TF: rework XLA generate tests (#16866)

tests/gpt2/test_modeling_tf_gpt2.py

@@ -16,7 +16,7 @@
 import unittest
 
 from transformers import GPT2Config, is_tf_available
-from transformers.testing_utils import require_tf, slow
+from transformers.testing_utils import get_gpu_count, require_tf, slow
 
 from ..test_configuration_common import ConfigTester
 from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
@@ -294,7 +294,7 @@ class TFGPT2ModelTester:
         result = model(inputs)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
-    def create_and_check_gpt2_xla_generate(self, config, input_ids, *args):
+    def create_and_check_gpt2_xla_generate_fast(self, config, input_ids, *args):
         config.eos_token_id = None
         config.max_length = 10
         model = TFGPT2LMHeadModel(config=config)
@@ -408,9 +408,9 @@ class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestC
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)
 
-    def test_gpt2_xla_generate(self):
+    def test_gpt2_xla_generate_fast(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_gpt2_xla_generate(*config_and_inputs)
+        self.model_tester.create_and_check_gpt2_xla_generate_fast(*config_and_inputs)
 
     def test_gpt2_double_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -536,41 +536,57 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
         self.assertListEqual(output_strings, expected_output_string)
 
     @slow
-    def test_lm_generate_gpt2(self):
+    @unittest.skipIf(not get_gpu_count(), "XLA not reliable on CPU")
+    # TODO: remove the skip when the XLA CPU softmax issue gets sorted
+    def test_lm_generate_gpt2_greedy_xla(self):
+        # TODO (Joao): convert this to an example with a batch size>1 with different input lengths that works (and fix
+        # the underlying problem)
         model = TFGPT2LMHeadModel.from_pretrained("gpt2")
-        input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32)  # The dog
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentences = ["The dog"]
+        expected_output_strings = [
+            "The dog was found in a field near the intersection of West and West Streets.\n\nThe dog",
+        ]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
 
-        # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
-        # fmt: off
-        expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290]
-        # fmt: on
         output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        self.assertListEqual(output_strings, expected_output_strings)
 
-    @slow
-    def test_lm_generate_gpt2_xla_greedy(self):
-        """This test gives the exact same results as the non-xla test above"""
-        model = TFGPT2LMHeadModel.from_pretrained("gpt2")
-        input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32)  # The dog
-
-        # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
-        # fmt: off
-        expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290]
-        # fmt: on
         xla_generate = tf.function(model.generate, jit_compile=True)
-
         output_ids = xla_generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        self.assertListEqual(output_strings, expected_output_strings)
 
     @slow
-    def test_lm_generate_gpt2_xla_sample(self):
+    @unittest.skipIf(not get_gpu_count(), "XLA not reliable on CPU")
+    # TODO: remove the skip when the XLA CPU softmax issue gets sorted
+    def test_lm_generate_gpt2_sample_xla(self):
+        # NOTE: due to the small numerical differences that are natural when we compile to XLA, sampling the same
+        # output out of the same seed is far from guaranteed. We can, however, confirm that the results are sensible
+        # and that we can seed both versions.
         model = TFGPT2LMHeadModel.from_pretrained("gpt2")
-        input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32)  # The dog
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentence = ["The dog"]
+        expected_output_string = [
+            "The dog must be well educated to do anything. If anything, this must be her best friend"
+        ]
+        expected_output_string_xla = ["The dog has been named in connection with the murder of a 20-year-old man in!"]
+        input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids
+
+        output_ids = model.generate(input_ids, do_sample=True, seed=[7, 0])
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        self.assertListEqual(output_strings, expected_output_string)
 
-        # fmt: off
-        expected_output_ids = [464, 3290, 550, 284, 307, 4376, 287, 281, 4044, 1363, 329, 734, 812, 878, 852, 4376, 757, 329, 2267, 0]
-        # fmt: on
         xla_generate = tf.function(model.generate, jit_compile=True)
-
+        output_ids = xla_generate(input_ids, do_sample=True, seed=[7, 0])
-        output_ids = xla_generate(input_ids, do_sample=True, seed=[42, 0])
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+        self.assertListEqual(output_strings, expected_output_string_xla)

tests/t5/test_modeling_tf_t5.py

@@ -16,7 +16,7 @@
 import unittest
 
 from transformers import T5Config, is_tf_available
-from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
+from transformers.testing_utils import get_gpu_count, require_sentencepiece, require_tf, require_tokenizers, slow
 from transformers.utils import cached_property
 
 from ..test_configuration_common import ConfigTester
@@ -227,7 +227,7 @@ class TFT5ModelTester:
         # test that outputs are equal for slice
         tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
 
