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.github/workflows/bazelBuildAndTestTcp.yml

@@ -56,6 +56,11 @@ jobs:
                    find . -type f -name "*.cpp" -o -name "*.h" | xargs clang-format -i
         if [ -n "$(git status --porcelain)" ]; then
           echo "Please run 'find . -type f -name "*.cpp" -o -name "*.h" | xargs clang-format -i' and commit changes."
+          echo "git reports the following changes: "
+          echo "$(git status --porcelain)"
+          echo "$(git diff -u)"
+          docker run --rm mlir-tcp:ci clang-format --version
+          docker run --rm mlir-tcp:ci uname -a
           exit 1
         fi
 

deps.bzl

@@ -22,8 +22,8 @@ def third_party_deps():
             path = local_llvm_repo_path(),
         )
     else:
-        LLVM_COMMIT = "5d6d982df61d16b6d498e6d59dd91c059679d3d8"
-        LLVM_SHA256 = "834184126812eecbdb2ed30de255554a6529295afaf44e9dfd3851d61195dbb5"
+        LLVM_COMMIT = "72144d119a7291f8b6b8e022a2947fbe31e66afc"
+        LLVM_SHA256 = "2caacb6925a13cb5886a5d7f225fa408b80ca8e1efe0736186954b2abc4ee1c3"
         http_archive(
             name = "llvm-raw",
             build_file_content = "# empty",
@@ -39,14 +39,17 @@ def third_party_deps():
             path = local_torch_mlir_repo_path(),
         )
     else:
-        TORCH_MLIR_COMMIT = "169032010793ee7fe3e305ab920e4119fdfc3b11"
-        TORCH_MLIR_SHA256 = "0f25459b0d6828983c8aa78d139adad4325508bff150b57e97345e9798377dd3"
+        TORCH_MLIR_COMMIT = "9f2ba5abaa85cefd95cc85579fafd0c53c1101e8"
+        TORCH_MLIR_SHA256 = "09444281839eeae4aff42c029d87b1728f307fa26511b896ff448d51aaa98049"
         http_archive(
             name = "torch-mlir-raw",
             build_file_content = "# empty",
             sha256 = TORCH_MLIR_SHA256,
             strip_prefix = "torch-mlir-" + TORCH_MLIR_COMMIT,
             urls = ["https://github.com/llvm/torch-mlir/archive/{commit}.tar.gz".format(commit = TORCH_MLIR_COMMIT)],
+            patches = [
+                "//third_party/patches:torch-mlir.1.patch",
+            ],
         )
 
     if use_local_stablehlo_repo():
@@ -55,8 +58,8 @@ def third_party_deps():
             path = local_stablehlo_repo_path(),
         )
     else:
-        STABLEHLO_COMMIT = "b62dc66da9946b4c400c0d99c9d5bb8e04edaee6"
-        STABLEHLO_SHA256 = "a51842f5cbcccc2dc74de232793e6fdc0b4403b616281a73bbc704cd227b50db"
+        STABLEHLO_COMMIT = "a54938f0651d3b4b7be9771848eda2463c92a8e7"
+        STABLEHLO_SHA256 = "edab2288f0b19e3efbf08815d17d4efb106984aa6fe02fed0cb2165284e6a5b7"
         http_archive(
             name = "stablehlo",
             sha256 = STABLEHLO_SHA256,
@@ -168,3 +171,19 @@ def third_party_deps():
         strip_prefix = "cnpy-4e8810b1a8637695171ed346ce68f6984e585ef4",
         urls = ["https://github.com/rogersce/cnpy/archive/4e8810b1a8637695171ed346ce68f6984e585ef4.tar.gz"],
     )
+
+    http_archive(
+        name = "nanobind",
+        build_file = "@llvm-raw//utils/bazel/third_party_build:nanobind.BUILD",
+        sha256 = "bb35deaed7efac5029ed1e33880a415638352f757d49207a8e6013fefb6c49a7",
+        strip_prefix = "nanobind-2.4.0",
+        url = "https://github.com/wjakob/nanobind/archive/refs/tags/v2.4.0.tar.gz",
+    )
+
+    http_archive(
+        name = "robin_map",
+        build_file = "@llvm-raw//utils/bazel/third_party_build:robin_map.BUILD",
+        sha256 = "a8424ad3b0affd4c57ed26f0f3d8a29604f0e1f2ef2089f497f614b1c94c7236",
+        strip_prefix = "robin-map-1.3.0",
+        url = "https://github.com/Tessil/robin-map/archive/refs/tags/v1.3.0.tar.gz",
+    )

