mirror of https://github.com/commaai/tinygrad.git
75 lines
3.4 KiB
Python
75 lines
3.4 KiB
Python
import sys, time
|
|
from typing import Callable, Optional, Tuple, TypeVar
|
|
import numpy as np
|
|
from test.external.process_replay.helpers import print_diff
|
|
from tinygrad import Tensor, Device, dtypes
|
|
from tinygrad.ops import UOp, UOps
|
|
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 Context, CI, OSX, getenv
|
|
from tinygrad.shape.symbolic import sint
|
|
|
|
def derandomize_model(model):
|
|
with Context(GRAPH=0):
|
|
for p in get_parameters(model):
|
|
p.lazydata = Tensor.empty(p.shape, device=p.device, dtype=p.dtype).lazydata
|
|
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, 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 is_dtype_supported(dtype: DType, device: str = Device.DEFAULT):
|
|
if dtype == dtypes.pyint and device != "PYTHON": return False
|
|
if dtype == dtypes.bfloat16:
|
|
# NOTE: this requires bf16 buffer support
|
|
return device in {"AMD"} or (device in {"CUDA", "NV"} and not CI and not getenv("PTX"))
|
|
if device in ["WEBGPU", "WEBGL"]: return dtype in [dtypes.float, dtypes.int32, dtypes.uint32]
|
|
# for CI GPU and OSX, cl_khr_fp16 isn't supported
|
|
# for CI LLVM, it segfaults because it can't link to the casting function
|
|
# CI CUDA architecture is sm_35 but we need at least sm_70 to run fp16 ALUs
|
|
# PYTHON supports half memoryview in 3.12+ https://github.com/python/cpython/issues/90751
|
|
if dtype == dtypes.half:
|
|
if device == "GPU": return not CI and not OSX
|
|
if device in ["LLVM", "CUDA", "NV"]: return not CI
|
|
if device == "PYTHON": return sys.version_info >= (3, 12)
|
|
if dtype == dtypes.float64: return device != "METAL" and not (OSX and device == "GPU")
|
|
return True
|
|
|
|
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 assert_equiv_uops(u1:UOp, u2:UOp) -> None:
|
|
if u1.key != u2.key:
|
|
print_diff(u1, u2)
|
|
raise AssertionError("uops aren't equal.")
|
|
|
|
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(),)
|
|
return UOp(UOps.VALID, dtypes.bool, st_src).where(UOp.const(dtype, val), UOp.const(dtype, 0))
|
|
|
|
T = TypeVar("T")
|
|
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
|