539961072c
Moved dispatch.cc to semantic.py Integer signedness now moved from C++ to python Cleaner frontend type Co-authored-by: Phil Tillet <phil@openai.com>
1354 lines
53 KiB
Python
1354 lines
53 KiB
Python
from __future__ import annotations
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import ast
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import builtins
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import functools
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import hashlib
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import inspect
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import os
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import pickle
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import subprocess
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import sys
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import tempfile
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import textwrap
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import time
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import warnings
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from typing import Dict, Optional, Set, Tuple, Union
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import torch
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from filelock import FileLock
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import triton
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import triton._C.libtriton.triton as _triton
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from .tools.disasm import extract
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class CodeGenerator(ast.NodeVisitor):
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def __init__(self, context, prototype, gscope, attributes, constants, kwargs):
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self.builder = _triton.ir.builder(context)
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self.module = _triton.ir.module('', self.builder)
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self.prototype = prototype
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self.gscope = gscope
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self.lscope = dict()
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self.is_arg_lscope = dict() # name => is_arg: {str: bool}
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self.attributes = attributes
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self.constants = constants
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self.kwargs = kwargs
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self.last_node = None
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self.builtins = {
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'range': range,
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'min': triton.language.minimum,
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'float': float,
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'int': int,
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'print': print,
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'isinstance': isinstance,
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'getattr': getattr,
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}
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# SSA-construction
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# [name, bb] => triton.language.tensor
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self.lvalues: Dict[Tuple[str, _triton.ir.basic_block], triton.language.tensor] = {}
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# bb => {name => phi}
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self.incomplete_phis = {}
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self.sealed_blocks: Set[_triton.ir.basic_block] = set()
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def get_value(self, name):
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''' This function:
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1. make sure `name` is defined
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2. if `name` is triton.language.tensor, get stored tensor by calling
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`self._get_tensor()`
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'''
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# search node.id in local scope
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ret = None
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if name in self.lscope:
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ret = self.lscope[name]
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# search node.id in global scope
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elif name in self.gscope:
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ret = self.gscope[name]
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# search node.id in builtins
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elif name in self.builtins:
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ret = self.builtins[name]
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else:
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raise ValueError(f'{name} is not defined')
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if self.is_triton_tensor(ret) and not self.is_arg_lscope[name]:
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return self._get_tensor(name)
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return ret
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def set_value(self, name: str,
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value: Union[triton.language.tensor, triton.language.constexpr],
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is_arg: bool = False) -> None:
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''' This function:
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called by visit_Assign() & visit_FuncDef() to store left value (lvalue)
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1. record local defined name (FIXME: should consider control flow)
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2. store tensor in self.lvalue
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'''
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self.lscope[name] = value
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# if this value is an argument, we don't need to create phis for it
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self.is_arg_lscope[name] = is_arg
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if isinstance(value, triton.language.tensor) and not is_arg:
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self._set_value(name, self.builder.get_insert_block(), value)
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#
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# SSA-construction
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#
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def _get_tensor(self, name: str, bb: Optional[_triton.ir.basic_block] = None) -> triton.language.tensor:
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if not bb:
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bb = self.builder.get_insert_block()
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# local value numbering
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if (name, bb) in self.lvalues:
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return self.lvalues[(name, bb)]
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# global value numbering
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saved_insert_point = self.builder.get_insert_point()
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result = self._get_tensor_recursive(name, bb)
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self.builder.set_insert_point(saved_insert_point)
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return result
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def _get_tensor_recursive(self, name: str, bb: _triton.ir.basic_block) -> triton.language.tensor:
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preds = bb.get_predecessors()
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type = self.lscope[name].type
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# some preds haven't been filled, create a phi as a proxy of the value
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if bb not in self.sealed_blocks:
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result = self._make_phi(type, len(preds), bb)
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if bb in self.incomplete_phis:
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self.incomplete_phis[bb][name] = result
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else:
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self.incomplete_phis[bb] = {name: result}
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elif len(preds) == 1:
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# one predecessor: no phi needed, try get value from pred
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result = self._get_tensor(name, preds[0])
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else: # multiple preds
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assert len(preds) > 1, f'{name} is an undefined name (cannot find in the entry block)'
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phi = self._make_phi(type, len(preds), bb)
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self._set_value(name, bb, phi)
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result = self._add_phi_operands(name, phi)
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self._set_value(name, bb, result)
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return result
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# returns a new phi tensor, which encausulate an ir.phi_node
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def _make_phi(self,
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type: triton.language.dtype,
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num_values: int,
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bb: _triton.ir.basic_block) -> triton.language.tensor:
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instr = bb.get_first_non_phi()
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self.builder.set_insert_point((bb, instr))
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ir_phi = self.builder.create_phi(type.to_ir(self.builder), num_values)
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if instr:
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self.builder.set_insert_block(bb)
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return triton.language.tensor(ir_phi, type)
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# complete a phi node. (TODO: rename this as _complete_phis?)
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# Note: since we try to remove tryival phi, the return tensor might not be a phi
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def _add_phi_operands(self, name: str,
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phi: triton.language.tensor) -> triton.language.tensor:
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bb = phi.handle.get_parent()
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for pred in bb.get_predecessors():
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v = self._get_tensor(name, pred)
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phi.handle.add_incoming(v.handle, pred)
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phi = self._try_remove_trivial_phi(phi)
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return phi
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def _set_value(self, name: str, bb: _triton.ir.basic_block, value: triton.language.tensor) -> None:
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self.lvalues[(name, bb)] = value
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# TODO: why we need this?
