Add generate() functionality to TF 2.0 (#3063)
* add first copy past test to tf 2 generate * add tf top_k_top_p_filter fn * add generate function for TF * add generate function for TF * implemented generate for all models expect transfoXL * implemented generate for all models expect transfoXL * implemented generate for all models expect transfoXL * make style * change permission of test file to correct ones * delete ipdb * delete ipdb * fix bug and finish simple gpt2 integration test * clean test file * clean test file * make style * make style * make style * make style * change import style * change import style * make style * make style * add decorators * add decorators * fix tf ctrl bug dim => axis in TF * make style * make style * refactored test file * refactored test file * take out test_torch_tf_conversion if nothing is defined * take out test_torch_tf_conversion if nothing is defined * remove useless files * remove useless files * fix conflicts * fix conflicts * fix conflicts * fix conflicts * fix conflicts * solve conflicts * solve conflicts * fix conflicts * fix conflicts * merge conflicts * delete ipdb * exposed top_k_top_p_filtering fns * delete weirdly created w! file * add comment to test tf common modeling * fix conflicts * fix conflicts * make style * merge conflicts * make style * change tf.tensor.shape to shape_list(tensor)
This commit is contained in:
Patrick von Platen
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GitHub
GitHub
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b31f715019
commit
4134100363
@@ -36,6 +36,7 @@ if is_torch_available():
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BertModel,
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BertConfig,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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top_k_top_p_filtering,
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)
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@@ -263,7 +264,7 @@ class ModelTesterMixin:
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# Prepare head_mask
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# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
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head_mask = torch.ones(
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self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device
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self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device,
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)
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head_mask[0, 0] = 0
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head_mask[-1, :-1] = 0
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@@ -303,7 +304,7 @@ class ModelTesterMixin:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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@@ -313,7 +314,10 @@ class ModelTesterMixin:
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]}
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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model.prune_heads(heads_to_prune)
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with torch.no_grad():
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outputs = model(**inputs_dict)
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@@ -329,7 +333,7 @@ class ModelTesterMixin:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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@@ -339,7 +343,10 @@ class ModelTesterMixin:
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]}
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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model.prune_heads(heads_to_prune)
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with tempfile.TemporaryDirectory() as temp_dir_name:
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@@ -359,7 +366,7 @@ class ModelTesterMixin:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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@@ -367,7 +374,10 @@ class ModelTesterMixin:
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config.output_attentions = True
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config.output_hidden_states = False
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]}
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heads_to_prune = {
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0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0],
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}
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config.pruned_heads = heads_to_prune
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model = model_class(config=config)
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@@ -387,7 +397,7 @@ class ModelTesterMixin:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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(config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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@@ -465,7 +475,7 @@ class ModelTesterMixin:
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)
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def test_resize_tokens_embeddings(self):
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original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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(original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.test_resize_embeddings:
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return
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@@ -634,6 +644,7 @@ class ModelTesterMixin:
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self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
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# batch_size > 1, greedy
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self._check_generated_tokens(model.generate(input_ids, do_sample=False))
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# batch_size > 1, num_beams > 1, sample
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self._check_generated_tokens(model.generate(input_ids, num_beams=3, num_return_sequences=3,))
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# batch_size > 1, num_beams > 1, greedy
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@@ -704,3 +715,110 @@ class ModelUtilsTest(unittest.TestCase):
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self.assertEqual(model.config.output_attentions, True)
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self.assertEqual(model.config.output_hidden_states, True)
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self.assertEqual(model.config, config)
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@require_torch
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class UtilsFunctionsTest(unittest.TestCase):
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# tests whether the top_k_top_p function behaves as expected
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def test_top_k_top_p_filtering(self):
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logits = torch.tensor(
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[
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[
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8.2220991, # 3rd highest value; idx. 0
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-0.5620044,
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5.23229752,
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4.0386393,
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-6.8798378,
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-0.54785802,
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-3.2012153,
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2.92777176,
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1.88171953,
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7.35341276, # 5th highest value; idx. 9
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8.43207833, # 2nd highest value; idx. 10
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-9.85711836,
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-5.96209236,
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-1.13039161,
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-7.1115294,
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-0.8369633,
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-5.3186408,
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7.06427407,
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0.81369344,
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-0.82023817,
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-5.9179796,
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0.58813443,
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-6.99778438,
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4.71551189,
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-0.18771637,
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7.44020759, # 4th highest value; idx. 25
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9.38450987, # 1st highest value; idx. 26
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2.12662941,
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-9.32562038,
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2.35652522,
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], # cummulative prob of 5 highest values <= 0.6
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[
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0.58425518,
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4.53139238,
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-5.57510464,
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-6.28030699,
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-7.19529503,
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-4.02122551,
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1.39337037,
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-6.06707057,
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1.59480517,
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-9.643119,
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0.03907799,
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0.67231762,
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-8.88206726,
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6.27115922, # 4th highest value; idx. 13
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2.28520723,
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4.82767506,
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4.30421368,
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8.8275313, # 2nd highest value; idx. 17
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5.44029958, # 5th highest value; idx. 18
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-4.4735794,
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7.38579536, # 3rd highest value; idx. 20
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-2.91051663,
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2.61946077,
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-2.5674762,
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-9.48959302,
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-4.02922645,
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-1.35416918,
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9.67702323, # 1st highest value; idx. 27
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-5.89478553,
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1.85370467,
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], # cummulative prob of 5 highest values <= 0.6
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],
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dtype=torch.float,
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device=torch_device,
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)
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non_inf_expected_idx = torch.tensor(
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[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
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dtype=torch.long,
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device=torch_device,
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) # expected non filtered idx as noted above
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non_inf_expected_output = torch.tensor(
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[
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8.2221,
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7.3534,
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8.4321,
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7.4402,
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9.3845,
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6.2712,
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8.8275,
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5.4403,
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7.3858,
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9.6770,
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], # expected non filtered values as noted above
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dtype=torch.float,
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device=torch_device,
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)
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output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
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non_inf_output = output[output != -float("inf")].to(device=torch_device)
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non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
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self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
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self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
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