5e83a5b475
Since the very first commits, the Python and C MLIR APIs have had mis-placed registration/load functionality for dialects, extensions, etc. This was done pragmatically in order to get bootstrapped and then just grew in. Downstreams largely bypass and do their own thing by providing various APIs to register things they need. Meanwhile, the C++ APIs have stabilized around this and it would make sense to follow suit.
The thing we have observed in canonical usage by downstreams is that each downstream tends to have native entry points that configure its installation to its preferences with one-stop APIs. This patch leans in to this approach with `RegisterEverything.h` and `mlir._mlir_libs._mlirRegisterEverything` being the one-stop entry points for the "upstream packages". The `_mlir_libs.__init__.py` now allows customization of the environment and Context by adding "initialization modules" to the `_mlir_libs` package. If present, `_mlirRegisterEverything` is treated as such a module. Others can be added by downstreams by adding a `_site_initialize_{i}.py` module, where '{i}' is a number starting with zero. The number will be incremented and corresponding module loaded until one is not found. Initialization modules can:
* Perform load time customization to the global environment (i.e. registering passes, hooks, etc).
* Define a `register_dialects(registry: DialectRegistry)` function that can extend the `DialectRegistry` that will be used to bootstrap the `Context`.
* Define a `context_init_hook(context: Context)` function that will be added to a list of callbacks which will be invoked after dialect registration during `Context` initialization.
Note that the `MLIRPythonExtension.RegisterEverything` is not included by default when building a downstream (its corresponding behavior was prior). For downstreams which need the default MLIR initialization to take place, they must add this back in to their Python CMake build just like they add their own components (i.e. to `add_mlir_python_common_capi_library` and `add_mlir_python_modules`). It is perfectly valid to not do this, in which case, only the things explicitly depended on and initialized by downstreams will be built/packaged. If the downstream has not been set up for this, it is recommended to simply add this back for the time being and pay the build time/package size cost.
CMake changes:
* `MLIRCAPIRegistration` -> `MLIRCAPIRegisterEverything` (renamed to signify what it does and force an evaluation: a number of places were incidentally linking this very expensive target)
* `MLIRPythonSoure.Passes` removed (without replacement: just drop)
* `MLIRPythonExtension.AllPassesRegistration` removed (without replacement: just drop)
* `MLIRPythonExtension.Conversions` removed (without replacement: just drop)
* `MLIRPythonExtension.Transforms` removed (without replacement: just drop)
Header changes:
* `mlir-c/Registration.h` is deleted. Dialect registration functionality is now in `IR.h`. Registration of upstream features are in `mlir-c/RegisterEverything.h`. When updating MLIR and a couple of downstreams, I found that proper usage was commingled so required making a choice vs just blind S&R.
Python APIs removed:
* mlir.transforms and mlir.conversions (previously only had an __init__.py which indirectly triggered `mlirRegisterTransformsPasses()` and `mlirRegisterConversionPasses()` respectively). Downstream impact: Remove these imports if present (they now happen as part of default initialization).
* mlir._mlir_libs._all_passes_registration, mlir._mlir_libs._mlirTransforms, mlir._mlir_libs._mlirConversions. Downstream impact: None expected (these were internally used).
C-APIs changed:
* mlirRegisterAllDialects(MlirContext) now takes an MlirDialectRegistry instead. It also used to trigger loading of all dialects, which was already marked with a TODO to remove -- it no longer does, and for direct use, dialects must be explicitly loaded. Downstream impact: Direct C-API users must ensure that needed dialects are loaded or call `mlirContextLoadAllAvailableDialects(MlirContext)` to emulate the prior behavior. Also see the `ir.c` test case (e.g. ` mlirContextGetOrLoadDialect(ctx, mlirStringRefCreateFromCString("func"));`).
* mlirDialectHandle* APIs were moved from Registration.h (which now is restricted to just global/upstream registration) to IR.h, arguably where it should have been. Downstream impact: include correct header (likely already doing so).
C-APIs added:
* mlirContextLoadAllAvailableDialects(MlirContext): Corresponds to C++ API with the same purpose.
Python APIs added:
* mlir.ir.DialectRegistry: Mapping for an MlirDialectRegistry.
* mlir.ir.Context.append_dialect_registry(MlirDialectRegistry)
* mlir.ir.Context.load_all_available_dialects()
* mlir._mlir_libs._mlirAllRegistration: New native extension that exposes a `register_dialects(MlirDialectRegistry)` entry point and performs all upstream pass/conversion/transforms registration on init. In this first step, we eagerly load this as part of the __init__.py and use it to monkey patch the Context to emulate prior behavior.
