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Conflicts: lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp lib/Target/LLVMIR/LLVMIRTranslation.cpp python/test/unit/language/assert_helper.py python/triton/third_party/cuda/bin/ptxas test/Conversion/tritongpu_to_llvm.mlir It looks like you may be committing a merge. If this is not correct, please remove the file .git/MERGE_HEAD and try again.
588 lines
21 KiB
Python
588 lines
21 KiB
Python
from __future__ import annotations
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import functools
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import hashlib
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import json
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import os
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import re
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import subprocess
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import tempfile
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from collections import namedtuple
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from pathlib import Path
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from typing import Any, Tuple
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import triton
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import triton._C.libtriton.triton as _triton
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from ..runtime import driver
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# TODO: runtime.errors
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from ..runtime.autotuner import OutOfResources
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from ..runtime.cache import get_cache_manager
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from ..tools.disasm import extract
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from .code_generator import ast_to_ttir
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from .make_launcher import make_stub
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def inline_triton_ir(mod):
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pm = _triton.ir.pass_manager(mod.context)
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pm.enable_debug()
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pm.add_inliner_pass()
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pm.run(mod)
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return mod
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def ttir_compute_capability_rewrite(mod, arch):
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# For hardware without support, we must rewrite all load/store
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# with block (tensor) pointers into tensors of pointers
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pm = _triton.ir.pass_manager(mod.context)
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pm.enable_debug()
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if _is_cuda(arch):
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pm.add_rewrite_tensor_pointer_pass(arch)
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pm.run(mod)
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return mod
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def optimize_ttir(mod, arch):
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mod = inline_triton_ir(mod)
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mod = ttir_compute_capability_rewrite(mod, arch)
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pm = _triton.ir.pass_manager(mod.context)
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pm.enable_debug()
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pm.add_inliner_pass()
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pm.add_triton_combine_pass()
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pm.add_canonicalizer_pass()
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pm.add_cse_pass()
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pm.add_licm_pass()
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pm.add_symbol_dce_pass()
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pm.run(mod)
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return mod
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def ttir_to_ttgir(mod, num_warps):
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pm = _triton.ir.pass_manager(mod.context)
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pm.add_convert_triton_to_tritongpu_pass(num_warps)
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pm.run(mod)
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return mod
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def optimize_ttgir(mod, num_stages, arch):
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pm = _triton.ir.pass_manager(mod.context)
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pm.enable_debug()
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pm.add_tritongpu_coalesce_pass()
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pm.add_tritongpu_remove_layout_conversions_pass()
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if _is_cuda(arch):
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pm.add_tritongpu_accelerate_matmul_pass(arch)
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pm.add_tritongpu_remove_layout_conversions_pass()
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pm.add_tritongpu_optimize_dot_operands_pass()
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# TODO enable this pass for AMD GPU when it is ready
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if torch.version.hip is None:
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pm.add_tritongpu_pipeline_pass(num_stages)
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pm.add_tritongpu_prefetch_pass()
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pm.add_tritongpu_optimize_dot_operands_pass()
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pm.add_tritongpu_remove_layout_conversions_pass()
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pm.add_tritongpu_decompose_conversions_pass()
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pm.add_tritongpu_reorder_instructions_pass()
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pm.add_cse_pass()
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pm.add_symbol_dce_pass()
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pm.run(mod)
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return mod
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def _add_external_libs(mod, libs):
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for name, path in libs.items():
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if len(name) == 0 or len(path) == 0:
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return
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_triton.add_external_libs(mod, list(libs.keys()), list(libs.values()))
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def ttgir_to_llir(mod, extern_libs, arch):
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if extern_libs:
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_add_external_libs(mod, extern_libs)
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# TODO: separate tritongpu_to_llvmir for different backends
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if _is_cuda(arch):
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return _triton.translate_triton_gpu_to_llvmir(mod, arch, False)
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else:
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return _triton.translate_triton_gpu_to_llvmir(mod, 0, True)
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# PTX translation
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@functools.lru_cache()
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def ptx_get_version(cuda_version) -> int:
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'''
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Get the highest PTX version supported by the current CUDA driver.
