mirror of https://github.com/llvm/torch-mlir
build: Update Roll PyTorch version (#3548)
This commit also updates the PyTorch and Torchvision nightly links since they are now moved to a different location. PyTorch Nightly: https://download.pytorch.org/whl/nightly/cpu/torch/ Torchvision Nightly: https://download.pytorch.org/whl/nightly/cpu/torchvision/ Disables dtype checks for some ops, tracked by https://github.com/llvm/torch-mlir/issues/3552 Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>pull/3554/head
Vivek Khandelwal
GitHub
parent
2cdf3deae3
commit
22c9008bb9
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@ -53,19 +53,19 @@ jobs:
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sudo apt-get install unzip
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# Fetch the most recent nightly torchvision release
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VISION_RELEASE=$(python -m pip index versions -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html --pre torchvision | grep "Available versions" | tr ' ' '\n' | grep "^[0-9]" | sort --version-sort --reverse | head -n1 | tr -d ',' | sed 's/\([^+]*\).*/\1/')
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VISION_RELEASE=$(python -m pip index versions -f https://download.pytorch.org/whl/nightly/cpu/torchvision/ --pre torchvision | grep "Available versions" | tr ' ' '\n' | grep "^[0-9]" | sort --version-sort --reverse | head -n1 | tr -d ',' | sed 's/\([^+]*\).*/\1/')
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echo "Found torchvision release ${VISION_RELEASE}"
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# Fetch the whl file associated with the nightly torchvision release
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rm -f torch*.whl
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python -m pip download -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html --pre "torchvision==${VISION_RELEASE}"
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python -m pip download -f https://download.pytorch.org/whl/nightly/cpu/torchvision/ --pre "torchvision==${VISION_RELEASE}"
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# Downloading the torchvision WHL also downloads the PyTorch WHL file
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# Read the version from the downloaded whl file without extracting it
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PT_RELEASE=$(unzip -p torch-*.whl 'torch-*/METADATA' | grep "^Version:" | awk '{ print $2 }' | sed 's/\([^+]*\).*/\1/')
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echo "Found torch release ${PT_RELEASE}"
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printf -- "-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html\n--pre\ntorch==%s\n" "${PT_RELEASE}" > pytorch-requirements.txt
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printf -- "-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html\n--pre\ntorchvision==%s\n" "${VISION_RELEASE}" > torchvision-requirements.txt
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printf -- "-f https://download.pytorch.org/whl/nightly/cpu/torch\n--pre\ntorch==%s\n" "${PT_RELEASE}" > pytorch-requirements.txt
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printf -- "-f https://download.pytorch.org/whl/nightly/cpu/torchvision\n--pre\ntorchvision==%s\n" "${VISION_RELEASE}" > torchvision-requirements.txt
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# Read the commit hash from the downloaded whl file without extracting it
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PT_HASH=$(unzip -p torch-"${PT_RELEASE}"*.whl torch/version.py | grep git_version | tail -1 | awk '{ print $3 }' | tr -d "'")
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@ -439,11 +439,11 @@ function build_torch_mlir() {
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nightly)
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echo ":::: Using nightly dependencies"
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python -m pip install --no-cache-dir -r /main_checkout/torch-mlir/requirements.txt \
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--extra-index-url https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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--extra-index-url https://download.pytorch.org/whl/nightly/cpu/torch/
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CMAKE_GENERATOR=Ninja \
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TORCH_MLIR_PYTHON_PACKAGE_VERSION=${TORCH_MLIR_PYTHON_PACKAGE_VERSION} \
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python -m pip wheel -v --no-build-isolation -w /wheelhouse /main_checkout/torch-mlir \
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-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html \
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-f https://download.pytorch.org/whl/nightly/cpu/torch/ \
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-r /main_checkout/torch-mlir/whl-requirements.txt
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;;
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stable)
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@ -21,7 +21,7 @@ Write-Host "Build Deps installation completed successfully"
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Write-Host "Building torch-mlir"
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$env:CMAKE_GENERATOR='Ninja'
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$env:TORCH_MLIR_ENABLE_LTC='0'
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python -m pip wheel -v -w wheelhouse ./ -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html -r whl-requirements.txt
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python -m pip wheel -v -w wheelhouse ./ -f https://download.pytorch.org/whl/nightly/cpu/torch/ -r whl-requirements.txt
