mirror of https://github.com/llvm/torch-mlir
[NFC] Update black version (#3256)
* Update black version to support 3.11/3.12 * Reformat codepull/3257/head
parent
aed2cf3351
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@ -11,7 +11,7 @@ repos:
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- id: check-yaml
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- id: check-added-large-files
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- repo: https://github.com/psf/black
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rev: 22.10.0
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rev: 24.4.2
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hooks:
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- id: black
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@ -2,6 +2,7 @@
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See https://github.com/llvm/torch-mlir/issues/1374
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"""
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import argparse
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import json
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@ -3,6 +3,7 @@ from torch_mlir import torchscript
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from transformers import BertForMaskedLM
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# Wrap the bert model to avoid multiple returns problem
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class BertTinyWrapper(torch.nn.Module):
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def __init__(self) -> None:
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@ -257,9 +257,9 @@ class _FXGraphImporter:
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# FakeTensor's in case of a tuple return with multiple elements.
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self._env: Dict[Tuple[torch.fx.Node, int], ir.Value] = {}
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self._module = ir.Module.create(ir.Location.unknown())
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self._module.operation.attributes[
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"torch.debug_module_name"
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] = ir.StringAttr.get(func_name)
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self._module.operation.attributes["torch.debug_module_name"] = (
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ir.StringAttr.get(func_name)
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)
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function_type = _extract_function_type_from_graph(g)
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func = func_dialect.FuncOp(
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func_name,
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@ -285,9 +285,9 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
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(ns, unqual + "_", overload if not is_functional_op else "")
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),
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emitter_td,
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traits=["IsTrailingUnderscoreInplaceVariant"]
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if not is_functional_op
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else [],
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traits=(
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["IsTrailingUnderscoreInplaceVariant"] if not is_functional_op else []
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),
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)
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# ==========================================================================
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@ -46,7 +46,7 @@ def convert_onnx(model, inputs):
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examples = []
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input_names = []
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dynamic_tensors = {}
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for (index, arg) in enumerate(inputs):
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for index, arg in enumerate(inputs):
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shape = map(lambda d: d if d >= 0 else 1, arg.shape)
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shape = tuple(shape)
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examples.append(torch.zeros(size=shape, dtype=arg.dtype))
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@ -55,7 +55,7 @@ def convert_onnx(model, inputs):
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input_names.append(input_name)
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dynamic_dims = {}
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for (dimindex, dim) in enumerate(arg.shape):
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for dimindex, dim in enumerate(arg.shape):
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if dim < 0:
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dynamic_dims[dimindex] = "dim_{}_{}".format(index, dimindex)
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@ -101,11 +101,13 @@ class RefBackendInvoker:
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def consume_return_funcs(*args):
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self.result = tuple(
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[
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(
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arg
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if type in elemental_type_to_ctype
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else unranked_memref_to_numpy(
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arg, memref_type_to_np_dtype[type]
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)
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)
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for arg, type in zip(args, ret_types)
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]
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)
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@ -803,9 +803,7 @@ class QuantizedReluInt32(torch.nn.Module):
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@register_test_case(module_factory=lambda: QuantizedReluInt32())
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def QuantizedReluInt32_basic(module, tu: TestUtils):
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module.forward(
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tu.randint(7, 4, low=(-(2**31)), high=(2**31 - 1)).to(torch.int32)
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)
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module.forward(tu.randint(7, 4, low=(-(2**31)), high=(2**31 - 1)).to(torch.int32))
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# ==============================================================================
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@ -342,6 +342,7 @@ def SelectIntNegativeDimAndIndexStaticModule_basic(module, tu: TestUtils):
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# ==============================================================================
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# For aten.slice_scatter op, The arguments are: SliceScatter(input, src, dim=0, start=None, end=None, step=1).
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# For aten.select_scatter op, The arguments are: SelectScatter(input, src, dim=0, index).
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class SliceScatterModule(torch.nn.Module):
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@ -11,6 +11,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK: module attributes {torch.debug_module_name = "TestModule"}
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class TestModule(torch.nn.Module):
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def __init__(self):
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@ -18,6 +18,7 @@ mb = ModuleBuilder()
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# `torch.Tensor` is just a pointer to a TensorImpl under the hood, and so
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# naively duplicating a Tensor retains the identity of the TensorImpl.
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# CHECK-LABEL: torch.class_type @__torch__.TestModule {
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class TestModule(torch.nn.Module):
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def __init__(self):
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@ -12,6 +12,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK-LABEL: torch.class_type @__torch__.TestModule {
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class TestModule(torch.nn.Module):
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def __init__(self):
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@ -9,6 +9,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK-LABEL: func.func @__torch__.add3
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# Note that line-level debug information for parts unannotated in the Torch
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# graph are ascribed to the first op that carries source information. Presently
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@ -9,6 +9,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK-LABEL: @__torch__.f
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@mb.import_function
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@torch.jit.script
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@ -11,6 +11,7 @@ import typing
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mb = ModuleBuilder()
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# CHECK-LABEL: func.func @__torch__.optional_return(
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# CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.optional<int> {
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# CHECK: %[[RET:.*]] = torch.derefine %[[ARG]] : !torch.int to !torch.optional<int>
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@ -13,6 +13,7 @@ mb = ModuleBuilder()
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# else branch and making all defined values optional, so no special handling
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# is needed.
