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/llvm-project/mlir/utils/tree-sitter-mlir/dialect/
H A Dlinalg.js6 'linalg.batch_matmul',
7 'linalg.batch_matmul_transpose_b',
8 'linalg.batch_matvec',
9 'linalg.batch_reduce_matmul', 'linalg.broadcast',
10 'linalg.conv_1d_ncw_fcw', 'linalg.conv_1d_nwc_wcf',
11 'linalg.conv_1d', 'linalg.conv_2d_nchw_fchw',
12 'linalg.conv_2d_ngchw_fgchw',
13 'linalg.conv_2d_nhwc_fhwc',
14 'linalg.conv_2d_nhwc_hwcf',
15 'linalg.conv_2d_nhwc_hwcf_q', 'linalg.conv_2d',
[all …]
/llvm-project/mlir/utils/tree-sitter-mlir/queries/
H A Dhighlights.scm215 "linalg.batch_matmul"
216 "linalg.batch_matmul_transpose_b"
217 "linalg.batch_matvec"
218 "linalg.batch_reduce_matmul"
219 "linalg.broadcast"
220 "linalg.conv_1d_ncw_fcw"
221 "linalg.conv_1d_nwc_wcf"
222 "linalg.conv_1d"
223 "linalg.conv_2d_nchw_fchw"
224 "linalg.conv_2d_ngchw_fgchw"
[all …]
/llvm-project/mlir/include/mlir/Dialect/Linalg/
H A DPasses.td1 //===-- Passes.td - Linalg pass definition file ------------*- tablegen -*-===//
14 def ConvertElementwiseToLinalgPass : Pass<"convert-elementwise-to-linalg", ""> {
15 let summary = "Convert ElementwiseMappable ops to linalg";
17 Convert ops with the `ElementwiseMappable` trait to linalg parallel loops.
20 run on op which contains linalg ops (most commonly a
23 let dependentDialects = ["linalg::LinalgDialect", "memref::MemRefDialect"];
26 def ConvertLinalgToAffineLoopsPass : Pass<"convert-linalg-to-affine-loops"> {
27 let summary = "Lower the operations from the linalg dialect into affine "
30 "affine::AffineDialect", "linalg::LinalgDialect", "memref::MemRefDialect"];
33 def ConvertLinalgToLoopsPass : Pass<"convert-linalg-to-loops"> {
[all …]
/llvm-project/mlir/test/Dialect/Linalg/
H A Dinvalid.mlir22 linalg.yield %arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>
29 linalg.index 0 : index
36 linalg.generic {
41 linalg.index 2 : index
42 linalg.yield %0 : f32
50 linalg.generic {
55 linalg.index -1 : index
56 linalg.yield %0 : f32
64 linalg.generic {
74 linalg
[all...]
H A Dnamed-ops-fail.mlir5 …linalg.add ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf3…
13 …linalg.add ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
21 …linalg.sub ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf3…
29 …linalg.sub ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
37 …linalg.mul ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf3…
45 …linalg.mul ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
53 …linalg.div ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf3…
61 …linalg.div ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
69 …linalg.div_unsigned ins(%arg0, %arg1 : memref<4x8x16xi32>, memref<4x8x16xi16>) outs(%arg2: memref<…
77 …linalg.div_unsigned ins(%arg0, %arg1 : memref<8x16xi32>, memref<4x8x16xi32>) outs(%arg2: memref<4x…
[all …]
H A Done-shot-bufferize-analysis.mlir6 // CHECK: linalg.elemwise_binary
7 …// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "true"], fun = #linalg.binary_fn<add>}
8 %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
18 // CHECK: linalg.elemwise_binary
19 …// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "true"], fun = #linalg.binary_fn<add>}
20 %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
31 // CHECK: linalg.elemwise_binary
32 …// CHECK-SAME: {__inplace_operands_attr__ = ["true", "none", "true"], fun = #linalg.binary_fn<add>}
33 %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
47 // CHECK: linalg.generic
[all …]
H A Dtransform-pack-greedily.mlir14 // CHECK: linalg.generic
19 %0 = linalg.matmul ins(%A, %B : !A_mk, !B_kn) outs(%C : !C_mn) -> !C_mn
25 %matmul = transform.structured.match ops{["linalg.matmul"]} in %module_op
26 : (!transform.any_op) -> !transform.op<"linalg.matmul">
29 : (!transform.op<"linalg.matmul">) -> !transform.op<"linalg.generic">
57 // CHECK: linalg.generic
62 %0 = linalg.generic #mkn_trait ins(%A, %B : !A_mk, !B_nk) outs(%C : !C_nm) {
66 linalg.yield %e : f32
73 …c = transform.structured.match ops{["linalg.generic"]} in %module_op : (!transform.any_op) -> !tra…
76 : (!transform.op<"linalg.generic">) -> !transform.op<"linalg.generic">
[all …]
H A Droundtrip.mlir33 linalg.matmul ins(%arg0, %arg0 : memref<?x?xf32, strided<[?, 1], offset: ?>>,
36 linalg.matvec ins(%arg0, %arg1: memref<?x?xf32, strided<[?, 1], offset: ?>>,
39 linalg.dot ins(%arg1, %arg2: memref<?xf32, strided<[1], offset: ?>>,
45 // CHECK: linalg.matmul
49 // CHECK: linalg.matvec
53 // CHECK: linalg.dot
61 linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?xf32, strided<[1], offset: ?>>)
66 // CHECK: linalg.fill ins(%{{.*}} : f32) outs(%{{.*}} : memref<?xf32, strided<[1], offset: ?>>)
82 linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
87 // CHECK: linalg
[all...]
