Merge remote-tracking branch 'upstream/main' (#57)

* update PyTorch version to 2.1.0.dev20230523 (llvm#2148) - torch version: 2.1.0.dev20230523 - torch commit hash: 981d4c2578d10d8a96d173471802fc2812541fb1 - torchvision version: 0.16.0.dev20230523 Co-authored-by: Roll PyTorch Action <[email protected]> * [Torch Dialect] Add split.tensor support + recompose rules (llvm#2102) * add split.tensor support + recompose rules * add e2e test * address comments * address comments * erase op in recomposeOp --------- Co-authored-by: zhekun.zhang <[email protected]> * [Stablehlo] Add `AtenIndexTensor` StableHlo support (llvm#2107) * Add AtenIndexTensor StableHlo support * clean up * Empty commit, trigger test * try to debug hanging test * fix segfulat * fix bad include --------- Co-authored-by: zhekun.zhang <[email protected]> * [arm64] Fix release builds for ARM64 (llvm#2157) Tested on Ubuntu 23.04 on Ampere Altra instance. * [Stablehlo] Add aten.uniform lowering (llvm#2101) * add uniform stablehlo lowering * add unit test * new line * rm redundant file * Empty commit, trigger test * fix include * address comments --------- Co-authored-by: zhekun.zhang <[email protected]> * update PyTorch version to 2.1.0.dev20230525 (llvm#2167) - torch version: 2.1.0.dev20230525 - torch commit hash: eb2ef134b4e834a9b8a8b6de86ddd7d2780ce0ac - torchvision version: 0.16.0.dev20230525 Co-authored-by: Roll PyTorch Action <[email protected]> * CI: disable caching for release builds (llvm#2168) This patch adds a (default-true) input called `cache-enabled` to the setup-build action, so that when the input is false, ccache is not setup on the host machine. This patch also sets the input to be false for the release builds. * Add alias analysis for cast-like ops to maximize-value-semantics (llvm#2160) When `use_tracing=True` is used to import a model into Torch-MLIR, several casts get inserted in the IR to bridge the untyped inputs and outputs with the typed body of the computation. These casts create extra aliases of tensors that cause the current analysis in `maximize-value-semantics` to fail. In particular, the `maximize-value-semantics` analysis assumes that the only valid alias right after an overwrite is the overwritten alias. So, if there is a use of a casted version of the overwritten alias after the overwrite, the analysis fails. This commit improves the analysis by identifying all cast-like aliases of the overwritten alias and allowing such aliases to be used after an overwrite. Because this issue only arises when using tracing, it cannot be currently tested e2e, so only lit test is added. * only setup python for non-docker platforms (llvm#2171) Original PR was accidentally merged to a branch. Re-landing same PR to main now * Remove spurious pip in Release builds (llvm#2172) (left over from a previous commit that was approved and landed in a branch on accident) * [Torch Op] Add AtenChunkOp support (llvm#2152) * add chunkOp support * update LTC xfail list * address comments * address comments --------- Co-authored-by: zhekun.zhang <[email protected]> * Add ARM64 release builds (llvm#2159) Creates a build_linux_arm64 job that builds the release on an arm64 self-hosted runner. Drop Python 3.10 support Pass TM_TORCH_VERSION to choose the Stable PyTorch version (since arm64 doesn't have nightly builds) Borrows nightly / stable Pytorch switch from the WIP llvm#2038 * Delete another spurious pip (llvm#2173) * update PyTorch version to 2.1.0.dev20230526 (llvm#2175) - torch version: 2.1.0.dev20230526 - torch commit hash: 10b46f7c7f69f9bf705d2b6ea53efb9c59145685 - torchvision version: 0.16.0.dev20230526 Co-authored-by: Roll PyTorch Action <[email protected]> * [Stablehlo] Enable Stablehlo backend with arith dialect (llvm#2139) * Add correct type checking for tm_tensor.attention * [TM_TENSOR] Add `aten.scatter.[src|value]` op This commit adds support of `aten.scatter.src` and `aten.scatter.value` ops. Signed-Off-by: Gaurav Shukla <[email protected]> * [MLIR][TORCH] Add support for the total_weight for aten.nll_loss_forward op Signed-Off By: Vivek Khandelwal <[email protected]> * Add Stable PyTorch CI Pipeline (llvm#2038) * feat: split pytorch requirements into stable and nightly * fix: add true to tests to see full output * refactor: add comments to explain true statement * feat: move some tests to experimental mode * refactor: refactor pipeline into more fine grained difference * feat: add version differentiation for some tests * feat: activate more configs * refactor: change implementation to use less requirement files * refactor: remove contraints used for testing * fix: revert some requirement file names * refactor: remove unnecessary ninja install * fix: fix version parsing * refactor: remove dependency on torchvision in main requirements file * refactor: remove index url * style: remove unnecesary line switch * fix: readd index url * Add `ReadOnly` trait to `copy.to_vtensor` (llvm#2179) Before inlining a global slot, the users of the global slot are checked to see if they are `ReadOnly` or `MemoryEffectFree` to make sure that the global slot is not being mutated. Because the op `copy.to_vtensor` currently does not have the `ReadOnly` trait, if a global slot is passed to `copy.to_vtensor`, the pass `InlineGlobalSlots` will fail. The op `copy.to_vtensor` is `ReadOnly`, since it does not modify the contents of the input tensor; it simply makes a new copy. This commit adds the trait as well as an e2e test that generates the case of a global slot being passed to a `copy.to_vtensor`. * [Importer] import constant tuple (llvm#2132) * [Importer] import constant tuple * update * update * update * update PyTorch version to 2.1.0.dev20230531 (llvm#2188) - torch version: 2.1.0.dev20230531 - torch commit hash: 48552338649ccc467060f5f93dbe19e2acbc4d1a - torchvision version: 0.16.0.dev20230531 Co-authored-by: Roll PyTorch Action <[email protected]> * [Torch Dialect] Add support for AtenScalarTensorOp (llvm#2085) * add scalar_tensor op * add dynamo pass test; needs PR2062 * try to fix * Empty commit, trigger test * Empty commit, trigger test * address comments * use dtype function * fix decompose rule * remove unused include * Empty commit, trigger test * fix test * disable ltc * fix dtype --------- Co-authored-by: zhekun.zhang <[email protected]> --------- Signed-off-by: Gaurav Shukla <[email protected]> Co-authored-by: Sean Silva <[email protected]> Co-authored-by: Roll PyTorch Action <[email protected]> Co-authored-by: Zhekun Zhang <[email protected]> Co-authored-by: zhekun.zhang <[email protected]> Co-authored-by: powderluv <[email protected]> Co-authored-by: Ashay Rane <[email protected]> Co-authored-by: Ramiro Leal-Cavazos <[email protected]> Co-authored-by: Yuanqiang Liu <[email protected]> Co-authored-by: George Petterson <[email protected]> Co-authored-by: Gaurav Shukla <[email protected]> Co-authored-by: Vivek Khandelwal <[email protected]> Co-authored-by: maxbartel <[email protected]>
Xilinx · Jun 2, 2023 · e53d054 · e53d054
1 parent b25c53a
commit e53d054
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diff --git a/.github/workflows/buildAndTest.yml b/.github/workflows/buildAndTest.yml
@@ -45,6 +45,9 @@ jobs:
             llvm-build: out-of-tree
           - os-arch: windows-x86_64
             torch-version: stable
+          # For PyTorch stable builds, we don't build PyTorch from source
+          - torch-version: stable
+            torch-binary: OFF
         include:
           # Specify OS versions
           - os-arch: ubuntu-x86_64
@@ -88,7 +91,7 @@ jobs:
         arch: x64
 