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'''
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assert isinstance(cuda_version, str)
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major, minor = map(int, cuda_version.split('.'))
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if major == 12:
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return 80 + minor
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if major == 11:
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return 70 + minor
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if major == 10:
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return 63 + minor
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raise RuntimeError("Triton only support CUDA 10.0 or higher")
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@functools.lru_cache()
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def path_to_ptxas():
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base_dir = os.path.join(os.path.dirname(__file__), os.pardir)
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paths = [
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os.environ.get("TRITON_PTXAS_PATH", ""),
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os.path.join(base_dir, "third_party", "cuda", "bin", "ptxas")
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]
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for ptxas in paths:
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if os.path.exists(ptxas) and os.path.isfile(ptxas):
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result = subprocess.check_output([ptxas, "--version"], stderr=subprocess.STDOUT)
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if result is not None:
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version = re.search(r".*release (\d+\.\d+).*", result.decode("utf-8"), flags=re.MULTILINE)
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if version is not None:
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return ptxas, version.group(1)
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raise RuntimeError("Cannot find ptxas")
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def llir_to_ptx(mod: Any, arch: int, ptx_version: int = None) -> str:
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'''
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Translate TritonGPU module to PTX code.
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:param mod: a TritonGPU dialect module
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:return: PTX code
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'''
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if ptx_version is None:
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_, cuda_version = path_to_ptxas()
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ptx_version = ptx_get_version(cuda_version)
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return _triton.translate_llvmir_to_ptx(mod, arch, ptx_version)
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def ptx_to_cubin(ptx: str, arch: int):
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'''
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Compile TritonGPU module to cubin.
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:param ptx: ptx code
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:param compute_capability: compute capability
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:return: str
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'''
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ptxas, _ = path_to_ptxas()
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return _triton.compile_ptx_to_cubin(ptx, ptxas, arch)
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# AMDGCN translation
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def get_amdgcn_bitcode_paths(arch):
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gpu_arch_agnostic_bitcode_libraries = ["opencl.bc",
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"ocml.bc",
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"ockl.bc",
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"oclc_finite_only_off.bc",
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"oclc_daz_opt_off.bc",
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"oclc_correctly_rounded_sqrt_on.bc",
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"oclc_unsafe_math_off.bc",
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"oclc_wavefrontsize64_on.bc",
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"oclc_abi_version_400.bc",]
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gfx_arch = arch[1]
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gfx_arch_id = re.search('gfx(\\w+)', gfx_arch).group(1).strip()
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gpu_arch_specific_bitcode_library = 'oclc_isa_version_' + gfx_arch_id + ".bc"
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bitcode_path_dir = os.path.join(Path(__file__).parent.parent.resolve(), "third_party/rocm/lib/bitcode/")
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amdgcn_bitcode_paths = {}
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i = 0
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for bc_lib in gpu_arch_agnostic_bitcode_libraries:
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bc_path = bitcode_path_dir + bc_lib
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if os.path.exists(bc_path):
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amdgcn_bitcode_paths['library_' + str(i)] = bc_path
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i += 1
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bc_gfx_path = bitcode_path_dir + gpu_arch_specific_bitcode_library
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if os.path.exists(bc_gfx_path):
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amdgcn_bitcode_paths['library_' + str(i)] = bc_gfx_path
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return amdgcn_bitcode_paths
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def get_amdgpu_arch_fulldetails():
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"""
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get the amdgpu fulll ISA details for compiling:
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i.e., arch_triple: amdgcn-amd-amdhsa; arch_name: gfx906; arch_features: sramecc+:xnack-
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"""
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try:
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# TODO: package rocm.cc with Triton
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rocm_path_dir = os.getenv("ROCM_PATH", default="/opt/rocm")
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rocminfo = subprocess.check_output(rocm_path_dir + '/bin/rocminfo').decode()
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gfx_arch_details = re.search('amd.*', rocminfo).group(0).strip().split('--')
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arch_triple = gfx_arch_details[0]
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arch_name_features = gfx_arch_details[1].split(':')
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arch_name = arch_name_features[0]
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arch_features = ""
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if (len(arch_name_features) == 3):
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arch_features = "+" + re.search('\\w+', arch_name_features[1]).group(0) + ","\
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"-" + re.search('\\w+', arch_name_features[2]).group(0)
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return [arch_triple, arch_name, arch_features]
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except BaseException:
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return None
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def llir_to_amdgcn_and_hsaco(mod: Any, gfx_arch: str, gfx_triple: str, gfx_features: str) -> Tuple[str, str]:
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'''
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Translate TritonGPU module to HSACO code based on full details of gpu architecture.
