openpilot_comma/tinygrad_repo/tinygrad/renderer/cstyle.py

from typing import Optional, Union, Literal, Callable, cast
import os, math, sys
from collections import defaultdict, Counter
from tinygrad.ops import GroupOp, Ops, UOp, PatternMatcher, UPat
from tinygrad.helpers import strip_parens, getenv, prod, dedup, AMX
from tinygrad.dtype import ImageDType, dtypes, DType, PtrDType
from tinygrad.renderer import Renderer, TensorCore
from tinygrad.codegen.devectorizer import no_vectorized_alu

base_rewrite = PatternMatcher([
  (UPat(Ops.DEFINE_ACC, name="x"), lambda ctx,x: ctx[x.src[0]]),
  (UPat(Ops.ASSIGN, name="x"), lambda ctx,x: f"{ctx[x.src[0]]} = {ctx[x.src[1]]};"),
  (UPat(Ops.IF, name="x"), lambda ctx,x: f"if ({ctx[x.src[0]]}) {{"),
  (UPat((Ops.ENDIF, Ops.ENDRANGE)), lambda ctx: "}"),
  (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{x.arg[0]}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]})"),
  # r method accesses
  (UPat(Ops.RANGE, name="x"),
   lambda ctx,x: f"for ({ctx.render_dtype(x.dtype)} {ctx[x]} = {ctx[x.src[0]]}; {ctx[x]} < {ctx[x.src[1]]}; {ctx[x]}++) {{"),
  (UPat(Ops.VECTORIZE, name="x"),
   lambda ctx,x: f"{ctx.float4.replace('float4', ctx.render_dtype(x.dtype))}" + \
    (f"{{{','.join([ctx[y] for y in x.src])}}}" if ctx.device in {'CPU', 'DSP'} else f"({','.join([ctx[y] for y in x.src])})")),
  (UPat(Ops.CAST, name="x"), lambda ctx,x:
    f"__builtin_convertvector({ctx[x.src[0]]}, {ctx.render_dtype(x.dtype)})" if x.dtype.count > 1 and not isinstance(x.dtype, PtrDType) else None),
  (UPat(Ops.CAST, name="x"), lambda ctx,x: f"({ctx.render_cast(x.dtype, ctx[x.src[0]])})"),
  (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"(*(({ctx.buffer_prefix}{ctx.render_dtype(x.dtype)}*)&{ctx[x.src[0]]}))"),
  (UPat(Ops.DEFINE_LOCAL, name="x"), lambda ctx,x: f"{ctx.smem_align}{ctx.smem_prefix}{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
  (UPat(Ops.BARRIER), lambda ctx: ctx.barrier),
  (UPat(Ops.NOOP, name="x"), lambda ctx,x: ctx[x.src[0]]),
  (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; /* {x.arg[1]} */"),
  # const
  (UPat(Ops.CONST, arg=math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, ctx.infinity)})"),
  (UPat(Ops.CONST, arg=-math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, f'-{ctx.infinity}')})"),
  (UPat(Ops.CONST, dtype=dtypes.floats, name="x"), lambda ctx,x: f"({ctx.render_cast(x.dtype, ctx.nan)})" if math.isnan(x.arg) else None),
  (UPat(Ops.CONST, dtype=dtypes.float, name="x"), lambda ctx,x: f"{x.arg}f"),
  (UPat(Ops.CONST, dtype=dtypes.int64, name="x"), lambda ctx,x: f"{x.arg}ll"),
  (UPat(Ops.CONST, dtype=dtypes.uint64, name="x"), lambda ctx,x: f"{x.arg}ull"),
  (UPat(Ops.CONST, dtype=dtypes.uint32, name="x"), lambda ctx,x: f"{x.arg}u"),
  (UPat(Ops.CONST, dtype=dtypes.bool, name="x"), lambda ctx,x: "1" if x.arg else "0"),
  # consts are rendered to larger type and casted
  (UPat(Ops.CONST, (dtypes.bfloat16, dtypes.half), name="x"), lambda ctx,x: f"({ctx.render_cast(x.dtype, f'{x.arg}f')})"),
  (UPat(Ops.CONST, (dtypes.uint8, dtypes.uint16), name="x"), lambda ctx,x: f"({ctx.render_cast(x.dtype, f'{x.arg}u')})"),
  (UPat(Ops.CONST, (dtypes.int8, dtypes.int16), name="x"), lambda ctx,x: f"({ctx.render_cast(x.dtype, x.arg)})"),
  # default const render
  (UPat(Ops.CONST, name="x"), lambda ctx,x: str(x.arg)),
  # new load/store
  (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var('idx')), allow_any_len=True),
   lambda ctx,buf,idx: f"({ctx[buf]}+{strip_parens(ctx[idx]) if idx.arg == Ops.ADD else ctx[idx]})"),
  (UPat(Ops.LOAD, src=(UPat(Ops.INDEX, src=(UPat(), UPat(), UPat.var("gate"))).or_casted('bidx'), UPat.var("var")), allow_any_len=True),
   lambda ctx,bidx,var,gate: f"({ctx[gate]}?*{ctx[bidx]}:{ctx[var]})"),
  (UPat(Ops.LOAD, src=(UPat.var('bidx'),), allow_any_len=True), lambda ctx,bidx: f"*{ctx[bidx]}"),
  (UPat(Ops.STORE, src=(UPat.var('bidx'), UPat.var("var")), allow_any_len=True), lambda ctx,bidx,var: f"*{ctx[bidx]} = {ctx[var]};"),
  # alu/gep
  (UPat(GroupOp.ALU, name="x"), lambda ctx,x: ctx.code_for_op[x.op](
    *([strip_parens(ctx[v]) if v.op == x.op and x.op in {Ops.ADD, Ops.MUL, Ops.XOR} else ctx[v] for v in x.src]), x.dtype)),
  (UPat(Ops.GEP, name="x"), lambda ctx,x: ctx[x.src[0]] + \
    (f"[{x.arg[0]}]" if x.src[0].dtype.count > (8 if ctx.device in {"CUDA", "NV"} else 4) or ctx.device in {'CPU', 'DSP'} else \
     f".{'xyzwabcd'[x.arg[0]]}")),
  # custom passes through with format
  (UPat((Ops.CUSTOM, Ops.CUSTOMI), name="x"), lambda ctx,x: x.arg.format(*[ctx[y] for y in x.src])),
])