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self.module.set_instr_metadata(name, value.handle)
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def _seal_block(self, bb: _triton.ir.basic_block):
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# complete all incomplete phis
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if bb in self.incomplete_phis:
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for name, phi in self.incomplete_phis[bb].items():
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result = self._add_phi_operands(name, phi)
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# it's possible that this phi is trivial
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if self._get_tensor(name, bb).handle == phi.handle:
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self._set_value(name, bb, result)
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del self.incomplete_phis[bb]
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self.sealed_blocks.add(bb)
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def _try_remove_trivial_phi(self, phi: triton.language.tensor) -> triton.language.tensor:
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unique_handles = {op for op in phi.handle.ops() if op != phi.handle}
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if len(unique_handles) != 1: # non-trivial phi
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return phi
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v = unique_handles.pop()
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phi.handle.replace_all_uses_with(v)
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phi.handle.erase_from_parent()
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# TODO: remove trivial phis recursively
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return triton.language.tensor(v, phi.type)
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def is_triton_tensor(self, value):
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return isinstance(value, triton.language.tensor)
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#
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# AST visitor
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#
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def visit_compound_statement(self, stmts):
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for stmt in stmts:
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self.last_ret = self.visit(stmt)
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if isinstance(stmt, ast.Return):
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break
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return stmts and isinstance(stmt, ast.Return)
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def visit_Module(self, node):
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ast.NodeVisitor.generic_visit(self, node)
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def visit_List(self, node):
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ctx = self.visit(node.ctx)
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assert ctx is None
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elts = [self.visit(elt) for elt in node.elts]
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return elts
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# By design, only non-kernel functions can return
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def visit_Return(self, node):
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ret = self.visit(node.value)
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if ret is None:
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return self.builder.ret_void()
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return ret
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def visit_FunctionDef(self, node, inline=False, arg_values=None):
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arg_names, kwarg_names = self.visit(node.args)
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# initialize defaults
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for i, default_value in enumerate(node.args.defaults):
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arg_node = node.args.args[-i - 1]
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annotation = arg_node.annotation
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name = arg_node.arg
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st_target = ast.Name(id=name, ctx=ast.Store())
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if annotation is None:
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init_node = ast.Assign(targets=[st_target], value=default_value)
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else:
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init_node = ast.AnnAssign(target=st_target, value=default_value, annotation=annotation)
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self.visit(init_node)
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# store keyword arguments in local scope
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self.lscope[kwarg_names] = self.kwargs
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# initialize function
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if inline:
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pass
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else:
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fn = self.module.get_or_insert_function(node.name, self.prototype.to_ir(self.builder))
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arg_values = []
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idx = 0
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for i, arg_name in enumerate(arg_names):
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if i in self.constants:
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cst = self.constants[i]
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if not isinstance(cst, triton.language.constexpr):
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cst = triton.language.constexpr(self.constants[i])
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arg_values.append(cst)
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else:
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if i in self.attributes:
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is_ptr = fn.args[idx].type.is_ptr()
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attr = 'aligned' if is_ptr else 'multiple_of'
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attr = getattr(_triton.ir.attribute_kind, attr)
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attr = _triton.ir.attribute(attr, self.attributes[i])
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fn.add_attr(idx + 1, attr)
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fn.args[idx].name = arg_name
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arg_values.append(triton.language.tensor(fn.args[idx], self.prototype.param_types[idx]))
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idx += 1
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for arg_name, arg_value in zip(arg_names, arg_values):
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self.set_value(arg_name, arg_value, is_arg=True)
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if inline:
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self.visit_compound_statement(node.body)
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return self.last_ret
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else:
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entry = _triton.ir.basic_block.create(self.builder.context, "entry", fn)
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self._seal_block(entry)
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self.builder.set_insert_block(entry)
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# visit function body
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self.visit_compound_statement(node.body)
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# finalize function
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self.builder.ret_void()
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def visit_arguments(self, node):
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arg_names = []
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for arg in node.args:
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arg_names += [self.visit(arg)]
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kwarg_names = self.visit(node.kwarg)
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return arg_names, kwarg_names
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def visit_arg(self, node):
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ast.NodeVisitor.generic_visit(self, node)
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return node.arg
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def visit_AnnAssign(self, node):
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# extract attributes
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annotation = self.visit(node.annotation)
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target = self.visit(node.target)
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value = self.visit(node.value)
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# constexpr
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if annotation == triton.language.constexpr:
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if target in self.lscope:
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raise ValueError(f'{target} is already defined.'
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f' constexpr cannot be reassigned.')
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if not isinstance(value, triton.language.constexpr):
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value = triton.language.constexpr(value)
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self.lscope[target] = value
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return self.lscope[target]
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# default: call visit_Assign
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return self.visit_Assign(node)
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def visit_Assign(self, node):
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_names = []
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for target in node.targets:
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_names += [self.visit(target)]
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assert len(_names) == 1
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names = _names[0]
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values = self.visit(node.value)
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if not isinstance(names, tuple):
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names = [names]
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if not isinstance(values, tuple):
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values = [values]
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for name, value in zip(names, values):
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# TODO: can we store constexpr here to support constant folding?
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# by default, constexpr are assigned into python variable
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if isinstance(value, triton.language.constexpr):
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value = value.value
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if not isinstance(value, triton.language.tensor):
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value = triton.language.core._to_tensor(value, self.builder)
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self.set_value(name, value)
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def visit_AugAssign(self, node):
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name = node.target.id
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lhs = ast.Name(id=name, ctx=ast.Load())
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rhs = ast.BinOp(lhs, node.op, node.value)
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assign = ast.Assign(targets=[node.target], value=rhs)
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self.visit(assign)
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return self.get_value(name)
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def visit_Name(self, node):
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if type(node.ctx) == ast.Store:
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return node.id
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return self.get_value(node.id)
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def visit_Store(self, node):
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ast.NodeVisitor.generic_visit(self, node)
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def visit_Load(self, node):
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ast.NodeVisitor.generic_visit(self, node)
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def visit_Tuple(self, node):
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args = [self.visit(x) for x in node.elts]
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return tuple(args)
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def visit_BinOp(self, node):
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lhs = self.visit(node.left)
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rhs = self.visit(node.right)
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if isinstance(lhs, triton.language.constexpr):
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lhs = lhs.value
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if isinstance(rhs, triton.language.constexpr):
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rhs = rhs.value
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fn = {
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ast.Add: '__add__',
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ast.Sub: '__sub__',
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ast.Mult: '__mul__',
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ast.Div: '__truediv__',
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ast.FloorDiv: '__floordiv__',
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ast.Mod: '__mod__',
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ast.Pow: '__pow__',
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ast.LShift: '__lshift__',
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ast.RShift: '__rshift__',
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ast.BitAnd: '__and__',
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ast.BitOr: '__or__',
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ast.BitXor: '__xor__',
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}[type(node.op)]
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if self.is_triton_tensor(lhs):
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return getattr(lhs, fn)(rhs, _builder=self.builder)
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elif self.is_triton_tensor(rhs):
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fn = fn[:2] + 'r' + fn[2:]
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return getattr(rhs, fn)(lhs, _builder=self.builder)
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else:
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return getattr(lhs, fn)(rhs)
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def visit_If(self, node):
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cond = self.visit(node.test)
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if isinstance(cond, triton.language.tensor):
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cond = cond.to(triton.language.int1, _builder=self.builder)
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current_bb = self.builder.get_insert_block()
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then_bb = _triton.ir.basic_block.create(self.builder.context, "then", current_bb.parent)
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else_bb = _triton.ir.basic_block.create(self.builder.context, "else", current_bb.parent) if node.orelse else None
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endif_bb = _triton.ir.basic_block.create(self.builder.context, "endif", current_bb.parent)
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self._seal_block(then_bb)
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if else_bb:
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self._seal_block(else_bb)
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self.builder.cond_br(cond.handle, then_bb, else_bb)
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else:
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self.builder.cond_br(cond.handle, then_bb, endif_bb)
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self.builder.set_insert_block(then_bb)
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is_terminator = self.visit_compound_statement(node.body)
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# TODO: last statement is a terminator?
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if not is_terminator:
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self.builder.br(endif_bb)
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if else_bb:
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self.builder.set_insert_block(else_bb)
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is_terminator = self.visit_compound_statement(node.orelse)
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# TODO: last statement is a terminator?
