import numpy as np
import os
import sys
import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt
from tools import testing_utils as utils
# Define the CSR format.
csr = pt.format([pt.dense, pt.compressed], [0, 1])
# Read matrices A and B from file, infer size of output matrix C.
A = pt.read(os.path.join(_SCRIPT_PATH, "data/A.mtx"), csr)
B = pt.read(os.path.join(_SCRIPT_PATH, "data/B.mtx"), csr)
C = pt.tensor([A.shape[0], B.shape[1]], csr)
# Define the kernel.
i, j, k = pt.get_index_vars(3)
C[i, j] = A[i, k] * B[k, j]
# Force evaluation of the kernel by writing out C.
with tempfile.TemporaryDirectory() as test_dir:
golden_file = os.path.join(_SCRIPT_PATH, "data/gold_C.tns")
out_file = os.path.join(test_dir, "C.tns")
pt.write(out_file, C)
#
# CHECK: Compare result True
#
# See http://tensor-compiler.org/docs/scientific_computing/index.html. compressed = pt.compressed dense = pt.dense # Define formats for storing the sparse matrix and dense vectors. csr = pt.format([dense, compressed]) dv = pt.format([dense]) # Load a sparse matrix stored in the matrix market format) and store it # as a CSR matrix. The matrix in this test is a reduced version of the data # downloaded from here: # https://www.cise.ufl.edu/research/sparse/MM/Boeing/pwtk.tar.gz # In order to run the program using the matrix above, you can download the # matrix and replace this path to the actual path to the file. A = pt.read(os.path.join(_SCRIPT_PATH, "data/pwtk.mtx"), csr) # These two lines have been modified from the original program to use static # data to support result comparison. x = pt.from_array(np.full((A.shape[1], ), 1, dtype=np.float64)) z = pt.from_array(np.full((A.shape[0], ), 2, dtype=np.float64)) # Declare the result to be a dense vector y = pt.tensor([A.shape[0]], dv) # Declare index vars i, j = pt.get_index_vars(2) # Define the SpMV computation y[i] = A[i, j] * x[j] + z[i]
###### This PyTACO part is taken from the TACO open-source project. ###### # See http://tensor-compiler.org/docs/data_analytics/index.html. compressed = pt.compressed dense = pt.dense # Define formats for storing the sparse tensor and dense matrices. csf = pt.format([compressed, compressed, compressed]) rm = pt.format([dense, dense]) # Load a sparse three-dimensional tensor from file (stored in the FROSTT # format) and store it as a compressed sparse fiber tensor. We use a small # tensor for the purpose of testing. To run the program using the data from # the real application, please download the data from: # http://frostt.io/tensors/nell-2/ B = pt.read(os.path.join(_SCRIPT_PATH, "data/nell-2.tns"), csf) # These two lines have been modified from the original program to use static # data to support result comparison. C = pt.from_array(np.full((B.shape[1], 25), 1, dtype=np.float64)) D = pt.from_array(np.full((B.shape[2], 25), 2, dtype=np.float64)) # Declare the result to be a dense matrix. A = pt.tensor([B.shape[0], 25], rm) # Declare index vars. i, j, k, l = pt.get_index_vars(4) # Define the MTTKRP computation. A[i, j] = B[i, k, l] * D[l, j] * C[k, j]