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453129d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import torch
import torch.distributed as dist
import triton
import triton.language as tl
@triton.jit
def block_fp8_dequant_kernel(y_ptr, s_ptr, x_ptr, num_elements, BLOCK_SIZE: tl.constexpr):
pid = tl.program_id(axis=0)
offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offs < num_elements
s = tl.load(s_ptr + pid)
y = tl.load(y_ptr + offs, mask=mask).to(tl.float32)
tl.store(x_ptr + offs, y * s, mask=mask)
def solution(
local_y: torch.Tensor,
local_s: torch.Tensor,
block_size: int = 128,
) -> torch.Tensor:
world_size = dist.get_world_size()
chunk_shape = local_y.shape[1:]
chunk_numel = local_y.numel() // world_size
num_elements = local_y.numel()
assert chunk_numel % block_size == 0, (
f"Chunk size {chunk_numel} must be divisible by block_size ({block_size})"
)
y_flat = local_y.view(-1)
s_flat = local_s.view(-1)
x_flat = torch.empty(num_elements, device=local_y.device, dtype=torch.float32)
if num_elements > 0:
grid = (triton.cdiv(num_elements, block_size),)
block_fp8_dequant_kernel[grid](
y_flat, s_flat, x_flat, num_elements, BLOCK_SIZE=block_size
)
x = x_flat.view(world_size, *chunk_shape)
out = torch.empty_like(x)
dist.all_to_all_single(out, x)
return out
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