9048ea28da
This reverts commit 5561e17411
The logic was moved from cmake into lit fixing the issue that lead to the revert and potentially others with multi-config cmake generators
Differential Revision: https://reviews.llvm.org/D143925
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
# RUN: env SUPPORTLIB=%mlir_c_runner_utils %PYTHON %s | FileCheck %s
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import filecmp
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import numpy as np
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import os
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import sys
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import tempfile
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_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(_SCRIPT_PATH)
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from tools import mlir_pytaco_api as pt
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from tools import testing_utils as utils
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i, j, k = pt.get_index_vars(3)
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# Set up dense matrices.
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A = pt.from_array(np.full((8, 8), 2.0, dtype=np.float32))
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B = pt.from_array(np.full((8, 8), 3.0, dtype=np.float32))
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# Set up sparse matrices.
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S = pt.tensor([8, 8], pt.format([pt.compressed, pt.compressed]))
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X = pt.tensor([8, 8], pt.format([pt.compressed, pt.compressed]))
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Y = pt.tensor([8, 8], pt.compressed) # alternative syntax works too
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S.insert([0, 7], 42.0)
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# Define the SDDMM kernel. Since this performs the reduction as
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# sum(k, S[i, j] * A[i, k] * B[k, j])
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# we only compute the intermediate dense matrix product that are actually
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# needed to compute the result, with proper asymptotic complexity.
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X[i, j] = S[i, j] * A[i, k] * B[k, j]
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# Alternative way to define SDDMM kernel. Since this performs the reduction as
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# sum(k, A[i, k] * B[k, j]) * S[i, j]
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# the MLIR lowering results in two separate tensor index expressions that are
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# fused prior to running the sparse compiler in order to guarantee proper
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# asymptotic complexity.
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Y[i, j] = A[i, k] * B[k, j] * S[i, j]
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expected = """; extended FROSTT format
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2 1
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8 8
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1 8 2016
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"""
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# Force evaluation of the kernels by writing out X and Y.
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with tempfile.TemporaryDirectory() as test_dir:
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x_file = os.path.join(test_dir, "X.tns")
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y_file = os.path.join(test_dir, "Y.tns")
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pt.write(x_file, X)
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pt.write(y_file, Y)
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#
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# CHECK: Compare result True True
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#
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x_data = utils.file_as_string(x_file)
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y_data = utils.file_as_string(y_file)
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print(f"Compare result {x_data == expected} {y_data == expected}")
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