derek.holloway
7502bf64a4
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220 lines
15 KiB
MLIR
220 lines
15 KiB
MLIR
// RUN: mlir-opt -test-linalg-drop-unit-dims --split-input-file %s | FileCheck %s --check-prefixes=CHECK,NOENCODE
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// RUN: mlir-opt -test-linalg-drop-unit-dims=collapse-encoded --split-input-file %s | FileCheck %s --check-prefixes=CHECK,ENCODE
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// Drop only the outermost unit dimension (controlled using a control function)
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// This test does not use an encoding, therefore behavior in both modes is identical.
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func.func @drop_outermost_unit_dims(%arg0: tensor<1x1x42xf32>) -> tensor<1x1x42xf32> {
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%0 = tensor.empty() : tensor<1x1x42xf32>
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%1 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0 : tensor<1x1x42xf32>) outs(%0 : tensor<1x1x42xf32>) {
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^bb0(%b0: f32, %b1 : f32):
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%2 = arith.addf %b0, %b1 : f32
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linalg.yield %2 : f32
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} -> tensor<1x1x42xf32>
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return %1 : tensor<1x1x42xf32>
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}
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// CHECK-LABEL: func @drop_outermost_unit_dims
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// CHECK-SAME: %[[ARG0:.+]]: tensor<1x1x42xf32>
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// CHECK: %[[OUTS:.+]] = tensor.empty()
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// CHECK: %[[ARG0_RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1], [2]{{\]}}
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// CHECK: %[[OUTS_RESHAPE:.+]] = tensor.collapse_shape %[[OUTS]] {{\[}}[0, 1], [2]{{\]}}
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// CHECK: %[[GENERIC:.+]] = linalg.generic
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// CHECK-SAME: ins(%[[ARG0_RESHAPE]] :
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// CHECK-SAME: outs(%[[OUTS_RESHAPE]] :
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// CHECK: %[[EXPAND_SHAPE:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0, 1], [2]{{\]}}
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// CHECK: return %[[EXPAND_SHAPE]]
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// -----
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// Drop outermost unit dimension with operand that has an encoding.
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// With the default behavior, the transformation is aborted and operation remains unchanged.
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// With the custom behavior, the operand gets collapsed and encoding is preserved in the collapse.
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#encoding = #test.tensor_encoding<"encoding">
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func.func @drop_unit_dims_encoded_operand(%arg0: tensor<1x1x42xf32>, %arg1: tensor<1x1x42xf32, #encoding>) -> tensor<1x1x42xf32> {
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%0 = tensor.empty() : tensor<1x1x42xf32>
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%1 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<1x1x42xf32>, tensor<1x1x42xf32, #encoding>) outs(%0 : tensor<1x1x42xf32>) {
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^bb0(%in0: f32, %in1 : f32, %out : f32):
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%2 = arith.addf %in0, %in1 : f32
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linalg.yield %2 : f32
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} -> tensor<1x1x42xf32>
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return %1 : tensor<1x1x42xf32>
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}
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// NOENCODE-LABEL: @drop_unit_dims_encoded_operand(
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// NOENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>,
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// NOENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>) -> tensor<1x1x42xf32> {
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// NOENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32>
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// NOENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// NOENCODE-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
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// NOENCODE-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<1x1x42xf32>, tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>)
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// NOENCODE-SAME: outs(%[[EMPTY_0]] : tensor<1x1x42xf32>)
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// NOENCODE: return %[[GENERIC_0]] : tensor<1x1x42xf32>
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// ENCODE-LABEL: @drop_unit_dims_encoded_operand(
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// ENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>,
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// ENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>) -> tensor<1x1x42xf32> {
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// ENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_0:.*]] = tensor.collapse_shape %[[ARG0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_1:.*]] = tensor.collapse_shape %[[ARG1]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">> into tensor<1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE: %[[COLLAPSE_SHAPE_2:.*]] = tensor.collapse_shape %[[EMPTY_0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// ENCODE-SAME: iterator_types = ["parallel", "parallel"]
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// ENCODE-SAME: ins(%[[COLLAPSE_SHAPE_0]], %[[COLLAPSE_SHAPE_1]] : tensor<1x42xf32>, tensor<1x42xf32, #test.tensor_encoding<"encoding">>)
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// ENCODE-SAME: outs(%[[COLLAPSE_SHAPE_2]] : tensor<1x42xf32>)
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// ENCODE: %[[EXPAND_SHAPE_0:.*]] = tensor.expand_shape %[[GENERIC_0]] {{\[\[}}0, 1], [2]] output_shape [1, 1, 42] : tensor<1x42xf32> into tensor<1x1x42xf32>
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// ENCODE: return %[[EXPAND_SHAPE_0]] : tensor<1x1x42xf32>
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// -----
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// Drop outermost unit dimension with result that has an encoding.
