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* update * update * update * update * update * update * update * update * remove useless code * update --------- Co-authored-by: gyzhou2000 <[email protected]> Co-authored-by: gyzhou2000 <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| import os | ||
| # os.environ['TL_BACKEND'] = "torch" | ||
| os.environ["CUDA_VISIBLE_DEVICES"] = "-1" | ||
| import tensorlayerx as tlx | ||
| from gammagl.mpops import * | ||
| import numpy as np | ||
| import time | ||
|
|
||
| relative_path = '/home/zgy/review/zgy/GammaGL/profiler/mpops/edge_index/' | ||
| file_name = ['cora.npy', 'pubmed.npy', 'ogbn-arxiv.npy'] | ||
| embedding = [16, 64, 256] | ||
| iter = 10 | ||
|
|
||
| operations = { | ||
| 'segment_max': segment_max, | ||
| 'unsorted_segmnet_max': unsorted_segment_max, | ||
| 'segment_sum': segment_sum, | ||
| 'unsorted_segment_sum': unsorted_segment_sum, | ||
| 'segment_mean': segment_mean, | ||
| 'unsorted_segment_mean': unsorted_segment_mean | ||
| } | ||
|
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||
|
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| def run_test(operation, x, dst, num_nodes): | ||
| edge_weight = tlx.ones(shape=(edge_index.shape[1],), dtype=tlx.float32) | ||
| edge_weight = tlx.expand_dims(edge_weight, -1) | ||
|
|
||
| msg = tlx.gather(x, src) # warm up | ||
| operation(msg, dst, num_nodes) | ||
|
|
||
| start = time.time() | ||
| for j in range(iter): | ||
| msg = tlx.gather(x, src) | ||
| msg = msg * edge_weight | ||
| operation(msg, dst, num_nodes) | ||
| end = time.time() | ||
| return end - start | ||
|
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||
|
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||
| with open('test_results.txt', 'w') as result_file: | ||
| for name in file_name: | ||
| path = relative_path + name | ||
| result_file.write(f"Loading data from {path}\n") | ||
| print(f"Loading data from {path}") | ||
| edge_index = np.load(path) | ||
|
|
||
| num_nodes = np.max(edge_index) + 1 | ||
| src = tlx.convert_to_tensor(edge_index[0, :], tlx.int64) | ||
| dst = tlx.convert_to_tensor(edge_index[1, :], tlx.int64) | ||
| dst2 = None | ||
|
|
||
| # TensorFlow-specific preprocessing for sorted operations | ||
| if tlx.BACKEND == 'tensorflow' or tlx.BACKEND == 'paddle' or tlx.BACKEND == 'mindspore': | ||
| dst_numpy = tlx.convert_to_numpy(dst) | ||
| idx = np.argsort(dst_numpy) | ||
| dst2 = tlx.gather(tlx.convert_to_tensor(dst_numpy, dtype=tlx.int64), tlx.convert_to_tensor(idx, dtype=tlx.int64)) | ||
|
|
||
| for embedding_dim in embedding: | ||
| result_file.write(f"** Testing embedding dimension {embedding_dim} **\n") | ||
| print(f"** Testing embedding dimension {embedding_dim} **") | ||
| x = tlx.convert_to_tensor(np.random.randn(num_nodes, embedding_dim), dtype=tlx.float32) | ||
|
|
||
| # Running tests for all operations | ||
| for op_name, op_func in operations.items(): | ||
| dst_target = dst2 if dst2 is not None else dst | ||
| duration = run_test(op_func, x, dst_target, num_nodes) | ||
| result = f"{op_name}: {duration:.3f} seconds\n" | ||
| result_file.write(result) | ||
| print(result) | ||
|
|
||
| print(f"** Done testing embedding dimension {embedding_dim} **") | ||
| result_file.write(f"** Done testing embedding dimension {embedding_dim} **\n") | ||
|
|
||
| if tlx.BACKEND == 'paddle': | ||
| info = f"Data tensors are on device: {x.place}\n" | ||
| elif tlx.BACKEND == 'mindspore': | ||
| info = f"mindspore\n" | ||
| else: | ||
| info = f"Data tensors are on device: {x.device}\n" | ||
| print(info, end='') | ||
| result_file.write(info) | ||
|
|
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