extra_pm = PatternMatcher([
  # insert a NOOP before BITCAST to force it to be rendered. not needed on all backends?
  (UPat(Ops.BITCAST, name="x"),
   lambda x: UOp(Ops.BITCAST, x.dtype, (UOp(Ops.NOOP, x.src[0].dtype, x.src),)) if x.src[0].op is not Ops.NOOP else None),
  # rewrite MAX to CMPLT + WHERE (max function is annoying on many cstyle backends)
  (UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])),
  # devectorize any bools
  (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.ASSIGN, Ops.INDEX), dtype=dtypes.bool, name="alu"), no_vectorized_alu),
  # CAST (from bool) can't be vectorized
  (UPat(Ops.CAST, src=(UPat(dtype=dtypes.bool),), name="alu"), no_vectorized_alu),
  # WHERE can't be vectorized
  (UPat(Ops.WHERE, name="alu"), no_vectorized_alu),
])

def uops_to_dtypes(uops:list[UOp]) -> list[DType]: return dedup(u.dtype for u in uops if not isinstance(u.dtype, (ImageDType, PtrDType)))

class CStyleLanguage(Renderer):
  kernel_prefix: str = ""
  buffer_prefix: str = ""
  buffer_suffix: str = ""
  smem_align: str = ""
  smem_prefix: str = ""
  smem_prefix_for_cast: bool = True
  arg_int_prefix: str = "const int"
  barrier: str = ""
  code_for_workitem: dict[Union[Literal["g"], Literal["l"], Literal["i"]], Callable] = {}
  extra_args: list[str] = []
  float4: Optional[str] = None
  type_map: dict[DType, str] = {}
  infinity: str = "INFINITY"
  nan: str = "NAN"
  code_for_op: dict = {
    Ops.SQRT: lambda x,dtype: f"sqrt({x})", Ops.RECIP: lambda x,dtype: f"(1/{x})", Ops.NEG: lambda x,dtype: f"-{x}",
    Ops.EXP2: lambda x,dtype: f"exp2({x})", Ops.LOG2: lambda x,dtype: f"log2({x})", Ops.SIN: lambda x,dtype: f"sin({x})",
    Ops.AND: lambda a,b,dtype: f"({a}&{b})", Ops.XOR: lambda a,b,dtype: f"({a}^{b})", Ops.OR: lambda a,b,dtype: f"({a}|{b})",
    Ops.ADD: lambda a,b,dtype: f"({a}+{b})", Ops.SUB: lambda a,b,dtype: f"({a}-{b})", Ops.MUL: lambda a,b,dtype: f"({a}*{b})",
    Ops.MOD: lambda a,b,dtype: f"({a}%{b})", Ops.IDIV: lambda a,b,dtype: f"({a}/{b})", Ops.CMPNE: lambda a,b,dtype: f"({a}!={b})",
    Ops.SHR: lambda a,b,dtype: f"({a}>>{b})", Ops.SHL: lambda a,b,dtype: f"({a}<<{b})", Ops.CMPLT: lambda a,b,dtype: f"({a}<{b})",
    Ops.WHERE: lambda a,b,c,dtype: f"({a}?{b}:{c})" }

  string_rewrite = base_rewrite
  extra_matcher = extra_pm

  def get_kernel_modifier(self, uops:list[UOp]) -> str: return ""
  def render_kernel(self, function_name:str, kernel:list[str], bufs:list[tuple[str,tuple[DType,bool]]], uops:list[UOp], prefix=None) -> str:
    tmp = "const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;\n" if any(isinstance(dtype, ImageDType) for _,(dtype,_) in bufs) else ""  # noqa: E501
    buftypes = [(name, self.render_dtype(dtype, mutable)+self.buffer_suffix if isinstance(dtype, (ImageDType, PtrDType)) else
                self.arg_int_prefix if dtype == dtypes.int else None) for name,(dtype,mutable) in bufs]
    prg = ''.join([f"{self.kernel_prefix}void {self.get_kernel_modifier(uops)}{function_name}(",] +
    [', '.join([f'{t} {name}' for name,t in buftypes] + self.extra_args)] +
    [") {\n" + tmp] + ['\n'.join(kernel), "\n}"])
    return prg if prefix is None else "\n".join(prefix)+f"\n{prg}"