* Type caster and capsule support for MlirDialectRegistry
This should make it possible to build downstream Python dialects that only depend on a subset of MLIR. See: https://github.com/llvm/llvm-project/issues/56037
Here is an example PR, minimally adapting IREE to these changes: https://github.com/iree-org/iree/pull/9638/files In this situation, IREE is opting to not link everything, since it is already configuring the Context to its liking. For projects that would just like to not think about it and pull in everything, add `MLIRPythonExtension.RegisterEverything` to the list of Python sources getting built, and the old behavior will continue.
Reviewed By: mehdi_amini, ftynse
Differential Revision: https://reviews.llvm.org/D128593
550 lines
18 KiB
Python
550 lines
18 KiB
Python
# RUN: %PYTHON %s 2>&1 | FileCheck %s
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# REQUIRES: native
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import gc, sys
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from mlir.ir import *
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from mlir.passmanager import *
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from mlir.execution_engine import *
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from mlir.runtime import *
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# Log everything to stderr and flush so that we have a unified stream to match
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# errors/info emitted by MLIR to stderr.
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def log(*args):
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print(*args, file=sys.stderr)
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sys.stderr.flush()
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def run(f):
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log("\nTEST:", f.__name__)
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f()
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gc.collect()
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assert Context._get_live_count() == 0
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# Verify capsule interop.
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# CHECK-LABEL: TEST: testCapsule
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def testCapsule():
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with Context():
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module = Module.parse(r"""
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llvm.func @none() {
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llvm.return
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}
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""")
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execution_engine = ExecutionEngine(module)
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execution_engine_capsule = execution_engine._CAPIPtr
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# CHECK: mlir.execution_engine.ExecutionEngine._CAPIPtr
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log(repr(execution_engine_capsule))
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execution_engine._testing_release()
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execution_engine1 = ExecutionEngine._CAPICreate(execution_engine_capsule)
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# CHECK: _mlirExecutionEngine.ExecutionEngine
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log(repr(execution_engine1))
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run(testCapsule)
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# Test invalid ExecutionEngine creation
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# CHECK-LABEL: TEST: testInvalidModule
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def testInvalidModule():
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with Context():
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# Builtin function
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module = Module.parse(r"""
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func.func @foo() { return }
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""")
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# CHECK: Got RuntimeError: Failure while creating the ExecutionEngine.
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try:
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execution_engine = ExecutionEngine(module)
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except RuntimeError as e:
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log("Got RuntimeError: ", e)
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run(testInvalidModule)
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def lowerToLLVM(module):
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pm = PassManager.parse(
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"convert-complex-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts")
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pm.run(module)
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return module
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# Test simple ExecutionEngine execution
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# CHECK-LABEL: TEST: testInvokeVoid
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def testInvokeVoid():
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with Context():
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module = Module.parse(r"""
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func.func @void() attributes { llvm.emit_c_interface } {
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return
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}
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""")
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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# Nothing to check other than no exception thrown here.
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execution_engine.invoke("void")
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run(testInvokeVoid)
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# Test argument passing and result with a simple float addition.
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# CHECK-LABEL: TEST: testInvokeFloatAdd
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def testInvokeFloatAdd():
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with Context():
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module = Module.parse(r"""
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func.func @add(%arg0: f32, %arg1: f32) -> f32 attributes { llvm.emit_c_interface } {
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%add = arith.addf %arg0, %arg1 : f32
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return %add : f32
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}
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""")
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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# Prepare arguments: two input floats and one result.
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# Arguments must be passed as pointers.
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c_float_p = ctypes.c_float * 1
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arg0 = c_float_p(42.)
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arg1 = c_float_p(2.)
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res = c_float_p(-1.)
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execution_engine.invoke("add", arg0, arg1, res)
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# CHECK: 42.0 + 2.0 = 44.0
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log("{0} + {1} = {2}".format(arg0[0], arg1[0], res[0]))
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run(testInvokeFloatAdd)
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# Test callback
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# CHECK-LABEL: TEST: testBasicCallback
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def testBasicCallback():
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# Define a callback function that takes a float and an integer and returns a float.
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@ctypes.CFUNCTYPE(ctypes.c_float, ctypes.c_float, ctypes.c_int)
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def callback(a, b):
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return a / 2 + b / 2
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with Context():
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# The module just forwards to a runtime function known as "some_callback_into_python".
