def register_element_ops():
binary_op_list = [
["mul", lambda a, b: a * b],
["add", lambda a, b: a + b],
["sub", lambda a, b: a - b],
["div", lambda a, b: a / (b + 1e-4)],
["pow", lambda a, b: torch.pow(a, b),
lambda a, b: np.power(a, b)], # no fuson triggered
["max", lambda a, b: torch.max(a, b), lambda a, b: np.maximum(a, b)],
["min", lambda a, b: torch.min(a, b), lambda a, b: np.minimum(a, b)],
]
unary_op_list = [
["exp", lambda x: torch.exp(x), lambda x: np.exp(x)],
["sin", lambda x: torch.sin(x), lambda x: np.sin(x)],
["cos", lambda x: torch.cos(x), lambda x: np.cos(x)],
[
"rand_like", lambda x: torch.rand_like(x),
lambda x: np.random.rand(*x.shape)
],
]
for split_input, binary_op in itertools.product([True, False],
binary_op_list):
# Make a copy of ElementBench
if len(binary_op) == 2:
[op_str, op_pt_func] = binary_op
op_np_func = op_pt_func
elif len(binary_op) == 3:
[op_str, op_pt_func, op_np_func] = binary_op
split_str = 'split' if split_input else 'shared'
op_str = split_str + '_' + op_str
bm_cls = type('ElementBench_' + op_str, (ElementBench, ), {})
bm_cls.op_str = op_str
bm_cls.binary_op_pt_func = op_pt_func
bm_cls.binary_op_np_func = op_np_func
bm_cls.split_input = split_input
framework.register_benchmark_class(bm_cls)
for split_input, unary_op in itertools.product([True, False],
unary_op_list):
# Make a copy of ElementBench
if len(unary_op) == 2:
[op_str, op_pt_func] = unary_op
op_np_func = op_pt_func
elif len(unary_op) == 3:
[op_str, op_pt_func, op_np_func] = unary_op
split_str = 'split' if split_input else 'shared'
op_str = split_str + '_' + op_str
bm_cls = type('ElementBench_' + op_str, (ElementBench, ), {})
bm_cls.op_str = op_str
bm_cls.unary_op_pt_func = op_pt_func
bm_cls.unary_op_np_func = op_np_func
bm_cls.split_input = split_input
framework.register_benchmark_class(bm_cls)
if self.mode == 'fwd':
sol_count = 1
algorithmic_count = 1
else:
sol_count = 1 + 1
algorithmic_count = 1 + (1 + 1)
buffer_size = self.B * self.M * self.N + self.B * self.M * self.N + self.B * self.N * self.K
buffer_size *= 4
return {
'sol': buffer_size * sol_count,
'algorithmic': buffer_size * algorithmic_count
}
def compute_workload(self):
if self.mode == 'fwd':
count = 1
else:
count = 1 + (1 + 1)
op_count = 2 * self.B * self.M * self.N * self.K
return op_count * count
@staticmethod
def default_configs():
return [[128, 64, 128, 256]]
framework.register_benchmark_class(MatMulBench)
y = self.softmax(self.data, dim=1)
return y
def reference(self):
return scipy.special.softmax(self.numpy(self.data), axis=1)
def config(self):
return [self.M, self.N]
@staticmethod
def module():
return 'softmax'
def memory_workload(self):
if self.mode == 'fwd':
sol_count = 1 + 1
algorithmic_count = 3 + 1
else:
sol_count = (1 + 1) + (1 + 1)
algorithmic_count = (3 + 1) + (3 + 1)
buffer_size = self.M * self.N * 4
return {'sol': buffer_size * sol_count, 'algorithmic': buffer_size * algorithmic_count}
@staticmethod
def default_configs():
return [[128, 1<<16]]
framework.register_benchmark_class(SoftmaxBench)
class ReduceRowBench(ReduceBench):
def __init__(self, mode, device, M, N, K):
super(ReduceRowBench, self).__init__(mode, device, 'row', M, N, K)
@staticmethod
def module():
return 'reduce_row'
class ReduceMidBench(ReduceBench):
def __init__(self, mode, device, M, N, K):
super(ReduceMidBench, self).__init__(mode, device, 'mid', M, N, K)
@staticmethod
def module():
return 'reduce_mid'
class ReduceColBench(ReduceBench):
def __init__(self, mode, device, M, N, K):
super(ReduceColBench, self).__init__(mode, device, 'col', M, N, K)
@staticmethod
def module():
return 'reduce_col'
framework.register_benchmark_class(ReduceRowBench)
framework.register_benchmark_class(ReduceMidBench)
framework.register_benchmark_class(ReduceColBench)
def reference(self):
return self.numpy(self.forward(self.data))
def config(self):
return [self.M, self.N]
@staticmethod
def module():
return "swish"
def memory_workload(self):
if self.mode == "fwd":
sol_count = 1 + 1
algorithmic_count = 3 + 1
else:
sol_count = (1 + 1) + (1 + 1)
algorithmic_count = (3 + 1) + (3 + 1)
buffer_size = self.M * self.N * 4
return {
"sol": buffer_size * sol_count,
"algorithmic": buffer_size * algorithmic_count,
}
@staticmethod
def default_configs():
return [[128, 1 << 16]]
framework.register_benchmark_class(SwishBench)
return {
'sol': buffer_size * sol_count,
'algorithmic': buffer_size * algorithmic_count
}
@staticmethod
def default_configs():
return [[3, 16, 32, 256, 256]]
class MaxPoolBench(PoolingBench):
def __init__(self, *args):
super().__init__('maxpool', *args)
@staticmethod
def module():
return 'maxpool'
class AvgPoolBench(PoolingBench):
def __init__(self, *args):
super().__init__('avgpool', *args)
@staticmethod
def module():
return 'avgpool'
framework.register_benchmark_class(MaxPoolBench)
framework.register_benchmark_class(AvgPoolBench)
def module():
return 'batchnorm'
class InstanceNormBench(NormalizationBench):
def forward(self):
y = self.instance_norm(self.data)
return y
@staticmethod
def module():
return 'instance_norm'
def is_supported(self):
return tensor_engine.is_supported(self.instance_norm)
class LayerNormBench(NormalizationBench):
def forward(self):
y = self.layer_norm(self.data, [self.H, self.W])
return y
@staticmethod
def module():
return 'layernorm'
framework.register_benchmark_class(BatchNormBench)
framework.register_benchmark_class(InstanceNormBench)
framework.register_benchmark_class(LayerNormBench)
