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6f42c37359
* Move batch invariant pkg to Fastdeploy * fix problem and pre-commit * move test * Change testcase to FD style * Add testcase for log_softmax * Add testcase for mean * Add testcase for addmm * fix pre-commit * API check v0.9 * move to layers and add comment about log_softmax * Update fastdeploy/model_executor/layers/batch_invariant_ops/batch_invariant_ops.py 存在于原版代码注释中的版本控制遗留的内容，确实应该去除 Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/batch_invariant/test_batch_invariance_op_mean.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/batch_invariant/test_batch_invariance_op_logsoftmax.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update fastdeploy/model_executor/layers/batch_invariant_ops/batch_invariant_ops.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * change comment after copilot fix * fix bug about addmm * avoid global effect by enable_torch_proxy --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
# Adapted from https://github.com/thinking-machines-lab/batch_invariant_ops/blob/main/batch_invariant_ops/test_batch_invariance.py
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import unittest
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import paddle
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from fastdeploy.model_executor.layers.batch_invariant_ops import (
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set_batch_invariant_mode,
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)
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class TestBatchInvariantForMean(unittest.TestCase):
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def setUp(self):
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"""
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Initialize the test environment
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"""
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device = "gpu" if paddle.is_compiled_with_cuda() else "cpu"
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paddle.set_device(device)
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def test_batch_invariance(self, B: int = 2048, D: int = 4096, dtype=paddle.float32):
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a = paddle.linspace(-100, 100, B * D, dtype=dtype).reshape(B, D)
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# Method 1: Mean reduction over last axis (batch size 1)
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out1 = paddle.mean(a[:1], axis=-1)
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# Method 2: Mean reduction over last axis (full batch)
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out2 = paddle.mean(a, axis=-1)[:1]
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# Check if results are identical
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diff = (out1 - out2).abs().max()
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return diff.item() == 0, diff
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def run_iters(self, iters=10, ass=False):
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for dtype in [paddle.float32, paddle.bfloat16]:
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is_deterministic = True
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difflist = []
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for i in range(iters):
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isd, df = self.test_batch_invariance(dtype=dtype)
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is_deterministic = is_deterministic and isd
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difflist.append(df)
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print(
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f"Batch Deterministic: {is_deterministic} run-to-run max/min/diff {max(difflist)}/{min(difflist)}/{max(difflist)-min(difflist)} for {dtype} in {iters} iterations"
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)
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if ass:
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assert max(difflist) == 0
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def test_case(self):
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# Test with standard Paddle (likely to show differences)
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print("Standard Paddle:")
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with set_batch_invariant_mode(False):
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self.run_iters(ass=False)
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# Test with batch-invariant operations
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print("\nBatch-Invariant Mode:")
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with set_batch_invariant_mode(True):
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self.run_iters(ass=True)
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if __name__ == "__main__":
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unittest.main()
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"""
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Standard Paddle:
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Batch Deterministic: False run-to-run max/min/diff 7.62939453125e-06/7.62939453125e-06/0.0 for paddle.float32 in 10 iterations
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Batch Deterministic: True run-to-run max/min/diff 0.0/0.0/0.0 for paddle.bfloat16 in 10 iterations
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Batch-Invariant Mode:
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Batch Deterministic: True run-to-run max/min/diff 0.0/0.0/0.0 for paddle.float32 in 10 iterations
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Batch Deterministic: True run-to-run max/min/diff 0.0/0.0/0.0 for paddle.bfloat16 in 10 iterations
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"""
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