     - name: Try to Restore PyTorch Build Cache
-      if: matrix.os-arch != 'windows-x86_64'
+      if: ${{ matrix.torch-binary == 'OFF' }}
       id: cache-pytorch
       uses: actions/cache/restore@v3
       with:
@@ -146,7 +149,7 @@ jobs:
       run: ./build_tools/python_deploy/build_windows_ci.sh
 
     - name: Save PyTorch Build Cache
-      if: ${{ github.ref_name == 'main' && matrix.torch-binary == 'OFF' && matrix.os-arch != 'windows-x86_64' }}
+      if: ${{ github.ref_name == 'main' && matrix.torch-binary == 'OFF' }}
       uses: actions/cache/save@v3
       with:
         path: ${{ github.workspace }}/build_tools/python_deploy/wheelhouse

diff --git a/e2e_testing/xfail_sets.py b/e2e_testing/xfail_sets.py
@@ -619,8 +619,10 @@
     "RsubIntModule_basic",
     "RsubIntModule_noalpha_basic",
     "RsubInt0d_NumToTensor_Module_basic",
+    "ScalarTensorDefaultDtypeModule_basic",
     "ScalarTensorFloat32Module_basic",
-    "ScalarTensorIntModule_basic",
+    "ScalarTensorInt32Module_basic",
+    "ScalarTensorInt64Module_basic",
     "SelectScattertModule_basic",
     "SelectScattertStaticModule_basic",
     "SliceStaticModule_basic",
@@ -1135,8 +1137,10 @@
     "PrimsViewOfModule_basic",
     "PrimsViewOfZeroRankModule_basic",
     "DetachModule_basic",
+    "ScalarTensorDefaultDtypeModule_basic",
     "ScalarTensorFloat32Module_basic",
-    "ScalarTensorIntModule_basic",
+    "ScalarTensorInt32Module_basic",
+    "ScalarTensorInt64Module_basic",
     "UnbindIntListUnpack_Module_basic",
     "UnbindIntGetItem_Module_basic",
     "TensorsConcatStaticModule_basic",

