"""Triton fusion for H3's segmented BF16 modulation and residual gates.""" from __future__ import annotations import torch import triton import triton.language as tl _TILE = 1024 _segment_cache_key = None _segment_cache_value = None @triton.jit def _round_bf16_fp32(value): """Apply round-to-nearest-even BF16 precision while retaining FP32.""" bits = value.to(tl.int32, bitcast=True) bits = bits + 0x7FFF + ((bits >> 16) & 1) return (bits & -65536).to(tl.float32, bitcast=True) @triton.jit def _modulate_kernel( x_ptr, scale_ptr, shift_ptr, row_index_ptr, tokens, width, sx_t, sx_d, ss_t, ss_d, sh_t, sh_d, BLOCK: tl.constexpr, ): row = tl.program_id(0) columns = tl.program_id(1) * BLOCK + tl.arange(0, BLOCK) valid = (row < tokens) & (columns < width) table_row = tl.load(row_index_ptr + row) x = tl.load(x_ptr + row * sx_t + columns * sx_d, mask=valid).to(tl.float32) scale = tl.load( scale_ptr + table_row * ss_t + columns * ss_d, mask=valid, ).to(tl.float32) shift = tl.load( shift_ptr + table_row * sh_t + columns * sh_d, mask=valid, ).to(tl.float32) scale = _round_bf16_fp32(scale) shift = _round_bf16_fp32(shift) multiplied = _round_bf16_fp32(x * _round_bf16_fp32(1.0 + scale)) result = _round_bf16_fp32(multiplied + shift) tl.store(x_ptr + row * sx_t + columns * sx_d, result.to(tl.bfloat16), mask=valid) @triton.jit def _gate_add_kernel( residual_ptr, gate_ptr, update_ptr, row_index_ptr, tokens, width, sr_t, sr_d, sg_t, sg_d, su_t, su_d, BLOCK: tl.constexpr, ): row = tl.program_id(0) columns = tl.program_id(1) * BLOCK + tl.arange(0, BLOCK) valid = (row < tokens) & (columns < width) table_row = tl.load(row_index_ptr + row) residual = tl.load( residual_ptr + row * sr_t + columns * sr_d, mask=valid, ).to(tl.float32) update = tl.load( update_ptr + row * su_t + columns * su_d, mask=valid, ).to(tl.float32) gate = tl.load( gate_ptr + table_row * sg_t + columns * sg_d, mask=valid, ).to(tl.float32) result = residual + update * _round_bf16_fp32(gate) tl.store( residual_ptr + row * sr_t + columns * sr_d, result.to(tl.bfloat16), mask=valid, ) def segment_index( tokens: int, segments: list[tuple[int, int, int]], device: torch.device, ) -> torch.Tensor: """Return one cached row-to-modulation-table lookup for a packed layout.""" global _segment_cache_key, _segment_cache_value normalized = tuple((int(start), int(stop), int(row)) for start, stop, row in segments) key = (str(device), int(tokens), normalized) if key == _segment_cache_key: return _segment_cache_value host = torch.empty(tokens, dtype=torch.int32) cursor = 0 for start, stop, row in normalized: if start != cursor or stop < start or stop > tokens or row < 0: raise ValueError("segments must be ordered, contiguous, and in range") host[start:stop] = row cursor = stop if cursor != tokens: raise ValueError("segments must cover every packed token") _segment_cache_key = key _segment_cache_value = host.to(device=device) return _segment_cache_value def _validate_activation(tensor: torch.Tensor, name: str) -> None: if tensor.ndim != 2: raise ValueError(f"{name} must have shape [tokens, hidden]") if tensor.device.type != "cuda" or tensor.dtype != torch.bfloat16: raise TypeError(f"{name} must be a CUDA BF16 tensor") if tensor.stride(1) != 1: raise ValueError(f"{name}'s hidden dimension must be contiguous") def _validate_table(table: torch.Tensor, activation: torch.Tensor, name: str) -> None: if table.ndim != 2 or table.shape[1] != activation.shape[1]: raise ValueError(f"{name} must have shape [rows, {activation.shape[1]}]") if table.device != activation.device or table.dtype not in {torch.bfloat16, torch.float32}: raise TypeError(f"{name} must be CUDA BF16/FP32 on the activation device") if table.stride(1) != 1: raise ValueError(f"{name}'s hidden dimension must be contiguous") def fused_modulate_( activation: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor, row_index: torch.Tensor, ) -> torch.Tensor: """Apply segmented scale/shift in place with eager-equivalent BF16 rounding.""" _validate_activation(activation, "activation") _validate_table(shift, activation, "shift") _validate_table(scale, activation, "scale") if row_index.shape != (activation.shape[0],) or row_index.dtype != torch.int32: raise TypeError("row_index must be int32 with one entry per token") if row_index.device != activation.device or not row_index.is_contiguous(): raise TypeError("row_index must be contiguous on the activation device") grid = (activation.shape[0], triton.cdiv(activation.shape[1], _TILE)) _modulate_kernel[grid]( activation, scale, shift, row_index, activation.shape[0], activation.shape[1], activation.stride(0), activation.stride(1), scale.stride(0), scale.stride(1), shift.stride(0), shift.stride(1), BLOCK=_TILE, num_warps=4, ) return activation def fused_gate_add_( residual: torch.Tensor, update: torch.Tensor, gate: torch.Tensor, row_index: torch.Tensor, ) -> torch.Tensor: """Apply the segmented residual gate in place with addcmul-equivalent math.""" _validate_activation(residual, "residual") _validate_activation(update, "update") if residual.shape != update.shape or residual.device != update.device: raise ValueError("residual and update must share shape and device") _validate_table(gate, residual, "gate") if row_index.shape != (residual.shape[0],) or row_index.dtype != torch.int32: raise TypeError("row_index must be int32 with one entry per token") if row_index.device != residual.device or not row_index.is_contiguous(): raise TypeError("row_index must be contiguous on the residual device") grid = (residual.shape[0], triton.cdiv(residual.shape[1], _TILE)) _gate_add_kernel[grid]( residual, gate, update, row_index, residual.shape[0], residual.shape[1], residual.stride(0), residual.stride(1), gate.stride(0), gate.stride(1), update.stride(0), update.stride(1), BLOCK=_TILE, num_warps=4, ) return residual