mirror of https://github.com/hpcaitech/ColossalAI
37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import colossalai
|
|
import colossalai.nn as col_nn
|
|
from torch.fx import symbolic_trace
|
|
from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata
|
|
|
|
import pytest
|
|
|
|
BATCH_SIZE = 2
|
|
DIM_IN = 4
|
|
DIM_OUT = 16
|
|
|
|
|
|
def meta_check(meta_info_spec: TensorMetadata, orig_tensor: torch.Tensor):
|
|
assert meta_info_spec.shape == orig_tensor.shape
|
|
assert meta_info_spec.dtype == orig_tensor.dtype
|
|
assert meta_info_spec.stride == orig_tensor.stride()
|
|
assert meta_info_spec.numel == orig_tensor.numel()
|
|
|
|
|
|
def test_meta_info_prop():
|
|
model = torch.nn.Linear(DIM_IN, DIM_OUT)
|
|
input_sample = torch.rand(BATCH_SIZE, DIM_IN, device='meta')
|
|
orig_output = model(input_sample)
|
|
gm = symbolic_trace(model)
|
|
MetaInfoProp(gm).run(input_sample)
|
|
for node in gm.graph.nodes:
|
|
if node.op == 'placeholder':
|
|
meta_check(node.meta['tensor_meta'], input_sample)
|
|
if node.op == 'output':
|
|
meta_check(node.meta['tensor_meta'], orig_output)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_meta_info_prop()
|