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if not is_terminator:
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self.builder.br(endif_bb)
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self._seal_block(endif_bb)
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self.builder.set_insert_block(endif_bb)
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else:
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if isinstance(cond, triton.language.constexpr):
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cond = cond.value
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if cond:
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self.visit_compound_statement(node.body)
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else:
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self.visit_compound_statement(node.orelse)
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def visit_IfExp(self, node):
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cond = self.visit(node.test)
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if cond.value:
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return self.visit(node.body)
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else:
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return self.visit(node.orelse)
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def visit_Pass(self, node):
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pass
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def visit_Compare(self, node):
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assert len(node.comparators) == 1
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assert len(node.ops) == 1
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lhs = self.visit(node.left)
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rhs = self.visit(node.comparators[0])
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if isinstance(lhs, triton.language.constexpr):
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lhs = lhs.value
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if isinstance(rhs, triton.language.constexpr):
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rhs = rhs.value
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if type(node.ops[0]) == ast.Is:
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return triton.language.constexpr(lhs is rhs)
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if type(node.ops[0]) == ast.IsNot:
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return triton.language.constexpr(lhs is not rhs)
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fn = {
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ast.Eq: '__eq__',
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ast.NotEq: '__ne__',
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ast.Lt: '__lt__',
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ast.LtE: '__le__',
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ast.Gt: '__gt__',
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ast.GtE: '__ge__',
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}[type(node.ops[0])]
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if self.is_triton_tensor(lhs):
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return getattr(lhs, fn)(rhs, _builder=self.builder)
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elif self.is_triton_tensor(rhs):
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fn = fn[:2] + 'r' + fn[2:]
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return getattr(rhs, fn)(lhs, _builder=self.builder)
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else:
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return getattr(lhs, fn)(rhs)
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def visit_UnaryOp(self, node):
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op = self.visit(node.operand)
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if type(node.op) == ast.Not:
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assert isinstance(op, triton.language.constexpr), "`not` only supported for constexpr at the moment"
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return triton.language.constexpr(not op)
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if isinstance(op, triton.language.constexpr):
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op = op.value
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fn = {
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ast.USub: '__neg__',
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ast.UAdd: '__pos__',
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ast.Invert: '__invert__',
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}[type(node.op)]
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if self.is_triton_tensor(op):
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return getattr(op, fn)(_builder=self.builder)
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return getattr(op, fn)()
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def visit_While(self, node):
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current_bb = self.builder.get_insert_block()
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loop_bb = _triton.ir.basic_block.create(self.builder.context, "loop", current_bb.parent)
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next_bb = _triton.ir.basic_block.create(self.builder.context, "postloop", current_bb.parent)
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def continue_fn():
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cond = self.visit(node.test)
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return self.builder.cond_br(cond.handle, loop_bb, next_bb)
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continue_fn()
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self.builder.set_insert_block(loop_bb)
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self.visit_compound_statement(node.body)
|
|
continue_fn()
|
|
stop_bb = self.builder.get_insert_block()
|
|
self._seal_block(stop_bb)
|
|
self._seal_block(loop_bb)
|
|
self._seal_block(next_bb)
|
|
self.builder.set_insert_block(next_bb)
|
|
|
|
for stmt in node.orelse:
|
|
ast.NodeVisitor.generic_visit(self, stmt)
|
|
|
|
def visit_Subscript(self, node):
|
|
assert node.ctx.__class__.__name__ == "Load"
|
|
lhs = self.visit(node.value)
|
|
slices = self.visit(node.slice)
|
|
if self.is_triton_tensor(lhs):
|
|
return lhs.__getitem__(slices, _builder=self.builder)
