[numpy] torch.lgamma: promote integer inputs to float #50140

kshitij12345 · 2021-01-06T13:15:32Z

Reference: #42515

facebook-github-bot · 2021-01-06T13:15:56Z

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kshitij12345 · 2021-01-06T13:34:07Z

torch/testing/_internal/common_methods_invocations.py

+                       dtypesIfCUDA=all_types_and(torch.bool, torch.half),
+                       skips=(
+                           SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
+                                    dtypes=[torch.bfloat16]),


Skipping Bfloat16

>>> import torch >>> torch.tensor(500.0, dtype=torch.bfloat16) tensor(500., dtype=torch.bfloat16) >>> t = torch.tensor(500.0, dtype=torch.bfloat16) >>> torch.lgamma(t) tensor(2608., dtype=torch.bfloat16) >>> torch.lgamma(t.to(torch.float32)) tensor(2605.1160)

Good skip. This again highlights that we should break-up test_reference_numerics into different value ranges (like small values, large values, extremal values, for example).

It would be helpful if ranges can be specified per dtype or for group of dtype (maybe something similar to precision-override), because we don't want to decrease the range of all dtypes just to be able to support one dtype with smaller range.

kshitij12345 · 2021-01-07T14:28:49Z

Skipping on Windows
https://app.circleci.com/pipelines/github/pytorch/pytorch/257275/workflows/c7284761-c821-404b-847d-2f893f73c3d6/jobs/9986296

codecov · 2021-01-11T16:18:20Z

Codecov Report

Merging #50140 (341664f) into master (8690819) will decrease coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master   #50140      +/-   ##
==========================================
- Coverage   80.91%   80.91%   -0.01%     
==========================================
  Files        1926     1926              
  Lines      210014   210023       +9     
==========================================
+ Hits       169943   169945       +2     
- Misses      40071    40078       +7

mruberry · 2021-01-12T12:17:35Z

torch/testing/_internal/common_methods_invocations.py

@@ -1260,6 +1260,28 @@ def reference_sigmoid(x):
            return (1 / (1 + np.exp(-x)))
        return scipy.special.expit(x)

+    def reference_lgamma(x):
+        # scipy.special.gammaln returns `-inf` when input is `-inf`.


This is a bug in SciPy.

scipy.special.gammaln is documented as equivalent to math.lgamma, but math.lgamma returns inf when given -inf.

Would you file an issue with SciPy, @kshitij12345?

cc @rgommers

mruberry · 2021-01-12T12:18:00Z

torch/testing/_internal/common_methods_invocations.py

@@ -1260,6 +1260,28 @@ def reference_sigmoid(x):
            return (1 / (1 + np.exp(-x)))
        return scipy.special.expit(x)

+    def reference_lgamma(x):
+        # scipy.special.gammaln returns `-inf` when input is `-inf`.
+        # While Pytorch, C and C++, all return `inf` when input is `-inf`.


This is a good comment. Would you extend it to include Python's math.lgamma, too?

mruberry · 2021-01-12T12:19:41Z

torch/testing/_internal/common_methods_invocations.py

+        # https://en.cppreference.com/w/c/numeric/math/lgamma
+
+        # To handle the above discrepancy,
+        # we never pass `-inf` to scipy.special.gammaln.


This workaround is more clever than this comment suggests. It's not only that this never passes -inf to scipy.special.gammaln, but that it replaces -inf with inf so the mapping -inf->inf occurs as expected.

Should we phrase it as,

# To handle the above discrepancy, # we never pass `-inf` to scipy.special.gammaln and replace them with `inf`.

That's pretty good, but it requires the reader make the connection about how -inf is mapped to inf.

"this replaces -inf with inf so values that were originally -inf map to inf as expected"?

I think it is very verbose but atleast clear. Will use that thanks!

mruberry · 2021-01-12T12:23:38Z

torch/testing/_internal/common_methods_invocations.py

+                           # Reference: https://github.com/pytorch/pytorch/pull/50140#issuecomment-756150214
+                           SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
+                                    dtypes=[torch.float32, torch.float64], active_if=IS_WINDOWS),
+                           # Backward of `lgamma` uses `digamma` but `digamma`


This is interesting. From the perspective of this PR this skip is correct, but I thought test_variant_consistency_jit wrapped the backward call in a try/except block to catch these errors?

