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cleanup deps, no pyyaml, pillow to testing (#2231)

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George Hotz
2023-11-07 10:32:23 -08:00
committed by GitHub
parent ae5d1407ee
commit a48ccdb359
2 changed files with 5 additions and 5 deletions
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setup.py
+2 -2
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@@ -19,7 +19,7 @@ setup(name='tinygrad',
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"
],
install_requires=["numpy", "requests", "pillow", "tqdm", "networkx", "pyopencl", "PyYAML",
install_requires=["numpy", "requests", "tqdm", "networkx", "pyopencl",
"pyobjc-framework-Metal; platform_system=='Darwin'",
"pyobjc-framework-Cocoa; platform_system=='Darwin'",
"pyobjc-framework-libdispatch; platform_system=='Darwin'"],
@@ -37,11 +37,11 @@ setup(name='tinygrad',
"typing-extensions",
"pre-commit",
"ruff",
"types-PyYAML",
"types-tqdm",
],
'testing': [
"torch",
"pillow",
"pytest",
"pytest-xdist",
"onnx==1.14.1",
tinygrad/runtime/ops_torch.py
+3 -3
View File
@@ -18,8 +18,8 @@ def as_strided(x, arg):
torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
# TODO: torch.tensor should work here
#BufferOps.CONST: lambda val, dtype: torch.tensor(val, dtype=inverse_type_map[dtype]),
BufferOps.CONST: lambda val, dtype: torch.from_numpy(np.array(val, dtype=dtype.np)),
#BufferOps.CONST: lambda val, dtype: torch.tensor(val, device=device, dtype=inverse_type_map[dtype]),
BufferOps.CONST: lambda val, dtype: torch.from_numpy(np.array(val, dtype=dtype.np)).requires_grad_(False).to(device),
UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,
UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),
BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)), BinaryOps.SUB: lambda x,y: torch.logical_xor(x, y) if y.dtype is torch.bool else torch.sub(x, y),
@@ -32,7 +32,7 @@ torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
}}
class RawTorchBuffer(RawBuffer):
def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None): super().__init__(size, dtype, buf if buf is not None else torch.empty([size], dtype=inverse_type_map[dtype]))
def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None): super().__init__(size, dtype, buf if buf is not None else torch.empty([size], device=device, dtype=inverse_type_map[dtype]))
@classmethod
def fromCPU(cls, x):
buf = torch.from_numpy(x if all(s>=0 for s in x.strides) else x.copy()).requires_grad_(False).to(device)