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	# Copyright 2023 HuggingFace Inc.
	#
	# 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.

	import copy
	import unittest

	from parameterized import parameterized

	from transformers import set_seed
	from transformers.testing_utils import (
	CaptureStderr,
	get_gpu_count,
	is_torch_available,
	require_gptq,
	require_non_xpu,
	require_read_token,
	require_torch,
	require_torch_accelerator,
	require_torch_gpu,
	require_torch_multi_gpu,
	slow,
	torch_device,
	)


	if is_torch_available():
	import torch

	from transformers import (
	AutoModelForCausalLM,
	AutoTokenizer,
	ClvpForCausalLM,
	DynamicCache,
	GenerationConfig,
	LlamaConfig,
	SinkCache,
	StaticCache,
	convert_and_export_with_cache,
	)
	from transformers.utils import is_torch_greater_or_equal


	@require_torch
	class CacheTest(unittest.TestCase):
	def test_dynamic_cache_retrocompatibility(self):
	"""Tests that we can convert back and forth between the legacy cache format and DynamicCache"""
	legacy_cache = ()
	new_cache = DynamicCache()

	# Creates a new cache with 10 layers in both formats
	for layer_idx in range(10):
	new_key = torch.rand((2, 4, 8, 16))
	new_value = torch.rand((2, 4, 8, 16))
	new_cache.update(new_key, new_value, layer_idx)
	legacy_cache += ((new_key, new_value),)

	# Sanity check 1: they must have the same shapes
	self.assertTrue(len(legacy_cache), len(new_cache))
	for layer_idx in range(10):
	self.assertTrue(len(legacy_cache[layer_idx]), len(legacy_cache[layer_idx]))
	for key_value_idx in range(2):
	self.assertTrue(
	legacy_cache[layer_idx][key_value_idx].shape == new_cache[layer_idx][key_value_idx].shape
	)

	# Sanity check 2: we can get the sequence length in multiple ways with DynamicCache, and they return the
	# expected value
	self.assertTrue(legacy_cache[0][0].shape[-2] == new_cache[0][0].shape[-2] == new_cache.get_seq_length() == 8)

	# Sanity check 3: they must be equal, and both support indexing
	for layer_idx in range(10):
	for key_value_idx in range(2):
	self.assertTrue(
	torch.allclose(new_cache[layer_idx][key_value_idx], legacy_cache[layer_idx][key_value_idx])
	)

	# Test 1: We can convert from legacy to new with no changes
	from_legacy = DynamicCache.from_legacy_cache(legacy_cache)
	for layer_idx in range(10):
	for key_value_idx in range(2):
	self.assertTrue(
	torch.allclose(from_legacy[layer_idx][key_value_idx], legacy_cache[layer_idx][key_value_idx])
	)

	# Test 2: We can convert from new to legacy with no changes
	to_legacy = new_cache.to_legacy_cache()
	for layer_idx in range(10):
	for key_value_idx in range(2):
	self.assertTrue(
	torch.allclose(to_legacy[layer_idx][key_value_idx], new_cache[layer_idx][key_value_idx])
	)

	def test_reorder_cache_retrocompatibility(self):
	"""Tests that Cache.reorder_cache is retrocompatible with the legacy code path"""
	legacy_reorder_fn = ClvpForCausalLM._reorder_cache # An example of a legacy `_reorder_cache` function

	legacy_cache = ()
	new_cache = DynamicCache()

	# Creates a new cache with 10 layers in both formats
	for layer_idx in range(10):
	new_key = torch.rand((4, 4, 8, 16))
	new_value = torch.rand((4, 4, 8, 16))
	new_cache.update(new_key, new_value, layer_idx)
	legacy_cache += ((new_key, new_value),)

	# Let's create some dummy beam indices. From the shape above, it is equivalent to the case where num_beams=4
	# and batch_size=1
	beam_idx = torch.randint(low=0, high=4, size=(4,))

	legacy_cache_reordered = legacy_reorder_fn(legacy_cache, beam_idx)
	new_cache.reorder_cache(beam_idx)