I have verified. I don't see any try/execpt block in the failing test.

Stack Trace

====================================================================== ERROR: test_variant_consistency_jit_lgamma_cpu_bfloat16 (__main__.TestCommonCPU) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/kshiteej/.conda/envs/PytorchENV/lib/python3.7/site-packages/torch/testing/_internal/common_device_type.py", line 284, in instantiated_test raise rte File "/home/kshiteej/.conda/envs/PytorchENV/lib/python3.7/site-packages/torch/testing/_internal/common_device_type.py", line 279, in instantiated_test result = test_fn(self, *args) File "/home/kshiteej/.conda/envs/PytorchENV/lib/python3.7/site-packages/torch/testing/_internal/common_device_type.py", line 253, in test_wrapper return test(*args, **kwargs) File "test/test_ops.py", line 290, in test_variant_consistency_jit no_grad=(dtype not in dtypes_to_grad_check)) File "/home/kshiteej/.conda/envs/PytorchENV/lib/python3.7/site-packages/torch/testing/_internal/common_jit.py", line 77, in check_against_reference allow_unused=allow_unused) File "/home/kshiteej/.conda/envs/PytorchENV/lib/python3.7/site-packages/torch/autograd/__init__.py", line 225, in grad inputs, allow_unused, accumulate_grad=False) RuntimeError: "digamma" not implemented for 'BFloat16' ---------------------------------------------------------------------- Ran 94 tests in 5.282s FAILED (errors=1, skipped=8)

Code fails in check_against_reference

pytorch/test/test_ops.py

Lines 272 to 300 in 5546a12

with disable_autodiff_subgraph_inlining():

def fn(*inputs, **kwargs):

output = func(*inputs, **kwargs)

return op.output_func(output)

# bfloat16 grad doesn't work for some operators

dtypes_to_grad_check = floating_and_complex_types_and(torch.half) \

if op.skip_bfloat16_grad else floating_and_complex_types_and(torch.half, torch.bfloat16)

# Check scripted forward, grad, and grad grad

script_fn = create_script_fn(self, name, func_type, op.output_func)

check_against_reference(self,

script_fn,

fn,

(*sample.input,) + sample.args,

sample.kwargs,

no_grad=(dtype not in dtypes_to_grad_check))

# Check traced forward, grad, and grad grad

traced_fn = create_traced_fn(self, variant)

check_against_reference(self,

traced_fn,

fn,

(*sample.input,) + sample.args,

sample.kwargs,

no_grad=(dtype not in dtypes_to_grad_check))

# Check alias annotation schema for correctness (make

The code for check_against_reference also doesn't have any try/except

pytorch/torch/testing/_internal/common_jit.py

Lines 39 to 108 in 5546a12

def check_against_reference(self, func, reference_func, args, kwargs=None,

allow_unused=True, check_types=True, no_grad=False):

kwargs = kwargs if kwargs else {}

def allSum(vs):

if isinstance(vs, torch.Tensor):

vs = (vs,)

return sum((i + 1) * v.sum()

for i, v in enumerate(vs)

if v is not None and v.dtype in floating_and_complex_types_and(torch.half, torch.bfloat16))

def clone_inputs(requires_grad):

inputs = [

arg.detach().clone().requires_grad_(requires_grad and arg.requires_grad)

if isinstance(arg, torch.Tensor) else arg for arg in args

]

return inputs, [input for input in inputs if isinstance(input, torch.Tensor) and input.requires_grad]

nograd_inputs, nograd_tensors = clone_inputs(False)

recording_inputs, recording_tensors = clone_inputs(True)

# test no gradients case

outputs = self.runAndSaveRNG(reference_func, nograd_inputs, kwargs)

with enable_profiling_mode_for_profiling_tests():

outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)

self.assertEqual(outputs, outputs_test)

if check_types:

check_output_types(self, func, outputs_test, nograd_inputs, kwargs)

if no_grad:

# skip grad tests

return

with enable_profiling_mode_for_profiling_tests():

