ColossalAI/tests/test_fx/test_meta_info_prop.py

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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)
[fx] add profiler for fx nodes. (#1480) * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] merge development into main (#1) * [fx] activation checkpointing using Chen strategies. * [fx] add test for ckpt_solver_chen * [fx] add vanilla activation checkpoint search with test on resnet and densenet * [fx] add a namespace code for solver_chen. * [fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174. * [fx] fix lowercase naming conventions. * [fx] simplify test for ckpt. * [fx] add rules to linearize computation graphs for searching. (#2) * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] merge development into main (#1) * [fx] activation checkpointing using Chen strategies. * [fx] add test for ckpt_solver_chen * [fx] add vanilla activation checkpoint search with test on resnet and densenet * [fx] add a namespace code for solver_chen. * [fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174. * [fx] fix lowercase naming conventions. * [fx] simplify test for ckpt. * [fx] fix test and algorithm bugs in activation checkpointing. * [fx] polish ckpt_test. * [fx] add rules to linearize computation graphs for searching. * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] fix inconsistencies. * [fx] fix MetaInfoProp. * [fx] fix MetaInfoProp. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] fix error in tests. * [fx] unfix bug. * [fx] unfix bug.
2022-08-24 08:22:44 +00:00
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()