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[FlaxSpeechEncoderDecoderModel] More Rigorous PT-Flax Equivalence Tests #16589

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Merged
sanchit-gandhi merged 1 commit into from
Apr 6, 2022
Merged

[FlaxSpeechEncoderDecoderModel] More Rigorous PT-Flax Equivalence Tests #16589

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27 changes: 10 additions & 17 deletions tests/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -413,28 +413,22 @@ def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_outputs = pt_outputs.to_tuple()

fx_outputs = fx_model(**inputs_dict)
fx_logits = fx_outputs.logits
fx_outputs = fx_outputs.to_tuple()
pt_outputs = pt_model(**pt_inputs).to_tuple()

fx_outputs = fx_model(**inputs_dict).to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
for fx_output, pt_output in zip(fx_outputs, pt_outputs):
self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-5)

# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)

fx_outputs_loaded = fx_model_loaded(**inputs_dict)
fx_logits_loaded = fx_outputs_loaded.logits
fx_outputs_loaded = fx_outputs_loaded.to_tuple()

fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2)
for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 1e-5)

# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
Expand All @@ -445,12 +439,11 @@ def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
pt_model_loaded.eval()

with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
pt_logits_loaded = pt_outputs_loaded.logits
pt_outputs_loaded = pt_outputs_loaded.to_tuple()
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()

self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
for fx_output, pt_output_loaded in zip(fx_outputs, pt_outputs_loaded):
self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 1e-5)

def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict):

Expand Down