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:param mod: a TritonGPU dialect module
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:return:
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- AMDGCN code
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- Path to HSACO object
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'''
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return _triton.translate_llvmir_to_hsaco(mod, gfx_arch, gfx_triple, gfx_features)
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# ------------------------------------------------------------------------------
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# compiler
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# ------------------------------------------------------------------------------
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def get_kernel_name(src: str, pattern: str) -> str:
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'''
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Get kernel name from PTX code.
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This Kernel name is required when launching the kernel.
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'''
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# There is a name mangling in PTX codegen, so the original kernel names in Triton IR are not available in PTX/cubin.
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assert src
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for line in src.split('\n'):
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line = line.strip()
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if line.startswith(pattern):
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return line.split()[-1]
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def convert_type_repr(x):
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match = re.search(r'!tt\.ptr<(.*)>', x)
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if match is not None:
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return '*' + convert_type_repr(match.group(1))
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return x
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def make_hash(fn, arch, **kwargs):
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if isinstance(fn, triton.runtime.JITFunction):
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configs = kwargs["configs"]
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signature = kwargs["signature"]
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constants = kwargs.get("constants", dict())
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num_warps = kwargs.get("num_warps", 4)
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num_stages = kwargs.get("num_stages", 3)
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debug = kwargs.get("debug", False)
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# Get unique key for the compiled code
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get_conf_key = lambda conf: (sorted(conf.divisible_by_16), sorted(conf.equal_to_1))
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configs_key = [get_conf_key(conf) for conf in configs]
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key = f"{fn.cache_key}-{''.join(signature.values())}-{configs_key}-{constants}-{num_warps}-{num_stages}-{debug}-{arch}"
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return hashlib.md5(key.encode("utf-8")).hexdigest()
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assert isinstance(fn, str)
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return hashlib.md5((Path(fn).read_text() + triton.runtime.jit.version_key()).encode("utf-8")).hexdigest()
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# - ^\s*tt\.func\s+ : match the start of the string, any leading whitespace, the keyword func,
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# and any following whitespace
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# - (public\s+)? : optionally match the keyword public and any following whitespace
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# - (@\w+) : match an @ symbol followed by one or more word characters
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# (letters, digits, or underscores), and capture it as group 1 (the function name)
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# - (\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\)) : match a pair of parentheses enclosing
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# zero or more arguments separated by commas, and capture it as group 2 (the argument list)
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mlir_prototype_pattern = r'^\s*tt\.func\s+(?:public\s+)?(@\w+)(\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\))\s*\{\s*$'
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ptx_prototype_pattern = r"\.(?:visible|extern)\s+\.(?:entry|func)\s+(\w+)\s*\(([^)]*)\)"
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prototype_pattern = {
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"ttir": mlir_prototype_pattern,
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"ttgir": mlir_prototype_pattern,
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"ptx": ptx_prototype_pattern,
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}
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mlir_arg_type_pattern = r'%\w+: ([^,^\)\s]+)(?: \{\S+ = \S+ : \S+\})?,?'