  def render_cast(self, dt:DType, val: str) -> str: return f"({self.render_dtype(dt)})({val})"
  def render_dtype(self, dt:DType, mutable=True) -> str:
    if isinstance(dt, ImageDType): return f"{'write_only' if mutable else 'read_only'} image2d_t"
    if isinstance(dt, PtrDType):
      return (self.smem_prefix if dt.local and self.smem_prefix_for_cast else self.buffer_prefix) + self.render_dtype(dt.base) + "*"
    if dt.count > 1: return self.type_map.get(scalar:=dt.scalar(), scalar.name).replace(" ", "_") + str(dt.count)
    return self.type_map.get(scalar:=dt.scalar(), scalar.name)

  def __getitem__(self, key): return self.r[key]  # hacky helper
  def _render(self, uops:list[UOp]) -> tuple[str, list[str], list[tuple[str,tuple[DType,bool]]]]:
    r: dict[UOp, str] = {}
    self.r = r

    child_count = Counter(v for ru in uops for v in ru.src)
    bufs: dict[UOp, tuple[str, tuple[DType, bool]]] = {}
    kernel = []
    depth = 1
    c: defaultdict[str, int] = defaultdict(int)
    name = "test"
    for u in uops:
      if u.op is Ops.NAME:
        name = u.arg
        continue
      if u.op in (Ops.DEFINE_GLOBAL, Ops.DEFINE_VAR):
        r[u] = f"data{u.arg}" if u.op is Ops.DEFINE_GLOBAL else u.arg[0]
        bufs[u] = (r[u], (u.dtype, False))
        continue

      # mark buffers that we store to writable
      if u.op is Ops.STORE:
        for up in u.src[0].toposort:
          if up.op is Ops.DEFINE_GLOBAL: bufs[up] = (bufs[up][0], (bufs[up][1][0], True))

      # naming
      prefix = None
      if u.op is Ops.SPECIAL:
        r[u] = u.arg[0]
      else:
        prefix = {Ops.RANGE: "ridx", Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
                  Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.NOOP: "precast",
                  Ops.INDEX: "bidx", Ops.DEFINE_ACC: "acc", Ops.LOAD: "val"}.get(u.op, "alu")
        r[u] = f"{prefix}{c[prefix]}"

      l = cast(str, self.string_rewrite.rewrite(u, ctx=self))
      assert l is not None, f"failed to render {u.op} {u.dtype} {[(x.op,x.dtype) for x in u.src]} {u.arg}"

      if u.op in {Ops.ENDIF, Ops.ENDRANGE}: depth -= 1
      if (u.op is not Ops.CAST or u.dtype.vcount == 1) and (u.op in {Ops.CONST, Ops.GEP, Ops.INDEX, Ops.CUSTOMI} or \
        (u.op in {Ops.VECTORIZE, *GroupOp.ALU, Ops.CAST, Ops.BITCAST} and child_count[u] == 1 and not getenv("EXPAND_SSA"))):
        r[u] = l
      else:
        if u.op in {Ops.RANGE, Ops.ASSIGN, Ops.DEFINE_LOCAL} or u.dtype == dtypes.void:
          if u.op is Ops.ASSIGN: r[u] = r[u.src[0]]
        else:
          l = f"{self.render_dtype(u.dtype)} {r[u]} = {l}" + (";" if u.op is not Ops.SPECIAL else "")
        kernel.append("  "*depth + l)
        if prefix: c[prefix] += 1  # if it was used, increment
      if u.op in {Ops.IF, Ops.RANGE}: depth += 1
    del self.r

    # NOTE: this relies on bufs dict preserving order
    return (name, kernel, list(bufs.values()))
  def render(self, uops:list[UOp]) -> str: return self.render_kernel(*self._render(uops), uops)

class ClangRenderer(CStyleLanguage):
  device = "CPU"
  float4 = "(float4)"
  has_local = False
  global_max = None
  infinity = "__builtin_inff()"
  nan = '__builtin_nanf("")'
  amx_tc = [TensorCore(dims=(sz,sz,1), threads=1, elements_per_thread=(sz,sz,sz*sz), dtype_in=dt, dtype_out=dt, swizzle=(None,((),(4,5,6,7,0,1,2,3))),
                      opts=("u0","u0","u0","u0","u1","u1","u1","u1")) for dt,sz in [(dt, 64 // dt.itemsize) for dt in [dtypes.float]]]
  if AMX: tensor_cores = amx_tc

