0f2ec35691
The old replacements will be removed soon: - `%linalg_test_lib_dir` - `%cuda_wrapper_library_dir` - `%spirv_wrapper_library_dir` - `%vulkan_wrapper_library_dir` - `%mlir_runner_utils_dir` - `%mlir_integration_test_dir` Reviewed By: herhut Differential Revision: https://reviews.llvm.org/D133270
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
# RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
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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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###### This PyTACO part is taken from the TACO open-source project. ######
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# See http://tensor-compiler.org/docs/scientific_computing/index.html.
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compressed = pt.compressed
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dense = pt.dense
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# Define formats for storing the sparse matrix and dense vectors.
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csr = pt.format([dense, compressed])
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dv = pt.format([dense])
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# Load a sparse matrix stored in the matrix market format) and store it
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# as a CSR matrix. The matrix in this test is a reduced version of the data
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# downloaded from here:
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# https://www.cise.ufl.edu/research/sparse/MM/Boeing/pwtk.tar.gz
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# In order to run the program using the matrix above, you can download the
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# matrix and replace this path to the actual path to the file.
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A = pt.read(os.path.join(_SCRIPT_PATH, "data/pwtk.mtx"), csr)
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# These two lines have been modified from the original program to use static
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# data to support result comparison.
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x = pt.from_array(np.full((A.shape[1],), 1, dtype=np.float32))
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z = pt.from_array(np.full((A.shape[0],), 2, dtype=np.float32))
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# Declare the result to be a dense vector
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y = pt.tensor([A.shape[0]], dv)
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# Declare index vars
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i, j = pt.get_index_vars(2)
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# Define the SpMV computation
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y[i] = A[i, j] * x[j] + z[i]
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##########################################################################
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# Perform the SpMV computation and write the result to file
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with tempfile.TemporaryDirectory() as test_dir:
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golden_file = os.path.join(_SCRIPT_PATH, "data/gold_y.tns")
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out_file = os.path.join(test_dir, "y.tns")
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pt.write(out_file, y)
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#
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# CHECK: Compare result True
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#
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print(f"Compare result {utils.compare_sparse_tns(golden_file, out_file)}")
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