from typing import Tuple from dataclasses import dataclass import torch import triton import triton.language as tl from triton import Config @dataclass class BlockConfig: """Configuration for block sizes in tensor operations.""" size: int = 128 size_m: int = 64 size_n: int = 64 size_k: int = 128 class QuantizationKernels: """Collection of Triton kernels for quantization operations.""" @staticmethod @triton.jit def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr): """ Quantizes activation values using block-wise scaling. Args: x_ptr: Input tensor pointer y_ptr: Output quantized tensor pointer s_ptr: Output scaling factors pointer BLOCK_SIZE: Size of processing block """ 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) @staticmethod @triton.jit def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr): """ Dequantizes weights using block-wise scaling. Args: x_ptr: Quantized weights pointer s_ptr: Scaling factors pointer y_ptr: Output dequantized tensor pointer M: Number of rows N: Number of columns BLOCK_SIZE: Size of processing block """ 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) class MatrixMultKernels: """Collection of Triton kernels for matrix multiplication operations.""" @staticmethod def get_configs(): """Generate configurations for FP8 GEMM autotuning.""" return [ 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] ] @staticmethod @triton.autotune(configs=get_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 FP8 matrix multiplication with scaling factors. Args: a_ptr: First input matrix pointer b_ptr: Second input matrix pointer c_ptr: Output matrix pointer a_s_ptr: First matrix scaling factors pointer b_s_ptr: Second matrix scaling factors pointer M: First matrix rows N: Second matrix columns K: Inner dimension BLOCK_SIZE_M/N/K: Block sizes for tiling """ pid_m = tl.program_id(axis=0) pid_n = tl.program_id(axis=1) k = tl.cdiv(K, BLOCK_SIZE_K) # Calculate offsets 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) # Initialize pointers 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) # Main computation loop 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, :] # Update pointers a_ptrs += BLOCK_SIZE_K b_ptrs += BLOCK_SIZE_K a_s_ptrs += 1 b_s_ptrs += 1 # Store results 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) class TensorOps: """High-level interface for tensor operations.""" @staticmethod def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]: """ Quantize activations using block-wise scaling. Args: x: Input tensor block_size: Block size for quantization Returns: Tuple of quantized tensor and scaling factors """ 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']),) QuantizationKernels.act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size) return y, s @staticmethod def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor: """ Dequantize weights using block-wise scaling. Args: x: Quantized weight tensor s: Scaling factors tensor block_size: Block size for dequantization Returns: Dequantized 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']) ) QuantizationKernels.weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size) return y @staticmethod def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor) -> torch.Tensor: """ Perform FP8 matrix multiplication. Args: a: First input matrix a_s: First matrix scaling factors b: Second input matrix b_s: Second matrix scaling factors Returns: Result matrix """ 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']) ) MatrixMultKernels.fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K) return c