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from . import benchmark
import numpy as np
import torch
class Concat2D2InputBench(benchmark.Benchmark):
def __init__(self, mode, device, dtype, I1_D1, I1_D2, I2_D1, I2_D2, concat_dim):
super().__init__(mode, device, dtype)
self.I1_D1 = I1_D1
self.I1_D2 = I1_D2
self.I2_D1 = I2_D1
self.I2_D2 = I2_D2
self.concat_dim = concat_dim
self.input1 = self.randn([I1_D1, I1_D2], device=device, dtype=dtype, requires_grad=self.requires_grad)
self.input2 = self.randn([I2_D1, I2_D2], device=device, dtype=dtype, requires_grad=self.requires_grad)
self.inputs = [self.input1, self.input2]
def forward(self, input1, input2):
x1 = self.add(input1, 0.00001)
x2 = self.add(input2, 0.00001)
y = self.cat((x1, x2), dim=self.concat_dim)
return y
def reference(self):
return np.concatenate((self.numpy(self.input1), self.numpy(self.input2)), axis=concat_dim)
def config(self):
return [self.I1_D1, self.I1_D2, self.I2_D1, self.I2_D2, self.concat_dim]
@staticmethod
def module():
return "concat2d2input"
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.I1_D1 * self.I1_D2 + self.I2_D1 * self.I2_D2
return {
"sol": buffer_size * sol_count,
"algorithmic": buffer_size * algorithmic_count,
}
@staticmethod
def default_configs():
return [
[1, 160, 1, 14, 1],
[1, 580, 1, 174, 1],
[20, 160, 20, 14, 1],
[20, 580, 20, 174, 1],
[8, 512, 8, 512, 1],
[1 << 13, 1060, 1 << 13, 1040, 1],
[1 << 13, 2000, 1 << 13, 1074, 1],
[1 << 15, 1060, 1 << 15, 2670, 1],
[1 << 15, 5120, 1 << 15, 2512, 1]
]
benchmark.register_benchmark_class(Concat2D2InputBench)
class ConcatGraphOptBench(benchmark.Benchmark):
def __init__(self, mode, device, dtype, I1_D1, I1_D2, I2_D1, I2_D2, concat_dim):
super().__init__(mode, device, dtype)
self.I1_D1 = I1_D1
self.I1_D2 = I1_D2
self.I2_D1 = I2_D1
self.I2_D2 = I2_D2
self.concat_dim = concat_dim
self.input1 = self.randn([I1_D1, I1_D2], device=device, dtype=dtype, requires_grad=self.requires_grad)
self.input2 = self.randn([I2_D1, I2_D2], device=device, dtype=dtype, requires_grad=self.requires_grad)
self.inputs = [self.input1, self.input2]
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_cat_wo_conditionals(True)
def forward(self, input1, input2):
x1 = self.add(input1, 0.00001)
x2 = self.add(input2, 0.00001)
y = self.cat((x1, x2), dim=self.concat_dim)
z = self.relu(y)
return z
def reference(self):
return np.concatenate((self.numpy(self.input1), self.numpy(self.input2)), axis=concat_dim)
def config(self):
return [self.I1_D1, self.I1_D2, self.I2_D1, self.I2_D2, self.concat_dim]
@staticmethod
def module():
return "concatGraphOpt"
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.I1_D1 * self.I1_D2 + self.I2_D1 * self.I2_D2
return {
"sol": buffer_size * sol_count,
"algorithmic": buffer_size * algorithmic_count,
}
@staticmethod
def default_configs():
return [
[1 << 13, 1060, 1 << 13, 1040, 1],
[1 << 13, 2000, 1 << 13, 1074, 1],
[1 << 15, 1060, 1 << 15, 2670, 1],
[1 << 15, 5120, 1 << 15, 2512, 1]
]
benchmark.register_benchmark_class(ConcatGraphOptBench)
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