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sycl_timer.py
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81 lines (67 loc) · 2.22 KB
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# Data Parallel Control (dpctl)
#
# Copyright 2020-2025 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import dpctl
import dpctl.tensor as dpt
from dpctl import SyclTimer
def matmul(m1, m2):
"""Naive matrix multiplication implementation"""
assert m1.ndim == 2
assert m2.ndim == 2
assert m1.shape[1] == m2.shape[0]
m1 = m1[:, dpt.newaxis, :]
m2 = dpt.permute_dims(m2, (1, 0))[dpt.newaxis, :, :]
# form m_prod[i, j, k] = m1[i,k] * m2[k, j]
m_prods = m1 * m2
# sum over k
return dpt.sum(m_prods, axis=-1)
n = 500
try:
q = dpctl.SyclQueue(property="enable_profiling")
except dpctl.SyclQueueCreationError:
print(
"Skipping the example, as dpctl.SyclQueue targeting "
"default device could not be created"
)
exit(0)
a_flat = dpt.arange(n * n, dtype=dpt.float32, sycl_queue=q)
a = dpt.reshape(a_flat, (n, n))
b_rand = np.random.random(n * n).astype(np.float32)
b_flat = dpt.asarray(b_rand, dtype=dpt.float32, sycl_queue=q)
b = dpt.reshape(b_flat, (n, n))
wall_times = []
device_times = []
print(
f"Computing naive matrix multiplication of two {n} by {n} matrices "
f"on {q.sycl_device.name}, repeating 5 times."
)
print()
for _ in range(5):
timer = SyclTimer(time_scale=1)
with timer(q):
a_matmul_b = matmul(a, b)
host_time, device_time = timer.dt
wall_times.append(host_time)
device_times.append(device_time)
c = dpt.asnumpy(a_matmul_b)
cc = np.dot(dpt.asnumpy(a), dpt.asnumpy(b))
print("Wall time: ", wall_times, "\nDevice time: ", device_times)
print()
print(
"Accuracy test: passed."
if np.allclose(c, cc)
else (f"Accuracy test: FAILED. \n Discrepancy = {np.max(np.abs(c-cc))}")
)