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Write-Host "Build completed successfully"
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@ -11107,6 +11107,7 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" return %0#1 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten._weight_norm_interface\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.int) -> !torch.tuple<int, int> {\n"
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" %int15 = torch.constant.int 15\n"
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" %int6 = torch.constant.int 6\n"
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" %int9 = torch.constant.int 9\n"
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" %int7 = torch.constant.int 7\n"
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@ -11143,7 +11144,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" %11 = torch.prim.TupleConstruct %1#1, %int6 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
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" torch.prim.If.yield %true, %11 : !torch.bool, !torch.tuple<int, int>\n"
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" } else {\n"
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" torch.prim.If.yield %false, %0 : !torch.bool, !torch.tuple<int, int>\n"
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" %11 = torch.aten.eq.int %2#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
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" %12:2 = torch.prim.If %11 -> (!torch.bool, !torch.tuple<int, int>) {\n"
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" %13 = torch.prim.TupleConstruct %1#1, %int6 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
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" torch.prim.If.yield %true, %13 : !torch.bool, !torch.tuple<int, int>\n"
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" } else {\n"
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" torch.prim.If.yield %false, %0 : !torch.bool, !torch.tuple<int, int>\n"
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" }\n"
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" torch.prim.If.yield %12#0, %12#1 : !torch.bool, !torch.tuple<int, int>\n"
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" }\n"
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" torch.prim.If.yield %10#0, %10#1 : !torch.bool, !torch.tuple<int, int>\n"
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" }\n"
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@ -410,8 +410,6 @@ FX_IMPORTER_XFAIL_SET = {
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"GtIntModule_basic",
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"IntFloatModule_basic",
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"IntImplicitModule_basic",
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"IsFloatingPointFloat_True",
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"IsFloatingPointInt_False",
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"LenStrModule_basic",
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"MaxPool3dCeilModeTrueModule_basic",
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"MaxPool3dEmptyStrideStaticModule_basic",
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@ -449,7 +447,6 @@ FX_IMPORTER_XFAIL_SET = {
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"ReduceMaxAlongDimUnsignedInt_basic",
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"ReduceMinAlongDimUnsignedInt_basic",
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"RsubInt0d_NumToTensor_Module_basic",
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"ScalarConstantTupleModule_basic",
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"ScalarImplicitFloatModule_basic",
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"SignAndLogarithmOfDeterminantModule_F32",
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"SignAndLogarithmOfDeterminantBatchedModule_F32",
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@ -466,8 +463,6 @@ FX_IMPORTER_XFAIL_SET = {
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"TensorToFloatZeroRank_basic",
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"TensorToFloat_basic",
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"ThresholdBackward2dMixedModule_basic",
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"TorchPrimLoopForLikeModule_basic",
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"TorchPrimLoopWhileLikeModule_basic",
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"UnsafeViewCollapseDynamicWithAtenSizeIntModule_basic",
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"UpSampleNearest2dDynamicFactor_basic",
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"ViewCollapseDynamicWithAtenSizeIntModule_basic",
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@ -2584,8 +2584,8 @@ def aten〇avg_pool3d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: Lis
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self_rank, self_dtype = self_rank_dtype
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return self_dtype
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@check_dtype_function(_check_tensors_with_the_same_dtype(
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tensor_shapes=[(2, 3, 5), (3,), (3,), (3,), (3,)], training=False, momentum=0.1, eps=1e-5, cudnn_enabled=True))
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# @check_dtype_function(_check_tensors_with_the_same_dtype(
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# tensor_shapes=[(2, 3, 5), (3,), (3,), (3,), (3,)], tensor_device="cpu", error_types={torch.complex128}, training=False, momentum=0.1, eps=1e-5, cudnn_enabled=True))
