# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch Mistral model.""" import gc import unittest import pytest from packaging import version from transformers import AutoTokenizer, MistralConfig, is_torch_available, set_seed from transformers.testing_utils import ( DeviceProperties, Expectations, backend_empty_cache, cleanup, get_device_properties, require_bitsandbytes, require_flash_attn, require_read_token, require_torch, require_torch_accelerator, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) if is_torch_available(): import torch from transformers import ( MistralForCausalLM, MistralForQuestionAnswering, MistralForSequenceClassification, MistralForTokenClassification, MistralModel, ) from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester class MistralModelTester(CausalLMModelTester): config_class = MistralConfig if is_torch_available(): base_model_class = MistralModel causal_lm_class = MistralForCausalLM sequence_class = MistralForSequenceClassification token_class = MistralForTokenClassification question_answering_class = MistralForQuestionAnswering @require_torch class MistralModelTest(CausalLMModelTest, unittest.TestCase): all_model_classes = ( ( MistralModel, MistralForCausalLM, MistralForSequenceClassification, MistralForTokenClassification, MistralForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": MistralModel, "text-classification": MistralForSequenceClassification, "token-classification": MistralForTokenClassification, "text-generation": MistralForCausalLM, "question-answering": MistralForQuestionAnswering, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False model_tester_class = MistralModelTester # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): return True @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest(reason="Mistral flash attention does not support right padding") @require_torch_accelerator @require_read_token class MistralIntegrationTest(unittest.TestCase): # This variable is used to determine which accelerator are we using for our runners (e.g. A10 or T4) # Depending on the hardware we get different logits / generations device_properties: DeviceProperties = (None, None, None) @classmethod def setUpClass(cls): cls.device_properties = get_device_properties() def setUp(self): cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) @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", 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.float().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, rtol=1e-2, atol=1e-2) # ("cuda", 8) for A100/A10, and ("cuda", 7) 7 for T4. # considering differences in hardware processing and potential deviations in output. # fmt: off EXPECTED_SLICES = Expectations( { ("cuda", 7): torch.tensor([-5.8828, -5.8633, -0.1042, -4.7266, -5.8828, -5.8789, -5.8789, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -1.0801, 1.7598, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828]), ("cuda", 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]), ("rocm", 9): torch.tensor([-5.8750, -5.8594, -0.1047, -4.7188, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -1.0781, 1.7578, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750]), } ) # fmt: on expected_slice = EXPECTED_SLICES.get_expectation() torch.testing.assert_close(out[0, 0, :30], expected_slice, atol=1e-4, rtol=1e-4) @slow @require_bitsandbytes def test_model_7b_generation(self): EXPECTED_TEXT_COMPLETION = "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={"": 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) @slow def test_model_7b_dola_generation(self): # ground truth text generated with dola_layers="low", repetition_penalty=1.2 EXPECTED_TEXT_COMPLETION = ( """My favourite condiment is 100% ketchup. I love it on everything, and I’m not ash""" ) 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", torch_dtype=torch.float16 ) 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, dola_layers="low", repetition_penalty=1.2 ) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) del model backend_empty_cache(torch_device) gc.collect() @require_flash_attn @require_bitsandbytes @slow @pytest.mark.flash_attn_test def test_model_7b_long_prompt(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # 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={"": torch_device}, load_in_4bit=True, attn_implementation="flash_attention_2", ) 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) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) @slow @require_torch_sdpa def test_model_7b_long_prompt_sdpa(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # 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", 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) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model backend_empty_cache(torch_device) gc.collect() EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) 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) @slow def test_speculative_generation(self): EXPECTED_TEXT_COMPLETION = "My favourite condiment is 100% Sriracha. I love it on everything. I have it on my" 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", torch_dtype=torch.float16 ) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs set_seed(0) generated_ids = model.generate( 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) @slow def test_compile_static_cache(self): # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2 # work as intended. See https://github.com/pytorch/pytorch/issues/121943 if version.parse(torch.__version__) < version.parse("2.3.0"): self.skipTest(reason="This test requires torch >= 2.3 to run.") if self.device_properties[0] == "cuda" and self.device_properties[1] == 7: self.skipTest(reason="This test is failing (`torch.compile` fails) on Nvidia T4 GPU.") NUM_TOKENS_TO_GENERATE = 40 EXPECTED_TEXT_COMPLETION = [ "My favourite condiment is 100% ketchup. I love it on everything. " "I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles" ] prompts = ["My favourite condiment is "] tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) tokenizer.pad_token = tokenizer.eos_token model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map=torch_device, torch_dtype=torch.float16 ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) # Dynamic Cache generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) # Static Cache generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Sliding Window Cache generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Static Cache + compile forward_function = model.forward model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text) # Sliding Window Cache + compile torch._dynamo.reset() model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window" ) static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text) @slow @require_torch_accelerator class Mask4DTestHard(unittest.TestCase): model_name = "mistralai/Mistral-7B-v0.1" model = None model_dtype = None @classmethod def setUpClass(cls): cleanup(torch_device, gc_collect=True) if cls.model_dtype is None: cls.model_dtype = torch.float16 if cls.model is None: cls.model = MistralForCausalLM.from_pretrained(cls.model_name, torch_dtype=cls.model_dtype).to( torch_device ) @classmethod def tearDownClass(cls): del cls.model_dtype del cls.model cleanup(torch_device, gc_collect=True) def setUp(self): cleanup(torch_device, gc_collect=True) self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False) def tearDown(self): cleanup(torch_device, gc_collect=True) def get_test_data(self): template = "my favorite {}" items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item batch_separate = [template.format(x) for x in items] # 3 separate lines batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device) input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device) mask_shared_prefix = torch.tensor( [ [ [ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1], ] ] ], device=torch_device, ) position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device) # building custom positions ids based on custom mask position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1) # effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device) # inverting the mask min_dtype = torch.finfo(self.model_dtype).min mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix def test_stacked_causal_mask(self): ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # single forward run with 4D custom mask logits_shared_prefix = self.model.forward( input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix ).logits logits_shared_prefix_last = logits_shared_prefix[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], : ] # last three tokens decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)] self.assertEqual(decoded, decoded_shared_prefix) def test_partial_stacked_causal_mask(self): # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # 2 forward runs with custom 4D masks part_a = 3 # split point input_1a = input_ids_shared_prefix[:, :part_a] position_ids_1a = position_ids_shared_prefix[:, :part_a] mask_1a = mask_shared_prefix[:, :, :part_a, :part_a] outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a) past_key_values_a = outs_1a["past_key_values"] # Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len]) input_1b = input_ids_shared_prefix[:, part_a:] position_ids_1b = position_ids_shared_prefix[:, part_a:] mask_1b = mask_shared_prefix[:, :, part_a:, :] outs_1b = self.model.forward( input_1b, attention_mask=mask_1b, position_ids=position_ids_1b, past_key_values=past_key_values_a ) decoded_1b = [ self.tokenizer.decode(t) for t in outs_1b.logits.argmax(-1)[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a ] ] self.assertEqual(decoded, decoded_1b)