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// With the default behavior, the transformation is aborted and operation remains unchanged.
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// With the custom behavior, the result gets expanded and encoding is preserved in the expansion.
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#encoding = #test.tensor_encoding<"encoding">
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func.func @drop_unit_dims_encoded_result(%arg0: tensor<1x1x42xf32>, %arg1: tensor<1x1x42xf32>) -> tensor<1x1x42xf32, #encoding> {
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%0 = tensor.empty() : tensor<1x1x42xf32, #encoding>
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%1 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<1x1x42xf32>, tensor<1x1x42xf32>) outs(%0 : tensor<1x1x42xf32, #encoding>) {
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^bb0(%in0: f32, %in1 : f32, %out : f32):
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%2 = arith.addf %in0, %in1 : f32
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linalg.yield %2 : f32
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} -> tensor<1x1x42xf32, #encoding>
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return %1 : tensor<1x1x42xf32, #encoding>
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}
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// NOENCODE-LABEL: @drop_unit_dims_encoded_result(
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// NOENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>,
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// NOENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32>) -> tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// NOENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// NOENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// NOENCODE-SAME: iterator_types = ["parallel", "parallel", "parallel"]
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// NOENCODE-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<1x1x42xf32>, tensor<1x1x42xf32>)
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// NOENCODE-SAME: outs(%[[EMPTY_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>)
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// NOENCODE-NOT: tensor.expand_shape
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// NOENCODE: return %[[GENERIC_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE-LABEL: @drop_unit_dims_encoded_result(
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// ENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>,
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// ENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32>) -> tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE: %[[COLLAPSE_SHAPE_0:.*]] = tensor.collapse_shape %[[ARG0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_1:.*]] = tensor.collapse_shape %[[ARG1]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_2:.*]] = tensor.collapse_shape %[[EMPTY_0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">> into tensor<1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// ENCODE-SAME: iterator_types = ["parallel", "parallel"]
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// ENCODE-SAME: ins(%[[COLLAPSE_SHAPE_0]], %[[COLLAPSE_SHAPE_1]] : tensor<1x42xf32>, tensor<1x42xf32>)
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// ENCODE-SAME: outs(%[[COLLAPSE_SHAPE_2]] : tensor<1x42xf32, #test.tensor_encoding<"encoding">>)
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// ENCODE: %[[EXPAND_SHAPE_0:.*]] = tensor.expand_shape %[[GENERIC_0]] {{\[\[}}0, 1], [2]] output_shape [1, 1, 42] : tensor<1x42xf32, #test.tensor_encoding<"encoding">> into tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// ENCODE: return %[[EXPAND_SHAPE_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding">>
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// -----
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#encoding1 = #test.tensor_encoding<"encoding1">
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#encoding2 = #test.tensor_encoding<"encoding2">
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func.func @drop_unit_dims_encoded_operand_and_result(%arg0: tensor<1x1x42xf32>, %arg1: tensor<1x1x42xf32, #encoding1>) -> tensor<1x1x42xf32, #encoding2> {
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%0 = tensor.empty() : tensor<1x1x42xf32, #encoding2>
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%1 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<1x1x42xf32>, tensor<1x1x42xf32, #encoding1>) outs(%0 : tensor<1x1x42xf32, #encoding2>) {
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^bb0(%in0: f32, %in1 : f32, %out : f32):
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%2 = arith.addf %in0, %in1 : f32
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linalg.yield %2 : f32
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} -> tensor<1x1x42xf32, #encoding2>
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return %1 : tensor<1x1x42xf32, #encoding2>
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}
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// NOENCODE-LABEL: @drop_unit_dims_encoded_operand_and_result
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// NOENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>
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// NOENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// NOENCODE-SAME: -> tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// NOENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// NOENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// NOENCODE-SAME: iterator_types = ["parallel", "parallel", "parallel"]
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// NOENCODE-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<1x1x42xf32>, tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>)
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// NOENCODE-SAME: outs(%[[EMPTY_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>)
