From 08a194fcd615dcf9406a7e319d637cc303097f46 Mon Sep 17 00:00:00 2001 From: Yih-Dar <2521628+ydshieh@users.noreply.github.com> Date: Tue, 9 Apr 2024 13:28:54 +0200 Subject: [PATCH] Fix slow tests for important models to be compatible with A10 runners (#29905) * fix mistral and mixtral * add pdb * fix mixtral tesst * fix * fix mistral ? * add fix gemma * fix mistral * fix * test * anoter test * fix * fix * fix mistral tests * fix them again * final fixes for mistral * fix padding right * fix whipser fa2 * fix * fix * fix gemma * test * fix llama * fix * fix * fix llama gemma * add class attribute * fix CI * clarify whisper * compute_capability * rename names in some comments * Add # fmt: skip * make style * Update tests/models/mistral/test_modeling_mistral.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * update * update --------- Co-authored-by: Younes Belkada Co-authored-by: ydshieh Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> --- tests/models/gemma/test_modeling_gemma.py | 141 ++++++++++-------- tests/models/llama/test_modeling_llama.py | 34 ++++- tests/models/mistral/test_modeling_mistral.py | 60 ++++++-- tests/models/mixtral/test_modeling_mixtral.py | 85 ++++++++--- tests/models/whisper/test_modeling_whisper.py | 18 +++ tests/test_modeling_common.py | 18 ++- 6 files changed, 246 insertions(+), 110 deletions(-) diff --git a/tests/models/gemma/test_modeling_gemma.py b/tests/models/gemma/test_modeling_gemma.py index 8c3aa392ba..e70dab3d95 100644 --- a/tests/models/gemma/test_modeling_gemma.py +++ b/tests/models/gemma/test_modeling_gemma.py @@ -21,6 +21,7 @@ from parameterized import parameterized from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available from transformers.testing_utils import ( + is_flaky, require_bitsandbytes, require_flash_attn, require_read_token, @@ -379,40 +380,6 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi def test_past_key_values_format(self): pass - @require_flash_attn - @require_torch_gpu - @pytest.mark.flash_attn_test - @slow - def test_flash_attn_2_generate_padding_right(self): - import torch - - for model_class in self.all_generative_model_classes: - config, _ = self.model_tester.prepare_config_and_inputs_for_common() - model = model_class(config) - - with tempfile.TemporaryDirectory() as tmpdirname: - model.save_pretrained(tmpdirname) - model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( - torch_device - ) - - dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) - dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device) - - model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False) - - model = model_class.from_pretrained( - tmpdirname, - torch_dtype=torch.float16, - attn_implementation="flash_attention_2", - low_cpu_mem_usage=True, - ).to(torch_device) - - with self.assertRaises(ValueError): - _ = model.generate( - dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False - ) - @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @@ -500,6 +467,7 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test + @is_flaky @slow def test_flash_attn_2_equivalence(self): for model_class in self.all_model_classes: @@ -531,12 +499,21 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi assert torch.allclose(logits_fa, logits, atol=3e-3) -@require_torch_gpu @slow -@require_read_token +@require_torch_gpu class GemmaIntegrationTest(unittest.TestCase): input_text = ["Hello I am doing", "Hi today"] + # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) + # Depending on the hardware we get different logits / generations + cuda_compute_capability_major_version = None + @classmethod + def setUpClass(cls): + if is_torch_available() and torch.cuda.is_available(): + # 8 is for A100 / A10 and 7 for T4 + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] + + @require_read_token def test_model_2b_fp32(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ @@ -554,6 +531,7 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) + @require_read_token def test_model_2b_fp16(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ @@ -573,6 +551,7 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) + @require_read_token def test_model_2b_fp16_static_cache(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ @@ -594,12 +573,19 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) + @require_read_token def test_model_2b_bf16(self): model_id = "google/gemma-2b" - EXPECTED_TEXTS = [ - "Hello I am doing a project on the 1990s and I need to know what the most popular music", - "Hi today I am going to share with you a very easy and simple recipe of Khichdi", - ] + EXPECTED_TEXTS = { + 7: [ + "Hello I am doing a project on the 1990s and I need to know what the most popular music", + "Hi today I am going to share with you a very easy and simple recipe of Khichdi", + ], + 8: [ + "Hello I am doing a project on the 1990s and I need to know what the most popular music", + "Hi today I am going to share with you a very easy and simple recipe of Kaju Kat", + ], + } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device @@ -611,14 +597,21 @@ class GemmaIntegrationTest(unittest.TestCase): output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) - self.assertEqual(output_text, EXPECTED_TEXTS) + self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) + @require_read_token def test_model_2b_eager(self): model_id = "google/gemma-2b" - EXPECTED_TEXTS = [ - "Hello I am doing a project on the 1990s and I am looking for some information on the ", - "Hi today I am going to share with you a very easy and simple recipe of Kaju Kat", - ] + EXPECTED_TEXTS = { + 7: [ + "Hello I am doing a project on the 1990s and I am looking for some information on the ", + "Hi today I am going to share with you a very easy and simple recipe of Kaju Kat", + ], + 8: [ + "Hello I am doing a project on the 1990s and I need to know what the most popular music", + "Hi today I am going to share with you a very easy and simple recipe of Kaju Kat", + ], + } model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager" @@ -631,15 +624,22 @@ class GemmaIntegrationTest(unittest.TestCase): output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) - self.assertEqual(output_text, EXPECTED_TEXTS) + self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @require_torch_sdpa + @require_read_token def test_model_2b_sdpa(self): model_id = "google/gemma-2b" - EXPECTED_TEXTS = [ - "Hello I am doing a project on the 1990s and I need to know what the most popular music", - "Hi today I am going to share with you a very easy and simple recipe of Khichdi", - ] + EXPECTED_TEXTS = { + 7: [ + "Hello I am doing a project on the 1990s and I need to know what the most popular music", + "Hi today I am going to share with you a very easy and simple recipe of Khichdi", + ], + 8: [ + "Hello I am doing a project on the 1990s and I need to know what the most popular music", + "Hi today I am going to share with you a very easy and simple recipe of Kaju Kat", + ], + } model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa" @@ -652,10 +652,11 @@ class GemmaIntegrationTest(unittest.TestCase): output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) - self.assertEqual(output_text, EXPECTED_TEXTS) + self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @pytest.mark.flash_attn_test @require_flash_attn + @require_read_token def test_model_2b_flash_attn(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ @@ -677,6 +678,7 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes + @require_read_token def test_model_2b_4bit(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ @@ -695,6 +697,7 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) @unittest.skip("The test will not fit our CI runners") + @require_read_token def test_model_7b_fp32(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ @@ -712,6 +715,7 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) + @require_read_token def test_model_7b_fp16(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ @@ -731,12 +735,19 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) + @require_read_token def test_model_7b_bf16(self): model_id = "google/gemma-7b" - EXPECTED_TEXTS = [ - """Hello I am doing a project on a 1991 240sx and I am trying to find""", - "Hi today I am going to show you how to make a very simple and easy to make a very simple and", - ] + EXPECTED_TEXTS = { + 7: [ + """Hello I am doing a project on a 1991 240sx and I