|
|
return lhs[slices]
|
|
|
|
def visit_ExtSlice(self, node):
|
|
return [self.visit(dim) for dim in node.dims]
|
|
|
|
def visit_For(self, node):
|
|
iterator = self.visit(node.iter.func)
|
|
if iterator != self.builtins['range']:
|
|
raise RuntimeError('Only `range` iterator currently supported')
|
|
# static for loops: all iterator arguments are constexpr
|
|
iter_args = [self.visit(arg) for arg in node.iter.args]
|
|
is_static = all([isinstance(x, triton.language.constexpr) for x in iter_args])
|
|
if is_static:
|
|
st_target = ast.Name(id=node.target.id, ctx=ast.Store())
|
|
iter_args = [arg.value for arg in iter_args]
|
|
range = iterator(*iter_args)
|
|
if len(range) <= 10:
|
|
for i in iterator(*iter_args):
|
|
self.lscope[node.target.id] = triton.language.constexpr(i)
|
|
self.visit_compound_statement(node.body)
|
|
for stmt in node.orelse:
|
|
ast.NodeVisitor.generic_visit(self, stmt)
|
|
return
|
|
# create nodes
|
|
st_target = ast.Name(id=node.target.id, ctx=ast.Store())
|
|
ld_target = ast.Name(id=node.target.id, ctx=ast.Load())
|
|
arg_0 = node.iter.args[0] if len(node.iter.args) > 1 else ast.Num(0)
|
|
arg_1 = node.iter.args[1] if len(node.iter.args) > 1 else node.iter.args[0]
|
|
arg_2 = node.iter.args[2] if len(node.iter.args) > 2 else ast.Num(1)
|
|
init_node = ast.Assign(targets=[st_target], value=arg_0)
|
|
pos_cond_node = ast.Compare(ld_target, [ast.Lt()], [arg_1])
|
|
neg_cond_node = ast.Compare(ld_target, [ast.Gt()], [arg_1])
|
|
pos_step_node = ast.Compare(arg_2, [ast.Gt()], [ast.Num(0)])
|
|
build_cond = lambda: triton.language.where(self.visit(pos_step_node),
|
|
self.visit(pos_cond_node),
|
|
self.visit(neg_cond_node),
|
|
_builder=self.builder)
|
|
# cond_node = neg_cond_node
|
|
step_node = ast.AugAssign(target=st_target, op=ast.Add(), value=arg_2)
|
|
# code generation
|
|
current_bb = self.builder.get_insert_block()
|
|
loop_bb = _triton.ir.basic_block.create(self.builder.context, "loop", current_bb.parent)
|
|
next_bb = _triton.ir.basic_block.create(self.builder.context, "postloop", current_bb.parent)
|
|
|
|
def continue_fn():
|
|
self.visit(step_node)
|
|
cond = build_cond()
|
|
return self.builder.cond_br(cond.handle, loop_bb, next_bb)
|
|
|
|
self.visit(init_node)
|
|
cond = build_cond()
|
|
self.builder.cond_br(cond.handle, loop_bb, next_bb)
|
|
self.builder.set_insert_block(loop_bb)
|
|
self.visit_compound_statement(node.body)
|
|
# TODO: handle case where body breaks control flow
|
|
continue_fn()
|
|
stop_bb = self.builder.get_insert_block()
|
|
self._seal_block(stop_bb)
|
|
self._seal_block(loop_bb)
|
|
self._seal_block(next_bb)
|
|
self.builder.set_insert_block(next_bb)
|
|
|
|
for stmt in node.orelse:
|
|
ast.NodeVisitor.generic_visit(self, stmt)
|
|
|
|
def visit_Slice(self, node):
|
|
lower = self.visit(node.lower)
|
|
upper = self.visit(node.upper)
|
|
step = self.visit(node.step)
|
|
return slice(lower, upper, step)
|
|
|
|
def visit_Index(self, node):
|
|
return self.visit(node.value)
|
|
|
|
def visit_keyword(self, node):
|
|
return {node.arg: self.visit(node.value)}
|
|
|
|
def visit_Call(self, node):
|
|
fn = self.visit(node.func)
|
|
if isinstance(fn, triton.language.constexpr):
|
|
fn = fn.value
|
|
kws = dict()
|
|
for keyword in node.keywords:
|
|
kws.update(self.visit(keyword))
|
|
args = [self.visit(arg) for arg in node.args]
|
|
if isinstance(fn, JITFunction):
|
|
return fn(*args, generator=self, **kws)
|
|
if hasattr(fn, '__self__') and self.is_triton_tensor(fn.__self__) or \
|
|
sys.modules[fn.__module__] is triton.language.core:
|
|
return fn(*args, _builder=self.builder, **kws)
|
|
if fn in self.builtins.values():
|
|
args = [arg.value if isinstance(arg, triton.language.constexpr) else arg
|
|
for arg in args]
|
|
return fn(*args, **kws)
|
|
|
|
def visit_Constant(self, node):
|
|
return triton.language.constexpr(node.value)
|
|
|
|
if sys.version_info < (3, 8):
|
|
def visit_NameConstant(self, node):
|
|
return triton.language.constexpr(node.value)
|
|
|
|
def visit_Num(self, node):
|
|
return triton.language.constexpr(node.n)
|
|
|
|
def visit_Str(self, node):
|
|
return triton.language.constexpr(ast.literal_eval(node))
|
|
|
|
def visit_Attribute(self, node):
|
|
lhs = self.visit(node.value)
|
|
return getattr(lhs, node.attr)
|
|
|
|
def visit_Expr(self, node):
|
|
ast.NodeVisitor.generic_visit(self, node)
|
|
|
|
def visit_NoneType(self, node):
|
|
return None
|
|
|
|
def visit(self, node):
|
|
if node is not None:
|
|
self.last_node = node
|
|
with warnings.catch_warnings():
|
|
# The ast library added visit_Constant and deprecated some other
|
|
# methods but we can't move to that without breaking Python 3.6 and 3.7.
|
|
warnings.simplefilter("ignore", DeprecationWarning) # python 3.9
|
|
warnings.simplefilter("ignore", PendingDeprecationWarning) # python 3.8
|
|
return super().visit(node)
|
|
|
|
def generic_visit(self, node):
|
|
typename = type(node).__name__
|
|
raise NotImplementedError("Unsupported node: {}".format(typename))
|
|
|
|
|
|
class Binary:
|
|
def __init__(self, backend, name, asm, shared_mem, num_warps):
|
|
self.backend = backend
|
|
self.name = name
|
|
self.asm = asm
|
|
self.shared_mem = shared_mem
|
|
self.num_warps = num_warps
|
|
|
|
|
|
class LoadedBinary:
|
|
def __init__(self, device: int, bin: Binary):
|
|
module, kernel = _triton.code_gen.load_binary(bin.backend,
|
|
bin.name,
|
|
bin.asm,
|
|
bin.shared_mem,
|
|
device)
|
|
self.bin = bin
|
|
self.asm = bin.asm
|
|
self.sass = ''
|
|
self.module = module
|
|
self.kernel = kernel
|
|
self.device = device
|
|
self.shared_mem = bin.shared_mem
|
|
|
|
def __call__(self, stream, args, grid_0, grid_1=1, grid_2=1):
|
|
_triton.runtime.enqueue(self.bin.backend, stream, self.kernel,
|
|
grid_0, grid_1, grid_2,
|
|
self.bin.num_warps * 32, 1, 1,
|
|
args, self.bin.shared_mem)
|
|
|
|
def get_sass(self, fun=None):
|
|
if self.sass:
|
|
return self.sass
|
|
fd, path = tempfile.mkstemp()
|
|
try:
|
|
with open(fd, 'wb') as cubin:
|
|
cubin.write(self.asm['cubin'])
|
|
self.sass = extract(path, fun)
|
|
finally:
|
|
os.remove(path)
|
|
self.asm['sass'] = self.sass
|
|
return self.sass
|
|
|
|
|
|
class CompilationError(Exception):
|
|
def __init__(self, src, node):
|
|
self.message = f'at {node.lineno}:{node.col_offset}:\n'
|
|
self.message += '\n'.join(src.split('\n')[:node.lineno])
|
|
self.message += '\n' + ' ' * node.col_offset + '^'
|
|
super().__init__(self.message)
|
|
# this is necessary to make CompilationError picklable
|
|
self.args = (src, node)
|
|
|
|
|
|
