# Copyright 2023 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 Mixtral model.""" import unittest import pytest from transformers import MixtralConfig, is_torch_available from transformers.testing_utils import ( Expectations, get_device_properties, require_flash_attn, require_torch, require_torch_accelerator, require_torch_gpu, slow, torch_device, ) if is_torch_available(): import torch from transformers import ( MixtralForCausalLM, MixtralForQuestionAnswering, MixtralForSequenceClassification, MixtralForTokenClassification, MixtralModel, ) from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester class MixtralModelTester(CausalLMModelTester): config_class = MixtralConfig if is_torch_available(): base_model_class = MixtralModel causal_lm_class = MixtralForCausalLM sequence_class = MixtralForSequenceClassification token_class = MixtralForTokenClassification question_answering_class = MixtralForQuestionAnswering @require_torch class MistralModelTest(CausalLMModelTest, unittest.TestCase): all_model_classes = ( ( MixtralModel, MixtralForCausalLM, MixtralForSequenceClassification, MixtralForTokenClassification, MixtralForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": MixtralModel, "text-classification": MixtralForSequenceClassification, "token-classification": MixtralForTokenClassification, "text-generation": MixtralForCausalLM, "question-answering": MixtralForQuestionAnswering, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False model_tester_class = MixtralModelTester # 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") # Ignore copy def test_load_balancing_loss(self): r""" Let's make sure we can actually compute the loss and do a backward on it. """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.num_local_experts = 8 config.output_router_logits = True input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = MixtralForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask) self.assertEqual(result.router_logits[0].shape, (91, config.num_local_experts)) torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2) # First, we make sure that adding padding tokens doesn't change the loss # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding) pad_length = 1000 # Add padding tokens (assume that pad_token_id=1) to input_ids padding_block = torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(torch_device) padded_input_ids = torch.cat((padding_block, input_ids), dim=1) # this is to simulate padding to the left padded_attention_mask = padded_input_ids.ne(1).to(torch_device) padded_result = model(padded_input_ids, attention_mask=padded_attention_mask) torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4) # We make sure that the loss of including padding tokens != the loss without padding tokens # if attention_mask=None --> we don't exclude padding tokens include_padding_result = model(padded_input_ids, attention_mask=None) # This is to mimic torch.testing.assert_not_close self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item()) @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 device_properties = None @classmethod def setUpClass(cls): cls.device_properties = get_device_properties() @slow @require_torch_accelerator def test_small_model_logits(self): model_id = "hf-internal-testing/Mixtral-tiny" dummy_input = torch.LongTensor([[0, 1, 0], [0, 1, 0]]).to(torch_device) model = MixtralForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, ).to(torch_device) # 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 implementation. # ("cuda", 8) for A100/A10, and ("cuda", 7) for T4 # considering differences in hardware processing and potential deviations in output. # fmt: off EXPECTED_LOGITS = Expectations( { ("cuda", 7): torch.Tensor([[0.1640, 0.1621, 0.6093], [-0.8906, -0.1640, -0.6093], [0.1562, 0.1250, 0.7226]]).to(torch_device), ("cuda", 8): torch.Tensor([[0.1631, 0.1621, 0.6094], [-0.8906, -0.1621, -0.6094], [0.1572, 0.1270, 0.7227]]).to(torch_device), ("rocm", 9): torch.Tensor([[0.1641, 0.1621, 0.6094], [-0.8906, -0.1631, -0.6094], [0.1572, 0.1260, 0.7227]]).to(torch_device), } ) # fmt: on expected_logit = EXPECTED_LOGITS.get_expectation() with torch.no_grad(): logits = model(dummy_input).logits logits = logits.float() torch.testing.assert_close(logits[0, :3, :3], expected_logit, atol=1e-3, rtol=1e-3) torch.testing.assert_close(logits[1, :3, :3], expected_logit, atol=1e-3, rtol=1e-3) @slow @require_torch_accelerator 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) attention_mask = dummy_input.ne(0).to(torch.long) model = MixtralForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, ).to(torch_device) # TODO: might need to tweak it in case the logits do not match on our daily runners # # ("cuda", 8) for A100/A10, and ("cuda", 7) for T4. # # considering differences in hardware processing and potential deviations in generated text. # fmt: off EXPECTED_LOGITS_LEFT_UNPADDED = Expectations( { ("cuda", 7): torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2275, 0.6054], [0.2656, -0.7070, 0.2460]]).to(torch_device), ("cuda", 8): torch.Tensor([[0.2207, 0.5234, -0.3828], [0.8203, -0.2285, 0.6055], [0.2656, -0.7109, 0.2451]]).to(torch_device), ("rocm", 9): torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2285, 0.6055], [0.2637, -0.7109, 0.2451]]).to(torch_device), } ) expected_left_unpadded = EXPECTED_LOGITS_LEFT_UNPADDED.get_expectation() EXPECTED_LOGITS_RIGHT_UNPADDED = Expectations( { ("cuda", 7): torch.Tensor([[0.2167, 0.1269, -0.1640], [-0.3496, 0.2988, -1.0312], [0.0688, 0.7929, 0.8007]]).to(torch_device), ("cuda", 8): torch.Tensor([[0.2178, 0.1270, -0.1621], [-0.3496, 0.3008, -1.0312], [0.0693, 0.7930, 0.7969]]).to(torch_device), ("rocm", 9): torch.Tensor([[0.2197, 0.1250, -0.1611], [-0.3516, 0.3008, -1.0312], [0.0684, 0.7930, 0.8008]]).to(torch_device), } ) expected_right_unpadded = EXPECTED_LOGITS_RIGHT_UNPADDED.get_expectation() # fmt: on with torch.no_grad(): logits = model(dummy_input, attention_mask=attention_mask).logits logits = logits.float() torch.testing.assert_close( logits[0, -3:, -3:], expected_left_unpadded, atol=1e-3, rtol=1e-3, ) torch.testing.assert_close( logits[1, -3:, -3:], expected_right_unpadded, atol=1e-3, rtol=1e-3, )