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https://github.com/tinygrad/tinygrad.git
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215 lines
11 KiB
Python
215 lines
11 KiB
Python
# pylint: disable=cell-var-from-loop
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# a python uops emulator
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# works to test the tensor cores, and all the uops in general
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# this is the (living) definition of uops
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from typing import Tuple, List, Optional, Any, Dict
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import pickle, base64, itertools, time, struct
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from tinygrad.dtype import DType, dtypes, ImageDType, PtrDType, truncate
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from tinygrad.helpers import all_same, getenv, flatten
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from tinygrad.device import Compiled, Compiler, Allocator
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from tinygrad.ops import BinaryOps, TernaryOps, exec_alu, UOps, UOp
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from tinygrad.renderer import Renderer
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from tinygrad.renderer.cstyle import CUDARenderer, MetalRenderer, AMDRenderer, IntelRenderer, ClangRenderer
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def _load(m, i):
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if i is None: return 0.0
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if i < 0 or i >= len(m): raise IndexError(f"load out of bounds, size is {len(m)} and access is {i}")
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return m[i]
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def load(inp, j=0):
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if len(inp) == 3: return [_load(m, x+j if x is not None else None) if gate else default for (m,x),default,gate in zip(*inp)]
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return [_load(m, x+j if x is not None else None) for m,x in inp[0]]
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def _store(m, i, v):
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if i < 0 or i >= len(m): raise IndexError(f"store out of bounds, size is {len(m)}, access is {i}, value is {v}")
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m[i] = v
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class PythonProgram:
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def __init__(self, name:str, lib:bytes):
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self.uops: List[Tuple[UOps, Optional[DType], List[int], Any]] = pickle.loads(lib)
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def __call__(self, *bufs, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1), vals:Tuple[int, ...]=(), wait=False):
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st = time.perf_counter()
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warp = list(itertools.product(*[range(x) for x in local_size[::-1]]))
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warp_size = len(warp)
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for idxs in itertools.product(*[range(x) for x in global_size[::-1]]):
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ul: Dict[int, Any] = {}
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dl: Dict[int, DType] = {}
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pbufs: List[memoryview] = list(bufs)
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pvals: List[int] = list(vals)
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i = 0
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loop_ends: Dict[int, int] = {}
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while i < len(self.uops):
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uop, dtype, idp, arg = self.uops[i]
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void_ops = {UOps.STORE, UOps.ENDRANGE, UOps.BARRIER, UOps.IF, UOps.ENDIF}
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if uop is UOps.DEFINE_ACC: idp = [idp[0]]
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inp = [ul[v] for v in idp if self.uops[v][0] not in void_ops]
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dtp = [dl[v] for v in idp if self.uops[v][0] not in void_ops]
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if getenv("TRACE"): print(i, uop, dtype, arg, inp, dtp)
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if uop is UOps.STORE:
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if len(inp) == 2: inp.append([True] * len(inp[0])) # set the gate to True
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if dtp[1].count > 1:
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for j,val in enumerate(inp[1]):
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for (m,o),v,g in zip(inp[0], val, inp[2]):
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if g: _store(m, o+j, v)
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else:
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for (m,o),v,g in zip(*inp):
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if g: _store(m, o, v)
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i += 1
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continue
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if uop is UOps.ENDRANGE:
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loop_ends[idp[0]] = i
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i = idp[0]
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continue
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if uop in (UOps.BARRIER, UOps.IF, UOps.ENDIF):
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# in the python emulator, the warp is always in sync
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i += 1
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continue
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assert dtype is not None, f"{uop} is missing a dtype"
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dl[i] = dtype
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if uop is UOps.DEFINE_GLOBAL:
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assert dtype.fmt is not None
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ul[i] = [pbufs.pop(0).cast(dtype.fmt)] * warp_size
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elif uop is UOps.DEFINE_LOCAL:
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assert dtype.fmt is not None
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lbuf = memoryview(bytearray(arg[1]*dtype.itemsize))
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ul[i] = [lbuf.cast(dtype.fmt)] * warp_size
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elif uop is UOps.DEFINE_VAR:
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ul[i] = [pvals.pop(0)] * warp_size
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elif uop is UOps.SPECIAL:
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if arg[0][0] == 'g': ul[i] = [idxs[2-int(arg[0][-1])]] * warp_size
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elif arg[0][0] == 'l': ul[i] = [x[2-int(arg[0][-1])] for x in warp]
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elif uop is UOps.CONST: ul[i] = [arg] * warp_size
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elif uop is UOps.DEFINE_ACC:
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ul[i] = [[inp[0][0][0]] * warp_size for _ in range(dtype.count)] if dtype.count > 1 else [inp[0][0]] * warp_size
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elif uop is UOps.INDEX:
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ret = []
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if isinstance(dtp[0], ImageDType):
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for m,ox,oy in zip(inp[0], inp[1][0], inp[1][1]):
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if ox < 0 or ox >= dtp[0].shape[1] or oy < 0 or oy >= dtp[0].shape[0]: ret.append((m, None))
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else: ret.append((m, ox*4 + oy*dtp[0].shape[1]*4))
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else:
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for m,o in zip(inp[0], inp[1]): ret.append((m,o))
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ul[i] = ret
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elif uop is UOps.CAST and isinstance(dtype, PtrDType):
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ul[i] = inp[0]
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elif uop is UOps.RANGE:
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if i not in ul: ul[i] = [inp[0][0]] * warp_size
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else:
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for j in range(len(ul[i])):
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ul[i][j] += 1
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if ul[i][0] == inp[1][0]:
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del ul[i]
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i = loop_ends[i] + 1
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continue
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elif uop is UOps.VECTORIZE: ul[i] = inp
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elif uop in {UOps.CAST, UOps.BITCAST}:
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assert dtp[0].fmt and dtype.fmt
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pack_format, unpack_format = str(warp_size) + dtp[0].fmt, str(warp_size) + dtype.fmt
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if uop is UOps.BITCAST: ul[i] = list(struct.unpack(unpack_format, struct.pack(pack_format, *inp[0])))
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else:
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casted = [dtypes.as_const(x, dtype) for x in inp[0]]
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if dtypes.is_int(dtype):
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overflow_adjust = 2**(dtype.itemsize*8 - 1) if not dtypes.is_unsigned(dtype) else 0
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casted = [((x + overflow_adjust) % 2**(dtype.itemsize*8) - overflow_adjust) for x in casted]
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elif dtypes.is_float(dtype):
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casted = [truncate.get(dtype, lambda dt: dt)(x) for x in casted]
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ul[i] = list(struct.unpack(unpack_format, struct.pack(unpack_format, *casted)))
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elif uop is UOps.LOAD:
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if dtype.count > 1:
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ul[i] = [load([inp[i][j] if i != 0 and dtp[i].count > 1 else inp[i] for i in range(len(inp))], j) for j in range(dtype.count)]
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else:
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ul[i] = load(inp)
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elif uop is UOps.ASSIGN:
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for j in range(len(inp[0])): inp[0][j] = inp[1][j]
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ul[i] = inp[0]
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elif uop is UOps.GEP:
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assert len(arg) == 1
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ul[i] = inp[0][arg[0]]
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elif uop is UOps.WMMA:
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# here are the models for the WMMA instruction on the different hardware
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def wmma_helper(WARP_THREADS, K, NUM_A, NUM_B, NUM_C, a_elem, b_elem, c_map):
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assert len(inp[0]) == NUM_A, f"A must have {NUM_A} elements per thread, it has {len(inp[0])}"
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assert len(inp[1]) == NUM_B, f"B must have {NUM_B} elements per thread, it has {len(inp[1])}"
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assert len(inp[2]) == NUM_C, f"C must have {NUM_C} elements per thread, it has {len(inp[2])}"
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assert len(flatten(inp[0])) == NUM_A * warp_size, f"WMMA must have {NUM_A * warp_size} total elements for A in WMMA"
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assert len(flatten(inp[1])) == NUM_B * warp_size, f"WMMA must have {NUM_B * warp_size} total elements for B in WMMA"
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assert len(flatten(inp[2])) == NUM_C * warp_size, f"WMMA must have {NUM_C * warp_size} total elements for C in WMMA"
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assert warp_size > 0 and warp_size % WARP_THREADS == 0, f"must have multiples of {WARP_THREADS} warp threads"
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out = [inp[2][elem_idx][:] for elem_idx in range(NUM_C)]
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for goff in range(0, warp_size, WARP_THREADS):
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for lane_id in range(WARP_THREADS):
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for elem_idx in range(NUM_C): # calculate new muls and add to acc
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(c_i, c_j) = c_map(lane_id, elem_idx)
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out[elem_idx][goff+lane_id] += sum(a_elem(inp[0], _k, c_j, goff) * b_elem(inp[1], c_i, _k, goff) for _k in range(K))
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return out