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ptx_arg_type_pattern = r"\.param\s+\.(\w+)"
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arg_type_pattern = {
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"ttir": mlir_arg_type_pattern,
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"ttgir": mlir_arg_type_pattern,
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"ptx": ptx_arg_type_pattern,
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}
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ttgir_num_warps_pattern = r'"triton_gpu.num-warps"\s?=\s?(\d+)\s?:'
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def _get_jsonable_constants(constants):
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def _is_jsonable(x):
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try:
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json.dumps(x)
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return True
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except (TypeError, OverflowError):
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return False
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serialized_constants = {}
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for constant in constants:
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if _is_jsonable(constants[constant]):
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serialized_constants[constant] = constants[constant]
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return serialized_constants
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def parse_mlir_module(path, context):
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module = _triton.ir.parse_mlir_module(path, context)
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# module takes ownership of the context
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module.context = context
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return module
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instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"], defaults=[set(), set()])
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# TODO: architecture descriptor class
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def _is_cuda(arch):
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return isinstance(arch, int)
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def is_hip():
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return torch.version.hip is not None
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def get_architecture_descriptor(capability):
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try:
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import torch
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except ImportError:
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raise ImportError("Triton requires PyTorch to be installed")
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if capability is None:
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if torch.version.hip is None:
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device = triton.runtime.jit.get_current_device()
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capability = triton.runtime.jit.get_device_capability(device)
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capability = capability[0] * 10 + capability[1]
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else:
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capability = get_amdgpu_arch_fulldetails()
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return capability
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def add_rocm_stages(arch, extern_libs, stages):
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extern_libs.update(get_amdgcn_bitcode_paths(arch))
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for key in list(extern_libs):
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if extern_libs[key] == '' or extern_libs[key] is None:
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extern_libs.pop(key)
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gfx_arch_full_details = arch
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gfx_arch = os.environ.get('MI_GPU_ARCH', gfx_arch_full_details[1])
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if gfx_arch is None:
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raise RuntimeError('gfx_arch is None (not specified)')
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stages["amdgcn"] = (lambda path: Path(path).read_text(),
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lambda src: llir_to_amdgcn_and_hsaco(src, gfx_arch,
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gfx_arch_full_details[0],
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gfx_arch_full_details[2]))
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def add_cuda_stages(arch, extern_libs, stages):
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stages["ptx"] = (lambda path: Path(path).read_text(),
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lambda src: llir_to_ptx(src, arch))
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stages["cubin"] = (lambda path: Path(path).read_bytes(),
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lambda src: ptx_to_cubin(src, arch))
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def compile(fn, **kwargs):
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if is_hip():
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capability = None
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else:
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capability = kwargs.get("cc", None)
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arch = get_architecture_descriptor(capability)
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is_cuda = _is_cuda(arch)
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context = _triton.ir.context()
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asm = dict()
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constants = kwargs.get("constants", dict())
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num_warps = kwargs.get("num_warps", 4)
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num_stages = kwargs.get("num_stages", 3 if is_cuda and arch >= 75 else 2)
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extern_libs = kwargs.get("extern_libs", dict())
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if extern_libs is None:
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extern_libs = dict()
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debug = kwargs.get("debug", False)
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# build compilation stages
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stages = dict()
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stages["ast"] = (lambda path: fn, None)
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stages["ttir"] = (lambda path: parse_mlir_module(path, context),
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lambda src: optimize_ttir(ast_to_ttir(src, signature, configs[0], constants, debug=debug), arch))
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stages["ttgir"] = (lambda path: parse_mlir_module(path, context),
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lambda src: optimize_ttgir(ttir_to_ttgir(src, num_warps), num_stages, arch))
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stages["llir"] = (lambda path: Path(path).read_text(),
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lambda src: ttgir_to_llir(src, extern_libs, arch))
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if is_cuda:
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add_cuda_stages(arch, extern_libs, stages)
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else:
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add_rocm_stages(arch, extern_libs, stages)
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# find out the signature of the function
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if isinstance(fn, triton.runtime.JITFunction):
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configs = kwargs.get("configs", None)
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signature = kwargs["signature"]
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if configs is None:
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configs = [instance_descriptor()]
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assert len(configs) == 1
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kwargs["configs"] = configs
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name = fn.__name__
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first_stage = 0
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if isinstance(signature, str):
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signature = {k: v.strip() for k, v in enumerate(signature.split(","))}
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kwargs["signature"] = signature
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else:
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assert isinstance(fn, str)
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_, ir = os.path.basename(fn).split(".")
