common generation changes

tests/generation/test_utils.py

@@ -2546,21 +2546,33 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
             "return_tensors": "pt",
         }
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_custom_logits_processor(self): 
         super().test_custom_logits_processor()
         pass
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_max_new_tokens_encoder_decoder(self):
         super().test_max_new_tokens_encoder_decoder()
         pass
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_eos_token_id_int_and_list_beam_search(self):
         super().test_eos_token_id_int_and_list_beam_search()
         pass
 
+    @skipIfRocm(arch='gfx942')
+    def test_transition_scores_greedy_search_normalized(self):
+        super().test_transition_scores_greedy_search_normalized()
+
+    @skipIfRocm(arch='gfx942')
+    def test_transition_scores_greedy_search(self):
+        super().test_transition_scores_greedy_search()
+
+    @skipIfRocm(arch='gfx942')
+    def test_generate_input_features_as_encoder_kwarg(self):
+        super().test_generate_input_features_as_encoder_kwarg()
+
     @slow
     def test_diverse_beam_search(self):
         # PT-only test: TF doesn't have a diverse beam search implementation
@@ -2596,6 +2608,7 @@ def test_diverse_beam_search(self):
             ],
         )
 
+    @skipIfRocm(arch='gfx942')
     def test_max_length_if_input_embeds(self):
         # PT-only test: TF doesn't have StoppingCriteria
         article = "Today a dragon flew over Paris."
@@ -2610,7 +2623,7 @@ def test_max_length_if_input_embeds(self):
         out_gen_embeds = model.generate(inputs_embeds=inputs_embeds, max_length=max_length)
         self.assertEqual(out_gen.shape[-1], input_len + out_gen_embeds.shape[-1])
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_min_length_if_input_embeds(self):
         # PT-only test: TF doesn't have StoppingCriteria
         article = "Today a dragon flew over Paris."
@@ -2663,7 +2676,7 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwa
         )
 
     # TODO (joao): replace `stop_sequence` in the pipeline by the more recent `generate` functionality
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_stop_sequence_stopping_criteria(self):
         # PT-only test: TF doesn't have StoppingCriteria
         prompt = """Hello I believe in"""
@@ -3199,6 +3212,7 @@ def test_decoder_start_id_from_config(self):
         with self.assertRaises(ValueError):
             outputs = bart_model.generate(input_ids, generation_config=GenerationConfig(do_sample=False))
 
+    @skipIfRocm(arch='gfx942')
     def test_contrastive_search_batched(self):
         # PT-only test: TF doesn't have constrained beam search
         # Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)
@@ -3246,7 +3260,7 @@ def test_logits_processor_not_inplace(self):
         self.assertListEqual(out.logits[-1].tolist(), out.scores[-1].tolist())
         self.assertNotEqual(out_with_temp.logits[-1].tolist(), out_with_temp.scores[-1].tolist())
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_eos_token_id_int_and_list_top_k_top_sampling(self):
         # Has TF equivalent: this test relies on random sampling
         generation_kwargs = {
@@ -3275,7 +3289,7 @@ def test_eos_token_id_int_and_list_top_k_top_sampling(self):
         generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
         self.assertTrue(expectation == len(generated_tokens[0]))
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_model_kwarg_encoder_signature_filtering(self):
         # Has TF equivalent: ample use of framework-specific code
         bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
@@ -3313,7 +3327,7 @@ def forward(self, input_ids, **kwargs):
             # FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo"
             bart_model.generate(input_ids, foo="bar")
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_default_max_length_warning(self):
         model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
@@ -3371,7 +3385,7 @@ def test_default_assisted_generation(self):
         self.assertEqual(config.assistant_confidence_threshold, 0.4)
         self.assertEqual(config.is_assistant, False)
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_generated_length_assisted_generation(self):
         # PT-only test: TF doesn't support assisted decoding yet.
         model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
@@ -3400,7 +3414,7 @@ def test_generated_length_assisted_generation(self):
         )
         self.assertTrue((input_length + 10) <= out.shape[-1] <= 20)
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gx1100'])
     def test_model_kwarg_assisted_decoding_decoder_only(self):
         # PT-only test: TF doesn't support assisted decoding yet.
         model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
@@ -3435,7 +3449,7 @@ def test_model_kwarg_assisted_decoding_decoder_only(self):
         )
         self.assertListEqual(outputs_assisted.tolist(), outputs_tti.tolist())
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_model_kwarg_assisted_decoding_encoder_decoder(self):
         """
         Tests that the following scenario is compatible with assisted generation:
@@ -3502,7 +3516,7 @@ def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None,
         )
         self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_assisted_decoding_encoder_decoder_shared_encoder(self):
         """
         Tests that the following scenario is compatible with assisted generation:
@@ -3581,7 +3595,7 @@ def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None,
         )
         self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_assisted_decoding_num_assistant_tokens_heuristic_schedule(self):
         # This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
 
@@ -3830,7 +3844,7 @@ def test_special_tokens_fall_back_to_model_default(self):
         self.assertTrue(test_bos_id == gen_output[0, 0])
         self.assertTrue(generation_config.bos_token_id is None)
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a'])
     def test_speculative_decoding_equals_regular_decoding(self):
         draft_name = "double7/vicuna-68m"
         target_name = "Qwen/Qwen2-0.5B-Instruct"
@@ -3861,7 +3875,7 @@ def test_speculative_decoding_equals_regular_decoding(self):
 
     @pytest.mark.generate
     @require_torch_multi_gpu
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a'])
     def test_generate_with_static_cache_multi_gpu(self):
         """
         Tests if the static cache has been set correctly and if generate works correctly when we are using multi-gpus.
@@ -3897,7 +3911,7 @@ def test_generate_with_static_cache_multi_gpu(self):
 
     @pytest.mark.generate
     @require_torch_multi_gpu
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a'])
     def test_init_static_cache_multi_gpu(self):
         """
         Tests if the static cache has been set correctly when we initialize it manually in a multi-gpu setup.
@@ -4079,7 +4093,7 @@ def test_padding_input_contrastive_search_t5(self):
         self.assertEqual(generated_text_no_padding, generated_text_with_padding)
         self.assertEqual(generated_text_no_padding, "Ich muss diese Aufgabe vor Ende des Tages beenden.")
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942'])
     def test_prepare_inputs_for_generation_decoder_llm(self):
         """Tests GenerationMixin.prepare_inputs_for_generation against expected usage with decoder-only llms."""
 
@@ -4196,7 +4210,7 @@ def test_prepare_inputs_for_generation_encoder_decoder_llm(self):
         self.assertTrue(model_inputs["encoder_outputs"] == "foo")
         # See the decoder-only test for more corner cases. The code is the same, so we don't repeat it here.
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a'])
     def test_generate_compile_fullgraph_tiny(self):
         """
         Tests that we can call end-to-end generation with a tiny model (i.e. doesn't crash)
@@ -4220,7 +4234,7 @@ def test_generate_compile_fullgraph_tiny(self):
         gen_out = compiled_generate(**model_inputs, generation_config=generation_config)
         self.assertTrue(gen_out.shape[1] > model_inputs["input_ids"].shape[1])  # some text was generated
 
-    @skipIfRocm(arch='gfx1201')
+    @skipIfRocm(arch=['gfx1201','gfx942','gfx90a','gfx1100'])
     def test_assisted_generation_early_exit(self):
         """
         Tests that assisted generation with early exit works as expected. Under the hood, this has complex cache