  # language options
  buffer_suffix = " restrict"
  type_map = {dtypes.bool:"_Bool", dtypes.half:"__fp16"}
  code_for_op = {**({k:v for k,v in CStyleLanguage.code_for_op.items() if k not in [Ops.EXP2, Ops.SIN, Ops.LOG2]}),
                 Ops.SQRT: lambda x,dtype: f"__builtin_sqrt({x})" if dtype == dtypes.float64 else f"__builtin_sqrtf({x})"}
  # LLVM legalizes double => half cast on systems that don't support it natively (like x86 cpus without AVX512-FP16) into a compiler-rt libcall.
  extra_matcher = PatternMatcher([(UPat.var("x", dtypes.float64).cast(dtypes.float16), lambda x: x.cast(dtypes.float32).cast(dtypes.float16)),
    (UPat(Ops.SQRT, name="alu"), no_vectorized_alu),]) + CStyleLanguage.extra_matcher

  if sys.platform == 'win32':
    kernel_prefix = "__attribute__((ms_abi)) "
  def render_vector_prefix(self, dt:DType) -> str:
    # round (down) to power of two (this is actually the default clang behavior)
    alignment = 2**int(math.log2(dt.itemsize)) if getenv("ALIGNED", 1) else 1
    return f"typedef {self.render_dtype(dt.scalar())} {self.render_dtype(dt)} __attribute__((aligned({alignment}),vector_size({dt.itemsize})));"

  def _render_defines(self, uops) -> list[str]:
    prefix = [self.render_vector_prefix(dt) for dt in uops_to_dtypes(uops) if dt.count > 1]
    # https://github.com/corsix/amx
    for name, (N, M, _), dtype_in, _, _, _, _, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
      prefix += [
        '#define AMX_SET(imm5) __asm("nop\\nnop\\nnop\\n.word (0x201000+(%0<<5)+%1)" : : "i"(17), "i"(imm5) : "memory")',
        '#define AMX(op, gpr, btf) __asm(".word (0x201000+(%0 << 5)+0%1-((0%1>>4)*6))" : : "i"(op), "r"((unsigned long long)(gpr)+(btf)) : "memory")',
      ]
      # 'static' in C roughly means that function symbol isn't exported. LLVM puts those symbols at the end of object file which allows Clang JIT
      # to just jump at the start of a shellcode whithout having to deal with symbols or trampolines at all. This is better than having to inline
      # wmma function every time it is called or wasting complexity on a symbol parsing and a memory page on trampoline.
      prefix += [f"""static {(out := self.render_dtype(dtype_in.vec(N*N)))} __{name}({self.render_dtype(dtype_in.vec(N))} data1, {self.render_dtype(dtype_in.vec(M))} data2, {out} data0){{
  AMX_SET(0);\n  for(int ridx0 = 0; ridx0 < 16; ridx0++){{ AMX(4, (int *)(&data0), 0ull<<62 | (ridx0*4ull)<<56 | ridx0*64ull); }}
  AMX(0, (int *)(&data2), 0ull<<62); AMX(1, (int *)(&data1), 0ull<<62); AMX(12, 0, 0ull);
  for(int ridx0 = 0; ridx0 < 16; ridx0++){{ AMX(5, (int *)(&data0), 0ull<<62 | (ridx0*4ull)<<56 | ridx0*64ull); }}\n  AMX_SET(1);\n  return data0;\n}}"""] # noqa: E501
    return prefix
  def _render_body(self, function_name, kernel, bufs, uops, pref=None) -> str: return super().render_kernel(function_name, kernel, bufs, uops, pref)
  def _render_entry(self, function_name:str, bufs:list[tuple[str,tuple[DType,bool]]]) -> str: return ""

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
    defines = '\n'.join(self._render_defines(uops))
    return defines + "\n" + self._render_body(function_name, kernel, bufs, uops, prefix) + "\n" + self._render_entry(function_name, bufs)

class OpenCLRenderer(CStyleLanguage):
  device = "GPU"

  # language options
  kernel_prefix = "__kernel "
  buffer_prefix = "__global "
  smem_align = "__attribute__ ((aligned (16))) "
  smem_prefix = "__local "
  barrier = "barrier(CLK_LOCAL_MEM_FENCE);"
  float4 = "(float4)"
  code_for_workitem = {"g": lambda x: f"get_group_id({x})", "l": lambda x: f"get_local_id({x})", "i": lambda x: f"get_global_id({x})"}
  type_map = { dtypes.int8: "char", dtypes.uint8: "uchar", dtypes.uint32: "uint", dtypes.uint16: "ushort", dtypes.uint64: "ulong",
              dtypes.bfloat16: "ushort" }

  string_rewrite = PatternMatcher([
    (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"as_{ctx.render_dtype(x.dtype)}({ctx[x.src[0]]})"),
    # load/store image (OpenCL)
    (UPat(Ops.LOAD, dtype=dtypes.float.vec(4), src=(UPat.var('buf').index(UPat.var('idx', dtypes.int.vec(2)), UPat.var("gate")), UPat.var("var"))),
      lambda ctx,buf,idx,var,gate: f"({ctx[gate]}?read_imagef({ctx[buf]}, smp, {ctx[idx]}):{ctx[var]})"),
    (UPat(Ops.LOAD, dtype=dtypes.float.vec(4), src=(UPat.var('buf').index(UPat.var('idx', dtypes.int.vec(2))),)),
      lambda ctx,buf,idx: f"read_imagef({ctx[buf]}, smp, {ctx[idx]})"),
    (UPat(Ops.STORE, src=(UPat.var('buf').index(UPat.var('idx', dtypes.int.vec(2))), UPat.var("var", dtypes.float.vec(4))), allow_any_len=True),
      lambda ctx,buf,idx,var: f"write_imagef({ctx[buf]}, {ctx[idx]}, {ctx[var]});"),
  ]) + base_rewrite

