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[mlir][Linalg] NFC - Rename Linalg and Vector EDSCs to avoid collisions

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A certain number of EDSCs have a named form (e.g. `linalg.matmul`) and a generic form (e.g. `linalg.generic` with matmul traits).
Despite living in different namespaces, using the same name is confusiong in clients.
Rename them as `linalg_matmul` and `linalg_generic_matmul` respectively.
This commit is contained in:
Nicolas Vasilache
2020-04-02 21:06:45 -04:00
parent 30f18ed387
commit aef0877b1b
7 changed files with 111 additions and 94 deletions
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50
mlir/test/EDSC/builder-api-test.cpp
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@@ -804,7 +804,7 @@ TEST_FUNC(affine_if_op) {
}
// clang-format off
// CHECK-LABEL: func @linalg_pointwise
// CHECK-LABEL: func @linalg_generic_pointwise
// CHECK: linalg.generic {args_in = 2 : i64, args_out = 1 : i64,
// CHECK-SAME: indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
// CHECK-SAME: iterator_types = ["parallel", "parallel"]}
@@ -822,14 +822,14 @@ TEST_FUNC(affine_if_op) {
// CHECK: tanh
// CHECK: }: memref<?x?xf32>, memref<?x?xf32>
// clang-format on
TEST_FUNC(linalg_pointwise_test) {
TEST_FUNC(linalg_generic_pointwise_test) {
using namespace edsc;
using namespace edsc::ops;
auto f32Type = FloatType::getF32(&globalContext());
auto memrefType = MemRefType::get(
{ShapedType::kDynamicSize, ShapedType::kDynamicSize}, f32Type, {}, 0);
auto f = makeFunction("linalg_pointwise", {},
auto f = makeFunction("linalg_generic_pointwise", {},
{memrefType, memrefType, memrefType});
OpBuilder builder(f.getBody());
@@ -838,16 +838,16 @@ TEST_FUNC(linalg_pointwise_test) {
AffineExpr i, j;
bindDims(&globalContext(), i, j);
StructuredIndexed SA(A), SB(B), SC(C);
linalg_pointwise_add(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_pointwise_max(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_pointwise_tanh(SA({i, j}), SC({i, j}));
linalg_generic_pointwise_add(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_generic_pointwise_max(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_generic_pointwise_tanh(SA({i, j}), SC({i, j}));
f.print(llvm::outs());
f.erase();
}
// clang-format off
// CHECK-LABEL: func @linalg_matmul
// CHECK-LABEL: func @linalg_generic_matmul
// CHECK: linalg.generic {args_in = 2 : i64, args_out = 1 : i64,
// CHECK-SAME: indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>],
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]}
@@ -857,7 +857,7 @@ TEST_FUNC(linalg_pointwise_test) {
// CHECK: linalg.yield %[[a4]] : f32
// CHECK: }: memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
// clang-format on
TEST_FUNC(linalg_matmul_test) {
TEST_FUNC(linalg_generic_matmul_test) {
using namespace edsc;
using namespace edsc::ops;
@@ -865,18 +865,18 @@ TEST_FUNC(linalg_matmul_test) {
auto memrefType = MemRefType::get(
{ShapedType::kDynamicSize, ShapedType::kDynamicSize}, f32Type, {}, 0);
auto f =
makeFunction("linalg_matmul", {}, {memrefType, memrefType, memrefType});
makeFunction("linalg_generic_matmul", {}, {memrefType, memrefType, memrefType});
OpBuilder builder(f.getBody());
ScopedContext scope(builder, f.getLoc());
linalg_matmul(makeValueHandles(llvm::to_vector<3>(f.getArguments())));
linalg_generic_matmul(makeValueHandles(llvm::to_vector<3>(f.getArguments())));
f.print(llvm::outs());
f.erase();
}
// clang-format off
// CHECK-LABEL: func @linalg_conv_nhwc
// CHECK-LABEL: func @linalg_generic_conv_nhwc
// CHECK: linalg.generic {args_in = 2 : i64, args_out = 1 : i64,
// CHECK-SAME: indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d2 * 3 + d4 * 5, d3 * 4 + d5 * 6, d6)>,
