mirror of https://github.com/llvm/torch-mlir
39 lines
1.4 KiB
Python
39 lines
1.4 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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def dot2d(a: np.ndarray, b: np.ndarray) -> np.ndarray:
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return np.dot(a, b)
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# TODO: Implement subclassing and deriving constraints by run
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exp = npc.Exporter()
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exp.dot2d = dot2d
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exp.dot2d.sig.args["a"] += Shape(4, 16)
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exp.dot2d.sig.args["a"] += DynamicDim(0)
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exp.dot2d.sig.args["a"] += DType(np.float32)
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exp.dot2d.sig.args["b"] += Shape(16, 32)
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exp.dot2d.sig.args["b"] += DType(np.float32)
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exp.dot2d.sig.result += Shape(4, 32)
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exp.dot2d.sig.result += DynamicDim(0)
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exp.dot2d.sig.result += DType(np.float32)
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mb = npc.tracing.ModuleBuilder()
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mb.trace(exp.dot2d)
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# CHECK-LABEL: func @dot2d(
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# CHECK-SAME: %[[VAL_0:.*]]: tensor<?x16xf32>,
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# CHECK-SAME: %[[VAL_1:.*]]: tensor<16x32xf32>) -> tensor<?x32xf32> {
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# CHECK: %[[VAL_2:.*]] = numpy.dot %[[VAL_0]], %[[VAL_1]] : (tensor<?x16xf32>, tensor<16x32xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %[[VAL_3:.*]] = numpy.narrow %[[VAL_2]] : (tensor<*x!basicpy.UnknownType>) -> tensor<?x32xf32>
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# CHECK: return %[[VAL_3]] : tensor<?x32xf32>
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# CHECK: }
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print(mb.module)
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