	# Let's check that the results are the same
	for layer_idx in range(10):
	for key_value_idx in range(2):
	self.assertTrue(
	torch.allclose(
	new_cache[layer_idx][key_value_idx], legacy_cache_reordered[layer_idx][key_value_idx]
	)
	)

	def test_static_cache_mha_mqa_gqa(self):
	"""
	Tests that static cache works with multi-head attention (MHA), grouped query attention (GQA), and multi-query
	attention (MQA)
	"""

	def _random_kvs(config):
	# shape for key and values: (batch_size, num_heads, seq_len, head_dim)
	random_keys = torch.rand(
	(1, config.num_key_value_heads, 1, config.hidden_size // config.num_attention_heads),
	device=torch_device,
	)
	random_values = torch.rand(
	(1, config.num_key_value_heads, 1, config.hidden_size // config.num_attention_heads),
	device=torch_device,
	)
	return random_keys, random_values

	mha_config = LlamaConfig(num_attention_heads=32)
	mha_static_cache = StaticCache(config=mha_config, max_batch_size=1, max_cache_len=10, device=torch_device)
	cached_keys, cached_values = mha_static_cache.update(
	*_random_kvs(mha_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)}
	)
	self.assertTrue(cached_keys.shape == (1, 32, 10, 128))
	self.assertTrue(cached_values.shape == (1, 32, 10, 128))

	gqa_config = LlamaConfig(num_attention_heads=32, num_key_value_heads=4)
	gqa_static_cache = StaticCache(config=gqa_config, max_batch_size=1, max_cache_len=10, device=torch_device)
	cached_keys, cached_values = gqa_static_cache.update(
	*_random_kvs(gqa_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)}
	)
	self.assertTrue(cached_keys.shape == (1, 4, 10, 128))
	self.assertTrue(cached_values.shape == (1, 4, 10, 128))

	mqa_config = LlamaConfig(num_attention_heads=32, num_key_value_heads=1)
	mqa_static_cache = StaticCache(config=mqa_config, max_batch_size=1, max_cache_len=10, device=torch_device)
	cached_keys, cached_values = mqa_static_cache.update(
	*_random_kvs(mqa_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)}
	)
	self.assertTrue(cached_keys.shape == (1, 1, 10, 128))
	self.assertTrue(cached_values.shape == (1, 1, 10, 128))

	def test_dynamic_cache_exportability(self):
	model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
	model = model.eval()
	tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
	prompt = "What is the best way to debug python script?"
	inputs = tokenizer(prompt, return_tensors="pt")
	attention_mask = inputs.attention_mask
	input_ids = inputs.input_ids

	past_key_values = DynamicCache()
	ep = torch.export.export(
	model,
	(),
	{
	"input_ids": input_ids,
	"attention_mask": attention_mask,
	"past_key_values": past_key_values,
	"use_cache": True,
	},
	strict=False,
	)
	res = ep.module()(
	input_ids=input_ids,
	attention_mask=attention_mask,
	past_key_values=past_key_values,
	use_cache=True,
	)
	self.assertTrue(len(res.past_key_values.key_cache) == model.config.num_hidden_layers)
	self.assertEqual(2 * model.config.num_hidden_layers + 1, len(ep.graph_signature.output_specs))
	self.assertEqual(
	3,
	len(
	[
	x
	for x in ep.graph_signature.input_specs
	if x.kind == torch.export.graph_signature.InputKind.USER_INPUT
	]
	),
	)

	past_key_values_eager = DynamicCache()
	res_eager = model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	past_key_values=past_key_values_eager,
	use_cache=True,
	)
	self.assertTrue(torch.allclose(res.logits, res_eager.logits))
	for k1, k2 in zip(res.past_key_values.key_cache, res_eager.past_key_values.key_cache):
	self.assertTrue(torch.allclose(k1, k2))

	for v1, v2 in zip(res.past_key_values.value_cache, res_eager.past_key_values.value_cache):
	self.assertTrue(torch.allclose(v1, v2))

	@slow
	@require_read_token
	def test_static_cache_exportability(self):
	"""
	Tests that static cache works with `torch.export()`
	"""
	if not is_torch_greater_or_equal("2.3"):
	self.skipTest(reason="This test requires torch >= 2.3 to run.")

