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# SOURCE: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py
from typing import Tuple, Optional
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, scale_fmt: tl.constexpr):
"""
Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factor in `s_ptr`.
Args:
x_ptr (triton.Pointer): Pointer to the input tensor.
y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored.
s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored.
BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance.
Returns:
None
"""
pid = tl.program_id(axis=0)
offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
x = tl.load(x_ptr + offs).to(tl.float32)
amax = tl.max(tl.abs(x)) # reduction
amax = tl.maximum(amax, 1e-4) # clamp to 1e-4
s = amax / 448.
if scale_fmt == "ue8m0":
exp = tl.math.ceil(tl.math.log2(s))
s = tl.math.exp2(exp)
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, scale_fmt: Optional[str] = None) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Quantizes the input tensor `x` using block-wise quantization.
Args:
x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`.
block_size (int, optional): The size of the blocks to be used for quantization. Default is 128.
scale_fmt (Optional[str], optional): The format of the scale. Default is None.
Returns:
Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
- The quantized tensor with dtype `torch.float8_e4m3fn`.
- A tensor of scaling factors with dtype `torch.float32`.
"""
assert x.is_contiguous(), 'Input tensor must be contiguous'
assert x.size(-1) % block_size == 0, f'Last dimension size must be divisible by block_size (block_size={block_size})'
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, scale_fmt=scale_fmt)
return y, s
@triton.jit
def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
"""
Dequantizes weights using the provided scaling factors and stores the result.
Args:
x_ptr (tl.pointer): Pointer to the quantized weights.
s_ptr (tl.pointer): Pointer to the scaling factors.
y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights.
M (int): Number of rows in the weight matrix.
N (int): Number of columns in the weight matrix.
BLOCK_SIZE (tl.constexpr): Size of the block for tiling.
Returns:
None
"""
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:
"""
Dequantizes the given weight tensor using the provided scale tensor.
Args:
x (torch.Tensor): The quantized weight tensor of shape (M, N).
s (torch.Tensor): The scale tensor of shape (M//block_size, N//block_size).
block_size (int, optional): The block size to use for dequantization. Defaults to 128.
Returns:
torch.Tensor: The dequantized weight tensor of the same shape as `x`.
Raises:
AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2.
"""
assert x.is_contiguous() and s.is_contiguous(), 'Input tensors must be contiguous'
assert x.dim() == 2 and s.dim() == 2, 'Input tensors must have 2 dimensions'
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):
"""
Performs a matrix multiplication operation on FP8 matrices with scaling factors.
Args:
a_ptr (tl.tensor): Pointer to the first input matrix A.
b_ptr (tl.tensor): Pointer to the second input matrix B.
c_ptr (tl.tensor): Pointer to the output matrix C.
a_s_ptr (tl.tensor): Pointer to the scaling factors for matrix A.
b_s_ptr (tl.tensor): Pointer to the scaling factors for matrix B.
M (int): Number of rows in matrix A and C.
N (tl.constexpr): Number of columns in matrix B and C.
K (tl.constexpr): Number of columns in matrix A and rows in matrix B.
BLOCK_SIZE_M (tl.constexpr): Block size for the M dimension.
BLOCK_SIZE_N (tl.constexpr): Block size for the N dimension.
BLOCK_SIZE_K (tl.constexpr): Block size for the K dimension.
Returns:
None
"""
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):
"""
Perform a matrix multiplication using FP8 precision.
Args:
a (torch.Tensor): The first input matrix, must be contiguous.
a_s (torch.Tensor): The scaling factor for the first input matrix, must be contiguous.
b (torch.Tensor): The second input matrix, must be contiguous.
b_s (torch.Tensor): The scaling factor for the second input matrix, must be contiguous.
Returns:
torch.Tensor: The result of the matrix multiplication.
"""
assert a.is_contiguous() and b.is_contiguous(), 'Input tensors must be contiguous'
assert a_s.is_contiguous() and b_s.is_contiguous(), 'Scaling factor tensors must be 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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