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module = Module.parse(r"""
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func.func @add(%arg0: f32, %arg1: i32) -> f32 attributes { llvm.emit_c_interface } {
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%resf = call @some_callback_into_python(%arg0, %arg1) : (f32, i32) -> (f32)
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return %resf : f32
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}
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func.func private @some_callback_into_python(f32, i32) -> f32 attributes { llvm.emit_c_interface }
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""")
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.register_runtime("some_callback_into_python", callback)
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# Prepare arguments: two input floats and one result.
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# Arguments must be passed as pointers.
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c_float_p = ctypes.c_float * 1
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c_int_p = ctypes.c_int * 1
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arg0 = c_float_p(42.)
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arg1 = c_int_p(2)
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res = c_float_p(-1.)
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execution_engine.invoke("add", arg0, arg1, res)
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# CHECK: 42.0 + 2 = 44.0
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log("{0} + {1} = {2}".format(arg0[0], arg1[0], res[0] * 2))
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run(testBasicCallback)
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# Test callback with an unranked memref
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# CHECK-LABEL: TEST: testUnrankedMemRefCallback
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def testUnrankedMemRefCallback():
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# Define a callback function that takes an unranked memref, converts it to a numpy array and prints it.
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@ctypes.CFUNCTYPE(None, ctypes.POINTER(UnrankedMemRefDescriptor))
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def callback(a):
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arr = unranked_memref_to_numpy(a, np.float32)
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log("Inside callback: ")
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log(arr)
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with Context():
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# The module just forwards to a runtime function known as "some_callback_into_python".
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module = Module.parse(r"""
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func.func @callback_memref(%arg0: memref<*xf32>) attributes { llvm.emit_c_interface } {
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call @some_callback_into_python(%arg0) : (memref<*xf32>) -> ()
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return
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}
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func.func private @some_callback_into_python(memref<*xf32>) -> () attributes { llvm.emit_c_interface }
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""")
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.register_runtime("some_callback_into_python", callback)
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inp_arr = np.array([[1.0, 2.0], [3.0, 4.0]], np.float32)
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# CHECK: Inside callback:
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# CHECK{LITERAL}: [[1. 2.]
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# CHECK{LITERAL}: [3. 4.]]
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execution_engine.invoke(
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"callback_memref",
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ctypes.pointer(ctypes.pointer(get_unranked_memref_descriptor(inp_arr))),
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)
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inp_arr_1 = np.array([5, 6, 7], dtype=np.float32)
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strided_arr = np.lib.stride_tricks.as_strided(
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inp_arr_1, strides=(4, 0), shape=(3, 4))
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# CHECK: Inside callback:
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# CHECK{LITERAL}: [[5. 5. 5. 5.]
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# CHECK{LITERAL}: [6. 6. 6. 6.]
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# CHECK{LITERAL}: [7. 7. 7. 7.]]
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execution_engine.invoke(
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"callback_memref",
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ctypes.pointer(
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ctypes.pointer(get_unranked_memref_descriptor(strided_arr))),
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)
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run(testUnrankedMemRefCallback)
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# Test callback with a ranked memref.
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# CHECK-LABEL: TEST: testRankedMemRefCallback
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def testRankedMemRefCallback():
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# Define a callback function that takes a ranked memref, converts it to a numpy array and prints it.
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@ctypes.CFUNCTYPE(
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None,
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ctypes.POINTER(
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make_nd_memref_descriptor(2,
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np.ctypeslib.as_ctypes_type(np.float32))),
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)
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def callback(a):
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arr = ranked_memref_to_numpy(a)
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log("Inside Callback: ")
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log(arr)
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with Context():
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# The module just forwards to a runtime function known as "some_callback_into_python".
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module = Module.parse(r"""
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func.func @callback_memref(%arg0: memref<2x2xf32>) attributes { llvm.emit_c_interface } {
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call @some_callback_into_python(%arg0) : (memref<2x2xf32>) -> ()
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return
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}
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func.func private @some_callback_into_python(memref<2x2xf32>) -> () attributes { llvm.emit_c_interface }
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""")
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.register_runtime("some_callback_into_python", callback)
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inp_arr = np.array([[1.0, 5.0], [6.0, 7.0]], np.float32)
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# CHECK: Inside Callback:
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# CHECK{LITERAL}: [[1. 5.]
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# CHECK{LITERAL}: [6. 7.]]