	set_seed(0)
	device = "cpu"
	dtype = "bfloat16"
	cache_implementation = "static"
	attn_implementation = "sdpa" # Export and ExecuTorch only works for SdpaAttention
	batch_size = 1
	max_cache_len = 1234
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2b",
	device_map=device,
	torch_dtype=dtype,
	attn_implementation=attn_implementation,
	generation_config=GenerationConfig(
	use_cache=True,
	cache_implementation=cache_implementation,
	max_length=max_cache_len,
	cache_config={
	"batch_size": batch_size,
	"max_cache_len": max_cache_len,
	"device": device,
	},
	),
	)
	# Check if cache config is passed through correctly
	self.assertEqual(model.generation_config.use_cache, True)
	self.assertEqual(model.generation_config.cache_implementation, cache_implementation)
	self.assertEqual(model.generation_config.max_length, max_cache_len)
	self.assertTrue(model.generation_config.cache_config is not None)
	self.assertEqual(model.generation_config.cache_config.batch_size, batch_size)
	self.assertEqual(model.generation_config.cache_config.max_cache_len, max_cache_len)

	exported_program = convert_and_export_with_cache(model)

	# Check if the exported model is configured with the `StaticCache` correctly
	n_static_key_caches = n_static_value_caches = 0
	for buffer_name, buffer in exported_program.named_buffers():
	if buffer_name.startswith("key_cache"):
	self.assertTrue(buffer.shape[0] == batch_size)
	self.assertTrue(buffer.shape[2] == max_cache_len)
	n_static_key_caches = n_static_key_caches + 1
	if buffer_name.startswith("value_cache"):
	self.assertTrue(buffer.shape[0] == batch_size)
	self.assertTrue(buffer.shape[2] == max_cache_len)
	n_static_value_caches = n_static_value_caches + 1
	self.assertEqual(n_static_key_caches, model.config.num_hidden_layers)
	self.assertEqual(n_static_value_caches, model.config.num_hidden_layers)


	@require_torch_accelerator
	@slow
	class CacheIntegrationTest(unittest.TestCase):
	def test_dynamic_cache_hard(self):
	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", padding_side="left")
	model = AutoModelForCausalLM.from_pretrained(
	"meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
	)
	inputs = tokenizer(["Here's everything I know about cats. Cats"], return_tensors="pt").to(model.device)

	# DynamicCache and the legacy cache format should be equivalent
	set_seed(0)
	gen_out_legacy = model.generate(**inputs, do_sample=True, max_new_tokens=256)
	set_seed(0)
	gen_out = model.generate(**inputs, do_sample=True, max_new_tokens=256, past_key_values=DynamicCache())
	self.assertListEqual(gen_out_legacy.tolist(), gen_out.tolist())

	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	expected_text = (
	"Here's everything I know about cats. Cats are mysterious creatures. They can't talk, and they don't like "
	"to be held. They don't play fetch, and they don't like to be hugged. But they do like to be petted.\n"
	"Cats are also very independent. They don't like to be told what to do, and they don't like to be told "
	"what to eat. They are also very territorial. They don't like to share their food or their toys.\nCats "
	"are also very curious. They like to explore, and they like to play. They are also very fast. They can "
	"run very fast, and they can jump very high.\nCats are also very smart. They can learn tricks, and they "
	"can solve problems. They are also very playful. They like to play with toys, and they like to play with "
	"other cats.\nCats are also very affectionate. They like to be petted, and they like to be held. They "
	"also like to be scratched.\nCats are also very clean. They like to groom themselves, and they like to "
	"clean their litter box.\nCats are also very independent. They don't"
	)
	self.assertEqual(decoded[0], expected_text)

	def test_dynamic_cache_batched(self):
	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", padding_side="left")
	tokenizer.pad_token = tokenizer.eos_token
	model = AutoModelForCausalLM.from_pretrained(
	"meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
	)
	inputs = tokenizer(["A sequence: 1, 2, 3, 4, 5", "A sequence: A, B, C"], padding=True, return_tensors="pt").to(
	model.device
	)

