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Tcc0403
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Jul 17, 2025
| W: (H,) | ||
| """ | ||
| Y, X, RSTD, BLOCK_SIZE, num_warps, casting_mode = rms_norm_forward(X, W, eps, offset, casting_mode, row_mode) | ||
| num_stages = calculate_num_stages() |
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nit: We can just return num_stages from rms_norm_forward() like num_warps to avoid calling it again.
| } | ||
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| def calculate_num_stages(): |
Collaborator
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Is there a table where we can look up these properties?
| ), | ||
| ], | ||
| ) | ||
| def test_large_64k_softmax_correctness(dtype, atol, rtol): |
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Is there any considerations of not just adding a test case to original tests?
| (GemmaRMSNorm, 1.0, "gemma"), | ||
| ], | ||
| ) | ||
| def test_large_64k_correctness(dtype, atol, rtol, reference, offset, casting_mode): |
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| device = torch.cuda.current_device() | ||
| torch_device_props = torch.cuda.get_device_properties(device) |
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We should make it xpu compatible
https://docs.pytorch.org/docs/stable/generated/torch.xpu.get_device_properties.html#torch.xpu.get_device_properties
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| num_warps = 4 | ||
| if BLOCK_SIZE >= 32768: | ||
| if BLOCK_SIZE >= 65536: |
Collaborator
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By the way, I'm always wondering why we don't take element_size into account. Do you have any idea?
Collaborator
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Can you show some plots comparing the perf before/after? |
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Summary
Optimizing Softmax and RMSNorm runtime performance on hidden_size >= 64k
Testing Done
Added large tests for 64K dim
make testto ensure correctnessmake checkstyleto ensure code stylemake test-convergenceto ensure convergence