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execution_engine.invoke(
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"callback_memref",
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ctypes.pointer(ctypes.pointer(get_ranked_memref_descriptor(inp_arr))))
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run(testRankedMemRefCallback)
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# Test addition of two memrefs.
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# CHECK-LABEL: TEST: testMemrefAdd
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def testMemrefAdd():
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with Context():
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module = Module.parse("""
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module {
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func.func @main(%arg0: memref<1xf32>, %arg1: memref<f32>, %arg2: memref<1xf32>) attributes { llvm.emit_c_interface } {
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%0 = arith.constant 0 : index
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%1 = memref.load %arg0[%0] : memref<1xf32>
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%2 = memref.load %arg1[] : memref<f32>
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%3 = arith.addf %1, %2 : f32
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memref.store %3, %arg2[%0] : memref<1xf32>
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return
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}
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} """)
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arg1 = np.array([32.5]).astype(np.float32)
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arg2 = np.array(6).astype(np.float32)
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res = np.array([0]).astype(np.float32)
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arg1_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg1)))
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arg2_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg2)))
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res_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(res)))
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.invoke("main", arg1_memref_ptr, arg2_memref_ptr,
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res_memref_ptr)
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# CHECK: [32.5] + 6.0 = [38.5]
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log("{0} + {1} = {2}".format(arg1, arg2, res))
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run(testMemrefAdd)
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# Test addition of two f16 memrefs
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# CHECK-LABEL: TEST: testF16MemrefAdd
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def testF16MemrefAdd():
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with Context():
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module = Module.parse("""
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module {
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func.func @main(%arg0: memref<1xf16>,
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%arg1: memref<1xf16>,
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%arg2: memref<1xf16>) attributes { llvm.emit_c_interface } {
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%0 = arith.constant 0 : index
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%1 = memref.load %arg0[%0] : memref<1xf16>
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%2 = memref.load %arg1[%0] : memref<1xf16>
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%3 = arith.addf %1, %2 : f16
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memref.store %3, %arg2[%0] : memref<1xf16>
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return
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}
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} """)
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arg1 = np.array([11.]).astype(np.float16)
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arg2 = np.array([22.]).astype(np.float16)
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arg3 = np.array([0.]).astype(np.float16)
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arg1_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg1)))
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arg2_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg2)))
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arg3_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg3)))
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.invoke("main", arg1_memref_ptr, arg2_memref_ptr,
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arg3_memref_ptr)
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# CHECK: [11.] + [22.] = [33.]
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log("{0} + {1} = {2}".format(arg1, arg2, arg3))
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# test to-numpy utility
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# CHECK: [33.]
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npout = ranked_memref_to_numpy(arg3_memref_ptr[0])
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log(npout)
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run(testF16MemrefAdd)
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# Test addition of two complex memrefs
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# CHECK-LABEL: TEST: testComplexMemrefAdd
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def testComplexMemrefAdd():
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with Context():
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module = Module.parse("""
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module {
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func.func @main(%arg0: memref<1xcomplex<f64>>,
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%arg1: memref<1xcomplex<f64>>,
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%arg2: memref<1xcomplex<f64>>) attributes { llvm.emit_c_interface } {
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%0 = arith.constant 0 : index
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%1 = memref.load %arg0[%0] : memref<1xcomplex<f64>>
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%2 = memref.load %arg1[%0] : memref<1xcomplex<f64>>
