mirror of https://github.com/llvm/torch-mlir
73 lines
5.6 KiB
MLIR
73 lines
5.6 KiB
MLIR
// RUN: npcomp-opt <%s -convert-tcf-to-linalg | FileCheck %s --dump-input=fail
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// CHECK-LABEL: func @tcf_matmul(
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// CHECK-SAME: %[[LHS:.*]]: tensor<?x?xf32>,
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// CHECK-SAME: %[[RHS:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
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// CHECK: %[[C0F32:.*]] = constant 0.000000e+00 : f32
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// CHECK: %[[C0:.*]] = constant 0 : index
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// CHECK: %[[C1:.*]] = constant 1 : index
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// CHECK: %[[LHSK:.*]] = dim %[[LHS]], %[[C1]] : tensor<?x?xf32>
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// CHECK: %[[RHSK:.*]] = dim %[[RHS]], %[[C0]] : tensor<?x?xf32>
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// CHECK: %[[KEQUAL:.*]] = cmpi eq, %[[LHSK]], %[[RHSK]] : index
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// CHECK: %[[WINESS:.*]] = shape.cstr_require %[[KEQUAL]], "mismatching contracting dimension for matmul"
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// CHECK: %[[RET:.*]] = shape.assuming %[[WINESS]] -> (tensor<?x?xf32>) {
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// CHECK: %[[LHSROWS:.*]] = dim %[[LHS]], %[[C0]] : tensor<?x?xf32>
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// CHECK: %[[RHSCOLS:.*]] = dim %[[RHS]], %[[C1]] : tensor<?x?xf32>
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// CHECK: %[[SHAPE:.*]] = tensor.from_elements %[[LHSROWS]], %[[RHSCOLS]] : tensor<2xindex>
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// CHECK: %[[INIT_TENSOR:.*]] = tcp.splatted %[[C0F32]], %[[SHAPE]] : (f32, tensor<2xindex>) -> tensor<?x?xf32>
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// CHECK: %[[MATMUL:.*]] = linalg.matmul ins(%[[LHS]], %[[RHS]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[INIT_TENSOR]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK: shape.assuming_yield %[[MATMUL]] : tensor<?x?xf32>
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// CHECK: }
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// CHECK: return %[[RET:.*]] : tensor<?x?xf32>
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func @tcf_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = tcf.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @tcf_conv_2d_nchw(
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// CHECK-SAME: %[[IN:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>
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// CHECK-SAME: %[[FILTER:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
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// CHECK: %[[C0F32:.*]] = constant 0.000000e+00 : f32
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// CHECK: %[[C1:.*]] = constant 1 : index
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// CHECK: %[[C0:.*]] = constant 0 : index
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// CHECK: %[[C2:.*]] = constant 2 : index
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// CHECK: %[[C3:.*]] = constant 3 : index
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// CHECK: %[[CHANNELS:.*]] = dim %[[IN]], %[[C1]] : tensor<?x?x?x?xf32>
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// CHECK: %[[HEIGHT:.*]] = dim %[[IN]], %[[C2]] : tensor<?x?x?x?xf32>
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// CHECK: %[[WIDTH:.*]] = dim %[[IN]], %[[C3]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERCHANNELS:.*]] = dim %[[FILTER]], %[[C1]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERHEIGHT:.*]] = dim %[[FILTER]], %[[C2]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERWIDTH:.*]] = dim %[[FILTER]], %[[C3]] : tensor<?x?x?x?xf32>
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// CHECK: %[[CMPCHANNELS:.*]] = cmpi eq, %[[CHANNELS]], %[[FILTERCHANNELS]] : index
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// CHECK: %[[CMPHEIGHT:.*]] = cmpi uge, %[[HEIGHT]], %[[FILTERHEIGHT]] : index
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// CHECK: %[[CMPWIDTH:.*]] = cmpi uge, %[[WIDTH]], %[[FILTERWIDTH]] : index
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// CHECK: %[[CSTRCHANNELS:.*]] = shape.cstr_require %[[CMPCHANNELS]], "input and filter in-channels must be equal"
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// CHECK: %[[CSTRHEIGHT:.*]] = shape.cstr_require %[[CMPHEIGHT]], "input height must be greater than or equal to filter KH-dimension"
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// CHECK: %[[CSTRWIDTH:.*]] = shape.cstr_require %[[CMPWIDTH]], "input width must be greater than or equal to filter KW-dimension"
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// CHECK: %[[WITNESS:.*]] = shape.assuming_all %[[CSTRCHANNELS]], %[[CSTRHEIGHT]], %[[CSTRWIDTH]]
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// CHECK: %[[RET:.*]] = shape.assuming %[[WITNESS]] -> (tensor<?x?x?x?xf32>) {
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// CHECK: %[[BATCH:.*]] = dim %[[IN]], %[[C0]] : tensor<?x?x?x?xf32>
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// CHECK: %[[HEIGHT:.*]] = dim %[[IN]], %[[C2]] : tensor<?x?x?x?xf32>
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// CHECK: %[[WIDTH:.*]] = dim %[[IN]], %[[C3]] : tensor<?x?x?x?xf32>
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// CHECK: %[[OUTCHANNELS:.*]] = dim %[[FILTER]], %[[C0]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERHEIGHT:.*]] = dim %[[FILTER]], %[[C2]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERWIDTH:.*]] = dim %[[FILTER]], %[[C3]] : tensor<?x?x?x?xf32>
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// CHECK: %[[FILTERHEIGHTM1:.*]] = subi %[[FILTERHEIGHT]], %[[C1]] : index
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// CHECK: %[[HEIGHTV0:.*]] = subi %[[HEIGHT]], %[[FILTERHEIGHTM1]] : index
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// CHECK: %[[HEIGHTV0M1:.*]] = subi %[[HEIGHTV0]], %[[C1]] : index
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// CHECK: %[[OUTHEIGHT:.*]] = addi %[[HEIGHTV0M1]], %[[C1]] : index
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// CHECK: %[[FILTERWIDTHM1:.*]] = subi %[[FILTERWIDTH]], %[[C1]] : index
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// CHECK: %[[WIDTHV0:.*]] = subi %[[WIDTH]], %[[FILTERWIDTHM1]] : index
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// CHECK: %[[WIDTHV0M1:.*]] = subi %[[WIDTHV0]], %[[C1]] : index
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// CHECK: %[[OUTWIDTH:.*]] = addi %[[WIDTHV0M1]], %[[C1]] : index
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// CHECK: %[[SHAPE:.*]] = tensor.from_elements %[[BATCH]], %[[OUTCHANNELS]], %[[OUTHEIGHT]], %[[OUTWIDTH]] : tensor<4xindex>
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// CHECK: %[[INIT_TENSOR:.*]] = tcp.splatted %[[C0F32]], %[[SHAPE]] : (f32, tensor<4xindex>) -> tensor<?x?x?x?xf32>
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// CHECK: %[[CONVNCHW:.*]] = linalg.conv_2d_nchw ins(%[[IN]], %[[FILTER]] : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) outs(%[[INIT_TENSOR]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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// CHECK: shape.assuming_yield %[[CONVNCHW]] : tensor<?x?x?x?xf32>
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// CHECK: }
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// CHECK: return %[[RET:.*]] : tensor<?x?x?x?xf32>
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func @tcf_conv_2d_nchw(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
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%0 = tcf.conv_2d_nchw %arg0, %arg1 : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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return %0 : tensor<?x?x?x?xf32>
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}
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