import unittest
import functools
import numpy as np
from tinygrad import Tensor, Device, dtypes
from tinygrad.uop.ops import UOp, Ops, KernelInfo
from tinygrad.engine.realize import run_linear, estimate_uop, compile_linear
from tinygrad.renderer import Estimates
from tinygrad.dtype import AddrSpace
from tinygrad.helpers import getenv
from tinygrad.runtime.autogen.amd.rdna3.ins import *
import tinygrad.runtime.autogen.amd.rdna3.ins as r3
import tinygrad.runtime.autogen.amd.rdna4.ins as r4
from tinygrad.renderer.amd.dsl import s, v, NULL
from test.amd.helpers import TARGET_TO_ARCH
from extra.gemm.amd_asm_matmul import Kernel

def custom_add_one(A:UOp) -> UOp:
  A = A.flatten()
  assert dtypes.is_float(A.dtype), f"buffer dtype must be float32, got {A.dtype}"
  threads = UOp.special(A.numel(), "lidx0")
  insts = [
    s_load_b64(s[0:1], s[0:1], soffset=NULL),
    s_waitcnt_lgkmcnt(sdst=NULL, simm16=0),
    v_lshlrev_b32_e32(v[0], 2, v[0]), # element offset
    global_load_b32(v[1], v[0], saddr=s[0:1]),
    s_waitcnt_vmcnt(sdst=NULL, simm16=0),
    v_mov_b32_e32(v[2], 1.0),
    v_add_f32_e32(v[1], v[1], v[2]),
    global_store_b32(addr=v[0], data=v[1], saddr=s[0:1]),
    s_endpgm(),
  ]
  sink = UOp.sink(A.base, threads, arg=KernelInfo(f"custom_add_one_{A.numel()}", estimates=Estimates(ops=A.numel(), mem=A.numel()*4*2)))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

def custom_add_var(A:UOp, B:UOp) -> UOp:
  A,B = A.flatten(), B.flatten()
  assert A.dtype == dtypes.uint32, f"buffer dtype must be uint32, got {A.dtype}"
  threads = UOp.special(A.numel(), "lidx0")
  var = UOp.param(2, dtypes.int, vmin_vmax=(0, 10), name="var", addrspace=AddrSpace.ALU)
  insts = [
    s_load_b128(s[4:7], s[0:1]),
    s_load_b32(s[8], s[0:1], offset=0x10), # all threads load the same variable
    s_waitcnt_lgkmcnt(sdst=NULL, simm16=0),
    v_lshlrev_b32_e32(v[0], 2, v[0]), # element offset, different per thread
    global_load_b32(v[1], v[0], saddr=s[6:7]),
    s_waitcnt_vmcnt(sdst=NULL, simm16=0),
    v_add_nc_u32_e32(v[1], s[8], v[1]),
    global_store_b32(addr=v[0], data=v[1], saddr=s[4:5]),
    s_endpgm(),
  ]
  sink = UOp.sink(A.base, B.base, var, threads, arg=KernelInfo(f"custom_add_var_{A.numel()}"))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

def custom_wave_sync(A:UOp, arch:str) -> UOp:
  # 4 waves across 1024 WG — enough to saturate a SIMD with many concurrent WGs
  # s_sleep yields the SIMD so waves from different WGs interleave, causing barrier packet reordering
  threads = UOp.special(128, "lidx0")
  wg = UOp.special(1024, "gidx0")
  insts = []
  for _ in range(4):
    insts.append(s_sleep(4))
    insts += [s_barrier()] if arch == "rdna3" else [r4.s_barrier_signal(), r4.s_barrier_wait()]
    insts += [s_nop(0)]*4
  insts.append(s_endpgm())
  sink = UOp.sink(A.base, threads, wg, arg=KernelInfo("custom_wave_sync"))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

