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Add pytree registration for static cache #41754

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157 changes: 105 additions & 52 deletions src/transformers/integrations/executorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,18 @@
from typing import Optional

import torch
import torch.utils._pytree as pytree

from ..cache_utils import (
Cache,
ChunkedSlidingLayer,
DynamicCache,
DynamicLayer,
DynamicSlidingWindowLayer,
EncoderDecoderCache,
SlidingWindowLayer,
StaticCache,
StaticLayer,
)
from ..generation.configuration_utils import GenerationConfig
from ..masking_utils import (
Expand All @@ -34,7 +39,6 @@
from ..pytorch_utils import (
is_torch_greater_or_equal,
is_torch_greater_or_equal_than_2_3,
is_torch_greater_or_equal_than_2_6,
)


Expand Down Expand Up @@ -855,7 +859,7 @@ def __init__(self, model, max_static_cache_length, batch_size):
self.static_cache.early_initialization(batch_size, num_heads, head_dim, torch.float32, model_device)
self.cache = EncoderDecoderCache(self.static_cache, DynamicCache(config=self.config))

register_dynamic_cache_export_support()
register_pytree_cache()

# Register cache buffers to make them exportable
for i in range(len(self.static_cache)):
Expand Down Expand Up @@ -1041,7 +1045,7 @@ def export_with_dynamic_cache(
ALL_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", ALL_ATTENTION_FUNCTIONS["sdpa"])
model.config._attn_implementation = "sdpa_without_vmap"

register_dynamic_cache_export_support()
register_pytree_cache()

with torch.no_grad():
exported_program = torch.export.export(
Expand All @@ -1058,57 +1062,106 @@ def export_with_dynamic_cache(
return exported_program


def register_dynamic_cache_export_support():
"""
Utilities for `DynamicCache` <> torch.export support
"""
def _register_pytree_cache_layer(cache_layer_cls):
def _flatten_layer(layer):
attributes = {
"keys": layer.keys,
"values": layer.values,
}

try:
torch.utils._pytree.register_pytree_node(
DynamicCache,
lambda dynamic_cache: torch.utils._pytree._dict_flatten(_get_cache_dict(dynamic_cache)),
_unflatten_dynamic_cache,
serialized_type_name=f"{DynamicCache.__module__}.{DynamicCache.__name__}",
flatten_with_keys_fn=lambda dynamic_cache: torch.utils._pytree._dict_flatten_with_keys(
_get_cache_dict(dynamic_cache)
),
)
# TODO (tmanlaibaatar) This won't be needed in torch 2.7.
torch.fx._pytree.register_pytree_flatten_spec(
DynamicCache,
lambda cache, spec: torch.fx._pytree._dict_flatten_spec(_get_cache_dict(cache), spec),
if isinstance(layer, StaticLayer):
attributes["max_cache_len"] = layer.max_cache_len
attributes["max_batch_size"] = getattr(layer, "max_batch_size", None)

if isinstance(layer, SlidingWindowLayer):
attributes["cumulative_length"] = layer.cumulative_length

return list(attributes.values()), list(attributes.keys())

def _unflatten_layer(values, context):
attributes = dict(zip(context, values))

if cache_layer_cls == StaticLayer:
static_layer = cache_layer_cls(
max_cache_len=attributes["max_cache_len"],
)
static_layer.keys = attributes["keys"]
static_layer.values = attributes["values"]
static_layer.max_batch_size = attributes["max_batch_size"]
return static_layer

elif cache_layer_cls in (SlidingWindowLayer, ChunkedSlidingLayer):
sliding_window_layer = cache_layer_cls(
max_cache_len=attributes["max_cache_len"],
cumulative_length=attributes["cumulative_length"],
)
sliding_window_layer.keys = attributes["keys"]
sliding_window_layer.values = attributes["values"]
static_layer.max_batch_size = attributes["max_batch_size"]
return sliding_window_layer

elif cache_layer_cls in (DynamicLayer, DynamicSlidingWindowLayer):
dynamic_layer = cache_layer_cls()
dynamic_layer.keys = attributes["keys"]
dynamic_layer.values = attributes["values"]
return dynamic_layer

def _flatten_layer_with_keys(static_layer):
values, context = _flatten_layer(static_layer)
return [(pytree.MappingKey(k), v) for k, v in zip(context, values)], context

pytree.register_pytree_node(
cache_layer_cls,
_flatten_layer,
_unflatten_layer,
serialized_type_name=f"{cache_layer_cls.__module__}.{cache_layer_cls.__name__}",
flatten_with_keys_fn=_flatten_layer_with_keys,
)