H A Dfusion-elementwise-ops.mlir1 // RUN: mlir-opt %s -linalg-fuse-elementwise-ops -split-input-file | FileCheck %s
14 …%3 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "paralle…
19 linalg.yield %4 : f32
21 // CHECK: linalg.generic {
23 …%4 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "paralle…
32 // CHECK-NOT: linalg.yield
34 // CHECK: linalg.yield
36 linalg.yield %5 : f32
56 …%3 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "paralle…
61 linalg.yield %4 : f32
[all …]
H A Dnamed-ops.mlir7 %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
9 %0 = linalg.depthwise_conv_1d_nwc_wcm {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
21 %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8xf32>) -> tensor<1x10x8xf32>
23 %0 = linalg.depthwise_conv_1d_nwc_wc {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
35 %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x8x10xf32>) -> tensor<1x8x10xf32>
37 %0 = linalg.depthwise_conv_1d_ncw_cw {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
49 %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x3x4x2x3xf32>) -> tensor<2x3x4x2x3xf32>
50 // CHECK: %{{.+}} = linalg.depthwise_conv_2d_nhwc_hwcm
54 %0 = linalg.depthwise_conv_2d_nhwc_hwcm
63 // CHECK: linalg
[all...]
H A Dtransform-op-gpu-map-copy-to-threads.mlir11 // CHECK: linalg.copy {{.*}} -> tensor<8xf16>
13 %0 = linalg.copy ins(%t0: !tt) outs(%out: !tt) -> !tt
19 %0 = transform.structured.match ops{["linalg.copy"]} in %arg1
23 : (!transform.any_op) -> (!transform.op<"scf.forall">, !transform.op<"linalg.copy">)
69 // CHECK: linalg.copy {{.*}} -> tensor<8xf16>
71 %0 = linalg.copy ins(%t0: !tt) outs(%out: !tt) -> !tt
77 %0 = transform.structured.match ops{["linalg.copy"]} in %arg1
81 : (!transform.any_op) -> (!transform.op<"scf.forall">, !transform.op<"linalg.copy">)
95 // CHECK: linalg.copy {{.*}} -> tensor<4xf16>
97 %0 = linalg.copy ins(%t0: !tt) outs(%out: !tt) -> !tt
[all …]
H A Dint-narrowing.mlir
H A Droundtrip-linalg-named-ops.mlir1 // The following test examples of linalg named ops lowered to linalg.generic and then
3 // RUN: mlir-opt %s -linalg-generalize-named-ops | mlir-opt --linalg-specialize-generic-ops | FileC…
6 linalg.exp ins(%A : memref<7x14x21xf32>) outs(%Out : memref<7x14x21xf32>)
12 // CHECK-NOT: linalg.generic
13 // CHECK: linalg.exp ins(%[[A]] : memref<7x14x21xf32>) outs(%[[Out]] : memref<7x14x21xf32>)
18 …%0 = linalg.add ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> ten…
24 // CHECK-NOT: linalg.generic
25 // CHECK: linalg.add ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[Out]] : tensor<…
30 …%0 = linalg.matmul ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> …
36 // CHECK-NOT: linalg.generic
[all …]
H A Dgeneralize-named-polymorphic-ops.mlir1 // RUN: mlir-opt %s -split-input-file -linalg-generalize-named-ops | FileCheck %s
5 %0 = linalg.matmul ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
17 // CHECK-NEXT: linalg.yield %[[ADD]] : f32
24 %0 = linalg.matmul ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
36 // CHECK-NEXT: linalg.yield %[[ADD]] : i32
44 %0 = linalg.matmul {cast = #linalg.type_fn<cast_unsigned>}
56 %0 = linalg.matmul ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
69 %0 = linalg.matmul ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
82 %0 = linalg
[all...]
