# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2023 DeepSeek. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Tuple import paddle 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): """ 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) s = tl.max(tl.abs(x)) / 448.0 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: paddle.Tensor, block_size: int = 128) -> Tuple[paddle.Tensor, paddle.Tensor]: """ Quantizes the input tensor `x` using block-wise quantization. Args: x (paddle.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. Returns: Tuple[paddle.Tensor, paddle.Tensor]: A tuple containing: - The quantized tensor with dtype `paddle.float8_e4m3fn`. - A tensor of scaling factors with dtype `paddle.float32`. """ assert x.is_contiguous(), "Input tensor must be contiguous" assert ( x.shape[-1] % block_size == 0 ), f"Last dimension size must be divisible by block_size (block_size={block_size})" y = paddle.empty_like(x, dtype=paddle.float8_e4m3fn) s = paddle.empty((*x.shape[:-1], x.shape[-1] // block_size), dtype=paddle.float32) grid = lambda meta: (triton.cdiv(x.numel().item(), 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): """ 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: paddle.Tensor, s: paddle.Tensor, block_size: int = 128) -> paddle.Tensor: """ Dequantizes the given weight tensor using the provided scale tensor. Args: x (paddle.Tensor): The quantized weight tensor of shape (M, N). s (paddle.Tensor): The scale tensor of shape (M, N). block_size (int, optional): The block size to use for dequantization. Defaults to 128. Returns: paddle.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.shape y = paddle.empty_like(x, dtype=paddle.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] # ] # FIXME @ZHUI, paddle not support triton autotune temporarily. # # @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: paddle.Tensor, a_s: paddle.Tensor, b: paddle.Tensor, b_s: paddle.Tensor): """ Modified for B matrix with shape [K, N] """ # FIXME @ZHUI, transposed b = b.T.contiguous() b_s = b_s.T.contiguous() 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.shape[-1] M = a.numel().item() // K # N = b.shape[-1] # Get N from the second dimension of B N = b.shape[0] # Get N from the second dimension of B c = paddle.empty((*a.shape[:-1], N), dtype=paddle.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, BLOCK_SIZE_M=32, BLOCK_SIZE_N=64, BLOCK_SIZE_K=128, ) return c