Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

dependencies: Update to numpy<1.24.4 #2693

Merged
merged 5 commits into from
Jun 9, 2023
Merged

dependencies: Update to numpy<1.24.4 #2693

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
da3c4fa
utilities: Handle numpy 1.24 rugged arrays
jvesely Jun 6, 2023
51ff068
tests/save_state_before_simulations: Extract mechanism values to form…
jvesely Jun 6, 2023
f4d1bdd
requirements: update numpy requirement
dependabot[bot] May 19, 2023
50fc46f
llvm: Represent integer types as unsigned in ctype
jvesely Jun 7, 2023
3eeea4b
Merge remote-tracking branch 'origin/devel' into numpy
jvesely Jun 8, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 8 additions & 0 deletions psyneulink/core/globals/utilities.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1022,6 +1022,14 @@ def safe_create_np_array(value):
return np.asarray(value)
except np.VisibleDeprecationWarning:
return np.asarray(value, dtype=object)
except ValueError as e:
# numpy 1.24 removed the above deprecation and raises
# ValueError instead. Note that the below call can still
# raise other ValueErrors
if 'The requested array has an inhomogeneous shape' in str(e):
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Was this getting sent as an np.VisibleDeprecationWarning before? It seems to be what's referenced in https://numpy.org/doc/stable/release/1.24.0-notes.html for ragged arrays, but I don't remember seeing this message before, just the "Creating an ndarray from ragged nested sequences..." one.

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Though I see now I referenced this https://numpy.org/neps/nep-0034-infer-dtype-is-object.html in the comment. Maybe I just didn't take note of the message.

Copy link
Collaborator Author

@jvesely jvesely Jun 9, 2023 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think the wording of the warning was different. the numpy commit (numpy/numpy@189eb6e, clumsily referenced in the commit that adds these lines)
shows that the removed warnings were: "Creating an ndarray from ragged nested sequences ... " and similar. while the remaining error messages are

"setting an array element with a sequence. The "
"requested array has an inhomogeneous shape after "
"%d dimensions. The detected shape was "

return np.asarray(value, dtype=object)
raise

except ValueError as e:
msg = str(e)
if 'cannot guess the desired dtype from the input' in msg:
Expand Down
8 changes: 4 additions & 4 deletions psyneulink/core/llvm/builder_context.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -636,13 +636,13 @@ def _convert_llvm_ir_to_ctype(t: ir.Type):
if t.width == 1:
return ctypes.c_bool
elif t.width == 8:
return ctypes.c_int8
return ctypes.c_uint8
elif t.width == 16:
return ctypes.c_int16
return ctypes.c_uint16
elif t.width == 32:
return ctypes.c_int32
return ctypes.c_uint32
elif t.width == 64:
return ctypes.c_int64
return ctypes.c_uint64
else:
assert False, "Unknown integer type: {}".format(type_t)
elif type_t is ir.DoubleType:
Expand Down
2 changes: 1 addition & 1 deletion requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ llvmlite<0.41
matplotlib<3.7.2
modeci_mdf<0.5, >=0.3.4; (platform_machine == 'AMD64' or platform_machine == 'x86_64') and platform_python_implementation == 'CPython' and implementation_name == 'cpython'
networkx<3.2
numpy>=1.19.0, <1.23.6
numpy>=1.19.0, <1.24.4
optuna<3.3.0
packaging<24.0
pandas<2.0.3
Expand Down
29 changes: 16 additions & 13 deletions tests/composition/test_composition.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6773,10 +6773,12 @@ def test_save_state_before_simulations(self):

comp.run(inputs={A: [[1.0], [1.0]]})

# All mechanism values are 2D but B has two elements,
# extract element 0 out of each.
run_1_values = [
A.parameters.value.get(comp),
A.parameters.value.get(comp)[0],
B.parameters.value.get(comp)[0],
C.parameters.value.get(comp)
C.parameters.value.get(comp)[0]
]