lib/Pipeline/Pipeline.cpp

@@ -80,10 +80,10 @@ static void createTcpToLlvmPipeline(OpPassManager &pm) {
 
   // One-shot bufferize tensor -> memref, from
   // https://mlir.llvm.org/docs/Bufferization/.
-  bufferization::OneShotBufferizationOptions bufferizationOptions;
+  bufferization::OneShotBufferizePassOptions bufferizationOptions;
   bufferizationOptions.bufferizeFunctionBoundaries = true;
-  bufferizationOptions.setFunctionBoundaryTypeConversion(
-      bufferization::LayoutMapOption::IdentityLayoutMap);
+  bufferizationOptions.functionBoundaryTypeConversion =
+      bufferization::LayoutMapOption::IdentityLayoutMap;
   pm.addPass(bufferization::createOneShotBufferizePass(bufferizationOptions));
   // Buffer deallocation pipeline for automatically inserting
   // buffer deallocation ops after one-shot bufferization.
@@ -95,14 +95,14 @@ static void createTcpToLlvmPipeline(OpPassManager &pm) {
   pm.addPass(bufferization::createLowerDeallocationsPass());
   pm.addPass(createCSEPass());
   pm.addPass(createCanonicalizerPass());
-  pm.addPass(createBufferizationToMemRefPass());
+  pm.addPass(createConvertBufferizationToMemRefPass());
 
   // Blanket-convert any remaining linalg ops to loops if any remain.
   pm.addNestedPass<func::FuncOp>(createConvertLinalgToLoopsPass());
   // Blanket-convert any remaining affine ops if any remain.
   pm.addPass(createLowerAffinePass());
   // Convert SCF to CF (always needed).
-  pm.addPass(createConvertSCFToCFPass());
+  pm.addPass(createSCFToControlFlowPass());
 
   // Sprinkle some cleanups.
   pm.addPass(createCanonicalizerPass());

