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Use helion.cdiv #852

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4 changes: 2 additions & 2 deletions examples/grouped_gemm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,9 +165,9 @@ def grouped_gemm_jagged_persistent(

if m_size > 0:
# Compute tile grid dimensions for current group
num_m_tiles = (m_size + BLOCK_M - 1) // BLOCK_M
num_m_tiles = helion.cdiv(m_size, BLOCK_M) # pyright: ignore[reportArgumentType]
# Calculate number of N tiles (shared across all groups)
num_n_tiles = (N + BLOCK_N - 1) // BLOCK_N
num_n_tiles = helion.cdiv(N, BLOCK_N) # pyright: ignore[reportArgumentType]
num_group_tiles = num_m_tiles * num_n_tiles

# Distribute tiles among workers using strided access pattern
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2 changes: 1 addition & 1 deletion examples/rms_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ def rms_norm_bwd(
m_block = hl.register_block_size(x.size(0))
grad_x = torch.empty_like(x)
grad_weight = x.new_empty(
[(x.size(0) + m_block - 1) // m_block, *weight.shape], dtype=torch.float32
[helion.cdiv(x.size(0), m_block), *weight.shape], dtype=torch.float32
)
weight_shape = hl.specialize(weight.size(0))
for mb_cta in hl.tile(x.size(0), block_size=m_block):
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3 changes: 2 additions & 1 deletion test/test_examples.expected
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Expand Up @@ -3153,6 +3153,7 @@ def moe_matmul_ogs(A: torch.Tensor, W: torch.Tensor, expert_token_counts: torch.
from __future__ import annotations

import torch
import helion
import triton
import triton.language as tl
from helion.runtime import default_launcher as _default_launcher
Expand Down Expand Up @@ -3218,7 +3219,7 @@ def rms_norm_bwd(grad_out: torch.Tensor, x: torch.Tensor, weight: torch.Tensor,
"""
m_block = 32
grad_x = torch.empty_like(x)
grad_weight = x.new_empty([(x.size(0) + m_block - 1) // m_block, *weight.shape], dtype=torch.float32)
grad_weight = x.new_empty([helion.cdiv(x.size(0), m_block), *weight.shape], dtype=torch.float32)
_BLOCK_SIZE_0 = 32
_RDIM_SIZE_2 = 64
_launcher(_helion_rms_norm_bwd, (triton.cdiv(x.size(0), _BLOCK_SIZE_0),), x, grad_out, rsqrt, weight, grad_x, grad_weight, x.size(0), grad_out.stride(0), grad_out.stride(1), grad_weight.stride(0), grad_weight.stride(1), grad_x.stride(0), grad_x.stride(1), rsqrt.stride(0), weight.stride(0), x.stride(0), x.stride(1), _BLOCK_SIZE_0, _RDIM_SIZE_2, num_warps=4, num_stages=3)
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