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
    if any(uop.dtype.base == dtypes.half for uop in uops): prefix = (["#pragma OPENCL EXTENSION cl_khr_fp16 : enable"] + (prefix or []))
    return super().render_kernel(function_name, kernel, bufs, uops, prefix)

class IntelRenderer(OpenCLRenderer):
  device, suffix, kernel_prefix = "GPU", "INTEL", "__attribute__((intel_reqd_sub_group_size(8)))\n" + "__kernel "
  tensor_cores = [TensorCore(dims=(8,8,16), threads=8, elements_per_thread=(16,16,8), dtype_in=dtypes.half, dtype_out=dtypes.float,
    opts=("l0","l0","l0","u1","u1","u1"), swizzle=(((4,5,6),(0,1,2,3,7,8,9)), ((0,1,2),(7,8,9,3,4,5,6))))]

  string_rewrite = PatternMatcher([
    (UPat(Ops.CAST, dtype=dtypes.bfloat16, src=(UPat.var('x', dtype=dtypes.float))), lambda ctx,x: f"intel_convert_bfloat16_as_ushort({ctx[x]})"),
    (UPat(Ops.CAST, dtype=dtypes.float, src=(UPat.var('x', dtype=dtypes.bfloat16))), lambda ctx,x: f"intel_convert_as_bfloat16_float({ctx[x]})"),
  ]) + OpenCLRenderer.string_rewrite

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
    prefix = []
    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
      dt_in = ("ushort", "bf16") if arg[2] == dtypes.bfloat16 else (arg[2].name, "f16")
      prefix.append(f"""{arg[3].name}8 __{arg[0]}({dt_in[0]}16 a, {dt_in[0]}16 b, {arg[3].name}8 c) {{
    return intel_sub_group_{dt_in[1]}_{dt_in[1]}_matrix_mad_k16(as_int8(a), as_int8(b), c);\n}}""")
    return super().render_kernel(function_name, kernel, bufs, uops, prefix or None)

class MetalRenderer(CStyleLanguage):
  device = "METAL"
  shared_max = 32768
  tensor_cores = [TensorCore(dims=(8,8,8), threads=32, elements_per_thread=(2,2,2), dtype_in=di, dtype_out=do, opts=("u0","l0","l1","l1","l0","l1"),
    swizzle=(((6,1,2,7,4),(8,0,3,5)), ((0,5,6,3,7),(1,2,4,8)))) for di,do in [(dtypes.float,dtypes.float),(dtypes.half,dtypes.float),
    (dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
  def __init__(self): self.tensor_cores = MetalRenderer.tensor_cores if hasattr(os, 'uname') and os.uname().machine == "arm64" else []

  # language options
  kernel_prefix = "kernel "
  buffer_prefix = "device "
  smem_prefix = "threadgroup "
  arg_int_prefix = "constant int&"
  barrier = "threadgroup_barrier(mem_flags::mem_threadgroup);"
  float4 = "float4"
  code_for_workitem = {"g": lambda x: f"gid.{chr(120+int(x))}", "l": lambda x: f"lid.{chr(120+int(x))}"}
  # uint3 used for gid/lid - TODO: this should probably be `ushort3 lid [[thread_position_in_threadgroup]]`
  extra_args = ['uint3 gid [[threadgroup_position_in_grid]]', 'uint3 lid [[thread_position_in_threadgroup]]']
  type_map = {dtypes.bfloat16: "bfloat"}

  # precise::sin
  code_for_op = {**CStyleLanguage.code_for_op, Ops.SIN: lambda x,dtype: f"precise::sin({x})"}

  # upcast to float32 all the ops that don't support bfloat16
  extra_matcher = PatternMatcher([
    # NOTE: this is copied from PTX
    (UPat((Ops.SQRT, Ops.EXP2, Ops.LOG2, Ops.SIN), dtype=dtypes.bfloat16, name="x"),
      lambda x: (UOp(x.op, dtypes.float, tuple(vv.cast(dtypes.float) for vv in x.src), x.arg).cast(dtypes.bfloat16))),
  ]) + extra_pm

  string_rewrite = PatternMatcher([
    (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"as_type<{ctx.render_dtype(x.dtype)}>({ctx[x.src[0]]})"),
  ]) + base_rewrite

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None):
    prefix, wmma_args = ["#include <metal_stdlib>","using namespace metal;"], set([uop.arg for uop in uops if uop.op is Ops.WMMA])
    for arg in wmma_args: prefix.append(
  f"""{(dtype_out:=self.render_dtype(arg[3].vec(2)))} __{arg[0]}({(dtype_in:=self.render_dtype(arg[2].vec(2)))} a, {dtype_in} b, {dtype_out} c){{
  simdgroup_{self.render_dtype(arg[2])}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(arg[3])}8x8 mat_c;
  mat_a.thread_elements()[0] = a[0]; mat_b.thread_elements()[0] = b[0]; mat_c.thread_elements()[0] = c[0];
  mat_a.thread_elements()[1] = a[1]; mat_b.thread_elements()[1] = b[1]; mat_c.thread_elements()[1] = c[1];
  simdgroup_multiply_accumulate(mat_c, mat_a, mat_b, mat_c);\n  return {dtype_out}(mat_c.thread_elements()[0], mat_c.thread_elements()[1]);\n}}""")
    return super().render_kernel(function_name, kernel, bufs, uops, prefix)