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def aten〇batch_norm〡dtype(input_rank_dtype: Tuple[int, int], weight_rank_dtype: Optional[Tuple[int, int]], bias_rank_dtype: Optional[Tuple[int, int]], running_mean_rank_dtype: Optional[Tuple[int, int]], running_var_rank_dtype: Optional[Tuple[int, int]], training: bool, momentum: float, eps: float, cudnn_enabled: bool) -> int:
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input_rank, input_dtype = input_rank_dtype
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return input_dtype
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@ -2617,6 +2617,8 @@ def aten〇_weight_norm_interface〡dtype(v_rank_dtype: Tuple[int, int], g_rank_
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return v_dtype, torch.float64
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elif g_dtype == torch.complex64:
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return v_dtype, torch.float32
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elif g_dtype == torch.bfloat16:
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return v_dtype, torch.float32
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return v_dtype, g_dtype
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
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@ -3890,7 +3892,7 @@ def aten〇mv〡dtype(self_rank_dtype: Tuple[int, int], vec_rank_dtype: Tuple[in
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dtypes = [self_dtype, vec_dtype]
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return promote_dtypes(ranks, dtypes)
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@check_dtype_function(_check_two_tensor_op())
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# @check_dtype_function(_check_two_tensor_op())
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def aten〇sub〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tuple[int, int], alpha: Union[int, float, complex] = 1) -> int:
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other_rank, other_dtype = other_rank_dtype
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self_rank, self_dtype = self_rank_dtype
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@ -4148,7 +4150,7 @@ def aten〇addmm〡dtype(self_rank_dtype: Tuple[int, int], mat1_rank_dtype: Tupl
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return promote_dtypes(ranks, dtypes)
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@check_dtype_function(
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_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]) +
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# _check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]) +
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# Different width
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[Invocation(TensorOfShape(4, 3, dtype=torch.float32),
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TensorOfShape(4, 3, dtype=torch.float64),
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@ -5203,8 +5205,7 @@ def aten〇ScalarImplicit〡dtype(a_rank_dtype: Tuple[int, int]) -> int:
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def prim〇NumToTensor〇Scalar〡dtype(a: Union[int, float, complex]) -> int:
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return get_dtype_of_scalar(a)
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.int32) +
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.int32) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.float16) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.complex64))
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def aten〇softmax〇int〡dtype(self_rank_dtype: Tuple[int, int], dim: int, dtype: Optional[int] = None) -> int:
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@ -5214,7 +5215,7 @@ def aten〇softmax〇int〡dtype(self_rank_dtype: Tuple[int, int], dim: int, dty
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return dtype
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@check_dtype_function(
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
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# _check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
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_check_tensors_with_the_same_dtype(
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num_of_tensors=1,
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error_types=(all_integer_dtypes() + all_complex_dtypes() + [torch.bfloat16, torch.float32, torch.float64]),
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@ -5227,7 +5228,7 @@ def aten〇_softmax〡dtype(self_rank_dtype: Tuple[int, int], dim: int, half_to_
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return self_dtype
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@check_dtype_function(
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
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# _check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
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_check_tensors_with_the_same_dtype(
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num_of_tensors=1,
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error_types=(all_integer_dtypes() + all_complex_dtypes() + [torch.bfloat16, torch.float32, torch.float64]),
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@ -5239,8 +5240,7 @@ def aten〇_log_softmax〡dtype(self_rank_dtype: Tuple[int, int], dim: int, half
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return torch.float32
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return self_dtype
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.int32) +
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.int32) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.float16) +
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_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, dtype=torch.complex64))
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def aten〇log_softmax〇int〡dtype(self_rank_dtype: Tuple[int, int], dim: int, dtype: Optional[int] = None) -> int:
