[autoparallel] Patch tensor related operations meta information (#2789)

* [autoparallel] tensor related meta information prototype * [autoparallel] tensor related meta information * [autoparallel] tensor related meta information * [autoparallel] tensor related meta information * [autoparallel] tensor related meta information
2025-09-24 19:17:30 +00:00 · 2023-02-20 17:38:55 +08:00
parent a5721229d9
commit 7ea6bc7f69
4 changed files with 183 additions and 1 deletions
--- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py
+++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py
@@ -0,0 +1,103 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    MemoryCost,
+    OperationData,
+    OperationDataType,
+    ShardingStrategy,
+    StrategiesVector,
+    TrainCycleItem,
+)
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
+
+if torch.__version__ >= '1.12.0':
+    from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register
+
+
+class SplitModule(nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x):
+        return x.split(512, dim=0)
+
+
+@pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+def test_tensor_meta_info():
+    """test tensor related meta information
+    We will just use torch.Tensor.split for the test
+    """
+    meta_func = meta_register.get(torch.Tensor.split)
+
+    # construct meta tensors
+    input_tensor = torch.rand(1024, 1024, device="meta")
+    output_tensor = input_tensor.split(512, dim=0)
+
+    # construct operation data
+    input_data = OperationData(
+        name="input",
+        data=input_tensor,
+        type=OperationDataType.ARG,
+        logical_shape=input_tensor.shape,
+    )
+    output_data = OperationData(
+        name="output",
+        data=output_tensor,
+        type=OperationDataType.OUTPUT,
+        logical_shape=input_tensor.shape,
+    )
+    split_info_data = OperationData(
+        name='split_info',
+        type=OperationDataType.ARG,
+        data=0,
+        logical_shape=None,
+    )
+
+    # construct args
+    args = [input_data, output_data, split_info_data]
+    kwargs = {'inplace': False}
+
+    # estimated results
+    compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out = meta_func(*args, **kwargs)
+
+    # actual results
+    model = SplitModule()
+    input_real_tensor = torch.rand(1024, 1024).cuda()
+
+    input_real_tensor.requires_grad = True
+
+    # fwd
+    torch.cuda.reset_peak_memory_stats()
+    mem_stamp0 = torch.cuda.memory_allocated()
+    output_real_tensor = model(input_real_tensor)
+    fwd_allocated = torch.cuda.memory_allocated() - mem_stamp0
+    fwd_peak = torch.cuda.max_memory_allocated() - mem_stamp0
+
+    # bwd
+    upstream_grad = [torch.rand_like(tensor) for tensor in output_real_tensor]
+    torch.cuda.reset_peak_memory_stats()
+    mem_stamp0 = torch.cuda.memory_allocated()
+    torch.autograd.backward(output_real_tensor, upstream_grad)
+    bwd_allocated = torch.cuda.memory_allocated() - mem_stamp0
+    bwd_peak = torch.cuda.max_memory_allocated() - mem_stamp0
+
+    print_results([input_real_tensor], output_real_tensor, compute_cost, memory_cost, fwd_allocated, fwd_peak,
+                  bwd_allocated, bwd_peak)
+
+
+if __name__ == "__main__":
+    test_tensor_meta_info()