# test single grad case

outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)

grads = torch.autograd.grad(allSum(outputs), recording_tensors,

allow_unused=allow_unused)

outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)

grads_test = torch.autograd.grad(allSum(outputs_test), recording_tensors,

allow_unused=allow_unused)

self.assertEqual(outputs, outputs_test)

self.assertEqual(grads, grads_test)

# test the grad grad case

if self._testMethodName in nn_functional_single_grad:

return

outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)

l1 = allSum(outputs)

grads = torch.autograd.grad(l1, recording_tensors, create_graph=True,

allow_unused=allow_unused)

l2 = (allSum(grads) * l1)

grads2 = torch.autograd.grad(l2, recording_tensors, allow_unused=allow_unused)

recording_inputs, recording_tensors = clone_inputs(True)

outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)

l1_test = allSum(outputs_test)

grads_test = torch.autograd.grad(

l1_test, recording_tensors, create_graph=True, allow_unused=allow_unused)

l2_test = (allSum(grads_test) * l1_test)

grads2_test = torch.autograd.grad(l2_test, recording_tensors, allow_unused=allow_unused)

self.assertEqual(outputs, outputs_test)

self.assertEqual(grads, grads_test)

for g2, g2_test in zip(grads2, grads2_test):

if g2 is None and g2_test is None:

continue

self.assertTrue(torch.allclose(g2, g2_test, atol=5e-4, rtol=1e-4))

Note:
The test_variant_consistency_eager test has a try/except block which checks if the eager mode raises or not.

pytorch/test/test_ops.py

Lines 183 to 237 in 5546a12

# Tests that the forward and backward passes of operations produce the

# same values for the cross-product of op variants (method, inplace)

# against eager's gold standard op function variant

@ops(op_db)

def test_variant_consistency_eager(self, device, dtype, op):

samples = op.sample_inputs(device, dtype, requires_grad=True)

if len(samples) == 0:

self.skipTest("Skipped! No sample inputs!")

for sample in samples:

# Acquires variants to test

method = op.get_method()

inplace = op.get_inplace()

variants = (v for v in (method, inplace) if v is not None)

# Computes expected forward

# below calls op's function variant

expected_forward = op(*sample.input, *sample.args, **sample.kwargs)

# Computes expected backward

# NOTE: backward may fail for some dtypes

exception_during_backwards = False

expected_grad = None

try:

expected_forward.sum().backward()

expected_grad = sample.input.grad

sample.input.grad = None

except Exception as e:

exception_during_backwards = True

# Test eager consistency

for variant in variants:

# Verifies that inplace operations that promote int->float fail

# on tensors with integer dtypes.

if (variant is inplace and op.promotes_integers_to_float and

dtype in (torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)):

try:

variant_forward = variant(*(clone_input_helper(input) for input in sample.input),

*sample.args,

**sample.kwargs)

except Exception as e:

continue

self.fail("Inplace operation on integer tensor that should be promoted to float didn't fail!")

# Compares variant's forward

# Note: copy the tensor-type inputs when testing inplace operation

variant_forward = variant(*(clone_input_helper(input) if variant is inplace else input

for input in sample.input),

*sample.args,

**sample.kwargs)

self.assertEqual(variant_forward, expected_forward)

# Compares variant's backward

if variant is not inplace or op.test_inplace_grad:

self.check_variant_backward(sample.input, variant_forward,

expected_grad, exception_during_backwards)

Aha! That's why I was confused. Thank you for verifying. I'll update the test suite tracker.

mruberry · 2021-01-12T12:25:38Z

Clever workarounds for that SciPy reference's bug on this one. Nice job, @kshitij12345. I think we can kill this, too:

pytorch/test/test_torch.py

Line 6920 in 78e71ce

    
           ('lgamma', '', _small_3d, lambda t, d: [], 1e-2, 1e-1, 1e-5, _float_types_no_half, [torch.bfloat16]),

Would you also take a look at test_variant_consistency_jit briefly to see what's going on in backward? I really thought we had written that test so backwards could fail and we would just verify it failed in both eager mode and the jit.

kshitij12345 · 2021-01-12T13:56:38Z

@mruberry

Thanks for the review.
Have made the necessary changes.

Changes:

Removed the redundant test.
Have updated the comment.