class OutOfResources(Exception):
|
|
def __init__(self, required, limit, name):
|
|
self.message = f'out of resource: {name}, '\
|
|
f'Required: {required}, '\
|
|
f'Hardware limit: {limit}'
|
|
super().__init__(self.message)
|
|
self.args = (required, limit, name)
|
|
|
|
|
|
class Kernel:
|
|
@staticmethod
|
|
def _type_name(obj):
|
|
type_names = {
|
|
triton.language.float8: 'f8',
|
|
torch.bfloat16: 'bf16',
|
|
torch.float16: 'f16',
|
|
torch.float32: 'f32',
|
|
torch.float64: 'f64',
|
|
torch.bool: 'i1',
|
|
torch.int8: 'i8',
|
|
torch.int16: 'i16',
|
|
torch.int32: 'i32',
|
|
torch.int64: 'i64',
|
|
triton.language.uint8: 'u8',
|
|
triton.language.uint16: 'u16',
|
|
triton.language.uint32: 'u32',
|
|
triton.language.uint64: 'u64',
|
|
}
|
|
if hasattr(obj, 'data_ptr'):
|
|
return type_names[obj.dtype]
|
|
if isinstance(obj, triton.language.constexpr):
|
|
obj = obj.value
|
|
if isinstance(obj, int):
|
|
if -2**31 <= obj < 2**31:
|
|
return 'i32'
|
|
elif 2**31 <= obj < 2**32:
|
|
return 'u32'
|
|
elif -2**63 <= obj < 2**63:
|
|
return 'i64'
|
|
elif 2**63 <= obj < 2**64:
|
|
return 'u64'
|
|
else:
|
|
raise ValueError(f'integer overflow representing {obj}')
|
|
if isinstance(obj, float):
|
|
return 'f'
|
|
if isinstance(obj, bool):
|
|
return 'B'
|
|
if isinstance(obj, str):
|
|
return 'str'
|
|
raise NotImplementedError(f'could not compute type name for {obj}')
|
|
|
|
@staticmethod
|
|
def _to_python_ir(obj):
|
|
# convert torch.Tensor to Triton IR pointers
|
|
if hasattr(obj, 'data_ptr'):
|
|
name = Kernel._type_name(obj)
|
|
return 'ptr', name
|
|
# default path returns triton.ir.type directly
|
|
name = Kernel._type_name(obj)
|
|
return 'scalar', name
|
|
|
|
@staticmethod
|
|
def _to_triton_ir(obj):
|
|
which, name = obj
|
|
type_map = {
|
|
'I': triton.language.int32,
|
|
'L': triton.language.int64,
|
|
'f': triton.language.float32,
|
|
'B': triton.language.int1,
|
|
'f8': triton.language.float8,
|
|
'f16': triton.language.float16,
|
|
'bf16': triton.language.bfloat16,
|
|
'f32': triton.language.float32,
|
|
'f64': triton.language.float64,
|
|
'i1': triton.language.int1,
|
|
'i8': triton.language.int8,
|
|
'i16': triton.language.int16,
|
|
'i32': triton.language.int32,
|
|
'i64': triton.language.int64,
|
|
'u8': triton.language.uint8,
|
|
'u16': triton.language.uint16,
|
|
'u32': triton.language.uint32,
|
|
'u64': triton.language.uint64,
|
|
}
|
|
# convert torch.Tensor to Triton IR pointers
|
|
if which == 'ptr':
|
|
elt_ty = type_map[name]
|
|
return triton.language.pointer_type(elt_ty, 1)
|
|
# default path returns triton.ir.type directly
|
|
return type_map[name]
|
|
|
|
@staticmethod
|
|
def pow2_divisor(N):
|
|
if N % 16 == 0:
|
|
return 16
|
|
if N % 8 == 0:
|
|
return 8
|
|
if N % 4 == 0:
|
|
return 4
|
|
if N % 2 == 0:
|
|
return 2
|
|
return 1
|
|
|
|
def __init__(self, fn):
|
|
self.fn = fn
|
|
|
|
def add_to_cache(self, key, wargs, device_idx, num_warps, num_stages):
|
|
tensor_idxs = [i for i, arg in enumerate(wargs) if hasattr(arg, 'data_ptr')]
|
|
# attributes
|
|
attributes = dict()
|
|
for i, arg in enumerate(wargs):
|
|
if i in self.fn.do_not_specialize:
|
|
continue
|
|
if isinstance(arg, int):
|
|
attributes[i] = Kernel.pow2_divisor(arg)
|
|
elif i in tensor_idxs:
|
|
addr = arg.data_ptr()
|
|
range_size = _triton.runtime.get_pointer_range_size(addr)
|
|
attributes[i] = min(Kernel.pow2_divisor(addr),
|
|
Kernel.pow2_divisor(range_size))
|
|
# transforms ints whose value is one into constants for just-in-time compilation
|
|
constants = {i: arg for i, arg in enumerate(wargs) if isinstance(arg, int) and arg == 1 and i not in self.fn.do_not_specialize}
|
|
constants.update({i: arg.value for i, arg in enumerate(wargs) if isinstance(arg, triton.language.constexpr)})
|
|
constants.update({i: None for i, arg in enumerate(wargs) if arg is None})
|
|
arg_types = [Kernel._to_python_ir(arg) for i, arg in enumerate(wargs) if i not in constants]
|
|
return self.fn._warmup(key, arg_types=arg_types, device=device_idx, attributes=attributes, constants=constants, num_warps=num_warps, num_stages=num_stages, is_manual_warmup=False)
|
|
|
|
def __call__(self, *wargs, grid, num_warps=4, num_stages=2, **kwargs):
|
|
# handle arguments passed by name
|
|
kwargs = {self.fn.arg_names.index(name): value for name, value in kwargs.items()}
|
|
wargs = list(wargs)
|
|
for i, pos in enumerate(sorted(kwargs)):
|
|
wargs.insert(pos + i, kwargs[pos])
|
|
if len(wargs) != len(self.fn.arg_names):
|
|
raise TypeError(f"Function takes {len(self.fn.arg_names)} positional arguments but {len(wargs)} were given")
|
|
# handle annotations
|
|
for pos, _type in self.fn.annotations.items():
|
|
wargs[pos] = _type(wargs[pos])
|
|
# check that tensors are on GPU.
|
|
for arg in wargs:
|
|
if hasattr(arg, 'data_ptr'):
|
|
assert arg.is_cuda, "All tensors must be on GPU!"
|
|
# query device index and cuda stream
|
|
device = torch.cuda.current_device()
|
|
torch.cuda.set_device(device)
|
|
cc = torch.cuda.get_device_capability(device)
|
|
cc = str(cc[0]) + '-' + str(cc[1])
|
|
# # query stream
|
|
# # this is hacky but much faster than `torch.cuda.current_stream(device).cuda_stream`
|
|
# # https://github.com/pytorch/pytorch/blob/master/c10/core/Stream.h#L154
|
|
# # building a C wrapper to re-use the unpack function would add a build-time torch dependency
|
|
# # and require different wheels for different torch versions -- undesirable!
|
|
# bits = torch._C._cuda_getCurrentStream(device)
|
|
# mask = 1 << 47
|
|
# stream = ((bits & 0xFFFFFFFFFFFF) ^ mask) - mask
|
|
stream = torch.cuda.current_stream(device).cuda_stream
|
|
# make key for cache
|
|
return _triton.runtime.launch(wargs, self.fn.do_not_specialize, self.fn.cache_key + cc, self.fn.arg_names, device, stream,
|
|
self.fn.bin_cache, num_warps, num_stages, self.add_to_cache, grid)
|
|
|
|
|
|
class Launcher:
|
|
def __init__(self, kernel, grid):
|
|
self.kernel = kernel
|
|
self.grid = grid
|
|
|
|
def __call__(self, *wargs, **kwargs):
|
|
return self.kernel(*wargs, **kwargs, grid=self.grid)
|
|
|
|
|
|
class Autotuner:
|
|
def __init__(self, kernel, arg_names, configs, key, reset_to_zero, prune_configs_by: Dict = None):
|
|
'''
|
|
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
|
|
'perf_model': performance model used to predicate running time with different configs, returns running time
|
|
'top_k': number of configs to bench
|
|
'prune_num_stages_by'(optional): a function used to prune num_stages. It take configs:List[Config] as its input, and returns pruned configs.
