class CPUUnaryBench(Benchmark):
args = utils.grid(
_common_arg(
{"function": TORCH_ONLY_FUNCTIONS, "framework": ("Torch",)}
)
)
def setupRun(self, state, arg):
_setupRun(self, state, arg)
def benchmark(self, state, arg):
_benchmark(self, state, arg)
class NumpyComparison(Benchmark):
args = utils.grid(
_common_arg(
{"function": ALL_UNARY_FUNCTIONS, "framework": ("Torch", "NumPy")}
)
)
def setupRun(self, state, arg):
_setupRun(self, state, arg)
def benchmark(self, state, arg):
_benchmark(self, state, arg)
class CPUUnaryBench(Benchmark):
args = utils.grid(
_common_arg({
"function": TORCH_ONLY_FUNCTIONS,
"framework": ("Torch", )
}))
user_counters = {"sizes": 30 * " ", "strides": 30 * " "}
def setupRun(self, state, arg):
_setupRun(self, state, arg)
def benchmark(self, state, arg):
_benchmark(self, state, arg)
class NumpyUnaryComparison(Benchmark):
args = utils.grid(
_common_arg({
"function": ALL_UNARY_FUNCTIONS,
"framework": ("Torch", "NumPy")
}))
user_counters = {"sizes": 30 * " ", "strides": 30 * " "}
def setupRun(self, state, arg):
_setupRun(self, state, arg)
def benchmark(self, state, arg):
_benchmark(self, state, arg)
class NumpyReduceComparison(Benchmark):
# NB: NumPy doesn't parallelize it's reductions
args = utils.grid({
"dims": ((3, None), (3, 2), (3, 1), (3, 0)),
"mag": (6, ),
"cont": (False, True),
"trans": (False, True),
"dtype": (torch.float, ),
"function": ALL_REDUCE_FUNCTIONS,
"framework": ("Torch", ), # "NumPy"),
})
user_counters = {"shape": 10 * " "}
def _benchmark(self, state, arg):
if arg.framework == "Torch":
if arg.dims[1]:
getattr(torch, arg.function[0])(state.torch_tensor,
arg.dims[1],
out=state.output)
else:
getattr(torch, arg.function[0])(state.torch_tensor)
else:
if arg.dims[1]:
getattr(np, arg.function[1])(state.numpy_tensor,
axis=arg.dims[1],
out=state.output)
else:
getattr(np, arg.function[1])(state.numpy_tensor)
def setupRun(self, state, arg):
size_ = int(math.pow(10, arg.mag))
tv = make_tensor(size_, arg.dtype, arg.cont, arg.dims[0], arg.trans)
state.shape = str(tv.size())
state.torch_tensor = tv
state.output = None
if arg.framework == "NumPy":
if arg.dtype == torch.float:
state.numpy_tensor = state.torch_tensor.numpy()
assert state.numpy_tensor.dtype == np.float32
if arg.dtype == torch.double:
state.numpy_tensor = state.torch_tensor.numpy()
assert state.numpy_tensor.dtype == np.float64
self._benchmark(state, arg)
def benchmark(self, state, arg):
self._benchmark(state, arg)
class CPULSTMBench(Benchmark):
sizes = [
[64, 15, 500, 500],
[64, 20, 500, 500],
[64, 25, 500, 500],
[64, 30, 500, 500],
[64, 35, 500, 500],
[64, 40, 500, 500],
[64, 45, 500, 500],
[64, 50, 500, 500],
[16, 25, 512, 512],
[32, 25, 512, 512],
[64, 25, 512, 512],
[128, 25, 512, 512],
[16, 25, 1024, 1024],
[32, 25, 1024, 1024],
[64, 25, 1024, 1024],
[128, 25, 1024, 1024],
[16, 25, 2048, 2048],
[32, 25, 2048, 2048],
[64, 25, 2048, 2048],
[128, 25, 2048, 2048],
[16, 25, 4096, 4096],
[32, 25, 4096, 4096],
[64, 25, 4096, 4096],
[128, 25, 4096, 4096],
]
args = utils.grid({"size": sizes, "train": (True, False)})
user_counters = {
"duration": 0,
"gflops": 10 * " ",
"GFLOPS": 10 * " ",
"SPS": 10 * " ",
}
def setupRun(self, state, arg):
size = arg.size
N = size[0] # batch size