_nms = "xyzwabcdefghijkl"
cuda_tc_opts = ("u0","l0","l0","l1","l1","l1","u1")  # shared by all shapes with M=16 N=8

class CUDARenderer(CStyleLanguage):
  device = "CUDA"
  global_max = (2147483647, 65535, 65535)
  local_max = (1024, 1024, 64)
  shared_max = 49152
  # https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-matrix-multiply-accumulate-instructions
  tc_81616 = [TensorCore(dims=(8,16,16), threads=32, elements_per_thread=(8,4,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
    swizzle=(((6,7,2,3,4),(0,1,9,5,10,8)), ((6,7,9,0,1),(2,3,4,10,5,8)))) for di,do in [(dtypes.half,dtypes.float), (dtypes.bfloat16,dtypes.float),
                                                                                        (dtypes.half,dtypes.half)]]
  tc_8168_f16 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
    swizzle=(((6,7,2,3,4),(0,1,8,5,9)), ((6,7,8,0,1),(2,3,4,9,5)))) for di,do in [(dtypes.half,dtypes.float), (dtypes.half,dtypes.half)]]
  tc_8168_tf32 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=dtypes.float, dtype_out=dtypes.float, opts=cuda_tc_opts,
    swizzle=(((5,6,2,3,4),(0,1,8,9,7)), ((5,6,8,0,1),(2,3,4,9,7))))]

  tc_sm80 = tc_81616 + tc_8168_f16
  if getenv("ALLOW_TF32", 0): tc_sm80 += tc_8168_tf32
  tc_sm75 = tc_8168_f16
  def __init__(self, arch:str):
    self.tensor_cores, self.arch = CUDARenderer.tc_sm80 if int(arch[3:]) >= 80 else CUDARenderer.tc_sm75 if int(arch[3:]) >= 75 else [], arch
  def __reduce__(self): return self.__class__, (self.arch,)

  # language options
  kernel_prefix = "extern \"C\" __global__ "
  smem_prefix = "__shared__ "
  smem_prefix_for_cast = False
  barrier = "__syncthreads();"
  float4 = "make_float4"
  code_for_workitem = {"g": lambda x: f"blockIdx.{chr(120+int(x))}", "l": lambda x: f"threadIdx.{chr(120+int(x))}",
                       "i": lambda x: f"(blockIdx.{chr(120+int(x))}*blockDim.{chr(120+int(x))}+threadIdx.{chr(120+int(x))})"}
  code_for_op = { **CStyleLanguage.code_for_op,
    Ops.SIN: lambda x,dtype: f"hsin({x})" if dtype in (dtypes.half, dtypes.bfloat16) else f"sin({x})",
    Ops.LOG2: lambda x,dtype: f"hlog2({x})" if dtype in (dtypes.half, dtypes.bfloat16) else f"log2({x})",
    Ops.EXP2: lambda x,dtype: f"hexp2({x})" if dtype in (dtypes.half, dtypes.bfloat16) else f"exp2({x})",
    Ops.SQRT: lambda x,dtype: f"hsqrt({x})" if dtype in (dtypes.half, dtypes.bfloat16) else f"sqrt({x})",
    Ops.RECIP: lambda x,dtype: f"hrcp({x})" if dtype in (dtypes.half, dtypes.bfloat16) else f"(1/{x})" }
  type_map = {dtypes.bfloat16: "nv_bfloat16"}

  def render_vector_prefix(self, dt:DType) -> str:
    vec, scal = self.render_dtype(dt), self.render_dtype(dt.scalar()),
    elems, header = ', '.join(_nms[:dt.count]), ', '.join([f"{scal} {x}" for x in _nms[:dt.count]])
    return f"struct __align__({dt.itemsize}) {vec} {{ {scal} {elems}; }}; __device__ {vec} make_{vec}({header}) {{ {vec} r={{{elems}}}; return r; }}"

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None):
    # TODO: why is dtypes.bfloat16.name == "__bf16"? would be easier not override dtypes.name
    prefix = ["#define INFINITY (__int_as_float(0x7f800000))","#define NAN (__int_as_float(0x7fffffff))"]

    used_dtypes = uops_to_dtypes(uops)
    if any(dt.scalar() == dtypes.half for dt in used_dtypes): prefix.append("#include <cuda_fp16.h>")
    if any(dt.scalar() == dtypes.bfloat16 for dt in used_dtypes): prefix.append("#include <cuda_bf16.h>")
    prefix += [self.render_vector_prefix(dt) for dt in used_dtypes if dt.count in (4,8) and dt.scalar() in {dtypes.half, dtypes.bfloat16}]

    dt_map_in = { dtypes.float: "tf32", dtypes.half: "f16", dtypes.bfloat16: "bf16" }
    dt_map_out = { dtypes.float: "f32", dtypes.half: "f16" }
    for name, (N, M, K), dtype_in, dtype_out, _, _, upcast_axes, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
      upcast_sizes = [prod(size for _, size in upcast) for upcast in upcast_axes]
      wmma_dtypes = [self.render_dtype(dtype.vec(size)) for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)]
      n_operands = [size*dtype.itemsize//4 for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)] # 4 => CUDA reg size in bytes
      operands = [f"%{i}" for i in range(sum(n_operands))]