/llvm-project/mlir/test/python/dialects/linalg/
H A Dops.py3 from mlir.dialects import arith, func, linalg, tensor, memref
4 from mlir.dialects.linalg.opdsl.lang import *
24 # CHECK-NEXT: %[[RES:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : tensor<12x?xf32>) -> tensor<12x?xf32>
33 return linalg.fill(zero, outs=[out])
38 # CHECK-NEXT: linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : memref<12x?xf32>)
47 linalg.fill(zero, outs=[out])
66 # CHECK: linalg.elemwise_unary
67 # CHECK-SAME: cast = #linalg.type_fn<cast_signed>
68 # CHECK-SAME: fun = #linalg.unary_fn<exp>
70 unary_result = linalg
[all...]
/llvm-project/mlir/lib/Dialect/Linalg/Transforms/
H A DTransposeMatmul.cpp1 //===- TransposeMatmul.cpp - Convert Linalg matmul to transposed variants -===//
12 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
16 #define DEBUG_TYPE "linalg-transpose-matmul"
19 using namespace mlir::linalg;
23 /// linalg.matmul(a, b)
27 /// linalg.matmul_transpose_a(linalg.transpose(a), b)
31 FailureOr<Operation *> mlir::linalg::transposeMatmul(RewriterBase &rewriter, in transposeMatmul()
32 linalg::MatmulOp matmulOp, in transposeMatmul()
59 auto transposeOp = rewriter.create<linalg in transposeMatmul()
[all...]
H A DBlockPackMatmul.cpp1 //===- BlockPackMatmul.cpp - Linalg matmul block packing ------------------===//
9 #include "mlir/Dialect/Linalg/Passes.h"
11 #include "mlir/Dialect/Linalg/IR/Linalg.h"
12 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
13 #include "mlir/Dialect/Linalg/Utils/Utils.h"
23 #include "mlir/Dialect/Linalg/Passes.h.inc"
27 using namespace mlir::linalg;
44 static bool validateFullTilesOnDims(linalg::LinalgOp linalgOp, in validateFullTilesOnDims()
90 transposePackedMatmul(RewriterBase &rewriter, linalg
[all...]
H A DTransposeConv2D.cpp10 #include "mlir/Dialect/Linalg/IR/Linalg.h"
25 namespace linalg { namespace
32 /// %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
41 /// %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<2x2x6x8xf32>) -> tensor<2x2x6x8xf32>
42 /// %transposed = linalg.transpose ins(%arg1 : tensor<8x2x2x6xf32>) outs(%1 : tensor<2x2x6x8xf32>)
44 /// %2 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
66 // Because linalg.transpose expects an "out" parameter we need to pass it a in transposeConv2DHelper()
87 rewriter.create<linalg::TransposeOp>(loc, filter, input, filterPerm); in transposeConv2DHelper()
128 linalg in transposeConv2D()
[all...]
/llvm-project/mlir/test/lib/Dialect/Linalg/
H A DTestLinalgElementwiseFusion.cpp1 //===- TestLinalgElementwiseFusion.cpp - Test Linalg elementwise fusion ---===//
10 // Linalg, mainly linalg options.
16 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
28 .Case<linalg::LinalgOp>([&](linalg::LinalgOp linalgOp) { in addOperands()
57 struct TestMultiUseProducerFusion : public OpRewritePattern<linalg::GenericOp> {
58 using OpRewritePattern<linalg::GenericOp>::OpRewritePattern;
60 LogicalResult matchAndRewrite(linalg::GenericOp genericOp, in matchAndRewrite()
64 if (linalg in matchAndRewrite()
[all...]