T = size[1] # sentence length
D = size[2] # embedding size
H = size[3] # hidden size
state.N, state.T, state.D, state.H = N, T, D, H
state.rnn = nn.LSTM(D, H, 1)
state.input = Variable(torch.randn(T, N, D))
state.h0 = Variable(torch.randn(1, N, H))
state.c0 = Variable(torch.randn(1, N, H))
state.output, state.hn = state.rnn(state.input, (state.h0, state.c0))
if arg.train:
state.loss_fn = torch.nn.L1Loss()
state.targets = Variable(torch.randn(T, N, D))
state.num_iter = 0
state.elapsed = 0
def benchmark(self, state, arg):
start = time.time()
state.output, state.hn = state.rnn(state.input, (state.h0, state.c0))
if arg.train:
loss = state.loss_fn(state.output, state.targets)
loss.backward()
state.elapsed += time.time() - start
state.num_iter += 1
def teardownRun(self, state, arg):
dura = (state.elapsed) / state.num_iter # time of ONE iteration
N, T, D, H = state.N, state.T, state.D, state.H
gflops = T * 4 * (N * H * D * 2 + N * H * H * 2) / 1e9
GFLOPS = gflops / dura # giga floating-point operations per second
SPS = N / dura # number of processed sentences per second
state.duration = "{:.4f}".format(dura)
state.gflops = "{:.4f}".format(gflops)
state.GFLOPS = "{:.4f}".format(GFLOPS)
state.SPS = "{:.4f}".format(SPS)
class CPUConvnets(Benchmark):
args = utils.grid({
("arch", "size"): (
("alexnet", (128, 3, 224, 224)),
("vgg11", (64, 3, 224, 224)),
("inception_v3", (128, 3, 299, 299)),
("resnet50", (128, 3, 224, 224)),
("squeezenet1_0", (128, 3, 224, 224)),
("densenet121", (32, 3, 224, 224)),
# ("mobilenet_v2", (128, 3, 224, 224)),
),
"single_batch_size": (True, False),
"inference": (True, False),
})
user_counters = {
"time_fwd_avg": 0,
"time_bwd_avg": 0,
"time_upt_avg": 0,
"time_total": 0,
}
def setupRun(self, state, arg):
arch, sizes = arg[("arch", "size")]
batch_size, c, h, w = sizes[0], sizes[1], sizes[2], sizes[3]
batch_size = 1 if arg.single_batch_size else batch_size
data_ = torch.randn(batch_size, c, h, w)
target_ = torch.arange(1, batch_size + 1).long()
state.net = models.__dict__[arch](
) # no need to load pre-trained weights for dummy data
state.optimizer = optim.SGD(state.net.parameters(), lr=0.01)
state.criterion = nn.CrossEntropyLoss()
state.net.eval()
state.data, state.target = Variable(data_), Variable(target_)
state.steps = 0
state.time_fwd = 0
state.time_bwd = 0
state.time_upt = 0
def benchmark(self, state, arg):
state.optimizer.zero_grad() # zero the gradient buffers
t1 = time.time()
output = state.net(state.data)
t2 = time.time()
if not arg.inference:
loss = state.criterion(output, state.target)
loss.backward()
t3 = time.time()
state.optimizer.step() # Does the update
t4 = time.time()
state.time_fwd += t2 - t1
if not arg.inference:
state.time_bwd += t3 - t2
state.time_upt += t4 - t3
state.steps += 1
def teardownRun(self, state, arg):
time_fwd_avg = state.time_fwd / state.steps * 1000
time_bwd_avg = state.time_bwd / state.steps * 1000
time_upt_avg = state.time_upt / state.steps * 1000
# update not included!
time_total = time_fwd_avg + time_bwd_avg
state.time_fwd_avg = "{:2.3f}".format(time_fwd_avg)
state.time_bwd_avg = "{:2.3f}".format(time_bwd_avg)
state.time_upt_avg = "{:2.3f}".format(time_upt_avg)
state.time_total = "{:2.3f}".format(time_total)