requirements_lock.txt

@@ -157,28 +157,30 @@ sympy==1.13.3 \
     --hash=sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73 \
     --hash=sha256:b27fd2c6530e0ab39e275fc9b683895367e51d5da91baa8d3d64db2565fec4d9
     # via torch
-torch==2.7.0.dev20250221+cpu \
-    --hash=sha256:03ed7e7e5186f2f0cadea798d1bbb249ca342618e90542b9634463c194ad8999 \
-    --hash=sha256:08c6edef82a3a11afcc8ec86910eb41837ca7e646416280de6ef352f6e216370 \
-    --hash=sha256:0fd00b3a1198f610fa706074243097d4448ee0a0efca96b809107c60af0a3f98 \
-    --hash=sha256:17672aba174f465fe90bee6972ee9a542980b59f410772e68091a5d044604b9d \
-    --hash=sha256:17db41853a494b1eb1b3c23e383297b2e4678d6629dfa539e8a9f71c4b05b32d \
-    --hash=sha256:1b3c5bc3a52cdacee11794a5221aea89a6207439a24aedfd458d79b6b5d38ad1 \
-    --hash=sha256:4201870a7d363dfb0c2015a26d847f71248685f352234c71834923f1b477b7ed \
-    --hash=sha256:494d8fa9c469cdcb042f61cfbf2c505ebfd344e923af8ba7f6223cd8b2a7742c \
-    --hash=sha256:5218559bd4c044977b3240aa0a2e188f7694865ef24611430bcb701ec10f5276 \
-    --hash=sha256:73f82e45e5f1707100751bd0bedbb8bf242e32268913959dbe9f0e4ab3b3cb99 \
-    --hash=sha256:9f80431e71a2e7d7795220eb9d549cf0490e6c5b36f0886539f7c9ef11b23a39 \
-    --hash=sha256:a89ed8084b88720fed36655cd8fe49c1b5c135483c61fb9ac5231210a502ef9c \
-    --hash=sha256:ac88604bf2dd8e4a53ed07877fb9e845a7c3c8a03aeeefe19f381655f19b056c \
-    --hash=sha256:ae3698e5caa6ddf1ad40712924f45788e15e2a838793969b0477868e02953011 \
-    --hash=sha256:aee87be29d490521806a414191e9e9afbc27e55167e79dbb66d06c18d87e0079 \
-    --hash=sha256:b3a63e0b2e8c495d0781735720a063c8924014cfcae8a6f63a360a696b657730 \
-    --hash=sha256:b8ac59ca5484c438a49b4d6a4ad3721256071c80687220ae8ffa355ea5c745ee \
-    --hash=sha256:c61d02f308414e7a2b95972e2b950168af86d5b24dcedb332e29ecb8419b64cd \
-    --hash=sha256:d9f4236bfb9b4dc39e7569ff5a2fb39f4b28b3f79dc2ff6b6ce70f7ca67fd40a \
-    --hash=sha256:f15bc3e3e51227f18068da9cd5b8153734385d817723c153bb0cdea7285b1eae \
-    --hash=sha256:f7433418e166b7a3e87e43f3e64115207a84dc50db8dc3b9a51428e737edcac7
+torch==2.8.0.dev20250506+cpu \
+    --hash=sha256:02abfdcdbb9ca15e3c561d31b1617f9d88f978af49b3b76cc048a5159c4bbb19 \
+    --hash=sha256:0304c11aa1a404a664a776dea4b61dab31707d5fecc1e165ea17b1c780049911 \
+    --hash=sha256:081ecdc2ced1285b92cce4684922710af244ccf4e4430d36c746f025e6872a30 \
+    --hash=sha256:0bdc6883695004803ea0e062382d21e432168d7ee93e6f77375d34fc43778ca8 \
+    --hash=sha256:1c82f3cd449bee2adcfc8c1dc25b087fc3ed9eba239ea46449e1a087ddbf5f97 \
+    --hash=sha256:370ae6fb1c8c132c4578973eb6066f14d10fb6cdc05a89e44660fec15bbce9a4 \
+    --hash=sha256:3c68844186c4d43db95f096b120b91c530c4e92540eeeece90e59fd6ec078f03 \
+    --hash=sha256:4017473f0a77cd2774a3c8245032fb9979ac08f92831f94f70d9e22612e2d5c1 \
+    --hash=sha256:4575a76e5459285311d1f94fb8835fec81d5509321192716fcff8631aa258ae3 \
+    --hash=sha256:48c682f8f369b573045d5922e989812b77183f4020a750b3339c3e64e42fd733 \
+    --hash=sha256:4a64fd103df112e2dbfb00ab04ffef839bc1838caa40ff8bf86647eb39daa7ad \
+    --hash=sha256:5f2a251b87dc7a359fe5b83772cb2830e01b0d75a585edc1ffe659a3e59ae17b \
+    --hash=sha256:690f44ae8974588810a6c58052e908fb1abc7c3d34e335faccec0baba852596b \
+    --hash=sha256:810c8106d575256c6e429e26a8edf58e4ab43fea0b10c4d56eed011f0712ee90 \
+    --hash=sha256:8701a35246db0aa148ea3bb6edb022a639c16115912d2dc90cbad9a56c0ded2e \
+    --hash=sha256:a5974f2958d12d01577e206417ee4d04dc2f2275505d266323cf23e828e46d96 \
+    --hash=sha256:b17959e888c65cef0765bfef3e4813f3dad7d3d55f73c976ca33a47d2ff875b5 \
+    --hash=sha256:b91059dce8f9c97fce586b1367c91c64912ba0866e2213510a3ffe522cee3aee \
+    --hash=sha256:c8a7058db5c6c478d2f93a14f911dcc045d5470ed0920797ec5a6008a0bce354 \
+    --hash=sha256:ce7960db4fb7899626a4a94c361b0d2c80c9b3bd6907b929380a176df27b9908 \
+    --hash=sha256:e23ba269a7f189dc65c1b0ff937beb0630dfbe9a810cd307d284a51cbc8409d6 \
+    --hash=sha256:e2cccdcc64938ede25afc43efaa4e70fdf45709c3f2b48549adc0d163aa7fadf \
+    --hash=sha256:fb30a20142ed498569649208d67f03e9e9f345be79ab340ceec734439a475d9a
     # via -r requirements.txt
 torch-mlir==20250127.357 \
     --hash=sha256:43c2362b6a5265405ac5d2291982d6b0d83afafc7ee37165f4cc6b845dec4c15 \