def _register_pytree_cache(cache_cls):
def _flatten_cache(cache):
attributes = {
"layers": cache.layers,
"offloading": cache.offloading,
"only_non_sliding": getattr(cache, "only_non_sliding", None),
}
return list(attributes.values()), list(attributes.keys())

def _flatten_cache_with_keys(cache):
values, context = _flatten_cache(cache)
return [(pytree.MappingKey(k), v) for k, v in zip(context, values)], context

def _unflatten_cache(values, context):
attributes = dict(zip(context, values))

cache = Cache(
layers=attributes["layers"],
offloading=attributes["offloading"],
offload_only_non_sliding=attributes["only_non_sliding"],
)
# Catching this in case there are multiple runs for some test runs
except ValueError as e:
if "already registered as pytree node" not in str(e):
raise


def _get_cache_dict(cache: DynamicCache):
"""Convert cache to dictionary format for pytree operations."""
if any(not isinstance(layer, (DynamicLayer, DynamicSlidingWindowLayer)) for layer in cache.layers):
raise RuntimeError("This pytree flattening function should only be applied to DynamicCache")

if not is_torch_greater_or_equal_than_2_6:
logging.warning("DynamicCache + torch.export is tested on torch 2.6.0+ and may not work on earlier versions.")

return {
"key_cache": [layer.keys for layer in cache.layers if layer.keys is not None],
"value_cache": [layer.values for layer in cache.layers if layer.values is not None],
}


def _unflatten_dynamic_cache(values, context: torch.utils._pytree.Context):
dictionary = torch.utils._pytree._dict_unflatten(values, context)
cache = DynamicCache()
# Reconstruct layers from keys and values lists
key_list = dictionary.get("key_cache", [])
value_list = dictionary.get("value_cache", [])
for idx in range(max(len(key_list), len(value_list))):
key = key_list[idx] if idx < len(key_list) else None
value = value_list[idx] if idx < len(value_list) else None
cache.update(key, value, idx)
return cache
cache.__class__ = cache_cls
cache.is_initialized = True
return cache

torch.utils._pytree.register_pytree_node(
cache_cls,
_flatten_cache,
_unflatten_cache,
serialized_type_name=f"{cache_cls.__module__}.{cache_cls.__name__}",
flatten_with_keys_fn=_flatten_cache_with_keys,
)


def register_pytree_cache():
_register_pytree_cache_layer(StaticLayer)
_register_pytree_cache_layer(SlidingWindowLayer)
_register_pytree_cache_layer(ChunkedSlidingLayer)
_register_pytree_cache_layer(DynamicLayer)
_register_pytree_cache_layer(DynamicSlidingWindowLayer)

_register_pytree_cache(StaticCache)
_register_pytree_cache(DynamicCache)


def sdpa_mask_without_vmap(
Expand Down
19 changes: 18 additions & 1 deletion tests/utils/test_cache_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@

if is_torch_available():
import torch
import torch.utils._pytree as pytree

from transformers import (
AutoModelForCausalLM,
Expand All @@ -56,7 +57,8 @@
convert_and_export_with_cache,
pipeline,
)
from transformers.integrations.executorch import export_with_dynamic_cache
from transformers.configuration_utils import PretrainedConfig
from transformers.integrations.executorch import export_with_dynamic_cache, register_pytree_cache


TEST_CACHE_IMPLEMENTATIONS = [
Expand Down Expand Up @@ -540,6 +542,21 @@ def test_cache_gptj_model(self, cache_implementation):
class CacheExportIntegrationTest(unittest.TestCase):
"""Cache tests that rely on `torch.export()` and model loading"""

@pytest.mark.torch_export_test
def test_static_cache_pytree(self):
cache = StaticCache(config=PretrainedConfig(num_hidden_layers=2), max_cache_len=1000)
cache.update(torch.ones(1, 1, 1, 1), torch.ones(1, 1, 1, 1), 0)

tree_spec = pytree.tree_flatten(cache)[1]
self.assertEqual(type(tree_spec), pytree.LeafSpec)

register_pytree_cache()

flattened, spec = pytree.tree_flatten(cache)
new_cache = pytree.tree_unflatten(flattened, spec)

torch.allclose(new_cache.layers[0].values, cache.layers[0].values)

@pytest.mark.torch_export_test
def test_dynamic_cache_exportability(self):
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
Expand Down