|
|
'''
|
|
if not configs:
|
|
self.configs = [Config(dict(), num_warps=4, num_stages=2)]
|
|
else:
|
|
self.configs = configs
|
|
self.key_idx = [arg_names.index(k) for k in key]
|
|
self.cache = dict()
|
|
self.kernel = kernel
|
|
# hook to reset all required tensor to zeros before relaunching a kernel
|
|
self.hook = lambda args: 0
|
|
if reset_to_zero is not None:
|
|
self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
|
|
|
|
def _hook(args):
|
|
for i in self.reset_idx:
|
|
args[i].zero_()
|
|
self.hook = _hook
|
|
self.arg_names = arg_names
|
|
# prune configs
|
|
if prune_configs_by:
|
|
perf_model, top_k = prune_configs_by['perf_model'], prune_configs_by['top_k']
|
|
if 'early_config_prune' in prune_configs_by:
|
|
early_config_prune = prune_configs_by['early_config_prune']
|
|
else:
|
|
perf_model, top_k, early_config_prune = None, None, None
|
|
self.perf_model, self.configs_top_k = perf_model, top_k
|
|
self.early_config_prune = early_config_prune
|
|
|
|
def _bench(self, *args, config, **meta):
|
|
# check for conflicts, i.e. meta-parameters both provided
|
|
# as kwargs and by the autotuner
|
|
conflicts = meta.keys() & config.kwargs.keys()
|
|
if conflicts:
|
|
raise ValueError(
|
|
f"Conflicting meta-parameters: {', '.join(conflicts)}."
|
|
" Make sure that you don't re-define auto-tuned symbols."
|
|
)
|
|
# augment meta-parameters with tunable ones
|
|
current = dict(meta, **config.kwargs)
|
|
|
|
def kernel_call():
|
|
if config.pre_hook:
|
|
config.pre_hook(self.nargs)
|
|
self.hook(args)
|
|
self.kernel(*args, num_warps=config.num_warps, num_stages=config.num_stages, **current)
|
|
return triton.testing.do_bench(kernel_call)
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
self.nargs = dict(zip(self.arg_names, args))
|
|
if len(self.configs) > 1:
|
|
key = tuple([args[i] for i in self.key_idx])
|
|
if key not in self.cache:
|
|
# prune configs
|
|
pruned_configs = self.configs
|
|
if self.early_config_prune:
|
|
pruned_configs = self.early_config_prune(self.configs, self.nargs)
|
|
if self.perf_model:
|
|
top_k = self.configs_top_k
|
|
if isinstance(top_k, float) and top_k <= 1.0:
|
|
top_k = int(len(self.configs) * top_k)
|
|
if len(pruned_configs) > top_k:
|
|
est_timing = {config: self.perf_model(**self.nargs, **kwargs, **config.kwargs, num_stages=config.num_stages, num_warps=config.num_warps) for config in pruned_configs}
|
|
pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
|
|
bench_start = time.time()
|
|
timings = {config: self._bench(*args, config=config, **kwargs)
|
|
for config in pruned_configs}
|
|
bench_end = time.time()
|
|
self.bench_time = bench_end - bench_start
|
|
self.cache[key] = builtins.min(timings, key=timings.get)
|
|
self.hook(args)
|
|
self.configs_timings = timings
|
|
config = self.cache[key]
|
|
else:
|
|
config = self.configs[0]
|
|
self.best_config = config
|
|
if config.pre_hook is not None:
|
|
config.pre_hook(self.nargs)
|
|
return self.kernel(*args, num_warps=config.num_warps, num_stages=config.num_stages, **kwargs, **config.kwargs)
|
|
|
|
|
|
@functools.lru_cache()
|
|
def version_key():
|
|
import pkgutil
|
|
contents = []
|
|
# frontend
|
|
with open(triton.code_gen.__file__, "rb") as f:
|
|
contents += [hashlib.md5(f.read()).hexdigest()]
|
|
# backend
|
|
with open(triton._C.libtriton.__file__, "rb") as f:
|
|
contents += [hashlib.md5(f.read()).hexdigest()]
|
|
# language
|
|
language_path = os.path.join(*triton.__path__, 'language')
|
|
for lib in pkgutil.iter_modules([language_path]):
|
|
with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f:
|
|
contents += [hashlib.md5(f.read()).hexdigest()]
|
|
# ptxas version
|
|
try:
|
|
ptxas_version = hashlib.md5(subprocess.check_output(["ptxas", "--version"])).hexdigest()
|
|
except Exception:
|
|
ptxas_version = ''
|
|
return '-'.join(triton.__version__) + '-' + ptxas_version + '-' + '-'.join(contents)
|
|
|
|
|
|
class DependenciesFinder(ast.NodeVisitor):
|
|
|
|
def __init__(self, globals, src) -> None:
|
|
super().__init__()
|
|
self.ret = hashlib.md5(src.encode("utf-8")).hexdigest()
|
|
self.globals = globals
|
|
|
|
def visit_Name(self, node):
|
|
return self.globals.get(node.id, None)
|
|
|
|
def visit_Attribute(self, node):
|
|
lhs = self.visit(node.value)
|
|
while isinstance(lhs, ast.Attribute):
|
|
lhs = self.visit(lhs.value)
|
|
if lhs is None or lhs is triton:
|
|
return None
|
|
return getattr(lhs, node.attr)
|
|
|
|
def visit_Call(self, node):
|
|
func = self.visit(node.func)
|
|
if func is None:
|
|
return
|
|
if inspect.isbuiltin(func):
|
|
return
|
|
if func.__module__ and func.__module__.startswith('triton.'):
|
|
return
|
|
assert isinstance(func, triton.JITFunction)
|
|
if func.hash is None:
|
|
tree = ast.parse(func.src)
|
|
finder = DependenciesFinder(func.__globals__, func.src)
|
|
finder.visit(tree)
|
|
func.hash = finder.ret
|
|
self.ret = (self.ret + func.hash).encode("utf-8")
|
|
self.ret = hashlib.md5(self.ret).hexdigest()
|
|
|
|
|
|
class JITFunction:
|
|
|
|
cache_hook = None
|
|
|
|
def __init__(self, fn, version=None, do_not_specialize=None):
|
|
# information of wrapped function
|
|
self.fn = fn
|
|
self.module = fn.__module__
|
|
signature = inspect.signature(fn)
|
|
self.arg_names = [v.name for v in signature.parameters.values()]
|
|
self.arg_defaults = [v.default for v in signature.parameters.values()]
|
|
|
|
self.version = version
|
|
self.src = textwrap.dedent(inspect.getsource(fn))
|
|
self.src = self.src[self.src.find("def"):]
|
|
self.do_not_specialize = [] if do_not_specialize is None else do_not_specialize
|
|
self.do_not_specialize = [self.arg_names.index(arg) if isinstance(arg, str) else arg for arg in self.do_not_specialize]
|
|
# cache for callable driver objects (e.g. CUkernel)
|
|
self.bin_cache = dict()
|
|
self.hash = None
|
|
# JITFunction can be instantiated as kernel
|
|
# when called with a grid using __getitem__
|
|
self.kernel_decorators = []
|
|
self.kernel = None
|
|
# annotations
|
|
self.annotations = {self.arg_names.index(name): ty for name, ty in fn.__annotations__.items()}
|
|
self.__annotations__ = fn.__annotations__
|
|
# forward docs
|
|
self.__doc__ = fn.__doc__
|
|
self.__name__ = fn.__name__
|
|
self.__globals__ = fn.__globals__
|
|
self.__module__ = fn.__module__
|
|
|
|
@property
|
|
@functools.lru_cache()
|
|
def cache_key(self):
|
|
# TODO : hash should be attribute of `self`
|
|
if self.hash is None:
|
|
dependencies_finder = DependenciesFinder(globals=self.__globals__, src=self.src)
|
|
dependencies_finder.visit(self.parse())
|
|
self.hash = dependencies_finder.ret + version_key()