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src = Path(fn).read_text()
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import re
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match = re.search(prototype_pattern[ir], src, re.MULTILINE)
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name, signature = match.group(1), match.group(2)
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types = re.findall(arg_type_pattern[ir], signature)
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if ir == 'ttgir':
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num_warps_matches = re.findall(ttgir_num_warps_pattern, src)
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assert len(num_warps_matches) == 1, "Expected exactly one match for num_warps"
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assert "num_warps" not in kwargs or int(num_warps_matches[0]) == num_warps, "num_warps in ttgir does not match num_warps in compile"
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num_warps = int(num_warps_matches[0])
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param_tys = [convert_type_repr(ty) for ty in types]
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signature = {k: v for k, v in enumerate(param_tys)}
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first_stage = list(stages.keys()).index(ir)
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# cache manager
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so_path = make_stub(name, signature, constants)
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# create cache manager
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fn_cache_manager = get_cache_manager(make_hash(fn, arch, **kwargs))
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# determine name and extension type of provided function
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if isinstance(fn, triton.runtime.JITFunction):
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name, ext = fn.__name__, "ast"
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else:
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name, ext = os.path.basename(fn).split(".")
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# load metadata if any
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metadata = None
|
|
metadata_filename = f"{name}.json"
|
|
|
|
# The group is addressed by the metadata
|
|
metadata_group = fn_cache_manager.get_group(
|
|
metadata_filename
|
|
) or {}
|
|
|
|
metadata_path = metadata_group.get(metadata_filename)
|
|
|
|
if metadata_path is not None:
|
|
with open(metadata_path) as f:
|
|
metadata = json.load(f)
|
|
else:
|
|
metadata = {"num_warps": num_warps,
|
|
"num_stages": num_stages,
|
|
"constants": _get_jsonable_constants(constants),
|
|
"debug": debug}
|
|
if ext == "ptx":
|
|
assert "shared" in kwargs, "ptx compilation must provide shared memory size"
|
|
metadata["shared"] = kwargs["shared"]
|
|
|
|
first_stage = list(stages.keys()).index(ext)
|
|
asm = dict()
|
|
module = fn
|
|
# run compilation pipeline and populate metadata
|
|
for ir, (parse, compile_kernel) in list(stages.items())[first_stage:]:
|
|
ir_filename = f"{name}.{ir}"
|
|
|
|
if ir == ext:
|
|
next_module = parse(fn)
|
|
else:
|
|
path = metadata_group.get(ir_filename)
|
|
if path is None:
|
|
next_module = compile_kernel(module)
|
|
if ir == "amdgcn":
|
|
extra_file_name = f"{name}.hsaco_path"
|
|
metadata_group[ir_filename] = fn_cache_manager.put(next_module[0], ir_filename)
|
|
metadata_group[extra_file_name] = fn_cache_manager.put(next_module[1], extra_file_name)