am trying to find""", + "Hi today I am going to show you how to make a very simple and easy to make a very simple and", + ], + 8: [ + "Hello I am doing a project for my school and I am trying to make a program that will read a .txt file", + "Hi today I am going to show you how to make a very simple and easy to make a very simple and", + ], + } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device @@ -748,8 +759,9 @@ class GemmaIntegrationTest(unittest.TestCase): output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) - self.assertEqual(output_text, EXPECTED_TEXTS) + self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) + @require_read_token def test_model_7b_fp16_static_cache(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ @@ -772,12 +784,19 @@ class GemmaIntegrationTest(unittest.TestCase): self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes + @require_read_token def test_model_7b_4bit(self): model_id = "google/gemma-7b" - EXPECTED_TEXTS = [ - "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", - """Hi today I am going to talk about the new update for the game called "The new update" and I""", - ] + EXPECTED_TEXTS = { + 7: [ + "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", + """Hi today I am going to talk about the new update for the game called "The new update" and I""", + ], + 8: [ + "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", + "Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very", + ], + } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True) @@ -787,4 +806,4 @@ class GemmaIntegrationTest(unittest.TestCase): output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) - self.assertEqual(output_text, EXPECTED_TEXTS) + self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) diff --git a/tests/models/llama/test_modeling_llama.py b/tests/models/llama/test_modeling_llama.py index 0fb4087dba..dc24fd848c 100644 --- a/tests/models/llama/test_modeling_llama.py +++ b/tests/models/llama/test_modeling_llama.py @@ -597,8 +597,18 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi pass -@require_torch +@require_torch_gpu class LlamaIntegrationTest(unittest.TestCase): + # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) + # Depending on the hardware we get different logits / generations + cuda_compute_capability_major_version = None + + @classmethod + def setUpClass(cls): + if is_torch_available() and torch.cuda.is_available(): + # 8 is for A100 / A10 and 7 for T4 + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] + @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!") @slow def test_model_7b_logits(self): @@ -675,16 +685,25 @@ class LlamaIntegrationTest(unittest.TestCase): @require_read_token def test_compile_static_cache(self): NUM_TOKENS_TO_GENERATE = 40 - EXPECTED_TEXT_COMPLETION = [ - "Simply put, the theory of relativity states that 1) the speed of light is constant, 2) the speed of light is the same for all observers, and 3) the laws of physics are the same for all observers.", - "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", - ] + EXPECTED_TEXT_COMPLETION = { + 7: [ + "Simply put, the theory of relativity states that 1) the speed of light is constant, 2) the speed of light is the same for all observers, and 3) the laws of physics are the same for all observers.", + "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", + ], + 8: [ + "Simply put, the theory of relativity states that 1) the speed of light is the same for all observers, and 2) the laws of physics are the same for all observers.\nThe first part of the theory of relativity", + "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", + ], + } + prompts = [ "Simply put, the theory of relativity states that ", "My favorite all time favorite condiment is ketchup.", ] tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="", padding_side="right") - model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="sequential") + model = LlamaForCausalLM.from_pretrained( + "meta-llama/Llama-2-7b-hf", device_map="sequential", torch_dtype=torch.float16 + ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) def decode_one_tokens(model, cur_token, input_pos, cache_position): @@ -718,7 +737,7 @@ class