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72d617a37d
running `GPU=1 python -m pytest -rA test/test_dtype.py::TestHalfDtype::test_casts_from` on mac would fail.
41 lines
1.9 KiB
Python
41 lines
1.9 KiB
Python
import sys
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.device import JITRunner
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from tinygrad.dtype import DType
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from tinygrad.nn.state import get_parameters
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from tinygrad.helpers import Context, CI, OSX, getenv
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def derandomize_model(model):
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with Context(GRAPH=0):
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for p in get_parameters(model):
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p.lazydata = Tensor.empty(p.shape, device=p.device, dtype=p.dtype).lazydata
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p.realize()
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def assert_jit_cache_len(fxn, expected_len):
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assert len(fxn.jit_cache) > 0
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# until we have a better way of typing the prg in JitItem
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if issubclass(type(fxn.jit_cache[0].prg), JITRunner) and not type(fxn.jit_cache[0].prg).__name__.endswith('Graph'):
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assert len(fxn.jit_cache) == expected_len
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else:
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assert len(fxn.jit_cache) == 1
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# until we have a better way of typing the prg in JitItem
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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 is_dtype_supported(dtype: DType, device: str = Device.DEFAULT):
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if dtype == dtypes.bfloat16:
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# NOTE: this requires bf16 buffer support
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return device in {"RHIP", "HSA"} or (device == "CUDA" and not CI)
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if device in ["WEBGPU", "WEBGL"]: return dtype in [dtypes.float, dtypes.int32, dtypes.uint32]
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if device == "CUDA" and getenv("PTX") and dtype in (dtypes.int8, dtypes.uint8): return False
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# for CI GPU and OSX, cl_khr_fp16 isn't supported
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# for CI LLVM, it segfaults because it can't link to the casting function
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# CUDACPU architecture is sm_35 but we need at least sm_70 to run fp16 ALUs
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# PYTHON supports half memoryview in 3.12+ https://github.com/python/cpython/issues/90751
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if dtype == dtypes.half:
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if device == "GPU": return not CI and not OSX
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if device in ["LLVM", "CUDA"]: return not CI
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if device == "PYTHON": return sys.version_info >= (3, 12)
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if dtype == dtypes.float64: return device != "METAL" and not (OSX and device == "GPU")
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return True
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