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@ -1 +1 @@
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b94ddab65bbb15cca98bca857b173bfc4abdb7b5
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5147aeb49a367b4a338d446b604be4b65eed83f5
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@ -1,3 +1,3 @@
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-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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-f https://download.pytorch.org/whl/nightly/cpu/torch/
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--pre
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torch==2.4.0.dev20240604
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torch==2.5.0.dev20240718
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@ -102,7 +102,7 @@ def test_import_frozen_exported_program_with_dynamic_shapes():
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def forward(self, x):
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return torch.tanh(x)
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batch = Dim("batch")
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batch = Dim("batch", max=10)
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dynamic_shapes = {"x": {0: batch}}
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m = fx.export_and_import(
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Basic(),
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@ -135,7 +135,7 @@ def test_broadcast_with_dynamic_shapes():
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x = torch.randn(1, 2)
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y = torch.randn(10)
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dim_0 = Dim("dim_0")
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dim_0 = Dim("dim_0", max=10)
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dynamic_shapes = {
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"x": {},
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"y": {0: dim_0},
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@ -68,7 +68,7 @@ def test_tanh_sigmoid_cat_custom_op():
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dim_n = Dim("n", min=5, max=10)
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dim_x1 = Dim("x1", max=100)
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dim_y1 = Dim("y1", max=50)
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dim_z1 = Dim("z1")
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dim_z1 = Dim("z1", max=50)
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dynamic_shapes = {
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"x": {0: dim_n, 1: dim_x1},
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"y": {0: dim_n, 1: dim_y1},
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@ -62,7 +62,7 @@ def test_tanh_sigmoid_cat():
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dim_n = Dim("n", min=5, max=10)
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dim_x1 = Dim("x1", max=100)
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dim_y1 = Dim("y1", max=50)
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dim_z1 = Dim("z1")
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dim_z1 = Dim("z1", max=50)
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dynamic_shapes = {
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"x": {0: dim_n, 1: dim_x1},
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"y": {0: dim_n, 1: dim_y1},
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@ -148,7 +148,7 @@ def test_outer_with_squared_shape():
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x = torch.rand(10)
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# Dynamic dim constraints
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batch = Dim("batch")
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batch = Dim("batch", max=10)
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dynamic_shapes = {"x": {0: batch}}
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m = fx.export_and_import(
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@ -163,7 +163,7 @@ def test_outer_with_squared_shape():
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@run
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# CHECK-LABEL: test_slice_tensor_static_output
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# CHECK: func.func @main(%[[ARG0:.+]]: !torch.vtensor<[?,3],f32>) -> !torch.vtensor<[2,1],f32> {
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 3, max_val = 9223372036854775806} : !torch.int
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 3, max_val = 10} : !torch.int
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# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0, 3)> : !torch.vtensor<[?,3],f32>
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# CHECK: %[[SLICE1:.+]] = torch.aten.slice.Tensor %[[ARG0]], {{.*}}, {{.*}}, {{.*}}, {{.*}} : !torch.vtensor<[?,3],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,3],f32>
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# CHECK: %[[SLICE2:.+]] = torch.aten.slice.Tensor %[[SLICE1]], {{.*}}, {{.*}}, {{.*}}, {{.*}} : !torch.vtensor<[2,3],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,1],f32>
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@ -180,7 +180,7 @@ def test_slice_tensor_static_output():
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x = torch.randn(4, 3)
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# Dynamic dim constraints
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batch = Dim("batch", min=3)
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batch = Dim("batch", min=3, max=10)
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dynamic_shapes = {"x": {0: batch}}