      # mma operands => {c}, {a}, {b}, {c}
      prefix.append(f"""__device__ {wmma_dtypes[2]} __{name}({wmma_dtypes[0]} a, {wmma_dtypes[1]} b, {wmma_dtypes[2]} c){{
  int *a_pk = (int *)(&a), *b_pk = (int *)(&b), *c_pk = (int *)(&c);
  asm("mma.sync.aligned.m{M}n{N}k{K}.row.col.{dt_map_out[dtype_out]}.{dt_map_in[dtype_in]}.{dt_map_in[dtype_in]}.{dt_map_out[dtype_out]}"
      "{{{", ".join(operands[:n_operands[2]])}}}, {{{", ".join(operands[n_operands[2]:n_operands[2]+n_operands[0]])}}},"
      "{{{", ".join(operands[-n_operands[1]:])}}}, {{{", ".join(operands[:n_operands[2]])}}};"
    : {", ".join([f'"+r"(c_pk[{i}])' for i in range(n_operands[2])])}
    : {", ".join([f'"r"(a_pk[{i}])' for i in range(n_operands[0])])}, {", ".join([f'"r"(b_pk[{i}])' for i in range(n_operands[1])])});
  return c;\n}}""")

    return super().render_kernel(function_name, kernel, bufs, uops, prefix=prefix)

  def get_kernel_modifier(self, uops:list[UOp]) -> str:
    maxThreadsPerBlock = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
    # https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
    return f"__launch_bounds__({maxThreadsPerBlock}) "

def cast_float_to_bf16(x: UOp) -> UOp:
  assert x.dtype == dtypes.float, "cast float -> bf16 must start with float"
  x = x.bitcast(dtypes.uint)
  x = (-x & 0x7f800000).where(x + ((x >> 16) & 1) + 0x7fff, (x & 0xffff).where((x | 0x10000), x))
  return (x >> 16).cast(dtypes.ushort).bitcast(dtypes.bfloat16)

class AMDRenderer(CStyleLanguage):
  device = "AMD"
  shared_max = 65536
  # NOTE: this is only really needed on gfx12, even though gfx11 reports the same limitation
  global_max = (2147483647, 65535, 65535)
  # https://gpuopen.com/learn/wmma_on_rdna3/
  tensor_cores = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(16,16,8), dtype_in=di, dtype_out=do,
    opts=("l0","l0","l0","l0","l1","u1","u1","u1"), swizzle=(((4,9,10,11,0),(1,2,3,5,6,7,8)), ((0,1,2,3,4),(9,10,11,5,6,7,8))))
    for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half)]]
  # https://gpuopen.com/learn/amd-lab-notes/amd-lab-notes-matrix-cores-readme
  tensor_cores_mfma = [TensorCore(dims=(16,16,16), threads=64, elements_per_thread=(4,4,4), dtype_in=di, dtype_out=do,
    opts=("l0","l0","l0","l0","u1","u1","l1","l1"), swizzle=(((10,11,4,5,8,9),(0,1,2,3,6,7)),((0,1,2,3,8,9),(4,5,10,11,6,7))))
    for di,do in [(dtypes.half,dtypes.float)]]

  def __init__(self, arch:str): # gfx942 => MI300, gfx1100 => RX 7900, gfx1201 => RX 9700
    self.arch = arch
    # TODO: fix tensor cores for gfx1201
    self.tensor_cores = \
      AMDRenderer.tensor_cores_mfma if arch.split(":")[0] == "gfx942" else AMDRenderer.tensor_cores if arch.split(":")[0] != "gfx1201" else []
    if self.arch.split(":")[0] == "gfx942":
      self.string_rewrite = PatternMatcher([
        (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{x.arg[0]}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]}, 0, 0, 0)")]) + base_rewrite
  def __reduce__(self): return self.__class__, (self.arch,)

  # language options
  ockl = [(f"__ockl_get_{name}", "unsigned int", "size_t", "const") for name in ["local_id", "group_id", "local_size"]]
  ocml = [(f"__ocml_{name}_f{n}", f"{dt}, {dt}" if "fmax" == name else dt, dt, atr)
            for dt, n in [(dtype.name, dtype.itemsize * 8) for dtype in [dtypes.float, dtypes.double, dtypes.half]]
            for name, atr in [("fmax", "const"), ("exp2", "pure"), ("log2", "pure"), ("sqrt", "const"), ("sin", "")]]