	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10, past_key_values=DynamicCache())
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	expected_text = ["A sequence: 1, 2, 3, 4, 5, 6, 7, 8,", "A sequence: A, B, C, D, E, F, G, H"]
	self.assertListEqual(decoded, expected_text)

	def test_dynamic_cache_beam_search(self):
	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", padding_side="left")
	model = AutoModelForCausalLM.from_pretrained(
	"meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
	)

	inputs = tokenizer(["The best color is"], return_tensors="pt").to(model.device)
	gen_out = model.generate(
	**inputs,
	do_sample=False,
	max_new_tokens=20,
	num_beams=2,
	num_return_sequences=2,
	)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	expected_text = [
	"The best color is the one that makes you feel good.\nThe best color is the one that makes you feel good",
	"The best color is the one that suits you.\nThe best color is the one that suits you. The",
	]
	self.assertListEqual(decoded, expected_text)

	def test_hybrid_cache_n_sequences(self):
	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2-9b",
	device_map="auto",
	torch_dtype=torch.bfloat16,
	attn_implementation="eager",
	)

	inputs = tokenizer(["Hello I am doing"], return_tensors="pt").to(model.device)

	gen_out = model.generate(
	**inputs,
	do_sample=False,
	max_new_tokens=20,
	num_return_sequences=2,
	num_beams=2,
	)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	expected_text = [
	"Hello I am doing a project for my school and I am trying to make a program that will allow me to input a",
	"Hello I am doing a project for my school and I am trying to make a program that will allow me to use a",
	]
	self.assertListEqual(decoded, expected_text)

	@require_non_xpu
	@require_gptq
	def test_sink_cache_hard(self):
	tokenizer = AutoTokenizer.from_pretrained("TheBloke/LLaMa-7B-GPTQ")
	model = AutoModelForCausalLM.from_pretrained("TheBloke/LLaMa-7B-GPTQ", device_map="auto")

	inputs = tokenizer(["Vaswani et al. (2017) introduced the Transformers"], return_tensors="pt").to(model.device)

	# Set up the SinkCache. Using a small window length to contain computational complexity. If this example is run
	# without a SinkCache, the last few tokens are gibberish (ends in "of the of the of a of a of")
	cache = SinkCache(window_length=508, num_sink_tokens=4)
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=3000, past_key_values=cache)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	self.assertTrue(decoded[0].endswith("to perform a variety of tasks. The Transformer is a neural network"))

	def test_sink_cache_iterative_prompts(self):
	"""Tests that SinkCache supports more than one new token at once, when shifting the cache"""
	tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
	model = AutoModelForCausalLM.from_pretrained(
	"HuggingFaceH4/zephyr-7b-beta", device_map="auto", torch_dtype=torch.float16
	)
	prompt = (
	"Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences "
	"and must-see attractions."
	)

	# Prepare generation settings
	cache = SinkCache(window_length=256, num_sink_tokens=4)
	input_ids = torch.tensor([], device=model.device, dtype=torch.int)
	for _ in range(3):
	# Tokenize the prompt with the correct chat template
	chat = [{"role": "user", "content": prompt}]
	tokenized_chat = tokenizer.apply_chat_template(chat, return_tensors="pt", add_generation_prompt=True).to(
	model.device
	)
	input_ids = torch.cat((input_ids, tokenized_chat), dim=1)

	# Perform the generation
	gen_out = model.generate(
	input_ids, do_sample=False, max_new_tokens=100, past_key_values=cache, use_cache=True
	)
	input_ids = gen_out

	# We went well beyond the cache length
	self.assertTrue(input_ids.shape[1] > cache.get_max_cache_shape() * 1.5)

	# And it still produces a coherent english
	decoded = tokenizer.batch_decode(input_ids, skip_special_tokens=True)
	last_output = (
	"<\|assistant\|>\nAs the sun began to set over the Pacific Ocean, I found myself standing on the shores of "
	"Waikiki Beach, my heart filled with awe and wonder. I had just returned from a two-week journey to the "
	"beautiful island of Hawaii, and it had been an unforgettable experience filled with cultural experiences "
	"and must-see attractions that left me breathless.\n\nOne of the most memorable experiences of my trip "
	"was visiting the historic district of Honolulu. Here,"
	)
	self.assertTrue(decoded[0].endswith(last_output))

