File size: 4,330 Bytes
bfa32d5 | 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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | from typing import Tuple
import torch
import triton
import triton.language as tl
from triton import Config
@triton.jit
def act_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)
s = tl.max(tl.abs(x)) / 448.
y = x / s
y = y.to(y_ptr.dtype.element_ty)
tl.store(y_ptr + offs, y)
tl.store(s_ptr + pid, s)
def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]:
assert x.is_contiguous()
assert x.size(-1) % block_size == 0
y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32)
grid = lambda meta: (triton.cdiv(x.numel(), meta['BLOCK_SIZE']), )
act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size)
return y, s
@triton.jit
def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
pid_m = tl.program_id(axis=0)
pid_n = tl.program_id(axis=1)
n = tl.cdiv(N, BLOCK_SIZE)
offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
offs = offs_m[:, None] * N + offs_n[None, :]
mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
x = tl.load(x_ptr + offs, mask=mask).to(tl.float32)
s = tl.load(s_ptr + pid_m * n + pid_n)
y = x * s
tl.store(y_ptr + offs, y, mask=mask)
def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor:
assert x.is_contiguous() and s.is_contiguous()
assert x.dim() == 2 and s.dim() == 2
M, N = x.size()
y = torch.empty_like(x, dtype=torch.get_default_dtype())
grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE']))
weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
return y
fp8_gemm_configs = [
Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': 128}, num_stages=num_stages, num_warps=8)
for block_m in [16, 32, 64] for block_n in [32, 64, 128] for num_stages in [3, 4, 5, 6]
]
@triton.autotune(configs=fp8_gemm_configs, key=['N', 'K'])
@triton.jit
def fp8_gemm_kernel(a_ptr, b_ptr, c_ptr,
a_s_ptr, b_s_ptr,
M, N: tl.constexpr, K: tl.constexpr,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr):
pid_m = tl.program_id(axis=0)
pid_n = tl.program_id(axis=1)
k = tl.cdiv(K, BLOCK_SIZE_K)
offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :]
b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None]
a_s_ptrs = a_s_ptr + offs_m * k
b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for i in range(k):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0)
a_s = tl.load(a_s_ptrs)
b_s = tl.load(b_s_ptrs)
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
a_ptrs += BLOCK_SIZE_K
b_ptrs += BLOCK_SIZE_K
a_s_ptrs += 1
b_s_ptrs += 1
c = accumulator.to(c_ptr.dtype.element_ty)
offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
tl.store(c_ptrs, c, mask=mask)
def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor):
assert a.is_contiguous() and b.is_contiguous()
assert a_s.is_contiguous() and b_s.is_contiguous()
K = a.size(-1)
M = a.numel() // K
N = b.size(0)
c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']), triton.cdiv(N, META['BLOCK_SIZE_N']))
fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K)
return c
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