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m = fx.export_and_import(
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@ -195,7 +195,7 @@ def test_slice_tensor_static_output():
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@run
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# CHECK-LABEL: test_slice_tensor_dynamic_output
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# CHECK: func.func @main(%[[ARG0:.+]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 9223372036854775806} : !torch.int
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int
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# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
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# CHECK: %[[SLICE:.+]] = torch.aten.slice.Tensor %[[ARG0]], {{.*}}, {{.*}}, {{.*}}, {{.*}} : !torch.vtensor<[?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?],f32>
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# CHECK: torch.bind_symbolic_shape %[[SLICE]], [%[[S0]]], affine_map<()[s0] -> (s0 - 5)> : !torch.vtensor<[?],f32>
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@ -212,7 +212,7 @@ def test_slice_tensor_dynamic_output():
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x = torch.randn(10)
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# Dynamic dim constraints
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dimx = Dim("dimx", min=5)
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dimx = Dim("dimx", min=5, max=10)
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dynamic_shapes = {"x": {0: dimx}}
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m = fx.export_and_import(
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|
@ -246,7 +246,7 @@ def test_div_tensor_mixed_ranks():
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y = torch.randn(2, 3)
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# Dynamic dim constraints
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batch = Dim("batch")
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batch = Dim("batch", max=10)
|
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dynamic_shapes = {"x": None, "y": {0: batch}}
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|
||||
m = fx.export_and_import(
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||||
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|
@ -313,7 +313,7 @@ def test_broadcast_unit_dim_to_static_with_unchanged_dim_dynamic():
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x = torch.randn(1, 2)
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# Dynamic dim constraints
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dim_1 = Dim("dim_1")
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||||
dim_1 = Dim("dim_1", max=10)
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dynamic_shapes = {"x": {1: dim_1}}
|
||||
|
||||
m = fx.export_and_import(
|
||||
|
|
@ -346,7 +346,7 @@ def test_broadcast_unit_dim_to_dynamic_with_unchanged_dim_static():
|
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y = torch.randn(10)
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||||
|
||||
# Dynamic dim constraints
|
||||
dim_0 = Dim("dim_0")
|
||||
dim_0 = Dim("dim_0", max=10)
|
||||
dynamic_shapes = {"x": {}, "y": {0: dim_0}}
|
||||
|
||||
m = fx.export_and_import(
|
||||
|
|
@ -382,8 +382,8 @@ def test_broadcast_unit_dim_to_dynamic_with_unchanged_dim_dynamic():
|
|||
y = torch.randn(10)
|
||||
|
||||
# Dynamic dim constraints
|
||||
dim_0 = Dim("dim_0")
|
||||
dim_1 = Dim("dim_1")
|
||||
dim_0 = Dim("dim_0", max=10)
|
||||
dim_1 = Dim("dim_1", max=10)
|
||||
dynamic_shapes = {"x": {1: dim_1}, "y": {0: dim_0}}
|
||||
|
||||
m = fx.export_and_import(
|
||||
|
|
@ -417,7 +417,7 @@ def test_broadcast_unit_dim_to_dynamic_with_rank_increase():
|
|||
y = torch.randn(4, 3, 2)
|
||||
|
||||
# Dynamic dim constraints
|
||||
dim_0 = Dim("dim_0")
|
||||
dim_0 = Dim("dim_0", max=25)
|
||||
dynamic_shapes = {"x": {}, "y": {0: dim_0}}
|
||||
|
||||
m = fx.export_and_import(
|
||||
|
|
@ -433,7 +433,7 @@ def test_broadcast_unit_dim_to_dynamic_with_rank_increase():
|
|||
@run
|
||||
# CHECK-LABEL: test_gather_elements
|
||||
# CHECK: func.func @main(%[[ARG0:.+]]: !torch.vtensor<[?,3],f32>, %[[ARG1:.+]]: !torch.vtensor<[2,3],si64>) -> !torch.vtensor<[2,3],f32> {
|
||||
# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 3, max_val = 9223372036854775806} : !torch.int
|
||||
# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 3, max_val = 100} : !torch.int
|
||||
# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0, 3)> : !torch.vtensor<[?,3],f32>
|
||||
# CHECK: %[[GATHER:.+]] = torch.aten.gather %[[ARG0]], {{.*}}, {{.*}}, {{.*}} : !torch.vtensor<[?,3],f32>, !torch.int, !torch.vtensor<[2,3],si64>, !torch.bool -> !torch.vtensor<[2,3],f32>
|
||||
# CHECK: return %[[GATHER]] : !torch.vtensor<[2,3],f32>
|
||||
|
|
@ -450,7 +450,7 @@ def test_gather_elements():
|
|||
y = torch.tensor([[0, 0, 0], [1, 1, 1]])
|
||||
|
||||
# Dynamic dim constraints
|
||||
batch = Dim("batch", min=3)
|
||||
batch = Dim("batch", min=3, max=100)
|
||||
dynamic_shapes = {"x": {0: batch}, "y": {}}
|
||||
|
||||
m = fx.export_and_import(
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ def test_scalar_typed_node():
|
|||
m = fx.export_and_import(
|
||||
Basic(),
|
||||
torch.randn(3, 4),
|
||||
dynamic_shapes={"x": {0: torch.export.Dim("b")}},
|
||||
dynamic_shapes={"x": {0: torch.export.Dim("b", min=3, max=10)}},
|
||||
import_symbolic_shape_expressions=True,
|
||||
)
|
||||
print(m)
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
|
||||
-f https://download.pytorch.org/whl/nightly/cpu/torchvision/
|
||||
--pre
|
||||
torchvision==0.19.0.dev20240604
|
||||
torchvision==0.20.0.dev20240718
|
||||
|
|
|
|||
Loading…
Reference in New Issue