mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
48 lines
1.3 KiB
48 lines
1.3 KiB
import torch |
|
import torch.nn as nn |
|
from torch.fx import GraphModule |
|
|
|
from colossalai.fx.proxy import ColoProxy |
|
from colossalai.fx.tracer.tracer import ColoTracer |
|
from colossalai.testing import clear_cache_before_run |
|
|
|
|
|
class Conv1D(nn.Module): |
|
def __init__(self, nf, nx): |
|
super().__init__() |
|
self.nf = nf |
|
w = torch.empty(nx, nf) |
|
nn.init.normal_(w, std=0.02) |
|
self.weight = nn.Parameter(w) |
|
self.bias = nn.Parameter(torch.zeros(nf)) |
|
|
|
def forward(self, x): |
|
size_out = x.shape[:-1] + (self.nf,) |
|
x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) |
|
x = x.view(size_out) |
|
return x |
|
|
|
|
|
@clear_cache_before_run() |
|
def test_coloproxy(): |
|
tracer = ColoTracer() |
|
model = Conv1D(3, 3) |
|
input_sample = {"x": torch.rand(3, 3).to("meta")} |
|
|
|
graph = tracer.trace(root=model, meta_args=input_sample) |
|
gm = GraphModule(model, graph, model.__class__.__name__) |
|
gm.recompile() |
|
node = list(gm.graph.nodes)[0] |
|
|
|
proxy = ColoProxy(node=node, tracer=tracer) |
|
proxy.meta_data = torch.empty(4, 2, device="meta") |
|
|
|
assert len(proxy) == 4 |
|
assert proxy.shape[0] == 4 and proxy.shape[1] == 2 |
|
assert proxy.dim() == 2 |
|
assert proxy.dtype == torch.float32 |
|
assert proxy.size(0) == 4 |
|
|
|
|
|
if __name__ == "__main__": |
|
test_coloproxy()
|
|
|