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make test_eager_matches_sdpa_inference less flaky #34512

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merged 13 commits into from
Oct 31, 2024
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make test_eager_matches_sdpa_inference less flaky #34512

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ydshieh Oct 30, 2024
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5 changes: 4 additions & 1 deletion tests/generation/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1263,6 +1263,9 @@ def test_dola_decoding_sample(self):

if model.get_output_embeddings() is None:
self.skipTest("DoLa is not supported for models that don't have output embeddings")

logits_processor_kwargs = self._get_logits_processor_kwargs(do_sample=True, config=model.config)
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@ArthurZucker ArthurZucker Oct 31, 2024

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do sample is random no?

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@ydshieh ydshieh Oct 31, 2024

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I am using the same value as in generation_kwargs = {...} a few line below.

Yes it is random but this method is test_...._sample so makes sense.


# Sets dola generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) there are at least two forward passes in the main model, to ensure the input preparation of
Expand All @@ -1280,7 +1283,7 @@ def test_dola_decoding_sample(self):
"use_cache": getattr(config, "use_cache", False), # Some models don't support the cache
"dola_layers": "low",
}
output_dola = model.generate(**generation_kwargs, **inputs_dict)
output_dola = model.generate(**generation_kwargs, **logits_processor_kwargs, **inputs_dict)
self._check_outputs(output_dola, model.config, use_cache=getattr(config, "use_cache", False))

@pytest.mark.generate
Expand Down
6 changes: 3 additions & 3 deletions tests/models/llava/test_modeling_llava.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def __init__(
},
is_training=True,
vision_config={
"image_size": 30,
"image_size": 8,
"patch_size": 2,
"num_channels": 3,
"is_training": True,
Expand Down Expand Up @@ -118,9 +118,9 @@ def __init__(
self.batch_size = 3
self.num_channels = 3
self.image_size = 336
self.encoder_seq_length = 232
self.num_image_tokens = 225
self.num_image_tokens = (self.vision_config["image_size"] // self.vision_config["patch_size"]) ** 2
self.seq_length = seq_length + self.num_image_tokens
self.encoder_seq_length = self.seq_length

def get_config(self):
return LlavaConfig(
Expand Down
6 changes: 3 additions & 3 deletions tests/models/vipllava/test_modeling_vipllava.py
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Expand Up @@ -79,7 +79,7 @@ def __init__(
is_training=True,
vision_config={
"batch_size": 12,
"image_size": 30,
"image_size": 8,
"patch_size": 2,
"num_channels": 3,
"is_training": True,
Expand Down Expand Up @@ -111,9 +111,9 @@ def __init__(
self.batch_size = 3
self.num_channels = 3
self.image_size = 336
self.encoder_seq_length = 232
self.num_image_tokens = 225
self.num_image_tokens = (self.vision_config["image_size"] // self.vision_config["patch_size"]) ** 2
self.seq_length = seq_length + self.num_image_tokens
self.encoder_seq_length = self.seq_length

def get_config(self):
return VipLlavaConfig(
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88 changes: 38 additions & 50 deletions tests/test_modeling_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3982,6 +3982,13 @@ def test_eager_matches_sdpa_inference(self, torch_dtype: str):
def get_mean_reldiff(failcase, x, ref, atol, rtol):
return f"{failcase}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}"

if hasattr(self.model_tester, "num_hidden_layers"):
self.model_tester.num_hidden_layers = 1
if hasattr(self.model_tester, "vision_config") and "num_hidden_layers" in self.model_tester.vision_config:
self.model_tester.vision_config["num_hidden_layers"] = 1
if hasattr(self.model_tester, "text_config") and "num_hidden_layers" in self.model_tester.text_config:
self.model_tester.text_config["num_hidden_layers"] = 1

for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
Expand Down Expand Up @@ -4013,7 +4020,8 @@ def get_mean_reldiff(failcase, x, ref, atol, rtol):
can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
if not (self.has_attentions and can_output_attn) and output_attentions:
continue
for batch_size in [1, 5]:
# TODO: if we can also check with `batch_size=1` without being flaky?
for batch_size in [7]:
dummy_input = inputs_dict[model.main_input_name]

if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]:
Expand Down Expand Up @@ -4064,14 +4072,14 @@ def get_mean_reldiff(failcase, x, ref, atol, rtol):

dummy_attention_mask[:] = 1
if padding_side == "left":
dummy_attention_mask[-1, :-1] = 1
dummy_attention_mask[-1, -4:] = 0
dummy_attention_mask[-1, :2] = 0
dummy_attention_mask[-1, 2:] = 1
elif padding_side == "right":
dummy_attention_mask[-1, 1:] = 1
dummy_attention_mask[-1, :3] = 0
dummy_attention_mask[-1, -2:] = 0
dummy_attention_mask[-1, :-2] = 1

for enable_kernels in [False, True]:
failcase = f"padding_side={padding_side}, use_mask={use_mask}, batch_size={batch_size}, enable_kernels={enable_kernels}"
failcase = f"padding_side={padding_side}, use_mask={use_mask}, enable_kernels={enable_kernels}"
if is_encoder_decoder:
decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)[
:batch_size
Expand Down Expand Up @@ -4161,52 +4169,32 @@ def get_mean_reldiff(failcase, x, ref, atol, rtol):

# Masked tokens output slightly deviates - we don't mind that.
if use_mask:
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
_logits_eager = torch.zeros_like(input=logits_eager)

_logits_sdpa[:-1] = logits_sdpa[:-1]
_logits_eager[:-1] = logits_eager[:-1]

if padding_side == "left":
sub_sdpa = logits_sdpa[:-1]
sub_eager = logits_eager[:-1]
if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
fail_cases.append(
get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol)
)

sub_sdpa = logits_sdpa[-1, :-4]
sub_eager = logits_eager[-1, :-4]
if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
fail_cases.append(
get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol)
)

# Testing the padding tokens is not really meaningful but anyway
# sub_sdpa = logits_sdpa[-1, -4:]
# sub_eager = logits_eager[-1, -4:]
# if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
# fail_cases.append(get_mean_reldiff(failcase, sub_sdpa, sub_eager, 4e-2, 4e-2))
elif padding_side == "right":
sub_sdpa = logits_sdpa[:-1]
sub_eager = logits_eager[:-1]
if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
fail_cases.append(
get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol)
)

sub_sdpa = logits_sdpa[-1, 3:]
sub_eager = logits_eager[-1, 3:]
if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
fail_cases.append(
get_mean_reldiff(failcase, sub_sdpa, sub_eager, atol, rtol)
)

# Testing the padding tokens is not really meaningful but anyway
# sub_sdpa = logits_sdpa[-1, :3]
# sub_eager = logits_eager[-1, :3]
# if not torch.allclose(sub_sdpa, sub_eager, atol=atol, rtol=rtol):
# fail_cases.append(get_mean_reldiff(failcase, sub_sdpa, sub_eager, 4e-2, 4e-2))
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]

else:
if not torch.allclose(logits_sdpa, logits_eager, atol=atol, rtol=rtol):
fail_cases.append(
get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol)
)
elif padding_side == "right":
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]

logits_sdpa = _logits_sdpa
logits_eager = _logits_eager

results = [
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
]
# If 80% batch elements have matched results, it's fine
if np.mean(results) < 0.8:
fail_cases.append(
get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol)
)

self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases))

Expand Down
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