|
|
return self.hash
|
|
|
|
# we do not parse `src` in the constructor because
|
|
# the user might want to monkey-patch self.src dynamically.
|
|
# Some unit tests do this, for example.
|
|
def parse(self):
|
|
tree = ast.parse(self.src)
|
|
assert isinstance(tree, ast.Module)
|
|
assert len(tree.body) == 1
|
|
assert isinstance(tree.body[0], ast.FunctionDef)
|
|
return tree
|
|
|
|
# Called by CodeGenerator.visit_Call()
|
|
def __call__(self, *args, generator: CodeGenerator, **kwargs):
|
|
try:
|
|
from inspect import getcallargs
|
|
arg_values = getcallargs(self.fn, *args, **kwargs)
|
|
arg_values = [arg_values[name] for name in self.arg_names]
|
|
arg_values = [arg if isinstance(arg, triton.language.tensor)
|
|
else triton.language.constexpr(arg) for arg in arg_values]
|
|
|
|
# Record values in the caller (parent scope)
|
|
gscope = generator.gscope.copy()
|
|
lscope = generator.lscope.copy()
|
|
|
|
# TODO: clear values other than args
|
|
lvalues = generator.lvalues.copy()
|
|
# types = generator.module.get_types().copy()
|
|
generator.gscope = sys.modules[self.fn.__module__].__dict__
|
|
generator.lscope = dict()
|
|
ret = generator.visit_FunctionDef(self.parse().body[0], inline=True, arg_values=arg_values)
|
|
generator.gscope = gscope
|
|
generator.lscope = lscope
|
|
|
|
generator.lvalues = lvalues
|
|
# generator.module.set_types(types)
|
|
|
|
return ret
|
|
except Exception as e:
|
|
node = generator.last_node
|
|
if node is None or isinstance(e, (NotImplementedError, CompilationError)):
|
|
raise e
|
|
raise CompilationError(self.src, node) from e
|
|
|
|
# - when `.src` attribute is set, cache path needs
|
|
# to be reinitialized
|
|
# - when kernel decorators change, cached kernel
|
|
# needs to be cleared
|
|
def __setattr__(self, name, value):
|
|
if name == 'kernel_decorators':
|
|
self.kernel = None
|
|
super(JITFunction, self).__setattr__(name, value)
|
|
if name == 'src':
|
|
self.hash = None
|
|
JITFunction.cache_key.fget.cache_clear()
|
|
|
|
def _init_kernel(self):
|
|
if self.kernel is None:
|
|
self.kernel = Kernel(self)
|
|
for decorator in reversed(self.kernel_decorators):
|
|
self.kernel = decorator(self.kernel)
|
|
return self.kernel
|
|
|
|
def warmup(self, compile):
|
|
return self._warmup(**compile, is_manual_warmup=True)
|
|
|
|
def _warmup(self, key, arg_types, device, attributes, constants, num_warps, num_stages, is_manual_warmup):
|
|
hashed_key = hashlib.md5(key.encode("utf-8")).hexdigest()
|
|
|
|
# create cache directory
|
|
cache_dir = os.environ.get('TRITON_CACHE_DIR', '/tmp/triton/')
|
|
if cache_dir:
|
|
os.makedirs(cache_dir, exist_ok=True)
|
|
|
|
if cache_dir:
|
|
bin_cache_path = os.path.join(cache_dir, hashed_key)
|
|
bin_lock_path = bin_cache_path + ".lock"
|
|
else:
|
|
bin_cache_path = None
|
|
bin_lock_path = None
|
|
|
|
binary = None
|
|
if bin_cache_path and os.path.exists(bin_cache_path):
|
|
assert bin_lock_path is not None
|
|
with FileLock(bin_lock_path):
|
|
with open(bin_cache_path, 'rb') as f:
|
|
binary = pickle.load(f)["binary"]
|
|
|
|
compile = dict(arg_types=arg_types, device=device, attributes=attributes, constants=constants, num_warps=num_warps, num_stages=num_stages)
|
|
if JITFunction.cache_hook is not None:
|
|
name = self.__name__
|
|
info = key.split('-')[-3:]
|
|
num_warps, num_stages, sig = info[0], info[1], info[2].split('_')[1:]
|
|
# make signature human-readable
|
|
arg_reprs = []
|
|
for arg_name, arg_sig in zip(self.arg_names, sig):
|
|
arg_reprs.append(f'{arg_name}: {arg_sig}')
|
|
# assemble the repr
|
|
arg_reprs = ", ".join(arg_reprs)
|
|
repr = f"{name}[num_warps={num_warps}, num_stages={num_stages}]({arg_reprs})"
|
|
noop = JITFunction.cache_hook(key=key, repr=repr, fn=self, compile={"key": key, **compile}, is_manual_warmup=is_manual_warmup, already_compiled=binary is not None)
|
|
if noop:
|
|
return True
|
|
|
|
if binary is None:
|
|
binary = self._compile(**compile)
|
|
|
|
if bin_cache_path:
|
|
assert bin_lock_path is not None
|
|
with FileLock(bin_lock_path):
|
|
with open(bin_cache_path + ".tmp", "wb") as f:
|
|
pickle.dump({"binary": binary, "key": key}, f)
|
|
os.rename(bin_cache_path + ".tmp", bin_cache_path)
|
|
|
|
self.bin_cache[key] = LoadedBinary(device, binary)
|
|
return False
|
|
|
|
def _compile(self, arg_types, device, attributes, constants, num_warps, num_stages):
|
|
# create IR module
|
|
context = _triton.ir.context()
|
|
# get just-in-time proto-type of kernel
|
|
arg_types = [Kernel._to_triton_ir(arg) for arg in arg_types]
|
|
ret_type = triton.language.void
|
|
prototype = triton.language.function_type(ret_type, arg_types)
|
|
# generate Triton-IR
|
|
# export symbols visible from self into code-generator object
|
|
gscope = self.__globals__
|
|
generator = CodeGenerator(context, prototype, gscope=gscope, attributes=attributes, constants=constants, kwargs=dict())
|
|
try:
|
|
generator.visit(self.parse())
|
|
except Exception as e:
|
|
node = generator.last_node
|
|
if node is None or isinstance(e, (NotImplementedError, CompilationError)):
|
|
raise e
|
|
raise CompilationError(self.src, node) from e
|
|
# Compile to machine code
|
|
if torch.version.hip is None:
|
|
backend = _triton.runtime.backend.CUDA
|
|
else:
|
|
backend = _triton.runtime.backend.ROCM
|
|
name, asm, shared_mem = _triton.code_gen.compile_ttir(backend, generator.module, device, num_warps, num_stages)
|
|
max_shared_memory = _triton.runtime.max_shared_memory(backend, device)
|
|
if shared_mem > max_shared_memory:
|
|
raise OutOfResources(shared_mem, max_shared_memory, "shared memory")
|
|
return Binary(backend, name, asm, shared_mem, num_warps)
|
|
|
|
def __getitem__(self, grid):
|
|
return Launcher(self._init_kernel(), grid)
|
|
|
|
def __repr__(self):
|
|
return f"JITFunction({self.module}:{self.fn.__name__})"
|
|
|
|
|
|
class Config:
|
|
"""
|
|
An object that represents a possible kernel configuration for the auto-tuner to try.