// CHECK-SAME: affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d1)>,
@@ -888,7 +888,7 @@ TEST_FUNC(linalg_matmul_test) {
// CHECK: linalg.yield %[[a4]] : f32
// CHECK: }: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>
// clang-format on
TEST_FUNC(linalg_conv_nhwc) {
TEST_FUNC(linalg_generic_conv_nhwc) {
using namespace edsc;
using namespace edsc::ops;
@@ -897,12 +897,12 @@ TEST_FUNC(linalg_conv_nhwc) {
MemRefType::get({ShapedType::kDynamicSize, ShapedType::kDynamicSize,
ShapedType::kDynamicSize, ShapedType::kDynamicSize},
f32Type, {}, 0);
auto f = makeFunction("linalg_conv_nhwc", {},
auto f = makeFunction("linalg_generic_conv_nhwc", {},
{memrefType, memrefType, memrefType});
OpBuilder builder(f.getBody());
ScopedContext scope(builder, f.getLoc());
linalg_conv_nhwc(makeValueHandles(llvm::to_vector<3>(f.getArguments())),
linalg_generic_conv_nhwc(makeValueHandles(llvm::to_vector<3>(f.getArguments())),
/*strides=*/{3, 4}, /*dilations=*/{5, 6});
f.print(llvm::outs());
@@ -910,7 +910,7 @@ TEST_FUNC(linalg_conv_nhwc) {
}
// clang-format off
// CHECK-LABEL: func @linalg_dilated_conv_nhwc
// CHECK-LABEL: func @linalg_generic_dilated_conv_nhwc
// CHECK: linalg.generic {args_in = 2 : i64, args_out = 1 : i64,
// CHECK-SAME: indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d3 * 3 + d5 * 5, d4 * 4 + d6 * 6, d2)>,
// CHECK-SAME: affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d2, d1)>,
@@ -922,7 +922,7 @@ TEST_FUNC(linalg_conv_nhwc) {
// CHECK: linalg.yield %[[a4]] : f32
// CHECK: }: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>
// clang-format on
TEST_FUNC(linalg_dilated_conv_nhwc) {
TEST_FUNC(linalg_generic_dilated_conv_nhwc) {
using namespace edsc;
using namespace edsc::ops;
@@ -931,12 +931,12 @@ TEST_FUNC(linalg_dilated_conv_nhwc) {
MemRefType::get({ShapedType::kDynamicSize, ShapedType::kDynamicSize,
ShapedType::kDynamicSize, ShapedType::kDynamicSize},
f32Type, {}, 0);
auto f = makeFunction("linalg_dilated_conv_nhwc", {},
auto f = makeFunction("linalg_generic_dilated_conv_nhwc", {},
{memrefType, memrefType, memrefType});
OpBuilder builder(f.getBody());
ScopedContext scope(builder, f.getLoc());
linalg_dilated_conv_nhwc(makeValueHandles(f.getArguments()),
linalg_generic_dilated_conv_nhwc(makeValueHandles(f.getArguments()),
/*depth_multiplier=*/7,
/*strides=*/{3, 4}, /*dilations=*/{5, 6});
@@ -1019,11 +1019,11 @@ TEST_FUNC(linalg_tensors_test) {
AffineExpr i, j;
bindDims(&globalContext(), i, j);
StructuredIndexed SA(A), SB(B), SC(tensorType);
linalg_pointwise_add(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_pointwise_max(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_pointwise_tanh(SA({i, j}), SC({i, j}));
Value o1 = linalg_matmul(A, B, tensorType)->getResult(0);
linalg_matmul(A, B, ValueHandle(o1), tensorType);
linalg_generic_pointwise_add(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_generic_pointwise_max(SA({i, j}), SB({i, j}), SC({i, j}));
linalg_generic_pointwise_tanh(SA({i, j}), SC({i, j}));
Value o1 = linalg_generic_matmul(A, B, tensorType)->getResult(0);
linalg_generic_matmul(A, B, ValueHandle(o1), tensorType);
f.print(llvm::outs());
f.erase();
@@ -1067,9 +1067,9 @@ TEST_FUNC(memref_vector_matmul_test) {
ValueHandle A(f.getArgument(0)), B(f.getArgument(1)), C(f.getArgument(2));
auto contractionBuilder = [](ArrayRef<BlockArgument> args) {
assert(args.size() == 3 && "expected 3 block arguments");
(linalg_yield(vector_matmul(args[0], args[1], args[2])));
(linalg_yield(vector_contraction_matmul(args[0], args[1], args[2])));
};
linalg_matmul(A, B, C, contractionBuilder);
linalg_generic_matmul(A, B, C, contractionBuilder);
f.print(llvm::outs());
f.erase();