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# TODO: refactor these to a shared TensorCoreLayout in kernel.py
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if arg[4] == "METAL":
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# A (2 elements on 32 threads): row major
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def a_b_elem(x, i, j, goff): return x[(i%2)][goff+(i//2)%2+(j%4)*2+(i//4)*8+(j//4)*16]
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# (i, j), C, D (2 elements on 32 threads): row major same as A/B
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def c_map(lane, elem): return (elem + ((lane%2)*2) + ((lane//8)%2)*4, ((lane//2)%4) + (lane//16)*4)
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ul[i] = wmma_helper(32, 8, 2, 2, 2, a_b_elem, a_b_elem, c_map)
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elif arg[4] == "AMD":
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# A (16 elements on 32 threads): col major, lane 16-32 == lane 0-15
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def a_elem(x, i, j, goff):
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assert x[i][goff+j] == x[i][goff+j+16], "warp elements not duplicated properly across lanes"
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return x[i][goff+j]
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# B (16 elements on 32 threads): row major, lane 16-32 == lane 0-15
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def b_elem(x, i, j, goff): return a_elem(x, j, i, goff) # pylint: disable=arguments-out-of-order
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def c_map(lane, elem): return (lane%16, lane//16+elem*2) # (i, j), C, D (8 elements on 32 threads): row major
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ul[i] = wmma_helper(32, 16, 16, 16, 8, a_elem, b_elem, c_map)
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elif arg[4] == "CUDA":
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# A (8 elements on 32 threads)
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def a_elem(x, i, j, goff): return x[(i%2)+(j//8)*2+(i//8)*4][goff+((i//2)%4)+(j%8)*4]
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# B (4 elements on 32 threads)
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def b_elem(x, i, j, goff): return x[(j%2)+(j//8)*2][goff+(j//2)%4+(i)*4]
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# (i, j), C, D (4 elements on 32 threads)
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def c_map(lane, elem): return ((elem%2)+(lane%4)*2, (lane//4)+(elem//2)*8)
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ul[i] = wmma_helper(32, 16, 8, 4, 4, a_elem, b_elem, c_map)
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elif arg[4] == "INTEL":
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# A (16 elements on 8 threads)
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def a_elem(x, i, j, goff): return x[i%2+j*2][goff+i//2]
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# B (16 elements on 8 threads)
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def b_elem(x, i, j, goff): return x[j][goff+i]
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# C, D (8 elements on 8 threads)
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def c_map(lane, elem): return (lane, elem)
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ul[i] = wmma_helper(8, 16, 16, 16, 8, a_elem, b_elem, c_map)
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elif arg[4] == "CLANG":
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def elem(x, i, j, _): return x[i+j][0]
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def c_map(_, elem): return (elem%16, elem//16)
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ul[i] = wmma_helper(1, 1, 16, 16, 256, elem, elem, c_map)
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else: raise NotImplementedError(f"unimplemented tensor core {arg}")
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elif uop is UOps.ALU:
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assert all_same([len(x) for x in inp]), f"{[len(x) for x in inp]} doesn't match on {arg}"
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assert all_same([dtype] + dtp) or arg in {BinaryOps.CMPNE, BinaryOps.CMPLT, TernaryOps.WHERE}, f"dtype mismatch on {arg}"
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ul[i] = [exec_alu(arg, dtype, p) for p in zip(*inp)]
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assert i in ul, (uop, dtype, idp, arg)
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i += 1
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return time.perf_counter() - st
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class PythonRenderer(Renderer):
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device = "PYTHON"
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def __init__(self):
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if getenv("EMULATE_METAL"): self.device, self.tensor_cores = "METAL", MetalRenderer.tensor_cores
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if getenv("EMULATE_AMD"): self.device, self.tensor_cores = "AMD", AMDRenderer.tensor_cores
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if getenv("EMULATE_CUDA"): self.device, self.tensor_cores = "CUDA", CUDARenderer.tensor_cores
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if getenv("EMULATE_INTEL"): self.device, self.suffix, self.tensor_cores = "INTEL", "INTEL", IntelRenderer.tensor_cores
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if getenv("EMULATE_AMX"): self.device, self.tensor_cores = "CLANG", ClangRenderer.tensor_cores
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def render(self, name:str, uops:List[UOp]) -> str:
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lops = [(u.op, u.dtype, [uops.index(v) for v in u.src], u.arg) for u in uops]
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return base64.b64encode(pickle.dumps(lops)).decode()
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class PythonCompiler(Compiler):
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def compile(self, src:str) -> bytes: return base64.b64decode(src)
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class PythonAllocator(Allocator):
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def _alloc(self, size, options): return memoryview(bytearray(size))
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def copyin(self, dest, src:memoryview): dest[:] = src
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def copyout(self, dest:memoryview, src): dest[:] = src
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class PythonDevice(Compiled):
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def __init__(self, device:str):
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super().__init__(device, PythonAllocator(), PythonRenderer(), PythonCompiler(), PythonProgram)
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