LlamaIntegrationTest(unittest.TestCase): cache_position += 1 text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) - self.assertEqual(EXPECTED_TEXT_COMPLETION, text) + self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text) @require_torch @@ -763,6 +782,7 @@ end @require_torch_accelerator @slow + @unittest.skip("Model is too large") def test_model_7b_logits(self): model = LlamaForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf").to(torch_device) tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf") diff --git a/tests/models/mistral/test_modeling_mistral.py b/tests/models/mistral/test_modeling_mistral.py index 432097e9d1..59f3cdea69 100644 --- a/tests/models/mistral/test_modeling_mistral.py +++ b/tests/models/mistral/test_modeling_mistral.py @@ -470,39 +470,68 @@ class MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi self.skipTest("Mistral flash attention does not support right padding") -@require_torch +@require_torch_gpu class MistralIntegrationTest(unittest.TestCase): + # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) + # Depending on the hardware we get different logits / generations + cuda_compute_capability_major_version = None + + @classmethod + def setUpClass(cls): + if is_torch_available() and torch.cuda.is_available(): + # 8 is for A100 / A10 and 7 for T4 + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] + + def tearDown(self): + torch.cuda.empty_cache() + gc.collect() + @slow def test_model_7b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] - model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto") + model = MistralForCausalLM.from_pretrained( + "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16 + ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) with torch.no_grad(): out = model(input_ids).logits.cpu() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) - # slicing logits[0, 0, 0:30] - EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]) # fmt: skip + + EXPECTED_SLICE = { + 7: torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]), + 8: torch.tensor([-5.8711, -5.8555, -0.1050, -4.7148, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -1.0781, 1.7568, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711]), + } # fmt: skip + print(out[0, 0, :30]) - torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4) + torch.testing.assert_close( + out[0, 0, :30], EXPECTED_SLICE[self.cuda_compute_capability_major_version], atol=1e-4, rtol=1e-4 + ) del model backend_empty_cache(torch_device) gc.collect() @slow + @require_bitsandbytes def test_model_7b_generation(self): - EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" + EXPECTED_TEXT_COMPLETION = { + 7: "My favourite condiment is 100% ketchup. I love it on everything. I'm not a big", + 8: "My favourite condiment is 100% ketchup. I’m not a fan of mustard, mayo,", + } + prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) - model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto") + model = MistralForCausalLM.from_pretrained( + "mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True + ) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) - self.assertEqual(EXPECTED_TEXT_COMPLETION, text) + self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text) del model backend_empty_cache(torch_device) @@ -517,7 +546,7 @@ class MistralIntegrationTest(unittest.TestCase): input_ids = [1] + [306, 338] * 2048 model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", - device_map="auto", + device_map={"": torch_device}, load_in_4bit=True, attn_implementation="flash_attention_2", ) @@ -544,9 +573,7 @@ class MistralIntegrationTest(unittest.TestCase): # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = MistralForCausalLM.from_pretrained( - "mistralai/Mistral-7B-v0.1", - device_map="auto", - attn_implementation="sdpa", + "mistralai/Mistral-7B-v0.1", device_map="auto", attn_implementation="sdpa", torch_dtype=torch.float16 ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) @@ -577,9 +604,10 @@ class MistralIntegrationTest(unittest.TestCase): @slow def test_speculative_generation(self): - EXPECTED_TEXT_COMPLETION = ( - "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs" - ) + EXPECTED_TEXT_COMPLETION = { + 7: "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs", + 8: "My favourite condiment is 100% Sriracha. I love the heat, the sweetness, the tang", + } prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) model = MistralForCausalLM.from_pretrained( @@ -593,7 +621,7 @@ class MistralIntegrationTest(unittest.TestCase): input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=model ) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) - self.assertEqual(EXPECTED_TEXT_COMPLETION, text) + self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text) del model backend_empty_cache(torch_device) diff --git a/tests/models/mixtral/test_modeling_mixtral.py b/tests/models/mixtral/test_modeling_mixtral.py index 98654c5133..0cc8c9fc44 100644 --- a/tests/models/mixtral/test_modeling_mixtral.py +++ b/tests/models/mixtral/test_modeling_mixtral.py @@ -507,6 +507,16 @@ class MixtralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi @require_torch class MixtralIntegrationTest(unittest.TestCase): + # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) + # Depending on the hardware we get different logits / generations + cuda_compute_capability_major_version = None + + @classmethod + def setUpClass(cls): + if is_torch_available() and torch.cuda.is_available(): + # 8 is for A100 / A10 and 7 for T4 + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] + @slow @require_torch_gpu def test_small_model_logits(self): @@ -518,18 +528,26 @@ class MixtralIntegrationTest(unittest.TestCase): ) # TODO: might need to tweak it in case the logits do not match on our daily runners # these logits have been obtained with the original megablocks impelmentation. - EXPECTED_LOGITS = torch.Tensor( - [[0.1670, 0.1620, 0.6094], [-0.8906, -0.1588, -0.6060], [0.1572, 0.1290, 0.7246]] - ).to(torch_device) - + EXPECTED_LOGITS = { + 7: torch.Tensor([[0.1670, 0.1620, 0.6094], [-0.8906, -0.1588, -0.6060], [0.1572, 0.1290, 0.7246]]).to( + torch_device + ), + 8: torch.Tensor([[0.1631, 0.1621, 0.6094], [-0.8906, -0.1621, -0.6094], [0.1572, 0.1270, 0.7227]]).to( + torch_device + ), + } with torch.no_grad(): logits = model(dummy_input).logits - torch.testing.assert_close(logits[0, :3, :3].half(), EXPECTED_LOGITS, atol=1e-3, rtol=1e-3) - torch.testing.assert_close(logits[1, :3, :3].half(), EXPECTED_LOGITS, atol=1e-3, rtol=1e-3) + torch.testing.assert_close( + logits[0, :3, :3], EXPECTED_LOGITS[self.cuda_compute_capability_major_version], atol=1e-3, rtol=1e-3 + ) + torch.testing.assert_close( + logits[1, :3, :3], EXPECTED_LOGITS[self.cuda_compute_capability_major_version], atol=1e-3, rtol=1e-3 + ) @slow - # @require_torch_gpu + @require_torch_gpu def test_small_model_logits_batched(self): model_id = "hf-internal-testing/Mixtral-tiny" dummy_input = torch.LongTensor([[0, 0, 0, 0, 0, 0, 1, 2, 3], [1, 1, 2, 3, 4, 5, 6, 7, 8]]).to(torch_device) @@ -540,23 +558,48 @@ class MixtralIntegrationTest(unittest.TestCase): ) # TODO: might need to tweak it in case the logits do not match on our daily runners - EXPECTED_LOGITS_LEFT = torch.Tensor( - [[0.1750, 0.0537, 0.7007], [0.1750, 0.0537, 0.7007], [0.1750, 0.0537, 0.7007]], - ) + EXPECTED_LOGITS_LEFT = { + 7: torch.Tensor( + [[0.1750, 0.0537, 0.7007], [0.1750, 0.0537, 0.7007], [0.1750, 0.0537, 0.7007]], + ).to(torch_device), + 8: torch.Tensor([[0.1914, 0.0508, 0.7188], [0.1953, 0.0510, 0.7227], [0.1973, 0.0562, 0.7148]]).to( + torch_device + ), + } - # logits[0, -3:, -3:].half() - EXPECTED_LOGITS_LEFT_UNPADDED = torch.Tensor( - [[0.2212, 0.5200, -0.3816], [0.8213, -0.2313, 0.6069], [0.2664, -0.7090, 0.2468]], - ) + EXPECTED_LOGITS_LEFT_UNPADDED = { + 7: torch.Tensor( + [[0.2212, 0.5200, -0.3816], [0.8213, -0.2313, 0.6069], [0.2664, -0.7090, 0.2468]], + ).to(torch_device), + 8: torch.Tensor([[0.2217, 0.5195, -0.3828], [0.8203, -0.2295, 0.6055], [0.2676, -0.7109, 0.2461]]).to( + torch_device + ), + } - # logits[1, -3:, -3:].half() - EXPECTED_LOGITS_RIGHT_UNPADDED = torch.Tensor( - [[0.2205, 0.1232, -0.1611], [-0.3484, 0.3030, -1.0312], [0.0742, 0.7930, 0.7969]] - ) + EXPECTED_LOGITS_RIGHT_UNPADDED = { + 7: torch.Tensor([[0.2205, 0.1232, -0.1611], [-0.3484, 0.3030, -1.0312], [0.0742, 0.7930, 0.7969]]).to( + torch_device + ), + 8: torch.Tensor([[0.2178, 