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7bc4864bc4
* Make `dev` a property of `Allocator` (this is a prereq refactor for #10285) At least `BufferXfer.copy` accesses it assuming it's always present, currently most devices just add this property on their own repeating the same code over and over again. This is also a bit footguny, see `RemoteAllocator` that named this property `device` instead of `dev`, i could obviously just change that in one place but doing it globally seems like a better solution (and it reduces code duplication too). `MallocAllocator` is a bit special, but passing `None` works just fine. * typing * ignore type instead of cast
72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
import time, struct
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from typing import Any, Callable, Optional
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import numpy as np
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from tinygrad import Tensor, dtypes, Device
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from tinygrad.ops import UOp, Ops, sint
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.tensor import _to_np_dtype
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from tinygrad.engine.realize import Runner
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from tinygrad.dtype import ConstType, DType
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from tinygrad.nn.state import get_parameters
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from tinygrad.helpers import T, unwrap, CI
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from tinygrad.codegen import full_rewrite
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from tinygrad.runtime.ops_python import PythonProgram, PythonRenderer, PythonCompiler
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def derandomize_model(model):
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for p in get_parameters(model):
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p.replace(Tensor.empty(p.shape, device=p.device, dtype=p.dtype))
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p.realize()
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def assert_jit_cache_len(fxn, expected_len):
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if not fxn.jit_cache:
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assert expected_len == 0, expected_len
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return
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# until we have a better way of typing the prg in ExecItem
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if issubclass(type(fxn.jit_cache[0].prg), Runner) and not type(fxn.jit_cache[0].prg).__name__.endswith('Graph'):
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assert len(fxn.jit_cache) == expected_len, f"expected {expected_len}, got {len(fxn.jit_cache)}"
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else:
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assert len(fxn.jit_cache) == 1, len(fxn.jit_cache)
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# until we have a better way of typing the prg in ExecItem
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assert type(fxn.jit_cache[0].prg).__name__.endswith('Graph')
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assert len(fxn.jit_cache[0].prg.jit_cache) == expected_len
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def rand_for_dtype(dt:DType, size:int):
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if dtypes.is_unsigned(dt):
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return np.random.randint(0, 100, size=size, dtype=_to_np_dtype(dt))
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elif dtypes.is_int(dt):
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return np.random.randint(-100, 100, size=size, dtype=_to_np_dtype(dt))
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elif dt == dtypes.bool:
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return np.random.choice([True, False], size=size)
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return np.random.uniform(-10, 10, size=size).astype(_to_np_dtype(dt))
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def ast_const(dtype:DType, val:ConstType, shape:tuple[sint, ...]=(), st:Optional[ShapeTracker]=None, st_src:Optional[tuple[UOp]]=None) -> UOp:
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if st_src is None:
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st_src = (st.to_uop() if st is not None else ShapeTracker.from_shape(()).reshape((1,)*len(shape)).expand(shape).to_uop(),)
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st = unwrap(st_src[0].st)
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if all(v.mask is None for v in st.views): return UOp.const(dtype, val).replace(src=(st.to_uop(),))
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return UOp.const(dtype, val).valid(st)
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def timeit(fxn:Callable[..., T], *args, **kwargs) -> tuple[T, float]:
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st = time.perf_counter_ns()
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ret = fxn(*args, **kwargs)
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return ret, (time.perf_counter_ns()-st)*1e-6
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def eval_uop(uop:UOp, inputs:list[tuple[DType, list[Any]]]|None=None):
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allocator = Device['PYTHON'].allocator
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bufs = []
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for buf_dt, data in inputs or []:
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bufs.append(buf:=allocator.alloc(len(data) * buf_dt.itemsize))
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allocator._copyin(buf, memoryview(struct.pack(str(len(data)) + buf_dt.fmt, *data)))
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g = UOp(Ops.DEFINE_GLOBAL, uop.dtype.ptr(), arg=0, src=())
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lst = full_rewrite(UOp.store(g.index(UOp.const(dtypes.int, 0)), uop).sink(), PythonRenderer)
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prog = PythonProgram("run", PythonCompiler().compile(PythonRenderer().render(lst)))
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prog(out_buf:=allocator.alloc(uop.dtype.itemsize), *bufs)
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return out_buf.cast(uop.dtype.fmt).tolist()[0]
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def not_support_multi_device():
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# GPU and CUDA don't support multi device if in CI
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return CI and REAL_DEV in ("GPU", "CUDA")
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# NOTE: This will open REMOTE if it's the default device
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REAL_DEV = (Device.DEFAULT if Device.DEFAULT != "REMOTE" else Device['REMOTE'].properties.real_device)
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