# "Save state" code from EVCaux
Expand All @@ -6802,25 +6804,26 @@ def test_save_state_before_simulations(self):
# Allow values to continue accumulating so that we can set them back to the saved state
comp.run(inputs={A: [[1.0], [1.0]]})

run_2_values = [A.parameters.value.get(comp),
# All mechanism values are 2D but B has two elements,
# extract element 0 out of each.
run_2_values = [A.parameters.value.get(comp)[0],
B.parameters.value.get(comp)[0],
C.parameters.value.get(comp)]
C.parameters.value.get(comp)[0]]

comp.run(
inputs={A: [[1.0], [1.0]]},
reset_stateful_functions_to=reinitialization_values
)

run_3_values = [A.parameters.value.get(comp),
# All mechanism values are 2D but B has two elements,
# extract element 0 out of each.
run_3_values = [A.parameters.value.get(comp)[0],
B.parameters.value.get(comp)[0],
C.parameters.value.get(comp)]

np.testing.assert_allclose(np.asfarray(run_2_values),
np.asfarray(run_3_values))
np.testing.assert_allclose(np.asfarray(run_1_values),
[np.array([0.36]), np.array([0.056]), np.array([0.056])])
np.testing.assert_allclose(np.asfarray(run_2_values),
[np.array([0.5904]), np.array([0.16384]), np.array([0.16384])])
C.parameters.value.get(comp)[0]]

np.testing.assert_allclose(run_2_values, run_3_values)
np.testing.assert_allclose(np.asfarray(run_1_values), [[0.36], [0.056], [0.056]])
np.testing.assert_allclose(np.asfarray(run_2_values), [[0.5904], [0.16384], [0.16384]])


class TestNodeRoles:
Expand Down
4 changes: 2 additions & 2 deletions tests/llvm/test_builtins_mt_random.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def f():
init_fun(state, SEED)

gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_int32')
out = ctypes.c_longlong()
out = ctypes.c_ulonglong()
def f():
gen_fun(state, out)
return out.value
Expand All @@ -39,7 +39,7 @@ def f():
init_fun.cuda_call(gpu_state, np.int32(SEED))

gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_int32')
out = np.asarray([0], dtype=np.int64)
out = np.asarray([0], dtype=np.uint64)
gpu_out = pnlvm.jit_engine.pycuda.driver.Out(out)
def f():
gen_fun.cuda_call(gpu_state, gpu_out)
Expand Down
8 changes: 4 additions & 4 deletions tests/llvm/test_builtins_philox_random.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
def test_random_int64(benchmark, mode, seed, expected):
res = []
if mode == 'numpy':
state = np.random.Philox([np.uint64(seed)])
state = np.random.Philox([np.int64(seed).astype(np.uint64)])
prng = np.random.Generator(state)
def f():
# Get uint range [0, MAX] to avoid any intermediate caching of random bits
Expand All @@ -31,7 +31,7 @@ def f():
init_fun(state, seed)

gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_philox_rand_int64')
out = ctypes.c_longlong()
out = ctypes.c_ulonglong()
def f():
gen_fun(state, out)
return np.uint64(out.value)
Expand Down Expand Up @@ -75,10 +75,10 @@ def f():
init_fun(state, SEED)

gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_philox_rand_int32')
out = ctypes.c_int()
out = ctypes.c_uint()
def f():
gen_fun(state, out)
return np.uint32(out.value)
return out.value
elif mode == 'PTX':
init_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_philox_rand_init')
state_size = ctypes.sizeof(init_fun.byref_arg_types[0])
Expand Down
8 changes: 4 additions & 4 deletions tests/llvm/test_custom_func.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,10 +8,10 @@
@pytest.mark.llvm
@pytest.mark.parametrize('mode', ['CPU',
pytest.param('PTX', marks=pytest.mark.cuda)])
@pytest.mark.parametrize('val', [np.int8(0x7e),
np.int16(0x7eec),
np.int32(0x7eedbeee),
np.int64(0x7eedcafedeadbeee)
@pytest.mark.parametrize('val', [np.uint8(0x7e),
np.uint16(0x7eec),
np.uint32(0x7eedbeee),
np.uint64(0x7eedcafedeadbeee)
], ids=lambda x: str(x.dtype))
def test_integer_broadcast(mode, val):
custom_name = None
Expand Down
4 changes: 2 additions & 2 deletions tests/llvm/test_helpers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -212,11 +212,11 @@ def test_helper_all_close(mode, var1, var2, atol, rtol):
ct_ty = ctypes.POINTER(bin_f.byref_arg_types[0])
ct_vec1 = vec1.ctypes.data_as(ct_ty)
ct_vec2 = vec2.ctypes.data_as(ct_ty)
res = ctypes.c_int32()
res = ctypes.c_uint32()

bin_f(ct_vec1, ct_vec2, ctypes.byref(res))
else:
res = np.array([5], dtype=np.int32)
res = np.array([5], dtype=np.uint32)
bin_f.cuda_wrap_call(vec1, vec2, res)
res = res[0]

Expand Down