0.1260, -0.1621], [-0.3496, 0.2988, -1.0312], [0.0693, 0.7930, 0.8008]]).to( + torch_device + ), + } with torch.no_grad(): logits = model(dummy_input, attention_mask=attention_mask).logits - torch.testing.assert_close(logits[0, :3, :3].half(), EXPECTED_LOGITS_LEFT, atol=1e-3, rtol=1e-3) - torch.testing.assert_close(logits[0, -3:, -3:].half(), EXPECTED_LOGITS_LEFT_UNPADDED, atol=1e-3, rtol=1e-3) - torch.testing.assert_close(logits[1, -3:, -3:].half(), EXPECTED_LOGITS_RIGHT_UNPADDED, atol=1e-3, rtol=1e-3) + torch.testing.assert_close( + logits[0, :3, :3], EXPECTED_LOGITS_LEFT[self.cuda_compute_capability_major_version], atol=1e-3, rtol=1e-3 + ) + torch.testing.assert_close( + logits[0, -3:, -3:], + EXPECTED_LOGITS_LEFT_UNPADDED[self.cuda_compute_capability_major_version], + atol=1e-3, + rtol=1e-3, + ) + torch.testing.assert_close( + logits[1, -3:, -3:], + EXPECTED_LOGITS_RIGHT_UNPADDED[self.cuda_compute_capability_major_version], + atol=1e-3, + rtol=1e-3, + ) diff --git a/tests/models/whisper/test_modeling_whisper.py b/tests/models/whisper/test_modeling_whisper.py index a36bd5f216..a078eb375c 100644 --- a/tests/models/whisper/test_modeling_whisper.py +++ b/tests/models/whisper/test_modeling_whisper.py @@ -3339,3 +3339,21 @@ class WhisperStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, @unittest.skip("The model doesn't support fast init from base") def test_save_load_fast_init_from_base(self): pass + + @unittest.skip( + "Duplicated test with WhisperModelTest + the FA2 testing suite needs to be refactored to be compatible with WhisperDecoder for that test" + ) + def test_flash_attn_2_generate_padding_right(self): + pass + + @unittest.skip( + "Duplicated test with WhisperModelTest + the FA2 testing suite needs to be refactored to be compatible with WhisperDecoder for that test" + ) + def test_flash_attn_2_inference(self): + pass + + @unittest.skip( + "Duplicated test with WhisperModelTest + the FA2 testing suite needs to be refactored to be compatible with WhisperDecoder for that test" + ) + def test_flash_attn_2_inference_padding_right(self): + pass diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 7241993b6d..e92aca1cd7 100755 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -3245,6 +3245,7 @@ class ModelTesterMixin: @require_torch_gpu @mark.flash_attn_test @slow + @is_flaky def test_flash_attn_2_inference_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: @@ -3338,6 +3339,7 @@ class ModelTesterMixin: @require_torch_gpu @mark.flash_attn_test @slow + @is_flaky def test_flash_attn_2_inference_equivalence_right_padding(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: @@ -3427,6 +3429,7 @@ class ModelTesterMixin: @require_torch_gpu @mark.flash_attn_test @slow + @is_flaky def test_flash_attn_2_generate_left_padding(self): for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: @@ -3470,6 +3473,7 @@ class ModelTesterMixin: @require_flash_attn @require_torch_gpu @mark.flash_attn_test + @is_flaky @slow def test_flash_attn_2_generate_padding_right(self): for model_class in self.all_generative_model_classes: @@ -3888,19 +3892,20 @@ class ModelTesterMixin: for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") - config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) - with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_input = inputs_dict[model.main_input_name] dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) + batch_size = dummy_attention_mask.shape[0] - if model.config.is_encoder_decoder: - dummy_decoder_input_ids = inputs_dict["decoder_input_ids"] - dummy_decoder_attention_mask = inputs_dict["decoder_attention_mask"] + is_padding_right = dummy_attention_mask[:, -1].sum().item() != batch_size + + # To avoid errors with padding_side=="right" + if is_padding_right: + dummy_attention_mask = torch.ones_like(dummy_input) model = model_class.from_pretrained( tmpdirname, @@ -3916,6 +3921,9 @@ class ModelTesterMixin: param.data = param.data.to(torch.float32) if model.config.is_encoder_decoder: + dummy_decoder_input_ids = inputs_dict["decoder_input_ids"] + dummy_decoder_attention_mask = inputs_dict["decoder_attention_mask"] + _ = model(dummy_input, decoder_input_ids=dummy_decoder_input_ids) # with attention mask _ = model(