/llvm-project/mlir/docs/Dialects/Linalg/
H A D_index.md1 # 'linalg' Dialect
9 Linalg is designed to solve the High-level Hierarchical Optimization (HHO box)
19 Linalg. They are all implemented in terms of the properties of the
20 `linalg.generic` OpInterface and avoid the pitfall of relying on hardcoded
25 Linalg IR and that have influenced its design:
35 1. Partially Lower to Iterations Over a Finer-Grained Linalg Op.
37 ## High-Level Description of Linalg Ops<a name="linalg_ops"></a>
39 Linalg takes at least some inspiration from all previously
62 [Linalg and Shapes](https://llvm.discourse.group/t/linalg
[all...]
/llvm-project/mlir/docs/Tutorials/transform/
H A DCh1.md18 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)
22 %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
28 %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
43 %arg1: !transform.op<"linalg.matmul">,
44 %arg2: !transform.op<"linalg.elemwise_binary">):
74 %arg1: !transform.op<"linalg.matmul">,
75 %arg2: !transform.op<"linalg.elemwise_binary">):
77 : !transform.op<"linalg.matmul">
79 : !transform.op<"linalg.elemwise_binary">
92 debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary})"
[all …]
/llvm-project/mlir/test/Examples/transform/Ch1/
H A Dinvalidation-1.mlir3 // RUN: debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary},\
18 %arg1: !transform.op<"linalg.matmul">,
19 %arg2: !transform.op<"linalg.elemwise_binary">) {
23 : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)
27 transform.debug.emit_remark_at %arg1, "remark" : !transform.op<"linalg.matmul">
38 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)
42 %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
48 %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
59 %arg1: !transform.op<"linalg.matmul">,
60 %arg2: !transform.op<"linalg.elemwise_binary">) {
[all …]
/llvm-project/mlir/docs/Rationale/
H A DRationaleLinalgDialect.md1 # Linalg Dialect Rationale: The Case For Compiler-Friendly Custom Operations
12 that led to the existing implementation of Linalg and aims at exposing
16 Linalg is designed to solve the High-level Hierarchical Optimization
26 this doc and the architecture of Linalg can help inform the community on a
31 Linalg started as a pragmatic dialect to bootstrap code generation in MLIR, by
34 into fast library implementations when available. Linalg **defines ops and
44 However, as the design of Linalg co-evolved with the design of MLIR, it became
48 The design and evolution of Linalg follow a *codegen-friendly* approach where
55 necessary. For example, `linalg.matmul` remains `linalg.matmul` after tiling
58 Furthermore, Linalg decouples transformation validity from profitability
[all …]
/llvm-project/mlir/lib/Dialect/Linalg/IR/
H A DLinalgDialect.cpp1 //===- Dialect.cpp - Implementation of the linalg dialect and types -------===//
9 // This file implements the Linalg dialect types and dialect.
16 #include "mlir/Dialect/Linalg/IR/Linalg.h"
37 using namespace mlir::linalg;
54 // Operations in Linalg dialect are always legal to inline.
104 void mlir::linalg::LinalgDialect::initialize() { in initialize()
107 #include "mlir/Dialect/Linalg/IR/LinalgOpsAttrDefs.cpp.inc" in initialize()
111 #include "mlir/Dialect/Linalg/IR/LinalgOps.cpp.inc" in initialize()
115 #include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc" in initialize()
118 // Fill the Linalg-specific OpName to RegionBuilder map. in initialize()
[all …]
/llvm-project/mlir/lib/CAPI/Dialect/
H A DLinalg.cpp1 //===- Linalg.cpp - C Interface for Linalg dialect ------------------------===//
9 #include "mlir-c/Dialect/Linalg.h"
11 #include "mlir/Dialect/Linalg/IR/Linalg.h"
14 using namespace mlir::linalg;
16 /// Apply the special region builder for the builtin named Linalg op.
17 /// Assert that `op` is a builtin named Linalg op.
25 assert(fun && "Expected a builtin named Linalg op."); in mlirLinalgFillBuiltinNamedOpRegion()
26 assert(op->getNumRegions() == 1 && "Expected Linalg op with 1 region"); in mlirLinalgFillBuiltinNamedOpRegion()
28 "Expected Linalg op with 0 blocks"); in mlirLinalgFillBuiltinNamedOpRegion()
44 MLIR_DEFINE_CAPI_DIALECT_REGISTRATION(Linalg, linalg, LinalgDialect)

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