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Make mlir.custom_call() more general and expose it as jax.interpreter…
…s.mlir.custom_call(). This change is in preparation for deprecating the XlaBuilder APIs for building non-MLIR HLO. In general JAX would be best served by adding a more user-friendly "custom kernel" API that doesn't require the user to build IR directly, but for the moment the best we can do is migrate users to use MLIR/StableHLO utilities instead of classic HLO utilities. Since most users of custom kernels probably want to build a custom-call we can get most of the benefit by providing an ergonomic helper function for building the IR for custom calls that can be called by external primitive lowering rules. This function has two benefits over just building the stablehlo directly: a) it is a JAX API, and we can be more confident the API won't change because of upstream MLIR changes b) the Python API to build stablehlo.custom_call generated by the bindings isn't that easy to use (e.g. it doesn't have sensible defaults). Next step will be to deprecate XlaBuilder and encourage users to switch to lowering rules using this helper. PiperOrigin-RevId: 561042402
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| # Copyright 2023 The JAX Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # https://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| # Tests for mlir.custom_call(). | ||
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| # RUN: %PYTHON %s | FileCheck %s | ||
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| from absl import app | ||
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| import jax | ||
| from jax.interpreters import mlir | ||
| from jax._src.lib.mlir import ir | ||
| from jax._src.lib.mlir.dialects import func as func_dialect | ||
| import numpy as np | ||
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| ShapedArray = jax.core.ShapedArray | ||
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| def print_custom_call(name, arg_avals, result_avals, **kw): | ||
| print(f"TEST: {name}") | ||
| ctx = mlir.make_ir_context() | ||
| loc = ir.Location.unknown(context=ctx) | ||
| with ctx, loc: | ||
| module = ir.Module.create(loc=ir.Location.unknown()) | ||
| ip = ir.InsertionPoint(module.body) | ||
| arg_types = [mlir.aval_to_ir_type(aval) for aval in arg_avals] | ||
| result_types = [mlir.aval_to_ir_type(aval) for aval in result_avals] | ||
| ftype = ir.FunctionType.get(arg_types, result_types) | ||
| func = func_dialect.FuncOp("func", ftype, ip=ip) | ||
| entry_block = func.add_entry_block() | ||
| with ir.InsertionPoint(entry_block): | ||
| outs = mlir.custom_call( | ||
| name, result_types=result_types, operands=entry_block.arguments, **kw | ||
| ) | ||
| func_dialect.ReturnOp(outs) | ||
| module.operation.verify() | ||
| print(str(module)) | ||
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| def main(_): | ||
| aval1 = ShapedArray((2, 3), np.dtype(np.float32)) | ||
| aval2 = ShapedArray((3, 4), np.dtype(np.int64)) | ||
| # CHECK-LABEL: TEST: simple | ||
| # CHECK: stablehlo.custom_call @simple(%arg0) {api_version = 2 : i32} : (tensor<2x3xf32>) -> tensor<3x4xi64> | ||
| print_custom_call("simple", [aval1], [aval2]) | ||
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| # CHECK-LABEL: TEST: sideeffect | ||
| # CHECK: stablehlo.custom_call @sideeffect(%arg0) {has_side_effect = true} : (tensor<2x3xf32>) -> tensor<3x4xi64> | ||
| print_custom_call("sideeffect", [aval1], [aval2], api_version=1, | ||
| has_side_effect=True) | ||
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| # CHECK-LABEL: TEST: backendconfig | ||
| # CHECK: stablehlo.custom_call @backendconfig(%arg0) {backend_config = "hello"} : (tensor<2x3xf32>) -> tensor<3x4xi64> | ||
| print_custom_call("backendconfig", [aval1], [aval2], api_version=1, | ||
| backend_config=b"hello") | ||
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| # CHECK-LABEL: TEST: calledcomputations | ||
| # CHECK: stablehlo.custom_call @calledcomputations(%arg0) {called_computations = [@a, @b]} : (tensor<2x3xf32>) -> tensor<3x4xi64> | ||
| print_custom_call("calledcomputations", [aval1], [aval2], api_version=1, | ||
| called_computations=["a", "b"]) | ||
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| # CHECK-LABEL: TEST: aliases | ||
| # CHECK: stablehlo.custom_call @aliases(%arg0, %arg1) {output_operand_aliases = [#stablehlo.output_operand_alias<output_tuple_indices = [0], operand_index = 1, operand_tuple_indices = []>]} : (tensor<2x3xf32>, tensor<3x4xi64>) -> (tensor<3x4xi64>, tensor<2x3xf32>) | ||
| print_custom_call("aliases", [aval1, aval2], [aval2, aval1], api_version=1, | ||
| operand_output_aliases={1: 0}) | ||
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| # CHECK-LABEL: TEST: layouts | ||
| # CHECK: stablehlo.custom_call @layouts(%arg0, %arg1) {operand_layouts = [dense<[0, 1]> : tensor<2xindex>, dense<[1, 0]> : tensor<2xindex>], result_layouts = [dense<[1, 0]> : tensor<2xindex>, dense<[0, 1]> : tensor<2xindex>]} : (tensor<2x3xf32>, tensor<3x4xi64>) -> (tensor<3x4xi64>, tensor<2x3xf32>) | ||
| print_custom_call("layouts", [aval1, aval2], [aval2, aval1], api_version=1, | ||
| operand_layouts=[[0, 1], [1, 0]], | ||
| result_layouts=[[1, 0], [0, 1]]) | ||
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| if __name__ == "__main__": | ||
| app.run(main) |