-    def create_and_check_t5_xla_generate(self, config, input_ids, *args):
+    def create_and_check_t5_xla_generate_fast(self, config, input_ids, *args):
         config.eos_token_id = None
         config.max_length = 10
         config.do_sample = False
@@ -297,9 +297,9 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_t5_decoder_model_past_large_inputs(*config_and_inputs)
 
-    def test_t5_model_xla_generate(self):
+    def test_t5_model_xla_generate_fast(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_t5_xla_generate(*config_and_inputs)
+        self.model_tester.create_and_check_t5_xla_generate_fast(*config_and_inputs)
 
     def test_model_common_attributes(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -481,12 +481,18 @@ class TFT5EncoderOnlyModelTest(TFModelTesterMixin, unittest.TestCase):
 @require_tokenizers
 class TFT5GenerationIntegrationTests(unittest.TestCase):
     @slow
+    @unittest.skipIf(not get_gpu_count(), "XLA not reliable on CPU")
+    # TODO: remove the skip when the XLA CPU softmax issue gets sorted
     def test_greedy_xla_generate_simple(self):
         model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
         tokenizer = T5Tokenizer.from_pretrained("t5-small")
 
-        sentence = "Translate English to German: Today is a beautiful day."
+        # two examples with different lengths to confirm that attention masks are operational in XLA
-        input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids
+        sentences = [
+            "Translate English to German: Today is a beautiful day.",
+            "Translate English to German: I have four cats, three dogs, two birds, and a horse.",
+        ]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
 
         xla_generate = tf.function(model.generate, jit_compile=True)
 
@@ -496,7 +502,10 @@ class TFT5GenerationIntegrationTests(unittest.TestCase):
         output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
         output_strings_xla = tokenizer.batch_decode(output_ids_xla, skip_special_tokens=True)
 
-        expected_output_string = ["Heute ist ein schöner Tag."]
+        expected_output_string = [
+            "Heute ist ein schöner Tag.",
+            "Ich habe vier Katzen, drei Hunde, zwei Vögel und ein Pferd.",
+        ]
 
         self.assertListEqual(expected_output_string, output_strings)
         self.assertListEqual(expected_output_string, output_strings_xla)
@@ -525,28 +534,25 @@ class TFT5GenerationIntegrationTests(unittest.TestCase):
         self.assertListEqual(expected_output_string, output_strings)
 
     @slow
+    @unittest.skipIf(not get_gpu_count(), "XLA not reliable on CPU")
+    # TODO: remove the skip when the XLA CPU softmax issue gets sorted
     def test_sample_xla_generate_simple(self):
+        # NOTE: due to the small numerical differences that are natural when we compile to XLA, sampling the same
+        # output out of the same seed is far from guaranteed (unlike this example). We can, however, confirm that the
+        # results are sensible and that we can seed both versions.
         model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
         tokenizer = T5Tokenizer.from_pretrained("t5-small")
 
-        sentence = "Translate English to German: Today is a beautiful day."
+        sentence = "Translate English to German: I have two bananas"
         input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids
-        # XLA reorder ops, which causes operations like FP matmul to have slightly different results, causing
+        expected_output_string = ["Ich habe 2 Bananen"]
-        # divergences in generate -- especially with sampling.
+        expected_output_string_xla = ["Ich habe 2 Bananen"]
-        expected_output_string = ["Heute ist ein schöner Tag."]
-        expected_output_string_xla = ["Heute ist ein schöne Tage."]
-        # However, notice that the first tokens are the same, for the same seed
-        assert expected_output_string[0][:15] == expected_output_string_xla[0][:15]
 
-        # forces the generation to happen on CPU, to avoid GPU-related quirks
-        with tf.device(":/CPU:0"):
         # seed set -> deterministic sampling sequence -> deterministic generation
         output_ids = model.generate(input_ids, do_sample=True, seed=[42, 0])
         output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
         self.assertListEqual(expected_output_string, output_strings)
 
-        # forces the generation to happen on CPU, to avoid GPU-related quirks
-        with tf.device(":/CPU:0"):
         xla_generate = tf.function(model.generate, jit_compile=True)
         # seed set -> deterministic sampling sequence -> deterministic generation
         output_ids_xla = xla_generate(input_ids, do_sample=True, seed=[42, 0])