  kernel_prefix = "\n".join(f'extern "C" __attribute__((device{f", {atr}" if atr else ""})) {dto} {meth}({dti});' for meth,dti,dto,atr in ockl+ocml)
  kernel_prefix += '\nextern "C" __attribute__((global))'
  code_for_workitem = {"g": lambda x: f"__ockl_get_group_id({x})", "l": lambda x: f"__ockl_get_local_id({x})",
                       "i": lambda x: f"(__ockl_get_group_id({x})*__ockl_get_local_size({x})+__ockl_get_local_id({x}))"}
  code_for_op = { **CStyleLanguage.code_for_op,
    Ops.SIN: lambda x,dtype: f"__ocml_sin_f{ {dtypes.half:16, dtypes.double:64}.get(dtype, 32)}({x})",
    Ops.LOG2: lambda x,dtype: f"__ocml_log2_f{ {dtypes.half:16, dtypes.double:64}.get(dtype, 32)}({x})",
    Ops.EXP2: lambda x,dtype: f"__ocml_exp2_f{ {dtypes.half:16, dtypes.double:64}.get(dtype, 32)}({x})",
    Ops.SQRT: lambda x,dtype: f"__ocml_sqrt_f{ {dtypes.half:16, dtypes.double:64}.get(dtype, 32)}({x})" }
  smem_prefix = "__attribute__((shared))"
  smem_prefix_for_cast: bool = False
  barrier = '__builtin_amdgcn_fence(__ATOMIC_RELEASE, "workgroup");' + '__builtin_amdgcn_s_barrier();' + \
            '__builtin_amdgcn_fence(__ATOMIC_ACQUIRE, "workgroup");'
  float4 = "make_float4"
  type_map = {dtypes.bfloat16: "hip_bfloat16"}
  extra_matcher = PatternMatcher([
    # cast bfloat16 alus to float
    (UPat(Ops.WHERE, src=(UPat.var("b"), UPat.var("x", dtype=dtypes.bfloat16), UPat.var("y", dtype=dtypes.bfloat16))),
      lambda b,x,y: UOp(Ops.WHERE, dtype=dtypes.float, src=(b,x.cast(dtypes.float),y.cast(dtypes.float))).cast(dtypes.bfloat16)),
    (UPat(GroupOp.ALU, dtype=dtypes.bfloat16, name="x"),
      lambda x: UOp(x.op, dtypes.float, tuple(vv.cast(dtypes.float) for vv in x.src), x.arg).cast(dtypes.bfloat16)),
    (UPat(GroupOp.ALU, dtypes.bool, name="alu", src=(UPat.var("x", dtype=dtypes.bfloat16), UPat.var("y", dtype=dtypes.bfloat16))),
      lambda alu,x,y: UOp(alu.op, dtypes.bool, (x.cast(dtypes.float), y.cast(dtypes.float)), alu.arg)),
    # add float intermediate casting for bfloat16
    (UPat(Ops.CAST, name="x", src=UPat.var("y", dtypes.bfloat16)),lambda x,y: y.cast(dtypes.float).cast(x.dtype) if x.dtype!=dtypes.float else None),
    (UPat(Ops.CAST, dtypes.bfloat16, UPat.var("x")),lambda x: x.cast(dtypes.float).cast(dtypes.bfloat16) if x.dtype!=dtypes.float else None),
    # bfloat16 casting
    (UPat.cvar('x', dtypes.bfloat16), lambda x: cast_float_to_bf16(UOp.const(dtypes.float, x.arg))),
    (UPat(Ops.CAST, dtypes.float, UPat.var("x", dtypes.bfloat16)), lambda x: (x.bitcast(dtypes.ushort).cast(dtypes.uint)<<16).bitcast(dtypes.float)),
    (UPat(Ops.CAST, dtype=dtypes.bfloat16, src=UPat.var("x", dtype=dtypes.float)), cast_float_to_bf16)]) + extra_pm

  def render_vector_prefix(self, dtype:DType) -> str:
    vec, scal = self.render_dtype(dtype), self.render_dtype(dtype.scalar())
    return f"typedef {scal} {vec} __attribute__((ext_vector_type({dtype.count})));\nstatic inline __attribute__((device)) "+ \
           f"{vec} make_{vec}({', '.join([f'{scal} {x}' for x in _nms[:dtype.count]])}) {{ return {{ {', '.join(_nms[:dtype.count])} }}; }}"

  def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
    prefix = ["#define INFINITY (__builtin_inff())","#define NAN (__builtin_nanf(\"\"))","typedef long unsigned int size_t;","#define half _Float16"]

    used_dtypes = uops_to_dtypes(uops)
    if any(dt.scalar() == dtypes.bfloat16 for dt in used_dtypes): prefix.append("typedef unsigned short hip_bfloat16;")
    prefix += [self.render_vector_prefix(dt) for dt in used_dtypes if dt.count > 1]

    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper
      if self.arch.split(":")[0] == "gfx942": prefix.append(f"#define __{arg[0]} __builtin_amdgcn_mfma_f32_16x16x16f16")
      elif arg[3] == dtypes.float: prefix.append(f"#define __{arg[0]} __builtin_amdgcn_wmma_f32_16x16x16_f16_w32")
      else: prefix.append(f"static inline __attribute__((device)) half8 __{arg[0]}"+"""(half16 a, half16 b, half8 c) {
  half16 c_frag = {}; half8 d; for (int n = 0; n < 8; n++) { c_frag[n*2] = c[n]; }
  c_frag = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32(a, b, c_frag, false);
  for (int n = 0; n < 8; n++) { d[n] = c_frag[n*2]; } return d;\n}""")
    return super().render_kernel(function_name, kernel, bufs, uops, prefix)

  def get_kernel_modifier(self, uops:list[UOp]) -> str:
    requiredMaxThreadsPerBlock = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
    # https://clang.llvm.org/docs/AttributeReference.html#amdgpu-flat-work-group-size
    # NOTE: this makes hlb_cifar10 twice as fast, there may be more gains in tweaking these parameters
    return f"__attribute__((amdgpu_flat_work_group_size(1, {requiredMaxThreadsPerBlock})))"

class NVRenderer(CUDARenderer): device = "NV"
class HIPRenderer(AMDRenderer): device = "HIP"
class QCOMRenderer(OpenCLRenderer): device = "QCOM"