diff --git a/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp b/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp
@@ -7188,22 +7188,17 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %0 : !torch.list<int>\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.scalar_tensor\"(%arg0: !torch.union<float, int>, %arg1: !torch.optional<int>, %arg2: !torch.optional<int>, %arg3: !torch.optional<Device>, %arg4: !torch.optional<bool>) -> !torch.int {\n"
+"    %int6 = torch.constant.int 6\n"
 "    %none = torch.constant.none\n"
 "    %0 = torch.aten.__isnot__ %arg1, %none : !torch.optional<int>, !torch.none -> !torch.bool\n"
 "    %1 = torch.prim.If %0 -> (!torch.int) {\n"
 "      %2 = torch.prim.unchecked_cast %arg1 : !torch.optional<int> -> !torch.int\n"
 "      torch.prim.If.yield %2 : !torch.int\n"
 "    } else {\n"
-"      %2 = func.call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.get_dtype_of_scalar(%arg0) : (!torch.union<float, int>) -> !torch.int\n"
-"      torch.prim.If.yield %2 : !torch.int\n"
+"      torch.prim.If.yield %int6 : !torch.int\n"
 "    }\n"
 "    return %1 : !torch.int\n"
 "  }\n"
-"  func.func @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.get_dtype_of_scalar(%arg0: !torch.union<float, int>) -> !torch.int {\n"
-"    %0 = torch.prim.NumToTensor.Scalar %arg0 : !torch.union<float, int> -> !torch.tensor\n"
-"    %1 = torch.prim.dtype %0 : !torch.tensor -> !torch.int\n"
-"    return %1 : !torch.int\n"
-"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten._shape_as_tensor\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
 "    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
 "    %1 = torch.prim.ListConstruct %0 : (!torch.int) -> !torch.list<int>\n"
@@ -8573,6 +8568,11 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.get_dtype_of_scalar(%arg0) : (!torch.union<float, int>) -> !torch.int\n"
 "    return %0 : !torch.int\n"
 "  }\n"
+"  func.func @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.get_dtype_of_scalar(%arg0: !torch.union<float, int>) -> !torch.int {\n"
+"    %0 = torch.prim.NumToTensor.Scalar %arg0 : !torch.union<float, int> -> !torch.tensor\n"
+"    %1 = torch.prim.dtype %0 : !torch.tensor -> !torch.int\n"
+"    return %1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.nll_loss_backward\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.optional<tuple<int, int>>, %arg4: !torch.int, %arg5: !torch.int, %arg6: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %int4 = torch.constant.int 4\n"
 "    %int11 = torch.constant.int 11\n"

diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -4379,10 +4379,12 @@ class DecomposeAtenScalarTensor : public OpRewritePattern<AtenScalarTensorOp> {
 
     Value cstNone = rewriter.create<ConstantNoneOp>(op.getLoc());
     Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
+    Value dtype =
+        getDtypeIntValueForType(rewriter, op.getLoc(), resultTy.getDtype());
     Value toDTypeLayout = rewriter.create<AtenToDtypeLayoutOp>(
-        op.getLoc(), resultTy, numToTensor, op.getDtype(), op.getLayout(),
-        op.getDevice(), op.getPinMemory(), /*non_blocking*/ cstFalse,
-        /*copy*/ cstFalse, /*memory_format*/ cstNone);
+        op.getLoc(), op.getType(), numToTensor, dtype, op.getLayout(),
+        op.getDevice(), op.getPinMemory(), /*non_blocking=*/cstFalse,
+        /*copy=*/cstFalse, /*memory_format=*/cstNone);
     rewriter.replaceOp(op, toDTypeLayout);
     return success();
   }

diff --git a/lib/Dialect/Torch/Transforms/RecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/RecomposeComplexOps.cpp
@@ -47,6 +47,7 @@ class RecomposeSliceCopy_ : public OpRewritePattern<AtenCopy_Op> {
     if (!matchPattern(sliceOp.getEnd(), m_TorchConstantInt(&end)))
       return failure();
 
+    Value newStart = sliceOp.getStart();
     Value newEnd = sliceOp.getEnd();
     Value dimSize = rewriter.create<AtenSizeIntOp>(
         op.getLoc(), sliceOp.getSelf(), sliceOp.getDim());
@@ -56,6 +57,9 @@ class RecomposeSliceCopy_ : public OpRewritePattern<AtenCopy_Op> {
     }
     newEnd = rewriter.create<PrimMinIntOp>(op.getLoc(), newEnd, dimSize);
 