Have checked the failing test_variant_consistency_jit and looks like it doesn't have a try/except. (test_variant_consistency_eager does check for that and that test passes). Have replied with more details on the relevant thread.

Note : clang-format looks irrelevant for now but not sure.

Thanks!

kshitij12345 · 2021-01-18T07:32:33Z

@mruberry PTAL :)

mruberry

Great work as usual, @kshitij12345. The test failure looks unrelated.

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mruberry · 2021-01-25T12:17:20Z

torch/testing/_internal/common_methods_invocations.py

+                           SkipInfo('TestCommon', 'test_variant_consistency_jit',
+                                    dtypes=[torch.bfloat16]),
+                       ),
+                       promotes_integers_to_float=True),


This PR has to be rebased because this will cause a logical merge conflict with @peterbell10's

0436ea1.

I think it just needs to be changed to safe_casts_outputs.

Thanks! Updated.

facebook-github-bot

@mruberry has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

facebook-github-bot · 2021-01-27T20:12:33Z

@mruberry merged this pull request in 6d09809.

kshitij12345 added 3 commits January 6, 2021 07:11

lgamma: support int -> float promotion

2aceaf5

remove redundant test

075d371

fix whitespace

fa21355

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kshitij12345 commented Jan 6, 2021

View reviewed changes

add reference

02a5397

kshitij12345 added 4 commits January 7, 2021 08:29

skip test on WINDOWS

fe34450

Merge branch 'master' into develop/numpy/unary-float-op/lgamma

20a0c21

remove use_c10_dispatcher: full as per master

33f1148

retrigger CI: confirm windows failure was due to upstream

75532e9

kshitij12345 requested a review from mruberry January 12, 2021 09:12

kshitij12345 marked this pull request as ready for review January 12, 2021 09:12

mruberry reviewed Jan 12, 2021

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kshitij12345 added 4 commits January 12, 2021 07:12

remove redundant test

59f8bf3

update comment

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Merge branch 'master' into develop/numpy/unary-float-op/lgamma

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retrigger CI to verify clang-format failure

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Merge branch 'master' into develop/numpy/unary-float-op/lgamma

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retrigger CI

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Unary Ufunc tracking issue #42515

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[numpy] torch.lgamma: promote integer inputs to float #50140

[numpy] torch.lgamma: promote integer inputs to float #50140

kshitij12345 commented Jan 6, 2021

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edited

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	with disable_autodiff_subgraph_inlining():
	def fn(inputs, *kwargs):
	output = func(inputs, *kwargs)
	return op.output_func(output)

	# bfloat16 grad doesn't work for some operators
	dtypes_to_grad_check = floating_and_complex_types_and(torch.half) \
	if op.skip_bfloat16_grad else floating_and_complex_types_and(torch.half, torch.bfloat16)

	# Check scripted forward, grad, and grad grad
	script_fn = create_script_fn(self, name, func_type, op.output_func)

	check_against_reference(self,
	script_fn,
	fn,
	(*sample.input,) + sample.args,
	sample.kwargs,
	no_grad=(dtype not in dtypes_to_grad_check))

	# Check traced forward, grad, and grad grad
	traced_fn = create_traced_fn(self, variant)
	check_against_reference(self,
	traced_fn,
	fn,
	(*sample.input,) + sample.args,
	sample.kwargs,
	no_grad=(dtype not in dtypes_to_grad_check))

	# Check alias annotation schema for correctness (make

	def check_against_reference(self, func, reference_func, args, kwargs=None,
	allow_unused=True, check_types=True, no_grad=False):
	kwargs = kwargs if kwargs else {}

	def allSum(vs):
	if isinstance(vs, torch.Tensor):
	vs = (vs,)
	return sum((i + 1) * v.sum()
	for i, v in enumerate(vs)
	if v is not None and v.dtype in floating_and_complex_types_and(torch.half, torch.bfloat16))

	def clone_inputs(requires_grad):
	inputs = [
	arg.detach().clone().requires_grad_(requires_grad and arg.requires_grad)
	if isinstance(arg, torch.Tensor) else arg for arg in args
	]
	return inputs, [input for input in inputs if isinstance(input, torch.Tensor) and input.requires_grad]

	nograd_inputs, nograd_tensors = clone_inputs(False)
	recording_inputs, recording_tensors = clone_inputs(True)

