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configure derandomizing CI tests (#4793)

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qazal
2024-05-31 22:06:58 +08:00
committed by GitHub
parent ed0a740fe4
commit 637f482588
6 changed files with 10 additions and 10 deletions
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2
test/test_dtype.py
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@@ -8,7 +8,7 @@ from tinygrad import Device, Tensor, dtypes
from hypothesis import given, settings, strategies as strat
from test.helpers import is_dtype_supported, rand_for_dtype
settings.register_profile("my_profile", max_examples=200, deadline=None)
settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
settings.load_profile("my_profile")
core_dtypes = list(DTYPES_DICT.values())

2
test/test_dtype_alu.py
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@@ -11,7 +11,7 @@ from tinygrad.engine.realize import run_schedule
from tinygrad.ops import UnaryOps
from test.helpers import is_dtype_supported
settings.register_profile("my_profile", max_examples=200, deadline=None)
settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
settings.load_profile("my_profile")
print(settings.default)

4
test/test_fuzz_shape_ops.py
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@@ -7,11 +7,11 @@ from hypothesis.extra import numpy as stn
import numpy as np
import torch
from tinygrad import Tensor, Device
from tinygrad.helpers import CI
from tinygrad.helpers import CI, getenv
settings.register_profile(__file__, settings.default,
max_examples=100 if CI else 250, deadline=None)
max_examples=100 if CI else 250, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
# torch wraparound for large numbers

4
test/test_multitensor.py
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@@ -2,7 +2,7 @@ import unittest, functools, random
from typing import List
from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit, dtypes
from tinygrad.ops import LoadOps, ReduceOps
from tinygrad.helpers import CI, prod, Context
from tinygrad.helpers import CI, getenv, prod, Context
from tinygrad.nn.state import get_parameters, get_state_dict
from tinygrad.engine.schedule import create_schedule
from tinygrad.engine.realize import lower_schedule, BufferCopy, CompiledRunner
@@ -12,7 +12,7 @@ import numpy as np
from hypothesis import given, strategies as strat, settings
from test.helpers import is_dtype_supported
settings.register_profile("my_profile", max_examples=200, deadline=None)
settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
settings.load_profile("my_profile")
d_zero = f"{Device.DEFAULT}:0"

4
test/test_randomness.py
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@@ -4,11 +4,11 @@ from functools import partial
import numpy as np
import torch
from tinygrad import nn, dtypes, Tensor, Device
from tinygrad.helpers import THREEFRY
from tinygrad.helpers import THREEFRY, getenv
from test.helpers import is_dtype_supported
from hypothesis import given, settings, strategies as strat
settings.register_profile("my_profile", max_examples=200, deadline=None)
settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
settings.load_profile("my_profile")
# https://gist.github.com/devries/11405101

4
test/test_tensor.py
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@@ -3,11 +3,11 @@ import torch
import unittest, copy
import mmap
from tinygrad import Tensor, Device, dtypes
from tinygrad.helpers import temp, CI
from tinygrad.helpers import getenv, temp, CI
from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
from hypothesis import given, settings, strategies as strat
settings.register_profile("my_profile", max_examples=200, deadline=None)
settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
settings.load_profile("my_profile")
x_init = np.random.randn(1,3).astype(np.float32)