Disable O rescale too for NO-OP test
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@@ -276,22 +276,11 @@ class FmhaV3RealSoftmax:
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acc_scale = Float32(0.0)
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row_sum *= acc_scale
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# O rescale in TMEM: multiply existing O by acc_scale = exp2(old_max - new_max)
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# Only for kt > 0 (first tile: no existing O to rescale)
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if kt > 0:
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n_corr = HEAD_DIM // corr_tile_size
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for ci in range(n_corr):
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tTMrO_rs = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
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tTMEM_LOAD_OtO_ci = cute.make_tensor(
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tTMEM_LOAD_OtO.iterator + ci * corr_tile_size, tTMEM_LOAD_OtO.layout
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)
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tTMEM_STORE_OtO_ci = cute.make_tensor(
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tTMEM_STORE_OtO.iterator + ci * corr_tile_size, tTMEM_STORE_OtO.layout
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)
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cute.copy(tiled_tmem_load_o, tTMEM_LOAD_OtO_ci, tTMrO_rs)
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for j in cutlass.range(cute.size(tTMrO_rs), vectorize=True):
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tTMrO_rs[j] = tTMrO_rs[j] * acc_scale
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cute.copy(tiled_tmem_store_o, tTMrO_rs, tTMEM_STORE_OtO_ci)
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# O rescale: DISABLED for NO-OP test
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# if kt > 0:
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# n_corr = HEAD_DIM // corr_tile_size
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# for ci in range(n_corr):
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# ...
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# Pass 2: P = exp2(S * scale_log2 - row_max), accumulate row_sum
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rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
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rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
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