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# CHECK-LABEL: @__torch__.prim_If(
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# CHECK-SAME: %[[B:.*]]: !torch.bool,
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# CHECK-SAME: %[[I:.*]]: !torch.int) -> !torch.int {
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@ -11,6 +11,7 @@ import typing
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mb = ModuleBuilder()
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# CHECK-LABEL: func.func @__torch__.prim_Loop_forlike(
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# CHECK-SAME: %[[MAX_ITERATIONS:.*]]: !torch.int) -> !torch.float {
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# CHECK: %[[BOOL_TRUE:.*]] = torch.constant.bool true
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@ -15,6 +15,7 @@ import typing
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mb = ModuleBuilder()
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# CHECK-LABEL: func.func @__torch__.prim_NumToTensor(
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# CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.tensor {
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# CHECK: %[[RET:.*]] = torch.prim.NumToTensor.Scalar %[[ARG]] : !torch.int -> !torch.tensor
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@ -13,6 +13,7 @@ from utils import create_script_function
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mb = ModuleBuilder()
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NT = NamedTuple("NT", [("f1", Optional[torch.Tensor]), ("f2", Optional[torch.Tensor])])
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# CHECK-LABEL: func.func @__torch__.tuple(
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# CHECK-SAME: %[[T0:.*]]: !torch.tensor,
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# CHECK-SAME: %[[T1:.*]]: !torch.tensor) ->
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@ -9,6 +9,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK: @__torch__.returns_bool
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@mb.import_function
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@torch.jit.script
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@ -9,6 +9,7 @@ from torch_mlir.jit_ir_importer import ModuleBuilder
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mb = ModuleBuilder()
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# CHECK: @__torch__.returns_none
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@mb.import_function
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@torch.jit.script
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@ -9,6 +9,7 @@ from torch._C import CompilationUnit
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# RUN: %PYTHON %s
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# Import TorchScript IR string as ScriptFunction.
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def create_script_function(func_name, ts_ir_str, **kwargs):
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cu = CompilationUnit()
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@ -1849,8 +1849,7 @@ def _emit_operation(
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# Opaque value to indicate something is empty. Used in cases where 'None'
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# may have a different meaning.
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class EmptyType:
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...
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class EmptyType: ...
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Empty = EmptyType()
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@ -156,8 +156,7 @@ class GraphInfo:
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return ""
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class OnnxImportError(Exception):
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...
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class OnnxImportError(Exception): ...
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class NodeImporter:
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@ -235,22 +234,22 @@ class NodeImporter:
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else:
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default_opset_version = opset_import.version
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if default_opset_version:
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container_op.attributes[
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"torch.onnx_meta.opset_version"
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] = IntegerAttr.get(i64_type, default_opset_version)
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container_op.attributes["torch.onnx_meta.opset_version"] = (
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IntegerAttr.get(i64_type, default_opset_version)
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)
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if opset_versions:
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container_op.attributes[
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"torch.onnx_meta.opset_versions"
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] = DictAttr.get(opset_versions)
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container_op.attributes["torch.onnx_meta.opset_versions"] = (
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DictAttr.get(opset_versions)
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)
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container_op.attributes["torch.onnx_meta.ir_version"] = IntegerAttr.get(
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IntegerType.get_signed(64), m.ir_version
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)
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container_op.attributes["torch.onnx_meta.producer_name"] = StringAttr.get(
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m.producer_name
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)
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container_op.attributes[
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"torch.onnx_meta.producer_version"
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] = StringAttr.get(m.producer_version)
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container_op.attributes["torch.onnx_meta.producer_version"] = (
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StringAttr.get(m.producer_version)
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)
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def import_all(self, func=True):
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"""Imports all nodes topologically."""
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@ -658,9 +657,11 @@ ELEM_TYPE_SPLAT_TENSOR_PROTO_CB = {
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RankedTensorType.get(shape, IntegerType.get_signed(64)),
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IntegerAttr.get(
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IntegerType.get_signed(64),
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(
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int.from_bytes(tp.raw_data, "little", signed=True)
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if tp.HasField("raw_data")
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else tp.int64_data[0],
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else tp.int64_data[0]
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),
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),
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),
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# TODO: All the rest from ELEM_TYPE_TO_IR_TYPE_CB
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),
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onnx.TensorProto.DataType.UINT64: lambda tp: DenseElementsAttr.get(
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np.asarray(tp.uint64_data, dtype=np.uint64).reshape(tp.dims), signless=False
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)
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),
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# Intentionally unsupported: STRING
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}
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