|
|
else:
|
|
metadata_group[ir_filename] = fn_cache_manager.put(next_module, ir_filename)
|
|
fn_cache_manager.put(next_module, ir_filename)
|
|
else:
|
|
if ir == "amdgcn":
|
|
extra_file_name = f"{name}.hsaco_path"
|
|
hasco_path = metadata_group.get(extra_file_name)
|
|
assert hasco_path is not None, "Expected to have hsaco_path in metadata when we have the amdgcn"
|
|
next_module = (parse(path), parse(hasco_path))
|
|
else:
|
|
next_module = parse(path)
|
|
|
|
if ir == "cubin":
|
|
asm[ir] = next_module
|
|
elif ir == "amdgcn":
|
|
asm[ir] = str(next_module[0])
|
|
else:
|
|
asm[ir] = str(next_module)
|
|
if ir == "llir" and "shared" not in metadata:
|
|
metadata["shared"] = _triton.get_shared_memory_size(module)
|
|
if ir == "ptx":
|
|
metadata["name"] = get_kernel_name(next_module, pattern='// .globl')
|
|
if ir == "amdgcn":
|
|
metadata["name"] = get_kernel_name(next_module[0], pattern='.globl')
|
|
asm["hsaco_path"] = next_module[1]
|
|
module = next_module
|
|
# write-back metadata, if it didn't come from the cache
|
|
if metadata_path is None:
|
|
metadata_group[metadata_filename] = fn_cache_manager.put(json.dumps(metadata), metadata_filename, binary=False)
|
|
fn_cache_manager.put_group(metadata_filename, metadata_group)
|
|
|
|
# return handle to compiled kernel
|
|
return CompiledKernel(fn, so_path, metadata, asm)
|
|
|
|
|
|
class CompiledKernel:
|
|
|
|
# Hooks for external tools to monitor the execution of triton kernels
|
|
launch_enter_hook = None
|
|
launch_exit_hook = None
|
|
|
|
def __init__(self, fn, so_path, metadata, asm):
|
|
# initialize launcher
|
|
import importlib.util
|
|
spec = importlib.util.spec_from_file_location("__triton_launcher", so_path)
|
|
mod = importlib.util.module_from_spec(spec)
|
|
self.fn = fn
|
|
spec.loader.exec_module(mod)
|
|
self.c_wrapper = getattr(mod, "launch")
|
|
# initialize metadata
|
|
self.shared = metadata["shared"]
|
|
self.num_warps = metadata["num_warps"]
|
|
self.num_stages = metadata["num_stages"]
|
|
self.constants = metadata["constants"]
|
|
# initialize asm dict
|
|
self.asm = asm
|
|
# binaries are lazily initialized
|
|
# because it involves doing runtime things
|
|
# (e.g., checking amount of shared memory on current device)
|
|
self.metadata = metadata
|
|
self.cu_module = None
|
|
self.cu_function = None
|
|
|
|
def _init_handles(self):
|
|
if self.cu_module is not None:
|
|
return
|
|
device = triton.runtime.jit.get_current_device()
|
|
bin_path = {
|
|
driver.HIP: "hsaco_path",
|
|
driver.CUDA: "cubin"
|
|
}[driver.backend]
|
|
max_shared = driver.utils.get_device_properties(device)["max_shared_mem"]
|
|
if self.shared > max_shared:
|
|
raise OutOfResources(self.shared, max_shared, "shared memory")
|
|
mod, func, n_regs, n_spills = driver.utils.load_binary(self.metadata["name"], self.asm[bin_path], self.shared, device)
|
|
|
|
self.n_spills = n_spills
|
|
self.n_regs = n_regs
|
|
self.cu_module = mod
|
|
self.cu_function = func
|
|
|
|
def __getattribute__(self, name):
|
|
if name == 'c_wrapper':
|
|
self._init_handles()
|
|
return super().__getattribute__(name)
|
|
|
|
def __getitem__(self, grid):
|
|
self._init_handles()
|
|
|
|
def runner(*args, stream=None):
|
|
if stream is None:
|
|
stream = triton.runtime.jit.get_cuda_stream()
|
|
self.c_wrapper(grid[0], grid[1], grid[2], self.num_warps, self.shared, stream, self.cu_function,
|
|
CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, self, *args)
|
|
return runner
|
|
|
|
def get_sass(self, fun=None):
|
|
if 'sass' in self.asm:
|
|
return self.asm['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
|