	@require_torch_gpu
	@parameterized.expand(
	[
	("eager", "static"),
	("sdpa", "static"),
	]
	)
	def test_static_cache_greedy_decoding_pad_left(self, attn_implementation, cache_implementation):
	EXPECTED_GENERATION = [
	"The best color is the one that complements the skin tone of the",
	"We should not undermind the issues at hand.\nWe should not undermind the issues",
	]

	tokenizer = AutoTokenizer.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf", padding_side="left", pad_token="<s>"
	)
	model = AutoModelForCausalLM.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf",
	torch_dtype=torch.bfloat16,
	attn_implementation=attn_implementation,
	).to(torch_device)
	inputs = tokenizer(
	["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt"
	).to(model.device)

	set_seed(0)
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	with self.subTest(f"{attn_implementation}, dynamic"):
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	set_seed(0)
	model.generation_config.cache_implementation = cache_implementation
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	with self.subTest(f"{attn_implementation}, static, eager"):
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	set_seed(0)
	model.forward = torch.compile(model.forward)
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	with self.subTest(f"{attn_implementation}, static, compiled"):
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	@require_torch_gpu
	@parameterized.expand(
	[
	("eager", "static"),
	("sdpa", "static"),
	]
	)
	def test_static_cache_greedy_decoding_pad_right(self, attn_implementation, cache_implementation):
	EXPECTED_GENERATION = [
	"The best color isЋ the one that complements the skin tone of",
	"We should not undermind the issues at hand.\nWe should not undermind the issues",
	]

	tokenizer = AutoTokenizer.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf", padding_side="right", pad_token="<s>"
	)
	model = AutoModelForCausalLM.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf",
	torch_dtype=torch.bfloat16,
	attn_implementation=attn_implementation,
	).to(torch_device)
	inputs = tokenizer(
	["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt"
	).to(model.device)

	set_seed(0)
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	with self.subTest(f"{attn_implementation}, dynamic"):
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	set_seed(0)
	model.generation_config.cache_implementation = cache_implementation
	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	with self.subTest(f"{attn_implementation}, static, eager"):
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	def test_dynamic_cache_extra_left_padding(self):
	"""Tests that adding extra left-padding does not affect the generation with the dynamic cache"""
	EXPECTED_GENERATION = [
	"The best color is the one that complements the skin tone of the",
	"We should not undermind the issues at hand.\nWe should not undermind the issues",
	]

	tokenizer = AutoTokenizer.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf", padding_side="left", pad_token="<s>"
	)
	model = AutoModelForCausalLM.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf",
	torch_dtype=torch.bfloat16,
	).to(torch_device)
	inputs = tokenizer(
	["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt"
	).to(model.device)

	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	# Now with extra left-padding
	inputs_expanded = tokenizer(
	["The best color is", "We should not undermind the issues at hand"],
	padding=True,
	return_tensors="pt",
	pad_to_multiple_of=32,
	).to(model.device)
	self.assertTrue(inputs.input_ids.shape[1] < inputs_expanded.input_ids.shape[1])
	gen_out = model.generate(**inputs_expanded, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	@parameterized.expand(
	[
	"static",
	]
	)
	def test_static_cache_extra_left_padding(self, cache_implementation):
	"""Tests that adding extra left-padding does not affect the generation with the static cache"""
	EXPECTED_GENERATION = [
	"The best color is the one that complements the skin tone of the",
	"We should not undermind the issues at hand.\nWe should not undermind the issues",
	]

	tokenizer = AutoTokenizer.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf", padding_side="left", pad_token="<s>"
	)
	model = AutoModelForCausalLM.from_pretrained(
	"NousResearch/Llama-2-7b-chat-hf",
	torch_dtype=torch.bfloat16,
	).to(torch_device)
	inputs = tokenizer(
	["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt"
	).to(model.device)

	model.generation_config.cache_implementation = cache_implementation

	gen_out = model.generate(**inputs, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	# Now with extra left-padding
	inputs_expanded = tokenizer(
	["The best color is", "We should not undermind the issues at hand"],
	padding=True,
	return_tensors="pt",
	pad_to_multiple_of=32,
	).to(model.device)
	self.assertTrue(inputs.input_ids.shape[1] < inputs_expanded.input_ids.shape[1])
	gen_out = model.generate(**inputs_expanded, do_sample=False, max_new_tokens=10)
	decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
	self.assertListEqual(decoded, EXPECTED_GENERATION)