|
|
|
|
:ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments.
|
|
:type meta: dict[Str, Any]
|
|
:ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if
|
|
`num_warps=8`, then each kernel instance will be automatically parallelized to
|
|
cooperatively execute using `8 * 32 = 256` threads.
|
|
:type num_warps: int
|
|
:ivar num_stages: the number of stages that the compiler should use when software-pipelining loops.
|
|
Mostly useful for matrix multiplication workloads on SM80+ GPUs.
|
|
:type num_stages: int
|
|
:ivar pre_hook: a function that will be called before the kernel is called. Parameters of this
|
|
function are args.
|
|
"""
|
|
|
|
def __init__(self, kwargs, num_warps=4, num_stages=2, pre_hook=None):
|
|
self.kwargs = kwargs
|
|
self.num_warps = num_warps
|
|
self.num_stages = num_stages
|
|
self.pre_hook = pre_hook
|
|
|
|
def __str__(self):
|
|
res = []
|
|
for k, v in self.kwargs.items():
|
|
res.append(f'{k}: {v}')
|
|
res.append(f'num_warps: {self.num_warps}')
|
|
res.append(f'num_stages: {self.num_stages}')
|
|
return ', '.join(res)
|
|
|
|
|
|
def autotune(configs, key, prune_configs_by=None, reset_to_zero=None):
|
|
"""
|
|
Decorator for auto-tuning a :code:`triton.jit`'d function.
|
|
|
|
.. highlight:: python
|
|
.. code-block:: python
|
|
|
|
@triton.autotune(configs=[
|
|
triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
|
|
triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
|
|
],
|
|
key=['x_size'] # the two above configs will be evaluated anytime
|
|
# the value of x_size changes
|
|
)
|
|
@triton.jit
|
|
def kernel(x_ptr, x_size, **META):
|
|
BLOCK_SIZE = META['BLOCK_SIZE']
|
|
|
|
:note: When all the configurations are evaluated, the kernel will run multiple time.
|
|
This means that whatever value the kernel updates will be updated multiple times.
|
|
To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
|
|
reset the value of the provided tensor to `zero` before running any configuration.
|
|
|
|
:param configs: a list of :code:`triton.Config` objects
|
|
:type configs: list[triton.Config]
|
|
:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
|
|
:type key: list[str]
|
|
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
|
|
'perf_model': performance model used to predicate running time with different configs, returns running time
|
|
'top_k': number of configs to bench
|
|
'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
|
|
:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
|
|
:type reset_to_zero: list[str]
|
|
"""
|
|
def decorator(fn):
|
|
def wrapper(kernel):
|
|
return Autotuner(kernel, fn.arg_names, configs, key, reset_to_zero, prune_configs_by)
|
|
|
|
fn.kernel_decorators.append(wrapper)
|
|
return fn
|
|
|
|
return decorator
|
|
|
|
|
|
def heuristics(values):
|
|
"""
|
|
Decorator for specifying how the values of certain meta-parameters may be computed.
|
|
This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable.
|
|
|
|
.. highlight:: python
|
|
.. code-block:: python
|
|
|
|
@triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))})
|
|
@triton.jit
|
|
def kernel(x_ptr, x_size, **META):
|
|
BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size
|
|
|
|
|
|
.param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter.
|
|
each such function takes a list of positional arguments as input.
|
|
.type values: dict[str, Callable[[list[Any]], Any]]
|
|
"""
|
|
def decorator(fn):
|
|
def wrapper(kernel):
|
|
def fun(*args, **meta):
|
|
for v, heur in values.items():
|
|
assert v not in meta
|
|
meta[v] = heur({**dict(zip(fn.arg_names, args)), **meta})
|
|
return kernel(*args, **meta)
|
|
return fun
|
|
|
|
fn.kernel_decorators.append(wrapper)
|
|
return fn
|
|
|
|
return decorator
|
|
|
|
|
|
def jit(*args, **kwargs):
|
|
"""
|
|
Decorator for JIT-compiling a function using the Triton compiler.
|
|
|
|
:note: When a jit'd function is called, :code:`torch.tensor` arguments are implicitly converted to pointers using the :code:`.data_ptr()` method.
|
|
|
|
:note: This function will be compiled and run on the GPU. It will only have access to:
|
|
|
|
* python primitives,
|
|
* objects within the triton.language package,
|
|
* arguments to this function,
|
|
* other jit'd functions
|
|
|
|
:param fn: the function to be jit-compiled
|
|
:type fn: Callable
|
|
"""
|
|
if args:
|
|
assert len(args) == 1
|
|
assert callable(args[0])
|
|
return JITFunction(args[0], **kwargs)
|
|
else:
|
|
def decorator(fn):
|
|
return JITFunction(fn, **kwargs)
|
|
return decorator
|
|
|
|
|
|
######
|
|
|
|
def cdiv(x, y):
|
|
return (x + y - 1) // y
|
|
|
|
|
|
def next_power_of_2(n):
|
|
"""Return the smallest power of 2 greater than or equal to n"""
|
|
n -= 1
|
|
n |= n >> 1
|
|
n |= n >> 2
|
|
n |= n >> 4
|
|
n |= n >> 8
|
|
n |= n >> 16
|
|
n += 1
|
|
return n
|
|
|
|
######
|
|
|
|
|
|
class TensorWrapper:
|
|
def __init__(self, base, dtype):
|
|
self.dtype = dtype
|
|
self.base = base
|
|
self.is_cuda = base.is_cuda
|
|
self.device = base.device
|
|
|
|
def data_ptr(self):
|
|
return self.base.data_ptr()
|
|
|
|
def __str__(self) -> str:
|
|
return f'TensorWrapper[{self.dtype}]({self.base})'
|
|
|
|
|
|
def reinterpret(tensor, dtype):
|
|
if isinstance(tensor, TensorWrapper):
|
|
if dtype == tensor.base.dtype:
|
|
# Reinterpreting to the original interpretation; return the base.
|
|
return tensor.base
|
|
else:
|
|
# Reinterpreting a wrapped tensor to a different type.
|
|
return TensorWrapper(tensor.base, dtype)
|
|
elif isinstance(tensor, torch.Tensor):
|
|
# A new wrapper is needed around an unwrapped tensor.
|
|
return TensorWrapper(tensor, dtype)
|
|
else:
|
|
raise TypeError(f'Cannot reinterpret a {type(tensor)}.')
|