+    newStart = rewriter.create<PrimMinIntOp>(op.getLoc(), newStart, dimSize);
+    newEnd = rewriter.create<PrimMinIntOp>(op.getLoc(), newEnd, dimSize);
+
     Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
     Value falseVal = rewriter.create<ConstantBoolOp>(op.getLoc(), false);
 
@@ -64,7 +68,7 @@ class RecomposeSliceCopy_ : public OpRewritePattern<AtenCopy_Op> {
     Type rangeType = tensorType.getWithSizesAndDtype(
         {kUnknownSize}, tensorType.getOptionalDtype());
     Value range = rewriter.create<AtenArangeStartStepOp>(
-        op.getLoc(), rangeType, sliceOp.getStart(), newEnd, sliceOp.getStep(),
+        op.getLoc(), rangeType, newStart, newEnd, sliceOp.getStep(),
         /*dtype=*/noneVal, /*layout=*/noneVal, /*device=*/noneVal,
         /*pin_memory=*/noneVal);
 

diff --git a/python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py b/python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py
@@ -774,11 +774,12 @@ def aten〇tensor〇bool〡shape(t: bool, dtype: Optional[int] = None, device: O
 def aten〇scalar_tensor〡shape(s: float, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
     return []
 
+@check_dtype_function([Invocation(-1), Invocation(-1.0)]) 
 def aten〇scalar_tensor〡dtype(s: Union[int, float], dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> int:
     if dtype is not None:
         return dtype
     else:
-        return get_dtype_of_scalar(s)
+        return torch.float32
 
 @check_shape_function([
     Invocation(TensorOfShape()),

diff --git a/python/torch_mlir_e2e_test/test_suite/basic.py b/python/torch_mlir_e2e_test/test_suite/basic.py
@@ -3965,7 +3965,29 @@ def ScalarTensorFloat32Module_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
-class ScalarTensorIntModule(torch.nn.Module):
+class ScalarTensorDefaultDtypeModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        scalar = torch.ops.aten.scalar_tensor(1.0)
+        return scalar
+
+
+@register_test_case(module_factory=lambda: ScalarTensorDefaultDtypeModule())
+def ScalarTensorDefaultDtypeModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+# ==============================================================================
+
+
+class ScalarTensorInt64Module(torch.nn.Module):
 
     def __init__(self):
         super().__init__()
@@ -3979,10 +4001,33 @@ def forward(self):
         return scalar
 
 
-@register_test_case(module_factory=lambda: ScalarTensorIntModule())
-def ScalarTensorIntModule_basic(module, tu: TestUtils):
+@register_test_case(module_factory=lambda: ScalarTensorInt64Module())
+def ScalarTensorInt64Module_basic(module, tu: TestUtils):
     module.forward()
 
+
+# ==============================================================================
+
+
+class ScalarTensorInt32Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        scalar = torch.ops.aten.scalar_tensor(1, dtype=torch.int32)
+        return scalar
+
+
+@register_test_case(module_factory=lambda: ScalarTensorInt32Module())
+def ScalarTensorInt32Module_basic(module, tu: TestUtils):
+    module.forward()
+
+
 # ==============================================================================
 
 

diff --git a/python/torch_mlir_e2e_test/test_suite/slice_like.py b/python/torch_mlir_e2e_test/test_suite/slice_like.py
@@ -591,6 +591,30 @@ def SliceCopyMax_Module_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class SliceCopyStartGreaterThanDimSize_Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, x, y):
+        xslice = torch.ops.aten.slice(x, 0, 100, 10, 1)
+        xslice.copy_(y)
+        return x
+
+
+@register_test_case(module_factory=lambda: SliceCopyStartGreaterThanDimSize_Module())
+def SliceCopyStartGreaterThanDimSize_Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(10, 4, 4), tu.rand(0, 4, 4))
+
+
+# ==============================================================================
+
+
 class SliceCopyEndGreaterThanDimSize_Module(torch.nn.Module):
     def __init__(self):
         super().__init__()

diff --git a/pytorch-hash.txt b/pytorch-hash.txt
@@ -1 +1 @@
-10b46f7c7f69f9bf705d2b6ea53efb9c59145685
+a14be7981bcef6186441a6c5780976e27e6246ea
diff --git a/pytorch-requirements.txt b/pytorch-requirements.txt
@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torch==2.1.0.dev20230526
+torch==2.1.0.dev20230601
diff --git a/torchvision-requirements.txt b/torchvision-requirements.txt
@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
-torchvision==0.16.0.dev20230526
+torchvision==0.16.0.dev20230601
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		10b46f7c7f69f9bf705d2b6ea53efb9c59145685
		a14be7981bcef6186441a6c5780976e27e6246ea