	# test no gradients case
	outputs = self.runAndSaveRNG(reference_func, nograd_inputs, kwargs)
	with enable_profiling_mode_for_profiling_tests():
	outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)
	self.assertEqual(outputs, outputs_test)

	if check_types:
	check_output_types(self, func, outputs_test, nograd_inputs, kwargs)

	if no_grad:
	# skip grad tests
	return

	with enable_profiling_mode_for_profiling_tests():
	# test single grad case
	outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)
	grads = torch.autograd.grad(allSum(outputs), recording_tensors,
	allow_unused=allow_unused)
	outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)
	grads_test = torch.autograd.grad(allSum(outputs_test), recording_tensors,
	allow_unused=allow_unused)
	self.assertEqual(outputs, outputs_test)
	self.assertEqual(grads, grads_test)
	# test the grad grad case
	if self._testMethodName in nn_functional_single_grad:
	return

	outputs = self.runAndSaveRNG(reference_func, recording_inputs, kwargs)
	l1 = allSum(outputs)
	grads = torch.autograd.grad(l1, recording_tensors, create_graph=True,
	allow_unused=allow_unused)

	l2 = (allSum(grads) * l1)
	grads2 = torch.autograd.grad(l2, recording_tensors, allow_unused=allow_unused)
	recording_inputs, recording_tensors = clone_inputs(True)
	outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)
	l1_test = allSum(outputs_test)
	grads_test = torch.autograd.grad(
	l1_test, recording_tensors, create_graph=True, allow_unused=allow_unused)

	l2_test = (allSum(grads_test) * l1_test)
	grads2_test = torch.autograd.grad(l2_test, recording_tensors, allow_unused=allow_unused)

	self.assertEqual(outputs, outputs_test)
	self.assertEqual(grads, grads_test)
	for g2, g2_test in zip(grads2, grads2_test):
	if g2 is None and g2_test is None:
	continue
	self.assertTrue(torch.allclose(g2, g2_test, atol=5e-4, rtol=1e-4))

	# Tests that the forward and backward passes of operations produce the
	# same values for the cross-product of op variants (method, inplace)
	# against eager's gold standard op function variant
	@ops(op_db)
	def test_variant_consistency_eager(self, device, dtype, op):
	samples = op.sample_inputs(device, dtype, requires_grad=True)
	if len(samples) == 0:
	self.skipTest("Skipped! No sample inputs!")

	for sample in samples:
	# Acquires variants to test
	method = op.get_method()
	inplace = op.get_inplace()
	variants = (v for v in (method, inplace) if v is not None)
	# Computes expected forward

	# below calls op's function variant
	expected_forward = op(sample.input, sample.args, **sample.kwargs)

	# Computes expected backward
	# NOTE: backward may fail for some dtypes
	exception_during_backwards = False
	expected_grad = None
	try:
	expected_forward.sum().backward()
	expected_grad = sample.input.grad
	sample.input.grad = None
	except Exception as e:
	exception_during_backwards = True

	# Test eager consistency
	for variant in variants:
	# Verifies that inplace operations that promote int->float fail
	# on tensors with integer dtypes.
	if (variant is inplace and op.promotes_integers_to_float and
	dtype in (torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)):
	try:
	variant_forward = variant(*(clone_input_helper(input) for input in sample.input),
	*sample.args,
	**sample.kwargs)
	except Exception as e:
	continue
	self.fail("Inplace operation on integer tensor that should be promoted to float didn't fail!")
	# Compares variant's forward
	# Note: copy the tensor-type inputs when testing inplace operation
	variant_forward = variant(*(clone_input_helper(input) if variant is inplace else input
	for input in sample.input),
	*sample.args,
	**sample.kwargs)
	self.assertEqual(variant_forward, expected_forward)

	# Compares variant's backward
	if variant is not inplace or op.test_inplace_grad:
	self.check_variant_backward(sample.input, variant_forward,
	expected_grad, exception_during_backwards)

[numpy] torch.lgamma: promote integer inputs to float #50140

[numpy] torch.lgamma: promote integer inputs to float #50140

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kshitij12345 commented Jan 6, 2021

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