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c2f8cf1 | 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 49 50 51 52 53 54 55 | import torch
import torch.distributed as dist
import triton
import triton.language as tl
from typing import Tuple
@triton.jit
def block_fp8_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr):
pid = tl.program_id(axis=0)
offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
x = tl.load(x_ptr + offs).to(tl.float32)
# FP8 E4M3 max value is 448.0
s = tl.max(tl.abs(x)) / 448.0
# Prevent division by zero if all elements in the block are 0
s_safe = tl.where(s == 0.0, 1.0, s)
y = (x / s_safe).to(y_ptr.dtype.element_ty)
tl.store(y_ptr + offs, y)
tl.store(s_ptr + pid, s)
def solution(local_tensor: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]:
assert local_tensor.size(-1) % block_size == 0, "Last dimension must be divisible by block_size"
y_local = torch.empty_like(local_tensor, dtype=torch.float8_e4m3fn)
s_local = local_tensor.new_empty(
*local_tensor.size()[:-1], local_tensor.size(-1) // block_size, dtype=torch.float32
)
grid = (triton.cdiv(local_tensor.numel(), block_size),)
block_fp8_quant_kernel[grid](local_tensor, y_local, s_local, BLOCK_SIZE=block_size)
if dist.is_initialized():
world_size = dist.get_world_size()
y_local_u8 = y_local.view(torch.uint8)
y_gather_u8 = [torch.empty_like(y_local_u8) for _ in range(world_size)]
dist.all_gather(y_gather_u8, y_local_u8)
y_global_u8 = torch.cat(y_gather_u8, dim=0)
y_global = y_global_u8.view(torch.float8_e4m3fn)
s_gather = [torch.empty_like(s_local) for _ in range(world_size)]
dist.all_gather(s_gather, s_local)
s_global = torch.cat(s_gather, dim=0)
else:
y_global = y_local
s_global = s_local
return y_global, s_global
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