xref: /aosp_15_r20/external/pytorch/benchmarks/tensorexpr/pt_engine.py (revision da0073e96a02ea20f0ac840b70461e3646d07c45) 
1import torch
2
3
4class TorchTensorEngine:
5    def rand(self, shape, device=None, dtype=None, requires_grad=False):
6        return torch.rand(
7            shape, device=device, dtype=dtype, requires_grad=requires_grad
8        )
9
10    def randn(self, shape, device=None, dtype=None, requires_grad=False):
11        return torch.randn(
12            shape, device=device, dtype=dtype, requires_grad=requires_grad
13        )
14
15    def nchw_rand(self, shape, device=None, requires_grad=False):
16        return self.rand(shape, device=device, requires_grad=requires_grad)
17
18    def reset(self, _):
19        pass
20
21    def rand_like(self, v):
22        return torch.rand_like(v)
23
24    def numpy(self, t):
25        return t.cpu().numpy()
26
27    def mul(self, t1, t2):
28        return t1 * t2
29
30    def add(self, t1, t2):
31        return t1 + t2
32
33    def batch_norm(self, data, mean, var, training):
34        return torch.nn.functional.batch_norm(data, mean, var, training=training)
35
36    def instance_norm(self, data):
37        return torch.nn.functional.instance_norm(data)
38
39    def layer_norm(self, data, shape):
40        return torch.nn.functional.layer_norm(data, shape)
41
42    def sync_cuda(self):
43        torch.cuda.synchronize()
44
45    def backward(self, tensors, grad_tensors, _):
46        torch.autograd.backward(tensors, grad_tensors=grad_tensors)
47
48    def sum(self, data, dims):
49        return torch.sum(data, dims)
50
51    def softmax(self, data, dim=None, dtype=None):
52        return torch.nn.functional.softmax(data, dim, dtype)
53
54    def cat(self, inputs, dim=0):
55        return torch.cat(inputs, dim=dim)
56
57    def clamp(self, data, min, max):
58        return torch.clamp(data, min=min, max=max)
59
60    def relu(self, data):
61        return torch.nn.functional.relu(data)
62
63    def tanh(self, data):
64        return torch.tanh(data)
65
66    def max_pool2d(self, data, kernel_size, stride=1):
67        return torch.nn.functional.max_pool2d(data, kernel_size, stride=stride)
68
69    def avg_pool2d(self, data, kernel_size, stride=1):
70        return torch.nn.functional.avg_pool2d(data, kernel_size, stride=stride)
71
72    def conv2d_layer(self, ic, oc, kernel_size, groups=1):
73        return torch.nn.Conv2d(ic, oc, kernel_size, groups=groups)
74
75    def matmul(self, t1, t2):
76        return torch.matmul(t1, t2)
77
78    def to_device(self, module, device):
79        return module.to(device)
80