mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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48 lines
1.2 KiB
48 lines
1.2 KiB
import torch |
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import torch.nn as nn |
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from colossalai.fx.proxy import ColoProxy |
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from colossalai.fx.tracer.tracer import ColoTracer |
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from torch.fx import GraphModule |
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import pytest |
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class Conv1D(nn.Module): |
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def __init__(self, nf, nx): |
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super().__init__() |
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self.nf = nf |
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w = torch.empty(nx, nf) |
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nn.init.normal_(w, std=0.02) |
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self.weight = nn.Parameter(w) |
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self.bias = nn.Parameter(torch.zeros(nf)) |
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def forward(self, x): |
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size_out = x.shape[:-1] + (self.nf,) |
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x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) |
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x = x.view(size_out) |
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return x |
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def test_coloproxy(): |
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tracer = ColoTracer() |
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model = Conv1D(3, 3) |
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input_sample = {'x': torch.rand(3, 3).to('meta')} |
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graph = tracer.trace(root=model, meta_args=input_sample) |
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gm = GraphModule(model, graph, model.__class__.__name__) |
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gm.recompile() |
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node = list(gm.graph.nodes)[0] |
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proxy = ColoProxy(node=node, tracer=tracer) |
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proxy.meta_data = torch.empty(4, 2, device='meta') |
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assert len(proxy) == 4 |
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assert proxy.shape[0] == 4 and proxy.shape[1] == 2 |
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assert proxy.dim() == 2 |
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assert proxy.dtype == torch.float32 |
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assert proxy.size(0) == 4 |
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if __name__ == '__main__': |
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test_coloproxy()
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