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gemm.py
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gemm.py
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#!/usr/bin/env python
from __future__ import print_function
import argparse
import dace
import numpy as np
M = dace.symbol('M')
K = dace.symbol('K')
N = dace.symbol('N')
@dace.program(dace.float64[M, K], dace.float64[K, N], dace.float64[M, N])
def gemm(A, B, C):
# Transient variable
tmp = dace.define_local([M, N, K], dtype=A.dtype)
@dace.map(_[0:M, 0:N, 0:K])
def multiplication(i, j, k):
in_A << A[i, k]
in_B << B[k, j]
out >> tmp[i, j, k]
out = in_A * in_B
dace.reduce(lambda a, b: a + b, tmp, C, axis=2, identity=0)
if __name__ == "__main__":
print("==== Program start ====")
parser = argparse.ArgumentParser()
parser.add_argument("M", type=int, nargs="?", default=24)
parser.add_argument("K", type=int, nargs="?", default=24)
parser.add_argument("N", type=int, nargs="?", default=24)
args = vars(parser.parse_args())
M.set(args["M"])
K.set(args["K"])
N.set(args["N"])
print('Matrix multiplication %dx%dx%d' % (M.get(), K.get(), N.get()))
# Initialize arrays: Randomize A and B, zero C
A = np.random.rand(M.get(), K.get()).astype(np.float64)
B = np.random.rand(K.get(), N.get()).astype(np.float64)
C = np.zeros([M.get(), N.get()], dtype=np.float64)
C_regression = np.zeros_like(C)
gemm(A, B, C)
if dace.Config.get_bool('profiling'):
dace.timethis('gemm', 'numpy', (2 * M.get() * K.get() * N.get()),
np.dot, A, B, C_regression)
else:
np.dot(A, B, C_regression)
diff = np.linalg.norm(C_regression - C) / (M.get() * N.get())
print("Difference:", diff)
print("==== Program end ====")
exit(0 if diff <= 1e-5 else 1)