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@ -72,13 +72,9 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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block_ms = (get_m_alignment_for_contiguous_layout(), )
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block_ns = tuple(range(16, 129, 8)) + (144, 160, )
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def fix_wave_saturate(x): return num_sms if x == 0 else x
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def get_num_waves(bm, bn): return (ceil_div(
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ceil_div(m, bm) * ceil_div(n, bn) * num_groups, num_sms) if bm else None)
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def get_last_wave_util(bm, bn): return fix_wave_saturate(
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(ceil_div(m, bm) * ceil_div(n, bn) * num_groups) % num_sms)
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fix_wave_saturate = lambda x: num_sms if x == 0 else x
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get_num_waves = lambda bm, bn: (ceil_div(ceil_div(m, bm) * ceil_div(n, bn) * num_groups, num_sms) if bm else None)
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get_last_wave_util = lambda bm, bn: fix_wave_saturate((ceil_div(m, bm) * ceil_div(n, bn) * num_groups) % num_sms)
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# Decide block sizes by waves
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best_block_m, best_block_n = None, None
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@ -86,8 +82,7 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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# NOTES: the block sizes cannot be too large, so at least one dim less than 128
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for block_n in filter(lambda bn: block_m <= 128 or bn <= 128, block_ns):
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success = False
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num_waves, best_num_waves = get_num_waves(
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block_m, block_n), get_num_waves(best_block_m, best_block_n)
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num_waves, best_num_waves = get_num_waves(block_m, block_n), get_num_waves(best_block_m, best_block_n)
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if best_block_m is None or best_block_n is None:
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success = True
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elif num_waves < best_num_waves:
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@ -104,8 +99,7 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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success |= block_n == best_block_n and block_m < best_block_m
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# Case 3: different for both `block_m` and `block_n`, `block_n` larger is better
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success |= block_m != best_block_m and block_n > best_block_n
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best_block_m, best_block_n = (block_m, block_n) if success else (
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best_block_m, best_block_n)
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best_block_m, best_block_n = (block_m, block_n) if success else (best_block_m, best_block_n)
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assert best_block_m is not None and best_block_n is not None
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# Always pick the longest one
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@ -116,8 +110,7 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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# Unrolling both stages and `num_former_iters` will cause large code size
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stage_candidates = (4, 3)
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for num_stages in stage_candidates:
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best_smem_config = get_smem_config(
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num_stages, k, best_block_m, best_block_n)
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best_smem_config = get_smem_config(num_stages, k, best_block_m, best_block_n)
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if best_smem_config[0] <= sm90_capacity:
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best_num_stages = num_stages
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break
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@ -141,10 +134,8 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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# Recompute the minimal number of SMs required
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# NOTES: less L2 cache usage and less GPU frequency drop
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num_waves = get_num_waves(best_block_m, best_block_n)
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num_min_sms = ceil_div(ceil_div(m, best_block_m) *
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ceil_div(n, best_block_n) * num_groups, num_waves)
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num_min_sms = ceil_div(
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num_min_sms, best_tma_multicast_config[0]) * best_tma_multicast_config[0]
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num_min_sms = ceil_div(ceil_div(m, best_block_m) * ceil_div(n, best_block_n) * num_groups, num_waves)
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num_min_sms = ceil_div(num_min_sms, best_tma_multicast_config[0]) * best_tma_multicast_config[0]
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assert num_min_sms <= num_sms
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return num_min_sms, best_block_m, best_block_n, best_num_stages, best_tma_multicast_config, best_smem_config
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