mirror of https://github.com/llvm/torch-mlir
35 lines
1.5 KiB
Python
35 lines
1.5 KiB
Python
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# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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import numpy as np
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import npcomp as npc
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from npcomp.types import *
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weights = np.random.uniform(size=(16, 4)).astype(np.float32)
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bias = np.random.uniform(size=(4,)).astype(np.float32)
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def constants(a: np.ndarray) -> np.ndarray:
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return np.dot(a, weights) + bias
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# TODO: Implement subclassing and deriving constraints by run
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exp = npc.Exporter()
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exp.constants = constants
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mb = npc.tracing.ModuleBuilder()
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mb.trace(exp.constants)
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# CHECK-LABEL: func @constants(
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# CHECK-SAME: %[[VAL_0:.*]]: tensor<*x!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype> {
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# CHECK: %[[VAL_1:.*]] = constant dense<{{.*}}> : tensor<16x4xf32>
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# CHECK: %[[VAL_2:.*]] = numpy.dot %[[VAL_0]], %[[VAL_1]] : (tensor<*x!numpy.any_dtype>, tensor<16x4xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %[[VAL_3:.*]] = constant dense<{{.*}}> : tensor<4xf32>
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# CHECK: %[[VAL_4:.*]] = numpy.builtin_ufunc_call<"numpy.add"> (%[[VAL_2]], %[[VAL_3]]) : (tensor<*x!basicpy.UnknownType>, tensor<4xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %[[VAL_5:.*]] = numpy.narrow %[[VAL_4]] : (tensor<*x!basicpy.UnknownType>) -> tensor<*x!numpy.any_dtype>
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# CHECK: return %[[VAL_5]] : tensor<*x!numpy.any_dtype>
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# CHECK: }
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print(mb.module)
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