test/Pipeline/tcp_to_llvm_pipeline.mlir

@@ -2,7 +2,6 @@
 
 // CHECK-LABEL: llvm.func @main
 // CHECK:         llvm.mlir.constant
-// CHECK:         llvm.mlir.undef
 // CHECK:         llvm.insertvalue
 // CHECK:         llvm.extractvalue
 // CHECK:         llvm.alloca

test/python_lit/fx_import/basic_test.py

@@ -24,18 +24,17 @@ def run(f):
 @run
 # CHECK-LABEL: test_import_frozen_exported_program
 # CHECK:     func.func @main(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
-# CHECK-DAG: %[[a:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_1_4_torch.float32> : tensor<1x4xf32>) : !torch.vtensor<[1,4],f32>
+# CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
+# CHECK-DAG: %[[a:.+]] = torch.aten.rand{{.*}} -> !torch.vtensor<[1,4],f32>
 # CHECK-DAG: %[[b:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_3_1_torch.float32> : tensor<3x1xf32>) : !torch.vtensor<[3,1],f32>
 # CHECK-DAG: %[[p:.+]] = torch.vtensor.literal(dense<{{.*>+}} : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
-# CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
 # CHECK-DAG: %[[mul_a:.+]] = torch.aten.mul.Tensor %[[tanh]], %[[a]]
 # CHECK-DAG: %[[mul_b:.+]] = torch.aten.mul.Tensor %[[mul_a]], %[[b]]
 # CHECK-DAG: %[[mul_p:.+]] = torch.aten.mul.Tensor %[[mul_b]], %[[p]]
 # CHECK:     return %[[mul_p]]
 #
 # Validate dialect resources exist.
 # CHECK: dialect_resources:
-# CHECK-DAG: torch_tensor_1_4_torch.float32
 # CHECK-DAG: torch_tensor_3_1_torch.float32
 def test_import_frozen_exported_program():
     # Tests the basic structural premises of import_frozen_exported_program,

test/python_lit/fx_import/custom_op_test.py

@@ -26,15 +26,15 @@ def run(f):
 # CHECK-SAME:       %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
 # CHECK-SAME:       %[[ARG1:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
 # CHECK-SAME:       %[[ARG2:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {
-# CHECK:        %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int
-# CHECK:        %[[S1:.+]] = torch.symbolic_int "s1" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
-# CHECK:        %[[S2:.+]] = torch.symbolic_int "s3" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
-# CHECK:        %[[S3:.+]] = torch.symbolic_int "s5" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
-# CHECK:        torch.bind_symbolic_shape %[[ARG0]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        %[[S0:.+]] = torch.symbolic_int "s{{.*}}" {min_val = 5, max_val = 10} : !torch.int
+# CHECK:        %[[S1:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
+# CHECK:        %[[S2:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
+# CHECK:        %[[S3:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
+# CHECK:        torch.bind_symbolic_shape %[[ARG0]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        torch.bind_symbolic_shape %[[ARG1]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
-# CHECK:        torch.bind_symbolic_shape %[[ARG2]], [%[[S0]], %[[S3]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        torch.bind_symbolic_shape %[[ARG2]], [%[[S3]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        %[[OP:.+]] = torch.operator "torch.my_custom_library.tanh_sigmoid_cat_op"(%[[ARG0]], %[[ARG1]], %[[ARG2]]) : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32>
-# CHECK:        torch.bind_symbolic_shape %[[OP]], [%[[S0]], %[[S1]], %[[S2]], %[[S3]]], affine_map<()[s0, s1, s2, s3] -> (s0, s2 + s3 + s1 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        torch.bind_symbolic_shape %[[OP]], [%[[S1]], %[[S3]], %[[S0]], %[[S2]]], affine_map<()[s0, s1, s2, s3] -> (s2, s1 + s3 + s0 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        return %[[OP]] : !torch.vtensor<[?,?,3],f32>
 def test_tanh_sigmoid_cat_custom_op():
 

