[FP6-LLM] Port splitK map from DeepSpeed #283
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/283
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit b89ec10 with merge base 42c2376 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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| @@ -111,6 +112,143 @@ def from_tc_float6_e3m2(tensor: Tensor, M: int, N: int, dtype: torch.dtype = tor | |||
| return from_float6_e3m2(tensor_fp6, no_bit_packing=True, dtype=dtype) | |||
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| # https://github.com/microsoft/DeepSpeed/blob/3a3a6db3332e339cc9fd94efd4982f6d60635a3d/deepspeed/inference/v2/kernels/core_ops/cuda_linear/cuda_linear.py | |||
| _SPLIT_K_MAP = [ | |||
| { # tokens: [1, 64] | |||
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n00b q: what is meant by token counts here?
Also can these values be autotuned? I don't necesarily wanna merge in something that's only fast on A100
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I think it's the batch size. Yea, it will be great if we can autotune CUDA kernel also.
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* aoti runner test * change to cpu
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https://github.com/microsoft/DeepSpeed/blob/3a3a6db3332e339cc9fd94efd4982f6d60635a3d/deepspeed/inference/v2/kernels/core_ops/cuda_linear/cuda_linear.py
Optimal splitK for a given
(batch_size, out_dim)pair on A100-80G.