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# 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,
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):
@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,
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,
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.
EXPECTED_LOGITS_LEFT_UNPADDED = Expectations(
{
("xpu", 3): [[0.2236, 0.5195, -0.3828], [0.8203, -0.2295, 0.6055], [0.2676, -0.7070, 0.2461]],
("cuda", 7): [[0.2236, 0.5195, -0.3828], [0.8203, -0.2275, 0.6054], [0.2656, -0.7070, 0.2460]],
("cuda", 8): [[0.2217, 0.5195, -0.3828], [0.8203, -0.2295, 0.6055], [0.2676, -0.7109, 0.2461]],
("rocm", 9): [[0.2236, 0.5195, -0.3828], [0.8203, -0.2285, 0.6055], [0.2637, -0.7109, 0.2451]],
}
)
expected_left_unpadded = torch.tensor(EXPECTED_LOGITS_LEFT_UNPADDED.get_expectation(), device=torch_device)
EXPECTED_LOGITS_RIGHT_UNPADDED = Expectations(
{
("xpu", 3): [[0.2178, 0.1270, -0.1641], [-0.3496, 0.2988, -1.0312], [0.0693, 0.7930, 0.8008]],
("cuda", 7): [[0.2167, 0.1269, -0.1640], [-0.3496, 0.2988, -1.0312], [0.0688, 0.7929, 0.8007]],
("cuda", 8): [[0.2178, 0.1260, -0.1621], [-0.3496, 0.2988, -1.0312], [0.0693, 0.7930, 0.8008]],
("rocm", 9): [[0.2197, 0.1250, -0.1611], [-0.3516, 0.3008, -1.0312], [0.0684, 0.7930, 0.8008]],
}
)
expected_right_unpadded = torch.tensor(EXPECTED_LOGITS_RIGHT_UNPADDED.get_expectation(), device=torch_device)
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,
)