def check_errors(thr,
a_shape,
a_dtype,
b_shape,
b_dtype,
transposed_a=False,
transposed_b=False):
a = get_test_array(a_shape, a_dtype)
b = get_test_array(b_shape, b_dtype)
a_ref = transpose(a) if transposed_a else a
b_ref = transpose(b) if transposed_b else b
res_ref = ref_dot(a_ref, b_ref)
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_dev = thr.empty_like(res_ref)
dot = MatrixMul(a_dev,
b_dev,
out_arr=res_dev,
transposed_a=transposed_a,
transposed_b=transposed_b)
dotc = dot.compile(thr)
dotc(res_dev, a_dev, b_dev)
assert diff_is_negligible(res_dev.get(), res_ref)
def modified_gemm_gpu(A, B, C):
shape = (A.shape[0], B.shape[1])
api = cluda.cuda_api()
thr = api.Thread.create()
res_arr = thr.array((shape[0], shape[1]), dtype=A.dtype)
mul = MatrixMul(A, B, out_arr=res_arr)
mulc = mul.compile(thr)
mulc(res_arr, A, B)
return res_arr + C
def check_performance(thr_and_double,
perf_shape,
bwo=None,
transposed_a=False,
transposed_b=False):
thr, double = thr_and_double
dtype = numpy.float64 if double else numpy.float32
batch, size = perf_shape
shape = (batch, size, size)
a = get_test_array(shape, dtype)
b = get_test_array(shape, dtype)
a_ref = transpose(a) if transposed_a else a
b_ref = transpose(b) if transposed_b else b
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_ref = ref_dot(a_ref, b_ref)
res_dev = thr.array(res_ref.shape, dtype=dtype)
dot = MatrixMul(a_dev,
b_dev,
out_arr=res_dev,
block_width_override=bwo,
transposed_a=transposed_a,
transposed_b=transposed_b)
try:
dotc = dot.compile(thr)
except ValueError:
pytest.skip()
attempts = 10
times = []
for i in range(attempts):
t1 = time.time()
dotc(res_dev, a_dev, b_dev)
thr.synchronize()
times.append(time.time() - t1)
assert diff_is_negligible(thr.from_device(res_dev), res_ref)
return min(times), batch * size**3 * 2
def check_errors(thr, a_shape, a_dtype, b_shape, b_dtype, transposed_a=False, transposed_b=False):
a = get_test_array(a_shape, a_dtype)
b = get_test_array(b_shape, b_dtype)
a_ref = transpose(a) if transposed_a else a
b_ref = transpose(b) if transposed_b else b
res_ref = ref_dot(a_ref, b_ref)
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_dev = thr.empty_like(res_ref)
dot = MatrixMul(a_dev, b_dev, out_arr=res_dev,
transposed_a=transposed_a, transposed_b=transposed_b)
dotc = dot.compile(thr)
dotc(res_dev, a_dev, b_dev)
assert diff_is_negligible(res_dev.get(), res_ref)
def main():
api = cluda.ocl_api()
thr = api.Thread.create()
print thr
shape1 = (100, 200)
shape2 = (200, 100)
a = numpy.random.randn(*shape1).astype(numpy.float32)
b = numpy.random.randn(*shape2).astype(numpy.float32)
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_dev = thr.array((shape1[0], shape2[1]), dtype=numpy.float32)
dot = MatrixMul(a_dev, b_dev, out_arr=res_dev)
dotc = dot.compile(thr)
dotc(res_dev, a_dev, b_dev)
res_reference = numpy.dot(a, b)
print res_reference
def main():
api = cluda.ocl_api()
# thr = api.Thread.create()
thr = api.Thread.create({'exclude_devices': 'Iris Pro'})
n = 6000
m = 3000
shape1 = (n, m)
shape2 = (m, n)
a = numpy.random.randn(*shape1).astype(numpy.float32)
b = numpy.random.randn(*shape2).astype(numpy.float32)
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_dev = thr.array((shape1[0], shape2[1]), dtype=numpy.float32)
dot = MatrixMul(a_dev, b_dev, out_arr=res_dev)
dotc = dot.compile(thr)
gt = 0
for i in range(10):
thr.synchronize()
gpu_start = time.time()
dotc(res_dev, a_dev, b_dev)
thr.synchronize()
gt += time.time() - gpu_start
print(gt)
ct = 0
res_reference = None
for i in range(10):
t = time.time()
res_reference = numpy.dot(a, b)
ct += time.time() - t
print(ct)
print(norm(res_dev.get() - res_reference) / norm(res_reference) < 1e-6)
def run():
api = cluda.ocl_api()
thr = api.Thread.create()
n = 3000
shape1 = (n, n)
shape2 = (n, n)
a = numpy.random.randn(*shape1).astype(numpy.float32)
b = numpy.random.randn(*shape2).astype(numpy.float32)
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_dev = thr.array((shape1[0], shape2[1]), dtype=numpy.float32)
dot = MatrixMul(a_dev, b_dev, out_arr=res_dev)
dotc = dot.compile(thr)
dotc(res_dev, a_dev, b_dev)
res_reference = numpy.dot(a, b)
print(norm(res_dev.get() - res_reference) / norm(res_reference) < 1e-6)
def check_performance(thr_and_double, perf_shape,
bwo=None, transposed_a=False, transposed_b=False):
thr, double = thr_and_double
dtype = numpy.float64 if double else numpy.float32
batch, size = perf_shape
shape = (batch, size, size)
a = get_test_array(shape, dtype)
b = get_test_array(shape, dtype)
a_ref = transpose(a) if transposed_a else a
b_ref = transpose(b) if transposed_b else b
a_dev = thr.to_device(a)
b_dev = thr.to_device(b)
res_ref = ref_dot(a_ref, b_ref)
res_dev = thr.array(res_ref.shape, dtype=dtype)
dot = MatrixMul(a_dev, b_dev, out_arr=res_dev, block_width_override=bwo,
transposed_a=transposed_a, transposed_b=transposed_b)
try:
dotc = dot.compile(thr)
except ValueError:
pytest.skip()
attempts = 10
times = []
for i in range(attempts):
t1 = time.time()
dotc(res_dev, a_dev, b_dev)
thr.synchronize()
times.append(time.time() - t1)
assert diff_is_negligible(thr.from_device(res_dev), res_ref)
return min(times), batch * size ** 3 * 2
def create(thr, a_size, b_size, dtype=np.complex128, compile_=True):
mm = MatrixMul(thr.array(a_size, dtype=dtype),
thr.array(b_size, dtype=dtype))
if compile_:
mm = mm.compile(thr)
return mm
def create(thr, a_size, b_size, dtype=np.complex128, compile_=True):
mm = MatrixMul(thr.array(a_size, dtype=dtype), thr.array(b_size, dtype=dtype))
if compile_:
mm = mm.compile(thr)
return mm