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%3 = complex.add %1, %2 : complex<f64>
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memref.store %3, %arg2[%0] : memref<1xcomplex<f64>>
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return
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}
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} """)
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arg1 = np.array([1.+2.j]).astype(np.complex128)
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arg2 = np.array([3.+4.j]).astype(np.complex128)
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arg3 = np.array([0.+0.j]).astype(np.complex128)
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arg1_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg1)))
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arg2_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg2)))
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arg3_memref_ptr = ctypes.pointer(
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ctypes.pointer(get_ranked_memref_descriptor(arg3)))
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execution_engine = ExecutionEngine(lowerToLLVM(module))
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execution_engine.invoke("main",
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arg1_memref_ptr,
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arg2_memref_ptr,
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arg3_memref_ptr)
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# CHECK: [1.+2.j] + [3.+4.j] = [4.+6.j]
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log("{0} + {1} = {2}".format(arg1, arg2, arg3))
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# test to-numpy utility
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# CHECK: [4.+6.j]
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npout = ranked_memref_to_numpy(arg3_memref_ptr[0])
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log(npout)
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run(testComplexMemrefAdd)
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# Test addition of two complex unranked memrefs
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# CHECK-LABEL: TEST: testComplexUnrankedMemrefAdd
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def testComplexUnrankedMemrefAdd():
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with Context():
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module = Module.parse("""
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module {
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func.func @main(%arg0: memref<*xcomplex<f32>>,
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%arg1: memref<*xcomplex<f32>>,
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%arg2: memref<*xcomplex<f32>>) attributes { llvm.emit_c_interface } {
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%A = memref.cast %arg0 : memref<*xcomplex<f32>> to memref<1xcomplex<f32>>
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%B = memref.cast %arg1 : memref<*xcomplex<f32>> to memref<1xcomplex<f32>>
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%C = memref.cast %arg2 : memref<*xcomplex<f32>> to memref<1xcomplex<f32>>
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%0 = arith.constant 0 : index
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%1 = memref.load %A[%0] : memref<1xcomplex<f32>>
|
|
%2 = memref.load %B[%0] : memref<1xcomplex<f32>>
|
|
%3 = complex.add %1, %2 : complex<f32>
|
|
memref.store %3, %C[%0] : memref<1xcomplex<f32>>
|
|
return
|
|
}
|
|
} """)
|
|
|
|
arg1 = np.array([5.+6.j]).astype(np.complex64)
|
|
arg2 = np.array([7.+8.j]).astype(np.complex64)
|
|
arg3 = np.array([0.+0.j]).astype(np.complex64)
|
|
|
|
arg1_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_unranked_memref_descriptor(arg1)))
|
|
arg2_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_unranked_memref_descriptor(arg2)))
|
|
arg3_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_unranked_memref_descriptor(arg3)))
|
|
|
|
execution_engine = ExecutionEngine(lowerToLLVM(module))
|
|
execution_engine.invoke("main",
|
|
arg1_memref_ptr,
|
|
arg2_memref_ptr,
|
|
arg3_memref_ptr)
|
|
# CHECK: [5.+6.j] + [7.+8.j] = [12.+14.j]
|
|
log("{0} + {1} = {2}".format(arg1, arg2, arg3))
|
|
|
|
# test to-numpy utility
|
|
# CHECK: [12.+14.j]
|
|
npout = unranked_memref_to_numpy(arg3_memref_ptr[0],
|
|
np.dtype(np.complex64))
|
|
log(npout)
|
|
|
|
|
|
run(testComplexUnrankedMemrefAdd)
|
|
|
|
|
|
# Test addition of two 2d_memref
|
|
# CHECK-LABEL: TEST: testDynamicMemrefAdd2D
|
|
def testDynamicMemrefAdd2D():
|
|
with Context():
|
|
module = Module.parse("""
|
|
module {
|
|
func.func @memref_add_2d(%arg0: memref<2x2xf32>, %arg1: memref<?x?xf32>, %arg2: memref<2x2xf32>) attributes {llvm.emit_c_interface} {
|
|
%c0 = arith.constant 0 : index
|
|
%c2 = arith.constant 2 : index
|
|
%c1 = arith.constant 1 : index
|
|
cf.br ^bb1(%c0 : index)
|
|
^bb1(%0: index): // 2 preds: ^bb0, ^bb5
|
|
%1 = arith.cmpi slt, %0, %c2 : index
|
|
cf.cond_br %1, ^bb2, ^bb6
|
|
^bb2: // pred: ^bb1
|
|
%c0_0 = arith.constant 0 : index
|
|
%c2_1 = arith.constant 2 : index
|
|
%c1_2 = arith.constant 1 : index
|
|
cf.br ^bb3(%c0_0 : index)
|
|
^bb3(%2: index): // 2 preds: ^bb2, ^bb4
|
|
%3 = arith.cmpi slt, %2, %c2_1 : index
|
|
cf.cond_br %3, ^bb4, ^bb5
|
|
^bb4: // pred: ^bb3
|
|
%4 = memref.load %arg0[%0, %2] : memref<2x2xf32>
|
|
%5 = memref.load %arg1[%0, %2] : memref<?x?xf32>
|
|
%6 = arith.addf %4, %5 : f32
|
|
memref.store %6, %arg2[%0, %2] : memref<2x2xf32>
|
|
%7 = arith.addi %2, %c1_2 : index
|
|
cf.br ^bb3(%7 : index)
|
|
^bb5: // pred: ^bb3
|
|
%8 = arith.addi %0, %c1 : index
|
|
cf.br ^bb1(%8 : index)
|
|
^bb6: // pred: ^bb1
|
|
return
|
|
}
|
|
}
|
|
""")
|
|
arg1 = np.random.randn(2, 2).astype(np.float32)
|
|
arg2 = np.random.randn(2, 2).astype(np.float32)
|
|
res = np.random.randn(2, 2).astype(np.float32)
|
|
|
|
arg1_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_ranked_memref_descriptor(arg1)))
|
|
arg2_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_ranked_memref_descriptor(arg2)))
|
|
res_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_ranked_memref_descriptor(res)))
|
|
|
|
execution_engine = ExecutionEngine(lowerToLLVM(module))
|
|
execution_engine.invoke("memref_add_2d", arg1_memref_ptr, arg2_memref_ptr,
|
|
res_memref_ptr)
|
|
# CHECK: True
|
|
log(np.allclose(arg1 + arg2, res))
|
|
|
|
|
|
run(testDynamicMemrefAdd2D)