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// NOENCODE-NOT: tensor.expand_shape
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// NOENCODE: return %[[GENERIC_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE-LABEL: @drop_unit_dims_encoded_operand_and_result
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// ENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32>,
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// ENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE-SAME: -> tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE: %[[COLLAPSE_SHAPE_0:.*]] = tensor.collapse_shape %[[ARG0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_1:.*]] = tensor.collapse_shape %[[ARG1]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE-SAME: into tensor<1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE: %[[COLLAPSE_SHAPE_2:.*]] = tensor.collapse_shape %[[EMPTY_0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE-SAME: into tensor<1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// ENCODE-SAME: iterator_types = ["parallel", "parallel"]
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// ENCODE-SAME: ins(%[[COLLAPSE_SHAPE_0]], %[[COLLAPSE_SHAPE_1]] : tensor<1x42xf32>, tensor<1x42xf32, #test.tensor_encoding<"encoding1">>)
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// ENCODE-SAME: outs(%[[COLLAPSE_SHAPE_2]] : tensor<1x42xf32, #test.tensor_encoding<"encoding2">>)
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// ENCODE: %[[EXPAND_SHAPE_0:.*]] = tensor.expand_shape %[[GENERIC_0]] {{\[\[}}0, 1], [2]] output_shape [1, 1, 42] : tensor<1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE-SAME: into tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE: return %[[EXPAND_SHAPE_0]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// -----
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#encoding1 = #test.tensor_encoding<"encoding1">
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#encoding2 = #test.tensor_encoding<"encoding2">
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func.func @drop_unit_dims_two_encoded_operands(%arg0: tensor<1x1x42xf32, #encoding1>, %arg1: tensor<1x1x42xf32, #encoding2>) -> tensor<1x1x42xf32> {
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%0 = tensor.empty() : tensor<1x1x42xf32>
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%1 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<1x1x42xf32, #encoding1>, tensor<1x1x42xf32, #encoding2>) outs(%0 : tensor<1x1x42xf32>) {
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^bb0(%in0: f32, %in1 : f32, %out : f32):
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%2 = arith.addf %in0, %in1 : f32
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linalg.yield %2 : f32
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} -> tensor<1x1x42xf32>
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return %1 : tensor<1x1x42xf32>
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}
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// NOENCODE-LABEL: @drop_unit_dims_two_encoded_operands
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// NOENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// NOENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>) -> tensor<1x1x42xf32>
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// NOENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32>
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// NOENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// NOENCODE-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
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// NOENCODE-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>, tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>)
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// NOENCODE-SAME: outs(%[[EMPTY_0]] : tensor<1x1x42xf32>)
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// NOENCODE: return %[[GENERIC_0]] : tensor<1x1x42xf32>
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// ENCODE-LABEL: @drop_unit_dims_two_encoded_operands
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// ENCODE-SAME: %[[ARG0:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE-SAME: %[[ARG1:.*]]: tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>) -> tensor<1x1x42xf32>
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// ENCODE: %[[EMPTY_0:.*]] = tensor.empty() : tensor<1x1x42xf32>
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// ENCODE: %[[COLLAPSE_SHAPE_0:.*]] = tensor.collapse_shape %[[ARG0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE-SAME: into tensor<1x42xf32, #test.tensor_encoding<"encoding1">>
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// ENCODE: %[[COLLAPSE_SHAPE_1:.*]] = tensor.collapse_shape %[[ARG1]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE-SAME: into tensor<1x42xf32, #test.tensor_encoding<"encoding2">>
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// ENCODE: %[[COLLAPSE_SHAPE_2:.*]] = tensor.collapse_shape %[[EMPTY_0]] {{\[\[}}0, 1], [2]] : tensor<1x1x42xf32> into tensor<1x42xf32>
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// ENCODE: %[[GENERIC_0:.*]] = linalg.generic
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// ENCODE-SAME: iterator_types = ["parallel", "parallel"]
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// ENCODE-SAME: ins(%[[COLLAPSE_SHAPE_0]], %[[COLLAPSE_SHAPE_1]] : tensor<1x42xf32, #test.tensor_encoding<"encoding1">>, tensor<1x42xf32, #test.tensor_encoding<"encoding2">>)
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// ENCODE-SAME: outs(%[[COLLAPSE_SHAPE_2]] : tensor<1x42xf32>)
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// ENCODE: %[[EXPAND_SHAPE_0:.*]] = tensor.expand_shape %[[GENERIC_0]] {{\[\[}}0, 1], [2]] output_shape [1, 1, 42] : tensor<1x42xf32> into tensor<1x1x42xf32>
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// ENCODE: return %[[EXPAND_SHAPE_0]] : tensor<1x1x42xf32>
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