test/python_lit/fx_import/symbolic_shape_expr_test.py

@@ -26,20 +26,20 @@ def run(f):
 # CHECK-SAME:       %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
 # CHECK-SAME:       %[[ARG1:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
 # CHECK-SAME:       %[[ARG2:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {
-# CHECK:        %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int
-# CHECK:        %[[S1:.+]] = torch.symbolic_int "s1" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
-# CHECK:        %[[S2:.+]] = torch.symbolic_int "s3" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
-# CHECK:        %[[S3:.+]] = torch.symbolic_int "s5" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
-# CHECK:        torch.bind_symbolic_shape %[[ARG0]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        %[[S0:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = 5, max_val = 10} : !torch.int
+# CHECK:        %[[S1:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
+# CHECK:        %[[S2:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
+# CHECK:        %[[S3:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
+# CHECK:        torch.bind_symbolic_shape %[[ARG0]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        torch.bind_symbolic_shape %[[ARG1]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
-# CHECK:        torch.bind_symbolic_shape %[[ARG2]], [%[[S0]], %[[S3]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        torch.bind_symbolic_shape %[[ARG2]], [%[[S3]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        %[[TANH:.+]] = torch.aten.tanh %[[ARG0]] : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>
-# CHECK:        torch.bind_symbolic_shape %[[TANH]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        torch.bind_symbolic_shape %[[TANH]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        %[[SIG:.+]] = torch.aten.sigmoid %[[ARG1]] : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>
 # CHECK:        torch.bind_symbolic_shape %[[SIG]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        %[[LIST:.+]] = torch.prim.ListConstruct %[[TANH]], %[[TANH]], %[[SIG]], %[[ARG2]] : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.list<vtensor>
 # CHECK:        %[[CAT:.+]] = torch.aten.cat %[[LIST]], {{.*}} : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[?,?,3],f32>
-# CHECK:        torch.bind_symbolic_shape %[[CAT]], [%[[S0]], %[[S1]], %[[S2]], %[[S3]]], affine_map<()[s0, s1, s2, s3] -> (s0, s2 + s3 + s1 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
+# CHECK:        torch.bind_symbolic_shape %[[CAT]], [%[[S1]], %[[S3]], %[[S0]], %[[S2]]], affine_map<()[s0, s1, s2, s3] -> (s2, s1 + s3 + s0 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
 # CHECK:        return %[[CAT]] : !torch.vtensor<[?,?,3],f32>
 def test_tanh_sigmoid_cat():
     class TanhSigmoidCat(nn.Module):

third_party/patches/BUILD

@@ -0,0 +1,10 @@
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+filegroup(
+    name = "all_files",
+    srcs = glob(["*"]),
+    visibility = ["//visibility:public"],
+)

third_party/patches/torch-mlir.1.patch

@@ -0,0 +1,12 @@
+diff --git lib/InitAll.cpp lib/InitAll.cpp
+index d9096929..2a9be6cc 100644
+--- lib/InitAll.cpp
++++ lib/InitAll.cpp
+@@ -33,6 +33,7 @@
+ #ifdef TORCH_MLIR_ENABLE_STABLEHLO
+ #include "stablehlo/conversions/linalg/transforms/Passes.h"
+ #include "stablehlo/transforms/Passes.h"
++#include "stablehlo/transforms/optimization/Passes.h"
+ #endif
+
+ #ifdef TORCH_MLIR_ENABLE_TOSA