|
|
|
|
|
|
# Test loading of shared libraries.
|
|
# CHECK-LABEL: TEST: testSharedLibLoad
|
|
def testSharedLibLoad():
|
|
with Context():
|
|
module = Module.parse("""
|
|
module {
|
|
func.func @main(%arg0: memref<1xf32>) attributes { llvm.emit_c_interface } {
|
|
%c0 = arith.constant 0 : index
|
|
%cst42 = arith.constant 42.0 : f32
|
|
memref.store %cst42, %arg0[%c0] : memref<1xf32>
|
|
%u_memref = memref.cast %arg0 : memref<1xf32> to memref<*xf32>
|
|
call @printMemrefF32(%u_memref) : (memref<*xf32>) -> ()
|
|
return
|
|
}
|
|
func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface }
|
|
} """)
|
|
arg0 = np.array([0.0]).astype(np.float32)
|
|
|
|
arg0_memref_ptr = ctypes.pointer(
|
|
ctypes.pointer(get_ranked_memref_descriptor(arg0)))
|
|
|
|
if sys.platform == 'win32':
|
|
shared_libs = [
|
|
"../../../../bin/mlir_runner_utils.dll",
|
|
"../../../../bin/mlir_c_runner_utils.dll"
|
|
]
|
|
else:
|
|
shared_libs = [
|
|
"../../../../lib/libmlir_runner_utils.so",
|
|
"../../../../lib/libmlir_c_runner_utils.so"
|
|
]
|
|
|
|
execution_engine = ExecutionEngine(
|
|
lowerToLLVM(module),
|
|
opt_level=3,
|
|
shared_libs=shared_libs)
|
|
execution_engine.invoke("main", arg0_memref_ptr)
|
|
# CHECK: Unranked Memref
|
|
# CHECK-NEXT: [42]
|
|
|
|
|
|
run(testSharedLibLoad)
|
|
|
|
|
|
# Test that nano time clock is available.
|
|
# CHECK-LABEL: TEST: testNanoTime
|
|
def testNanoTime():
|
|
with Context():
|
|
module = Module.parse("""
|
|
module {
|
|
func.func @main() attributes { llvm.emit_c_interface } {
|
|
%now = call @nanoTime() : () -> i64
|
|
%memref = memref.alloca() : memref<1xi64>
|
|
%c0 = arith.constant 0 : index
|
|
memref.store %now, %memref[%c0] : memref<1xi64>
|
|
%u_memref = memref.cast %memref : memref<1xi64> to memref<*xi64>
|
|
call @printMemrefI64(%u_memref) : (memref<*xi64>) -> ()
|
|
return
|
|
}
|
|
func.func private @nanoTime() -> i64 attributes { llvm.emit_c_interface }
|
|
func.func private @printMemrefI64(memref<*xi64>) attributes { llvm.emit_c_interface }
|
|
}""")
|
|
|
|
if sys.platform == 'win32':
|
|
shared_libs = [
|
|
"../../../../bin/mlir_runner_utils.dll",
|
|
"../../../../bin/mlir_c_runner_utils.dll"
|
|
]
|
|
else:
|
|
shared_libs = [
|
|
"../../../../lib/libmlir_runner_utils.so",
|
|
"../../../../lib/libmlir_c_runner_utils.so"
|
|
]
|
|
|
|
execution_engine = ExecutionEngine(
|
|
lowerToLLVM(module),
|
|
opt_level=3,
|
|
shared_libs=shared_libs)
|
|
execution_engine.invoke("main")
|
|
# CHECK: Unranked Memref
|
|
# CHECK: [{{.*}}]
|
|
|
|
|
|
run(testNanoTime)
|