %[[END_OFFSET:.+]] = arith.addi %[[START_OFFSET]], %[[OFFSET]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[END_OFFSET]], %[[ONE]]
// CHECK: %[[KHEIGHT:.+]] = arith.select %[[CMP]], %[[ONE]], %[[END_OFFSET]]

// Compute how much of the width does not include padding:
// CHECK: %[[STRIDE:.+]] = arith.constant 1
// CHECK: %[[KSIZE:.+]] = arith.constant 4
// CHECK: %[[START:.+]] = linalg.index 2
// CHECK: %[[END:.+]] = arith.subi %[[WIDTH]], %[[START]]
// CHECK: %[[SRC_START:.+]] = arith.muli %[[START]], %[[STRIDE]]
// CHECK: %[[SRC_END:.+]] = arith.muli %[[END]], %[[STRIDE]]
// CHECK: %[[PAD_START:.+]] = arith.constant 1
// CHECK: %[[START_SUB:.+]] = arith.subi %[[SRC_START]], %[[PAD_START]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[START_SUB]], %[[ZERO]]
// CHECK: %[[OFFSET:.+]] = arith.select %[[CMP]], %[[START_SUB]], %[[ZERO]]
// CHECK: %[[START_OFFSET:.+]] = arith.addi %[[KSIZE]], %[[OFFSET]]
// CHECK: %[[PAD_END:.+]] = arith.constant 1
// CHECK: %[[END_SUB:.+]] = arith.subi %[[SRC_END]], %[[PAD_END]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[END_SUB]], %[[ZERO]]
// CHECK: %[[OFFSET:.+]] = arith.select %[[CMP]], %[[END_SUB]], %[[ZERO]]
// CHECK: %[[END_OFFSET:.+]] = arith.addi %[[START_OFFSET]], %[[OFFSET]]
// CHECK: %[[CMP:.+]] = arith.cmpi slt, %[[END_OFFSET]], %[[ONE]]
// CHECK: %[[KWIDTH:.+]] = arith.select %[[CMP]], %[[ONE]], %[[END_OFFSET]]

// Divide the summed value by the number of values summed.
// CHECK: %[[COUNT:.+]] = arith.muli %[[KHEIGHT]], %[[KWIDTH]]
// CHECK: %[[CAST:.+]] = arith.index_cast %[[COUNT]]
// CHECK: %[[FLT:.+]] = arith.sitofp %[[CAST]]
// CHECK: %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
// CHECK: %[[TRUNC:.+]] = arith.truncf %[[DIV]]
// CHECK: linalg.yield %[[TRUNC]]
%0 = tosa.avg_pool2d %arg0 {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf16>) -> tensor<1x5x33x62xf16>
return %0 : tensor<1x5x33x62xf16>
}

// -----

// CHECK-LABEL: @avg_pool_i8
func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
// CHECK: %[[GENERIC:.+]] = linalg.generic
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