	@unittest.skip(reason="TODO @gante static cache's does not support beam search yet")
	def test_static_cache_beam_search(self):
	pass

	@require_torch_accelerator
	def test_offloaded_cache_equivalent_to_dynamic_cache(self):
	"""Tests that OffloadedCache produces the same result as the default DynamicCache"""
	model_name = "microsoft/Phi-3-mini-4k-instruct"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
	device = model.device

	if not is_torch_greater_or_equal("2.7", accept_dev=True) and device.type == "xpu":
	self.skipTest(reason="This test requires torch >= 2.7 to run on xpu.")

	input_text = "Fun fact:"
	inputs = tokenizer(input_text, return_tensors="pt").to(device)
	common = {
	"num_beams": 4,
	"num_beam_groups": 2,
	"num_return_sequences": 4,
	"diversity_penalty": 1.0,
	"max_new_tokens": 20,
	"early_stopping": True,
	}
	original = GenerationConfig(**common)
	offloaded = GenerationConfig(cache_implementation="offloaded", **common)
	original_outputs = model.generate(generation_config=original, **inputs)
	offloaded_outputs = model.generate(generation_config=offloaded, **inputs)
	for original_output, offloaded_output in zip(original_outputs, offloaded_outputs):
	assert torch.all(original_output == offloaded_output).item()

	@require_torch_accelerator
	def test_offloaded_cache_uses_less_memory_than_dynamic_cache(self):
	"""Tests that OffloadedCache uses less memory than the default DynamicCache"""
	model_name = "microsoft/Phi-3-mini-4k-instruct"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
	device = model.device

	if not is_torch_greater_or_equal("2.7", accept_dev=True) and device.type == "xpu":
	self.skipTest(reason="This test requires torch >= 2.7 to run on xpu.")

	input_text = "Fun fact:"
	inputs = tokenizer(input_text, return_tensors="pt").to(device)
	common = {
	"num_beams": 4,
	"num_beam_groups": 2,
	"num_return_sequences": 4,
	"diversity_penalty": 1.0,
	"max_new_tokens": 20,
	"early_stopping": True,
	}
	original = GenerationConfig(**common)
	offloaded = GenerationConfig(cache_implementation="offloaded", **common)

	torch_accelerator_module = None
	if device.type == "cuda":
	torch_accelerator_module = torch.cuda
	elif device.type == "xpu":
	torch_accelerator_module = torch.xpu

	torch_accelerator_module.reset_peak_memory_stats(device)
	model.generate(generation_config=original, **inputs)
	original_peak_memory = torch_accelerator_module.max_memory_allocated(device)
	torch_accelerator_module.reset_peak_memory_stats(device)
	model.generate(generation_config=offloaded, **inputs)
	offloaded_peak_memory = torch_accelerator_module.max_memory_allocated(device)
	print(f"original_peak_memory: {original_peak_memory}, offloaded_peak_memory: {offloaded_peak_memory}")
	assert offloaded_peak_memory < original_peak_memory

	@require_torch_gpu
	def test_cache_copy(self):
	model_name = "microsoft/Phi-3-mini-4k-instruct"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype=torch.bfloat16)

	prompt_cache = StaticCache(
	config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16
	)

