mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
ba17786068
* new lines that exist in codegen/ops * update tests * update sops.gz (13071 -> 13070 asts) * fix viz too * remove that TODO * diff pruning * mask assert + device * work * diff pruning * re: fix viz too --------- Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
62 lines
2.8 KiB
Python
62 lines
2.8 KiB
Python
import time
|
|
from typing import Callable, Optional, Tuple
|
|
import numpy as np
|
|
from tinygrad import Tensor, dtypes
|
|
from tinygrad.ops import UOp, Ops, sint
|
|
from tinygrad.shape.shapetracker import ShapeTracker
|
|
from tinygrad.tensor import _to_np_dtype
|
|
from tinygrad.engine.realize import Runner
|
|
from tinygrad.dtype import ConstType, DType
|
|
from tinygrad.nn.state import get_parameters
|
|
from tinygrad.helpers import T, unwrap
|
|
from tinygrad.codegen.linearize import linearize_uop
|
|
from tinygrad.codegen.rewriter import full_graph_rewrite
|
|
from tinygrad.runtime.ops_python import PythonProgram, PythonRenderer, PythonCompiler, PythonAllocator
|
|
|
|
def derandomize_model(model):
|
|
for p in get_parameters(model):
|
|
p.replace(Tensor.empty(p.shape, device=p.device, dtype=p.dtype))
|
|
p.realize()
|
|
|
|
def assert_jit_cache_len(fxn, expected_len):
|
|
if not fxn.jit_cache:
|
|
assert expected_len == 0, expected_len
|
|
return
|
|
# until we have a better way of typing the prg in ExecItem
|
|
if issubclass(type(fxn.jit_cache[0].prg), Runner) and not type(fxn.jit_cache[0].prg).__name__.endswith('Graph'):
|
|
assert len(fxn.jit_cache) == expected_len, f"expected {expected_len}, got {len(fxn.jit_cache)}"
|
|
else:
|
|
assert len(fxn.jit_cache) == 1, len(fxn.jit_cache)
|
|
# until we have a better way of typing the prg in ExecItem
|
|
assert type(fxn.jit_cache[0].prg).__name__.endswith('Graph')
|
|
assert len(fxn.jit_cache[0].prg.jit_cache) == expected_len
|
|
|
|
def rand_for_dtype(dt:DType, size:int):
|
|
if dtypes.is_unsigned(dt):
|
|
return np.random.randint(0, 100, size=size, dtype=_to_np_dtype(dt))
|
|
elif dtypes.is_int(dt):
|
|
return np.random.randint(-100, 100, size=size, dtype=_to_np_dtype(dt))
|
|
elif dt == dtypes.bool:
|
|
return np.random.choice([True, False], size=size)
|
|
return np.random.uniform(-10, 10, size=size).astype(_to_np_dtype(dt))
|
|
|
|
def ast_const(dtype:DType, val:ConstType, shape:Tuple[sint, ...]=(), st:Optional[ShapeTracker]=None, st_src:Optional[Tuple[UOp]]=None) -> UOp:
|
|
if st_src is None:
|
|
st_src = (st.to_uop() if st is not None else ShapeTracker.from_shape(()).reshape((1,)*len(shape)).expand(shape).to_uop(),)
|
|
st = unwrap(st_src[0].st)
|
|
if all(v.mask is None for v in st.views): return UOp.const(dtype, val).replace(src=(st.to_uop(),))
|
|
return UOp.const(dtype, val).valid(st)
|
|
|
|
def timeit(fxn:Callable[..., T], *args, **kwargs) -> Tuple[T, float]:
|
|
st = time.perf_counter_ns()
|
|
ret = fxn(*args, **kwargs)
|
|
return ret, (time.perf_counter_ns()-st)*1e-6
|
|
|
|
def eval_uop(uop:UOp):
|
|
g = UOp(Ops.DEFINE_GLOBAL, uop.dtype.ptr(), arg=0, src=())
|
|
rw = full_graph_rewrite(UOp.store(g.index(UOp.const(dtypes.int, 0)), uop).sink(), PythonRenderer)
|
|
prog = PythonProgram("run", PythonCompiler().compile(PythonRenderer().render("run", linearize_uop(rw))))
|
|
buf = PythonAllocator().alloc(uop.dtype.itemsize)
|
|
prog(buf)
|
|
return buf.cast(uop.dtype.fmt).tolist()[0]
|