def custom_lds_sync(A:UOp, arch:str) -> UOp:
  A = A.flatten()
  num_threads = A.shape[0]
  threads = UOp.special(num_threads, "lidx0")
  wg = UOp.special(1, "gidx0")
  lds = UOp.placeholder((512,), dtypes.uint8, 0, AddrSpace.LOCAL)  # 128 * 4 bytes
  isa = r4 if arch == "rdna4" else r3
  wait_kmcnt = [isa.s_wait_kmcnt(simm16=0)] if arch == "rdna4" else [isa.s_waitcnt_lgkmcnt(sdst=NULL, simm16=0)]
  wait_dscnt = [isa.s_wait_dscnt(simm16=0)] if arch == "rdna4" else [isa.s_waitcnt_lgkmcnt(sdst=NULL, simm16=0)]
  barrier = [isa.s_barrier_signal(ssrc0=-1), isa.s_barrier_wait(simm16=-1)] if arch == "rdna4" else [isa.s_barrier()]
  global_store = [isa.global_store_b32(vaddr=v[6:7], saddr=s[0:1], vsrc=v[5])] if arch == "rdna4" \
      else [isa.global_store_b32(addr=v[6], data=v[5], saddr=s[0:1])]
  insts = [
    isa.s_load_b64(s[0:1], s[0:1], soffset=NULL),
    *wait_kmcnt,
    isa.v_lshlrev_b32_e32(v[1], 2, v[0]),
    # lds[thread_idx] = thread_idx
    isa.ds_store_b32(addr=v[1], data0=v[0]),
    *wait_dscnt,
    *barrier,
    # out[threaed_idx] = thread_idx == num_threads ? -1 : lds[thread_idx + 1]
    isa.v_add_nc_u32_e32(v[2], 4, v[1]),
    isa.v_cmp_gt_u32_e32(num_threads-1, v[0]),
    isa.ds_load_b32(vdst=v[3], addr=v[2]),
    *wait_dscnt,
    isa.v_mov_b32_e32(v[4], -1),
    isa.v_cndmask_b32_e32(v[5], v[4], v[3]),
    isa.v_lshlrev_b32_e32(v[6], 2, v[0]),
    *global_store,
    isa.s_endpgm(),
  ]
  sink = UOp.sink(A.base, lds, threads, wg, arg=KernelInfo("custom_lds_sync"))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

def custom_handwritten(A:UOp) -> UOp:
  A = A.flatten()
  threads = UOp.special(128, "lidx0")
  wg = UOp.special(1, "gidx0")
  lds = UOp.placeholder((512,), dtypes.uint8, 0, AddrSpace.LOCAL)  # 128 * 4 bytes
  pipes = {getenv("PIPE", "")} if getenv("PIPE", "") else {"SALU", "VALU", "TRANSCENDENTAL", "WMMA"}
  k = Kernel()
  # wrap in loop to filter out icache misses
  LOOP_N, UNROLL_N = 8, 5
  k.emit(r4.s_mov_b32(s[1], LOOP_N))
  k.label("loop")
  if "SALU" in pipes:
    for i in range(UNROLL_N):
      k.emit(r4.s_mov_b32(s[20+i], i))
      k.emit(r4.s_min_i32(s[30+i], i))
      k.emit(r4.s_mov_b32(s[40+i], i))
      k.emit(r4.s_mul_i32(s[14+i], s[12+i], 32))
  if "VALU" in pipes:
    for i in range(UNROLL_N):
      k.emit(r4.v_mov_b32_e32(v[20+i], i))
      k.emit(r4.v_lshlrev_b64_e32(v[30+2*i:31+2*i], 2, v[12+i:13+i]))
      k.emit(r4.v_mad_co_u64_u32(v[40+2*i:41+2*i], NULL, v[12+i], v[13+i], v[14+i:15+i]))
  if "TRANSCENDENTAL" in pipes:
    # transcendental VALU runs on the TFU, it can run regular VALU at the same time
    for i in range(UNROLL_N):
      k.emit(r4.v_mov_b32_e32(v[20+i], i))
      k.emit(r4.v_s_rcp_f32(s[20+i], s[12+i]))
      k.emit(r4.v_rcp_f32_e32(v[30+i], v[12+i]))
      k.emit(r4.v_s_exp_f32(s[30+i], s[12+i]))
  if "WMMA" in pipes:
    base = 30
    for i in range(UNROLL_N):
      a = base + i*40
      b, cd = a + 4, a + 8
      k.emit(r4.v_wmma_f32_16x16x16_f16(v[cd:cd+7], v[a:a+3], v[b:b+3], v[cd:cd+7]))
      a = base + i*40 + 16
      b, cd = a + 2, a + 4
      k.emit(r4.v_wmma_i32_16x16x16_iu8(v[cd:cd+7], v[a:a+1], v[b:b+1], v[cd:cd+7]))
  k.emit(r4.s_add_co_i32(s[1], s[1], -1))
  k.emit(r4.s_cmp_eq_i32(s[1], 0))
  k.emit(r4.s_cbranch_scc0(), target="loop")
  k.emit(r4.s_endpgm())
  insts = k.finalize()
  sink = UOp.sink(A.base, threads, wg, lds, arg=KernelInfo("custom_handwritten"))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