	INITIAL_PROMPT = "You are a helpful assistant. "
	inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda")
	# This is the common prompt cached, we need to run forward without grad to be abel to copy
	with torch.no_grad():
	prompt_cache = model(**inputs_initial_prompt, past_key_values=prompt_cache).past_key_values

	prompts = ["Help me to write a blogpost about travelling.", "What is the capital of France?"]
	responses = []
	for prompt in prompts:
	new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
	past_key_values = copy.deepcopy(prompt_cache)
	outputs = model.generate(**new_inputs, past_key_values=past_key_values, max_new_tokens=40)
	response = tokenizer.batch_decode(outputs)[0]
	responses.append(response)

	EXPECTED_DECODED_TEXT = [
	"You are a helpful assistant. Help me to write a blogpost about travelling.\n\nTraveling is an enriching experience that broadens our horizons and exposes us to new cultures, landscapes, and people. Whether it's a week",
	'You are a helpful assistant. What is the capital of France?\n\n\n## Response:Paris is the capital of France.\n\n\n\n\n\n## Query:\n\nIn a detailed analysis, compare the economic impacts of the introduction of the'
	] # fmt: skip
	self.assertEqual(responses, EXPECTED_DECODED_TEXT)

	@require_torch_multi_gpu
	def test_data_parallel_dynamic_cache(self):
	"""
	Tests that the dynamic cache works with nn.DataParallel. Under the hood, `DynamicCache` is rebuilt from
	multiple `DynamicCache` in the gather step.
	"""

	model_repo = "hf-internal-testing/tiny-random-MistralForCausalLM"
	model = AutoModelForCausalLM.from_pretrained(model_repo).to(torch_device)
	tokenizer = AutoTokenizer.from_pretrained(model_repo)

	# w/o DP: batch_size = num_gpu
	# w DP: batch_size = 1 (with num_gpus replicas)
	num_gpus = get_gpu_count()
	model_inputs = tokenizer(["foo bar"] * num_gpus, return_tensors="pt").to(model.device)

	# w/o DP
	no_parallelism_cache = model(**model_inputs).past_key_values
	self.assertIsInstance(no_parallelism_cache, DynamicCache)

	# w DP
	model = torch.nn.DataParallel(model)
	parallelism_cache = model(**model_inputs).past_key_values
	self.assertIsInstance(parallelism_cache, DynamicCache)

	# Check that the caches are the same
	for layer_idx in range(len(no_parallelism_cache)):
	for kv_idx in range(2): # 0 = key, 1 = value
	torch.testing.assert_close(
	actual=parallelism_cache[layer_idx][kv_idx], expected=no_parallelism_cache[layer_idx][kv_idx]
	)

	@require_torch_gpu
	def test_static_cache_no_cuda_graph_skips(self):
	"""
	Tests generating with static cache and compilation doesn't skip cuda graphs. Regression test for #36543.

	(? We set `fullgraph=True`, which according to torch docs means it should raise an exception. Instead,
	messages are being thrown to stderr?)
	"""
	model_repo = "hf-internal-testing/tiny-random-MistralForCausalLM"
	model = AutoModelForCausalLM.from_pretrained(model_repo).to(torch_device)
	tokenizer = AutoTokenizer.from_pretrained(model_repo)
	inputs = tokenizer(["foo bar"], return_tensors="pt").to(torch_device)

	# on `main`, prior to #36543, this would send stderr messages about cuda graphs being skipped.
	with CaptureStderr() as cap:
	model.generate(**inputs, max_new_tokens=2, cache_implementation="static")
	self.assertEqual(cap.err, "")

	@require_torch_multi_gpu
	def test_static_cache_multi_gpu(self):
	"""Regression test for #35164: static cache with multi-gpu"""

	model_id = "google/gemma-2-2b-it"
	tokenizer = AutoTokenizer.from_pretrained(model_id)

	device_map = {"model.embed_tokens": 0, "model.norm": 1, "model.rotary_emb": 1, "lm_head": 0}
	num_hidden_layers = 26
	for i in range(num_hidden_layers):
	device_map[f"model.layers.{i}"] = 0 if i < 13 else 1

	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="bfloat16",
	device_map=device_map,
	)
	inputs = tokenizer("Today is a beautiful day!", return_tensors="pt").to(0)
	_ = model(**inputs)
	_ = model.generate(**inputs, max_new_tokens=2, cache_implementation="hybrid")