def custom_data_deps(A:UOp) -> UOp:
  A = A.flatten()
  threads = UOp.special(A.numel(), "lidx0")
  k = Kernel()
  k.emit(s_load_b64(s[0:1], s[0:1], soffset=NULL))
  k.emit(s_waitcnt_lgkmcnt(sdst=NULL, simm16=0))
  k.emit(v_lshlrev_b32_e32(v[0], 2, v[0]))
  k.emit(global_load_b32(v[1], v[0], saddr=s[0:1]))
  k.emit(s_waitcnt_vmcnt(sdst=NULL, simm16=0))
  k.emit(v_add_f32_e32(v[1], 1.0, v[1]))
  k.emit(global_store_b32(addr=v[0], data=v[1], saddr=s[0:1]))
  k.emit(s_endpgm())
  insts = k.finalize()
  sink = UOp.sink(A.base, threads, arg=KernelInfo("custom_data_deps"))
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=(x, dtypes.void)) for x in insts]))))

@unittest.skipUnless(Device.DEFAULT == "AMD", "requires AMD device")
class TestAsmKernel(unittest.TestCase):
  def setUp(self): self.arch = TARGET_TO_ARCH[Device["AMD"].arch]

  def test_simple(self):
    if self.arch != "rdna3": self.skipTest("only rdna3")
    a = Tensor.full((16, 16), 1.).contiguous().realize()
    a = Tensor.custom_kernel(a, fxn=custom_add_one)[0]
    linear = compile_linear(a.schedule_linear())
    est = estimate_uop(linear.src[-1])
    self.assertEqual(est.ops, a.numel())
    self.assertEqual(est.mem, a.nbytes()*2)
    run_linear(linear)
    self.assertTrue((a.numpy() == 2.).all())

  def test_variable(self):
    if self.arch != "rdna3": self.skipTest("only rdna3")
    b = Tensor.full((16, 16), 1, dtype=dtypes.uint32).contiguous().realize()
    a = Tensor.zeros_like(b).contiguous().realize()
    a = Tensor.custom_kernel(a, b, fxn=custom_add_var)[0]
    linear = a.schedule_linear()
    for i in range(4):
      run_linear(linear, var_vals={"var":i})
      self.assertTrue((a.numpy() == 1+i).all())

  def test_lds_sync(self):
    if self.arch not in ("rdna3", "rdna4"): self.skipTest("only rdna3/rdna4")
    a = Tensor.empty(128, dtype=dtypes.int32).contiguous().realize()
    a = Tensor.custom_kernel(a, fxn=functools.partial(custom_lds_sync, arch=self.arch))[0]
    a.realize()
    ref = Tensor.arange(1, 129, dtype=dtypes.int32)
    ref[127] = -1
    self.assertListEqual(a.tolist(), ref.tolist())

  def test_handwritten(self):
    if self.arch != "rdna4": self.skipTest("only tested on rdna4")
    a = Tensor.empty(1024, dtype=dtypes.int32).contiguous().realize()
    a = Tensor.custom_kernel(a, fxn=custom_handwritten)[0]
    a.realize()

  def test_data_deps(self):
    if self.arch != "rdna3": self.skipTest("only tested on rdna3")
    a = Tensor(np.full(32, 5.0, dtype=np.float32)).realize()
    a = Tensor.custom_kernel(a, fxn=custom_data_deps)[0]
    a.realize()
    self.assertTrue((a.numpy() == 6.0).all())

if __name__ == "__main__":
  unittest.main()